15_pandas.ipynb

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   "source": [
    "# Foundations of Computational Economics #15\n",
    "\n",
    "by Fedor Iskhakov, ANU\n",
    "\n",
    "<img src=\"_static/img/dag3logo.png\" style=\"width:256px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Introduction to Data Manipulation in Python (Pandas)\n",
    "\n",
    "<img src=\"_static/img/lecture.png\" style=\"width:64px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<img src=\"_static/img/youtube.png\" style=\"width:65px;\">\n",
    "\n",
    "[https://youtu.be/61pHVbZubmo](https://youtu.be/61pHVbZubmo)\n",
    "\n",
    "Description: Introduction into DataFrames, grouping and data merging."
   ]
  },
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    "### Scientific stack in Python\n",
    "\n",
    "Collection of modules (libraries) used in scientific numerical computations:\n",
    "\n",
    "- **``NumPy``** is widely-used scientific computing package for implements fast array processing — vectorization  \n",
    "- **``SciPy``** is a collection of functions that perform common scientific operations (optimization, root finding, interpolation, numerical integration, etc.)  \n",
    "- **``Pandas``** is data manipulation package with special data types and methods  \n",
    "- **``Numba``** is just in time (JIT) compiler for a subset of Python and NumPy functions  \n",
    "- **``Matplotlib``** serves for making figures and plots  "
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### What is Pandas?\n",
    "\n",
    "- Pandas provides classes for working with data (`Series`, `DataFrame`)  \n",
    "- Data objects have **methods** for manipulating data eg. indexing, sorting, grouping, filling in missing data  \n",
    "- Pandas does not provide modeling tools eg. regression, prediction  \n",
    "- These tools are found in packages such as `scikit-learn` and `statsmodels`, which are built on top of pandas  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "#### DataFrames\n",
    "\n",
    "A `DataFrame` combines multiple ‘columns’ of data into a\n",
    "two-dimensional object, similar to a spreadsheet\n",
    "\n",
    "<img src=\"_static/img/dataframe.jpg\" style=\"width:800px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Create and explore the dataframe object"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('./_static/data/recent-grads.csv')\n",
    "# help(data) # more help on dataframe object and its methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Major</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Major_category</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Sample_size</th>\n",
       "      <th>Employed</th>\n",
       "      <th>...</th>\n",
       "      <th>Part_time</th>\n",
       "      <th>Full_time_year_round</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Median</th>\n",
       "      <th>P25th</th>\n",
       "      <th>P75th</th>\n",
       "      <th>College_jobs</th>\n",
       "      <th>Non_college_jobs</th>\n",
       "      <th>Low_wage_jobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2419</td>\n",
       "      <td>PETROLEUM ENGINEERING</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>2057.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>36</td>\n",
       "      <td>1976</td>\n",
       "      <td>...</td>\n",
       "      <td>270</td>\n",
       "      <td>1207</td>\n",
       "      <td>37</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>110000</td>\n",
       "      <td>95000</td>\n",
       "      <td>125000</td>\n",
       "      <td>1534</td>\n",
       "      <td>364</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2416</td>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>756.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.101852</td>\n",
       "      <td>7</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>388</td>\n",
       "      <td>85</td>\n",
       "      <td>0.117241</td>\n",
       "      <td>75000</td>\n",
       "      <td>55000</td>\n",
       "      <td>90000</td>\n",
       "      <td>350</td>\n",
       "      <td>257</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2415</td>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>856.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.153037</td>\n",
       "      <td>3</td>\n",
       "      <td>648</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>340</td>\n",
       "      <td>16</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>73000</td>\n",
       "      <td>50000</td>\n",
       "      <td>105000</td>\n",
       "      <td>456</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2417</td>\n",
       "      <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>16</td>\n",
       "      <td>758</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>692</td>\n",
       "      <td>40</td>\n",
       "      <td>0.050125</td>\n",
       "      <td>70000</td>\n",
       "      <td>43000</td>\n",
       "      <td>80000</td>\n",
       "      <td>529</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2405</td>\n",
       "      <td>CHEMICAL ENGINEERING</td>\n",
       "      <td>32260.0</td>\n",
       "      <td>21239.0</td>\n",
       "      <td>11021.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.341631</td>\n",
       "      <td>289</td>\n",
       "      <td>25694</td>\n",
       "      <td>...</td>\n",
       "      <td>5180</td>\n",
       "      <td>16697</td>\n",
       "      <td>1672</td>\n",
       "      <td>0.061098</td>\n",
       "      <td>65000</td>\n",
       "      <td>50000</td>\n",
       "      <td>75000</td>\n",
       "      <td>18314</td>\n",
       "      <td>4440</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2418</td>\n",
       "      <td>NUCLEAR ENGINEERING</td>\n",
       "      <td>2573.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.144967</td>\n",
       "      <td>17</td>\n",
       "      <td>1857</td>\n",
       "      <td>...</td>\n",
       "      <td>264</td>\n",
       "      <td>1449</td>\n",
       "      <td>400</td>\n",
       "      <td>0.177226</td>\n",
       "      <td>65000</td>\n",
       "      <td>50000</td>\n",
       "      <td>102000</td>\n",
       "      <td>1142</td>\n",
       "      <td>657</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>6202</td>\n",
       "      <td>ACTUARIAL SCIENCE</td>\n",
       "      <td>3777.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>0.441356</td>\n",
       "      <td>51</td>\n",
       "      <td>2912</td>\n",
       "      <td>...</td>\n",
       "      <td>296</td>\n",
       "      <td>2482</td>\n",
       "      <td>308</td>\n",
       "      <td>0.095652</td>\n",
       "      <td>62000</td>\n",
       "      <td>53000</td>\n",
       "      <td>72000</td>\n",
       "      <td>1768</td>\n",
       "      <td>314</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5001</td>\n",
       "      <td>ASTRONOMY AND ASTROPHYSICS</td>\n",
       "      <td>1792.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>960.0</td>\n",
       "      <td>Physical Sciences</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>10</td>\n",
       "      <td>1526</td>\n",
       "      <td>...</td>\n",
       "      <td>553</td>\n",
       "      <td>827</td>\n",
       "      <td>33</td>\n",
       "      <td>0.021167</td>\n",
       "      <td>62000</td>\n",
       "      <td>31500</td>\n",
       "      <td>109000</td>\n",
       "      <td>972</td>\n",
       "      <td>500</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2414</td>\n",
       "      <td>MECHANICAL ENGINEERING</td>\n",
       "      <td>91227.0</td>\n",
       "      <td>80320.0</td>\n",
       "      <td>10907.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.119559</td>\n",
       "      <td>1029</td>\n",
       "      <td>76442</td>\n",
       "      <td>...</td>\n",
       "      <td>13101</td>\n",
       "      <td>54639</td>\n",
       "      <td>4650</td>\n",
       "      <td>0.057342</td>\n",
       "      <td>60000</td>\n",
       "      <td>48000</td>\n",
       "      <td>70000</td>\n",
       "      <td>52844</td>\n",
       "      <td>16384</td>\n",
       "      <td>3253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2408</td>\n",
       "      <td>ELECTRICAL ENGINEERING</td>\n",
       "      <td>81527.0</td>\n",
       "      <td>65511.0</td>\n",
       "      <td>16016.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.196450</td>\n",
       "      <td>631</td>\n",
       "      <td>61928</td>\n",
       "      <td>...</td>\n",
       "      <td>12695</td>\n",
       "      <td>41413</td>\n",
       "      <td>3895</td>\n",
       "      <td>0.059174</td>\n",
       "      <td>60000</td>\n",
       "      <td>45000</td>\n",
       "      <td>72000</td>\n",
       "      <td>45829</td>\n",
       "      <td>10874</td>\n",
       "      <td>3170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank  Major_code                                      Major    Total  \\\n",
       "0     1        2419                      PETROLEUM ENGINEERING   2339.0   \n",
       "1     2        2416             MINING AND MINERAL ENGINEERING    756.0   \n",
       "2     3        2415                  METALLURGICAL ENGINEERING    856.0   \n",
       "3     4        2417  NAVAL ARCHITECTURE AND MARINE ENGINEERING   1258.0   \n",
       "4     5        2405                       CHEMICAL ENGINEERING  32260.0   \n",
       "5     6        2418                        NUCLEAR ENGINEERING   2573.0   \n",
       "6     7        6202                          ACTUARIAL SCIENCE   3777.0   \n",
       "7     8        5001                 ASTRONOMY AND ASTROPHYSICS   1792.0   \n",
       "8     9        2414                     MECHANICAL ENGINEERING  91227.0   \n",
       "9    10        2408                     ELECTRICAL ENGINEERING  81527.0   \n",
       "\n",
       "       Men    Women     Major_category  ShareWomen  Sample_size  Employed  \\\n",
       "0   2057.0    282.0        Engineering    0.120564           36      1976   \n",
       "1    679.0     77.0        Engineering    0.101852            7       640   \n",
       "2    725.0    131.0        Engineering    0.153037            3       648   \n",
       "3   1123.0    135.0        Engineering    0.107313           16       758   \n",
       "4  21239.0  11021.0        Engineering    0.341631          289     25694   \n",
       "5   2200.0    373.0        Engineering    0.144967           17      1857   \n",
       "6   2110.0   1667.0           Business    0.441356           51      2912   \n",
       "7    832.0    960.0  Physical Sciences    0.535714           10      1526   \n",
       "8  80320.0  10907.0        Engineering    0.119559         1029     76442   \n",
       "9  65511.0  16016.0        Engineering    0.196450          631     61928   \n",
       "\n",
       "   ...  Part_time  Full_time_year_round  Unemployed  Unemployment_rate  \\\n",
       "0  ...        270                  1207          37           0.018381   \n",
       "1  ...        170                   388          85           0.117241   \n",
       "2  ...        133                   340          16           0.024096   \n",
       "3  ...        150                   692          40           0.050125   \n",
       "4  ...       5180                 16697        1672           0.061098   \n",
       "5  ...        264                  1449         400           0.177226   \n",
       "6  ...        296                  2482         308           0.095652   \n",
       "7  ...        553                   827          33           0.021167   \n",
       "8  ...      13101                 54639        4650           0.057342   \n",
       "9  ...      12695                 41413        3895           0.059174   \n",
       "\n",
       "   Median  P25th   P75th  College_jobs  Non_college_jobs  Low_wage_jobs  \n",
       "0  110000  95000  125000          1534               364            193  \n",
       "1   75000  55000   90000           350               257             50  \n",
       "2   73000  50000  105000           456               176              0  \n",
       "3   70000  43000   80000           529               102              0  \n",
       "4   65000  50000   75000         18314              4440            972  \n",
       "5   65000  50000  102000          1142               657            244  \n",
       "6   62000  53000   72000          1768               314            259  \n",
       "7   62000  31500  109000           972               500            220  \n",
       "8   60000  48000   70000         52844             16384           3253  \n",
       "9   60000  45000   72000         45829             10874           3170  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview of the dataset\n",
    "data.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Description of the data\n",
    "\n",
    "**The Economic Guide To Picking A College Major**\n",
    "\n",
    "[https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/](https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/)\n",
    "\n",
    "Data dictionary available at\n",
    "\n",
    "[https://github.com/fivethirtyeight/data/tree/master/college-majors](https://github.com/fivethirtyeight/data/tree/master/college-majors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 173 entries, 0 to 172\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Rank                  173 non-null    int64  \n",
