PANDAS.ipynb

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   "execution_count": 75,
   "metadata": {
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    "id": "K3v6y0tJcyAc"
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1599472530430,
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    "id": "tx8-7xS2c-Mm"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\african_crises.csv')"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1870</td>\n",
       "      <td>1</td>\n",
       "      <td>0.052264</td>\n",
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       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1871</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052798</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.149140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1872</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.718593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1873</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051680</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.203897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1874</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.848561</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1875</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051546</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.924178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1876</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051867</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.769547</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1877</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051867</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.116045</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1878</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051948</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.492537</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1879</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052029</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-16.831357</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   case  cc3  country  year  systemic_crisis  exch_usd  \\\n",
       "0     1  DZA  Algeria  1870                1  0.052264   \n",
       "1     1  DZA  Algeria  1871                0  0.052798   \n",
       "2     1  DZA  Algeria  1872                0  0.052274   \n",
       "3     1  DZA  Algeria  1873                0  0.051680   \n",
       "4     1  DZA  Algeria  1874                0  0.051308   \n",
       "5     1  DZA  Algeria  1875                0  0.051546   \n",
       "6     1  DZA  Algeria  1876                0  0.051867   \n",
       "7     1  DZA  Algeria  1877                0  0.051867   \n",
       "8     1  DZA  Algeria  1878                0  0.051948   \n",
       "9     1  DZA  Algeria  1879                0  0.052029   \n",
       "\n",
       "   domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "0                         0                                0   \n",
       "1                         0                                0   \n",
       "2                         0                                0   \n",
       "3                         0                                0   \n",
       "4                         0                                0   \n",
       "5                         0                                0   \n",
       "6                         0                                0   \n",
       "7                         0                                0   \n",
       "8                         0                                0   \n",
       "9                         0                                0   \n",
       "\n",
       "   gdp_weighted_default  inflation_annual_cpi  independence  currency_crises  \\\n",
       "0                   0.0              3.441456             0                0   \n",
       "1                   0.0             14.149140             0                0   \n",
       "2                   0.0             -3.718593             0                0   \n",
       "3                   0.0             11.203897             0                0   \n",
       "4                   0.0             -3.848561             0                0   \n",
       "5                   0.0            -20.924178             0                0   \n",
       "6                   0.0             -1.769547             0                0   \n",
       "7                   0.0             29.116045             0                0   \n",
       "8                   0.0             -1.492537             0                0   \n",
       "9                   0.0            -16.831357             0                0   \n",
       "\n",
       "   inflation_crises banking_crisis  \n",
       "0                 0         crisis  \n",
       "1                 0      no_crisis  \n",
       "2                 0      no_crisis  \n",
       "3                 0      no_crisis  \n",
       "4                 0      no_crisis  \n",
       "5                 0      no_crisis  \n",
       "6                 0      no_crisis  \n",
       "7                 1      no_crisis  \n",
       "8                 0      no_crisis  \n",
       "9                 0      no_crisis  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=1059, step=1)\n"
     ]
    }
   ],
   "source": [
    "print(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2064,
     "status": "ok",
     "timestamp": 1599472708891,
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
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       "      <th>1049</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2004</td>\n",
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       "      <td>5.600000e-23</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1050</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>8.460000e-22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>5.858444e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
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       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000e-19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.281114e+03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
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       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>1.900000e-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.627989e+04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.198970e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>3.548000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.670000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>3.782000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.217000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>3.619000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.920000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>3.619000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.720000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>3.619000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.632000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      case  cc3   country  year  systemic_crisis      exch_usd  \\\n",
       "1049    70  ZWE  Zimbabwe  2004                1  5.600000e-23   \n",
       "1050    70  ZWE  Zimbabwe  2005                1  8.460000e-22   \n",
       "1051    70  ZWE  Zimbabwe  2006                1  3.000000e-19   \n",
       "1052    70  ZWE  Zimbabwe  2007                1  1.900000e-16   \n",
       "1053    70  ZWE  Zimbabwe  2008                1  2.000000e-03   \n",
       "1054    70  ZWE  Zimbabwe  2009                1  3.548000e+02   \n",
       "1055    70  ZWE  Zimbabwe  2010                0  3.782000e+02   \n",
       "1056    70  ZWE  Zimbabwe  2011                0  3.619000e+02   \n",
       "1057    70  ZWE  Zimbabwe  2012                0  3.619000e+02   \n",
       "1058    70  ZWE  Zimbabwe  2013                0  3.619000e+02   \n",
       "\n",
       "      domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "1049                         1                                1   \n",
       "1050                         1                                1   \n",
       "1051                         1                                1   \n",
       "1052                         1                                1   \n",
       "1053                         1                                1   \n",
       "1054                         1                                1   \n",
       "1055                         1                                1   \n",
       "1056                         1                                1   \n",
       "1057                         1                                1   \n",
       "1058                         1                                1   \n",
       "\n",
       "      gdp_weighted_default  inflation_annual_cpi  independence  \\\n",
       "1049                   0.0          1.327468e+02             1   \n",
       "1050                   0.0          5.858444e+02             1   \n",
       "1051                   0.0          1.281114e+03             1   \n",
       "1052                   0.0          6.627989e+04             1   \n",
       "1053                   0.0          2.198970e+07             1   \n",
       "1054                   0.0         -7.670000e+00             1   \n",
       "1055                   0.0          3.217000e+00             1   \n",
       "1056                   0.0          4.920000e+00             1   \n",
       "1057                   0.0          3.720000e+00             1   \n",
       "1058                   0.0          1.632000e+00             1   \n",
       "\n",
       "      currency_crises  inflation_crises banking_crisis  \n",
       "1049                1                 1         crisis  \n",
       "1050                1                 1         crisis  \n",
       "1051                1                 1         crisis  \n",
       "1052                1                 1         crisis  \n",
       "1053                1                 1         crisis  \n",
       "1054                1                 0         crisis  \n",
       "1055                0                 0      no_crisis  \n",
       "1056                0                 0      no_crisis  \n",
       "1057                0                 0      no_crisis  \n",
       "1058                0                 0      no_crisis  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6002,
     "status": "ok",
     "timestamp": 1599472718854,
     "user": {
      "displayName": "ANKIT NAYAN",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgQ1RJNBljXXp8QbwEt49E95VDahZfyFVAbvPbThA=s64",
      "userId": "00579363849121494836"
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     "user_tz": -330
    },
    "id": "9rIMCa9cdaMZ",
    "outputId": "ceb3d8d1-8a46-44c6-ca88-81b7679623a6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1059.000000</td>\n",
       "      <td>1059.000000</td>\n",
       "      <td>1059.000000</td>\n",
       "      <td>1059.000000</td>\n",
       "      <td>1059.000000</td>\n",
       "      <td>1059.000000</td>\n",
       "      <td>1059.000000</td>\n",
       "      <td>1.059000e+03</td>\n",
       "      <td>1059.000000</td>\n",
       "      <td>1059.000000</td>\n",
       "      <td>1059.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.613787</td>\n",
       "      <td>1967.767705</td>\n",
       "      <td>0.077432</td>\n",
       "      <td>43.140831</td>\n",
       "      <td>0.039660</td>\n",
       "      <td>0.152975</td>\n",
       "      <td>0.006402</td>\n",
       "      <td>2.084889e+04</td>\n",
       "      <td>0.776204</td>\n",
       "      <td>0.132200</td>\n",
       "      <td>0.129367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.692402</td>\n",
       "      <td>33.530632</td>\n",
       "      <td>0.267401</td>\n",
       "      <td>111.475380</td>\n",
       "      <td>0.195251</td>\n",
       "      <td>0.360133</td>\n",
       "      <td>0.043572</td>\n",
       "      <td>6.757274e+05</td>\n",
       "      <td>0.416984</td>\n",
       "      <td>0.349847</td>\n",
       "      <td>0.335765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1860.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.850214e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.195350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.086162e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.762330e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.462750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.164405e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>744.306139</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2.198970e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              case         year  systemic_crisis     exch_usd  \\\n",
       "count  1059.000000  1059.000000      1059.000000  1059.000000   \n",
       "mean     35.613787  1967.767705         0.077432    43.140831   \n",
       "std      23.692402    33.530632         0.267401   111.475380   \n",
       "min       1.000000  1860.000000         0.000000     0.000000   \n",
       "25%      15.000000  1951.000000         0.000000     0.195350   \n",
       "50%      38.000000  1973.000000         0.000000     0.868400   \n",
       "75%      56.000000  1994.000000         0.000000     8.462750   \n",
       "max      70.000000  2014.000000         1.000000   744.306139   \n",
       "\n",
       "       domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "count               1059.000000                      1059.000000   \n",
       "mean                   0.039660                         0.152975   \n",
       "std                    0.195251                         0.360133   \n",
       "min                    0.000000                         0.000000   \n",
       "25%                    0.000000                         0.000000   \n",
       "50%                    0.000000                         0.000000   \n",
       "75%                    0.000000                         0.000000   \n",
       "max                    1.000000                         1.000000   \n",
       "\n",
       "       gdp_weighted_default  inflation_annual_cpi  independence  \\\n",
       "count           1059.000000          1.059000e+03   1059.000000   \n",
       "mean               0.006402          2.084889e+04      0.776204   \n",
       "std                0.043572          6.757274e+05      0.416984   \n",
       "min                0.000000         -2.850214e+01      0.000000   \n",
       "25%                0.000000          2.086162e+00      1.000000   \n",
       "50%                0.000000          5.762330e+00      1.000000   \n",
       "75%                0.000000          1.164405e+01      1.000000   \n",
       "max                0.400000          2.198970e+07      1.000000   \n",
       "\n",
       "       currency_crises  inflation_crises  \n",
       "count      1059.000000       1059.000000  \n",
       "mean          0.132200          0.129367  \n",
       "std           0.349847          0.335765  \n",
       "min           0.000000          0.000000  \n",
       "25%           0.000000          0.000000  \n",
       "50%           0.000000          0.000000  \n",
       "75%           0.000000          0.000000  \n",
       "max           2.000000          1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5546,
     "status": "ok",
     "timestamp": 1599472725213,
     "user": {
      "displayName": "ANKIT NAYAN",
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    },
    "id": "UqpMmKstdaIb",
    "outputId": "66f1b573-ab82-41da-9dd8-f0d23d7d070f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1059 entries, 0 to 1058\n",
      "Data columns (total 14 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   case                             1059 non-null   int64  \n",
      " 1   cc3                              1059 non-null   object \n",
      " 2   country                          1059 non-null   object \n",
      " 3   year                             1059 non-null   int64  \n",
      " 4   systemic_crisis                  1059 non-null   int64  \n",
      " 5   exch_usd                         1059 non-null   float64\n",
      " 6   domestic_debt_in_default         1059 non-null   int64  \n",
      " 7   sovereign_external_debt_default  1059 non-null   int64  \n",
      " 8   gdp_weighted_default             1059 non-null   float64\n",
      " 9   inflation_annual_cpi             1059 non-null   float64\n",
      " 10  independence                     1059 non-null   int64  \n",
      " 11  currency_crises                  1059 non-null   int64  \n",
      " 12  inflation_crises                 1059 non-null   int64  \n",
      " 13  banking_crisis                   1059 non-null   object \n",
      "dtypes: float64(3), int64(8), object(3)\n",
      "memory usage: 116.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2799,
     "status": "ok",
     "timestamp": 1599472728935,
     "user": {
      "displayName": "ANKIT NAYAN",
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    },
    "id": "r7nqBdygdaE5",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1059, 14)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZXT9LfUTlzpc"
   },
   "source": [
    "**Operations on DataFrame**\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "Access of columns in a DataFrame¶\n",
    "\n",
    "We can select specified set of columns of a dataframe and copy it to a new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9650,
     "status": "ok",
     "timestamp": 1599472741408,
     "user": {
      "displayName": "ANKIT NAYAN",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgQ1RJNBljXXp8QbwEt49E95VDahZfyFVAbvPbThA=s64",
      "userId": "00579363849121494836"
     },
     "user_tz": -330
    },
    "id": "XV5wpbnFdaAg",
    "outputId": "90a504f1-985b-44b4-e587-206ca834d7a1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1870</td>\n",
       "      <td>0.052264</td>\n",
       "      <td>3.441456</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1871</td>\n",
       "      <td>0.052798</td>\n",
       "      <td>14.149140</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1872</td>\n",
       "      <td>0.052274</td>\n",
       "      <td>-3.718593</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1873</td>\n",
       "      <td>0.051680</td>\n",
       "      <td>11.203897</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1874</td>\n",
       "      <td>0.051308</td>\n",
       "      <td>-3.848561</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  exch_usd  inflation_annual_cpi banking_crisis\n",
       "0  Algeria  1870  0.052264              3.441456         crisis\n",
       "1  Algeria  1871  0.052798             14.149140      no_crisis\n",
       "2  Algeria  1872  0.052274             -3.718593      no_crisis\n",
       "3  Algeria  1873  0.051680             11.203897      no_crisis\n",
       "4  Algeria  1874  0.051308             -3.848561      no_crisis"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new=df[['country','year','exch_usd','inflation_annual_cpi','banking_crisis']]\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5453,
     "status": "ok",
     "timestamp": 1599472743874,
     "user": {
      "displayName": "ANKIT NAYAN",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgQ1RJNBljXXp8QbwEt49E95VDahZfyFVAbvPbThA=s64",
      "userId": "00579363849121494836"
     },
     "user_tz": -330
    },
    "id": "CsIH9cSrdZ8_",
    "outputId": "5bd36a3f-cc89-4498-abc5-46b17238688e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.052264\n",
       "1    0.052798\n",
       "2    0.052274\n",
       "3    0.051680\n",
       "4    0.051308\n",
       "Name: exch_usd, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['exch_usd'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 126
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1522,
     "status": "ok",
     "timestamp": 1599472749644,
     "user": {
      "displayName": "ANKIT NAYAN",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgQ1RJNBljXXp8QbwEt49E95VDahZfyFVAbvPbThA=s64",
      "userId": "00579363849121494836"
     },
