0102_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "\n",
    "## Pandas Objects\n",
    "\n",
    "In the previous chapter we discussed the very basics of Python and NumPy. Here we go one step further and introduce the Pandas package and its data structures. At the very basic level, Pandas can be thought of as enhanced versions of NumPy arrays in which rows and columns come with labels (rather than simple integer indices). Pandas obviously provides more features than this, but it is important to first get an understanding of Pandas' data structure before delving into more advanced topics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We start by importing the NumPy, Pandas packages\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Series\n",
    "\n",
    "While a NumPy array has an *implicitly defined* index, Pandas allows for an *explicitly defined* index. That could mean strings or nonsequential indices. We first look at Pandas `Series` objects. These are one-dimensional arrays of indexed data. We can use lists or arrays to create it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.25\n",
       "1    0.50\n",
       "2    0.25\n",
       "3    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.25, 1])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "w    0.25\n",
       "x    0.50\n",
       "y    0.25\n",
       "z    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.25, 1], index=['w', 'x', 'y', 'z'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Item access works as we would expect\n",
    "data['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.25\n",
       "7    0.50\n",
       "4    0.75\n",
       "1    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another example\n",
    "data = pd.Series([0.25, 0.5, 0.75, 1],\n",
    "                 index=[2, 7, 4, 1])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.2\n",
       "1    0.4\n",
       "2    0.6\n",
       "3    0.8\n",
       "4    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas series from NumPy array\n",
    "vec = np.linspace(start=0.2, stop=1, num=5)\n",
    "pd.Series(vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pd.Series(vec))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy array operations (as discussed in the previous chapter) such as filtering with a boolean array, scalar multiplication, or applying math functions, will preserve the index-value link. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas' Data Frames\n",
    "\n",
    "### Constructing Data Frames\n",
    "\n",
    "While Pandas' `Series` are comparable to a one-dimensional array with flexible indices, `DataFrame`s are comparable to two-dimensional arrays with both flexible row and column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>Company</th>\n",
       "      <th>yrEndClose</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schindler</td>\n",
       "      <td>179.60</td>\n",
       "      <td>7.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABB</td>\n",
       "      <td>21.48</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GF</td>\n",
       "      <td>834.00</td>\n",
       "      <td>53.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sulzer</td>\n",
       "      <td>105.00</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  yrEndClose    eps\n",
       "0  Schindler      179.60   7.14\n",
       "1        ABB       21.48   0.87\n",
       "2         GF      834.00  53.00\n",
       "3     Sulzer      105.00   1.73"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Company': ['Schindler', 'ABB', 'GF', 'Sulzer'],\n",
    "        'yrEndClose': [179.6, 21.48, 834, 105],\n",
    "        'eps': [7.14, 0.87, 53, 1.73]}\n",
    "\n",
    "comps = pd.DataFrame(data)\n",
    "comps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Company</th>\n",
       "      <th>yrEndClose</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schindler</td>\n",
       "      <td>179.60</td>\n",
       "      <td>7.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABB</td>\n",
       "      <td>21.48</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GF</td>\n",
       "      <td>834.00</td>\n",
       "      <td>53.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sulzer</td>\n",
       "      <td>105.00</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  yrEndClose    eps\n",
       "0  Schindler      179.60   7.14\n",
       "1        ABB       21.48   0.87\n",
       "2         GF      834.00  53.00\n",
       "3     Sulzer      105.00   1.73"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [{'Company': 'Schindler', 'yrEndClose': 179.6, 'eps': 7.14},\n",
    "        {'Company': 'ABB',  'yrEndClose': 21.48, 'eps': 0.87},\n",
    "        {'Company': 'GF',  'yrEndClose': 834,  'eps': 53},\n",
    "        {'Company': 'Sulzer',  'yrEndClose': 105,  'eps': 1.73}]\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ultimately, there are many more ways to create a `DataFrame` but we'll leave it with the above two examples. Reason being, as we will see later on, that data for a `DataFrame` is usually imported from a txt, csv or xls file. This process is fairly simple and will be discussed later on. Regarding `DataFrames`, check the excellent overview by [Chris Moffit](http://pbpython.com/pandas-list-dict.html) or see the help page `pd.DataFrame?` for more examples on how to create a `DataFrame`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we would expect, each `DataFrame` has some callable attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=4, step=1)\n",
      "12 (4, 3) 2\n"
     ]
    }
   ],
   "source": [
    "print(comps.index)\n",
    "print(comps.size, comps.shape, comps.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working with data and adding new data columns is straight forward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Company</th>\n",
       "      <th>yrEndClose</th>\n",
       "      <th>eps</th>\n",
       "      <th>PE</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schindler</td>\n",