      " 1   Major_code            173 non-null    int64  \n",
      " 2   Major                 173 non-null    object \n",
      " 3   Total                 172 non-null    float64\n",
      " 4   Men                   172 non-null    float64\n",
      " 5   Women                 172 non-null    float64\n",
      " 6   Major_category        173 non-null    object \n",
      " 7   ShareWomen            172 non-null    float64\n",
      " 8   Sample_size           173 non-null    int64  \n",
      " 9   Employed              173 non-null    int64  \n",
      " 10  Full_time             173 non-null    int64  \n",
      " 11  Part_time             173 non-null    int64  \n",
      " 12  Full_time_year_round  173 non-null    int64  \n",
      " 13  Unemployed            173 non-null    int64  \n",
      " 14  Unemployment_rate     173 non-null    float64\n",
      " 15  Median                173 non-null    int64  \n",
      " 16  P25th                 173 non-null    int64  \n",
      " 17  P75th                 173 non-null    int64  \n",
      " 18  College_jobs          173 non-null    int64  \n",
      " 19  Non_college_jobs      173 non-null    int64  \n",
      " 20  Low_wage_jobs         173 non-null    int64  \n",
      "dtypes: float64(5), int64(14), object(2)\n",
      "memory usage: 28.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Info on the dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Access individual columns of data\n",
    "\n",
    "This returns a `Series` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data1 is  <class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0                        PETROLEUM ENGINEERING\n",
       "1               MINING AND MINERAL ENGINEERING\n",
       "2                    METALLURGICAL ENGINEERING\n",
       "3    NAVAL ARCHITECTURE AND MARINE ENGINEERING\n",
       "4                         CHEMICAL ENGINEERING\n",
       "Name: Major, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = data['Major']\n",
    "print('Type of data1 is ',type(data1))\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Access multiple columns of data\n",
    "\n",
    "This returns a `DataFrame` object again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data2 is  <class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>Major</th>\n",
       "      <th>ShareWomen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PETROLEUM ENGINEERING</td>\n",
       "      <td>0.120564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>0.101852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>0.153037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
       "      <td>0.107313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMICAL ENGINEERING</td>\n",
       "      <td>0.341631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Major  ShareWomen\n",
       "0                      PETROLEUM ENGINEERING    0.120564\n",
       "1             MINING AND MINERAL ENGINEERING    0.101852\n",
       "2                  METALLURGICAL ENGINEERING    0.153037\n",
       "3  NAVAL ARCHITECTURE AND MARINE ENGINEERING    0.107313\n",
       "4                       CHEMICAL ENGINEERING    0.341631"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data[['Major', 'ShareWomen']]\n",
    "print('Type of data2 is ',type(data2))\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Add a new column Stata style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>Total</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Employment rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2339.0</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.844805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>756.0</td>\n",
       "      <td>640</td>\n",
       "      <td>0.846561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>856.0</td>\n",
       "      <td>648</td>\n",
       "      <td>0.757009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1258.0</td>\n",
       "      <td>758</td>\n",
       "      <td>0.602544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32260.0</td>\n",
       "      <td>25694</td>\n",
       "      <td>0.796466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Total  Employed  Employment rate\n",
       "0   2339.0      1976         0.844805\n",
       "1    756.0       640         0.846561\n",
       "2    856.0       648         0.757009\n",
       "3   1258.0       758         0.602544\n",
       "4  32260.0     25694         0.796466"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Employment rate'] = data['Employed'] / data['Total']\n",
    "data[['Total', 'Employed', 'Employment rate']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Average unemployment rate…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.819083091329481"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Unemployment_rate'].mean() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Simple summary statistics\n",
    "\n",
    "`.describe()` returns useful summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    173.000000\n",
       "mean       0.068191\n",
       "std        0.030331\n",
       "min        0.000000\n",
       "25%        0.050306\n",
       "50%        0.067961\n",
       "75%        0.087557\n",
       "max        0.177226\n",
       "Name: Unemployment_rate, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Unemployment_rate'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Simple plots\n",
    "\n",
    "Pandas also provides a simple way to generate matplotlib plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median salary')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "data.plot(x='ShareWomen', y='Median', kind='scatter', figsize=(10, 8), color='red')\n",
    "plt.xlabel('Share of women')\n",
    "plt.ylabel('Median salary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Selecting and filtering\n",
    "\n",
    "We can use integer slicing to select rows as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Major</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Major_category</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Sample_size</th>\n",
       "      <th>Employed</th>\n",
       "      <th>...</th>\n",
       "      <th>Full_time_year_round</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Median</th>\n",
       "      <th>P25th</th>\n",
       "      <th>P75th</th>\n",
       "      <th>College_jobs</th>\n",
       "      <th>Non_college_jobs</th>\n",
       "      <th>Low_wage_jobs</th>\n",
       "      <th>Employment rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2419</td>\n",
       "      <td>PETROLEUM ENGINEERING</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>2057.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>36</td>\n",
       "      <td>1976</td>\n",
       "      <td>...</td>\n",
       "      <td>1207</td>\n",
       "      <td>37</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>110000</td>\n",
       "      <td>95000</td>\n",
       "      <td>125000</td>\n",
       "      <td>1534</td>\n",
       "      <td>364</td>\n",
       "      <td>193</td>\n",
       "      <td>0.844805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2416</td>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>756.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.101852</td>\n",
       "      <td>7</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>388</td>\n",
       "      <td>85</td>\n",
       "      <td>0.117241</td>\n",
       "      <td>75000</td>\n",
       "      <td>55000</td>\n",
       "      <td>90000</td>\n",
       "      <td>350</td>\n",
       "      <td>257</td>\n",
       "      <td>50</td>\n",
       "      <td>0.846561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2415</td>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>856.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.153037</td>\n",
       "      <td>3</td>\n",
       "      <td>648</td>\n",
       "      <td>...</td>\n",
       "      <td>340</td>\n",
       "      <td>16</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>73000</td>\n",
       "      <td>50000</td>\n",
       "      <td>105000</td>\n",
       "      <td>456</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "      <td>0.757009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank  Major_code                           Major   Total     Men  Women  \\\n",
       "0     1        2419           PETROLEUM ENGINEERING  2339.0  2057.0  282.0   \n",
       "1     2        2416  MINING AND MINERAL ENGINEERING   756.0   679.0   77.0   \n",
       "2     3        2415       METALLURGICAL ENGINEERING   856.0   725.0  131.0   \n",
       "\n",
       "  Major_category  ShareWomen  Sample_size  Employed  ...  \\\n",
       "0    Engineering    0.120564           36      1976  ...   \n",
       "1    Engineering    0.101852            7       640  ...   \n",
       "2    Engineering    0.153037            3       648  ...   \n",
       "\n",
       "   Full_time_year_round  Unemployed  Unemployment_rate  Median  P25th   P75th  \\\n",
       "0                  1207          37           0.018381  110000  95000  125000   \n",
       "1                   388          85           0.117241   75000  55000   90000   \n",
       "2                   340          16           0.024096   73000  50000  105000   \n",
       "\n",
       "   College_jobs  Non_college_jobs  Low_wage_jobs  Employment rate  \n",
       "0          1534               364            193         0.844805  \n",
       "1           350               257             50         0.846561  \n",
       "2           456               176              0         0.757009  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Majors with the highest share of women\n",
    "\n",
    "First we will sort our values by a column in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Major</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Major_category</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Sample_size</th>\n",
       "      <th>Employed</th>\n",
       "      <th>...</th>\n",
       "      <th>Full_time_year_round</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Median</th>\n",
       "      <th>P25th</th>\n",
       "      <th>P75th</th>\n",
       "      <th>College_jobs</th>\n",
       "      <th>Non_college_jobs</th>\n",
       "      <th>Low_wage_jobs</th>\n",
       "      <th>Employment rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>165</td>\n",
       "      <td>2307</td>\n",
       "      <td>EARLY CHILDHOOD EDUCATION</td>\n",
       "      <td>37589.0</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>36422.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>0.968954</td>\n",
       "      <td>342</td>\n",
       "      <td>32551</td>\n",
       "      <td>...</td>\n",
       "      <td>20748</td>\n",
       "      <td>1360</td>\n",
       "      <td>0.040105</td>\n",
       "      <td>28000</td>\n",
       "      <td>21000</td>\n",
       "      <td>35000</td>\n",
       "      <td>23515</td>\n",
       "      <td>7705</td>\n",
       "      <td>2868</td>\n",
       "      <td>0.865971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>164</td>\n",
       "      <td>6102</td>\n",
       "      <td>COMMUNICATION DISORDERS SCIENCES AND SERVICES</td>\n",
       "      <td>38279.0</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>37054.0</td>\n",
       "      <td>Health</td>\n",
       "      <td>0.967998</td>\n",
       "      <td>95</td>\n",
       "      <td>29763</td>\n",
       "      <td>...</td>\n",
       "      <td>14460</td>\n",
       "      <td>1487</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>28000</td>\n",
       "      <td>20000</td>\n",
       "      <td>40000</td>\n",
       "      <td>19957</td>\n",
       "      <td>9404</td>\n",
       "      <td>5125</td>\n",
       "      <td>0.777528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>6104</td>\n",
       "      <td>MEDICAL ASSISTING SERVICES</td>\n",
       "      <td>11123.0</td>\n",
       "      <td>803.0</td>\n",
       "      <td>10320.0</td>\n",
       "      <td>Health</td>\n",
       "      <td>0.927807</td>\n",
       "      <td>67</td>\n",
       "      <td>9168</td>\n",
       "      <td>...</td>\n",
       "      <td>4290</td>\n",
       "      <td>407</td>\n",
       "      <td>0.042507</td>\n",
       "      <td>42000</td>\n",
       "      <td>30000</td>\n",
       "      <td>65000</td>\n",
       "      <td>2091</td>\n",
       "      <td>6948</td>\n",
       "      <td>1270</td>\n",
       "      <td>0.824238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank  Major_code                                          Major    Total  \\\n",
       "164   165        2307                      EARLY CHILDHOOD EDUCATION  37589.0   \n",
       "163   164        6102  COMMUNICATION DISORDERS SCIENCES AND SERVICES  38279.0   \n",
       "51     52        6104                     MEDICAL ASSISTING SERVICES  11123.0   \n",
       "\n",
       "        Men    Women Major_category  ShareWomen  Sample_size  Employed  ...  \\\n",
       "164  1167.0  36422.0      Education    0.968954          342     32551  ...   \n",
       "163  1225.0  37054.0         Health    0.967998           95     29763  ...   \n",
       "51    803.0  10320.0         Health    0.927807           67      9168  ...   \n",
       "\n",
       "     Full_time_year_round  Unemployed  Unemployment_rate  Median  P25th  \\\n",