     "user_tz": -330
    },
    "id": "ETfleArPox1_",
    "outputId": "30e5f68f-d614-4056-ec3d-5686101f6206"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.052264\n",
       "1    0.052798\n",
       "2    0.052274\n",
       "3    0.051680\n",
       "4    0.051308\n",
       "Name: exch_usd, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.exch_usd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GMH6RnhSm5k5"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KoLJ_SkeptuR"
   },
   "source": [
    "**Indexing / Slicing a DataFrame**\n",
    "\n",
    "---\n",
    "Pandas Series.reset_index() function generate a new DataFrame or Series with the index reset. This comes handy when index is need to be used as a column.\n",
    "Syntax: Series.reset_index(level=None, drop=False, name=None, inplace=False)\n",
    "\n",
    "Parameter :\n",
    "\n",
    "level : For a Series with a MultiIndex\n",
    "\n",
    "drop : Just reset the index, without inserting it as a column in the new DataFrame.\n",
    "\n",
    "name : The name to use for the column containing the original Series values.\n",
    "\n",
    "inplace : Modify the Series in place\n",
    "\n",
    "Returns : result : Series\n",
    "\n",
    "\n",
    "Resetting Index of a column in a DataFrame\n",
    "\n",
    "Let us consider the column \"case\" as index of dataframe \"df\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 310
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1426,
     "status": "ok",
     "timestamp": 1599472893250,
     "user": {
      "displayName": "ANKIT NAYAN",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgQ1RJNBljXXp8QbwEt49E95VDahZfyFVAbvPbThA=s64",
      "userId": "00579363849121494836"
     },
     "user_tz": -330
    },
    "id": "wN_rs9B-ozV3",
    "outputId": "785471b3-5a27-4998-d697-ab1f560f5706"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of       cc3   country  year  systemic_crisis    exch_usd  \\\n",
       "case                                                     \n",
       "1     DZA   Algeria  1870                1    0.052264   \n",
       "1     DZA   Algeria  1871                0    0.052798   \n",
       "1     DZA   Algeria  1872                0    0.052274   \n",
       "1     DZA   Algeria  1873                0    0.051680   \n",
       "1     DZA   Algeria  1874                0    0.051308   \n",
       "...   ...       ...   ...              ...         ...   \n",
       "70    ZWE  Zimbabwe  2009                1  354.800000   \n",
       "70    ZWE  Zimbabwe  2010                0  378.200000   \n",
       "70    ZWE  Zimbabwe  2011                0  361.900000   \n",
       "70    ZWE  Zimbabwe  2012                0  361.900000   \n",
       "70    ZWE  Zimbabwe  2013                0  361.900000   \n",
       "\n",
       "      domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "case                                                              \n",
       "1                            0                                0   \n",
       "1                            0                                0   \n",
       "1                            0                                0   \n",
       "1                            0                                0   \n",
       "1                            0                                0   \n",
       "...                        ...                              ...   \n",
       "70                           1                                1   \n",
       "70                           1                                1   \n",
       "70                           1                                1   \n",
       "70                           1                                1   \n",
       "70                           1                                1   \n",
       "\n",
       "      gdp_weighted_default  inflation_annual_cpi  independence  \\\n",
       "case                                                             \n",
       "1                      0.0              3.441456             0   \n",
       "1                      0.0             14.149140             0   \n",
       "1                      0.0             -3.718593             0   \n",
       "1                      0.0             11.203897             0   \n",
       "1                      0.0             -3.848561             0   \n",
       "...                    ...                   ...           ...   \n",
       "70                     0.0             -7.670000             1   \n",
       "70                     0.0              3.217000             1   \n",
       "70                     0.0              4.920000             1   \n",
       "70                     0.0              3.720000             1   \n",
       "70                     0.0              1.632000             1   \n",
       "\n",
       "      currency_crises  inflation_crises banking_crisis  \n",
       "case                                                    \n",
       "1                   0                 0         crisis  \n",
       "1                   0                 0      no_crisis  \n",
       "1                   0                 0      no_crisis  \n",
       "1                   0                 0      no_crisis  \n",
       "1                   0                 0      no_crisis  \n",
       "...               ...               ...            ...  \n",
       "70                  1                 0         crisis  \n",
       "70                  0                 0      no_crisis  \n",
       "70                  0                 0      no_crisis  \n",
       "70                  0                 0      no_crisis  \n",
       "70                  0                 0      no_crisis  \n",
       "\n",
       "[1059 rows x 13 columns]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('case').head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1331,
     "status": "ok",
     "timestamp": 1599428772543,
     "user": {
      "displayName": "ANKIT NAYAN",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgQ1RJNBljXXp8QbwEt49E95VDahZfyFVAbvPbThA=s64",
      "userId": "00579363849121494836"
     },
     "user_tz": -330
    },
    "id": "5Z1CwZ_gozlK",
    "outputId": "4c2bbd05-34c4-4140-bf10-3a654fa7631d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of       case  cc3   country  year  systemic_crisis    exch_usd  \\\n",
       "0        1  DZA   Algeria  1870                1    0.052264   \n",
       "1        1  DZA   Algeria  1871                0    0.052798   \n",
       "2        1  DZA   Algeria  1872                0    0.052274   \n",
       "3        1  DZA   Algeria  1873                0    0.051680   \n",
       "4        1  DZA   Algeria  1874                0    0.051308   \n",
       "...    ...  ...       ...   ...              ...         ...   \n",
       "1054    70  ZWE  Zimbabwe  2009                1  354.800000   \n",
       "1055    70  ZWE  Zimbabwe  2010                0  378.200000   \n",
       "1056    70  ZWE  Zimbabwe  2011                0  361.900000   \n",
       "1057    70  ZWE  Zimbabwe  2012                0  361.900000   \n",
       "1058    70  ZWE  Zimbabwe  2013                0  361.900000   \n",
       "\n",
       "      domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "0                            0                                0   \n",
       "1                            0                                0   \n",
       "2                            0                                0   \n",
       "3                            0                                0   \n",
       "4                            0                                0   \n",
       "...                        ...                              ...   \n",
       "1054                         1                                1   \n",
       "1055                         1                                1   \n",
       "1056                         1                                1   \n",
       "1057                         1                                1   \n",
       "1058                         1                                1   \n",
       "\n",
       "      gdp_weighted_default  inflation_annual_cpi  independence  \\\n",
       "0                      0.0              3.441456             0   \n",
       "1                      0.0             14.149140             0   \n",
       "2                      0.0             -3.718593             0   \n",
       "3                      0.0             11.203897             0   \n",
       "4                      0.0             -3.848561             0   \n",
       "...                    ...                   ...           ...   \n",
       "1054                   0.0             -7.670000             1   \n",
       "1055                   0.0              3.217000             1   \n",
       "1056                   0.0              4.920000             1   \n",
       "1057                   0.0              3.720000             1   \n",
       "1058                   0.0              1.632000             1   \n",
       "\n",
       "      currency_crises  inflation_crises banking_crisis  \n",
       "0                   0                 0         crisis  \n",
       "1                   0                 0      no_crisis  \n",
       "2                   0                 0      no_crisis  \n",
       "3                   0                 0      no_crisis  \n",
       "4                   0                 0      no_crisis  \n",
       "...               ...               ...            ...  \n",
       "1054                1                 0         crisis  \n",
       "1055                0                 0      no_crisis  \n",
       "1056                0                 0      no_crisis  \n",
       "1057                0                 0      no_crisis  \n",
       "1058                0                 0      no_crisis  \n",
       "\n",
       "[1059 rows x 14 columns]>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resetting Index ( Extra columnn(index) is added 0.1.2.3.4.5.6......)\n",
    "df.reset_index(drop=True, inplace=False).head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1741,
     "status": "ok",
     "timestamp": 1599472914383,
     "user": {
      "displayName": "ANKIT NAYAN",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgQ1RJNBljXXp8QbwEt49E95VDahZfyFVAbvPbThA=s64",
      "userId": "00579363849121494836"
     },
     "user_tz": -330
    },
    "id": "qjMwOTbsoyFM",
    "outputId": "c6cada91-903f-4c28-e525-5242c10b8023"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of       index  case  cc3   country  year  systemic_crisis    exch_usd  \\\n",
       "0         0     1  DZA   Algeria  1870                1    0.052264   \n",
       "1         1     1  DZA   Algeria  1871                0    0.052798   \n",
       "2         2     1  DZA   Algeria  1872                0    0.052274   \n",
       "3         3     1  DZA   Algeria  1873                0    0.051680   \n",
       "4         4     1  DZA   Algeria  1874                0    0.051308   \n",
       "...     ...   ...  ...       ...   ...              ...         ...   \n",
       "1054   1054    70  ZWE  Zimbabwe  2009                1  354.800000   \n",
       "1055   1055    70  ZWE  Zimbabwe  2010                0  378.200000   \n",
       "1056   1056    70  ZWE  Zimbabwe  2011                0  361.900000   \n",
       "1057   1057    70  ZWE  Zimbabwe  2012                0  361.900000   \n",
       "1058   1058    70  ZWE  Zimbabwe  2013                0  361.900000   \n",
       "\n",
       "      domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "0                            0                                0   \n",
       "1                            0                                0   \n",
       "2                            0                                0   \n",
       "3                            0                                0   \n",
       "4                            0                                0   \n",
       "...                        ...                              ...   \n",
       "1054                         1                                1   \n",
       "1055                         1                                1   \n",
       "1056                         1                                1   \n",
       "1057                         1                                1   \n",
       "1058                         1                                1   \n",
       "\n",
       "      gdp_weighted_default  inflation_annual_cpi  independence  \\\n",
       "0                      0.0              3.441456             0   \n",
       "1                      0.0             14.149140             0   \n",
       "2                      0.0             -3.718593             0   \n",
       "3                      0.0             11.203897             0   \n",
       "4                      0.0             -3.848561             0   \n",
       "...                    ...                   ...           ...   \n",
       "1054                   0.0             -7.670000             1   \n",
       "1055                   0.0              3.217000             1   \n",
       "1056                   0.0              4.920000             1   \n",
       "1057                   0.0              3.720000             1   \n",
       "1058                   0.0              1.632000             1   \n",
       "\n",
       "      currency_crises  inflation_crises banking_crisis  \n",
       "0                   0                 0         crisis  \n",
       "1                   0                 0      no_crisis  \n",
       "2                   0                 0      no_crisis  \n",
       "3                   0                 0      no_crisis  \n",
       "4                   0                 0      no_crisis  \n",
       "...               ...               ...            ...  \n",
       "1054                1                 0         crisis  \n",
       "1055                0                 0      no_crisis  \n",
       "1056                0                 0      no_crisis  \n",
       "1057                0                 0      no_crisis  \n",
       "1058                0                 0      no_crisis  \n",
       "\n",
       "[1059 rows x 15 columns]>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(drop=False, inplace=False).head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fPGurvCSoyOn"
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-15-ecb6e75e2218>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-15-ecb6e75e2218>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    cuml_df.loc[:,['koi_disposition':'koi_period']]  correct\u001b[0m\n\u001b[1;37m                                    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "cuml_df.loc[:,'koi_disposition':'koi_period']\n",
    "cuml_df.loc[:,['koi_disposition':'koi_period']]  correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iOg7Zfylox-a"
   },
   "source": [
    "# Indexing with iloc\n",
    "It is purely integer-location based indexing for selection by position\n",
    "\n",
    "Allowed inputs are:\n",
    "\n",
    "1) An integer, e.g. 5.\n",
    "\n",
    "2) A list or array of integers, e.g. [4, 3, 0].\n",
    "\n",
    "3) A slice object with ints, e.g. 1:7.\n",
    "\n",
    "4) A boolean array.\n",
    "\n",
    "5) A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zJXrXLBjox6f"
   },
   "outputs": [],
   "source": [
    "# 1. Selecting a single row using iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "case                                      1\n",
       "cc3                                     DZA\n",
       "country                             Algeria\n",
       "year                                   1870\n",
       "systemic_crisis                           1\n",
       "exch_usd                           0.052264\n",
       "domestic_debt_in_default                  0\n",
       "sovereign_external_debt_default           0\n",
       "gdp_weighted_default                      0\n",
       "inflation_annual_cpi                3.44146\n",
       "independence                              0\n",
       "currency_crises                           0\n",
       "inflation_crises                          0\n",
       "banking_crisis                       crisis\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slice=df.iloc[0]    # It presents the 2nd row of the dataframe.\n",
    "df_slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Selecting row and column number using iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Algeria'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slice1=df.iloc[1,2]  #It represents the value present in the ith row and ith column ie Algeria. You can cross-verify by checking the dataframe.\n",
    "df_slice1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Selecting range of rows and a specified column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    Algeria\n",
       "2    Algeria\n",
       "3    Algeria\n",
       "4    Algeria\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slice2=df.iloc[1:5,2] # It represents the values of 1,2,3,4 rows and 2nd column.\n",
    "df_slice2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Accessing multiple rows and multiple column values at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1874</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1875</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051546</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1876</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051867</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1877</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051867</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  systemic_crisis  exch_usd  domestic_debt_in_default\n",
       "4  1874                0  0.051308                         0\n",
       "5  1875                0  0.051546                         0\n",
       "6  1876                0  0.051867                         0\n",
       "7  1877                0  0.051867                         0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slice3=df.iloc[4:8,3:7]\n",
    "df_slice3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Selecting all rows and specified columns using iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.052264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.441456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.052798</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.149140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.052274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.718593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.051680</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.203897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.051308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.848561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>354.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>378.200000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.217000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>361.900000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>361.900000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>361.900000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.632000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        exch_usd  domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "0       0.052264                         0                                0   \n",
       "1       0.052798                         0                                0   \n",
       "2       0.052274                         0                                0   \n",
       "3       0.051680                         0                                0   \n",
       "4       0.051308                         0                                0   \n",
       "...          ...                       ...                              ...   \n",
       "1054  354.800000                         1                                1   \n",
       "1055  378.200000                         1                                1   \n",
       "1056  361.900000                         1                                1   \n",
