01_getting_started.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading files into DataFrames\n",
    "\n",
    "In Pandas, data is accessed through a [DataFrame](https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe). A DataFrame is a 2D data structure where each column may contain different data types, from numeric series to complex structures. In most cases, you can think on DataFrames as _tables_.\n",
    "\n",
    "The [IO API](https://pandas.pydata.org/pandas-docs/stable/io.html) has different methods to read different formats, most common one is text-delimited files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"./data/goog.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this point we can access the data using different methods. For instance, `head(n)` will retrieve the first `n` columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>692.200012</td>\n",
       "      <td>700.650024</td>\n",
       "      <td>692.130005</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1342700</td>\n",
       "      <td>699.210022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>685.469971</td>\n",
       "      <td>692.320007</td>\n",
       "      <td>683.650024</td>\n",
       "      <td>692.099976</td>\n",
       "      <td>1590500</td>\n",
       "      <td>692.099976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Volume  \\\n",
       "0  2016-07-01  692.200012  700.650024  692.130005  699.210022  1342700   \n",
       "1  2016-06-30  685.469971  692.320007  683.650024  692.099976  1590500   \n",
       "\n",
       "    Adj Close  \n",
       "0  699.210022  \n",
       "1  692.099976  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame also allows _range_ expressions, by using `[]` or `loc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-06-24</td>\n",
       "      <td>675.169983</td>\n",
       "      <td>689.400024</td>\n",
       "      <td>673.450012</td>\n",
       "      <td>675.219971</td>\n",
       "      <td>4399800</td>\n",
       "      <td>675.219971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-06-23</td>\n",
       "      <td>697.450012</td>\n",
       "      <td>701.950012</td>\n",
       "      <td>687.000000</td>\n",
       "      <td>701.869995</td>\n",
       "      <td>2157400</td>\n",
       "      <td>701.869995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Volume  \\\n",
       "5  2016-06-24  675.169983  689.400024  673.450012  675.219971  4399800   \n",
       "6  2016-06-23  697.450012  701.950012  687.000000  701.869995  2157400   \n",
       "\n",
       "    Adj Close  \n",
       "5  675.219971  \n",
       "6  701.869995  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "data[5:7]"
   ]
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   "execution_count": 4,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-06-24</td>\n",
       "      <td>675.169983</td>\n",
       "      <td>689.400024</td>\n",
       "      <td>673.450012</td>\n",
       "      <td>675.219971</td>\n",
       "      <td>4399800</td>\n",
       "      <td>675.219971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-06-23</td>\n",
       "      <td>697.450012</td>\n",
       "      <td>701.950012</td>\n",
       "      <td>687.000000</td>\n",
       "      <td>701.869995</td>\n",
       "      <td>2157400</td>\n",
       "      <td>701.869995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-06-22</td>\n",
       "      <td>699.059998</td>\n",
       "      <td>700.859985</td>\n",
       "      <td>693.081970</td>\n",
       "      <td>697.460022</td>\n",
       "      <td>1179300</td>\n",
       "      <td>697.460022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "         Date        Open        High         Low       Close   Volume  \\\n",
       "5  2016-06-24  675.169983  689.400024  673.450012  675.219971  4399800   \n",
       "6  2016-06-23  697.450012  701.950012  687.000000  701.869995  2157400   \n",
       "7  2016-06-22  699.059998  700.859985  693.081970  697.460022  1179300   \n",
       "\n",
       "    Adj Close  \n",
       "5  675.219971  \n",
       "6  701.869995  \n",
       "7  697.460022  "
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[5:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Single row access is done using `iloc` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         2016-06-24\n",
       "Open         675.169983\n",
       "High         689.400024\n",
       "Low          673.450012\n",
       "Close        675.219971\n",
       "Volume          4399800\n",
       "Adj Close    675.219971\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "675.169983"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[5].Open"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All methods and attributes a DataFrame supports can be checked in the [API documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html). Note that althought Pandas support Python's index operator `[]`, the recommended way to access values or ranges is to use `loc` for labels and `iloc` for positions. There's more information about it in the [Indexing and selecting data](https://pandas.pydata.org/docs/user_guide/indexing.html) chapter of Pandas documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data formats and compression\n",
    "\n",
    "Pandas can read and write multiple data formats, using `read_X` and `to_X` methods. Parameters are described in the [IO API documentation](https://pandas.pydata.org/pandas-docs/stable/io.html).\n",
    "\n",
    "One of the popular formats is [PyArrow](https://arrow.apache.org/docs/python/pandas.html), that has relatively good integration with Pandas. Normally, you will be interested to use a binary column-oriented format called [Feather](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#feather), created from the Arrow project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          object\n",
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Volume         int64\n",
       "Adj Close    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_feather('./data/goog.feather')\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the index was lost when saving the data. You may want to create it again by doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2016-07-01</th>\n",
       "      <td>692.200012</td>\n",
       "      <td>700.650024</td>\n",
       "      <td>692.130005</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1342700</td>\n",
       "      <td>699.210022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                  Open        High         Low       Close   Volume  \\\n",
       "Date                                                                  \n",
       "2016-07-01  692.200012  700.650024  692.130005  699.210022  1342700   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2016-07-01  699.210022  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_indexed = data.set_index('Date')\n",
    "data_indexed.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also replace the index in-place, without creating a copy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2016-07-01</th>\n",
       "      <td>692.200012</td>\n",
       "      <td>700.650024</td>\n",
       "      <td>692.130005</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1342700</td>\n",
       "      <td>699.210022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Volume  \\\n",
       "Date                                                                  \n",
       "2016-07-01  692.200012  700.650024  692.130005  699.210022  1342700   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2016-07-01  699.210022  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.set_index('Date', inplace=True)\n",
    "data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, you can write Feather files using `to_feather` function. Remember to drop the index first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./data/goog.csv\", index_col=0)\n",
    "data.reset_index().to_feather('data/goog.feather')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas also accepts compressed files, for instance in `gzip` format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>692.200012</td>\n",
       "      <td>700.650024</td>\n",
       "      <td>692.130005</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1342700</td>\n",