       "164                 20748        1360           0.040105   28000  21000   \n",
       "163                 14460        1487           0.047584   28000  20000   \n",
       "51                   4290         407           0.042507   42000  30000   \n",
       "\n",
       "     P75th  College_jobs  Non_college_jobs  Low_wage_jobs  Employment rate  \n",
       "164  35000         23515              7705           2868         0.865971  \n",
       "163  40000         19957              9404           5125         0.777528  \n",
       "51   65000          2091              6948           1270         0.824238  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by='ShareWomen', ascending=False)[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Using row index\n",
    "\n",
    "Another way to select rows is to use row labels, i.e. set a row index\n",
    "\n",
    "Similar to the column labels, we can add row labels (the index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Rank</th>\n",
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       "      <th>2419</th>\n",
       "      <td>1</td>\n",
       "      <td>PETROLEUM ENGINEERING</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>2057.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>36</td>\n",
       "      <td>1976</td>\n",
       "      <td>1849</td>\n",
       "      <td>...</td>\n",
       "      <td>1207</td>\n",
       "      <td>37</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>110000</td>\n",
       "      <td>95000</td>\n",
       "      <td>125000</td>\n",
       "      <td>1534</td>\n",
       "      <td>364</td>\n",
       "      <td>193</td>\n",
       "      <td>0.844805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2416</th>\n",
       "      <td>2</td>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>756.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.101852</td>\n",
       "      <td>7</td>\n",
       "      <td>640</td>\n",
       "      <td>556</td>\n",
       "      <td>...</td>\n",
       "      <td>388</td>\n",
       "      <td>85</td>\n",
       "      <td>0.117241</td>\n",
       "      <td>75000</td>\n",
       "      <td>55000</td>\n",
       "      <td>90000</td>\n",
       "      <td>350</td>\n",
       "      <td>257</td>\n",
       "      <td>50</td>\n",
       "      <td>0.846561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>3</td>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>856.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.153037</td>\n",
       "      <td>3</td>\n",
       "      <td>648</td>\n",
       "      <td>558</td>\n",
       "      <td>...</td>\n",
       "      <td>340</td>\n",
       "      <td>16</td>\n",
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       "      <td>73000</td>\n",
       "      <td>50000</td>\n",
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       "      <td>456</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "      <td>0.757009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>4</td>\n",
       "      <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>16</td>\n",
       "      <td>758</td>\n",
       "      <td>1069</td>\n",
       "      <td>...</td>\n",
       "      <td>692</td>\n",
       "      <td>40</td>\n",
       "      <td>0.050125</td>\n",
       "      <td>70000</td>\n",
       "      <td>43000</td>\n",
       "      <td>80000</td>\n",
       "      <td>529</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0.602544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>5</td>\n",
       "      <td>CHEMICAL ENGINEERING</td>\n",
       "      <td>32260.0</td>\n",
       "      <td>21239.0</td>\n",
       "      <td>11021.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.341631</td>\n",
       "      <td>289</td>\n",
       "      <td>25694</td>\n",
       "      <td>23170</td>\n",
       "      <td>...</td>\n",
       "      <td>16697</td>\n",
       "      <td>1672</td>\n",
       "      <td>0.061098</td>\n",
       "      <td>65000</td>\n",
       "      <td>50000</td>\n",
       "      <td>75000</td>\n",
       "      <td>18314</td>\n",
       "      <td>4440</td>\n",
       "      <td>972</td>\n",
       "      <td>0.796466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank                                      Major    Total      Men  \\\n",
       "Major_code                                                                      \n",
       "2419           1                      PETROLEUM ENGINEERING   2339.0   2057.0   \n",
       "2416           2             MINING AND MINERAL ENGINEERING    756.0    679.0   \n",
       "2415           3                  METALLURGICAL ENGINEERING    856.0    725.0   \n",
       "2417           4  NAVAL ARCHITECTURE AND MARINE ENGINEERING   1258.0   1123.0   \n",
       "2405           5                       CHEMICAL ENGINEERING  32260.0  21239.0   \n",
       "\n",
       "              Women Major_category  ShareWomen  Sample_size  Employed  \\\n",
       "Major_code                                                              \n",
       "2419          282.0    Engineering    0.120564           36      1976   \n",
       "2416           77.0    Engineering    0.101852            7       640   \n",
       "2415          131.0    Engineering    0.153037            3       648   \n",
       "2417          135.0    Engineering    0.107313           16       758   \n",
       "2405        11021.0    Engineering    0.341631          289     25694   \n",
       "\n",
       "            Full_time  ...  Full_time_year_round  Unemployed  \\\n",
       "Major_code             ...                                     \n",
       "2419             1849  ...                  1207          37   \n",
       "2416              556  ...                   388          85   \n",
       "2415              558  ...                   340          16   \n",
       "2417             1069  ...                   692          40   \n",
       "2405            23170  ...                 16697        1672   \n",
       "\n",
       "            Unemployment_rate  Median  P25th   P75th  College_jobs  \\\n",
       "Major_code                                                           \n",
       "2419                 0.018381  110000  95000  125000          1534   \n",
       "2416                 0.117241   75000  55000   90000           350   \n",
       "2415                 0.024096   73000  50000  105000           456   \n",
       "2417                 0.050125   70000  43000   80000           529   \n",
       "2405                 0.061098   65000  50000   75000         18314   \n",
       "\n",
       "            Non_college_jobs  Low_wage_jobs  Employment rate  \n",
       "Major_code                                                    \n",
       "2419                     364            193         0.844805  \n",
       "2416                     257             50         0.846561  \n",
       "2415                     176              0         0.757009  \n",
       "2417                     102              0         0.602544  \n",
       "2405                    4440            972         0.796466  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.set_index('Major_code').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Need to overwrite dataframe\n",
    "\n",
    "Note: we haven’t actually changed the DataFrame `data`\n",
    "\n",
    "Need to overwrite `data` with the new copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>2419</td>\n",
       "      <td>PETROLEUM ENGINEERING</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>2057.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>36</td>\n",
       "      <td>1976</td>\n",
       "      <td>...</td>\n",
       "      <td>1207</td>\n",
       "      <td>37</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>110000</td>\n",
       "      <td>95000</td>\n",
       "      <td>125000</td>\n",
       "      <td>1534</td>\n",
       "      <td>364</td>\n",
       "      <td>193</td>\n",
       "      <td>0.844805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2416</td>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>756.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.101852</td>\n",
       "      <td>7</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>388</td>\n",
       "      <td>85</td>\n",
       "      <td>0.117241</td>\n",
       "      <td>75000</td>\n",
       "      <td>55000</td>\n",
       "      <td>90000</td>\n",
       "      <td>350</td>\n",
       "      <td>257</td>\n",
       "      <td>50</td>\n",
       "      <td>0.846561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2415</td>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>856.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.153037</td>\n",
       "      <td>3</td>\n",
       "      <td>648</td>\n",
       "      <td>...</td>\n",
       "      <td>340</td>\n",
       "      <td>16</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>73000</td>\n",
       "      <td>50000</td>\n",
       "      <td>105000</td>\n",
       "      <td>456</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "      <td>0.757009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2417</td>\n",
       "      <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>16</td>\n",
       "      <td>758</td>\n",
       "      <td>...</td>\n",
       "      <td>692</td>\n",
       "      <td>40</td>\n",
       "      <td>0.050125</td>\n",
       "      <td>70000</td>\n",
       "      <td>43000</td>\n",
       "      <td>80000</td>\n",
       "      <td>529</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0.602544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2405</td>\n",
       "      <td>CHEMICAL ENGINEERING</td>\n",
       "      <td>32260.0</td>\n",
       "      <td>21239.0</td>\n",
       "      <td>11021.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.341631</td>\n",
       "      <td>289</td>\n",
       "      <td>25694</td>\n",
       "      <td>...</td>\n",
       "      <td>16697</td>\n",
       "      <td>1672</td>\n",
       "      <td>0.061098</td>\n",
       "      <td>65000</td>\n",
       "      <td>50000</td>\n",
       "      <td>75000</td>\n",
       "      <td>18314</td>\n",
       "      <td>4440</td>\n",
       "      <td>972</td>\n",
       "      <td>0.796466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank  Major_code                                      Major    Total  \\\n",
       "0     1        2419                      PETROLEUM ENGINEERING   2339.0   \n",
       "1     2        2416             MINING AND MINERAL ENGINEERING    756.0   \n",
       "2     3        2415                  METALLURGICAL ENGINEERING    856.0   \n",
       "3     4        2417  NAVAL ARCHITECTURE AND MARINE ENGINEERING   1258.0   \n",
       "4     5        2405                       CHEMICAL ENGINEERING  32260.0   \n",
       "\n",
       "       Men    Women Major_category  ShareWomen  Sample_size  Employed  ...  \\\n",
       "0   2057.0    282.0    Engineering    0.120564           36      1976  ...   \n",
       "1    679.0     77.0    Engineering    0.101852            7       640  ...   \n",
       "2    725.0    131.0    Engineering    0.153037            3       648  ...   \n",
       "3   1123.0    135.0    Engineering    0.107313           16       758  ...   \n",
       "4  21239.0  11021.0    Engineering    0.341631          289     25694  ...   \n",
       "\n",
       "   Full_time_year_round  Unemployed  Unemployment_rate  Median  P25th   P75th  \\\n",
       "0                  1207          37           0.018381  110000  95000  125000   \n",
       "1                   388          85           0.117241   75000  55000   90000   \n",
       "2                   340          16           0.024096   73000  50000  105000   \n",
       "3                   692          40           0.050125   70000  43000   80000   \n",
       "4                 16697        1672           0.061098   65000  50000   75000   \n",
       "\n",
       "   College_jobs  Non_college_jobs  Low_wage_jobs  Employment rate  \n",
       "0          1534               364            193         0.844805  \n",
       "1           350               257             50         0.846561  \n",
       "2           456               176              0         0.757009  \n",
       "3           529               102              0         0.602544  \n",
       "4         18314              4440            972         0.796466  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
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   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2419</th>\n",
       "      <td>1</td>\n",
       "      <td>PETROLEUM ENGINEERING</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>2057.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>36</td>\n",
       "      <td>1976</td>\n",
       "      <td>1849</td>\n",
       "      <td>...</td>\n",
       "      <td>1207</td>\n",
       "      <td>37</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>110000</td>\n",
       "      <td>95000</td>\n",
       "      <td>125000</td>\n",
       "      <td>1534</td>\n",
       "      <td>364</td>\n",
       "      <td>193</td>\n",
       "      <td>0.844805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2416</th>\n",
       "      <td>2</td>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>756.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.101852</td>\n",
