00. Pandas Review.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>Time Series Analysis</h1>\n",
    "<h1>Pandas Review</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.5\n",
      "IPython version      : 7.19.0\n",
      "\n",
      "Compiler    : Clang 10.0.0 \n",
      "OS          : Darwin\n",
      "Release     : 20.5.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: cb359d5f1b6a097e176b437218ac5ab9b4f6dec9\n",
      "\n",
      "pandas    : 1.1.3\n",
      "watermark : 2.1.0\n",
      "matplotlib: 3.3.2\n",
      "numpy     : 1.19.2\n",
      "json      : 2.0.9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrames and Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series and DataFrames can be thought of as dictionaries associating keys to lists of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"id\": [23, 42, 12, 86], \"Name\":[\"Bob\", \"Karen\", \"Kate\", \"Bill\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': [23, 42, 12, 86], 'Name': ['Bob', 'Karen', 'Kate', 'Bill']}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Series corresponds to just a sigle list of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = pd.Series(data[\"id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23\n",
       "1    42\n",
       "2    12\n",
       "3    86\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While a DataFrame can have multiple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    " df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   Name\n",
       "0  23    Bob\n",
       "1  42  Karen\n",
       "2  12   Kate\n",
       "3  86   Bill"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way of looking at it, is that DataFrames are essentially groups of individual Series. Each Series can have it's own datatype **dtype**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id       int64\n",
       "Name    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get general information about how the DataFrame is being stored by calling __info()__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      4 non-null      int64 \n",
      " 1   Name    4 non-null      object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 192.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subsetting a DataFrame by column name we retrieve the underlying Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both columns and index values have types and possibly names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'Name'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can query the shape and number of dimensions of the DataFrame easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And relable both index and column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = [\"row\" + str(i) for i in range(4)]\n",
    "df.columns = ['ID', 'First Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row0</th>\n",
       "      <td>23</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID First Name\n",
       "row0  23        Bob\n",
       "row1  42      Karen\n",
       "row2  12       Kate\n",
       "row3  86       Bill"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsetting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top/bottom N number of values are easy to access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row0</th>\n",
       "      <td>23</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      ID First Name\n",
       "row0  23        Bob\n",
       "row1  42      Karen"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>ID</th>\n",
       "      <th>First Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "      ID First Name\n",
       "row2  12       Kate\n",
       "row3  86       Bill"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As are individual columns, as we already saw above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row0    23\n",
       "row1    42\n",
       "row2    12\n",
       "row3    86\n",
       "Name: ID, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And individual rows, which can be indexed by Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID              12\n",
       "First Name    Kate\n",
       "Name: row2, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['row2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID              12\n",