       "      <td>699.210022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Volume  \\\n",
       "0  2016-07-01  692.200012  700.650024  692.130005  699.210022  1342700   \n",
       "\n",
       "    Adj Close  \n",
       "0  699.210022  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./data/goog.csv.gz')\n",
    "data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that you don't need anything fancy to compress files, just use shell tools:\n",
    "\n",
    "```\n",
    "$ gzip -9 data_file.csv\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing Data\n",
    "\n",
    "### Column Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `read_csv` will not assume any index in the data. In our case we have a _natural_ index in the first column, so we can read the file again to make sure it's incorporated in the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2016-07-01</th>\n",
       "      <td>692.200012</td>\n",
       "      <td>700.650024</td>\n",
       "      <td>692.130005</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1342700</td>\n",
       "      <td>699.210022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>685.469971</td>\n",
       "      <td>692.320007</td>\n",
       "      <td>683.650024</td>\n",
       "      <td>692.099976</td>\n",
       "      <td>1590500</td>\n",
       "      <td>692.099976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Volume  \\\n",
       "Date                                                                  \n",
       "2016-07-01  692.200012  700.650024  692.130005  699.210022  1342700   \n",
       "2016-06-30  685.469971  692.320007  683.650024  692.099976  1590500   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2016-07-01  699.210022  \n",
       "2016-06-30  692.099976  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./data/goog.csv\", index_col=0)\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can use range expressions to fetch _years_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2015-12-31</th>\n",
       "      <td>769.500000</td>\n",
       "      <td>769.500000</td>\n",
       "      <td>758.340027</td>\n",
       "      <td>758.880005</td>\n",
       "      <td>1489600</td>\n",
       "      <td>758.880005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>776.599976</td>\n",
       "      <td>777.599976</td>\n",
       "      <td>766.900024</td>\n",
       "      <td>771.000000</td>\n",
       "      <td>1293300</td>\n",
       "      <td>771.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Volume  \\\n",
       "Date                                                                  \n",
       "2015-12-31  769.500000  769.500000  758.340027  758.880005  1489600   \n",
       "2015-12-30  776.599976  777.599976  766.900024  771.000000  1293300   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2015-12-31  758.880005  \n",
       "2015-12-30  771.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"2016\":\"2015\"].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or _month_ ranges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2016-04-29</th>\n",
       "      <td>690.700012</td>\n",
       "      <td>697.619995</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>693.01001</td>\n",
       "      <td>2484300</td>\n",
       "      <td>693.01001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-28</th>\n",
       "      <td>708.260010</td>\n",
       "      <td>714.169983</td>\n",
       "      <td>689.549988</td>\n",
       "      <td>691.02002</td>\n",
       "      <td>2851100</td>\n",
       "      <td>691.02002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low      Close   Volume  Adj Close\n",
       "Date                                                                         \n",
       "2016-04-29  690.700012  697.619995  689.000000  693.01001  2484300  693.01001\n",
       "2016-04-28  708.260010  714.169983  689.549988  691.02002  2851100  691.02002"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"2016-05\":\"2015-04\"].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the index is set as an string instead of a timestamp. This could produce weird results when getting date ranges from the DataFrame. If you want to parse the dates, you have to set the flag `parse_dates=True` when reading the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-07-01 00:00:00')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./data/goog.csv\", index_col=0, parse_dates=True)\n",
    "data.index[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, for accessing a particular range remember the to use the `.loc` function instead of the array accessor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2016-07-01</th>\n",
       "      <td>692.200012</td>\n",
       "      <td>700.650024</td>\n",
       "      <td>692.130005</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1342700</td>\n",
       "      <td>699.210022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>685.469971</td>\n",
       "      <td>692.320007</td>\n",
       "      <td>683.650024</td>\n",
       "      <td>692.099976</td>\n",
       "      <td>1590500</td>\n",
       "      <td>692.099976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Volume  \\\n",
       "Date                                                                  \n",
       "2016-07-01  692.200012  700.650024  692.130005  699.210022  1342700   \n",
       "2016-06-30  685.469971  692.320007  683.650024  692.099976  1590500   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2016-07-01  699.210022  \n",
       "2016-06-30  692.099976  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc['2016'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's also a method called `.query` that would allow us to perform queries over the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2007-11-08</th>\n",
       "      <td>734.60125</td>\n",
       "      <td>734.891274</td>\n",
       "      <td>677.181165</td>\n",
       "      <td>693.841184</td>\n",
       "      <td>33148100</td>\n",
       "      <td>346.574018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open        High         Low       Close    Volume  \\\n",
       "Date                                                                  \n",
       "2007-11-08  734.60125  734.891274  677.181165  693.841184  33148100   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2007-11-08  346.574018  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.query('Close > 600 & Volume > 33000000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding or removing columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "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>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>692.200012</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>7.010010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>685.469971</td>\n",
       "      <td>692.099976</td>\n",
       "      <td>6.630005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open       Close      Diff\n",
       "Date                                        \n",
       "2016-07-01  692.200012  699.210022  7.010010\n",
       "2016-06-30  685.469971  692.099976  6.630005"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Diff\"] = data.Close - data.Open\n",
    "data[[\"Open\", \"Close\", \"Diff\"]].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'], dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del data[\"Diff\"]\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming a Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>adj_close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2016-07-01</th>\n",
       "      <td>692.200012</td>\n",
       "      <td>700.650024</td>\n",
       "      <td>692.130005</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1342700</td>\n",
       "      <td>699.210022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>685.469971</td>\n",
       "      <td>692.320007</td>\n",
       "      <td>683.650024</td>\n",
       "      <td>692.099976</td>\n",
       "      <td>1590500</td>\n",
       "      <td>692.099976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>683.000000</td>\n",
       "      <td>687.429016</td>\n",
       "      <td>681.409973</td>\n",
       "      <td>684.109985</td>\n",