       "      <td>7</td>\n",
       "      <td>640</td>\n",
       "      <td>556</td>\n",
       "      <td>...</td>\n",
       "      <td>388</td>\n",
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       "      <td>0.117241</td>\n",
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       "      <td>55000</td>\n",
       "      <td>90000</td>\n",
       "      <td>350</td>\n",
       "      <td>257</td>\n",
       "      <td>50</td>\n",
       "      <td>0.846561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>3</td>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>856.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.153037</td>\n",
       "      <td>3</td>\n",
       "      <td>648</td>\n",
       "      <td>558</td>\n",
       "      <td>...</td>\n",
       "      <td>340</td>\n",
       "      <td>16</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>73000</td>\n",
       "      <td>50000</td>\n",
       "      <td>105000</td>\n",
       "      <td>456</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "      <td>0.757009</td>\n",
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       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>4</td>\n",
       "      <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>16</td>\n",
       "      <td>758</td>\n",
       "      <td>1069</td>\n",
       "      <td>...</td>\n",
       "      <td>692</td>\n",
       "      <td>40</td>\n",
       "      <td>0.050125</td>\n",
       "      <td>70000</td>\n",
       "      <td>43000</td>\n",
       "      <td>80000</td>\n",
       "      <td>529</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0.602544</td>\n",
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       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>5</td>\n",
       "      <td>CHEMICAL ENGINEERING</td>\n",
       "      <td>32260.0</td>\n",
       "      <td>21239.0</td>\n",
       "      <td>11021.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.341631</td>\n",
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       "      <td>0.796466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank                                      Major    Total      Men  \\\n",
       "Major_code                                                                      \n",
       "2419           1                      PETROLEUM ENGINEERING   2339.0   2057.0   \n",
       "2416           2             MINING AND MINERAL ENGINEERING    756.0    679.0   \n",
       "2415           3                  METALLURGICAL ENGINEERING    856.0    725.0   \n",
       "2417           4  NAVAL ARCHITECTURE AND MARINE ENGINEERING   1258.0   1123.0   \n",
       "2405           5                       CHEMICAL ENGINEERING  32260.0  21239.0   \n",
       "\n",
       "              Women Major_category  ShareWomen  Sample_size  Employed  \\\n",
       "Major_code                                                              \n",
       "2419          282.0    Engineering    0.120564           36      1976   \n",
       "2416           77.0    Engineering    0.101852            7       640   \n",
       "2415          131.0    Engineering    0.153037            3       648   \n",
       "2417          135.0    Engineering    0.107313           16       758   \n",
       "2405        11021.0    Engineering    0.341631          289     25694   \n",
       "\n",
       "            Full_time  ...  Full_time_year_round  Unemployed  \\\n",
       "Major_code             ...                                     \n",
       "2419             1849  ...                  1207          37   \n",
       "2416              556  ...                   388          85   \n",
       "2415              558  ...                   340          16   \n",
       "2417             1069  ...                   692          40   \n",
       "2405            23170  ...                 16697        1672   \n",
       "\n",
       "            Unemployment_rate  Median  P25th   P75th  College_jobs  \\\n",
       "Major_code                                                           \n",
       "2419                 0.018381  110000  95000  125000          1534   \n",
       "2416                 0.117241   75000  55000   90000           350   \n",
       "2415                 0.024096   73000  50000  105000           456   \n",
       "2417                 0.050125   70000  43000   80000           529   \n",
       "2405                 0.061098   65000  50000   75000         18314   \n",
       "\n",
       "            Non_college_jobs  Low_wage_jobs  Employment rate  \n",
       "Major_code                                                    \n",
       "2419                     364            193         0.844805  \n",
       "2416                     257             50         0.846561  \n",
       "2415                     176              0         0.757009  \n",
       "2417                     102              0         0.602544  \n",
       "2405                    4440            972         0.796466  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.set_index('Major_code')\n",
    "# Could also use data.set_index('Major_code', inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### After index is set, we can access with `loc`\n",
    "\n",
    "Using `Major_code` variable values as labels for rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                                       5\n",
       "Major                   CHEMICAL ENGINEERING\n",
       "Total                                  32260\n",
       "Men                                    21239\n",
       "Women                                  11021\n",
       "Major_category                   Engineering\n",
       "ShareWomen                          0.341631\n",
       "Sample_size                              289\n",
       "Employed                               25694\n",
       "Full_time                              23170\n",
       "Part_time                               5180\n",
       "Full_time_year_round                   16697\n",
       "Unemployed                              1672\n",
       "Unemployment_rate                  0.0610977\n",
       "Median                                 65000\n",
       "P25th                                  50000\n",
       "P75th                                  75000\n",
       "College_jobs                           18314\n",
       "Non_college_jobs                        4440\n",
       "Low_wage_jobs                            972\n",
       "Employment rate                     0.796466\n",
       "Name: 2405, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[2405]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <td>COMMUNICATION DISORDERS SCIENCES AND SERVICES</td>\n",
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       "      <td>...</td>\n",
       "      <td>14460</td>\n",
       "      <td>1487</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>28000</td>\n",
       "      <td>20000</td>\n",
       "      <td>40000</td>\n",
       "      <td>19957</td>\n",
       "      <td>9404</td>\n",
       "      <td>5125</td>\n",
       "      <td>0.777528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001</th>\n",
       "      <td>8</td>\n",
       "      <td>ASTRONOMY AND ASTROPHYSICS</td>\n",
       "      <td>1792.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>960.0</td>\n",
       "      <td>Physical Sciences</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>10</td>\n",
       "      <td>1526</td>\n",
       "      <td>1085</td>\n",
       "      <td>...</td>\n",
       "      <td>827</td>\n",
       "      <td>33</td>\n",
       "      <td>0.021167</td>\n",
       "      <td>62000</td>\n",
       "      <td>31500</td>\n",
       "      <td>109000</td>\n",
       "      <td>972</td>\n",
       "      <td>500</td>\n",
       "      <td>220</td>\n",
       "      <td>0.851562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank                                          Major    Total  \\\n",
       "Major_code                                                                 \n",
       "6102         164  COMMUNICATION DISORDERS SCIENCES AND SERVICES  38279.0   \n",
       "5001           8                     ASTRONOMY AND ASTROPHYSICS   1792.0   \n",
       "\n",
       "               Men    Women     Major_category  ShareWomen  Sample_size  \\\n",
       "Major_code                                                                \n",
       "6102        1225.0  37054.0             Health    0.967998           95   \n",
       "5001         832.0    960.0  Physical Sciences    0.535714           10   \n",
       "\n",
       "            Employed  Full_time  ...  Full_time_year_round  Unemployed  \\\n",
       "Major_code                       ...                                     \n",
       "6102           29763      19975  ...                 14460        1487   \n",
       "5001            1526       1085  ...                   827          33   \n",
       "\n",
       "            Unemployment_rate  Median  P25th   P75th  College_jobs  \\\n",
       "Major_code                                                           \n",
       "6102                 0.047584   28000  20000   40000         19957   \n",
       "5001                 0.021167   62000  31500  109000           972   \n",
       "\n",
       "            Non_college_jobs  Low_wage_jobs  Employment rate  \n",
       "Major_code                                                    \n",
       "6102                    9404           5125         0.777528  \n",
       "5001                     500            220         0.851562  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "code_list = [6102, 5001]\n",
    "\n",
    "data.loc[code_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Sorting index\n",
    "\n",
    "Recommended for efficient selecting and filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major</th>\n",
       "      <th>Total</th>\n",
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       "      <th>Unemployment_rate</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major_code</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>65</td>\n",
       "      <td>GENERAL AGRICULTURE</td>\n",
       "      <td>10399.0</td>\n",
       "      <td>6053.0</td>\n",
       "      <td>4346.0</td>\n",
       "      <td>Agriculture &amp; Natural Resources</td>\n",
       "      <td>0.417925</td>\n",
       "      <td>158</td>\n",
       "      <td>8884</td>\n",
       "      <td>7589</td>\n",
       "      <td>...</td>\n",
       "      <td>5888</td>\n",
       "      <td>178</td>\n",
       "      <td>0.019642</td>\n",
       "      <td>40000</td>\n",
       "      <td>30000</td>\n",
       "      <td>50000</td>\n",
       "      <td>2418</td>\n",
       "      <td>4717</td>\n",
       "      <td>839</td>\n",
       "      <td>0.854313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>64</td>\n",
       "      <td>AGRICULTURE PRODUCTION AND MANAGEMENT</td>\n",
       "      <td>14240.0</td>\n",
       "      <td>9658.0</td>\n",
       "      <td>4582.0</td>\n",
       "      <td>Agriculture &amp; Natural Resources</td>\n",
       "      <td>0.321770</td>\n",
       "      <td>273</td>\n",
       "      <td>12323</td>\n",
       "      <td>11119</td>\n",
       "      <td>...</td>\n",
       "      <td>9093</td>\n",
       "      <td>649</td>\n",
       "      <td>0.050031</td>\n",
       "      <td>40000</td>\n",
       "      <td>25000</td>\n",
       "      <td>50000</td>\n",
       "      <td>1925</td>\n",
       "      <td>6221</td>\n",
       "      <td>1362</td>\n",
       "      <td>0.865379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>72</td>\n",
       "      <td>AGRICULTURAL ECONOMICS</td>\n",
       "      <td>2439.0</td>\n",
       "      <td>1749.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>Agriculture &amp; Natural Resources</td>\n",
       "      <td>0.282903</td>\n",
       "      <td>44</td>\n",
       "      <td>2174</td>\n",
       "      <td>1819</td>\n",
       "      <td>...</td>\n",
       "      <td>1528</td>\n",
       "      <td>182</td>\n",
       "      <td>0.077250</td>\n",
       "      <td>40000</td>\n",
       "      <td>27000</td>\n",
       "      <td>54000</td>\n",
       "      <td>535</td>\n",
       "      <td>893</td>\n",
       "      <td>94</td>\n",
       "      <td>0.891349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>153</td>\n",
       "      <td>ANIMAL SCIENCES</td>\n",
       "      <td>21573.0</td>\n",
       "      <td>5347.0</td>\n",
       "      <td>16226.0</td>\n",
       "      <td>Agriculture &amp; Natural Resources</td>\n",
       "      <td>0.752144</td>\n",
       "      <td>255</td>\n",
       "      <td>17112</td>\n",
       "      <td>14479</td>\n",
       "      <td>...</td>\n",
       "      <td>10824</td>\n",
       "      <td>917</td>\n",
       "      <td>0.050862</td>\n",
       "      <td>30000</td>\n",
       "      <td>22000</td>\n",
       "      <td>40000</td>\n",
       "      <td>5443</td>\n",
       "      <td>9571</td>\n",
       "      <td>2125</td>\n",
       "      <td>0.793214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>22</td>\n",
       "      <td>FOOD SCIENCE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Agriculture &amp; Natural Resources</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>3149</td>\n",
       "      <td>2558</td>\n",
       "      <td>...</td>\n",