       "First Name    Kate\n",
       "Name: row2, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows behave a named tuples, so you can access individual elements by position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2].ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Karen'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['row1', 'First Name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ranges can also be used with iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
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      "text/plain": [
       "      ID First Name\n",
       "row1  42      Karen\n",
       "row2  12       Kate"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And loc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One important difference is that loc automatically __includes__ the last value of the range, while iloc does not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>ID</th>\n",
       "      <th>First Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID First Name\n",
       "row1  42      Karen\n",
       "row2  12       Kate\n",
       "row3  86       Bill"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"row1\":\"row3\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since each column is just a numpy array, we can easily manipulate the values and create new columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['id2'] = df.ID+45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>id2</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row0</th>\n",
       "      <td>23</td>\n",
       "      <td>Bob</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID First Name  id2\n",
       "row0  23        Bob   68\n",
       "row1  42      Karen   87\n",
       "row2  12       Kate   57\n",
       "row3  86       Bill  131"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also append new rows to the dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>id2</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row0</th>\n",
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       "      <td>Bob</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "      <td>131</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>Bruno</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID First Name  id2   Name\n",
       "row0  23        Bob   68    NaN\n",
       "row1  42      Karen   87    NaN\n",
       "row2  12       Kate   57    NaN\n",
       "row3  86       Bill  131    NaN\n",
       "0     15        NaN   30  Bruno"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append([{\"Name\":\"Bruno\", \"ID\":15, \"id2\":30}])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__append__ returns the modified version of the dataframe so you should assign it to a new variable if you wish to keep it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>First Name</th>\n",
       "      <th>id2</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>23</td>\n",
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       "      <td>68</td>\n",
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       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID First Name  id2\n",
       "row0  23        Bob   68\n",
       "row1  42      Karen   87\n",
       "row2  12       Kate   57\n",
       "row3  86       Bill  131"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To stack two dataframes on top of one another, we use __concat__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.concat([df, df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
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       "      <th>id2</th>\n",
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       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "      ID First Name  id2\n",
       "row0  23        Bob   68\n",
       "row1  42      Karen   87\n",
       "row2  12       Kate   57\n",
       "row3  86       Bill  131\n",
       "row0  23        Bob   68\n",
       "row1  42      Karen   87\n",
       "row2  12       Kate   57\n",
       "row3  86       Bill  131"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__concat__ can also be used to place two DataFrames side by side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>Bob</td>\n",
       "      <td>68</td>\n",