       "      <td>1928500</td>\n",
       "      <td>684.109985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>678.969971</td>\n",
       "      <td>680.330017</td>\n",
       "      <td>673.000000</td>\n",
       "      <td>680.039978</td>\n",
       "      <td>2116600</td>\n",
       "      <td>680.039978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>671.000000</td>\n",
       "      <td>672.299988</td>\n",
       "      <td>663.283997</td>\n",
       "      <td>668.260010</td>\n",
       "      <td>2629000</td>\n",
       "      <td>668.260010</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>104.960181</td>\n",
       "      <td>108.000187</td>\n",
       "      <td>103.880180</td>\n",
       "      <td>106.000184</td>\n",
       "      <td>9232100</td>\n",
       "      <td>52.947145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>111.240189</td>\n",
       "      <td>111.600192</td>\n",
       "      <td>103.570177</td>\n",
       "      <td>104.870176</td>\n",
       "      <td>15319700</td>\n",
       "      <td>52.382705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>110.750191</td>\n",
       "      <td>113.480193</td>\n",
       "      <td>109.050183</td>\n",
       "      <td>109.400185</td>\n",
       "      <td>18342800</td>\n",
       "      <td>54.645447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>101.010175</td>\n",
       "      <td>109.080187</td>\n",
       "      <td>100.500174</td>\n",
       "      <td>108.310183</td>\n",
       "      <td>22942800</td>\n",
       "      <td>54.100990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>100.000168</td>\n",
       "      <td>104.060182</td>\n",
       "      <td>95.960165</td>\n",
       "      <td>100.340176</td>\n",
       "      <td>44871300</td>\n",
       "      <td>50.119968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2989 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close    Volume  \\\n",
       "Date                                                                   \n",
       "2016-07-01  692.200012  700.650024  692.130005  699.210022   1342700   \n",
       "2016-06-30  685.469971  692.320007  683.650024  692.099976   1590500   \n",
       "2016-06-29  683.000000  687.429016  681.409973  684.109985   1928500   \n",
       "2016-06-28  678.969971  680.330017  673.000000  680.039978   2116600   \n",
       "2016-06-27  671.000000  672.299988  663.283997  668.260010   2629000   \n",
       "...                ...         ...         ...         ...       ...   \n",
       "2004-08-25  104.960181  108.000187  103.880180  106.000184   9232100   \n",
       "2004-08-24  111.240189  111.600192  103.570177  104.870176  15319700   \n",
       "2004-08-23  110.750191  113.480193  109.050183  109.400185  18342800   \n",
       "2004-08-20  101.010175  109.080187  100.500174  108.310183  22942800   \n",
       "2004-08-19  100.000168  104.060182   95.960165  100.340176  44871300   \n",
       "\n",
       "             adj_close  \n",
       "Date                    \n",
       "2016-07-01  699.210022  \n",
       "2016-06-30  692.099976  \n",
       "2016-06-29  684.109985  \n",
       "2016-06-28  680.039978  \n",
       "2016-06-27  668.260010  \n",
       "...                ...  \n",
       "2004-08-25   52.947145  \n",
       "2004-08-24   52.382705  \n",
       "2004-08-23   54.645447  \n",
       "2004-08-20   54.100990  \n",
       "2004-08-19   50.119968  \n",
       "\n",
       "[2989 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(columns={'Adj Close': 'adj_close'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing Dates\n",
    "\n",
    "You would normally use `parse_dates=True` or `parse_dates=[0]` to automatically process dates when reading files. In some cases you would like to perform that manually by using `pd.to_datetime` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>692.200012</td>\n",
       "      <td>700.650024</td>\n",
       "      <td>692.130005</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1342700</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>685.469971</td>\n",
       "      <td>692.320007</td>\n",
       "      <td>683.650024</td>\n",
       "      <td>692.099976</td>\n",
       "      <td>1590500</td>\n",
       "      <td>692.099976</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Volume  \\\n",
       "0  2016-07-01  692.200012  700.650024  692.130005  699.210022  1342700   \n",
       "1  2016-06-30  685.469971  692.320007  683.650024  692.099976  1590500   \n",
       "\n",
       "    Adj Close  Month  \n",
       "0  699.210022      7  \n",
       "1  692.099976      6  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"./data/goog.csv\")\n",
    "df2['Month'] = pd.to_datetime(df2['Date']).dt.month\n",
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1     583.353097\n",
       "2     572.136671\n",
       "3     557.874017\n",
       "4     527.056556\n",
       "5     531.708374\n",
       "6     531.205082\n",
       "7     538.988607\n",
       "8     519.061782\n",
       "9     505.104736\n",
       "10    540.488037\n",
       "11    565.204085\n",
       "12    576.999005\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(df2['Month'])['Close'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-Indexes\n",
    "\n",
    "Pandas is able to treat data that has indexes in multiple levels. For instance, the `prices.csv` data set has two initial columns with the stock symbol and the date of the prices. That's described in [Hierarchical indexing](https://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced-hierarchical) documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "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>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aapl</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>6.535084</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>7.660714</td>\n",
       "      <td>7.585</td>\n",
       "      <td>7.622500</td>\n",
       "      <td>493729600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>msft</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>23.800220</td>\n",
       "      <td>30.950001</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>30.590</td>\n",
       "      <td>30.620001</td>\n",
       "      <td>38409100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol        Date  Adj Close      Close       High     Low       Open  \\\n",
       "0   aapl  2010-01-04   6.535084   7.643214   7.660714   7.585   7.622500   \n",
       "1   msft  2010-01-04  23.800220  30.950001  31.100000  30.590  30.620001   \n",
       "\n",
       "        Volume  \n",
       "0  493729600.0  \n",
       "1   38409100.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('./data/prices.csv').head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily tell Pandas that first two columns are the index by using the `index_col` parameter. Let's create a data frame named `prices`, containing price data for several stocks in a time range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
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       "        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></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</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>aapl</th>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>6.535084</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>7.660714</td>\n",
       "      <td>7.585000</td>\n",
       "      <td>7.622500</td>\n",
       "      <td>4.937296e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>msft</th>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>23.800220</td>\n",
       "      <td>30.950001</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>30.590000</td>\n",
       "      <td>30.620001</td>\n",
       "      <td>3.840910e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goog</th>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>312.204773</td>\n",
       "      <td>312.204773</td>\n",
       "      <td>313.579620</td>\n",
       "      <td>310.954468</td>\n",
       "      <td>312.304413</td>\n",
       "      <td>3.927065e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>^GSPC</th>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>1132.989990</td>\n",
       "      <td>1132.989990</td>\n",
       "      <td>1133.869995</td>\n",