       "      <td>1735</td>\n",
       "      <td>338</td>\n",
       "      <td>0.096931</td>\n",
       "      <td>53000</td>\n",
       "      <td>32000</td>\n",
       "      <td>70000</td>\n",
       "      <td>1183</td>\n",
       "      <td>1274</td>\n",
       "      <td>485</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank                                  Major    Total     Men  \\\n",
       "Major_code                                                                 \n",
       "1100          65                    GENERAL AGRICULTURE  10399.0  6053.0   \n",
       "1101          64  AGRICULTURE PRODUCTION AND MANAGEMENT  14240.0  9658.0   \n",
       "1102          72                 AGRICULTURAL ECONOMICS   2439.0  1749.0   \n",
       "1103         153                        ANIMAL SCIENCES  21573.0  5347.0   \n",
       "1104          22                           FOOD SCIENCE      NaN     NaN   \n",
       "\n",
       "              Women                   Major_category  ShareWomen  Sample_size  \\\n",
       "Major_code                                                                      \n",
       "1100         4346.0  Agriculture & Natural Resources    0.417925          158   \n",
       "1101         4582.0  Agriculture & Natural Resources    0.321770          273   \n",
       "1102          690.0  Agriculture & Natural Resources    0.282903           44   \n",
       "1103        16226.0  Agriculture & Natural Resources    0.752144          255   \n",
       "1104            NaN  Agriculture & Natural Resources         NaN           36   \n",
       "\n",
       "            Employed  Full_time  ...  Full_time_year_round  Unemployed  \\\n",
       "Major_code                       ...                                     \n",
       "1100            8884       7589  ...                  5888         178   \n",
       "1101           12323      11119  ...                  9093         649   \n",
       "1102            2174       1819  ...                  1528         182   \n",
       "1103           17112      14479  ...                 10824         917   \n",
       "1104            3149       2558  ...                  1735         338   \n",
       "\n",
       "            Unemployment_rate  Median  P25th  P75th  College_jobs  \\\n",
       "Major_code                                                          \n",
       "1100                 0.019642   40000  30000  50000          2418   \n",
       "1101                 0.050031   40000  25000  50000          1925   \n",
       "1102                 0.077250   40000  27000  54000           535   \n",
       "1103                 0.050862   30000  22000  40000          5443   \n",
       "1104                 0.096931   53000  32000  70000          1183   \n",
       "\n",
       "            Non_college_jobs  Low_wage_jobs  Employment rate  \n",
       "Major_code                                                    \n",
       "1100                    4717            839         0.854313  \n",
       "1101                    6221           1362         0.865379  \n",
       "1102                     893             94         0.891349  \n",
       "1103                    9571           2125         0.793214  \n",
       "1104                    1274            485              NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_index(inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Selecting rows\n",
    "\n",
    "Alternatively, we can filter our dataframe (select rows) using *boolean conditions*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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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       "      <th>Rank</th>\n",
       "      <th>Major</th>\n",
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       "      <th>Men</th>\n",
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       "      <th>Major_category</th>\n",
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       "      <th>Full_time</th>\n",
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       "      <th>Major_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>150</td>\n",
       "      <td>FINE ARTS</td>\n",
       "      <td>74440.0</td>\n",
       "      <td>24786.0</td>\n",
       "      <td>49654.0</td>\n",
       "      <td>Arts</td>\n",
       "      <td>0.667034</td>\n",
       "      <td>623</td>\n",
       "      <td>59679</td>\n",
       "      <td>42764</td>\n",
       "      <td>...</td>\n",
       "      <td>31877</td>\n",
       "      <td>5486</td>\n",
       "      <td>0.084186</td>\n",
       "      <td>30500</td>\n",
       "      <td>21000</td>\n",
       "      <td>41000</td>\n",
       "      <td>20792</td>\n",
       "      <td>32725</td>\n",
       "      <td>11880</td>\n",
       "      <td>0.801706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6001</th>\n",
       "      <td>167</td>\n",
       "      <td>DRAMA AND THEATER ARTS</td>\n",
       "      <td>43249.0</td>\n",
       "      <td>14440.0</td>\n",
       "      <td>28809.0</td>\n",
       "      <td>Arts</td>\n",
       "      <td>0.666119</td>\n",
       "      <td>357</td>\n",
       "      <td>36165</td>\n",
       "      <td>25147</td>\n",
       "      <td>...</td>\n",
       "      <td>16891</td>\n",
       "      <td>3040</td>\n",
       "      <td>0.077541</td>\n",
       "      <td>27000</td>\n",
       "      <td>19200</td>\n",
       "      <td>35000</td>\n",
       "      <td>6994</td>\n",
       "      <td>25313</td>\n",
       "      <td>11068</td>\n",
       "      <td>0.836204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6002</th>\n",
       "      <td>147</td>\n",
       "      <td>MUSIC</td>\n",
       "      <td>60633.0</td>\n",
       "      <td>29909.0</td>\n",
       "      <td>30724.0</td>\n",
       "      <td>Arts</td>\n",
       "      <td>0.506721</td>\n",
       "      <td>419</td>\n",
       "      <td>47662</td>\n",
       "      <td>29010</td>\n",
       "      <td>...</td>\n",
       "      <td>21425</td>\n",
       "      <td>3918</td>\n",
       "      <td>0.075960</td>\n",
       "      <td>31000</td>\n",
       "      <td>22300</td>\n",
       "      <td>42000</td>\n",
       "      <td>13752</td>\n",
       "      <td>28786</td>\n",
       "      <td>9286</td>\n",
       "      <td>0.786074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6003</th>\n",
       "      <td>154</td>\n",
       "      <td>VISUAL AND PERFORMING ARTS</td>\n",
       "      <td>16250.0</td>\n",
       "      <td>4133.0</td>\n",
       "      <td>12117.0</td>\n",
       "      <td>Arts</td>\n",
       "      <td>0.745662</td>\n",
       "      <td>132</td>\n",
       "      <td>12870</td>\n",
       "      <td>8447</td>\n",
       "      <td>...</td>\n",
       "      <td>6322</td>\n",
       "      <td>1465</td>\n",
       "      <td>0.102197</td>\n",
       "      <td>30000</td>\n",
       "      <td>22000</td>\n",
       "      <td>40000</td>\n",
       "      <td>3849</td>\n",
       "      <td>7635</td>\n",
       "      <td>2840</td>\n",
       "      <td>0.792000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6004</th>\n",
       "      <td>96</td>\n",
       "      <td>COMMERCIAL ART AND GRAPHIC DESIGN</td>\n",
       "      <td>103480.0</td>\n",
       "      <td>32041.0</td>\n",
       "      <td>71439.0</td>\n",
       "      <td>Arts</td>\n",
       "      <td>0.690365</td>\n",
       "      <td>1186</td>\n",
       "      <td>83483</td>\n",
       "      <td>67448</td>\n",
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       "      <td>52243</td>\n",
       "      <td>8947</td>\n",
       "      <td>0.096798</td>\n",
       "      <td>35000</td>\n",
       "      <td>25000</td>\n",
       "      <td>45000</td>\n",
       "      <td>37389</td>\n",
       "      <td>38119</td>\n",
       "      <td>14839</td>\n",
       "      <td>0.806755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank                              Major     Total      Men  \\\n",
       "Major_code                                                               \n",
       "6000         150                          FINE ARTS   74440.0  24786.0   \n",
       "6001         167             DRAMA AND THEATER ARTS   43249.0  14440.0   \n",
       "6002         147                              MUSIC   60633.0  29909.0   \n",
       "6003         154         VISUAL AND PERFORMING ARTS   16250.0   4133.0   \n",
       "6004          96  COMMERCIAL ART AND GRAPHIC DESIGN  103480.0  32041.0   \n",
       "\n",
       "              Women Major_category  ShareWomen  Sample_size  Employed  \\\n",
       "Major_code                                                              \n",
       "6000        49654.0           Arts    0.667034          623     59679   \n",
       "6001        28809.0           Arts    0.666119          357     36165   \n",
       "6002        30724.0           Arts    0.506721          419     47662   \n",
       "6003        12117.0           Arts    0.745662          132     12870   \n",
       "6004        71439.0           Arts    0.690365         1186     83483   \n",
       "\n",
       "            Full_time  ...  Full_time_year_round  Unemployed  \\\n",
       "Major_code             ...                                     \n",
       "6000            42764  ...                 31877        5486   \n",
       "6001            25147  ...                 16891        3040   \n",
       "6002            29010  ...                 21425        3918   \n",
       "6003             8447  ...                  6322        1465   \n",
       "6004            67448  ...                 52243        8947   \n",
       "\n",
       "            Unemployment_rate  Median  P25th  P75th  College_jobs  \\\n",
       "Major_code                                                          \n",
       "6000                 0.084186   30500  21000  41000         20792   \n",
       "6001                 0.077541   27000  19200  35000          6994   \n",
       "6002                 0.075960   31000  22300  42000         13752   \n",
       "6003                 0.102197   30000  22000  40000          3849   \n",
       "6004                 0.096798   35000  25000  45000         37389   \n",
       "\n",
       "            Non_college_jobs  Low_wage_jobs  Employment rate  \n",
       "Major_code                                                    \n",
       "6000                   32725          11880         0.801706  \n",
       "6001                   25313          11068         0.836204  \n",
       "6002                   28786           9286         0.786074  \n",
       "6003                    7635           2840         0.792000  \n",
       "6004                   38119          14839         0.806755  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection = data['Major_category'] == 'Arts'\n",
    "data[selection].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "            Rank                              Major     Total      Men  \\\n",
       "Major_code                                                               \n",
       "6004          96  COMMERCIAL ART AND GRAPHIC DESIGN  103480.0  32041.0   \n",
       "\n",
       "              Women Major_category  ShareWomen  Sample_size  Employed  \\\n",
       "Major_code                                                              \n",
       "6004        71439.0           Arts    0.690365         1186     83483   \n",
       "\n",
       "            Full_time  ...  Full_time_year_round  Unemployed  \\\n",
       "Major_code             ...                                     \n",
       "6004            67448  ...                 52243        8947   \n",
       "\n",
       "            Unemployment_rate  Median  P25th  P75th  College_jobs  \\\n",
       "Major_code                                                          \n",
       "6004                 0.096798   35000  25000  45000         37389   \n",
       "\n",
       "            Non_college_jobs  Low_wage_jobs  Employment rate  \n",
       "Major_code                                                    \n",
       "6004                   38119          14839         0.806755  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection = data['Major_category'] == 'Arts'\n",
    "data[selection & (data['Total'] > 100000)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Grouping and aggregating data\n",
    "\n",
    "We might want to summarize our data by grouping it by major categories\n",
    "\n",
    "To do this, we will use the `.groupby()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f9ac0802990>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = data.groupby('Major_category')\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n",
      "<class 'dict'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Agriculture & Natural Resources': Int64Index([1100, 1101, 1102, 1103, 1104, 1105, 1106, 1199, 1302, 1303], dtype='int64', name='Major_code'),\n",
       " 'Arts': Int64Index([6000, 6001, 6002, 6003, 6004, 6005, 6007, 6099], dtype='int64', name='Major_code'),\n",
       " 'Biology & Life Science': Int64Index([1301, 3600, 3601, 3602, 3603, 3604, 3605, 3606, 3607, 3608, 3609,\n",
       "             3611, 3699, 4006],\n",
       "            dtype='int64', name='Major_code'),\n",
       " 'Business': Int64Index([6200, 6201, 6202, 6203, 6204, 6205, 6206, 6207, 6209, 6210, 6211,\n",
       "             6212, 6299],\n",
