08_College_Tuition.ipynb

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        "<a href=\"https://colab.research.google.com/github/wcj365/python-stats-dataviz/blob/master/08_College_Tuition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "asl80-xxIcok",
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      "source": [
        "### 08 College Scorecard Data\n",
        "Exploration of In State Tuition.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jh6PHH2dIcom",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import useful Python libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import statistics\n",
        "from pylab import rcParams\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "outputId": "5186798b-e7f5-443e-bf7e-2a3aff269563"
      },
      "source": [
        "# use college scorecard 2017-18 acedemic year data\n",
        "\n",
        "column_list = [\"INSTNM\", \"STABBR\",\"CONTROL\", \"TUITIONFEE_IN\"]\n",
        "df = pd.read_csv(\"MERGED2017_18_PP.csv\", usecols=column_list)\n",
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7115 entries, 0 to 7114\n",
            "Data columns (total 4 columns):\n",
            "INSTNM           7115 non-null object\n",
            "STABBR           7115 non-null object\n",
            "CONTROL          7115 non-null int64\n",
            "TUITIONFEE_IN    4028 non-null float64\n",
            "dtypes: float64(1), int64(1), object(2)\n",
            "memory usage: 222.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFpABy3XIco5",
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        "colab": {},
        "outputId": "46baa847-8020-4f65-d70c-b54a176ee212"
      },
      "source": [
        "# Find out the existance of missing values\n",
        "\n",
        "df.isna().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "INSTNM              0\n",
              "STABBR              0\n",
              "CONTROL             0\n",
              "TUITIONFEE_IN    3087\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 3
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      "cell_type": "code",
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        "id": "uBXTEfugIcpB",
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        "outputId": "807fbc01-ef00-4312-86bc-cce0196d54fc"
      },
      "source": [
        "# Take a look at what colleges have missing tuition\n",
        "\n",
        "df[df[\"TUITIONFEE_IN\"].isna()].sample(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>INSTNM</th>\n",
              "      <th>STABBR</th>\n",
              "      <th>CONTROL</th>\n",
              "      <th>TUITIONFEE_IN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5924</th>\n",
              "      <td>New England Tractor Trailer Training School of...</td>\n",
              "      <td>MA</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2657</th>\n",
              "      <td>Cincinnati College of Mortuary Science</td>\n",
              "      <td>OH</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>6135</th>\n",
              "      <td>Ukiah Adult School</td>\n",
              "      <td>CA</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>6001</th>\n",
              "      <td>Tulsa Welding School-Jacksonville</td>\n",
              "      <td>FL</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>5439</th>\n",
              "      <td>Milan Institute of Cosmetology-Reno</td>\n",
              "      <td>NV</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>6424</th>\n",
              "      <td>The Training Domain</td>\n",
              "      <td>FL</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6653</th>\n",
              "      <td>Bull City Durham Beauty and Barber College</td>\n",
              "      <td>NC</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5481</th>\n",
              "      <td>Urshan Graduate School of Theology</td>\n",
              "      <td>MO</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5619</th>\n",
              "      <td>Southern Worcester County Regional Vocational ...</td>\n",
              "      <td>MA</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6452</th>\n",
              "      <td>Unitech Training Academy-Metairie</td>\n",
              "      <td>LA</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1372</th>\n",
              "      <td>John Jay Beauty College</td>\n",
              "      <td>LA</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5052</th>\n",
              "      <td>The Beauty Institute</td>\n",
              "      <td>FL</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5620</th>\n",
              "      <td>L'esprit Academy</td>\n",
              "      <td>MI</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5270</th>\n",
              "      <td>Make-up Designory</td>\n",
              "      <td>CA</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4032</th>\n",
              "      <td>Austin Graduate School of Theology</td>\n",
