Pandas_Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"http://rcs.bu.edu/examples/python/data_analysis/Salaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>WEEKDAY</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>ORG_AIR</th>\n",
       "      <th>DEST_AIR</th>\n",
       "      <th>SCHED_DEP</th>\n",
       "      <th>DEP_DELAY</th>\n",
       "      <th>AIR_TIME</th>\n",
       "      <th>DIST</th>\n",
       "      <th>SCHED_ARR</th>\n",
       "      <th>ARR_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>WN</td>\n",
       "      <td>LAX</td>\n",
       "      <td>SLC</td>\n",
       "      <td>1625</td>\n",
       "      <td>58.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>590</td>\n",
       "      <td>1905</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>UA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>IAD</td>\n",
       "      <td>823</td>\n",
       "      <td>7.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>1452</td>\n",
       "      <td>1333</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>MQ</td>\n",
       "      <td>DFW</td>\n",
       "      <td>VPS</td>\n",
       "      <td>1305</td>\n",
       "      <td>36.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>641</td>\n",
       "      <td>1453</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>DFW</td>\n",
       "      <td>DCA</td>\n",
       "      <td>1555</td>\n",
       "      <td>7.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>1192</td>\n",
       "      <td>1935</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>WN</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MCI</td>\n",
       "      <td>1720</td>\n",
       "      <td>48.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1363</td>\n",
       "      <td>2225</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MONTH  DAY  WEEKDAY AIRLINE ORG_AIR DEST_AIR  SCHED_DEP  DEP_DELAY  \\\n",
       "0      1    1        4      WN     LAX      SLC       1625       58.0   \n",
       "1      1    1        4      UA     DEN      IAD        823        7.0   \n",
       "2      1    1        4      MQ     DFW      VPS       1305       36.0   \n",
       "3      1    1        4      AA     DFW      DCA       1555        7.0   \n",
       "4      1    1        4      WN     LAX      MCI       1720       48.0   \n",
       "\n",
       "   AIR_TIME  DIST  SCHED_ARR  ARR_DELAY  DIVERTED  CANCELLED  \n",
       "0      94.0   590       1905       65.0         0          0  \n",
       "1     154.0  1452       1333      -13.0         0          0  \n",
       "2      85.0   641       1453       35.0         0          0  \n",
       "3     126.0  1192       1935       -7.0         0          0  \n",
       "4     166.0  1363       2225       39.0         0          0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rank', 'discipline', 'phd', 'service', 'sex', 'salary'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rank', 'discipline', 'phd', 'service', 'sex', 'salary'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank          object\n",
       "discipline    object\n",
       "phd            int64\n",
       "service        int64\n",
       "sex           object\n",
       "salary         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>77500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>116450</td>\n",
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       "      <th>70</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>78500</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>71065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>161101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "68   AsstProf          A    4        2  Female   77500\n",
       "69       Prof          A   28        7  Female  116450\n",
       "70   AsstProf          A    8        3  Female   78500\n",
       "71  AssocProf          B   12        9  Female   71065\n",
       "72       Prof          B   24       15  Female  161101\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.705128</td>\n",
       "      <td>15.051282</td>\n",
       "      <td>108023.782051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.498425</td>\n",
       "      <td>12.139768</td>\n",
       "      <td>28293.661022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.250000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>88612.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.500000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>104671.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.750000</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>126774.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>186960.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             phd    service         salary\n",
       "count  78.000000  78.000000      78.000000\n",
       "mean   19.705128  15.051282  108023.782051\n",
       "std    12.498425  12.139768   28293.661022\n",
       "min     1.000000   0.000000   57800.000000\n",
       "25%    10.250000   5.250000   88612.500000\n",
       "50%    18.500000  14.500000  104671.000000\n",
       "75%    27.750000  20.750000  126774.750000\n",
       "max    56.000000  51.000000  186960.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank            Prof\n",
       "discipline         B\n",
       "phd               56\n",
       "service           51\n",
       "sex             Male\n",
       "salary        186960\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank          AssocProf\n",
       "discipline            A\n",
       "phd                   1\n",
       "service               0\n",
       "sex              Female\n",
       "salary            57800\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd            19.705128\n",