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       "      <th>row1</th>\n",
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       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "      <td>57</td>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "      <td>131</td>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID First Name  id2  ID First Name  id2\n",
       "row0  23        Bob   68  23        Bob   68\n",
       "row1  42      Karen   87  42      Karen   87\n",
       "row2  12       Kate   57  12       Kate   57\n",
       "row3  86       Bill  131  86       Bill  131"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df, df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>id2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row0</th>\n",
       "      <td>23</td>\n",
       "      <td>Bob</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID First Name  id2\n",
       "row0  23        Bob   68\n",
       "row1  42      Karen   87\n",
       "row3  86       Bill  131"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('row2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row0</th>\n",
       "      <td>23</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID First Name\n",
       "row0  23        Bob\n",
       "row1  42      Karen\n",
       "row2  12       Kate\n",
       "row3  86       Bill"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('id2', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apple stock information from https://finance.yahoo.com/quote/AAPL/history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/AAPL.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data file contains a column with dates, as well as several columns with various metrics of the stock price fluctuations for each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-12-12</td>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.406782</td>\n",
       "      <td>117258400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-12-15</td>\n",
       "      <td>0.488839</td>\n",
       "      <td>0.488839</td>\n",
       "      <td>0.486607</td>\n",
       "      <td>0.486607</td>\n",
       "      <td>0.385558</td>\n",
       "      <td>43971200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.450893</td>\n",
       "      <td>0.450893</td>\n",
       "      <td>0.357260</td>\n",
       "      <td>26432000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-12-17</td>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.366103</td>\n",
       "      <td>21610400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-12-18</td>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.477679</td>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.376715</td>\n",
       "      <td>18362400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9882</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>297.260010</td>\n",
       "      <td>304.179993</td>\n",
       "      <td>289.230011</td>\n",
       "      <td>298.179993</td>\n",
       "      <td>298.179993</td>\n",
       "      <td>55548800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9883</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>300.950012</td>\n",
       "      <td>302.529999</td>\n",
       "      <td>286.130005</td>\n",
       "      <td>288.079987</td>\n",
       "      <td>288.079987</td>\n",
       "      <td>57668400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9884</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>286.529999</td>\n",
       "      <td>297.880005</td>\n",
       "      <td>286.500000</td>\n",
       "      <td>292.649994</td>\n",
       "      <td>292.649994</td>\n",
       "      <td>49513700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9885</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>281.100006</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>272.959991</td>\n",
       "      <td>273.519989</td>\n",
       "      <td>273.519989</td>\n",
       "      <td>80151400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9886</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>257.260010</td>\n",
       "      <td>278.410004</td>\n",
       "      <td>256.369995</td>\n",
       "      <td>273.359985</td>\n",
       "      <td>273.359985</td>\n",