              "      <td>TX</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7061</th>\n",
              "      <td>Professional Hands Institute</td>\n",
              "      <td>FL</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>492</th>\n",
              "      <td>Marshall B Ketchum University</td>\n",
              "      <td>CA</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4994</th>\n",
              "      <td>Jay's Technical Institute</td>\n",
              "      <td>TX</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>960</th>\n",
              "      <td>Hair Professionals School of Cosmetology</td>\n",
              "      <td>IL</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6400</th>\n",
              "      <td>American College of Barbering</td>\n",
              "      <td>KY</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "                                                 INSTNM STABBR  CONTROL  \\\n",
              "5924  New England Tractor Trailer Training School of...     MA        3   \n",
              "2657             Cincinnati College of Mortuary Science     OH        2   \n",
              "6135                                 Ukiah Adult School     CA        1   \n",
              "6001                  Tulsa Welding School-Jacksonville     FL        3   \n",
              "5439                Milan Institute of Cosmetology-Reno     NV        3   \n",
              "6424                                The Training Domain     FL        3   \n",
              "6653         Bull City Durham Beauty and Barber College     NC        3   \n",
              "5481                 Urshan Graduate School of Theology     MO        2   \n",
              "5619  Southern Worcester County Regional Vocational ...     MA        1   \n",
              "6452                  Unitech Training Academy-Metairie     LA        3   \n",
              "1372                            John Jay Beauty College     LA        3   \n",
              "5052                               The Beauty Institute     FL        3   \n",
              "5620                                   L'esprit Academy     MI        3   \n",
              "5270                                  Make-up Designory     CA        3   \n",
              "4032                 Austin Graduate School of Theology     TX        2   \n",
              "7061                       Professional Hands Institute     FL        3   \n",
              "492                       Marshall B Ketchum University     CA        2   \n",
              "4994                          Jay's Technical Institute     TX        3   \n",
              "960            Hair Professionals School of Cosmetology     IL        3   \n",
              "6400                      American College of Barbering     KY        3   \n",
              "\n",
              "      TUITIONFEE_IN  \n",
              "5924            NaN  \n",
              "2657            NaN  \n",
              "6135            NaN  \n",
              "6001            NaN  \n",
              "5439            NaN  \n",
              "6424            NaN  \n",
              "6653            NaN  \n",
              "5481            NaN  \n",
              "5619            NaN  \n",
              "6452            NaN  \n",
              "1372            NaN  \n",
              "5052            NaN  \n",
              "5620            NaN  \n",
              "5270            NaN  \n",
              "4032            NaN  \n",
              "7061            NaN  \n",
              "492             NaN  \n",
              "4994            NaN  \n",
              "960             NaN  \n",
              "6400            NaN  "
            ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "__pXlVX6IcpI",
        "colab_type": "code",
        "colab": {},
        "outputId": "7caeaf97-038d-48fa-cb2d-4e574d69522e"
      },
      "source": [
        "# Drop the colleges with missing tuition\n",
        "\n",
        "df = df[df[\"TUITIONFEE_IN\"].isna() == False]\n",
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4028 entries, 0 to 7114\n",
            "Data columns (total 4 columns):\n",
            "INSTNM           4028 non-null object\n",
            "STABBR           4028 non-null object\n",
            "CONTROL          4028 non-null int64\n",
            "TUITIONFEE_IN    4028 non-null float64\n",
            "dtypes: float64(1), int64(1), object(2)\n",
            "memory usage: 157.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMTCuJUJIcpO",
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        "colab": {},
        "outputId": "549193c9-cd6a-400c-f8b9-3b8883d9a3a7"
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      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CONTROL</th>\n",
              "      <th>TUITIONFEE_IN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4028.000000</td>\n",
              "      <td>4028.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.776812</td>\n",