       "      <td>1116.560059</td>\n",
       "      <td>1116.560059</td>\n",
       "      <td>3.991400e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aapl</th>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>6.546383</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>7.699643</td>\n",
       "      <td>7.616071</td>\n",
       "      <td>7.664286</td>\n",
       "      <td>6.019048e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Adj Close        Close         High          Low  \\\n",
       "Symbol Date                                                             \n",
       "aapl   2010-01-04     6.535084     7.643214     7.660714     7.585000   \n",
       "msft   2010-01-04    23.800220    30.950001    31.100000    30.590000   \n",
       "goog   2010-01-04   312.204773   312.204773   313.579620   310.954468   \n",
       "^GSPC  2010-01-04  1132.989990  1132.989990  1133.869995  1116.560059   \n",
       "aapl   2010-01-05     6.546383     7.656429     7.699643     7.616071   \n",
       "\n",
       "                          Open        Volume  \n",
       "Symbol Date                                   \n",
       "aapl   2010-01-04     7.622500  4.937296e+08  \n",
       "msft   2010-01-04    30.620001  3.840910e+07  \n",
       "goog   2010-01-04   312.304413  3.927065e+06  \n",
       "^GSPC  2010-01-04  1116.560059  3.991400e+09  \n",
       "aapl   2010-01-05     7.664286  6.019048e+08  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = pd.read_csv('./data/prices.csv', index_col=[0,1])\n",
    "prices.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([( 'aapl', '2010-01-04'),\n",
       "            ( 'msft', '2010-01-04'),\n",
       "            ( 'goog', '2010-01-04'),\n",
       "            ('^GSPC', '2010-01-04'),\n",
       "            ( 'aapl', '2010-01-05'),\n",
       "            ( 'msft', '2010-01-05'),\n",
       "            ( 'goog', '2010-01-05'),\n",
       "            ('^GSPC', '2010-01-05'),\n",
       "            ( 'aapl', '2010-01-06'),\n",
       "            ( 'msft', '2010-01-06'),\n",
       "            ...\n",
       "            ( 'aapl', '2022-06-30'),\n",
       "            ( 'msft', '2022-06-30'),\n",
       "            ( 'goog', '2022-06-30'),\n",
       "            ( 'tsla', '2022-06-30'),\n",
       "            ('^GSPC', '2022-06-30'),\n",
       "            ( 'aapl', '2022-07-01'),\n",
       "            ( 'msft', '2022-07-01'),\n",
       "            ( 'goog', '2022-07-01'),\n",
       "            ( 'tsla', '2022-07-01'),\n",
       "            ('^GSPC', '2022-07-01')],\n",
       "           names=['Symbol', 'Date'], length=15608)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To query for a particular index, you can use `get_level_values` function and `isin`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</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 rowspan=\"5\" valign=\"top\">msft</th>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>23.800220</td>\n",
       "      <td>30.950001</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>30.590000</td>\n",
       "      <td>30.620001</td>\n",
       "      <td>38409100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>23.807911</td>\n",
       "      <td>30.959999</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>30.639999</td>\n",
       "      <td>30.850000</td>\n",
       "      <td>49749600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>23.661804</td>\n",
       "      <td>30.770000</td>\n",
       "      <td>31.080000</td>\n",
       "      <td>30.520000</td>\n",
       "      <td>30.879999</td>\n",
       "      <td>58182400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>23.415728</td>\n",
       "      <td>30.450001</td>\n",
       "      <td>30.700001</td>\n",
       "      <td>30.190001</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>50559700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>23.577215</td>\n",
       "      <td>30.660000</td>\n",
       "      <td>30.879999</td>\n",
       "      <td>30.240000</td>\n",
       "      <td>30.280001</td>\n",
       "      <td>51197400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Adj Close      Close       High        Low       Open  \\\n",
       "Symbol Date                                                                \n",
       "msft   2010-01-04  23.800220  30.950001  31.100000  30.590000  30.620001   \n",
       "       2010-01-05  23.807911  30.959999  31.100000  30.639999  30.850000   \n",
       "       2010-01-06  23.661804  30.770000  31.080000  30.520000  30.879999   \n",
       "       2010-01-07  23.415728  30.450001  30.700001  30.190001  30.629999   \n",
       "       2010-01-08  23.577215  30.660000  30.879999  30.240000  30.280001   \n",
       "\n",
       "                       Volume  \n",
       "Symbol Date                    \n",
       "msft   2010-01-04  38409100.0  \n",
       "       2010-01-05  49749600.0  \n",
       "       2010-01-06  58182400.0  \n",
       "       2010-01-07  50559700.0  \n",
       "       2010-01-08  51197400.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft = prices[prices.index.get_level_values('Symbol').isin(['msft'])]\n",
    "msft.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, `query` method is also available for multi-indexes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</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 rowspan=\"5\" valign=\"top\">msft</th>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>23.800220</td>\n",
       "      <td>30.950001</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>30.590000</td>\n",
       "      <td>30.620001</td>\n",
       "      <td>38409100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>23.807911</td>\n",
       "      <td>30.959999</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>30.639999</td>\n",
       "      <td>30.850000</td>\n",
       "      <td>49749600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>23.661804</td>\n",
       "      <td>30.770000</td>\n",
       "      <td>31.080000</td>\n",
       "      <td>30.520000</td>\n",
       "      <td>30.879999</td>\n",
       "      <td>58182400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>23.415728</td>\n",
       "      <td>30.450001</td>\n",
       "      <td>30.700001</td>\n",
       "      <td>30.190001</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>50559700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>23.577215</td>\n",
       "      <td>30.660000</td>\n",
       "      <td>30.879999</td>\n",
       "      <td>30.240000</td>\n",
       "      <td>30.280001</td>\n",
       "      <td>51197400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Adj Close      Close       High        Low       Open  \\\n",
       "Symbol Date                                                                \n",
       "msft   2010-01-04  23.800220  30.950001  31.100000  30.590000  30.620001   \n",
       "       2010-01-05  23.807911  30.959999  31.100000  30.639999  30.850000   \n",
       "       2010-01-06  23.661804  30.770000  31.080000  30.520000  30.879999   \n",
       "       2010-01-07  23.415728  30.450001  30.700001  30.190001  30.629999   \n",
       "       2010-01-08  23.577215  30.660000  30.879999  30.240000  30.280001   \n",
       "\n",
       "                       Volume  \n",
       "Symbol Date                    \n",
       "msft   2010-01-04  38409100.0  \n",
       "       2010-01-05  49749600.0  \n",
       "       2010-01-06  58182400.0  \n",
       "       2010-01-07  50559700.0  \n",
       "       2010-01-08  51197400.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.query('Symbol == \"msft\"').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once filtering is done, you might want to drop `Symbol` index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2010-01-04</th>\n",
       "      <td>23.800220</td>\n",
       "      <td>30.950001</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>30.590000</td>\n",
       "      <td>30.620001</td>\n",
       "      <td>38409100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>23.807911</td>\n",
       "      <td>30.959999</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>30.639999</td>\n",
       "      <td>30.850000</td>\n",