       "            dtype='int64', name='Major_code'),\n",
       " 'Communications & Journalism': Int64Index([1901, 1902, 1903, 1904], dtype='int64', name='Major_code'),\n",
       " 'Computers & Mathematics': Int64Index([2001, 2100, 2101, 2102, 2105, 2106, 2107, 3700, 3701, 3702, 4005], dtype='int64', name='Major_code'),\n",
       " 'Education': Int64Index([2300, 2301, 2303, 2304, 2305, 2306, 2307, 2308, 2309, 2310, 2311,\n",
       "             2312, 2313, 2314, 2399, 3501],\n",
       "            dtype='int64', name='Major_code'),\n",
       " 'Engineering': Int64Index([1401, 2400, 2401, 2402, 2403, 2404, 2405, 2406, 2407, 2408, 2409,\n",
       "             2410, 2411, 2412, 2413, 2414, 2415, 2416, 2417, 2418, 2419, 2499,\n",
       "             2500, 2501, 2502, 2503, 2504, 2599, 5008],\n",
       "            dtype='int64', name='Major_code'),\n",
       " 'Health': Int64Index([4002, 6100, 6102, 6103, 6104, 6105, 6106, 6107, 6108, 6109, 6110,\n",
       "             6199],\n",
       "            dtype='int64', name='Major_code'),\n",
       " 'Humanities & Liberal Arts': Int64Index([1501, 2601, 2602, 2603, 3301, 3302, 3401, 3402, 4001, 4801, 4901,\n",
       "             5502, 6006, 6402, 6403],\n",
       "            dtype='int64', name='Major_code'),\n",
       " 'Industrial Arts & Consumer Services': Int64Index([2201, 2901, 3801, 4101, 5601, 5701, 5901], dtype='int64', name='Major_code'),\n",
       " 'Interdisciplinary': Int64Index([4000], dtype='int64', name='Major_code'),\n",
       " 'Law & Public Policy': Int64Index([3201, 3202, 5301, 5401, 5402], dtype='int64', name='Major_code'),\n",
       " 'Physical Sciences': Int64Index([5000, 5001, 5002, 5003, 5004, 5005, 5006, 5007, 5098, 5102], dtype='int64', name='Major_code'),\n",
       " 'Psychology & Social Work': Int64Index([5200, 5201, 5202, 5203, 5205, 5206, 5299, 5403, 5404], dtype='int64', name='Major_code'),\n",
       " 'Social Science': Int64Index([4007, 5500, 5501, 5503, 5504, 5505, 5506, 5507, 5599], dtype='int64', name='Major_code')}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(grouped))\n",
    "print(type(grouped.groups))\n",
    "grouped.groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Reduce functions for the grouped data\n",
    "\n",
    "To return an *aggregated* dataframe, we need to specify the function we\n",
    "would like pandas to use to aggregate our groups\n",
    "\n",
    "- Mean  \n",
    "- Sum  \n",
    "- Count  \n",
    "\n",
    "\n",
    "A list of built-in aggregatation functions [https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>Agriculture &amp; Natural Resources</th>\n",
       "      <td>101.500000</td>\n",
       "      <td>8402.222222</td>\n",
       "      <td>4484.111111</td>\n",
       "      <td>3918.111111</td>\n",
       "      <td>0.405267</td>\n",
       "      <td>110.400000</td>\n",
       "      <td>6694.300000</td>\n",
       "      <td>5814.300000</td>\n",
       "      <td>1659.100000</td>\n",
       "      <td>4362.600000</td>\n",
       "      <td>382.400000</td>\n",
       "      <td>0.056328</td>\n",
       "      <td>36900.000000</td>\n",
       "      <td>25400.000000</td>\n",
       "      <td>48010.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>3449.100000</td>\n",
       "      <td>789.900000</td>\n",
       "      <td>0.860603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arts</th>\n",
       "      <td>131.125000</td>\n",
       "      <td>44641.250000</td>\n",
       "      <td>16798.750000</td>\n",
       "      <td>27842.500000</td>\n",
       "      <td>0.603658</td>\n",
       "      <td>407.500000</td>\n",
       "      <td>36014.250000</td>\n",
       "      <td>25971.625000</td>\n",
       "      <td>14348.875000</td>\n",
       "      <td>19138.875000</td>\n",
       "      <td>3528.500000</td>\n",
       "      <td>0.090173</td>\n",
       "      <td>33062.500000</td>\n",
       "      <td>21962.500000</td>\n",
       "      <td>43662.500000</td>\n",
       "      <td>11848.125000</td>\n",
       "      <td>20465.000000</td>\n",
       "      <td>7514.500000</td>\n",
       "      <td>0.815670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biology &amp; Life Science</th>\n",
       "      <td>95.357143</td>\n",
       "      <td>32418.714286</td>\n",
       "      <td>13208.500000</td>\n",
       "      <td>19210.214286</td>\n",
       "      <td>0.587193</td>\n",
       "      <td>165.500000</td>\n",
       "      <td>21628.357143</td>\n",
       "      <td>17169.785714</td>\n",
       "      <td>8338.285714</td>\n",
       "      <td>11843.000000</td>\n",
       "      <td>1632.428571</td>\n",
       "      <td>0.060918</td>\n",
       "      <td>36421.428571</td>\n",
       "      <td>26614.285714</td>\n",
       "      <td>46085.714286</td>\n",
       "      <td>10802.357143</td>\n",
       "      <td>9084.428571</td>\n",
       "      <td>3053.000000</td>\n",
       "      <td>0.700543</td>\n",
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       "      <th>Business</th>\n",
       "      <td>55.846154</td>\n",
       "      <td>100182.769231</td>\n",
       "      <td>51373.230769</td>\n",
       "      <td>48809.538462</td>\n",
       "      <td>0.483198</td>\n",
       "      <td>1192.692308</td>\n",
       "      <td>83749.384615</td>\n",
       "      <td>76066.923077</td>\n",
       "      <td>15148.923077</td>\n",
       "      <td>60801.923077</td>\n",
       "      <td>6144.384615</td>\n",
       "      <td>0.071064</td>\n",
       "      <td>43538.461538</td>\n",
       "      <td>33461.538462</td>\n",
       "      <td>54846.153846</td>\n",
       "      <td>11426.000000</td>\n",
       "      <td>38197.692308</td>\n",
       "      <td>9752.923077</td>\n",
       "      <td>0.829638</td>\n",
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       "      <th>Communications &amp; Journalism</th>\n",
       "      <td>104.000000</td>\n",
       "      <td>98150.250000</td>\n",
       "      <td>32980.250000</td>\n",
       "      <td>65170.000000</td>\n",
       "      <td>0.658384</td>\n",
       "      <td>1127.000000</td>\n",
       "      <td>82665.000000</td>\n",
       "      <td>68332.500000</td>\n",
       "      <td>22454.250000</td>\n",
       "      <td>53557.000000</td>\n",
       "      <td>6713.000000</td>\n",
       "      <td>0.075538</td>\n",
       "      <td>34500.000000</td>\n",
       "      <td>26250.000000</td>\n",
       "      <td>44975.000000</td>\n",
       "      <td>21639.000000</td>\n",
       "      <td>43248.000000</td>\n",
       "      <td>12398.750000</td>\n",
       "      <td>0.844534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computers &amp; Mathematics</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>27182.545455</td>\n",
       "      <td>18975.000000</td>\n",
       "      <td>8207.545455</td>\n",
       "      <td>0.311772</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>21626.727273</td>\n",
       "      <td>18867.727273</td>\n",
       "      <td>4842.727273</td>\n",
       "      <td>14468.727273</td>\n",
       "      <td>1670.272727</td>\n",
       "      <td>0.084256</td>\n",
       "      <td>42745.454545</td>\n",
       "      <td>29290.909091</td>\n",
       "      <td>58090.909091</td>\n",
       "      <td>12532.636364</td>\n",
       "      <td>6769.363636</td>\n",
       "      <td>1466.909091</td>\n",
       "      <td>0.799617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>130.375000</td>\n",
       "      <td>34945.562500</td>\n",
       "      <td>6470.375000</td>\n",
       "      <td>28475.187500</td>\n",
       "      <td>0.748507</td>\n",
       "      <td>296.375000</td>\n",
       "      <td>29989.937500</td>\n",
       "      <td>24878.687500</td>\n",
       "      <td>7537.062500</td>\n",
       "      <td>18001.937500</td>\n",
       "      <td>1560.562500</td>\n",
       "      <td>0.051702</td>\n",
       "      <td>32350.000000</td>\n",
       "      <td>26590.625000</td>\n",
       "      <td>38562.500000</td>\n",
       "      <td>21169.562500</td>\n",
       "      <td>7610.062500</td>\n",
       "      <td>2554.375000</td>\n",
       "      <td>0.856697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering</th>\n",
       "      <td>22.620690</td>\n",
       "      <td>18537.344828</td>\n",
       "      <td>14079.551724</td>\n",
       "      <td>4457.793103</td>\n",
       "      <td>0.238889</td>\n",
       "      <td>169.862069</td>\n",
       "      <td>14495.586207</td>\n",
       "      <td>13167.827586</td>\n",
       "      <td>2935.724138</td>\n",
       "      <td>9963.862069</td>\n",
       "      <td>1028.172414</td>\n",
       "      <td>0.063334</td>\n",
       "      <td>57382.758621</td>\n",
       "      <td>41555.172414</td>\n",
       "      <td>70448.275862</td>\n",
       "      <td>9302.310345</td>\n",
       "      <td>3530.448276</td>\n",
       "      <td>864.793103</td>\n",
       "      <td>0.787311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health</th>\n",
       "      <td>96.500000</td>\n",
       "      <td>38602.500000</td>\n",
       "      <td>6293.083333</td>\n",
       "      <td>32309.416667</td>\n",
       "      <td>0.795152</td>\n",
       "      <td>326.166667</td>\n",
       "      <td>31012.250000</td>\n",
       "      <td>24568.250000</td>\n",
       "      <td>9549.333333</td>\n",
       "      <td>19034.833333</td>\n",
       "      <td>1851.083333</td>\n",
       "      <td>0.065920</td>\n",
       "      <td>36825.000000</td>\n",
       "      <td>26166.666667</td>\n",
       "      <td>50250.000000</td>\n",
       "      <td>20453.416667</td>\n",
       "      <td>9208.000000</td>\n",
       "      <td>2605.833333</td>\n",
       "      <td>0.758078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humanities &amp; Liberal Arts</th>\n",
       "      <td>135.066667</td>\n",
       "      <td>47564.533333</td>\n",
       "      <td>18189.733333</td>\n",
       "      <td>29374.800000</td>\n",
       "      <td>0.631790</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>36274.533333</td>\n",
       "      <td>27795.933333</td>\n",
       "      <td>14268.666667</td>\n",
       "      <td>19704.066667</td>\n",
       "      <td>3406.733333</td>\n",
       "      <td>0.081008</td>\n",
       "      <td>31913.333333</td>\n",
       "      <td>23493.333333</td>\n",
       "      <td>42073.333333</td>\n",
       "      <td>12843.333333</td>\n",
       "      <td>18435.466667</td>\n",
       "      <td>6282.666667</td>\n",
       "      <td>0.767983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrial Arts &amp; Consumer Services</th>\n",
       "      <td>105.142857</td>\n",
       "      <td>32827.428571</td>\n",
       "      <td>14825.857143</td>\n",
       "      <td>18001.571429</td>\n",
       "      <td>0.349523</td>\n",
       "      <td>309.285714</td>\n",
       "      <td>27006.142857</td>\n",
       "      <td>21626.142857</td>\n",
       "      <td>8731.714286</td>\n",
       "      <td>16311.285714</td>\n",
       "      <td>1646.571429</td>\n",
       "      <td>0.048071</td>\n",
       "      <td>36342.857143</td>\n",
       "      <td>26771.428571</td>\n",
       "      <td>45142.857143</td>\n",
       "      <td>8171.428571</td>\n",
       "      <td>14945.714286</td>\n",
       "      <td>3798.571429</td>\n",
       "      <td>0.715442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interdisciplinary</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>12296.000000</td>\n",
       "      <td>2817.000000</td>\n",
       "      <td>9479.000000</td>\n",
       "      <td>0.770901</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>9821.000000</td>\n",
       "      <td>8032.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>6234.000000</td>\n",
       "      <td>749.000000</td>\n",
       "      <td>0.070861</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>44000.000000</td>\n",
       "      <td>5176.000000</td>\n",
       "      <td>3903.000000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>0.798715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law &amp; Public Policy</th>\n",
       "      <td>64.600000</td>\n",
       "      <td>35821.400000</td>\n",
       "      <td>18225.800000</td>\n",
       "      <td>17595.600000</td>\n",
       "      <td>0.483649</td>\n",
       "      <td>387.000000</td>\n",
       "      <td>28958.000000</td>\n",
       "      <td>25388.000000</td>\n",
       "      <td>7642.600000</td>\n",
       "      <td>20090.800000</td>\n",
       "      <td>2699.000000</td>\n",
       "      <td>0.090805</td>\n",
       "      <td>42200.000000</td>\n",
       "      <td>32640.000000</td>\n",
       "      <td>55000.000000</td>\n",
       "      <td>5844.200000</td>\n",
       "      <td>20004.800000</td>\n",
       "      <td>4144.000000</td>\n",
       "      <td>0.770304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physical Sciences</th>\n",
       "      <td>67.600000</td>\n",
       "      <td>18547.900000</td>\n",
       "      <td>9539.000000</td>\n",
       "      <td>9008.900000</td>\n",
       "      <td>0.508683</td>\n",
       "      <td>113.700000</td>\n",
       "      <td>13923.100000</td>\n",
       "      <td>11285.200000</td>\n",
       "      <td>4344.400000</td>\n",
       "      <td>8563.500000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>0.046511</td>\n",
       "      <td>41890.000000</td>\n",
       "      <td>28350.000000</td>\n",
       "      <td>57290.000000</td>\n",