       "1057  361.900000                         1                                1   \n",
       "1058  361.900000                         1                                1   \n",
       "\n",
       "      gdp_weighted_default  inflation_annual_cpi  \n",
       "0                      0.0              3.441456  \n",
       "1                      0.0             14.149140  \n",
       "2                      0.0             -3.718593  \n",
       "3                      0.0             11.203897  \n",
       "4                      0.0             -3.848561  \n",
       "...                    ...                   ...  \n",
       "1054                   0.0             -7.670000  \n",
       "1055                   0.0              3.217000  \n",
       "1056                   0.0              4.920000  \n",
       "1057                   0.0              3.720000  \n",
       "1058                   0.0              1.632000  \n",
       "\n",
       "[1059 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slice4=df.iloc[:,5:10] #\n",
    "df_slice4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Selecting all rows and columns starting from 0th position to specified column position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      case  cc3   country  year\n",
       "0        1  DZA   Algeria  1870\n",
       "1        1  DZA   Algeria  1871\n",
       "2        1  DZA   Algeria  1872\n",
       "3        1  DZA   Algeria  1873\n",
       "4        1  DZA   Algeria  1874\n",
       "...    ...  ...       ...   ...\n",
       "1054    70  ZWE  Zimbabwe  2009\n",
       "1055    70  ZWE  Zimbabwe  2010\n",
       "1056    70  ZWE  Zimbabwe  2011\n",
       "1057    70  ZWE  Zimbabwe  2012\n",
       "1058    70  ZWE  Zimbabwe  2013\n",
       "\n",
       "[1059 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slice5=df.iloc[:,:4]\n",
    "df_slice5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Passing values to iloc() using lambda function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1870</td>\n",
       "      <td>1</td>\n",
       "      <td>0.052264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.441456</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1872</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.718593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1874</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.848561</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1876</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051867</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.769547</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1878</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051948</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.492537</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case  cc3  country  year  systemic_crisis  exch_usd  \\\n",
       "0     1  DZA  Algeria  1870                1  0.052264   \n",
       "2     1  DZA  Algeria  1872                0  0.052274   \n",
       "4     1  DZA  Algeria  1874                0  0.051308   \n",
       "6     1  DZA  Algeria  1876                0  0.051867   \n",
       "8     1  DZA  Algeria  1878                0  0.051948   \n",
       "\n",
       "   domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "0                         0                                0   \n",
       "2                         0                                0   \n",
       "4                         0                                0   \n",
       "6                         0                                0   \n",
       "8                         0                                0   \n",
       "\n",
       "   gdp_weighted_default  inflation_annual_cpi  independence  currency_crises  \\\n",
       "0                   0.0              3.441456             0                0   \n",
       "2                   0.0             -3.718593             0                0   \n",
       "4                   0.0             -3.848561             0                0   \n",
       "6                   0.0             -1.769547             0                0   \n",
       "8                   0.0             -1.492537             0                0   \n",
       "\n",
       "   inflation_crises banking_crisis  \n",
       "0                 0         crisis  \n",
       "2                 0      no_crisis  \n",
       "4                 0      no_crisis  \n",
       "6                 0      no_crisis  \n",
       "8                 0      no_crisis  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slice_func = df.iloc[lambda x: x.index % 2 == 0]   # We see all even indexes being selected.\n",
    "df_slice_func.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task \n",
    "Try,\n",
    "\n",
    "cuml_df.iloc[1:3, 0:3].head(),\n",
    "\n",
    "cuml_df.iloc[[0, 2], [1, 3]].head(),\n",
    "\n",
    "cuml_df.iloc[:, [True, False, True, False]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slicing the dataframe using 'loc'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Access of a single row values using loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "case                     1\n",
       "cc3                    DZA\n",
       "country            Algeria\n",
       "year                  1870\n",
       "systemic_crisis          1\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selection by single row\n",
    "df_sl_loc = df.loc[0]\n",
    "df_sl_loc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Access of multiple rows using loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1872</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.718593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1873</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051680</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.203897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1874</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.848561</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case  cc3  country  year  systemic_crisis  exch_usd  \\\n",
       "2     1  DZA  Algeria  1872                0  0.052274   \n",
       "3     1  DZA  Algeria  1873                0  0.051680   \n",
       "4     1  DZA  Algeria  1874                0  0.051308   \n",
       "\n",
       "   domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "2                         0                                0   \n",
       "3                         0                                0   \n",
       "4                         0                                0   \n",
       "\n",
       "   gdp_weighted_default  inflation_annual_cpi  independence  currency_crises  \\\n",
       "2                   0.0             -3.718593             0                0   \n",
       "3                   0.0             11.203897             0                0   \n",
       "4                   0.0             -3.848561             0                0   \n",
       "\n",
       "   inflation_crises banking_crisis  \n",
       "2                 0      no_crisis  \n",
       "3                 0      no_crisis  \n",
       "4                 0      no_crisis  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sl_loc1 = df.loc[[2,3,4]]\n",
    "df_sl_loc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Access of multiple rows using loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1872</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.718593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1873</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051680</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.203897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1874</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.848561</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case  cc3  country  year  systemic_crisis  exch_usd  \\\n",
       "2     1  DZA  Algeria  1872                0  0.052274   \n",
       "3     1  DZA  Algeria  1873                0  0.051680   \n",
       "4     1  DZA  Algeria  1874                0  0.051308   \n",
       "\n",
       "   domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "2                         0                                0   \n",
       "3                         0                                0   \n",
       "4                         0                                0   \n",
       "\n",
       "   gdp_weighted_default  inflation_annual_cpi  independence  currency_crises  \\\n",
       "2                   0.0             -3.718593             0                0   \n",
       "3                   0.0             11.203897             0                0   \n",
       "4                   0.0             -3.848561             0                0   \n",
       "\n",
       "   inflation_crises banking_crisis  \n",
       "2                 0      no_crisis  \n",
       "3                 0      no_crisis  \n",
       "4                 0      no_crisis  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sl_loc1 = df.loc[[2,3,4]]\n",
    "df_sl_loc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Selection by index of rows and column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.052264\n",
       "1     0.052798\n",
       "2     0.052274\n",
       "3     0.051680\n",
       "4     0.051308\n",
       "5     0.051546\n",
       "6     0.051867\n",
       "7     0.051867\n",
       "8     0.051948\n",
       "9     0.052029\n",
       "10    0.052356\n",
       "Name: exch_usd, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sl_loc2 = df.loc[:10, \"exch_usd\"]\n",
    "df_sl_loc2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Selection based on callable function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1870</td>\n",
       "      <td>1</td>\n",
       "      <td>0.052264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.441456</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1871</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052798</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.149140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1872</td>\n",
       "      <td>0</td>\n",
       "      <td>0.052274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.718593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1873</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051680</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.203897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1874</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.848561</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>74.943700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.913000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>76.056300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.522000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>78.102500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.916000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>78.148701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.255000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>87.970698</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.917000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    case  cc3  country  year  systemic_crisis   exch_usd  \\\n",
       "0      1  DZA  Algeria  1870                1   0.052264   \n",
       "1      1  DZA  Algeria  1871                0   0.052798   \n",
       "2      1  DZA  Algeria  1872                0   0.052274   \n",
       "3      1  DZA  Algeria  1873                0   0.051680   \n",
       "4      1  DZA  Algeria  1874                0   0.051308   \n",
       "..   ...  ...      ...   ...              ...        ...   \n",
       "80     1  DZA  Algeria  2010                0  74.943700   \n",
       "81     1  DZA  Algeria  2011                0  76.056300   \n",
       "82     1  DZA  Algeria  2012                0  78.102500   \n",
       "83     1  DZA  Algeria  2013                0  78.148701   \n",
       "84     1  DZA  Algeria  2014                0  87.970698   \n",
       "\n",
       "    domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "0                          0                                0   \n",
       "1                          0                                0   \n",
       "2                          0                                0   \n",
       "3                          0                                0   \n",
       "4                          0                                0   \n",
       "..                       ...                              ...   \n",
       "80                         0                                0   \n",
       "81                         0                                0   \n",
       "82                         0                                0   \n",
       "83                         0                                0   \n",
       "84                         0                                0   \n",
       "\n",
       "    gdp_weighted_default  inflation_annual_cpi  independence  currency_crises  \\\n",
       "0                    0.0              3.441456             0                0   \n",
       "1                    0.0             14.149140             0                0   \n",
       "2                    0.0             -3.718593             0                0   \n",
       "3                    0.0             11.203897             0                0   \n",
       "4                    0.0             -3.848561             0                0   \n",
       "..                   ...                   ...           ...              ...   \n",
       "80                   0.0              3.913000             1                0   \n",
       "81                   0.0              4.522000             1                0   \n",
       "82                   0.0              8.916000             1                0   \n",
       "83                   0.0              3.255000             1                0   \n",
       "84                   0.0              2.917000             1                0   \n",
       "\n",
       "    inflation_crises banking_crisis  \n",
       "0                  0         crisis  \n",
       "1                  0      no_crisis  \n",
       "2                  0      no_crisis  \n",
       "3                  0      no_crisis  \n",
       "4                  0      no_crisis  \n",
       "..               ...            ...  \n",
       "80                 0      no_crisis  \n",
       "81                 0      no_crisis  \n",
       "82                 0      no_crisis  \n",
       "83                 0      no_crisis  \n",
       "84                 0      no_crisis  \n",
       "\n",
       "[85 rows x 14 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sl_loc3 = df.loc[lambda x: x['country'] == 'Algeria']\n",
    "df_sl_loc3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Difference between loc and iloc\n",
    "\n",
    "loc : It can select data for columns based on labels\n",
    "    \n",
    "iloc : It can select data only by integer location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df.iloc[0:5,3:5] ----->     year  systemic_crisis\n",
      "0  1870                1\n",
      "1  1871                0\n",
      "2  1872                0\n",
      "3  1873                0\n",
      "4  1874                0\n",
      "df.loc[0:5,'systemic_crisis':'exch_usd' ]    systemic_crisis  exch_usd\n",
      "0                1  0.052264\n",
      "1                0  0.052798\n",
      "2                0  0.052274\n",
      "3                0  0.051680\n",
      "4                0  0.051308\n",
      "5                0  0.051546\n"
     ]
    }
   ],
   "source": [
    "# Selecting particular value using loc and iloc\n",
    "print(\"df.iloc[0:5,3:5] -----> \",df.iloc[0:5,3:5])\n",
    "print(\"df.loc[0:5,'systemic_crisis':'exch_usd' ]\",df.loc[0:5,\"systemic_crisis\":\"exch_usd\" ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>5.600000e-23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.327468e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>8.460000e-22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.858444e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000e-19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.281114e+03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>1.900000e-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.627989e+04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.198970e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>3.548000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.670000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>3.782000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.217000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>3.619000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.920000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>3.619000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.720000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>3.619000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.632000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      case  cc3   country  year  systemic_crisis      exch_usd  \\\n",
       "1049    70  ZWE  Zimbabwe  2004                1  5.600000e-23   \n",
       "1050    70  ZWE  Zimbabwe  2005                1  8.460000e-22   \n",
       "1051    70  ZWE  Zimbabwe  2006                1  3.000000e-19   \n",
       "1052    70  ZWE  Zimbabwe  2007                1  1.900000e-16   \n",
       "1053    70  ZWE  Zimbabwe  2008                1  2.000000e-03   \n",
       "1054    70  ZWE  Zimbabwe  2009                1  3.548000e+02   \n",
       "1055    70  ZWE  Zimbabwe  2010                0  3.782000e+02   \n",
       "1056    70  ZWE  Zimbabwe  2011                0  3.619000e+02   \n",
       "1057    70  ZWE  Zimbabwe  2012                0  3.619000e+02   \n",
       "1058    70  ZWE  Zimbabwe  2013                0  3.619000e+02   \n",
       "\n",
       "      domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "1049                         1                                1   \n",
       "1050                         1                                1   \n",
       "1051                         1                                1   \n",
       "1052                         1                                1   \n",
       "1053                         1                                1   \n",
       "1054                         1                                1   \n",
       "1055                         1                                1   \n",
       "1056                         1                                1   \n",
       "1057                         1                                1   \n",
       "1058                         1                                1   \n",
       "\n",
       "      gdp_weighted_default  inflation_annual_cpi  independence  \\\n",
       "1049                   0.0          1.327468e+02             1   \n",
       "1050                   0.0          5.858444e+02             1   \n",
       "1051                   0.0          1.281114e+03             1   \n",
       "1052                   0.0          6.627989e+04             1   \n",
       "1053                   0.0          2.198970e+07             1   \n",
       "1054                   0.0         -7.670000e+00             1   \n",
       "1055                   0.0          3.217000e+00             1   \n",
       "1056                   0.0          4.920000e+00             1   \n",
       "1057                   0.0          3.720000e+00             1   \n",
       "1058                   0.0          1.632000e+00             1   \n",
       "\n",
       "      currency_crises  inflation_crises banking_crisis  \n",
       "1049                1                 1         crisis  \n",
       "1050                1                 1         crisis  \n",
       "1051                1                 1         crisis  \n",
       "1052                1                 1         crisis  \n",
       "1053                1                 1         crisis  \n",
       "1054                1                 0         crisis  \n",
       "1055                0                 0      no_crisis  \n",