       "      <td>179.60</td>\n",
       "      <td>7.14</td>\n",
       "      <td>25.154062</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABB</td>\n",
       "      <td>21.48</td>\n",
       "      <td>0.87</td>\n",
       "      <td>24.689655</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GF</td>\n",
       "      <td>834.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>15.735849</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sulzer</td>\n",
       "      <td>105.00</td>\n",
       "      <td>1.73</td>\n",
       "      <td>60.693642</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  yrEndClose    eps         PE  Year\n",
       "0  Schindler      179.60   7.14  25.154062  2020\n",
       "1        ABB       21.48   0.87  24.689655  2020\n",
       "2         GF      834.00  53.00  15.735849  2020\n",
       "3     Sulzer      105.00   1.73  60.693642  2020"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps['PE'] = comps['yrEndClose'] / comps['eps']\n",
    "comps['Year'] = 2020\n",
    "comps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Company  Year         PE    eps  yrEndClose\n",
      "0  Schindler  2020  25.154062   7.14      179.60\n",
      "1        ABB  2020  24.689655   0.87       21.48\n",
      "2         GF  2020  15.735849  53.00      834.00\n",
      "3     Sulzer  2020  60.693642   1.73      105.00\n"
     ]
    }
   ],
   "source": [
    "# Reorder columns\n",
    "comps = comps[['Company', 'Year', 'PE', 'eps', 'yrEndClose']]\n",
    "print(comps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Company', 'Year', 'PE', 'EPS', 'Price'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming columns\n",
    "comps.columns = ['Company', 'Year', 'PE', 'EPS', 'Price']\n",
    "comps.columns.values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Company', 'Year', 'PE', 'EPS', 'yrEndClose'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or renaming just one column\n",
    "colNms = comps.columns.values\n",
    "colNms[4] = 'yrEndClose'\n",
    "comps.columns = colNms\n",
    "comps.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing and Selection\n",
    "\n",
    "In the previous chapter we discussed how to access elements of NumPy arrays. In general, the same patterns are applicable to Pandas objects. However, there are a few quirks that we'll discuss to prevent confusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    2.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0, 1, 2], index=['a', 'b', 'c'])\n",
    "\n",
    "# Adding a float\n",
    "data['d'] = 2.5\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing by explicit index\n",
    "data['a':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing by implicit index\n",
    "data[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT:**\n",
    "\n",
    "**Notice that when using the explicit index, the final index is *included*. On the other hand, when you use the implicit index (i.e. `data[0:2]`), the final index is *excluded*.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider an example where the `Series` object has an explicit integer index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explicit index when indexing\n",
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Implicit index when slicing\n",
    "data[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because this was recognized as a source of confusion, Pandas introduced the `loc` and `iloc` attribute. `loc` allows indexing and slicing with the **explicit index**, `iloc` allows slicing that always references the **implicit index**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "3    b\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing for `DataFrame` works similar to what we discussed thus far."
   ]
  },
  {
   "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",
       "    }\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>Company</th>\n",
       "      <th>PE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schindler</td>\n",
       "      <td>25.154062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABB</td>\n",
       "      <td>24.689655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GF</td>\n",
       "      <td>15.735849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sulzer</td>\n",
       "      <td>60.693642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company         PE\n",
       "0  Schindler  25.154062\n",
       "1        ABB  24.689655\n",
       "2         GF  15.735849\n",
       "3     Sulzer  60.693642"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps[['Company', 'PE']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index will always be shown. Thus if we reset the index such that the company names represent the index, then we could simply use `comps['PE']`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Year         PE    EPS  yrEndClose\n",
      "Company                                      \n",
      "Schindler  2020  25.154062   7.14      179.60\n",
      "ABB        2020  24.689655   0.87       21.48\n",
      "GF         2020  15.735849  53.00      834.00\n",
      "Sulzer     2020  60.693642   1.73      105.00\n"
     ]
    }
   ],
   "source": [
    "compsInd = comps.set_index('Company')\n",
    "print(compsInd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company\n",
       "Schindler    25.154062\n",
       "ABB          24.689655\n",
       "GF           15.735849\n",
       "Sulzer       60.693642\n",
       "Name: PE, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compsInd['PE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use dot notation to access a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.14\n",
       "1    0.87\n",
       "Name: EPS, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps.EPS[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One slicing option you might come across when studying python scripts is the `.ix` indexer. It is a hybrid of the two functions `.loc` and `.iloc`. However, the `.ix` indexer is deprecated in favor of the more strict `.iloc` and `.loc` and thus we won't discuss it here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index Alignment\n",