       "      <td>49749600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>23.661804</td>\n",
       "      <td>30.770000</td>\n",
       "      <td>31.080000</td>\n",
       "      <td>30.520000</td>\n",
       "      <td>30.879999</td>\n",
       "      <td>58182400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>23.415728</td>\n",
       "      <td>30.450001</td>\n",
       "      <td>30.700001</td>\n",
       "      <td>30.190001</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>50559700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>23.577215</td>\n",
       "      <td>30.660000</td>\n",
       "      <td>30.879999</td>\n",
       "      <td>30.240000</td>\n",
       "      <td>30.280001</td>\n",
       "      <td>51197400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close      Close       High        Low       Open      Volume\n",
       "Date                                                                         \n",
       "2010-01-04  23.800220  30.950001  31.100000  30.590000  30.620001  38409100.0\n",
       "2010-01-05  23.807911  30.959999  31.100000  30.639999  30.850000  49749600.0\n",
       "2010-01-06  23.661804  30.770000  31.080000  30.520000  30.879999  58182400.0\n",
       "2010-01-07  23.415728  30.450001  30.700001  30.190001  30.629999  50559700.0\n",
       "2010-01-08  23.577215  30.660000  30.879999  30.240000  30.280001  51197400.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft.index = msft.index.droplevel(0)\n",
    "msft.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2010-01-04</th>\n",
       "      <td>6.535084</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>7.660714</td>\n",
       "      <td>7.585000</td>\n",
       "      <td>7.622500</td>\n",
       "      <td>493729600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>6.546383</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>7.699643</td>\n",
       "      <td>7.616071</td>\n",
       "      <td>7.664286</td>\n",
       "      <td>601904800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>6.442255</td>\n",
       "      <td>7.534643</td>\n",
       "      <td>7.686786</td>\n",
       "      <td>7.526786</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>552160000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>6.430345</td>\n",
       "      <td>7.520714</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.466071</td>\n",
       "      <td>7.562500</td>\n",
       "      <td>477131200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>6.473095</td>\n",
       "      <td>7.570714</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.466429</td>\n",
       "      <td>7.510714</td>\n",
       "      <td>447610800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close     Close      High       Low      Open       Volume\n",
       "Date                                                                      \n",
       "2010-01-04   6.535084  7.643214  7.660714  7.585000  7.622500  493729600.0\n",
       "2010-01-05   6.546383  7.656429  7.699643  7.616071  7.664286  601904800.0\n",
       "2010-01-06   6.442255  7.534643  7.686786  7.526786  7.656429  552160000.0\n",
       "2010-01-07   6.430345  7.520714  7.571429  7.466071  7.562500  477131200.0\n",
       "2010-01-08   6.473095  7.570714  7.571429  7.466429  7.510714  447610800.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl = prices[prices.index.get_level_values('Symbol').isin(['aapl'])]\n",
    "aapl.index = aapl.index.droplevel(0)\n",
    "aapl.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All that operations can be simplified by using `loc` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>6.535084</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>7.660714</td>\n",
       "      <td>7.585000</td>\n",
       "      <td>7.622500</td>\n",
       "      <td>493729600.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>6.546383</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>7.699643</td>\n",
       "      <td>7.616071</td>\n",
       "      <td>7.664286</td>\n",
       "      <td>601904800.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>6.442255</td>\n",
       "      <td>7.534643</td>\n",
       "      <td>7.686786</td>\n",
       "      <td>7.526786</td>\n",
       "      <td>7.656429</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>6.430345</td>\n",
       "      <td>7.520714</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.466071</td>\n",
       "      <td>7.562500</td>\n",
       "      <td>477131200.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>6.473095</td>\n",
       "      <td>7.570714</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.466429</td>\n",
       "      <td>7.510714</td>\n",
       "      <td>447610800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close     Close      High       Low      Open       Volume\n",
       "Date                                                                      \n",
       "2010-01-04   6.535084  7.643214  7.660714  7.585000  7.622500  493729600.0\n",
       "2010-01-05   6.546383  7.656429  7.699643  7.616071  7.664286  601904800.0\n",
       "2010-01-06   6.442255  7.534643  7.686786  7.526786  7.656429  552160000.0\n",
       "2010-01-07   6.430345  7.520714  7.571429  7.466071  7.562500  477131200.0\n",
       "2010-01-08   6.473095  7.570714  7.571429  7.466429  7.510714  447610800.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.loc['aapl'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking\n",
    "\n",
    "Using `stack` and `unstack` you can easily convert multi-indexed data into different columns. Notice that in the following example, we're using column `1` as the first one in the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Symbol</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2010-01-04</th>\n",
       "      <th>aapl</th>\n",
       "      <td>7.643214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>msft</th>\n",
       "      <td>30.950001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goog</th>\n",
       "      <td>312.204773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>^GSPC</th>\n",
       "      <td>1132.989990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <th>aapl</th>\n",
       "      <td>7.656429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Close\n",
       "Date       Symbol             \n",
       "2010-01-04 aapl       7.643214\n",
       "           msft      30.950001\n",
       "           goog     312.204773\n",
       "           ^GSPC   1132.989990\n",
       "2010-01-05 aapl       7.656429"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = pd.read_csv('./data/prices.csv', index_col=[1,0], usecols=[0,1,3], parse_dates=[1])\n",
    "prices.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th>^GSPC</th>\n",
       "      <th>aapl</th>\n",
       "      <th>goog</th>\n",
       "      <th>msft</th>\n",
       "      <th>tsla</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>1132.98999</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>312.204773</td>\n",
       "      <td>30.950001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>1136.52002</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>310.829926</td>\n",
       "      <td>30.959999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close                                      \n",
       "Symbol           ^GSPC      aapl        goog       msft tsla\n",
       "Date                                                        \n",
       "2010-01-04  1132.98999  7.643214  312.204773  30.950001  NaN\n",
       "2010-01-05  1136.52002  7.656429  310.829926  30.959999  NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.unstack().head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We recommend to read the Pandas documentation about [pivoting data](https://pandas.pydata.org/docs/user_guide/reshaping.html#pivot-tables)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series\n",
    "\n",
    "All columns in the previous DataFrame are defined as [_Series_](https://pandas.pydata.org/pandas-docs/stable/dsintro.html#series). A Serie is a data bag indexed by date, in most cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-01    699.210022\n",