       "      <td>106627500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9887 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     1980-12-12    0.513393    0.515625    0.513393    0.513393    0.406782   \n",
       "1     1980-12-15    0.488839    0.488839    0.486607    0.486607    0.385558   \n",
       "2     1980-12-16    0.453125    0.453125    0.450893    0.450893    0.357260   \n",
       "3     1980-12-17    0.462054    0.464286    0.462054    0.462054    0.366103   \n",
       "4     1980-12-18    0.475446    0.477679    0.475446    0.475446    0.376715   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "9882  2020-02-24  297.260010  304.179993  289.230011  298.179993  298.179993   \n",
       "9883  2020-02-25  300.950012  302.529999  286.130005  288.079987  288.079987   \n",
       "9884  2020-02-26  286.529999  297.880005  286.500000  292.649994  292.649994   \n",
       "9885  2020-02-27  281.100006  286.000000  272.959991  273.519989  273.519989   \n",
       "9886  2020-02-28  257.260010  278.410004  256.369995  273.359985  273.359985   \n",
       "\n",
       "           Volume  \n",
       "0     117258400.0  \n",
       "1      43971200.0  \n",
       "2      26432000.0  \n",
       "3      21610400.0  \n",
       "4      18362400.0  \n",
       "...           ...  \n",
       "9882   55548800.0  \n",
       "9883   57668400.0  \n",
       "9884   49513700.0  \n",
       "9885   80151400.0  \n",
       "9886  106627500.0  \n",
       "\n",
       "[9887 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can automatically conver the Date column to a datetime column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Date'] = pd.to_datetime(data['Date'].astype('str'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         datetime64[ns]\n",
       "Open                float64\n",
       "High                float64\n",
       "Low                 float64\n",
       "Close               float64\n",
       "Adj Close           float64\n",
       "Volume              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another alternative is to tell pandas directly which column to use for dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/AAPL.csv', parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the end result is similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         datetime64[ns]\n",
       "Open                float64\n",
       "High                float64\n",
       "Low                 float64\n",
       "Close               float64\n",
       "Adj Close           float64\n",
       "Volume              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we now set the Date column to be the index, we effectively create our first Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that pandas automatically generated a \"DatetimeIndex\" object that allos us to take advantage of the fact that we are dealing with dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 9887 entries, 1980-12-12 to 2020-02-28\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       9886 non-null   float64\n",
      " 1   High       9886 non-null   float64\n",
      " 2   Low        9886 non-null   float64\n",
      " 3   Close      9886 non-null   float64\n",
      " 4   Adj Close  9886 non-null   float64\n",
      " 5   Volume     9886 non-null   float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 540.7 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily access parts of the date object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
       "            ...\n",
       "             2,  2,  2,  2,  2,  2,  2,  2,  2,  2],\n",
       "           dtype='int64', name='Date', length=9887)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980,\n",
       "            ...\n",
       "            2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020],\n",
       "           dtype='int64', name='Date', length=9887)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([12, 15, 16, 17, 18, 19, 22, 23, 24, 26,\n",
       "            ...\n",
       "            14, 18, 19, 20, 21, 24, 25, 26, 27, 28],\n",
       "           dtype='int64', name='Date', length=9887)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And slice the DataFrame by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Adj Close</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>30.490000</td>\n",
       "      <td>30.642857</td>\n",