    "\n",
    "Pandas will align indices in the process of performing operations for both `Series` as well as `DataFrame`s. This proves to be very convenient when dealing with incomplete data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   C\n",
       "0  57  93\n",
       "1  48  63"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "A = pd.DataFrame(np.random.randint(low=10, high=99, size=(2,2)),\n",
    "                 columns=['A', 'C'])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   B  A  C\n",
       "0  4  8  9\n",
       "1  1  7  9\n",
       "2  6  8  0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = pd.DataFrame(np.random.randint(low=0, high=10, size=(3,3)),\n",
    "                 columns=list('BAC'))\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A   B      C\n",
       "0  65.0 NaN  102.0\n",
       "1  55.0 NaN   72.0\n",
       "2   NaN NaN    NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A + B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we wish to fill the blanks with another value than `NaN`, we can do so by using the `add()` method and specify the `fill_value`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B      C\n",
       "0  65.0  4.0  102.0\n",
       "1  55.0  1.0   72.0\n",
       "2   8.0  6.0    0.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.add(B, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Missing Data\n",
    "\n",
    "So far we have always dealt with complete data sets. Real world data, however, is hardly ever clean and homogeneous. Often data sets will have some amount of missing values. Further complicating the issue is the fact that different conventions exists to indicate missing data (`NaN`, `None`, `NA`, `null`, `-9999`).\n",
    "\n",
    "Developers of NumPy and Pandas chose to use `NaN` (acronym for *Not a Number*) as missing data representation. Operations on `np.arrays`, `Series` or `DataFrames` containing `NaN` values are possible. However, one needs to keep in mind that any arithmetic operation with `NaN` will be another `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1., nan,  2.,  3.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = np.array([0, np.nan, 1, 2])\n",
    "val + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0., nan,  0.,  0.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val * 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy provides special functions which can deal with `NaN` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan nan nan\n",
      "3.0 0.0 2.0\n"
     ]
    }
   ],
   "source": [
    "print(val.sum(), val.min(), val.max())\n",
    "print(np.nansum(val), np.nanmin(val), np.nanmax(val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While NumPy only accepts `np.nan`, Pandas is also able do handle `None` as input. Yet internally, Pandas will convert `None` values to `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq = pd.Series([1, np.nan, 2, None])\n",
    "seq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But how do we deal with `NaN` values? Python provides some specific methods:\n",
    "\n",
    "| **Method**   | **Description**                                                  |\n",
    "|:------------:|------------------------------------------------------------------|\n",
    "| `.isnull()`  | Generates boolean mask indicating missing values                 |\n",
    "| `.notnull()` | Opposite of `.isnull()`                                          |\n",
    "| `.dropna()`  | Returns a filtered version of the data                           |\n",
    "| `.fillna()`  | Returns a copy of the data with missing values filled or imputed |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1     True\n",
      "2    False\n",
      "3     True\n",
      "dtype: bool\n",
      "0    1.0\n",
      "2    2.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Sample Series\n",
    "ser = pd.Series([1, None, 2., np.nan])\n",
    "\n",
    "# Boolean mask\n",
    "print(ser.isnull())\n",
    "\n",
    "# Sliced Series\n",
    "print(ser[ser.notnull()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
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       "</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",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B   C\n",
       "0  10.0   NaN  12\n",
       "1  13.0  14.0  15\n",
       "2   NaN  17.0  18"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame\n",
    "df = pd.DataFrame(10 + np.arange(9).reshape(3, 3),\n",
    "                  columns= ['A', 'B', 'C'])\n",
    "df.iloc[0, 1] = np.nan; df.iloc[2, 0] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.dropna()` can not drop single values, but it can drop full columns or rows. For this, the method takes the parameter `axis='rows'` or `axis='columns'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B   C\n",
       "1  13.0  14.0  15"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()  # Similar to df.dropna(axis=0) or df.dropna(axis='rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
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      "text/plain": [
       "    C\n",
       "0  12\n",
       "1  15\n",
       "2  18"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis='columns')  # similar to df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beyond the axis you can specify the parameter `how` and `thresh`. \n",
    "\n",
    "For parameter `how`, default is set to `how='any'` which means that any row or column (depending on your selection) with `NaN` values will be dropped. Alternatively you could set it to `how='all'` to remove only those rows/columns where all entries are of sort `NaN`.\n",
    "\n",
    "For parameter `thresh`, default is set to `thresh=None`. For example setting a `thresh=3` will drop rows/columns with less than 3 non-null values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "      A     B   C   D\n",