       "2016-06-30    692.099976\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Close[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.Close)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Series API](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html#pandas.Series) has multiple methods, but one of the most interesting ones is the `describe` one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2989.000000\n",
       "mean      545.555584\n",
       "std       196.002935\n",
       "min       100.010169\n",
       "25%       435.110754\n",
       "50%       537.359985\n",
       "75%       625.651065\n",
       "max      1220.172036\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Close.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2011-04-20    525.730893\n",
       "2013-07-03    886.431543\n",
       "2010-06-02    493.370861\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Close.sample(n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two interesting parameters to separate training data from model testing data:\n",
    "- Use `frac` to get the % of items from the whole set to retrieve\n",
    "- If you want sets to be consistent between runs, use `random_state`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2009-12-17    593.941045\n",
       "2005-04-21    204.220356\n",
       "2014-12-24    528.772422\n",
       "2010-03-03    545.320926\n",
       "2007-02-07    470.010792\n",
       "2012-01-27    579.980993\n",
       "2014-09-30    577.362590\n",
       "2013-01-17    711.321223\n",
       "2008-07-08    554.530932\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Close.sample(frac=0.003, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also two functions to retrieve the largest and smallest values of a set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2014-02-26    1220.172036\n",
       "2014-02-25    1220.002089\n",
       "2014-03-06    1219.612082\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Close.nlargest(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Querying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2014-02-26</th>\n",
       "      <td>1224.002141</td>\n",
       "      <td>1228.882066</td>\n",
       "      <td>1213.762102</td>\n",
       "      <td>1220.172036</td>\n",
       "      <td>3979100</td>\n",
       "      <td>609.476541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-25</th>\n",
       "      <td>1215.942121</td>\n",
       "      <td>1224.502055</td>\n",
       "      <td>1210.422109</td>\n",
       "      <td>1220.002089</td>\n",
       "      <td>2888100</td>\n",
       "      <td>609.391653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close   Volume  \\\n",
       "Date                                                                      \n",
       "2014-02-26  1224.002141  1228.882066  1213.762102  1220.172036  3979100   \n",
       "2014-02-25  1215.942121  1224.502055  1210.422109  1220.002089  2888100   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2014-02-26  609.476541  \n",
       "2014-02-25  609.391653  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Close > 1220]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2014-02-26</th>\n",
       "      <td>1224.002141</td>\n",
       "      <td>1228.882066</td>\n",
       "      <td>1213.762102</td>\n",
       "      <td>1220.172036</td>\n",
       "      <td>3979100</td>\n",
       "      <td>609.476541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-25</th>\n",
       "      <td>1215.942121</td>\n",
       "      <td>1224.502055</td>\n",
       "      <td>1210.422109</td>\n",
       "      <td>1220.002089</td>\n",
       "      <td>2888100</td>\n",
       "      <td>609.391653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close   Volume  \\\n",
       "Date                                                                      \n",
       "2014-02-26  1224.002141  1228.882066  1213.762102  1220.172036  3979100   \n",
       "2014-02-25  1215.942121  1224.502055  1210.422109  1220.002089  2888100   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2014-02-26  609.476541  \n",
       "2014-02-25  609.391653  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.query(\"Close > 1220\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Masking "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's also two functions, `mask` and `where`, that you can use to discard or include values. In this case it's being is used to filter out values greater than 685, and fill the values with -42. If fill value is omited, `NaN` is used instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-01    699.210022\n",
       "2016-06-30    692.099976\n",
       "2016-06-29    -42.000000\n",
       "2016-06-28    -42.000000\n",
       "2016-06-27    -42.000000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Close.where(lambda x: x > 685, -42).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Notice that `mask` and `where` do the opposite:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-01    -42.000000\n",
       "2016-06-30    -42.000000\n",
       "2016-06-29    684.109985\n",
       "2016-06-28    680.039978\n",
       "2016-06-27    668.260010\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Close.mask(lambda x: x > 685, -42).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipes\n",
    "\n",
    "Is it possible to apply a function to every value of the Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-01    1403.420044\n",
       "2016-06-30    1389.199952\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def double(x):\n",
    "    return x * 2\n",
    "\n",
    "data.Close.pipe(double).pipe(lambda x: x + 5).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `pipe` operation can be also applied to DataFrames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Double Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>2016-07-01</th>\n",
       "      <td>692.200012</td>\n",
       "      <td>700.650024</td>\n",
       "      <td>692.130005</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1342700</td>\n",
       "      <td>699.210022</td>\n",
       "      <td>1398.420044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>685.469971</td>\n",
       "      <td>692.320007</td>\n",
       "      <td>683.650024</td>\n",
       "      <td>692.099976</td>\n",
       "      <td>1590500</td>\n",
       "      <td>692.099976</td>\n",
       "      <td>1384.199952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Volume  \\\n",
       "Date                                                                  \n",
       "2016-07-01  692.200012  700.650024  692.130005  699.210022  1342700   \n",
       "2016-06-30  685.469971  692.320007  683.650024  692.099976  1590500   \n",
       "\n",
       "             Adj Close  Double Close  \n",
       "Date                                  \n",
       "2016-07-01  699.210022   1398.420044  \n",
       "2016-06-30  692.099976   1384.199952  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def double_close(df):\n",
    "    df['Double Close'] = df['Close'] * 2\n",
    "    return df\n",
    "\n",
    "data.pipe(double_close).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply\n",
    "\n",
    "The `apply` method allows to execute arbitrary functions over a Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-01    1398.420044\n",
       "2016-06-30    1384.199952\n",
       "2016-06-29    1368.219970\n",
       "2016-06-28    1360.079956\n",
       "2016-06-27    1336.520020\n",
       "                 ...     \n",
       "2016-01-08    1428.939942\n",
       "2016-01-07    1452.780030\n",
       "2016-01-06    1487.239990\n",
       "2016-01-05    1485.160034\n",
       "2016-01-04    1483.680054\n",
       "Name: Close, Length: 126, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Close['2016'].apply(lambda x: x * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can also be applied to a DataFrame directly, by selecting the axis - normally `0` for the index and `1` for the DataFrame columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open            5.458180e+02\n",
       "High            5.510774e+02\n",
       "Low             5.399927e+02\n",
       "Close           5.455556e+02\n",
       "Volume          8.722729e+06\n",
       "Adj Close       3.310623e+02\n",
       "Double Close    1.091111e+03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data.apply(np.mean, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-01    1.925117e+05\n",