       "1056                0                 0      no_crisis  \n",
       "1057                0                 0      no_crisis  \n",
       "1058                0                 0      no_crisis  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sl_lst = df[-10:]\n",
    "df_sl_lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering out specific data from a column of a dataframe based on condition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to obtain the countries list which are under \"crisis\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1870</td>\n",
       "      <td>1</td>\n",
       "      <td>5.226400e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.441456e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "      <td>1.219080e+01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.272000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "      <td>2.139190e+01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.590000e+01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>2.278140e+01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>3.170000e+01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>5.500000e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.990970e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>8.460000e-22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.858444e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000e-19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.281114e+03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>1.900000e-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.627989e+04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.198970e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>70</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>3.548000e+02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.670000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      case  cc3   country  year  systemic_crisis      exch_usd  \\\n",
       "0        1  DZA   Algeria  1870                1  5.226400e-02   \n",
       "60       1  DZA   Algeria  1990                1  1.219080e+01   \n",
       "61       1  DZA   Algeria  1991                1  2.139190e+01   \n",
       "62       1  DZA   Algeria  1992                1  2.278140e+01   \n",
       "139      2  AGO    Angola  1992                0  5.500000e-07   \n",
       "...    ...  ...       ...   ...              ...           ...   \n",
       "1050    70  ZWE  Zimbabwe  2005                1  8.460000e-22   \n",
       "1051    70  ZWE  Zimbabwe  2006                1  3.000000e-19   \n",
       "1052    70  ZWE  Zimbabwe  2007                1  1.900000e-16   \n",
       "1053    70  ZWE  Zimbabwe  2008                1  2.000000e-03   \n",
       "1054    70  ZWE  Zimbabwe  2009                1  3.548000e+02   \n",
       "\n",
       "      domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "0                            0                                0   \n",
       "60                           0                                0   \n",
       "61                           0                                1   \n",
       "62                           0                                1   \n",
       "139                          1                                1   \n",
       "...                        ...                              ...   \n",
       "1050                         1                                1   \n",
       "1051                         1                                1   \n",
       "1052                         1                                1   \n",
       "1053                         1                                1   \n",
       "1054                         1                                1   \n",
       "\n",
       "      gdp_weighted_default  inflation_annual_cpi  independence  \\\n",
       "0                     0.00          3.441456e+00             0   \n",
       "60                    0.00          9.272000e+00             1   \n",
       "61                    0.23          2.590000e+01             1   \n",
       "62                    0.23          3.170000e+01             1   \n",
       "139                   0.00          2.990970e+02             1   \n",
       "...                    ...                   ...           ...   \n",
       "1050                  0.00          5.858444e+02             1   \n",
       "1051                  0.00          1.281114e+03             1   \n",
       "1052                  0.00          6.627989e+04             1   \n",
       "1053                  0.00          2.198970e+07             1   \n",
       "1054                  0.00         -7.670000e+00             1   \n",
       "\n",
       "      currency_crises  inflation_crises banking_crisis  \n",
       "0                   0                 0         crisis  \n",
       "60                  1                 0         crisis  \n",
       "61                  1                 1         crisis  \n",
       "62                  0                 1         crisis  \n",
       "139                 1                 1         crisis  \n",
       "...               ...               ...            ...  \n",
       "1050                1                 1         crisis  \n",
       "1051                1                 1         crisis  \n",
       "1052                1                 1         crisis  \n",
       "1053                1                 1         crisis  \n",
       "1054                1                 0         crisis  \n",
       "\n",
       "[94 rows x 14 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"banking_crisis\"]== \"crisis\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering out specific data from a column of a dataframe based on more than one condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_more_cond = (df['country']== 'Algeria') & (df['systemic_crisis'] == 1)\n",
    "df_more_cond.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1870</td>\n",
       "      <td>1</td>\n",
       "      <td>0.052264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.441456</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "      <td>12.190800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.272000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "      <td>21.391900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>22.781400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>31.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    case  cc3  country  year  systemic_crisis   exch_usd  \\\n",
       "0      1  DZA  Algeria  1870                1   0.052264   \n",
       "60     1  DZA  Algeria  1990                1  12.190800   \n",
       "61     1  DZA  Algeria  1991                1  21.391900   \n",
       "62     1  DZA  Algeria  1992                1  22.781400   \n",
       "\n",
       "    domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "0                          0                                0   \n",
       "60                         0                                0   \n",
       "61                         0                                1   \n",
       "62                         0                                1   \n",
       "\n",
       "    gdp_weighted_default  inflation_annual_cpi  independence  currency_crises  \\\n",
       "0                   0.00              3.441456             0                0   \n",
       "60                  0.00              9.272000             1                1   \n",
       "61                  0.23             25.900000             1                1   \n",
       "62                  0.23             31.700000             1                0   \n",
       "\n",
       "    inflation_crises banking_crisis  \n",
       "0                  0         crisis  \n",
       "60                 0         crisis  \n",
       "61                 1         crisis  \n",
       "62                 1         crisis  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df_more_cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figuring out the list of countries present in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Egypt                       155\n",
       "South Africa                114\n",
       "Zimbabwe                     90\n",
       "Algeria                      85\n",
       "Angola                       77\n",
       "Tunisia                      75\n",
       "Morocco                      75\n",
       "Zambia                       72\n",
       "Mauritius                    68\n",
       "Kenya                        67\n",
       "Ivory Coast                  63\n",
       "Nigeria                      60\n",
       "Central African Republic     58\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping by columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central African Republic</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Egypt</th>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155.0</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivory Coast</th>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenya</th>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mauritius</th>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          case  cc3  year  systemic_crisis  exch_usd  \\\n",
       "country                                                                \n",
       "Algeria                     85   85    85               85      85.0   \n",
       "Angola                      77   77    77               77      77.0   \n",
       "Central African Republic    58   58    58               58      58.0   \n",
       "Egypt                      155  155   155              155     155.0   \n",
       "Ivory Coast                 63   63    63               63      63.0   \n",
       "Kenya                       67   67    67               67      67.0   \n",
       "Mauritius                   68   68    68               68      68.0   \n",
       "Morocco                     75   75    75               75      75.0   \n",
       "Nigeria                     60   60    60               60      60.0   \n",
       "South Africa               114  114   114              114     114.0   \n",
       "Tunisia                     75   75    75               75      75.0   \n",
       "Zambia                      72   72    72               72      72.0   \n",
       "Zimbabwe                    90   90    90               90      90.0   \n",
       "\n",
       "                          domestic_debt_in_default  \\\n",
       "country                                              \n",
       "Algeria                                         85   \n",
       "Angola                                          77   \n",
       "Central African Republic                        58   \n",
       "Egypt                                          155   \n",
       "Ivory Coast                                     63   \n",
       "Kenya                                           67   \n",
       "Mauritius                                       68   \n",
       "Morocco                                         75   \n",
       "Nigeria                                         60   \n",
       "South Africa                                   114   \n",
       "Tunisia                                         75   \n",
       "Zambia                                          72   \n",
       "Zimbabwe                                        90   \n",
       "\n",
       "                          sovereign_external_debt_default  \\\n",
       "country                                                     \n",
       "Algeria                                                85   \n",
       "Angola                                                 77   \n",
       "Central African Republic                               58   \n",
       "Egypt                                                 155   \n",
       "Ivory Coast                                            63   \n",
       "Kenya                                                  67   \n",
       "Mauritius                                              68   \n",
       "Morocco                                                75   \n",
       "Nigeria                                                60   \n",
       "South Africa                                          114   \n",
       "Tunisia                                                75   \n",
       "Zambia                                                 72   \n",
       "Zimbabwe                                               90   \n",
       "\n",
       "                          gdp_weighted_default  inflation_annual_cpi  \\\n",
       "country                                                                \n",
       "Algeria                                   85.0                  85.0   \n",
       "Angola                                    77.0                  77.0   \n",
       "Central African Republic                  58.0                  58.0   \n",
       "Egypt                                    155.0                 155.0   \n",
       "Ivory Coast                               63.0                  63.0   \n",
       "Kenya                                     67.0                  67.0   \n",
       "Mauritius                                 68.0                  68.0   \n",
       "Morocco                                   75.0                  75.0   \n",
       "Nigeria                                   60.0                  60.0   \n",
       "South Africa                             114.0                 114.0   \n",
       "Tunisia                                   75.0                  75.0   \n",
       "Zambia                                    72.0                  72.0   \n",
       "Zimbabwe                                  90.0                  90.0   \n",
       "\n",
       "                          independence  currency_crises  inflation_crises  \\\n",
       "country                                                                     \n",
       "Algeria                             85               85                85   \n",
       "Angola                              77               77                77   \n",
       "Central African Republic            58               58                58   \n",
       "Egypt                              155              155               155   \n",
       "Ivory Coast                         63               63                63   \n",
       "Kenya                               67               67                67   \n",
       "Mauritius                           68               68                68   \n",
       "Morocco                             75               75                75   \n",
       "Nigeria                             60               60                60   \n",
       "South Africa                       114              114               114   \n",
       "Tunisia                             75               75                75   \n",
       "Zambia                              72               72                72   \n",
       "Zimbabwe                            90               90                90   \n",
       "\n",
       "                          banking_crisis  \n",
       "country                                   \n",
       "Algeria                               85  \n",
       "Angola                                77  \n",
       "Central African Republic              58  \n",
       "Egypt                                155  \n",
       "Ivory Coast                           63  \n",
       "Kenya                                 67  \n",
       "Mauritius                             68  \n",
       "Morocco                               75  \n",
       "Nigeria                               60  \n",
       "South Africa                         114  \n",
       "Tunisia                               75  \n",
       "Zambia                                72  \n",
       "Zimbabwe                              90  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np # We see here that based on country we are obtaining the relevant data.\n",
    "df.groupby(['country']).agg(np.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binning a DataFrame\n",
    "Binning a dataframe means bins values into discrete intervals.\n",
    "\n",
    "Using \"pd.cut\" we need to segment and sort data values to bins. This function is useful for making a continuous variable to categorical variable. For example, using pd.cut we can convert ratings into good, very good and excellent. It supports binning into an equal number of bins or a pre-specified array of bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Photo</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Flag</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>...</th>\n",
       "      <th>Composure</th>\n",
       "      <th>Marking</th>\n",
       "      <th>StandingTackle</th>\n",
       "      <th>SlidingTackle</th>\n",
       "      <th>GKDiving</th>\n",
       "      <th>GKHandling</th>\n",
       "      <th>GKKicking</th>\n",
       "      <th>GKPositioning</th>\n",
       "      <th>GKReflexes</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>158023</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/158023.png</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>https://cdn.sofifa.org/flags/52.png</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>€226.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20801</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>33</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/20801.png</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>https://cdn.sofifa.org/flags/38.png</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>€127.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>190871</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/190871.png</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>https://cdn.sofifa.org/flags/54.png</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>€228.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>193080</td>\n",
       "      <td>De Gea</td>\n",
       "      <td>27</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/193080.png</td>\n",
       "      <td>Spain</td>\n",
       "      <td>https://cdn.sofifa.org/flags/45.png</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>€138.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192985</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/192985.png</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>https://cdn.sofifa.org/flags/7.png</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>€196.4M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      ID               Name  Age  \\\n",
       "0           0  158023           L. Messi   31   \n",
       "1           1   20801  Cristiano Ronaldo   33   \n",
       "2           2  190871          Neymar Jr   26   \n",
       "3           3  193080             De Gea   27   \n",
       "4           4  192985       K. De Bruyne   27   \n",
       "\n",
       "                                            Photo Nationality  \\\n",
       "0  https://cdn.sofifa.org/players/4/19/158023.png   Argentina   \n",
       "1   https://cdn.sofifa.org/players/4/19/20801.png    Portugal   \n",
       "2  https://cdn.sofifa.org/players/4/19/190871.png      Brazil   \n",
       "3  https://cdn.sofifa.org/players/4/19/193080.png       Spain   \n",
       "4  https://cdn.sofifa.org/players/4/19/192985.png     Belgium   \n",
       "\n",
       "                                  Flag  Overall  Potential  \\\n",
       "0  https://cdn.sofifa.org/flags/52.png       94         94   \n",
       "1  https://cdn.sofifa.org/flags/38.png       94         94   \n",
       "2  https://cdn.sofifa.org/flags/54.png       92         93   \n",
       "3  https://cdn.sofifa.org/flags/45.png       91         93   \n",
       "4   https://cdn.sofifa.org/flags/7.png       91         92   \n",
       "\n",
       "                  Club  ... Composure Marking StandingTackle  SlidingTackle  \\\n",
       "0         FC Barcelona  ...      96.0    33.0           28.0           26.0   \n",
       "1             Juventus  ...      95.0    28.0           31.0           23.0   \n",
       "2  Paris Saint-Germain  ...      94.0    27.0           24.0           33.0   \n",