       "0  10.0   NaN  12 NaN\n",
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       "2   NaN  17.0  18 NaN"
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     "execution_count": 42,
     "metadata": {},
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   ],
   "source": [
    "df['D'] = np.nan\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "      A     B   C\n",
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     "execution_count": 43,
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   "source": [
    "df.dropna(axis='columns', how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      A     B   C   D\n",
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    "df.dropna(axis='rows', thresh=3)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is also adequate to replace `NaN` cells with a specific value. For this method `.fillna()` is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "        A       B   C       D\n",
       "0    10.0 -9999.0  12 -9999.0\n",
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       "2 -9999.0    17.0  18 -9999.0"
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   "source": [
    "df.fillna(value=-9999)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
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       "      A     B   C   D\n",
       "0  10.0   NaN  12 NaN\n",
       "1  13.0  14.0  15 NaN\n",
       "2   NaN  17.0  18 NaN"
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     "execution_count": 46,
     "metadata": {},
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      A     B   C   D\n",
       "0  10.0   NaN  12 NaN\n",
       "1  13.0  14.0  15 NaN\n",
       "2  13.0  17.0  18 NaN"
      ]
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     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Forward-fill to propagate previous value forward\n",
    "df.fillna(axis='rows', method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B   C   D\n",
       "0  10.0  14.0  12 NaN\n",
       "1  13.0  14.0  15 NaN\n",
       "2   NaN  17.0  18 NaN"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backward-fill to propagate previous value forward\n",
    "df.fillna(axis='rows', method='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining Datasets\n",
    "\n",
    "### Concat and Append\n",
    "\n",
    "Concatenating, appending, merging or joining data sets is a deep (and some say 'dull') topic. Anyone who had the pleasure of learning relational algebra can tell. Pandas has four functions that will do the job for you and of which you should have heard:\n",
    "\n",
    "* `pd.append()`\n",
    "* `pd.concat()`\n",
    "* `pd.merge()`\n",
    "* `pd.join()`\n",
    "\n",
    "From time to time one of these functions will appear in this course. However, we will not properly discuss these functions in any detail. Unfortunately, doing it would consume too much time and would be beyond the purpose of this course. Nonetheless, I recommend to spend 15min in learning the basics by reading through Pandas' intuitive introduction which can be found [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html). It is kept fairly brief and with all the examples and visual representations, the functions are explained in a much better way than this tutorial could ever do. \n",
    "\n",
    "Another valuable resource is of course again Jake VanderPlas' Data Science Handbook. You might find his explanations and examples very helpful and since it's freely available on GitHub, why not give it a shot. [Here's the link (Combining Datasets)](https://github.com/jakevdp/PythonDataScienceHandbook). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Time Series\n",
    "\n",
    "### Timestamps and Periods\n",
    "\n",
    "Pandas was developed with a focus on financial data. It thus does not surprise that Pandas has incorporated an easy and reliable way of handling datetime formats. Pandas handling of date/time objects improves on the existing package `datetime` and NumPy's `numpy.datetime64` object and provides the necessary tools to efficiently handle datetimes. \n",
    "\n",
    "The most basic kind of time series object in Pandas are `pd.Series` or `pd.DataFrame` objects indexed with `timestamps`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-31 00:00:00\n",
      "2021-12-31 13:08:00\n",
      "2021-10-03 17:32:15\n",
      "2021-03-31 17:32:15\n",
      "2021-03-31 00:00:00\n",
      "2000-07-01 00:00:00\n",
      "1992-08-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Simple Time Stamps\n",
    "print(pd.Timestamp(day=31, year=2021, month=12))\n",
    "print(pd.Timestamp(2021, 12, 31, 13, 8))\n",
    "print(pd.Timestamp('10.03.2021 17:32:15'))\n",
    "print(pd.Timestamp('31.03.2021 17:32:15'))\n",
    "print(pd.Timestamp('2021-03-31'))\n",
    "print(pd.Timestamp('2000-07'))\n",
    "print(pd.to_datetime(\"1st of August, 1992\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that `pd.Timestamp('10.03.2020')` is interpreted as 3rd of October 2018 while `pd.Timestamp('31.03.2020')` as 31st of March. Here it is important to realize that the default format is the American way of writing a date: 'mm.dd.yyyy'. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides `pd.Timestamp` Pandas does also have a function for periods: `pd.Period`. The difference is subtle: The former is for a specific point in time, the latter represents a bounded interval. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2021Q3', 'Q-DEC')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time Periods\n",
    "Q3 = pd.Period('2021-09', freq='Q')\n",
    "Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2021-09', 'M')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M9 = pd.Period('2021-09', freq='M')\n",
    "M9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `pd.Period` function has specific properties such as `start_time` and `end_time`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-07-01 00:00:00')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q3.start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-09-30 23:59:59.999999999')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M9.end_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date Ranges\n",