       "      <td>30.340000</td>\n",
       "      <td>30.572857</td>\n",
       "      <td>26.538483</td>\n",
       "      <td>123432400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.657143</td>\n",
       "      <td>30.798571</td>\n",
       "      <td>30.464285</td>\n",
       "      <td>30.625713</td>\n",
       "      <td>26.584366</td>\n",
       "      <td>150476200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.625713</td>\n",
       "      <td>30.747143</td>\n",
       "      <td>30.107143</td>\n",
       "      <td>30.138571</td>\n",
       "      <td>26.161509</td>\n",
       "      <td>138040000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.250000</td>\n",
       "      <td>30.285715</td>\n",
       "      <td>29.864286</td>\n",
       "      <td>30.082857</td>\n",
       "      <td>26.113146</td>\n",
       "      <td>119282800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.042856</td>\n",
       "      <td>30.285715</td>\n",
       "      <td>29.865715</td>\n",
       "      <td>30.282858</td>\n",
       "      <td>26.286753</td>\n",
       "      <td>111902700.0</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>2010-06-04</th>\n",
       "      <td>36.887142</td>\n",
       "      <td>37.414288</td>\n",
       "      <td>36.375713</td>\n",
       "      <td>36.565716</td>\n",
       "      <td>31.740519</td>\n",
       "      <td>189576100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-07</th>\n",
       "      <td>36.898571</td>\n",
       "      <td>37.021427</td>\n",
       "      <td>35.792858</td>\n",
       "      <td>35.848572</td>\n",
       "      <td>31.118025</td>\n",
       "      <td>221735500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-08</th>\n",
       "      <td>36.177143</td>\n",
       "      <td>36.257141</td>\n",
       "      <td>35.092857</td>\n",
       "      <td>35.618572</td>\n",
       "      <td>30.918377</td>\n",
       "      <td>250192600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-09</th>\n",
       "      <td>35.924286</td>\n",
       "      <td>35.985714</td>\n",
       "      <td>34.641430</td>\n",
       "      <td>34.742859</td>\n",
       "      <td>30.158216</td>\n",
       "      <td>213657500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-10</th>\n",
       "      <td>34.977142</td>\n",
       "      <td>35.854286</td>\n",
       "      <td>34.599998</td>\n",
       "      <td>35.787144</td>\n",
       "      <td>31.064703</td>\n",
       "      <td>194089000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close       Volume\n",
       "Date                                                                          \n",
       "2010-01-04  30.490000  30.642857  30.340000  30.572857  26.538483  123432400.0\n",
       "2010-01-05  30.657143  30.798571  30.464285  30.625713  26.584366  150476200.0\n",
       "2010-01-06  30.625713  30.747143  30.107143  30.138571  26.161509  138040000.0\n",
       "2010-01-07  30.250000  30.285715  29.864286  30.082857  26.113146  119282800.0\n",
       "2010-01-08  30.042856  30.285715  29.865715  30.282858  26.286753  111902700.0\n",
       "...               ...        ...        ...        ...        ...          ...\n",
       "2010-06-04  36.887142  37.414288  36.375713  36.565716  31.740519  189576100.0\n",
       "2010-06-07  36.898571  37.021427  35.792858  35.848572  31.118025  221735500.0\n",
       "2010-06-08  36.177143  36.257141  35.092857  35.618572  30.918377  250192600.0\n",
       "2010-06-09  35.924286  35.985714  34.641430  34.742859  30.158216  213657500.0\n",
       "2010-06-10  34.977142  35.854286  34.599998  35.787144  31.064703  194089000.0\n",
       "\n",
       "[110 rows x 6 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc['2010':'2010-06-10']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we note that the last value is also included"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame Manipulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a new DataFrame with some fake data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "0  0  1\n",
       "1  1  2\n",
       "2  2  3"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Map allows us to easily apply a function to the rows of a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: A, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A'].map(lambda x:x+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a dataframe we need to use transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "0  1  2\n",
