Untitled.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "decimal-burner",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "worthy-status",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 355440 entries, 0 to 355439\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   system:index  355440 non-null  int64  \n",
      " 1   date          355440 non-null  float64\n",
      " 2   mean          355440 non-null  float64\n",
      " 3   .geo          0 non-null       float64\n",
      "dtypes: float64(3), int64(1)\n",
      "memory usage: 10.8 MB\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "          system:index          date      mean  .geo\n",
       "0       20000601000000  9.598176e+11  0.224942   NaN\n",
       "1       20000601003000  9.598194e+11  0.000000   NaN\n",
       "2       20000601010000  9.598212e+11  0.000000   NaN\n",
       "3       20000601013000  9.598230e+11  0.000000   NaN\n",
       "4       20000601020000  9.598248e+11  0.000000   NaN\n",
       "...                ...           ...       ...   ...\n",
       "355435  20200908213000  1.599601e+12  0.000000   NaN\n",
       "355436  20200908220000  1.599602e+12  0.000000   NaN\n",
       "355437  20200908223000  1.599604e+12  0.000000   NaN\n",
       "355438  20200908230000  1.599606e+12  0.000000   NaN\n",
       "355439  20200908233000  1.599608e+12  0.000000   NaN\n",
       "\n",
       "[355440 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"preci_all_delhi.csv\")\n",
    "df.info()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "referenced-mapping",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"system:index\"] = pd.to_datetime(df[\"system:index\"],format = '%Y%m%d%H%M%S')\n",
    "#dff = df[\"system:index\"].groupby(df[\"system:index\"].dt.day).sum()\n",
    "df = df.resample('D', on='system:index').sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "engaged-entry",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mean</th>\n",
       "      <th>.geo</th>\n",
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       "      <th>system:index</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-06-01</th>\n",
       "      <td>4.607328e+13</td>\n",
       "      <td>33.405911</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2000-06-02</th>\n",
       "      <td>4.607742e+13</td>\n",
       "      <td>0.022229</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2000-06-03</th>\n",
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       "    <tr>\n",
       "      <th>2020-09-05</th>\n",
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       "      <td>45.213516</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-06</th>\n",
       "      <td>7.677085e+13</td>\n",
       "      <td>0.192533</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-07</th>\n",
       "      <td>7.677500e+13</td>\n",
       "      <td>23.122570</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-08</th>\n",
       "      <td>7.677914e+13</td>\n",
       "      <td>0.088258</td>\n",
       "      <td>0.0</td>\n",
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       "<p>7405 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "                      date       mean  .geo\n",
       "system:index                               \n",
       "2000-06-01    4.607328e+13  33.405911   0.0\n",
       "2000-06-02    4.607742e+13   0.022229   0.0\n",
       "2000-06-03    4.608157e+13  25.713945   0.0\n",
       "2000-06-04    4.608572e+13  11.130651   0.0\n",
       "2000-06-05    4.608986e+13   0.006525   0.0\n",
       "...                    ...        ...   ...\n",
       "2020-09-04    7.676256e+13   0.481709   0.0\n",
       "2020-09-05    7.676670e+13  45.213516   0.0\n",
       "2020-09-06    7.677085e+13   0.192533   0.0\n",
       "2020-09-07    7.677500e+13  23.122570   0.0\n",
       "2020-09-08    7.677914e+13   0.088258   0.0\n",
       "\n",
       "[7405 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "funded-workstation",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['date']\n",
    "del df['.geo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "successful-reducing",
   "metadata": {},
   "outputs": [
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       "      <th>7401</th>\n",
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      "text/plain": [
       "           date      value\n",
       "0    2000-06-01  33.405911\n",
       "1    2000-06-02   0.022229\n",
       "2    2000-06-03  25.713945\n",
       "3    2000-06-04  11.130651\n",
       "4    2000-06-05   0.006525\n",
       "...         ...        ...\n",
       "7400 2020-09-04   0.481709\n",
       "7401 2020-09-05  45.213516\n",
       "7402 2020-09-06   0.192533\n",
       "7403 2020-09-07  23.122570\n",
       "7404 2020-09-08   0.088258\n",
       "\n",
       "[7405 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.reset_index()\n",
    "df.columns = [\"date\",\"value\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fixed-ribbon",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"preci_delhi_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "color-kingdom",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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