{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering Data\n",
    "Filtering means limiting rows and/or columns. Filtering is clearly central to any data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries\n",
    "I include the data import and library import commands at the start of each lesson so that the lessons are self-contained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "bank = pd.read_csv('Data/Bank.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean vectors\n",
    "Filtering in Pandas relies heavily on the concept of a _Boolean vectors_.  To illustrate, consider the following expression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4      False\n",
       "       ...  \n",
       "203    False\n",
       "204    False\n",
       "205    False\n",
       "206    False\n",
       "207     True\n",
       "Name: Gender, Length: 208, dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['Gender'] == 'Female'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The expression tests whether each value of the Gender column is equal to the string \"Female\".  As in R and other languages, the logical comparison operator is double equals (\"==\").  The result of the expression is a vector of trues and falses corresponding to whether each of the 209 values of Gender is equal to \"Female\".\n",
    "\n",
    "Now, once we have the vector of 209 values of true or false (the Boolean vector), we can apply that to the original data frame.  If the first value in the Boolean vector is true, the first row of the data frame is returned; if the first value is false, the row is skipped.\n",
    "\n",
    "This is what it looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Employee</th>\n",
       "      <th>EducLev</th>\n",
       "      <th>JobGrade</th>\n",
       "      <th>YrHired</th>\n",
       "      <th>YrBorn</th>\n",
       "      <th>Gender</th>\n",
       "      <th>YrsPrior</th>\n",
       "      <th>PCJob</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>57</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>60</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>55</td>\n",
       "      <td>Female</td>\n",
       "      <td>7</td>\n",
       "      <td>No</td>\n",
       "      <td>30.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>71</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>68</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>30.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>187</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>86</td>\n",
       "      <td>58</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>188</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>83</td>\n",
       "      <td>49</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>61.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>189</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>79</td>\n",
       "      <td>52</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>191</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>86</td>\n",
       "      <td>58</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>No</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>208</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>62</td>\n",
       "      <td>33</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Employee  EducLev  JobGrade  YrHired  YrBorn  Gender  YrsPrior PCJob  \\\n",
       "1           2        1         1       81      57  Female         1    No   \n",
       "2           3        1         1       83      60  Female         0    No   \n",
       "3           4        2         1       87      55  Female         7    No   \n",
       "5           6        3         1       92      71  Female         0    No   \n",
       "6           7        3         1       91      68  Female         0    No   \n",
       "..        ...      ...       ...      ...     ...     ...       ...   ...   \n",
       "186       187        5         5       86      58  Female         2    No   \n",
       "187       188        5         5       83      49  Female         2    No   \n",
       "188       189        4         5       79      52  Female         0    No   \n",
       "190       191        5         5       86      58  Female         6    No   \n",
       "207       208        5         6       62      33  Female         0    No   \n",
       "\n",
       "     Salary  \n",
       "1      39.1  \n",
       "2      33.2  \n",
       "3      30.6  \n",
       "5      30.5  \n",
       "6      30.0  \n",
       "..      ...  \n",
       "186    50.0  \n",
       "187    61.8  \n",
       "188    43.0  \n",
       "190    58.5  \n",
       "207    30.0  \n",
       "\n",
       "[140 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank[bank['Gender'] == \"Female\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you find this notation too cumbersome, you can assign the results to a separate data frame that contains only the 140 female employees:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FemaleEmployees = bank[bank['Gender'] == \"Female\"]\n",
    "type(FemaleEmployees)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python `type()` method is used to make sure the result is a Pandas data frame. It is, so we can calculate the mean salary for female employees:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.20992857142858"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FemaleEmployees['Salary'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python has some basic built-in functions that can be applied to the core data types, such as integers, floating point numbers, and so on.  For example, if I want to take the result of `mean()` and round it to two decimals, I can wrap the whole expression inside the `round()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.21"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(FemaleEmployees['Salary'].mean(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Complex filtering criteria\n",
    "Boolean vectors can be created by combining conditions with & (and) and | (or).  The only trick is that each condition must be in parentheses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4      False\n",
       "       ...  \n",
       "203    False\n",
       "204    False\n",
       "205    False\n",
       "206    False\n",
       "207    False\n",
       "Length: 208, dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(bank['Gender'] == 'Female') & (bank['JobGrade'] == 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vector can then be applied to the whole data set to filter the data frame to female employees with job grade 1.  Rather than listing the results, I call the `shape` property to confirm that only 48 employees are included in the resulting vector.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank[(bank['Gender'] == 'Female') & (bank['JobGrade'] == 1)].shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering by a list\n",