       "service        15.051282\n",
       "salary     108023.782051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    42\n",
       "A    36\n",
       "Name: discipline, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.discipline.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.705128</td>\n",
       "      <td>15.051282</td>\n",
       "      <td>108023.782051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.498425</td>\n",
       "      <td>12.139768</td>\n",
       "      <td>28293.661022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.250000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>88612.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.500000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>104671.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.750000</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>126774.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>186960.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             phd    service         salary\n",
       "count  78.000000  78.000000      78.000000\n",
       "mean   19.705128  15.051282  108023.782051\n",
       "std    12.498425  12.139768   28293.661022\n",
       "min     1.000000   0.000000   57800.000000\n",
       "25%    10.250000   5.250000   88612.500000\n",
       "50%    18.500000  14.500000  104671.000000\n",
       "75%    27.750000  20.750000  126774.750000\n",
       "max    56.000000  51.000000  186960.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd           12.498425\n",
       "service       12.139768\n",
       "salary     28293.661022\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_50=df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd            21.52\n",
       "service        17.60\n",
       "salary     113789.14\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_50.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd            21.52\n",
       "service        17.60\n",
       "salary     113789.14\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(50).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     186960\n",
       "1      93000\n",
       "2     110515\n",
       "3     131205\n",
       "4     104800\n",
       "       ...  \n",
       "73    105450\n",
       "74    104542\n",
       "75    124312\n",
       "76    109954\n",
       "77    109646\n",
       "Name: salary, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting a column\n",
    "df.salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     186960\n",
       "1      93000\n",
       "2     110515\n",
       "3     131205\n",
       "4     104800\n",
       "       ...  \n",
       "73    105450\n",
       "74    104542\n",
       "75    124312\n",
       "76    109954\n",
       "77    109646\n",
       "Name: salary, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    salary\n",
       "0   186960\n",
       "1    93000\n",
       "2   110515\n",
       "3   131205\n",
       "4   104800\n",
       "..     ...\n",
       "73  105450\n",
       "74  104542\n",
       "75  124312\n",
       "76  109954\n",
       "77  109646\n",
       "\n",
       "[78 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rank', 'discipline', 'phd', 'service', 'sex', 'salary'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the basic statistics for the Phd column;\n",
    "\n",
    "Find how many values in the Phd column (use count method);\n",
    "\n",
    "Calculate the average Phd;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    78.000000\n",
       "mean     19.705128\n",
       "std      12.498425\n",
       "min       1.000000\n",
       "25%      10.250000\n",
       "50%      18.500000\n",
       "75%      27.750000\n",
       "max      56.000000\n",
       "Name: phd, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.phd.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.phd.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.705128205128204"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.phd.mean()"
   ]
  },
  {
   "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",
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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>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AssocProf</th>\n",
       "      <td>15.076923</td>\n",
       "      <td>11.307692</td>\n",
       "      <td>91786.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AsstProf</th>\n",
       "      <td>5.052632</td>\n",
       "      <td>2.210526</td>\n",
       "      <td>81362.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof</th>\n",
       "      <td>27.065217</td>\n",
       "      <td>21.413043</td>\n",
       "      <td>123624.804348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 phd    service         salary\n",
       "rank                                          \n",
       "AssocProf  15.076923  11.307692   91786.230769\n",
       "AsstProf    5.052632   2.210526   81362.789474\n",
       "Prof       27.065217  21.413043  123624.804348"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['rank']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "      <th>phd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AssocProf</th>\n",
       "      <td>91786.230769</td>\n",
       "      <td>15.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AsstProf</th>\n",
       "      <td>81362.789474</td>\n",
       "      <td>5.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof</th>\n",
       "      <td>123624.804348</td>\n",
       "      <td>27.065217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  salary        phd\n",
       "rank                               \n",
       "AssocProf   91786.230769  15.076923\n",