       "1  2  3\n",
       "2  3  4"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.transform(lambda x: x + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    3\n",
       "B    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.sum, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    3\n",
       "2    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.sum, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['year'] = data.index.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "groupby gives us a simple way to aggregate values based on the entries of a specific column to generate summary statistics, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>3.362128e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>2.049236e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>5.341252e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1.112825e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1.049476e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>1.137307e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1.333081e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1.494283e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>1.032324e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1.272646e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1.110049e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>1.433691e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1.028448e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1.411323e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>1.428897e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1.856663e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1.329856e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1.799084e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>2.879855e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>3.427568e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>3.007540e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.366445e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1.925348e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1.780756e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>3.045042e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>4.560025e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>5.392474e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>6.174900e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>7.149530e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>3.581342e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>3.775623e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>3.101483e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>3.299105e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2.560539e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1.591449e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1.306605e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>9.682478e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>6.810776e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>8.539036e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>7.063803e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.488462e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Volume\n",
       "year              \n",
       "1980  3.362128e+08\n",
       "1981  2.049236e+09\n",
       "1982  5.341252e+09\n",
       "1983  1.112825e+10\n",
       "1984  1.049476e+10\n",
       "1985  1.137307e+10\n",
       "1986  1.333081e+10\n",
       "1987  1.494283e+10\n",
       "1988  1.032324e+10\n",
       "1989  1.272646e+10\n",
       "1990  1.110049e+10\n",
       "1991  1.433691e+10\n",
       "1992  1.028448e+10\n",
       "1993  1.411323e+10\n",
       "1994  1.428897e+10\n",
       "1995  1.856663e+10\n",
       "1996  1.329856e+10\n",
       "1997  1.799084e+10\n",
       "1998  2.879855e+10\n",
       "1999  3.427568e+10\n",
       "2000  3.007540e+10\n",
       "2001  2.366445e+10\n",
       "2002  1.925348e+10\n",
       "2003  1.780756e+10\n",
       "2004  3.045042e+10\n",
       "2005  4.560025e+10\n",
       "2006  5.392474e+10\n",
       "2007  6.174900e+10\n",
       "2008  7.149530e+10\n",