    "\n",
    "The command `pd.date_range` generates an index with indicated length according to a particular frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-01-04', '2021-01-05', '2021-01-06', '2021-01-07',\n",
       "               '2021-01-08'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='20210104', end='20210108')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could also pass just a start or end date combined with a number of periods to generate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-12-17', '2021-12-18'], dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2021-12-17', periods=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-09-27', '2021-09-28', '2021-09-29', '2021-09-30'], dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(end='2021-09-30', periods=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As became obvious from above examples, `pd.date_range` by default generates daily timestamps. If you wish another frequency - such as monthly, annual, etc. - you add the `freq=` argument to your command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-01-01', '2021-04-01', '2021-07-01', '2021-10-01'], dtype='datetime64[ns]', freq='BQS-JAN')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(end='2021-12-31', periods=4, freq='BQS')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an overview of Pandas frequency codes:\n",
    "\n",
    "| **Code** | **Description**        || **Code**  | ** Description** |\n",
    "|:--------:|------------------------||:---------:|------------------| \n",
    "| D        | Calendar day           ||  A        | Year end |\n",
    "| B        | Business day           ||  AS       | Year start |\n",
    "| W        | Weekly                 ||  BA       | Business year end |\n",
    "| M        | Month end              ||  BAS      | Business year start |\n",
    "| MS       | Month start            ||  H        | Hours |\n",
    "| BM       | Business month end     ||  BH       | Business hours |\n",
    "| BMS      | Business month start   ||  T        | Minutes |\n",
    "| Q        | Quarter end            ||  S        | Seconds |\n",
    "| QS       | Quarter start          ||  L        | Miliseconds |\n",
    "| BQ       | Business quarter end   ||  U        | Microseconds |\n",
    "| BQS      | Business quarter start ||  N        | Nanoseconds |\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beyond the above frequencies, Pandas has one more useful further option: \"week of month\". This enables you to\n",
    "get dates like the third Friday of each month. Anyone dealing with options will recoginze these dates as the standard dates of monthly expiry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-01-15', '2021-02-19', '2021-03-19', '2021-04-16',\n",
       "               '2021-05-21', '2021-06-18', '2021-07-16', '2021-08-20'],\n",
       "              dtype='datetime64[ns]', freq='WOM-3FRI')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Third Friday of each month between Jan-18 and Sep-18\n",
    "pd.date_range('2021-01-01', '2021-09-01', freq='WOM-3FRI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing, Selection, Subsetting of Time Series\n",
    "\n",
    "Both `Timestamp` and `Period` can be used as index. Lists of `Timestamp` and `Period` are automatically coerced to `DatetimeIndex` and `PeriodIndex`, respectively. This is convenient as it allows us to index and slice the data object as if it were a regular `Series` or `DataFrame`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-01   -2.073882\n",
      "2019-01-02   -0.161588\n",
      "2019-01-03   -0.085147\n",
      "2019-01-04   -0.659354\n",
      "2019-01-05    0.824481\n",
      "Freq: D, dtype: float64 \n",
      "\n",
      "2021-12-27   -0.389967\n",
      "2021-12-28    0.255279\n",
      "2021-12-29   -0.609727\n",
      "2021-12-30    1.379011\n",
      "2021-12-31   -1.081839\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "dates = pd.date_range(start='2019-01-01', end='2021-12-31', freq='D')\n",
    "ts = pd.Series(np.random.randn(len(dates)), index=dates)\n",
    "print(ts.head(), '\\n')\n",
    "print(ts.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select a subset, we can apply the same logic as shown before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-02-28    2.413975\n",
       "2021-03-01   -0.102841\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fancy indexing\n",
    "rng = pd.date_range(start='2021-02-28', end='2021-03-01')\n",
    "ts[rng]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.081838541671564\n",
      "0.4531105462753985\n"
     ]
    }
   ],
   "source": [
    "# Indexing by string\n",
    "print(ts['20211231'])\n",
    "print(ts['2021-06-30'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, you could choose a full year or a specific month with `ts['2021']` or `ts['2021-05']`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-12-25   -0.945551\n",
       "2021-12-26    0.311025\n",
       "2021-12-27   -0.389967\n",
       "2021-12-28    0.255279\n",
       "2021-12-29   -0.609727\n",
       "2021-12-30    1.379011\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing\n",
    "ts['2021-12-25':'2021-12-30']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data\n",
    "\n",
    "### File Path\n",
    "\n",