       "AsstProf    81362.789474   5.052632\n",
       "Prof       123624.804348  27.065217"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('rank')[['salary','phd']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped=df.groupby('rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.generic.DataFrameGroupBy"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>150500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>148750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>155865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>151768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>140096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>122960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>127512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>144651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>161101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank discipline  phd  service     sex  salary\n",
       "0   Prof          B   56       49    Male  186960\n",
       "3   Prof          A   40       31    Male  131205\n",
       "5   Prof          A   20       20    Male  122400\n",
       "7   Prof          A   18       18    Male  126300\n",
       "10  Prof          B   39       33    Male  128250\n",
       "11  Prof          B   23       23    Male  134778\n",
       "13  Prof          B   35       33    Male  162200\n",
       "14  Prof          B   25       19    Male  153750\n",
       "15  Prof          B   17        3    Male  150480\n",
       "19  Prof          A   29       27    Male  150500\n",
       "26  Prof          A   38       19    Male  148750\n",
       "27  Prof          A   45       43    Male  155865\n",
       "29  Prof          B   21       20    Male  123683\n",
       "31  Prof          B   22       21    Male  155750\n",
       "35  Prof          B   28       23    Male  126933\n",
       "36  Prof          B   45       45    Male  146856\n",
       "39  Prof          B   18       18  Female  129000\n",
       "40  Prof          A   39       36  Female  137000\n",
       "44  Prof          B   23       19  Female  151768\n",
       "45  Prof          B   25       25  Female  140096\n",
       "49  Prof          B   17       18  Female  122960\n",
       "51  Prof          B   20       14  Female  127512\n",
       "58  Prof          B   36       26  Female  144651\n",
       "72  Prof          B   24       15  Female  161101\n",
       "75  Prof          B   17       17  Female  124312"
      ]
     },
     "execution_count": 45,
     "metadata": {},
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   ],
   "source": [
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   ]
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       "      <th>73</th>\n",
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       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
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       "      <th>74</th>\n",
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       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
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       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
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       "    <tr>\n",
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       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
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       "         rank discipline  phd  service     sex  salary\n",
       "0        Prof          B   56       49    Male  186960\n",
       "1        Prof          A   12        6    Male   93000\n",
       "2        Prof          A   23       20    Male  110515\n",
       "3        Prof          A   40       31    Male  131205\n",
       "4        Prof          B   20       18    Male  104800\n",
       "..        ...        ...  ...      ...     ...     ...\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
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       "   rank discipline  phd  service   sex  salary\n",
       "0  Prof          B   56       49  Male  186960\n",
       "1  Prof          A   12        6  Male   93000\n",
       "2  Prof          A   23       20  Male  110515\n",
       "3  Prof          A   40       31  Male  131205\n",
       "4  Prof          B   20       18  Male  104800"
      ]
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       "   rank discipline  phd\n",
       "1  Prof          A   12\n",
       "2  Prof          A   23\n",
       "3  Prof          A   40"
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       "      <th>6</th>\n",
       "      <td>AssocProf</td>\n",
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       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "        rank discipline  phd\n",
       "0       Prof          B   56\n",
       "1       Prof          A   12\n",
       "2       Prof          A   23\n",
       "3       Prof          A   40\n",
       "4       Prof          B   20\n",
       "5       Prof          A   20\n",
       "6  AssocProf          A   20\n",
       "7       Prof          A   18\n",
       "8       Prof          A   29\n",
       "9       Prof          A   51"
      ]
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       "   rank discipline  phd  service   sex  salary\n",
       "0  Prof          B   56       49  Male  186960\n",
       "1  Prof          A   12        6  Male   93000"
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       "2       Male\n",
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       "       ...  \n",
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       "Name: sex, Length: 78, dtype: object"
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