    "It is not uncommon when you have categorical data to need to filter or recode on a specific list of values. To reuse the example used previously, assume we want to create a list of managerial employees.  The easiest way to do this is to use a greater-than condition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employee</th>\n",
       "      <th>EducLev</th>\n",
       "      <th>JobGrade</th>\n",
       "      <th>YrHired</th>\n",
       "      <th>YrBorn</th>\n",
       "      <th>Gender</th>\n",
       "      <th>YrsPrior</th>\n",
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       "      <th>145</th>\n",
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       "      <td>Male</td>\n",
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       "      <td>No</td>\n",
       "      <td>44.5</td>\n",
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       "      <td>65</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>41.0</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>89</td>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
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       "      <td>No</td>\n",
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       "      <th>148</th>\n",
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       "      <td>5</td>\n",
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       "      <td>90</td>\n",
       "      <td>63</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
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       "      <td>35</td>\n",
       "      <td>Male</td>\n",
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       "      <td>No</td>\n",
       "      <td>95.0</td>\n",
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       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>206</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>63</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>207</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>208</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>62</td>\n",
       "      <td>33</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Employee  EducLev  JobGrade  YrHired  YrBorn  Gender  YrsPrior PCJob  \\\n",
       "145       146        5         4       90      62    Male         3    No   \n",
       "146       147        5         4       91      65    Male         1    No   \n",
       "147       148        5         4       89      58    Male         3    No   \n",
       "148       149        5         4       89      65    Male         0    No   \n",
       "149       150        5         4       90      63  Female         4    No   \n",
       "..        ...      ...       ...      ...     ...     ...       ...   ...   \n",
       "203       204        3         6       61      35    Male         0    No   \n",
       "204       205        5         6       59      34    Male         0    No   \n",
       "205       206        5         6       63      33    Male         0    No   \n",
       "206       207        5         6       60      36    Male         0    No   \n",
       "207       208        5         6       62      33  Female         0    No   \n",
       "\n",
       "     Salary  \n",
       "145    44.5  \n",
       "146    41.0  \n",
       "147    44.0  \n",
       "148    44.0  \n",
       "149    42.5  \n",
       "..      ...  \n",
       "203    95.0  \n",
       "204    97.0  \n",
       "205    88.0  \n",
       "206    94.0  \n",
       "207    30.0  \n",
       "\n",
       "[63 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank[bank['JobGrade'] >= 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But an alternative approach (and the _only_ approach that works with categorical data) is to create a list and use the `isin()` method to check membership in the list.  This gives the same result as above."
   ]
  },
  {
   "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>Employee</th>\n",
       "      <th>EducLev</th>\n",
       "      <th>JobGrade</th>\n",
       "      <th>YrHired</th>\n",
       "      <th>YrBorn</th>\n",
       "      <th>Gender</th>\n",
       "      <th>YrsPrior</th>\n",
       "      <th>PCJob</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>90</td>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>65</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>89</td>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>89</td>\n",
       "      <td>65</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>90</td>\n",
       "      <td>63</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>42.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>204</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>61</td>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>206</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>63</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>207</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>208</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>62</td>\n",
       "      <td>33</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Employee  EducLev  JobGrade  YrHired  YrBorn  Gender  YrsPrior PCJob  \\\n",
       "145       146        5         4       90      62    Male         3    No   \n",
       "146       147        5         4       91      65    Male         1    No   \n",
       "147       148        5         4       89      58    Male         3    No   \n",
       "148       149        5         4       89      65    Male         0    No   \n",
       "149       150        5         4       90      63  Female         4    No   \n",
       "..        ...      ...       ...      ...     ...     ...       ...   ...   \n",
       "203       204        3         6       61      35    Male         0    No   \n",
       "204       205        5         6       59      34    Male         0    No   \n",
       "205       206        5         6       63      33    Male         0    No   \n",
       "206       207        5         6       60      36    Male         0    No   \n",
       "207       208        5         6       62      33  Female         0    No   \n",
       "\n",
       "     Salary  \n",
       "145    44.5  \n",
       "146    41.0  \n",
       "147    44.0  \n",
       "148    44.0  \n",
       "149    42.5  \n",
       "..      ...  \n",
       "203    95.0  \n",
       "204    97.0  \n",
       "205    88.0  \n",
       "206    94.0  \n",
       "207    30.0  \n",
       "\n",
       "[63 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mgmt = [4,5,6]\n",
    "bank[bank['JobGrade'].isin(mgmt)]"
   ]
  }
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