    "For most of this course we will use data stored in csv format which we will have to import. For this we can make use of Panda's `read_csv()` function. If you check the function's help page, you might be overwhelmed by all the possible parameter. Below follows an example which loads Swiss stock market data for the four companies Schindler, ABB, Georg Fischer, and Sulzer from a csv. To load it we necessarily need to specify the file name and its path."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas will start looking from where your current python file or notebook is located. Python's working directory is set to where your current `.py` or `.ipynb` file is stored. If you have stored your file in a subfolder, one can simply preced the file name with the path: `pd.read_csv('dataSubFolder/anotherSubFolder/data.csv)`. Given your file is located in another folder, one could either use an explicit path as in `pd.read_csv('C:/Users/Username/Path/To/Your/Folder/data.csv')` or you can move from your current directory to where your data is located with '..' For example `pd.read_csv('../../../dataFolder/data.csv')` will go 3 levels up and then into a dataFolder. If you wish to check the path of your current working directory, use `!cd` (Windows) or `!pwd` (Mac) to find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print working directory (uncomment to run)\n",
    "#!cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol_in_shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.02.2022</td>\n",
       "      <td>ABBN</td>\n",
       "      <td>31.40</td>\n",
       "      <td>31.40</td>\n",
       "      <td>31.55</td>\n",
       "      <td>31.04</td>\n",
       "      <td>5?361?030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.02.2022</td>\n",
       "      <td>ABBN</td>\n",
       "      <td>32.49</td>\n",
       "      <td>32.44</td>\n",
       "      <td>32.66</td>\n",
       "      <td>32.17</td>\n",
       "      <td>3?479?165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.02.2022</td>\n",
       "      <td>ABBN</td>\n",
       "      <td>33.12</td>\n",
       "      <td>32.93</td>\n",
       "      <td>33.24</td>\n",
       "      <td>32.73</td>\n",
       "      <td>4?115?307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Ticker   Open  Close   High    Low Vol_in_shares\n",
       "0  14.02.2022   ABBN  31.40  31.40  31.55  31.04     5?361?030\n",
       "1  11.02.2022   ABBN  32.49  32.44  32.66  32.17     3?479?165\n",
       "2  10.02.2022   ABBN  33.12  32.93  33.24  32.73     4?115?307"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('Data/ShareData.csv', sep=',').head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date              object\n",
       "Ticker            object\n",
       "Open             float64\n",
       "Close            float64\n",
       "High             float64\n",
       "Low              float64\n",
       "Vol_in_shares     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl = pd.read_csv('Data/ShareData.csv', sep=',')\n",
    "fl.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few notes: \n",
    "* CSV stands for comma separated values. The majority of csv-files indeed use commas to separate the values. Sometimes there are however other separators used such as semicolons or (worse) tabs. If that is the case, set argument e.g. `sep=';'` as the separator.\n",
    "* To make the function parse the 'Date' column as dates we have to add `parse_dates=['Date']`.\n",
    "* The dates in the csv have format 'dd.mm.yyy'. Pandas default is 'mm.dd.yyyy'. Thus we need to specify that the dates have days first, then months. For this we specify `dayfirst=True`.\n",
    "* 'Date' and 'Ticker' uniqueliy identify each row. Therefore we wish to set these two columns as index. This is done by adding `index_col=['NameOfColumn']`.\n",
    "* Due to the thousands separator sign, entries are not loaded as actual numbers but strings. This can be corrected by specifying the `thousands=\"'\"` parameter. \n",
    "* The above import shows that Pandas has taken the file's first row as the headers. Alternatively one could set `header=None` or add the argument `skiprows=n` where `n` defines the number of rows (from top) that should be skipped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol_in_shares</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Ticker</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>2022-02-14</th>\n",
       "      <th>ABBN</th>\n",
       "      <td>31.40</td>\n",
       "      <td>31.40</td>\n",
       "      <td>31.55</td>\n",
       "      <td>31.04</td>\n",
       "      <td>5361030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-11</th>\n",
       "      <th>ABBN</th>\n",
       "      <td>32.49</td>\n",
       "      <td>32.44</td>\n",
       "      <td>32.66</td>\n",
       "      <td>32.17</td>\n",
       "      <td>3479165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-10</th>\n",
       "      <th>ABBN</th>\n",
       "      <td>33.12</td>\n",
       "      <td>32.93</td>\n",
       "      <td>33.24</td>\n",
       "      <td>32.73</td>\n",
       "      <td>4115307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open  Close   High    Low  Vol_in_shares\n",
       "Date       Ticker                                           \n",
       "2022-02-14 ABBN    31.40  31.40  31.55  31.04        5361030\n",
       "2022-02-11 ABBN    32.49  32.44  32.66  32.17        3479165\n",
       "2022-02-10 ABBN    33.12  32.93  33.24  32.73        4115307"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data/ShareData.csv', sep=',',\n",
    "                 parse_dates=['Date'], dayfirst=True, \n",
    "                 index_col=['Date', 'Ticker'], thousands=\"?\")\n",
    "\n",
    "# Print first 3 data rows\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open             float64\n",
       "Close            float64\n",
       "High             float64\n",
       "Low              float64\n",
       "Vol_in_shares      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing from Web Link\n",
    "\n",
    "When data is updated on a regular basis, it is certainly more convenient to directly load a related file from an existing (static) url than to manually download it time and time again before running a script. Since Pandas version 0.19.2, `pd.read_csv()` is able to handle that. A simple example is provided below, where the csv file with historical closing prices of the 30 day volatility index on the SMI (VSMI) is downloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>ISIN</th>\n",