       "3    Manchester United  ...      68.0    15.0           21.0           13.0   \n",
       "4      Manchester City  ...      88.0    68.0           58.0           51.0   \n",
       "\n",
       "  GKDiving  GKHandling  GKKicking  GKPositioning GKReflexes Release Clause  \n",
       "0      6.0        11.0       15.0           14.0        8.0        €226.5M  \n",
       "1      7.0        11.0       15.0           14.0       11.0        €127.1M  \n",
       "2      9.0         9.0       15.0           15.0       11.0        €228.1M  \n",
       "3     90.0        85.0       87.0           88.0       94.0        €138.6M  \n",
       "4     15.0        13.0        5.0           10.0       13.0        €196.4M  \n",
       "\n",
       "[5 rows x 89 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa19 = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\end to end data sets\\fifa19.csv')\n",
    "fifa19.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we observe an \"Age\" column. We shall convert it into categories using pd.cut.\n",
    "\n",
    "Before binning, we need to know the min Age and max Age of the player in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(fifa19.Age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(fifa19.Age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, the age range of the player is between 16 to 45."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "fifa19['age_bins'] = pd.cut(fifa19.Age,bins=[15,25,35,45],labels=['Budding','Experienced','Well-versed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Club</th>\n",
       "      <th>...</th>\n",
       "      <th>Marking</th>\n",
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       "      <th>SlidingTackle</th>\n",
       "      <th>GKDiving</th>\n",
       "      <th>GKHandling</th>\n",
       "      <th>GKKicking</th>\n",
       "      <th>GKPositioning</th>\n",
       "      <th>GKReflexes</th>\n",
       "      <th>Release Clause</th>\n",
       "      <th>age_bins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>158023</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/158023.png</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>https://cdn.sofifa.org/flags/52.png</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>€226.5M</td>\n",
       "      <td>Experienced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20801</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>33</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/20801.png</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>https://cdn.sofifa.org/flags/38.png</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>€127.1M</td>\n",
       "      <td>Experienced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>190871</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/190871.png</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>https://cdn.sofifa.org/flags/54.png</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>€228.1M</td>\n",
       "      <td>Experienced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>193080</td>\n",
       "      <td>De Gea</td>\n",
       "      <td>27</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/193080.png</td>\n",
       "      <td>Spain</td>\n",
       "      <td>https://cdn.sofifa.org/flags/45.png</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>€138.6M</td>\n",
       "      <td>Experienced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192985</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/192985.png</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>https://cdn.sofifa.org/flags/7.png</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>€196.4M</td>\n",
       "      <td>Experienced</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      ID               Name  Age  \\\n",
       "0           0  158023           L. Messi   31   \n",
       "1           1   20801  Cristiano Ronaldo   33   \n",
       "2           2  190871          Neymar Jr   26   \n",
       "3           3  193080             De Gea   27   \n",
       "4           4  192985       K. De Bruyne   27   \n",
       "\n",
       "                                            Photo Nationality  \\\n",
       "0  https://cdn.sofifa.org/players/4/19/158023.png   Argentina   \n",
       "1   https://cdn.sofifa.org/players/4/19/20801.png    Portugal   \n",
       "2  https://cdn.sofifa.org/players/4/19/190871.png      Brazil   \n",
       "3  https://cdn.sofifa.org/players/4/19/193080.png       Spain   \n",
       "4  https://cdn.sofifa.org/players/4/19/192985.png     Belgium   \n",
       "\n",
       "                                  Flag  Overall  Potential  \\\n",
       "0  https://cdn.sofifa.org/flags/52.png       94         94   \n",
       "1  https://cdn.sofifa.org/flags/38.png       94         94   \n",
       "2  https://cdn.sofifa.org/flags/54.png       92         93   \n",
       "3  https://cdn.sofifa.org/flags/45.png       91         93   \n",
       "4   https://cdn.sofifa.org/flags/7.png       91         92   \n",
       "\n",
       "                  Club  ... Marking StandingTackle SlidingTackle  GKDiving  \\\n",
       "0         FC Barcelona  ...    33.0           28.0          26.0       6.0   \n",
       "1             Juventus  ...    28.0           31.0          23.0       7.0   \n",
       "2  Paris Saint-Germain  ...    27.0           24.0          33.0       9.0   \n",
       "3    Manchester United  ...    15.0           21.0          13.0      90.0   \n",
       "4      Manchester City  ...    68.0           58.0          51.0      15.0   \n",
       "\n",
       "  GKHandling  GKKicking  GKPositioning  GKReflexes Release Clause     age_bins  \n",
       "0       11.0       15.0           14.0         8.0        €226.5M  Experienced  \n",
       "1       11.0       15.0           14.0        11.0        €127.1M  Experienced  \n",
       "2        9.0       15.0           15.0        11.0        €228.1M  Experienced  \n",
       "3       85.0       87.0           88.0        94.0        €138.6M  Experienced  \n",
       "4       13.0        5.0           10.0        13.0        €196.4M  Experienced  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa19.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the end of the dataframe you see a category being created as \"age_bins\" with different labels being assigned to it depending on the ages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sort_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is used to sort by the values along either axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>cc3</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>systemic_crisis</th>\n",
       "      <th>exch_usd</th>\n",
       "      <th>domestic_debt_in_default</th>\n",
       "      <th>sovereign_external_debt_default</th>\n",
       "      <th>gdp_weighted_default</th>\n",
       "      <th>inflation_annual_cpi</th>\n",
       "      <th>independence</th>\n",
       "      <th>currency_crises</th>\n",
       "      <th>inflation_crises</th>\n",
       "      <th>banking_crisis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1870</td>\n",
       "      <td>1</td>\n",
       "      <td>0.052264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.441456</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>22.781400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>31.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "      <td>12.190800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.272000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1989</td>\n",
       "      <td>0</td>\n",
       "      <td>8.032400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.172000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>1988</td>\n",
       "      <td>0</td>\n",
       "      <td>6.730800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.938000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>no_crisis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    case  cc3  country  year  systemic_crisis   exch_usd  \\\n",
       "0      1  DZA  Algeria  1870                1   0.052264   \n",
       "62     1  DZA  Algeria  1992                1  22.781400   \n",
       "60     1  DZA  Algeria  1990                1  12.190800   \n",
       "59     1  DZA  Algeria  1989                0   8.032400   \n",
       "58     1  DZA  Algeria  1988                0   6.730800   \n",
       "\n",
       "    domestic_debt_in_default  sovereign_external_debt_default  \\\n",
       "0                          0                                0   \n",
       "62                         0                                1   \n",
       "60                         0                                0   \n",
       "59                         0                                0   \n",
       "58                         0                                0   \n",
       "\n",
       "    gdp_weighted_default  inflation_annual_cpi  independence  currency_crises  \\\n",
       "0                   0.00              3.441456             0                0   \n",
       "62                  0.23             31.700000             1                0   \n",
       "60                  0.00              9.272000             1                1   \n",
       "59                  0.00              9.172000             1                1   \n",
       "58                  0.00              5.938000             1                1   \n",
       "\n",
       "    inflation_crises banking_crisis  \n",
       "0                  0         crisis  \n",
       "62                 1         crisis  \n",
       "60                 0         crisis  \n",
       "59                 0      no_crisis  \n",
       "58                 0      no_crisis  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by=['country'],ascending=[True])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now see the values are displayed based on the country column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining DataFrames in Pandas\n",
    "In many of the Data Science competitions, we see that the datasets being provided are not limited to single file, but it is a combination of various files together. Each file convey it's own story, we need to combine all those stories together and then analysis the insights of the problem. In this tutorial, we will be using one such example i.e \"restaurant_data\".\n",
    "\n",
    "# Topics which will be covered are:-\n",
    "\n",
    "Concatenating DataFrame to a single dataframe\n",
    "Merging DataFrames on specific keys by different join logics like left-join, inner-join etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate DataFrames\n",
    "Importing various datasets and passing it to different dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User info\n",
    "\n",
    "usercuisine = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\restaurant-data-with-consumer-ratings\\usercuisine.csv')\n",
    "payment     = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\restaurant-data-with-consumer-ratings\\userpayment.csv')\n",
    "profile     = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\restaurant-data-with-consumer-ratings\\userprofile.csv') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# restaurant info\n",
    "rest_accepts = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\restaurant-data-with-consumer-ratings\\chefmozaccepts.csv')\n",
    "rest_cuisine = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\restaurant-data-with-consumer-ratings\\chefmozcuisine.csv')\n",
    "rest_hours   = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\restaurant-data-with-consumer-ratings\\chefmozhours4.csv')\n",
    "rest_parking = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\restaurant-data-with-consumer-ratings\\chefmozparking.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loc and rating info\n",
    "geo_loc = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\restaurant-data-with-consumer-ratings\\geoplaces2.csv')\n",
    "rating_final = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\restaurant-data-with-consumer-ratings\\rating_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  userID          Rcuisine\n",
       "0  U1001          American\n",
       "1  U1002           Mexican\n",
       "2  U1003           Mexican\n",
       "3  U1004            Bakery\n",
       "4  U1004  Breakfast-Brunch"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usercuisine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "  userID          Upayment\n",
       "0  U1001              cash\n",
       "1  U1002              cash\n",
       "2  U1003              cash\n",
       "3  U1004              cash\n",
       "4  U1004  bank_debit_cards"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>smoker</th>\n",
       "      <th>drink_level</th>\n",
       "      <th>dress_preference</th>\n",
       "      <th>ambience</th>\n",
       "      <th>transport</th>\n",
       "      <th>marital_status</th>\n",
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       "      <td>thrifty-protector</td>\n",
       "      <td>none</td>\n",
       "      <td>student</td>\n",
       "      <td>black</td>\n",
       "      <td>69</td>\n",
       "      <td>medium</td>\n",
       "      <td>1.77</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U1002</td>\n",
       "      <td>22.150087</td>\n",
       "      <td>-100.983325</td>\n",
       "      <td>false</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>informal</td>\n",
       "      <td>family</td>\n",
       "      <td>public</td>\n",
       "      <td>single</td>\n",
       "      <td>independent</td>\n",
       "      <td>1990</td>\n",
       "      <td>technology</td>\n",
       "      <td>hunter-ostentatious</td>\n",
       "      <td>Catholic</td>\n",
       "      <td>student</td>\n",
       "      <td>red</td>\n",
       "      <td>40</td>\n",
       "      <td>low</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U1003</td>\n",
       "      <td>22.119847</td>\n",
       "      <td>-100.946527</td>\n",
       "      <td>false</td>\n",
       "      <td>social drinker</td>\n",
       "      <td>formal</td>\n",
       "      <td>family</td>\n",
       "      <td>public</td>\n",
       "      <td>single</td>\n",
       "      <td>independent</td>\n",
       "      <td>1989</td>\n",
       "      <td>none</td>\n",
       "      <td>hard-worker</td>\n",
       "      <td>Catholic</td>\n",
       "      <td>student</td>\n",
       "      <td>blue</td>\n",
       "      <td>60</td>\n",
       "      <td>low</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U1004</td>\n",
       "      <td>18.867000</td>\n",
       "      <td>-99.183000</td>\n",
       "      <td>false</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>informal</td>\n",
       "      <td>family</td>\n",
       "      <td>public</td>\n",
       "      <td>single</td>\n",
       "      <td>independent</td>\n",
       "      <td>1940</td>\n",
       "      <td>variety</td>\n",
       "      <td>hard-worker</td>\n",
       "      <td>none</td>\n",
       "      <td>professional</td>\n",
       "      <td>green</td>\n",
       "      <td>44</td>\n",
       "      <td>medium</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U1005</td>\n",
       "      <td>22.183477</td>\n",
       "      <td>-100.959891</td>\n",
       "      <td>false</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>no preference</td>\n",
       "      <td>family</td>\n",
       "      <td>public</td>\n",
       "      <td>single</td>\n",
       "      <td>independent</td>\n",
       "      <td>1992</td>\n",
       "      <td>none</td>\n",
       "      <td>thrifty-protector</td>\n",
       "      <td>Catholic</td>\n",
       "      <td>student</td>\n",
       "      <td>black</td>\n",
       "      <td>65</td>\n",
       "      <td>medium</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  userID   latitude   longitude smoker     drink_level dress_preference  \\\n",
       "0  U1001  22.139997 -100.978803  false      abstemious         informal   \n",
       "1  U1002  22.150087 -100.983325  false      abstemious         informal   \n",
       "2  U1003  22.119847 -100.946527  false  social drinker           formal   \n",
       "3  U1004  18.867000  -99.183000  false      abstemious         informal   \n",
       "4  U1005  22.183477 -100.959891  false      abstemious    no preference   \n",
       "\n",
       "  ambience transport marital_status        hijos  birth_year    interest  \\\n",
       "0   family   on foot         single  independent        1989     variety   \n",
       "1   family    public         single  independent        1990  technology   \n",
       "2   family    public         single  independent        1989        none   \n",
       "3   family    public         single  independent        1940     variety   \n",
       "4   family    public         single  independent        1992        none   \n",
       "\n",
       "           personality  religion      activity  color  weight  budget  height  \n",
       "0    thrifty-protector      none       student  black      69  medium    1.77  \n",
       "1  hunter-ostentatious  Catholic       student    red      40     low    1.87  \n",
       "2          hard-worker  Catholic       student   blue      60     low    1.69  \n",
       "3          hard-worker      none  professional  green      44  medium    1.53  \n",
       "4    thrifty-protector  Catholic       student  black      65  medium    1.69  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>Rpayment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135110</td>\n",
       "      <td>cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135110</td>\n",
       "      <td>VISA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135110</td>\n",
       "      <td>MasterCard-Eurocard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135110</td>\n",
       "      <td>American_Express</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135110</td>\n",
       "      <td>bank_debit_cards</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID             Rpayment\n",
       "0   135110                 cash\n",
       "1   135110                 VISA\n",
       "2   135110  MasterCard-Eurocard\n",
       "3   135110     American_Express\n",
       "4   135110     bank_debit_cards"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_accepts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>Rcuisine</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135110</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135109</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135107</td>\n",
       "      <td>Latin_American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135106</td>\n",
       "      <td>Mexican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135105</td>\n",
       "      <td>Fast_Food</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID        Rcuisine\n",
       "0   135110         Spanish\n",
       "1   135109         Italian\n",
       "2   135107  Latin_American\n",
       "3   135106         Mexican\n",
       "4   135105       Fast_Food"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_cuisine.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135111</td>\n",
       "      <td>00:00-23:30;</td>\n",
       "      <td>Mon;Tue;Wed;Thu;Fri;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135111</td>\n",
       "      <td>00:00-23:30;</td>\n",
       "      <td>Sat;</td>\n",
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       "      <th>2</th>\n",
       "      <td>135111</td>\n",
       "      <td>00:00-23:30;</td>\n",
       "      <td>Sun;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135110</td>\n",
       "      <td>08:00-19:00;</td>\n",
       "      <td>Mon;Tue;Wed;Thu;Fri;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135110</td>\n",
       "      <td>00:00-00:00;</td>\n",
       "      <td>Sat;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID         hours                  days\n",
       "0   135111  00:00-23:30;  Mon;Tue;Wed;Thu;Fri;\n",
       "1   135111  00:00-23:30;                  Sat;\n",
       "2   135111  00:00-23:30;                  Sun;\n",