       "2016-06-30    2.279043e+05\n",
       "2016-06-29    2.761840e+05\n",
       "2016-06-28    3.030504e+05\n",
       "2016-06-27    3.762399e+05\n",
       "                  ...     \n",
       "2004-08-25    1.318970e+06\n",
       "2004-08-24    2.188628e+06\n",
       "2004-08-23    2.620502e+06\n",
       "2004-08-20    3.277641e+06\n",
       "2004-08-19    6.410279e+06\n",
       "Length: 2989, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(np.mean, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading directly into a series\n",
    "\n",
    "Instead of reading files as `DataFrames`, it's possible to read a `Series` directly, using column 0 as date, and colum 4 as the `Series` value. Do not forget to apply the `.squeze` function afterwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-01    699.210022\n",
       "2016-06-30    692.099976\n",
       "2016-06-29    684.109985\n",
       "2016-06-28    680.039978\n",
       "2016-06-27    668.260010\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = pd.read_csv(\"./data/goog.csv\", header=0, index_col=0, usecols=[0, 4], parse_dates=True).squeeze()\n",
    "series.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Exercise 1.1\n",
    "\n",
    "Read file `prices.csv` and filter symbol `tsla`. Select 90% of `Close` column for training and reserve 10% for model later checking. Try to randomise data picked.\n",
    "\n",
    "You may want to check [Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/).\n",
    "\n",
    "### Exercise 1.2\n",
    "\n",
    "From `prices.csv` file, read `msft` data and write it as a Feather file in `data/msft.feather`.\n",
    "\n",
    "### Exercise 1.3\n",
    "\n",
    "Add a boolean column to previous `msft` data frame indicating if trades open with values greater than 100.\n",
    "\n",
    "### Exercise 1.4\n",
    "\n",
    "Look in Pandas documentation for __styling functions__ and mark in red the `close` values below 30.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time-Series\n",
    "\n",
    "Our series can be [resampled](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html) to months, using mean close price for each one.\n",
    "\n",
    "- M: Months\n",
    "- Q: Quarters\n",
    "- A: Years\n",
    "\n",
    "The `resample` function just returns an object represeting the resampling. We have to pick one of the many functions to extract the value for each new of the rows generated (months, quarters or year rows). The available offset strings can be found [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.resample.DatetimeIndexResampler object at 0x116270c50>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = pd.read_csv(\"./data/goog.csv\", header=0, index_col=0, usecols=[0, 4], parse_dates=True).squeeze()\n",
    "series.resample(\"A\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Padding\n",
    "\n",
    "First thing we can do is to get the latest value known in each range, using `fillna` with `ffill` method. Don't use `pad` function, since it's deprecated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192.790325"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series['2004-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004-12-31    192.790325\n",
       "2005-12-31    414.860723\n",
       "2006-12-31    460.480804\n",
       "2007-12-31    691.481171\n",
       "2008-12-31    307.650529\n",
       "Freq: A-DEC, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample(\"A\").fillna(method=\"ffill\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For brevity, there's a function called `ffill` that calls to `fillna` internally:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004-12-31    192.790325\n",
       "2005-12-31    414.860723\n",
       "2006-12-31    460.480804\n",
       "2007-12-31    691.481171\n",
       "2008-12-31    307.650529\n",
       "Freq: A-DEC, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample(\"A\").ffill().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing range mean, min or max values\n",
    "\n",
    "And we can also get the mean values for each range. Instead of `fillna`, use `mean` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151.80153629787233"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series[\"2004\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004-12-31    151.801536\n",
       "2005-12-31    277.759564\n",
       "2006-12-31    411.186004\n",
       "2007-12-31    538.751362\n",
       "2008-12-31    464.852813\n",
       "Freq: A-DEC, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample(\"A\").mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004-12-31    197.600333\n",
       "2005-12-31    432.040752\n",
       "2006-12-31    509.650860\n",
       "2007-12-31    741.791259\n",
       "2008-12-31    685.331181\n",
       "Freq: A-DEC, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample(\"A\").max().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or even `sum` all the values in a range - although in this case it doesn't make too much sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004-12-31     14269.344412\n",
       "2005-12-31     69995.410151\n",
       "2006-12-31    103207.687074\n",
       "2007-12-31    135226.591787\n",
       "2008-12-31    117607.761661\n",
       "Freq: A-DEC, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample(\"A\").sum().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ploting resampled series for better understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-01    699.210022\n",
       "2016-06-30    692.099976\n",
       "2016-06-29    684.109985\n",
       "2016-06-28    680.039978\n",
       "2016-06-27    668.260010\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "import matplotlib.pyplot as plt\n",
    "series.plot(figsize=(10,5))\n",
    "series.resample(\"M\").mean().plot(linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OHLC: Open, High, Low, Close\n",
    "\n",
    "A very interesting function is `ohlc`, that computes several values for a date range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>100.340176</td>\n",
       "      <td>197.600333</td>\n",
       "      <td>100.010169</td>\n",
       "      <td>192.790325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>202.710351</td>\n",
       "      <td>432.040752</td>\n",
       "      <td>174.990304</td>\n",
       "      <td>414.860723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>435.230740</td>\n",
       "      <td>509.650860</td>\n",
       "      <td>337.060574</td>\n",
       "      <td>460.480804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>467.590802</td>\n",
       "      <td>741.791259</td>\n",
       "      <td>438.680763</td>\n",
       "      <td>691.481171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>685.191192</td>\n",
       "      <td>685.331181</td>\n",
       "      <td>257.440455</td>\n",
       "      <td>307.650529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>321.320557</td>\n",
       "      <td>622.871087</td>\n",
       "      <td>282.750497</td>\n",
       "      <td>619.981079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>626.751061</td>\n",
       "      <td>626.771094</td>\n",
       "      <td>436.070761</td>\n",
       "      <td>593.971003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>604.351030</td>\n",
       "      <td>645.901096</td>\n",
       "      <td>474.880824</td>\n",
       "      <td>645.901096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>665.411118</td>\n",
       "      <td>768.051291</td>\n",
       "      <td>559.050931</td>\n",
       "      <td>707.381210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>723.251230</td>\n",
       "      <td>1120.711956</td>\n",
       "      <td>702.871197</td>\n",
       "      <td>1120.711956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>1113.121955</td>\n",
       "      <td>1220.172036</td>\n",
       "      <td>495.392242</td>\n",
       "      <td>526.402397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>524.812404</td>\n",
       "      <td>776.599976</td>\n",
       "      <td>492.552239</td>\n",
       "      <td>758.880005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>741.840027</td>\n",
       "      <td>766.609985</td>\n",