       "      <th>Indexvalue</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>2023-02-08</th>\n",
       "      <td>CH0019900841</td>\n",
       "      <td>14.7161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-09</th>\n",
       "      <td>CH0019900841</td>\n",
       "      <td>14.7514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10</th>\n",
       "      <td>CH0019900841</td>\n",
       "      <td>16.3852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-13</th>\n",
       "      <td>CH0019900841</td>\n",
       "      <td>15.9320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-14</th>\n",
       "      <td>CH0019900841</td>\n",
       "      <td>15.0867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ISIN  Indexvalue\n",
       "Date                                \n",
       "2023-02-08  CH0019900841     14.7161\n",
       "2023-02-09  CH0019900841     14.7514\n",
       "2023-02-10  CH0019900841     16.3852\n",
       "2023-02-13  CH0019900841     15.9320\n",
       "2023-02-14  CH0019900841     15.0867"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://www.six-group.com/exchanges/downloads/indexdata/h_vsmi_30.csv'\n",
    "data = pd.read_csv(url, sep=';', parse_dates=['Date'], \n",
    "                   dayfirst=True, index_col='Date')\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further details on how to load/import data to Python check [Pandas' tutorial](https://pandas.pydata.org/pandas-docs/stable/tutorials.html) on the topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Working with Stock Data\n",
    "\n",
    "In what follows it is shown how `DataFrames` are helpful in analyzing data. For that we will make use of the previously loaded stock data. The functions run below will not be introduced individually. But based on the annotated code, the comments in class and the output, the functions should easily be understood. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort df for dates (ascending)\n",
    "df = df.sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us say we want to have a statistical summary of the closing prices per share. We can use the `.groupby()` method to first split the values, select the closing prices, and then apply the `.describe()` method to have the desired summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></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>ABBN</th>\n",
       "      <td>1286.0</td>\n",
       "      <td>24.001998</td>\n",
       "      <td>4.513471</td>\n",
       "      <td>14.93</td>\n",
       "      <td>20.8875</td>\n",
       "      <td>23.235</td>\n",
       "      <td>25.835</td>\n",
       "      <td>35.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLN</th>\n",
       "      <td>1286.0</td>\n",
       "      <td>175.217667</td>\n",
       "      <td>17.691088</td>\n",
       "      <td>141.03</td>\n",
       "      <td>158.8100</td>\n",
       "      <td>174.140</td>\n",
       "      <td>191.200</td>\n",
       "      <td>222.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCHN</th>\n",
       "      <td>1286.0</td>\n",
       "      <td>298.909953</td>\n",
       "      <td>57.580314</td>\n",
       "      <td>152.20</td>\n",
       "      <td>256.0000</td>\n",
       "      <td>294.375</td>\n",
       "      <td>337.150</td>\n",
       "      <td>478.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCHN</th>\n",
       "      <td>1286.0</td>\n",
       "      <td>224.133515</td>\n",
       "      <td>23.681899</td>\n",
       "      <td>177.50</td>\n",
       "      <td>205.9250</td>\n",
       "      <td>221.400</td>\n",
       "      <td>239.800</td>\n",
       "      <td>293.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count        mean        std     min       25%      50%      75%  \\\n",
       "Ticker                                                                      \n",
       "ABBN    1286.0   24.001998   4.513471   14.93   20.8875   23.235   25.835   \n",
       "ALLN    1286.0  175.217667  17.691088  141.03  158.8100  174.140  191.200   \n",
       "BCHN    1286.0  298.909953  57.580314  152.20  256.0000  294.375  337.150   \n",
       "SCHN    1286.0  224.133515  23.681899  177.50  205.9250  221.400  239.800   \n",
       "\n",
       "           max  \n",
       "Ticker          \n",
       "ABBN     35.79  \n",
       "ALLN    222.00  \n",
       "BCHN    478.00  \n",
       "SCHN    293.60  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Ticker'])['Close'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open             0\n",
       "Close            0\n",
       "High             0\n",
       "Low              0\n",
       "Vol_in_shares    0\n",
       "Return           4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column with the returns \n",
    "shft = len(df.index.levels[1])\n",
    "df['Return'] = np.log(df['Close'] / df['Close'].shift(shft))\n",
    "\n",
    "# Check for NA values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume we wish to investigate ABB's stock a bit further. For this we need to slice the multiindex objcet `df`. Slicing multiindex objects is a bit trickier than doing the same on a simple data frame with only a single index. Below is an example how you slice the `DataFrame` based on a date range (here we take all dates) and on the ticker 'ABBN'. For further examples on how to slice multiindex objects, see [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign ABB data to variable abb\n",
    "idx = pd.IndexSlice\n",
    "abb = df.loc[idx[:, ['ABBN']], idx[:]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add column indicating the quarter (but excl. year)\n",
    "abb['Quarter'] = pd.PeriodIndex(abb.index.levels[0], freq='Q').strftime('Q%q')\n",
    "\n",
    "# Add rolling 252d mean\n",
    "abb['Rol252dMean'] = abb['Close'].rolling(window=252).mean()\n",
    "\n",
    "# Add (annualized) historical rolling volatility\n",
    "abb['Rol252dVol'] = abb['Return'].rolling(window=252).std() * np.sqrt(252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Ticker Index as it is all ABB data now\n",