       "      <td>7655.200000</td>\n",
       "      <td>4946.900000</td>\n",
       "      <td>1407.800000</td>\n",
       "      <td>0.776080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychology &amp; Social Work</th>\n",
       "      <td>143.000000</td>\n",
       "      <td>53445.222222</td>\n",
       "      <td>10901.666667</td>\n",
       "      <td>42543.555556</td>\n",
       "      <td>0.794397</td>\n",
       "      <td>353.333333</td>\n",
       "      <td>42260.444444</td>\n",
       "      <td>32111.111111</td>\n",
       "      <td>15332.444444</td>\n",
       "      <td>24233.888889</td>\n",
       "      <td>3699.111111</td>\n",
       "      <td>0.072065</td>\n",
       "      <td>30100.000000</td>\n",
       "      <td>25333.333333</td>\n",
       "      <td>38777.777778</td>\n",
       "      <td>18256.111111</td>\n",
       "      <td>18818.444444</td>\n",
       "      <td>6249.555556</td>\n",
       "      <td>0.795633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Science</th>\n",
       "      <td>91.666667</td>\n",
       "      <td>58885.111111</td>\n",
       "      <td>28537.111111</td>\n",
       "      <td>30348.000000</td>\n",
       "      <td>0.553962</td>\n",
       "      <td>509.000000</td>\n",
       "      <td>44610.333333</td>\n",
       "      <td>38571.222222</td>\n",
       "      <td>13507.666667</td>\n",
       "      <td>28357.666667</td>\n",
       "      <td>4775.000000</td>\n",
       "      <td>0.095729</td>\n",
       "      <td>37344.444444</td>\n",
       "      <td>28355.555556</td>\n",
       "      <td>50111.111111</td>\n",
       "      <td>12662.222222</td>\n",
       "      <td>21138.444444</td>\n",
       "      <td>6020.000000</td>\n",
       "      <td>0.770296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Rank          Total           Men  \\\n",
       "Major_category                                                                 \n",
       "Agriculture & Natural Resources      101.500000    8402.222222   4484.111111   \n",
       "Arts                                 131.125000   44641.250000  16798.750000   \n",
       "Biology & Life Science                95.357143   32418.714286  13208.500000   \n",
       "Business                              55.846154  100182.769231  51373.230769   \n",
       "Communications & Journalism          104.000000   98150.250000  32980.250000   \n",
       "Computers & Mathematics               57.000000   27182.545455  18975.000000   \n",
       "Education                            130.375000   34945.562500   6470.375000   \n",
       "Engineering                           22.620690   18537.344828  14079.551724   \n",
       "Health                                96.500000   38602.500000   6293.083333   \n",
       "Humanities & Liberal Arts            135.066667   47564.533333  18189.733333   \n",
       "Industrial Arts & Consumer Services  105.142857   32827.428571  14825.857143   \n",
       "Interdisciplinary                    110.000000   12296.000000   2817.000000   \n",
       "Law & Public Policy                   64.600000   35821.400000  18225.800000   \n",
       "Physical Sciences                     67.600000   18547.900000   9539.000000   \n",
       "Psychology & Social Work             143.000000   53445.222222  10901.666667   \n",
       "Social Science                        91.666667   58885.111111  28537.111111   \n",
       "\n",
       "                                            Women  ShareWomen  Sample_size  \\\n",
       "Major_category                                                               \n",
       "Agriculture & Natural Resources       3918.111111    0.405267   110.400000   \n",
       "Arts                                 27842.500000    0.603658   407.500000   \n",
       "Biology & Life Science               19210.214286    0.587193   165.500000   \n",
       "Business                             48809.538462    0.483198  1192.692308   \n",
       "Communications & Journalism          65170.000000    0.658384  1127.000000   \n",
       "Computers & Mathematics               8207.545455    0.311772   260.000000   \n",
       "Education                            28475.187500    0.748507   296.375000   \n",
       "Engineering                           4457.793103    0.238889   169.862069   \n",
       "Health                               32309.416667    0.795152   326.166667   \n",
       "Humanities & Liberal Arts            29374.800000    0.631790   356.000000   \n",
       "Industrial Arts & Consumer Services  18001.571429    0.349523   309.285714   \n",
       "Interdisciplinary                     9479.000000    0.770901   128.000000   \n",
       "Law & Public Policy                  17595.600000    0.483649   387.000000   \n",
       "Physical Sciences                     9008.900000    0.508683   113.700000   \n",
       "Psychology & Social Work             42543.555556    0.794397   353.333333   \n",
       "Social Science                       30348.000000    0.553962   509.000000   \n",
       "\n",
       "                                         Employed     Full_time     Part_time  \\\n",
       "Major_category                                                                  \n",
       "Agriculture & Natural Resources       6694.300000   5814.300000   1659.100000   \n",
       "Arts                                 36014.250000  25971.625000  14348.875000   \n",
       "Biology & Life Science               21628.357143  17169.785714   8338.285714   \n",
       "Business                             83749.384615  76066.923077  15148.923077   \n",
       "Communications & Journalism          82665.000000  68332.500000  22454.250000   \n",
       "Computers & Mathematics              21626.727273  18867.727273   4842.727273   \n",
       "Education                            29989.937500  24878.687500   7537.062500   \n",
       "Engineering                          14495.586207  13167.827586   2935.724138   \n",
       "Health                               31012.250000  24568.250000   9549.333333   \n",
       "Humanities & Liberal Arts            36274.533333  27795.933333  14268.666667   \n",
       "Industrial Arts & Consumer Services  27006.142857  21626.142857   8731.714286   \n",
       "Interdisciplinary                     9821.000000   8032.000000   3173.000000   \n",
       "Law & Public Policy                  28958.000000  25388.000000   7642.600000   \n",
       "Physical Sciences                    13923.100000  11285.200000   4344.400000   \n",
       "Psychology & Social Work             42260.444444  32111.111111  15332.444444   \n",
       "Social Science                       44610.333333  38571.222222  13507.666667   \n",
       "\n",
       "                                     Full_time_year_round   Unemployed  \\\n",
       "Major_category                                                           \n",
       "Agriculture & Natural Resources               4362.600000   382.400000   \n",
       "Arts                                         19138.875000  3528.500000   \n",
       "Biology & Life Science                       11843.000000  1632.428571   \n",
       "Business                                     60801.923077  6144.384615   \n",
       "Communications & Journalism                  53557.000000  6713.000000   \n",
       "Computers & Mathematics                      14468.727273  1670.272727   \n",
       "Education                                    18001.937500  1560.562500   \n",
       "Engineering                                   9963.862069  1028.172414   \n",
       "Health                                       19034.833333  1851.083333   \n",
       "Humanities & Liberal Arts                    19704.066667  3406.733333   \n",
       "Industrial Arts & Consumer Services          16311.285714  1646.571429   \n",
       "Interdisciplinary                             6234.000000   749.000000   \n",
       "Law & Public Policy                          20090.800000  2699.000000   \n",
       "Physical Sciences                             8563.500000   788.000000   \n",
       "Psychology & Social Work                     24233.888889  3699.111111   \n",
       "Social Science                               28357.666667  4775.000000   \n",
       "\n",
       "                                     Unemployment_rate        Median  \\\n",
       "Major_category                                                         \n",
       "Agriculture & Natural Resources               0.056328  36900.000000   \n",
       "Arts                                          0.090173  33062.500000   \n",
       "Biology & Life Science                        0.060918  36421.428571   \n",
       "Business                                      0.071064  43538.461538   \n",
       "Communications & Journalism                   0.075538  34500.000000   \n",
       "Computers & Mathematics                       0.084256  42745.454545   \n",
       "Education                                     0.051702  32350.000000   \n",
       "Engineering                                   0.063334  57382.758621   \n",
       "Health                                        0.065920  36825.000000   \n",
       "Humanities & Liberal Arts                     0.081008  31913.333333   \n",
       "Industrial Arts & Consumer Services           0.048071  36342.857143   \n",
       "Interdisciplinary                             0.070861  35000.000000   \n",
       "Law & Public Policy                           0.090805  42200.000000   \n",
       "Physical Sciences                             0.046511  41890.000000   \n",
       "Psychology & Social Work                      0.072065  30100.000000   \n",
       "Social Science                                0.095729  37344.444444   \n",
       "\n",
       "                                            P25th         P75th  College_jobs  \\\n",
       "Major_category                                                                  \n",
       "Agriculture & Natural Resources      25400.000000  48010.000000   1986.000000   \n",
       "Arts                                 21962.500000  43662.500000  11848.125000   \n",
       "Biology & Life Science               26614.285714  46085.714286  10802.357143   \n",
       "Business                             33461.538462  54846.153846  11426.000000   \n",
       "Communications & Journalism          26250.000000  44975.000000  21639.000000   \n",
       "Computers & Mathematics              29290.909091  58090.909091  12532.636364   \n",
       "Education                            26590.625000  38562.500000  21169.562500   \n",
       "Engineering                          41555.172414  70448.275862   9302.310345   \n",
       "Health                               26166.666667  50250.000000  20453.416667   \n",
       "Humanities & Liberal Arts            23493.333333  42073.333333  12843.333333   \n",
       "Industrial Arts & Consumer Services  26771.428571  45142.857143   8171.428571   \n",
       "Interdisciplinary                    25000.000000  44000.000000   5176.000000   \n",
       "Law & Public Policy                  32640.000000  55000.000000   5844.200000   \n",
       "Physical Sciences                    28350.000000  57290.000000   7655.200000   \n",
       "Psychology & Social Work             25333.333333  38777.777778  18256.111111   \n",
       "Social Science                       28355.555556  50111.111111  12662.222222   \n",
       "\n",
       "                                     Non_college_jobs  Low_wage_jobs  \\\n",
       "Major_category                                                         \n",
       "Agriculture & Natural Resources           3449.100000     789.900000   \n",
       "Arts                                     20465.000000    7514.500000   \n",
       "Biology & Life Science                    9084.428571    3053.000000   \n",
       "Business                                 38197.692308    9752.923077   \n",
       "Communications & Journalism              43248.000000   12398.750000   \n",
       "Computers & Mathematics                   6769.363636    1466.909091   \n",
       "Education                                 7610.062500    2554.375000   \n",
       "Engineering                               3530.448276     864.793103   \n",
       "Health                                    9208.000000    2605.833333   \n",
       "Humanities & Liberal Arts                18435.466667    6282.666667   \n",
       "Industrial Arts & Consumer Services      14945.714286    3798.571429   \n",
       "Interdisciplinary                         3903.000000    1061.000000   \n",
       "Law & Public Policy                      20004.800000    4144.000000   \n",
       "Physical Sciences                         4946.900000    1407.800000   \n",
       "Psychology & Social Work                 18818.444444    6249.555556   \n",
       "Social Science                           21138.444444    6020.000000   \n",
       "\n",
       "                                     Employment rate  \n",
       "Major_category                                        \n",
       "Agriculture & Natural Resources             0.860603  \n",
       "Arts                                        0.815670  \n",
       "Biology & Life Science                      0.700543  \n",
       "Business                                    0.829638  \n",
       "Communications & Journalism                 0.844534  \n",
       "Computers & Mathematics                     0.799617  \n",