       "      <td>668.260010</td>\n",
       "      <td>699.210022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   open         high         low        close\n",
       "Date                                                         \n",
       "2004-12-31   100.340176   197.600333  100.010169   192.790325\n",
       "2005-12-31   202.710351   432.040752  174.990304   414.860723\n",
       "2006-12-31   435.230740   509.650860  337.060574   460.480804\n",
       "2007-12-31   467.590802   741.791259  438.680763   691.481171\n",
       "2008-12-31   685.191192   685.331181  257.440455   307.650529\n",
       "2009-12-31   321.320557   622.871087  282.750497   619.981079\n",
       "2010-12-31   626.751061   626.771094  436.070761   593.971003\n",
       "2011-12-31   604.351030   645.901096  474.880824   645.901096\n",
       "2012-12-31   665.411118   768.051291  559.050931   707.381210\n",
       "2013-12-31   723.251230  1120.711956  702.871197  1120.711956\n",
       "2014-12-31  1113.121955  1220.172036  495.392242   526.402397\n",
       "2015-12-31   524.812404   776.599976  492.552239   758.880005\n",
       "2016-12-31   741.840027   766.609985  668.260010   699.210022"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample(\"A\").ohlc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom ranges\n",
    "\n",
    "Every two years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004-12-31    151.801536\n",
       "2006-12-31    344.340154\n",
       "2008-12-31    501.655463\n",
       "2010-12-31    487.656550\n",
       "2012-12-31    605.749265\n",
       "Freq: 2A-DEC, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample(\"2A\").mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is more information for those magic strings like `2A` in [the documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upsampling\n",
    "\n",
    "Apart of grouping data (or _downsampling_) we can also calculate interpolated values. For instence, we're going to calculate values for each quarter, using `\"Q\"` or `\"4M\"` parameters.\n",
    "\n",
    "First we need to get the data downsampled to year values, using latest one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2004-12-31    151.801536\n",
       "2005-12-31    277.759564\n",
       "2006-12-31    411.186004\n",
       "2007-12-31    538.751362\n",
       "2008-12-31    464.852813\n",
       "2009-12-31    439.689088\n",
       "2010-12-31    535.624013\n",
       "2011-12-31    568.975142\n",
       "2012-12-31    642.817582\n",
       "2013-12-31    884.244254\n",
       "2014-12-31    696.317865\n",
       "2015-12-31    602.462102\n",
       "2016-12-31    717.235795\n",
       "Freq: A-DEC, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft_year = series.resample(\"A\").mean()\n",
    "msft_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can interpolate some values. Normally you would use `ffill`, optionally setting the maximum number of rows to fill before stopping propagation and filling with `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\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>mean</th>\n",
       "      <th>ffill</th>\n",
       "      <th>ffill_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>151.801536</td>\n",
       "      <td>151.801536</td>\n",
       "      <td>151.801536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>151.801536</td>\n",
       "      <td>151.801536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>151.801536</td>\n",
       "      <td>151.801536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>151.801536</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>277.759564</td>\n",
       "      <td>277.759564</td>\n",
       "      <td>277.759564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>277.759564</td>\n",
       "      <td>277.759564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>277.759564</td>\n",
       "      <td>277.759564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>277.759564</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>411.186004</td>\n",
       "      <td>411.186004</td>\n",
       "      <td>411.186004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean       ffill   ffill_max\n",
       "Date                                          \n",
       "2004-12-31  151.801536  151.801536  151.801536\n",
       "2005-03-31         NaN  151.801536  151.801536\n",
       "2005-06-30         NaN  151.801536  151.801536\n",
       "2005-09-30         NaN  151.801536         NaN\n",
       "2005-12-31  277.759564  277.759564  277.759564\n",
       "2006-03-31         NaN  277.759564  277.759564\n",
       "2006-06-30         NaN  277.759564  277.759564\n",
       "2006-09-30         NaN  277.759564         NaN\n",
       "2006-12-31  411.186004  411.186004  411.186004"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = msft_year.resample(\"Q\")\n",
    "max_propagated = 2\n",
    "pd.DataFrame({ 'mean': rs.mean(), 'ffill': rs.ffill(), \n",
    "              'ffill_max': rs.ffill(max_propagated) })[:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Exercise 1.5\n",
    "\n",
    "Read CSV data for Tesla `tsla` directly into a `Series` and compute _closing_ values for each quarter.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cookbook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining Series into new DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>msft</th>\n",
       "      <th>aapl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.950001</td>\n",
       "      <td>7.643214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.959999</td>\n",
       "      <td>7.656429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.770000</td>\n",
       "      <td>7.534643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 msft      aapl\n",
       "Date                           \n",
       "2010-01-04  30.950001  7.643214\n",
       "2010-01-05  30.959999  7.656429\n",
       "2010-01-06  30.770000  7.534643"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft_aapl = pd.DataFrame({ 'msft': msft.Close, 'aapl': aapl.Close })\n",
    "msft_aapl[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default it uses closed ranges."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sample_data import stock_prices\n",
    "aapl = stock_prices('aapl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>2010-01-04</th>\n",
       "      <td>6.535084</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>7.660714</td>\n",
       "      <td>7.585000</td>\n",
       "      <td>7.622500</td>\n",
       "      <td>493729600.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>6.546383</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>7.699643</td>\n",
       "      <td>7.616071</td>\n",
       "      <td>7.664286</td>\n",
       "      <td>601904800.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close     Close      High       Low      Open       Volume  \\\n",
       "Date                                                                         \n",
       "2010-01-04   6.535084  7.643214  7.660714  7.585000  7.622500  493729600.0   \n",
       "2010-01-05   6.546383  7.656429  7.699643  7.616071  7.664286  601904800.0   \n",
       "\n",
       "            Year  \n",
       "Date              \n",
       "2010-01-04  2010  \n",
       "2010-01-05  2010  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl.loc[:,\"Year\"] = pd.to_datetime(aapl.index).map(lambda d: d.year)\n",
    "aapl[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using custom indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-10-31', '2015-11-30', '2015-12-31', '2016-01-31'], dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('2015-10', '2016-02', freq='M')\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-10-31           NaN\n",
       "2015-11-30    742.599976\n",
       "2015-12-31    758.880005\n",
       "2016-01-31           NaN\n",
       "Freq: M, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import Series\n",
    "Series(series, index=dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install packages in Colab Notebooks\n",
    "\n",
    "Very simple, use `pip` or `conda`, by appending a `!` in a cell for executing in a shell\n",
    "\n",
    "`!pip install package_name`."
   ]
  }
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