    "abb = abb.reset_index(level=1, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol_in_shares</th>\n",
       "      <th>Return</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Rol252dMean</th>\n",
       "      <th>Rol252dVol</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-10</th>\n",
       "      <td>33.12</td>\n",
       "      <td>32.93</td>\n",
       "      <td>33.24</td>\n",
       "      <td>32.73</td>\n",
       "      <td>4115307</td>\n",
       "      <td>-0.001214</td>\n",
       "      <td>Q1</td>\n",
       "      <td>31.665000</td>\n",
       "      <td>0.222180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-11</th>\n",
       "      <td>32.49</td>\n",
       "      <td>32.44</td>\n",
       "      <td>32.66</td>\n",
       "      <td>32.17</td>\n",
       "      <td>3479165</td>\n",
       "      <td>-0.014992</td>\n",
       "      <td>Q1</td>\n",
       "      <td>31.688175</td>\n",
       "      <td>0.222531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-14</th>\n",
       "      <td>31.40</td>\n",
       "      <td>31.40</td>\n",
       "      <td>31.55</td>\n",
       "      <td>31.04</td>\n",
       "      <td>5361030</td>\n",
       "      <td>-0.032584</td>\n",
       "      <td>Q1</td>\n",
       "      <td>31.708571</td>\n",
       "      <td>0.224610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open  Close   High    Low  Vol_in_shares    Return Quarter  \\\n",
       "Date                                                                      \n",
       "2022-02-10  33.12  32.93  33.24  32.73        4115307 -0.001214      Q1   \n",
       "2022-02-11  32.49  32.44  32.66  32.17        3479165 -0.014992      Q1   \n",
       "2022-02-14  31.40  31.40  31.55  31.04        5361030 -0.032584      Q1   \n",
       "\n",
       "            Rol252dMean  Rol252dVol  \n",
       "Date                                 \n",
       "2022-02-10    31.665000    0.222180  \n",
       "2022-02-11    31.688175    0.222531  \n",
       "2022-02-14    31.708571    0.224610  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abb.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having the data set up helps run further analysis. Note that plots will be discussed in a separate chapter and thus we will not get into it here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup for plotting\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import seaborn as sns\n",
    "import scipy as sp\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "abb[['Close', 'Rol252dMean']].plot(figsize=(16, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "abb[['Close', 'Rol252dVol', 'Return']].plot(subplots=True, figsize=(16, 10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if the returns follow a normal distribution. We have many approaches to check this, both with plots and statistics. Below are some options presented. We will make use of the `stats` sublibrary of the `scipy` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: -0.6842105027584504\n",
      "Kurtosis:  11.35081843224319\n"
     ]
    }
   ],
   "source": [
    "# Select returns\n",
    "rets = abb['Return'].dropna()\n",
    "\n",
    "# Calc skewness (Norm.dist: 0)\n",
    "print('Skewness:', sp.stats.skew(rets))\n",
    "\n",
    "# Calc kurtosis (Norm.dist: 3); for excess kurt set 'fisher=False'\n",
    "print('Kurtosis: ', sp.stats.kurtosis(rets, fisher=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often the Shapiro Wilk test is used to check if values follow a normal distribution. The function `sp.stats.shapiro()`  tests the null hypothesis that the data was drawn from a normal distribution. **If the p-value is very small, it means it is unlikely that the data came from a normal distribution.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro Wilk Test:\n",
      "Test Statistic:  0.9161418676376343\n",
      "p-Value:         4.424505113230884e-26\n"
     ]
    }
   ],
   "source": [
    "# Apply Shapiro-Wilk test\n",
    "print('Shapiro Wilk Test:')\n",
    "print('Test Statistic: ', sp.stats.shapiro(rets)[0])\n",
    "print('p-Value:        ', sp.stats.shapiro(rets)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the log-returns with a normal distribution\n",
    "plt.hist(rets, bins=50, density=True, label='frequency')\n",
    "plt.xlabel('log-returns')\n",
    "plt.ylabel('frequency')\n",
    "x = np.linspace(np.min(rets), np.max(rets))\n",
    "plt.plot(x, sp.stats.norm.pdf(x, loc=np.mean(rets), scale=np.std(rets)), \n",
    "         'r', lw=2.0, label='pdf')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or alternatively we could combine the histogram with a kernel density estimation (KDE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KDE plot in Seaborn\n",
    "sns.displot(data=rets, kind='hist', kde=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ben Zimmermann\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# qqplot\n",
    "sm.qqplot(rets, line='s');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Resources\n",
    "\n",
    "In writing this notebook, many ressources were consulted. For internet ressources the links are provided within the textflow above and will therefore not be listed again. Beyond these links, the following ressources are recommended as further reading on the discussed topic:\n",
    "\n",
    "* McKinney, Wes, 2022, *pandas: powerful Python data analysis toolkit*, from Website https://pandas.pydata.org/docs/pandas.pdf, 02/14/2022.\n",
    "* Vanderplas, Jake, 2016, *Python Data Science Handbook* (O'Reilly Media, Sebastopol, CA).\n",
    "* Hilpisch, Yves, 2019, *Python for Finance* (O'Rilly Media, Sebastopol, CA).\n",
    "* McKinney, Wes, 2012, *Python for Data Analysis* (O'Rilly Media, Sebastopol, CA).\n",
    "* Sheppard, Kevin, 2017, Introduction to Python for Econometrics, Statistics and Data Analysis from Website https://www.kevinsheppard.com/images/b/b3/Python_introduction-2016.pdf, 07/07/2017."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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