       "3   135110  08:00-19:00;  Mon;Tue;Wed;Thu;Fri;\n",
       "4   135110  00:00-00:00;                  Sat;"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_hours.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>parking_lot</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135111</td>\n",
       "      <td>public</td>\n",
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       "      <th>1</th>\n",
       "      <td>135110</td>\n",
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       "      <th>2</th>\n",
       "      <td>135109</td>\n",
       "      <td>none</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135108</td>\n",
       "      <td>none</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135107</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID parking_lot\n",
       "0   135111      public\n",
       "1   135110        none\n",
       "2   135109        none\n",
       "3   135108        none\n",
       "4   135107        none"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_parking.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>service_rating</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U1077</td>\n",
       "      <td>135085</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U1077</td>\n",
       "      <td>135038</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U1077</td>\n",
       "      <td>132825</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U1077</td>\n",
       "      <td>135060</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U1068</td>\n",
       "      <td>135104</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  userID  placeID  rating  food_rating  service_rating\n",
       "0  U1077   135085       2            2               2\n",
       "1  U1077   135038       2            2               1\n",
       "2  U1077   132825       2            2               2\n",
       "3  U1077   135060       1            2               2\n",
       "4  U1068   135104       1            1               2"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>fax</th>\n",
       "      <th>zip</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>smoking_area</th>\n",
       "      <th>dress_code</th>\n",
       "      <th>accessibility</th>\n",
       "      <th>price</th>\n",
       "      <th>url</th>\n",
       "      <th>Rambience</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134999</td>\n",
       "      <td>18.915421</td>\n",
       "      <td>-99.184871</td>\n",
       "      <td>0101000020957F000088568DE356715AC138C0A525FC46...</td>\n",
       "      <td>Kiku Cuernavaca</td>\n",
       "      <td>Revolucion</td>\n",
       "      <td>Cuernavaca</td>\n",
       "      <td>Morelos</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>No_Alcohol_Served</td>\n",
       "      <td>none</td>\n",
       "      <td>informal</td>\n",
       "      <td>no_accessibility</td>\n",
       "      <td>medium</td>\n",
       "      <td>kikucuernavaca.com.mx</td>\n",
       "      <td>familiar</td>\n",
       "      <td>f</td>\n",
       "      <td>closed</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>132825</td>\n",
       "      <td>22.147392</td>\n",
       "      <td>-100.983092</td>\n",
       "      <td>0101000020957F00001AD016568C4858C1243261274BA5...</td>\n",
       "      <td>puesto de tacos</td>\n",
       "      <td>esquina santos degollado y leon guzman</td>\n",
       "      <td>s.l.p.</td>\n",
       "      <td>s.l.p.</td>\n",
       "      <td>mexico</td>\n",
       "      <td>?</td>\n",
       "      <td>78280</td>\n",
       "      <td>No_Alcohol_Served</td>\n",
       "      <td>none</td>\n",
       "      <td>informal</td>\n",
       "      <td>completely</td>\n",
       "      <td>low</td>\n",
       "      <td>?</td>\n",
       "      <td>familiar</td>\n",
       "      <td>f</td>\n",
       "      <td>open</td>\n",
       "      <td>none</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135106</td>\n",
       "      <td>22.149709</td>\n",
       "      <td>-100.976093</td>\n",
       "      <td>0101000020957F0000649D6F21634858C119AE9BF528A3...</td>\n",
       "      <td>El Rinc�n de San Francisco</td>\n",
       "      <td>Universidad 169</td>\n",
       "      <td>San Luis Potosi</td>\n",
       "      <td>San Luis Potosi</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>?</td>\n",
       "      <td>78000</td>\n",
       "      <td>Wine-Beer</td>\n",
       "      <td>only at bar</td>\n",
       "      <td>informal</td>\n",
       "      <td>partially</td>\n",
       "      <td>medium</td>\n",
       "      <td>?</td>\n",
       "      <td>familiar</td>\n",
       "      <td>f</td>\n",
       "      <td>open</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132667</td>\n",
       "      <td>23.752697</td>\n",
       "      <td>-99.163359</td>\n",
       "      <td>0101000020957F00005D67BCDDED8157C1222A2DC8D84D...</td>\n",
       "      <td>little pizza Emilio Portes Gil</td>\n",
       "      <td>calle emilio portes gil</td>\n",
       "      <td>victoria</td>\n",
       "      <td>tamaulipas</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>No_Alcohol_Served</td>\n",
       "      <td>none</td>\n",
       "      <td>informal</td>\n",
       "      <td>completely</td>\n",
       "      <td>low</td>\n",
       "      <td>?</td>\n",
       "      <td>familiar</td>\n",
       "      <td>t</td>\n",
       "      <td>closed</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>132613</td>\n",
       "      <td>23.752903</td>\n",
       "      <td>-99.165076</td>\n",
       "      <td>0101000020957F00008EBA2D06DC8157C194E03B7B504E...</td>\n",
       "      <td>carnitas_mata</td>\n",
       "      <td>lic. Emilio portes gil</td>\n",
       "      <td>victoria</td>\n",
       "      <td>Tamaulipas</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>No_Alcohol_Served</td>\n",
       "      <td>permitted</td>\n",
       "      <td>informal</td>\n",
       "      <td>completely</td>\n",
       "      <td>medium</td>\n",
       "      <td>?</td>\n",
       "      <td>familiar</td>\n",
       "      <td>t</td>\n",
       "      <td>closed</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID   latitude   longitude  \\\n",
       "0   134999  18.915421  -99.184871   \n",
       "1   132825  22.147392 -100.983092   \n",
       "2   135106  22.149709 -100.976093   \n",
       "3   132667  23.752697  -99.163359   \n",
       "4   132613  23.752903  -99.165076   \n",
       "\n",
       "                                      the_geom_meter  \\\n",
       "0  0101000020957F000088568DE356715AC138C0A525FC46...   \n",
       "1  0101000020957F00001AD016568C4858C1243261274BA5...   \n",
       "2  0101000020957F0000649D6F21634858C119AE9BF528A3...   \n",
       "3  0101000020957F00005D67BCDDED8157C1222A2DC8D84D...   \n",
       "4  0101000020957F00008EBA2D06DC8157C194E03B7B504E...   \n",
       "\n",
       "                             name                                 address  \\\n",
       "0                 Kiku Cuernavaca                              Revolucion   \n",
       "1                 puesto de tacos  esquina santos degollado y leon guzman   \n",
       "2      El Rinc�n de San Francisco                         Universidad 169   \n",
       "3  little pizza Emilio Portes Gil                 calle emilio portes gil   \n",
       "4                   carnitas_mata                  lic. Emilio portes gil   \n",
       "\n",
       "              city            state country fax    zip            alcohol  \\\n",
       "0       Cuernavaca          Morelos  Mexico   ?      ?  No_Alcohol_Served   \n",
       "1           s.l.p.           s.l.p.  mexico   ?  78280  No_Alcohol_Served   \n",
       "2  San Luis Potosi  San Luis Potosi  Mexico   ?  78000          Wine-Beer   \n",
       "3        victoria        tamaulipas       ?   ?      ?  No_Alcohol_Served   \n",
       "4         victoria       Tamaulipas  Mexico   ?      ?  No_Alcohol_Served   \n",
       "\n",
       "  smoking_area dress_code     accessibility   price                    url  \\\n",
       "0         none   informal  no_accessibility  medium  kikucuernavaca.com.mx   \n",
       "1         none   informal        completely     low                      ?   \n",
       "2  only at bar   informal         partially  medium                      ?   \n",
       "3         none   informal        completely     low                      ?   \n",
       "4    permitted   informal        completely  medium                      ?   \n",
       "\n",
       "  Rambience franchise    area other_services  \n",
       "0  familiar         f  closed           none  \n",
       "1  familiar         f    open           none  \n",
       "2  familiar         f    open           none  \n",
       "3  familiar         t  closed           none  \n",
       "4  familiar         t  closed           none  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns',25)\n",
    "geo_loc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function pd.set_option('display.max_columns') is used to display all columns present in dataframe , instead of printing (...)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining all data frames into a single data using \"pd.concat\" function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>Rcuisine</th>\n",
       "      <th>Upayment</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>smoker</th>\n",
       "      <th>drink_level</th>\n",
       "      <th>dress_preference</th>\n",
       "      <th>ambience</th>\n",
       "      <th>transport</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>hijos</th>\n",
       "      <th>...</th>\n",
       "      <th>smoking_area</th>\n",
       "      <th>dress_code</th>\n",
       "      <th>accessibility</th>\n",
       "      <th>price</th>\n",
       "      <th>url</th>\n",
       "      <th>Rambience</th>\n",
       "      <th>franchise</th>\n",
       "      <th>area</th>\n",
       "      <th>other_services</th>\n",
       "      <th>rating</th>\n",
       "      <th>food_rating</th>\n",
       "      <th>service_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U1001</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U1002</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U1003</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Breakfast-Brunch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  userID          Rcuisine Upayment  latitude  longitude smoker drink_level  \\\n",
       "0  U1001          American      NaN       NaN        NaN    NaN         NaN   \n",
       "1  U1002           Mexican      NaN       NaN        NaN    NaN         NaN   \n",
       "2  U1003           Mexican      NaN       NaN        NaN    NaN         NaN   \n",
       "3  U1004            Bakery      NaN       NaN        NaN    NaN         NaN   \n",
       "4  U1004  Breakfast-Brunch      NaN       NaN        NaN    NaN         NaN   \n",
       "\n",
       "  dress_preference ambience transport marital_status hijos  ...  smoking_area  \\\n",
       "0              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "1              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "2              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "3              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "4              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "\n",
       "  dress_code accessibility price  url Rambience  franchise area  \\\n",
       "0        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "1        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "2        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "3        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "4        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "\n",
       "   other_services  rating food_rating service_rating  \n",
       "0             NaN     NaN         NaN            NaN  \n",
       "1             NaN     NaN         NaN            NaN  \n",
       "2             NaN     NaN         NaN            NaN  \n",
       "3             NaN     NaN         NaN            NaN  \n",
       "4             NaN     NaN         NaN            NaN  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_data = pd.concat([usercuisine,payment,profile,rest_accepts,rest_cuisine,rest_hours,rest_parking,geo_loc, rating_final])\n",
    "rest_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that entire data is now copied into a single dataframe. \n",
    "Concatenating technique is similar to Union used in set theory, that is the reason we observe null values in it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us modify above \"concat\" function by including \"ignore_index\" parameter in it. This parameter is very beneficial in indexing as different dataframes shows it's own index while combining and can lead to a ambiguity. Inorder to remove ambiguity we can make use of ignore_index option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>Rcuisine</th>\n",
       "      <th>Upayment</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>smoker</th>\n",
       "      <th>drink_level</th>\n",
       "      <th>dress_preference</th>\n",
       "      <th>ambience</th>\n",
       "      <th>transport</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>hijos</th>\n",
       "      <th>...</th>\n",
       "      <th>smoking_area</th>\n",
       "      <th>dress_code</th>\n",
       "      <th>accessibility</th>\n",
       "      <th>price</th>\n",
       "      <th>url</th>\n",
       "      <th>Rambience</th>\n",
       "      <th>franchise</th>\n",
       "      <th>area</th>\n",
       "      <th>other_services</th>\n",
       "      <th>rating</th>\n",
       "      <th>food_rating</th>\n",
       "      <th>service_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U1001</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U1002</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U1003</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Breakfast-Brunch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  userID          Rcuisine Upayment  latitude  longitude smoker drink_level  \\\n",
       "0  U1001          American      NaN       NaN        NaN    NaN         NaN   \n",
       "1  U1002           Mexican      NaN       NaN        NaN    NaN         NaN   \n",
       "2  U1003           Mexican      NaN       NaN        NaN    NaN         NaN   \n",
       "3  U1004            Bakery      NaN       NaN        NaN    NaN         NaN   \n",
       "4  U1004  Breakfast-Brunch      NaN       NaN        NaN    NaN         NaN   \n",
       "\n",
       "  dress_preference ambience transport marital_status hijos  ...  smoking_area  \\\n",
       "0              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "1              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "2              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "3              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "4              NaN      NaN       NaN            NaN   NaN  ...           NaN   \n",
       "\n",
       "  dress_code accessibility price  url Rambience  franchise area  \\\n",
       "0        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "1        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "2        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "3        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "4        NaN           NaN   NaN  NaN       NaN        NaN  NaN   \n",
       "\n",
       "   other_services  rating food_rating service_rating  \n",
       "0             NaN     NaN         NaN            NaN  \n",
       "1             NaN     NaN         NaN            NaN  \n",
       "2             NaN     NaN         NaN            NaN  \n",
       "3             NaN     NaN         NaN            NaN  \n",
       "4             NaN     NaN         NaN            NaN  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_data = pd.concat([usercuisine,payment,profile,rest_accepts,rest_cuisine,rest_hours,rest_parking,geo_loc,rating_final], ignore_index=True)\n",
    "rest_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are inquistive to know what data came from which dataframe, we can make use of keys parameter specifying the label names of the DataFrames in a list. \n",
    "Here, we will perform concatenation with the keys for DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [usercuisine,payment,profile,rest_accepts,rest_cuisine,rest_hours,rest_parking,geo_loc,rating_final]\n",
    "\n",
    "rest_keys = pd.concat(frames, keys = ['usr_cu','pay','pro','rst_accpt','rst_cu','rst_hrs','rst_prkg','glc','rating_fnl'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>placeID</th>\n",
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       "      <th>days</th>\n",
       "      <th>parking_lot</th>\n",
       "      <th>the_geom_meter</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         userID          Rcuisine Upayment  latitude  longitude smoker  \\\n",
       "usr_cu 0  U1001          American      NaN       NaN        NaN    NaN   \n",
       "       1  U1002           Mexican      NaN       NaN        NaN    NaN   \n",
       "       2  U1003           Mexican      NaN       NaN        NaN    NaN   \n",
       "       3  U1004            Bakery      NaN       NaN        NaN    NaN   \n",
       "       4  U1004  Breakfast-Brunch      NaN       NaN        NaN    NaN   \n",
       "\n",
       "         drink_level dress_preference ambience transport marital_status hijos  \\\n",
       "usr_cu 0         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "       1         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "       2         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "       3         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "       4         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "\n",
       "          birth_year interest personality religion activity color  weight  \\\n",
       "usr_cu 0         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "       1         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "       2         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "       3         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "       4         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "\n",
       "         budget  height  placeID Rpayment hours days parking_lot  \\\n",
       "usr_cu 0    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "       1    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "       2    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "       3    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "       4    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "\n",
       "         the_geom_meter name address city state country  fax  zip alcohol  \\\n",
       "usr_cu 0            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "       1            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "       2            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "       3            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "       4            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "\n",
       "         smoking_area dress_code accessibility price  url Rambience franchise  \\\n",
       "usr_cu 0          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "       1          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "       2          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "       3          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "       4          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "\n",
       "         area other_services  rating  food_rating  service_rating  \n",
       "usr_cu 0  NaN            NaN     NaN          NaN             NaN  \n",
       "       1  NaN            NaN     NaN          NaN             NaN  \n",
       "       2  NaN            NaN     NaN          NaN             NaN  \n",
       "       3  NaN            NaN     NaN          NaN             NaN  \n",
       "       4  NaN            NaN     NaN          NaN             NaN  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns',50)\n",