       "Education                                   0.856697  \n",
       "Engineering                                 0.787311  \n",
       "Health                                      0.758078  \n",
       "Humanities & Liberal Arts                   0.767983  \n",
       "Industrial Arts & Consumer Services         0.715442  \n",
       "Interdisciplinary                           0.798715  \n",
       "Law & Public Policy                         0.770304  \n",
       "Physical Sciences                           0.776080  \n",
       "Psychology & Social Work                    0.795633  \n",
       "Social Science                              0.770296  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Major_category\n",
       "Agriculture & Natural Resources        36900.000000\n",
       "Arts                                   33062.500000\n",
       "Biology & Life Science                 36421.428571\n",
       "Business                               43538.461538\n",
       "Communications & Journalism            34500.000000\n",
       "Computers & Mathematics                42745.454545\n",
       "Education                              32350.000000\n",
       "Engineering                            57382.758621\n",
       "Health                                 36825.000000\n",
       "Humanities & Liberal Arts              31913.333333\n",
       "Industrial Arts & Consumer Services    36342.857143\n",
       "Interdisciplinary                      35000.000000\n",
       "Law & Public Policy                    42200.000000\n",
       "Physical Sciences                      41890.000000\n",
       "Psychology & Social Work               30100.000000\n",
       "Social Science                         37344.444444\n",
       "Name: Median, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped['Median'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>36300</td>\n",
       "      <td>4528.912006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business</th>\n",
       "      <td>43538.461538</td>\n",
       "      <td>40000</td>\n",
       "      <td>7774.052832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Communications &amp; Journalism</th>\n",
       "      <td>34500.000000</td>\n",
       "      <td>35000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computers &amp; Mathematics</th>\n",
       "      <td>42745.454545</td>\n",
       "      <td>45000</td>\n",
       "      <td>5108.691346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>32350.000000</td>\n",
       "      <td>32750</td>\n",
       "      <td>3892.728263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering</th>\n",
       "      <td>57382.758621</td>\n",
       "      <td>57000</td>\n",
       "      <td>13626.079747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health</th>\n",
       "      <td>36825.000000</td>\n",
       "      <td>35000</td>\n",
       "      <td>5776.460854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humanities &amp; Liberal Arts</th>\n",
       "      <td>31913.333333</td>\n",
       "      <td>32000</td>\n",
       "      <td>3393.032076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrial Arts &amp; Consumer Services</th>\n",
       "      <td>36342.857143</td>\n",
       "      <td>35000</td>\n",
       "      <td>7290.829204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interdisciplinary</th>\n",
       "      <td>35000.000000</td>\n",
       "      <td>35000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law &amp; Public Policy</th>\n",
       "      <td>42200.000000</td>\n",
       "      <td>36000</td>\n",
       "      <td>9066.421565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physical Sciences</th>\n",
       "      <td>41890.000000</td>\n",
       "      <td>39500</td>\n",
       "      <td>8251.659766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychology &amp; Social Work</th>\n",
       "      <td>30100.000000</td>\n",
       "      <td>30000</td>\n",
       "      <td>5381.914158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Science</th>\n",
       "      <td>37344.444444</td>\n",
       "      <td>38000</td>\n",
       "      <td>4750.555523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             mean  median           std\n",
       "Major_category                                                         \n",
       "Agriculture & Natural Resources      36900.000000   35000   6935.416354\n",
       "Arts                                 33062.500000   30750   7223.164621\n",
       "Biology & Life Science               36421.428571   36300   4528.912006\n",
       "Business                             43538.461538   40000   7774.052832\n",
       "Communications & Journalism          34500.000000   35000   1000.000000\n",
       "Computers & Mathematics              42745.454545   45000   5108.691346\n",
       "Education                            32350.000000   32750   3892.728263\n",
       "Engineering                          57382.758621   57000  13626.079747\n",
       "Health                               36825.000000   35000   5776.460854\n",
       "Humanities & Liberal Arts            31913.333333   32000   3393.032076\n",
       "Industrial Arts & Consumer Services  36342.857143   35000   7290.829204\n",
       "Interdisciplinary                    35000.000000   35000           NaN\n",
       "Law & Public Policy                  42200.000000   36000   9066.421565\n",
       "Physical Sciences                    41890.000000   39500   8251.659766\n",
       "Psychology & Social Work             30100.000000   30000   5381.914158\n",
       "Social Science                       37344.444444   38000   4750.555523"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped['Median'].agg(['mean', 'median', 'std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Plot from GroupBy objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b20472a10>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped['Median'].mean().plot(kind='bar', figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Merging and appending data\n",
    "\n",
    "Simple example with fictitious data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "raw_data_1 = {'subject_id': ['1', '2', '3', '4', '5'],\n",
    "              'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],\n",
    "              'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "raw_data_2 = {'subject_id': ['4', '5', '6', '7', '8'],\n",
    "              'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],\n",
    "              'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "\n",
    "raw_data_3 = {'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "              'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Assign each to a dataframe called data1, data2, data3\n",
    "data1 = pd.DataFrame(raw_data_1, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "data2 = pd.DataFrame(raw_data_2, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "data3 = pd.DataFrame(raw_data_3, columns = ['subject_id','test_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          1       Alex  Anderson\n",
       "1          2        Amy  Ackerman\n",
       "2          3      Allen       Ali\n",
       "3          4      Alice      Aoni\n",
       "4          5     Ayoung   Atiches"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print data 1\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          4      Billy    Bonder\n",
       "1          5      Brian     Black\n",
       "2          6       Bran   Balwner\n",
       "3          7      Bryce     Brice\n",
       "4          8      Betty    Btisan"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print data 2\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>test_id</th>\n",
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       "      <td>15</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>15</td>\n",
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       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id  test_id\n",
       "0          1       51\n",
       "1          2       15\n",
       "2          3       15\n",
       "3          4       61\n",
       "4          5       16\n",
       "5          7       14\n",
       "6          8       15\n",
       "7          9        1\n",
       "8         10       61\n",
       "9         11       16"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print data 3\n",
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          1       Alex  Anderson\n",
       "1          2        Amy  Ackerman\n",
       "2          3      Allen       Ali\n",
       "3          4      Alice      Aoni\n",
       "4          5     Ayoung   Atiches\n",
       "0          4      Billy    Bonder\n",
       "1          5      Brian     Black\n",
       "2          6       Bran   Balwner\n",
       "3          7      Bryce     Brice\n",
       "4          8      Betty    Btisan"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the two dataframes along rows\n",
    "data_all_rows = pd.concat([data1, data2])\n",
    "data_all_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  subject_id first_name last_name subject_id first_name last_name\n",
       "0          1       Alex  Anderson          4      Billy    Bonder\n",
       "1          2        Amy  Ackerman          5      Brian     Black\n",
       "2          3      Allen       Ali          6       Bran   Balwner\n",
       "3          4      Alice      Aoni          7      Bryce     Brice\n",
       "4          5     Ayoung   Atiches          8      Betty    Btisan"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the two dataframes along columns\n",
    "data_all_col = pd.concat([data1, data2], axis = 1)\n",
    "data_all_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name  test_id\n",
       "0          1       Alex  Anderson       51\n",
       "1          2        Amy  Ackerman       15\n",
       "2          3      Allen       Ali       15\n",
       "3          4      Alice      Aoni       61\n",
       "4          4      Billy    Bonder       61\n",
       "5          5     Ayoung   Atiches       16\n",
       "6          5      Brian     Black       16\n",
       "7          7      Bryce     Brice       14\n",
       "8          8      Betty    Btisan       15"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge data_all_rows and data3 along the subject_id value\n",
    "pd.merge(data_all_rows, data3, on='subject_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hide-output": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>first_name_x</th>\n",
       "      <th>last_name_x</th>\n",
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       "      <td>Black</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  subject_id first_name_x last_name_x first_name_y last_name_y\n",
       "0          4        Alice        Aoni        Billy      Bonder\n",
       "1          5       Ayoung     Atiches        Brian       Black"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inner merge\n",
    "pd.merge(data1, data2, on='subject_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hide-output": false,
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    {
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       "<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>subject_id</th>\n",
       "      <th>first_name_x</th>\n",
       "      <th>last_name_x</th>\n",
       "      <th>first_name_y</th>\n",
       "      <th>last_name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name_x last_name_x first_name_y last_name_y\n",
       "0          1         Alex    Anderson          NaN         NaN\n",
       "1          2          Amy    Ackerman          NaN         NaN\n",
       "2          3        Allen         Ali          NaN         NaN\n",
       "3          4        Alice        Aoni        Billy      Bonder\n",
       "4          5       Ayoung     Atiches        Brian       Black\n",
       "5          6          NaN         NaN         Bran     Balwner\n",
       "6          7          NaN         NaN        Bryce       Brice\n",
       "7          8          NaN         NaN        Betty      Btisan"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outer merge\n",
    "pd.merge(data1, data2, on='subject_id', how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "### Further learning resources\n",
    "\n",
    "- Reference manual for Pandas\n",
    "  [https://pandas.pydata.org/pandas-docs/stable/getting_started/](https://pandas.pydata.org/pandas-docs/stable/getting_started/)  \n",
    "- Pandas at QuantEcon lectures\n",
    "  [https://lectures.quantecon.org/py/pandas.html](https://lectures.quantecon.org/py/pandas.html)\n",
    "  [https://lectures.quantecon.org/py/pandas_panel.html](https://lectures.quantecon.org/py/pandas_panel.html)  \n",
    "- Pandas at QuantEcon DataScience\n",
    "  [https://datascience.quantecon.org/pandas/](https://datascience.quantecon.org/pandas/)  \n",
    "- QuantEcon [Stata-R-Pandas\n",
    "  cheatsheet](https://cheatsheets.quantecon.org/stats-cheatsheet.html)  \n",
    "- 📖 Kevin Sheppard “Introduction to Python for Econometrics, Statistics\n",
    "  and Data Analysis.” *Chapter: 9, 16*  "
   ]
  }
 ],
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  "title": "Foundations of Computational Economics #15"
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