       "2009  3.581342e+10\n",
       "2010  3.775623e+10\n",
       "2011  3.101483e+10\n",
       "2012  3.299105e+10\n",
       "2013  2.560539e+10\n",
       "2014  1.591449e+10\n",
       "2015  1.306605e+10\n",
       "2016  9.682478e+09\n",
       "2017  6.810776e+09\n",
       "2018  8.539036e+09\n",
       "2019  7.063803e+09\n",
       "2020  1.488462e+09"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Volume', 'year']].groupby('year').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['month'] = data.index.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pivot tables are a particularly powerful way to manipulate a DataFrame to generate summary tables of values. For example, we can easily slice our data to create time series of the average monthly value for each year in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sTZ4fAAAAAACgMzFMU6fffqYGTh6sd34wXzs/a5tzOL7Y9fkOPXX2XzT+lxfptNvPkOliWiYABAqBTAAAAADoJGzb1q5yW1n5trLyLW0qsWV1zEGYkrwhzMxUU6f1MJXe3ZDbJIgJAIBTynaWKvelHOW+tEplO0udbqdVQqNDNeyyEUqfnaHeZ/ZhwjYAAB3Unj179Nhjj/m09sYbb2yTmvv37/dpXbAHMn19HQg+a9eu1Z133qk//OEPuu+++3TjjTfyfhcAAAAAAOAkJPRL0PcWXK+s51Zq2a+XqKEqMBembapt1Pu/eE/r316rGU/OVOIgLpwGAIFAIBMAAAAAOrD6Jltr9nkDmFn5loprnO7If0xDGtjVUGaKqcxUQ33jDDaQAQDgoIbqBm18Z71WvZitHZ8G7kqwbaXPOf00ek6Ghs4YrtCoUKfbAQAAfvbTn/5UNTUtnziJiYnRnXfe2SY1S0pKfFqXmJjYJvVay9epl76+DgSv3bt36+abb9bzzz+vf/7znxowYIDTLQEAAAAAALQ7hmlq7I2nKe2iQVr4o7e17cO8gNXe8/VuPX3uX3XBvRfqzLlny3S7AlYbADojApkAAAAA0MHsqz4cwFy7z1aDx+mO/Cc6VMrobmpMqqExKaZiwghgAgDgJNu2tfvLXcqdl6N189eqobLe6ZZaJa53nNJnZ2jUNRmK7xvvdDtAwHne+1LW4q+dbgMBYNu2wi3rqGNNpslFbU7AnHKaXFPPcLoN+Mkbb7yhl19+2ae1d911l+Lj2+Z9woEDB3xaFxMT0yb1WsvXur6+DgS/Tz75ROnp6XrhhRd02WWXOd0OAAAAAABAuxTXO05z3vieVr2Qrfd/sVj1FXUBqeupb9IH9y/VhrfXa8ZfZip5WLeA1AWAzohAJgAAAAC0cx7L1sbi5hBmga3d5bbTLflV7y6GMlMNjU01NairIZfJhmEAAJxWvqdcq1/OUe68HB3Y1r425IdEhmjojOEaPWeM+pzdR4ZpOt0S4Bi7vFr27n1Ot4EAOdZ3u4792+SpscurnW4BflJYWKjbb7/dp7U9e/bUvffe22a1fQ0yxsbGtlnN1vC1LoHMjqW6ulqXX365HnroIf385z93uh0AAAAAAIB2yTAMZVybqQETBurdu9/WliWbA1Y7P2evnjn/bzrvp+fr7LvPkyuEaZkA0NYIZAIAAABAO1RRbyunwFJWvve2utHpjvwn1CWN7GYoM8XUmFRTyVEEMAEACAaNtY3auHC9cuflaNvybZLdvmI8vc/so/TZGRp22QiFxYQ53Q4AAHCAx+PRNddco+LiYp/WP/LII4qKimqz+jU1NT6ti46ObrOareHrhExfXwfaD9u2dc899xy6BQAAAAAAwMmJTY3VNS9/V2tezdXiny9SXVltQOpajR4t//2H2rDAOy0zJT01IHUBoLMgkAkAAAAA7YBt29pRZisr3zsJc3OJ3aEnlyRFSpmppjJTTY1INhTmJoQJAEAwsG1be/67W7nzcrTuzTWqr6h3uqVWie3ZRenXjFb67Awl9O/qdDsAAMBh99xzjz7++GOf1l5++eW6+uqr27R+Y6NvV9gKCQlp07q+8rVuQ0ODnztBYmKi0tPTNWzYMA0fPlxDhw5VQkKCunTpotjYWEVHR6uxsVE1NTUqKCjQ7t27tXr1aq1cuVIffPDBSU8xvffee5WQkKBbb721jV8RAAAAAABA52EYhkbNGq3+FwzQoh+/o40LNwSsdtHaQv1jwtM6+0fn6tyfXiB3GBEiAGgLfDcFAAAAgCBV12RrdaE3gJlVYOlAYC6Q5gjTkIYkGhqTampsqqFesYYMgxAmAADBoiK/QqtfWaXceTkq2eLbBKlg4Q53a+iMYUqfPUb9zusnwzSdbgkAAASBF198UX/60598Wtu9e3c9/fTTbd6Dr0FGpwKZbrdv2wl8DZbCdy6XS6effrqmTp2qKVOmKDMzs8VzZS6XS+Hh4UpISNDw4cM1ZcoUSZJlWfroo4/07LPP6rXXXpPH42lVLz/4wQ+UkZGhcePGnfTrAQAAAAAAgBTdLUZX/ec7Wj9/rd77yULVlNQEpK7VZOnTP32sje9u0Iy/zFSPMT0DUhcAOjICmQAAAAAQRAqrmgOY+ZbW7rPVZDndkf/EhEpjUkxlphoanWIqOpQAJgAAwaSprlGbFm3Uqnk52vZhnmyrfc3n7nl6b42enaFhl41QeJdwp9sBAABBZMWKFbrpppt8Wmuapv79738rMTGxzfvwNcjoazCyrTEhM/AGDBigm2++WTfccIO6devWJs9pmqYmTJigCRMm6De/+Y3uuecevfnmmz5/fUNDg2bNmqV169YpIiKiTXoCAAAAAADorAzD0PCZI9X33P5a/LOFWvfm2oDV3r9hn/550TM68wdn6/x7xiskwpkLwQFAR0AgEwAAAAAc1GTZ2rD/8BTMvRVOd+Rf/eIMZaYaykw1lZZgyGUSwgQAIJjYtq38nL3KfTFHa19frbryOqdbapWY1FiNuma00r8zWokDk5xuBwAABKG8vDxddtllqq+v92n9/fffr0mTJvmlF8vy7UpcLU1G9BfTx8nivr4OHN/IkSO1bNkyjR8/3q//vQcOHKg33nhDb7zxhm688UZVVPh2MnL79u363e9+pwcffNBvvQEAAAAAAHQmUYlRuuKfszR85ki9++N3VL2vKiB1bcvW53/+TJsWbdSMJ2eq1+m9A1IXADoaApkAAAAAEGBldbaymwOYuYW2anwbhtAuhbmkUd0NZaaYGpNqKjGSACYAAMGoqqhSq1/NVe68HO3fsM/pdlrFFebWkOlDlD5njPpfMECmy7fgAAAA6HyKioo0ZcoUFRcX+7R+2rRp+uUvf+m3fnydQNnU1OS3Hk7E1wmevr4OHF9mZmZA611xxRUaPny4Jk2apN27d/v0NQ8//LBuv/129ezZ08/dAQAAAAAAdB5DLhmmPuf01ZJ7Fmn1K7kBq1uypVj/mvKsTr/9DI3/1UUKiQwNWG0A6AgIZAIAAACAn1m2re2ltrLyvZMw8w7Ysp1uyo+6RUmZqaYyU00NTzYU6iKECQBAMGqqb9KWxZu0al628pblyfa0r8lGqZk9NXpOhoZfPlIRcRFOtwMAAIJceXm5pkyZoq1bt/q0fvTo0XrppZf8Oq0w2AOZvtYNDWWzVns0ZMgQLV++XOecc44KCgpaXN/Q0KDHHntMjzzySAC6AwAAAAAA6Dwi4iN12dNXatjMkXr37rdVWVAZmMK2ra/+9oU2L96kS564TH3P6ReYugDQARDIBAAAAAA/qG20lVvoDWBmFVgqq3O6I/9xGdLQJONQCLNHjPy6WREAAJw827ZVmFug3Hk5WvNarmpLa51uqVWiu0Vr1DWjlf6dDCUNSXa6HQAA0E7U1NRo+vTpWrVqlU/rBw4cqCVLlig2NtavffkaZCSQCX/p37+/3njjDV1wwQVqaGhocf0zzzyj3/zmN4qKigpAdwAAAAAAAJ3LoCmD1fuMH2jpr5Yo5z9ZAatbuv2Anr/4nxp782macN8khcWEBaw2ALRXBDIBAAAAoI3kV9jKKrCUlW9p/X5bTe1ryFSrdAmTxqR4A5jp3Q1FhRLABAAgmFXvr9KaV1crd162itYVOd1Oq7hCXRo0dYhGzxmjAeMHyHS7nG4JAAC0I/X19br00ku1YsUKn9b36tVLy5YtU3Ky/y/+EBbm28ammpoaP3dybNXV1T6tI5DZvp155pn62c9+pgcffLDFtVVVVVq4cKFmzZoVgM4AAAAAAAA6n/C4CF3yxGUaNnOEFt41X+V7ygNWe+WzX2vLks265InL1P+CAQGrCwDtEYFMAAAAADhJjR5b6/cfnoJZUOl0R/41IN5QZqp3EuaABEMmUzABAAhqnkaPtizZpNx5Odry/mZZ7exqESmjU5U+O0MjrhylyIRIp9sBAADtUGNjo66++motW7bMp/XdunXTsmXL1Lt3bz935tWlSxef1lVWOnPSyde6cXFx/m0EfveLX/xC//znP5Wfn9/i2tdff51AJgAAAAAAgJ8NGJ+m27/4gT64/32t/MfXAatbvrtML1z2nDK+l6mJv52i8C7hAasNAO0JgUwAAAAAaIUDtbaymwOYuYW26pqc7sh/wt3S6O7eAGZGiqmECAKYAAC0B0VrC7XqxWyteW21aop9m2oULKKSojTy6nSlz85Qt+HdnW4HAAC0Yx6PR3PmzNGCBQt8Wp+QkKClS5dq0KBBfu7ssK5du/q0rqKiws+dnFpdX18Hgld4eLjuvvtu/fSnP21x7fLly/3fEAAAAAAAABQWE6Zpj1yioZcO18K75qt0R2nAauc8n6W8ZVt08f9dqoGTAnfOFADaCwKZAAAAAHAClm0r70DzFMx8W9tKbadb8quUGCkzxVRmqqlhSYZCXIQwAQBoD2pKqrX29TVa9WK2ClcXON1Oq5huU4OmDFb67AylTRwkV4jL6ZYAAEA7Z1mWrr/+er322ms+rY+NjdWSJUs0cuRIP3d2tISEBJ/WlZWV+beRU6zr6+tAcLv22mt1zz33yOPxnHBdcXGxNm3apMGDBweoMwAAAAAAgM6t33n9dduKufrot8v01dNfSnZg9q9V5lfopav/o1HXjNbkh6YqIj4yIHUBoD0gkAkAAAAA31DdYCu30BvCzC6wVF7vdEf+4zalYUneKZiZqaZSYwhgAgDQXlhNHuUty9OqF7O1efEmWY0n3jgdbLqN6K7Rc8ZoxFWjFJUY5XQ7AACgg7BtW7feeqteeOEFn9ZHRUVp0aJFGjt2rJ87+zZfJ0sWFhb6uZNTq8uEzI6hW7duOvPMM/XZZ5+1uDYnJ4dAJgAAAAAAQACFRoVq8h+maeilw/XOD95SSV5JwGqvfnmVtn2Up+mPztDg6UMDVhcAghmBTAAAAACdnm3b2lshrSywlJ1vaZu1v/wAAQAASURBVMN+W54OPAgzLlyHApjp3QxFhBDCBAC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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.pivot_table(data[data['year']>=2010], index='month', columns=\"year\", values='Open', aggfunc=np.mean).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge and Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define 2 toy DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.DataFrame({\"lkey\":[\"foo\", \"bar\", \"baz\", \"foo\"], \"value\":[1,2,3,4]})\n",
    "B = pd.DataFrame({\"rkey\":[\"foo\", \"bar\", \"qux\", \"bar\"], \"value\":[5,6,7,8]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge allows us to join them by specifying an arbitrary column on each of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lkey</th>\n",
       "      <th>value_x</th>\n",
       "      <th>rkey</th>\n",
       "      <th>value_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1.0</td>\n",
       "      <td>foo</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>4.0</td>\n",
       "      <td>foo</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bar</td>\n",
       "      <td>2.0</td>\n",
       "      <td>bar</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>2.0</td>\n",
       "      <td>bar</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baz</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>qux</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lkey  value_x rkey  value_y\n",
       "0  foo      1.0  foo      5.0\n",
       "1  foo      4.0  foo      5.0\n",
       "2  bar      2.0  bar      6.0\n",
       "3  bar      2.0  bar      8.0\n",
       "4  baz      3.0  NaN      NaN\n",
       "5  NaN      NaN  qux      7.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.merge(B, left_on=\"lkey\", right_on=\"rkey\", how=\"outer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, join performs the join using the respective Indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.set_index('lkey', inplace=True)\n",
    "B.set_index('rkey', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>value_l</th>\n",
       "      <th>value_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     value_l  value_r\n",
       "bar        2        6\n",
       "bar        2        8\n",
       "foo        1        5\n",
       "foo        4        5"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.join(B, lsuffix=\"_l\", rsuffix=\"_r\", how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Open', ylabel='Close'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.scatter(x='Open', y='Close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('year').mean().plot(y='High')"
   ]
  },
  {
   "cell_type": "markdown",
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    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
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