    "rest_keys.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also retrieve the data by mentioning the specific key using the loc method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U1003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U1004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U1004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bank_debit_cards</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  userID Rcuisine          Upayment  latitude  longitude smoker drink_level  \\\n",
       "0  U1001      NaN              cash       NaN        NaN    NaN         NaN   \n",
       "1  U1002      NaN              cash       NaN        NaN    NaN         NaN   \n",
       "2  U1003      NaN              cash       NaN        NaN    NaN         NaN   \n",
       "3  U1004      NaN              cash       NaN        NaN    NaN         NaN   \n",
       "4  U1004      NaN  bank_debit_cards       NaN        NaN    NaN         NaN   \n",
       "\n",
       "  dress_preference ambience transport marital_status hijos  birth_year  \\\n",
       "0              NaN      NaN       NaN            NaN   NaN         NaN   \n",
       "1              NaN      NaN       NaN            NaN   NaN         NaN   \n",
       "2              NaN      NaN       NaN            NaN   NaN         NaN   \n",
       "3              NaN      NaN       NaN            NaN   NaN         NaN   \n",
       "4              NaN      NaN       NaN            NaN   NaN         NaN   \n",
       "\n",
       "  interest personality religion activity color  weight budget  height  \\\n",
       "0      NaN         NaN      NaN      NaN   NaN     NaN    NaN     NaN   \n",
       "1      NaN         NaN      NaN      NaN   NaN     NaN    NaN     NaN   \n",
       "2      NaN         NaN      NaN      NaN   NaN     NaN    NaN     NaN   \n",
       "3      NaN         NaN      NaN      NaN   NaN     NaN    NaN     NaN   \n",
       "4      NaN         NaN      NaN      NaN   NaN     NaN    NaN     NaN   \n",
       "\n",
       "   placeID Rpayment hours days parking_lot the_geom_meter name address city  \\\n",
       "0      NaN      NaN   NaN  NaN         NaN            NaN  NaN     NaN  NaN   \n",
       "1      NaN      NaN   NaN  NaN         NaN            NaN  NaN     NaN  NaN   \n",
       "2      NaN      NaN   NaN  NaN         NaN            NaN  NaN     NaN  NaN   \n",
       "3      NaN      NaN   NaN  NaN         NaN            NaN  NaN     NaN  NaN   \n",
       "4      NaN      NaN   NaN  NaN         NaN            NaN  NaN     NaN  NaN   \n",
       "\n",
       "  state country  fax  zip alcohol smoking_area dress_code accessibility price  \\\n",
       "0   NaN     NaN  NaN  NaN     NaN          NaN        NaN           NaN   NaN   \n",
       "1   NaN     NaN  NaN  NaN     NaN          NaN        NaN           NaN   NaN   \n",
       "2   NaN     NaN  NaN  NaN     NaN          NaN        NaN           NaN   NaN   \n",
       "3   NaN     NaN  NaN  NaN     NaN          NaN        NaN           NaN   NaN   \n",
       "4   NaN     NaN  NaN  NaN     NaN          NaN        NaN           NaN   NaN   \n",
       "\n",
       "   url Rambience franchise area other_services  rating  food_rating  \\\n",
       "0  NaN       NaN       NaN  NaN            NaN     NaN          NaN   \n",
       "1  NaN       NaN       NaN  NaN            NaN     NaN          NaN   \n",
       "2  NaN       NaN       NaN  NaN            NaN     NaN          NaN   \n",
       "3  NaN       NaN       NaN  NaN            NaN     NaN          NaN   \n",
       "4  NaN       NaN       NaN  NaN            NaN     NaN          NaN   \n",
       "\n",
       "   service_rating  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns',50)\n",
    "rest_keys.loc['pay'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also pass a dictionary to concat() function in which the dictionary keys will be used as \"keys\" argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>Rcuisine</th>\n",
       "      <th>Upayment</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>smoker</th>\n",
       "      <th>drink_level</th>\n",
       "      <th>dress_preference</th>\n",
       "      <th>ambience</th>\n",
       "      <th>transport</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>hijos</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>interest</th>\n",
       "      <th>personality</th>\n",
       "      <th>religion</th>\n",
       "      <th>activity</th>\n",
       "      <th>color</th>\n",
       "      <th>weight</th>\n",
       "      <th>budget</th>\n",
       "      <th>height</th>\n",
       "      <th>placeID</th>\n",
       "      <th>Rpayment</th>\n",
       "      <th>hours</th>\n",
       "      <th>days</th>\n",
       "      <th>parking_lot</th>\n",
       "      <th>the_geom_meter</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>fax</th>\n",
       "      <th>zip</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>smoking_area</th>\n",
       "      <th>dress_code</th>\n",
       "      <th>accessibility</th>\n",
       "      <th>price</th>\n",
       "      <th>url</th>\n",
       "      <th>Rambience</th>\n",
       "      <th>franchise</th>\n",
       "      <th>area</th>\n",
       "      <th>other_services</th>\n",
       "      <th>rating</th>\n",
       "      <th>food_rating</th>\n",
       "      <th>service_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">usr_cu</th>\n",
       "      <th>0</th>\n",
       "      <td>U1001</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U1002</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U1003</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Breakfast-Brunch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         userID          Rcuisine Upayment  latitude  longitude smoker  \\\n",
       "usr_cu 0  U1001          American      NaN       NaN        NaN    NaN   \n",
       "       1  U1002           Mexican      NaN       NaN        NaN    NaN   \n",
       "       2  U1003           Mexican      NaN       NaN        NaN    NaN   \n",
       "       3  U1004            Bakery      NaN       NaN        NaN    NaN   \n",
       "       4  U1004  Breakfast-Brunch      NaN       NaN        NaN    NaN   \n",
       "\n",
       "         drink_level dress_preference ambience transport marital_status hijos  \\\n",
       "usr_cu 0         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "       1         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "       2         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "       3         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "       4         NaN              NaN      NaN       NaN            NaN   NaN   \n",
       "\n",
       "          birth_year interest personality religion activity color  weight  \\\n",
       "usr_cu 0         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "       1         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "       2         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "       3         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "       4         NaN      NaN         NaN      NaN      NaN   NaN     NaN   \n",
       "\n",
       "         budget  height  placeID Rpayment hours days parking_lot  \\\n",
       "usr_cu 0    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "       1    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "       2    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "       3    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "       4    NaN     NaN      NaN      NaN   NaN  NaN         NaN   \n",
       "\n",
       "         the_geom_meter name address city state country  fax  zip alcohol  \\\n",
       "usr_cu 0            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "       1            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "       2            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "       3            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "       4            NaN  NaN     NaN  NaN   NaN     NaN  NaN  NaN     NaN   \n",
       "\n",
       "         smoking_area dress_code accessibility price  url Rambience franchise  \\\n",
       "usr_cu 0          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "       1          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "       2          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "       3          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "       4          NaN        NaN           NaN   NaN  NaN       NaN       NaN   \n",
       "\n",
       "         area other_services  rating  food_rating  service_rating  \n",
       "usr_cu 0  NaN            NaN     NaN          NaN             NaN  \n",
       "       1  NaN            NaN     NaN          NaN             NaN  \n",
       "       2  NaN            NaN     NaN          NaN             NaN  \n",
       "       3  NaN            NaN     NaN          NaN             NaN  \n",
       "       4  NaN            NaN     NaN          NaN             NaN  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_dict_keys = {'usr_cu':usercuisine,'pay':payment,'pro':profile,'rst_accpt':rest_accepts,\n",
    "                  'rst_cu':rest_cuisine,'rst_hrs':rest_hours,'rst_prkg':\n",
    "                  rest_parking,'glc':geo_loc,'rating_fnl':rating_final}\n",
    "\n",
    "rest_keys_frame = pd.concat(rest_dict_keys)\n",
    "rest_keys_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To concatenate dataframes along columns we make use of axis=1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "rest_col = pd.concat([usercuisine,payment,profile],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>Rcuisine</th>\n",
       "      <th>userID</th>\n",
       "      <th>Upayment</th>\n",
       "      <th>userID</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>smoker</th>\n",
       "      <th>drink_level</th>\n",
       "      <th>dress_preference</th>\n",
       "      <th>ambience</th>\n",
       "      <th>transport</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>hijos</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>interest</th>\n",
       "      <th>personality</th>\n",
       "      <th>religion</th>\n",
       "      <th>activity</th>\n",
       "      <th>color</th>\n",
       "      <th>weight</th>\n",
       "      <th>budget</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U1001</td>\n",
       "      <td>American</td>\n",
       "      <td>U1001</td>\n",
       "      <td>cash</td>\n",
       "      <td>U1001</td>\n",
       "      <td>22.139997</td>\n",
       "      <td>-100.978803</td>\n",
       "      <td>false</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>informal</td>\n",
       "      <td>family</td>\n",
       "      <td>on foot</td>\n",
       "      <td>single</td>\n",
       "      <td>independent</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>variety</td>\n",
       "      <td>thrifty-protector</td>\n",
       "      <td>none</td>\n",
       "      <td>student</td>\n",
       "      <td>black</td>\n",
       "      <td>69.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U1002</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>U1002</td>\n",
       "      <td>cash</td>\n",
       "      <td>U1002</td>\n",
       "      <td>22.150087</td>\n",
       "      <td>-100.983325</td>\n",
       "      <td>false</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>informal</td>\n",
       "      <td>family</td>\n",
       "      <td>public</td>\n",
       "      <td>single</td>\n",
       "      <td>independent</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>technology</td>\n",
       "      <td>hunter-ostentatious</td>\n",
       "      <td>Catholic</td>\n",
       "      <td>student</td>\n",
       "      <td>red</td>\n",
       "      <td>40.0</td>\n",
       "      <td>low</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U1003</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>U1003</td>\n",
       "      <td>cash</td>\n",
       "      <td>U1003</td>\n",
       "      <td>22.119847</td>\n",
       "      <td>-100.946527</td>\n",
       "      <td>false</td>\n",
       "      <td>social drinker</td>\n",
       "      <td>formal</td>\n",
       "      <td>family</td>\n",
       "      <td>public</td>\n",
       "      <td>single</td>\n",
       "      <td>independent</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>none</td>\n",
       "      <td>hard-worker</td>\n",
       "      <td>Catholic</td>\n",
       "      <td>student</td>\n",
       "      <td>blue</td>\n",
       "      <td>60.0</td>\n",
       "      <td>low</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>U1004</td>\n",
       "      <td>cash</td>\n",
       "      <td>U1004</td>\n",
       "      <td>18.867000</td>\n",
       "      <td>-99.183000</td>\n",
       "      <td>false</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>informal</td>\n",
       "      <td>family</td>\n",
       "      <td>public</td>\n",
       "      <td>single</td>\n",
       "      <td>independent</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>variety</td>\n",
       "      <td>hard-worker</td>\n",
       "      <td>none</td>\n",
       "      <td>professional</td>\n",
       "      <td>green</td>\n",
       "      <td>44.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Breakfast-Brunch</td>\n",
       "      <td>U1004</td>\n",
       "      <td>bank_debit_cards</td>\n",
       "      <td>U1005</td>\n",
       "      <td>22.183477</td>\n",
       "      <td>-100.959891</td>\n",
       "      <td>false</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>no preference</td>\n",
       "      <td>family</td>\n",
       "      <td>public</td>\n",
       "      <td>single</td>\n",
       "      <td>independent</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>none</td>\n",
       "      <td>thrifty-protector</td>\n",
       "      <td>Catholic</td>\n",
       "      <td>student</td>\n",
       "      <td>black</td>\n",
       "      <td>65.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  userID          Rcuisine userID          Upayment userID   latitude  \\\n",
       "0  U1001          American  U1001              cash  U1001  22.139997   \n",
       "1  U1002           Mexican  U1002              cash  U1002  22.150087   \n",
       "2  U1003           Mexican  U1003              cash  U1003  22.119847   \n",
       "3  U1004            Bakery  U1004              cash  U1004  18.867000   \n",
       "4  U1004  Breakfast-Brunch  U1004  bank_debit_cards  U1005  22.183477   \n",
       "\n",
       "    longitude smoker     drink_level dress_preference ambience transport  \\\n",
       "0 -100.978803  false      abstemious         informal   family   on foot   \n",
       "1 -100.983325  false      abstemious         informal   family    public   \n",
       "2 -100.946527  false  social drinker           formal   family    public   \n",
       "3  -99.183000  false      abstemious         informal   family    public   \n",
       "4 -100.959891  false      abstemious    no preference   family    public   \n",
       "\n",
       "  marital_status        hijos  birth_year    interest          personality  \\\n",
       "0         single  independent      1989.0     variety    thrifty-protector   \n",
       "1         single  independent      1990.0  technology  hunter-ostentatious   \n",
       "2         single  independent      1989.0        none          hard-worker   \n",
       "3         single  independent      1940.0     variety          hard-worker   \n",
       "4         single  independent      1992.0        none    thrifty-protector   \n",
       "\n",
       "   religion      activity  color  weight  budget  height  \n",
       "0      none       student  black    69.0  medium    1.77  \n",
       "1  Catholic       student    red    40.0     low    1.87  \n",
       "2  Catholic       student   blue    60.0     low    1.69  \n",
       "3      none  professional  green    44.0  medium    1.53  \n",
       "4  Catholic       student  black    65.0  medium    1.69  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns',25)\n",
    "rest_col.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another important operation related to DataFrame is the merging operation.\n",
    "Two or more DataFrames might hold different kinds of information about the same entity and linked by some common feature/column.\n",
    "To join these dataframes , we used above concat() function , likewise we can make use of merge(), join() functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the above dataframes , we will merge them using a common column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>Rcuisine</th>\n",
       "      <th>Upayment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U1001</td>\n",
       "      <td>American</td>\n",
       "      <td>cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U1002</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U1003</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U1004</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>bank_debit_cards</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  userID  Rcuisine          Upayment\n",
       "0  U1001  American              cash\n",
       "1  U1002   Mexican              cash\n",
       "2  U1003   Mexican              cash\n",
       "3  U1004    Bakery              cash\n",
       "4  U1004    Bakery  bank_debit_cards"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_merge = pd.merge(usercuisine, payment , on = 'userID')\n",
    "rest_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Also, if the columns names are with different names in dataframes , we can use the syntax, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "download.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Join DataFrames![download.png](attachment:download.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also combine various dataframes using the \"Join\" operation based on some common column/key.\n",
    "The logic behind these joins is similar to SQL where you join tables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full-Outer Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Full Outer Join is similar to concat() function discussed above, where it combines the results of both left and right outer joins. The joined dataframe contains all records from both dataframes and fills null values for missing matches on either side. We can perform full outer join by specifying the \"how\" argument as \"outer\" in the merge() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "rest_full_outer_join = pd.merge(rest_accepts, rest_cuisine, on='placeID', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>Rpayment</th>\n",
       "      <th>Rcuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135110</td>\n",
       "      <td>cash</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135110</td>\n",
       "      <td>VISA</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135110</td>\n",
       "      <td>MasterCard-Eurocard</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135110</td>\n",
       "      <td>American_Express</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135110</td>\n",
       "      <td>bank_debit_cards</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID             Rpayment Rcuisine\n",
       "0   135110                 cash  Spanish\n",
       "1   135110                 VISA  Spanish\n",
       "2   135110  MasterCard-Eurocard  Spanish\n",
       "3   135110     American_Express  Spanish\n",
       "4   135110     bank_debit_cards  Spanish"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_full_outer_join.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inner Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inner Join produces only set of records that matches in both dataframes. We have to pass \"inner\" in the \"how\" argument of the merge() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "rest_inner_join = pd.merge(rest_accepts, rest_cuisine, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>Rpayment</th>\n",
       "      <th>Rcuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135110</td>\n",
       "      <td>cash</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135110</td>\n",
       "      <td>VISA</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135110</td>\n",
       "      <td>MasterCard-Eurocard</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135110</td>\n",
       "      <td>American_Express</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135110</td>\n",
       "      <td>bank_debit_cards</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID             Rpayment Rcuisine\n",
       "0   135110                 cash  Spanish\n",
       "1   135110                 VISA  Spanish\n",
       "2   135110  MasterCard-Eurocard  Spanish\n",
       "3   135110     American_Express  Spanish\n",
       "4   135110     bank_debit_cards  Spanish"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_inner_join.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, we can observe that through \"Full Outer Join\" records obtained is around 1920 rows and using \"Inner Join\" it is 1441 rows. Thus, 14.25% of data is reduced while using \"Inner Join\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Right Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Right Join\" produces a complete set of records of dataframe in the right i.e \"rest_cuisine\", with the matching records in left dataframe \"rest_accepts\" . If there is no match, the left side will contain null. We need to pass \"right\" to the \"how\" argument of merge() function to do right join."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>Rpayment</th>\n",
       "      <th>Rcuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135110</td>\n",
       "      <td>cash</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135110</td>\n",
       "      <td>VISA</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135110</td>\n",
       "      <td>MasterCard-Eurocard</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135110</td>\n",
       "      <td>American_Express</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135110</td>\n",
       "      <td>bank_debit_cards</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID             Rpayment Rcuisine\n",
       "0   135110                 cash  Spanish\n",
       "1   135110                 VISA  Spanish\n",
       "2   135110  MasterCard-Eurocard  Spanish\n",
       "3   135110     American_Express  Spanish\n",
       "4   135110     bank_debit_cards  Spanish"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_right_join=pd.merge(rest_accepts,rest_cuisine,how='right')\n",
    "rest_right_join.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Left Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Left Join produces a complete set of records from DataFrame \"rest_accepts\" in the left with the matching records in the right dataframe \"rest_cuisine\". If there is no match, right side will contain null.Here, we need to pass \"left\" to \"how\" argument of the merge() function :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "rest_left_join = pd.merge(rest_accepts, rest_cuisine, on='placeID', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>Rpayment</th>\n",
       "      <th>Rcuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135110</td>\n",
       "      <td>cash</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135110</td>\n",
       "      <td>VISA</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135110</td>\n",
       "      <td>MasterCard-Eurocard</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135110</td>\n",
       "      <td>American_Express</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135110</td>\n",
       "      <td>bank_debit_cards</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>132012</td>\n",
       "      <td>Diners_Club</td>\n",
       "      <td>Dutch-Belgian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>132002</td>\n",
       "      <td>MasterCard-Eurocard</td>\n",
       "      <td>Seafood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>132002</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Seafood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>132002</td>\n",
       "      <td>American_Express</td>\n",
       "      <td>Seafood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>132002</td>\n",
       "      <td>Diners_Club</td>\n",
       "      <td>Seafood</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1631 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      placeID             Rpayment       Rcuisine\n",
       "0      135110                 cash        Spanish\n",
       "1      135110                 VISA        Spanish\n",
       "2      135110  MasterCard-Eurocard        Spanish\n",
       "3      135110     American_Express        Spanish\n",
       "4      135110     bank_debit_cards        Spanish\n",
       "...       ...                  ...            ...\n",
       "1626   132012          Diners_Club  Dutch-Belgian\n",
       "1627   132002  MasterCard-Eurocard        Seafood\n",
       "1628   132002                 Visa        Seafood\n",
       "1629   132002     American_Express        Seafood\n",
       "1630   132002          Diners_Club        Seafood\n",
       "\n",
       "[1631 rows x 3 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_left_join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining on Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At times we need to perform the join on the indexes or the row labels. To do so, we have to specify the right_index and left_index as True:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "rest_index_df = pd.merge(rest_accepts, rest_cuisine, right_index=True, left_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID_x</th>\n",
       "      <th>Rpayment</th>\n",
       "      <th>placeID_y</th>\n",
       "      <th>Rcuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135110</td>\n",
       "      <td>cash</td>\n",
       "      <td>135110</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135110</td>\n",
       "      <td>VISA</td>\n",
       "      <td>135109</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135110</td>\n",
       "      <td>MasterCard-Eurocard</td>\n",
       "      <td>135107</td>\n",
       "      <td>Latin_American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135110</td>\n",
       "      <td>American_Express</td>\n",
       "      <td>135106</td>\n",
       "      <td>Mexican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135110</td>\n",
       "      <td>bank_debit_cards</td>\n",
       "      <td>135105</td>\n",
       "      <td>Fast_Food</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID_x             Rpayment  placeID_y        Rcuisine\n",
       "0     135110                 cash     135110         Spanish\n",
       "1     135110                 VISA     135109         Italian\n",
       "2     135110  MasterCard-Eurocard     135107  Latin_American\n",
       "3     135110     American_Express     135106         Mexican\n",
       "4     135110     bank_debit_cards     135105       Fast_Food"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_index_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus on joining by index we get total of 916 records."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregation Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crosstab\n",
    "Compute a simple cross-tabulation of two (or more) factors. By default computes a frequency table of the factors unless an array of values and an aggregation function are passed. Categorical data will have all of its categories included in the cross-tabulation, even if the actual data does not contain any instances of a particular category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "stu_perf = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\end to end data sets\\StudentsPerformance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu_perf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us apply crosstab on columns \"gender\" and \"race/ethnicity\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>group A</th>\n",
       "      <th>group B</th>\n",
       "      <th>group C</th>\n",
       "      <th>group D</th>\n",
       "      <th>group E</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>36</td>\n",
       "      <td>104</td>\n",
       "      <td>180</td>\n",
       "      <td>129</td>\n",
       "      <td>69</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>53</td>\n",
       "      <td>86</td>\n",
       "      <td>139</td>\n",
       "      <td>133</td>\n",
       "      <td>71</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>89</td>\n",
       "      <td>190</td>\n",
       "      <td>319</td>\n",
       "      <td>262</td>\n",
       "      <td>140</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race/ethnicity  group A  group B  group C  group D  group E   All\n",
       "gender                                                           \n",
       "female               36      104      180      129       69   518\n",
       "male                 53       86      139      133       71   482\n",
       "All                  89      190      319      262      140  1000"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(stu_perf['gender'], stu_perf['race/ethnicity'],margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Date and Time in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas mainly combines the ease-of-use of \"datetime\" and \"dateutil\", vectorized interface of numpy.datetime64. From a group of these objects pandas can construct a DateTime Index that can be used to index data in a \"Series\" or \"DataFrame\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try to understand \"Datetime\" Index by considering a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_data = pd.read_csv(r'C:\\Users\\Dell\\Jupyter Notebook\\Supervised Learning Practice\\end to end data sets\\Fremont_Bridge_Hourly_Bicycle_Counts_by_Month_October_2012_to_present.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Fremont Bridge East Sidewalk</th>\n",
       "      <th>Fremont Bridge West Sidewalk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04/30/2019 11:00:00 PM</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04/30/2019 10:00:00 PM</td>\n",
       "      <td>15.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04/30/2019 09:00:00 PM</td>\n",
       "      <td>17.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/30/2019 08:00:00 PM</td>\n",
       "      <td>58.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04/30/2019 07:00:00 PM</td>\n",
       "      <td>106.0</td>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Date  Fremont Bridge East Sidewalk  \\\n",
       "0  04/30/2019 11:00:00 PM                           8.0   \n",
       "1  04/30/2019 10:00:00 PM                          15.0   \n",
       "2  04/30/2019 09:00:00 PM                          17.0   \n",
       "3  04/30/2019 08:00:00 PM                          58.0   \n",
       "4  04/30/2019 07:00:00 PM                         106.0   \n",
       "\n",
       "   Fremont Bridge West Sidewalk  \n",
       "0                          16.0  \n",
       "1                          29.0  \n",
       "2                          54.0  \n",
       "3                          91.0  \n",
       "4                         175.0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_data.rename(columns={'Fremont Bridge East Sidewalk':'East','Fremont Bridge West Sidewalk':'West'}\n",
    "                 ,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57624, 3)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date     object\n",
       "East    float64\n",
       "West    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us convert the \"Date\" feature to \"datetime\" format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_data['Date'] = pd.to_datetime(time_data['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date    datetime64[ns]\n",
       "East           float64\n",
       "West           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will try to shorten the names of the cols and add an extra \"Total\" column to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_data['Total'] = time_data['East'] + time_data['West']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting 'year' column seperately\n",
    "time_data['year'] = time_data['Date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting 'month' column seperately\n",
    "time_data['month'] = time_data['Date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting 'day' column seperately\n",
    "time_data['day'] = time_data['Date'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting 'week' column seperately\n",
    "time_data['Week'] = time_data['Date'].dt.week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>East</th>\n",
       "      <th>West</th>\n",
       "      <th>Total</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-04-30 23:00:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-04-30 22:00:00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-04-30 21:00:00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-04-30 20:00:00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-04-30 19:00:00</td>\n",
       "      <td>106.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date   East   West  Total  year  month  day  Week\n",
       "0 2019-04-30 23:00:00    8.0   16.0   24.0  2019      4   30    18\n",
       "1 2019-04-30 22:00:00   15.0   29.0   44.0  2019      4   30    18\n",
       "2 2019-04-30 21:00:00   17.0   54.0   71.0  2019      4   30    18\n",
       "3 2019-04-30 20:00:00   58.0   91.0  149.0  2019      4   30    18\n",
       "4 2019-04-30 19:00:00  106.0  175.0  281.0  2019      4   30    18"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groupby over 'month' to get 'monthly_average'\n",
    "monthly_average = time_data.groupby(['month']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>East</th>\n",
       "      <th>West</th>\n",
       "      <th>Total</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "      <th>Week</th>\n",
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       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.538018</td>\n",
       "      <td>39.959485</td>\n",
       "      <td>76.497504</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.751152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.016497</td>\n",
       "      <td>37.925338</td>\n",
       "      <td>72.941836</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>14.573604</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.872189</td>\n",
       "      <td>50.692293</td>\n",
       "      <td>94.564482</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>11.216590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.635570</td>\n",
       "      <td>59.997023</td>\n",
       "      <td>113.632592</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>15.585714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74.505376</td>\n",
       "      <td>79.628584</td>\n",
       "      <td>154.133961</td>\n",
       "      <td>2015.5</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>19.930108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            East       West       Total    year        day       Week\n",
       "month                                                                \n",
       "1      36.538018  39.959485   76.497504  2016.0  16.000000   3.751152\n",
       "2      35.016497  37.925338   72.941836  2016.0  14.573604   7.000000\n",
       "3      43.872189  50.692293   94.564482  2016.0  16.000000  11.216590\n",
       "4      53.635570  59.997023  113.632592  2016.0  15.500000  15.585714\n",
       "5      74.505376  79.628584  154.133961  2015.5  16.000000  19.930108"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_average.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groupby by the week column\n",
    "week_average = time_data.groupby(['Week']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>East</th>\n",
       "      <th>West</th>\n",
       "      <th>Total</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.960884</td>\n",
       "      <td>30.977041</td>\n",
       "      <td>58.937925</td>\n",
       "      <td>2015.857143</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.173469</td>\n",
       "      <td>38.783163</td>\n",
       "      <td>74.956633</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.451531</td>\n",
       "      <td>40.829932</td>\n",
       "      <td>78.281463</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39.335034</td>\n",
       "      <td>42.745748</td>\n",
       "      <td>82.080782</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40.334184</td>\n",
       "      <td>45.494898</td>\n",
       "      <td>85.829082</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.510204</td>\n",
       "      <td>15.755102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           East       West      Total         year     month        day\n",
       "Week                                                                   \n",
       "1     27.960884  30.977041  58.937925  2015.857143  2.571429   8.000000\n",
       "2     36.173469  38.783163  74.956633  2016.000000  1.000000  10.571429\n",
       "3     37.451531  40.829932  78.281463  2016.000000  1.000000  17.571429\n",
       "4     39.335034  42.745748  82.080782  2016.000000  1.000000  24.571429\n",
       "5     40.334184  45.494898  85.829082  2016.000000  1.510204  15.755102"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_average.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>East</th>\n",
       "      <th>West</th>\n",
       "      <th>Total</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>57615.000000</td>\n",
       "      <td>57615.000000</td>\n",
       "      <td>57615.000000</td>\n",
       "      <td>57624.000000</td>\n",
       "      <td>57624.000000</td>\n",
       "      <td>57624.000000</td>\n",
       "      <td>57624.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.916914</td>\n",
       "      <td>58.359889</td>\n",
       "      <td>110.276803</td>\n",
       "      <td>2015.543940</td>\n",
       "      <td>6.491462</td>\n",
       "      <td>15.732611</td>\n",
       "      <td>26.430654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>67.368033</td>\n",
       "      <td>84.958511</td>\n",
       "      <td>139.743003</td>\n",
       "      <td>1.931325</td>\n",
       "      <td>3.536482</td>\n",
       "      <td>8.790200</td>\n",
       "      <td>15.432359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>698.000000</td>\n",
       "      <td>717.000000</td>\n",
       "      <td>957.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               East          West         Total          year         month  \\\n",
       "count  57615.000000  57615.000000  57615.000000  57624.000000  57624.000000   \n",
       "mean      51.916914     58.359889    110.276803   2015.543940      6.491462   \n",
       "std       67.368033     84.958511    139.743003      1.931325      3.536482   \n",
       "min        0.000000      0.000000      0.000000   2012.000000      1.000000   \n",
       "25%        6.000000      7.000000     14.000000   2014.000000      3.000000   \n",
       "50%       28.000000     29.000000     60.000000   2016.000000      6.000000   \n",
       "75%       69.000000     70.000000    143.000000   2017.000000     10.000000   \n",
       "max      698.000000    717.000000    957.000000   2019.000000     12.000000   \n",
       "\n",
       "                day          Week  \n",
       "count  57624.000000  57624.000000  \n",
       "mean      15.732611     26.430654  \n",
       "std        8.790200     15.432359  \n",
       "min        1.000000      1.000000  \n",
       "25%        8.000000     13.000000  \n",
       "50%       16.000000     26.000000  \n",
       "75%       23.000000     40.000000  \n",
       "max       31.000000     53.000000  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let us look at the summary statistics of the data\n",
    "time_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "Pandas.ipynb",
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  },
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