              "      <td>15081.399950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.754484</td>\n",
              "      <td>12723.724262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>5246.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>11296.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>19400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>74514.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           CONTROL  TUITIONFEE_IN\n",
              "count  4028.000000    4028.000000\n",
              "mean      1.776812   15081.399950\n",
              "std       0.754484   12723.724262\n",
              "min       1.000000       0.000000\n",
              "25%       1.000000    5246.250000\n",
              "50%       2.000000   11296.000000\n",
              "75%       2.000000   19400.000000\n",
              "max       3.000000   74514.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVa9OJVMIcpS",
        "colab_type": "code",
        "colab": {},
        "outputId": "2c1df0d4-acb6-4de1-ea09-6fe94a5047b8"
      },
      "source": [
        "# Since the min of tuition is 0, let's find out how many of them\n",
        "\n",
        "df[df[\"TUITIONFEE_IN\"] == 0 ].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FF3ReHuXIcpa",
        "colab_type": "code",
        "colab": {},
        "outputId": "35c4d409-63ed-43a9-b0fe-a738ab0c6e32"
      },
      "source": [
        "# Display the colleges with 0 tuition\n",
        "\n",
        "df[df[\"TUITIONFEE_IN\"] == 0 ]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>INSTNM</th>\n",
              "      <th>STABBR</th>\n",
              "      <th>CONTROL</th>\n",
              "      <th>TUITIONFEE_IN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>United States Air Force Academy</td>\n",
              "      <td>CO</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>United States Coast Guard Academy</td>\n",
              "      <td>CT</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1500</th>\n",
              "      <td>United States Naval Academy</td>\n",
              "      <td>MD</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2449</th>\n",
              "      <td>United States Military Academy</td>\n",
              "      <td>NY</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "                                 INSTNM STABBR  CONTROL  TUITIONFEE_IN\n",
              "577     United States Air Force Academy     CO        1            0.0\n",
              "629   United States Coast Guard Academy     CT        1            0.0\n",
              "1500        United States Naval Academy     MD        1            0.0\n",
              "2449     United States Military Academy     NY        1            0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECmuRsvJIcpf",
        "colab_type": "code",
        "colab": {},
        "outputId": "7eb6c82b-a3b7-4719-8de5-6b15cad349e4"
      },
      "source": [
        "# Drop these colleges with 0 tuition\n",
        "\n",
        "df = df[df[\"TUITIONFEE_IN\"] != 0]\n",
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4024 entries, 0 to 7114\n",
            "Data columns (total 4 columns):\n",
            "INSTNM           4024 non-null object\n",
            "STABBR           4024 non-null object\n",
            "CONTROL          4024 non-null int64\n",
            "TUITIONFEE_IN    4024 non-null float64\n",
            "dtypes: float64(1), int64(1), object(2)\n",
            "memory usage: 157.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6IrxaAHMIcpk",
        "colab_type": "text"
      },
      "source": [
        "The control is used to classify the type of institution:\n",
        "- 1 = \"Public\"\n",
        "- 2 = \"Private nonprofit\"\n",
        "- 3 = \"Private for-profit\"\n",
        "We need to convert the varibale from int to string."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JGg17mcIcpl",
        "colab_type": "code",
        "colab": {},
        "outputId": "50523c1c-9e62-4331-8dfd-5ff2f17f1889"
      },
      "source": [
        "# Convert the varibale from int to string\n",
        "\n",
        "df[\"CONTROL\"] = df[\"CONTROL\"].astype(str)\n",
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4024 entries, 0 to 7114\n",
            "Data columns (total 4 columns):\n",
            "INSTNM           4024 non-null object\n",
            "STABBR           4024 non-null object\n",
            "CONTROL          4024 non-null object\n",
            "TUITIONFEE_IN    4024 non-null float64\n",
            "dtypes: float64(1), object(3)\n",
            "memory usage: 157.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IilRxL55Icpq",
        "colab_type": "code",
        "colab": {},
        "outputId": "9c7b05c7-b72f-4df7-965b-b784fda3f589"
      },
      "source": [
        "# Replace the numbers with the explicit names\n",
        "\n",
        "control_dict = {\"1\": \"Public\",\n",
        "                \"2\": \"Private nonprofit\",\n",
        "                \"3\": \"Private for-profit\"}\n",
        "df[\"CONTROL\"] = df[\"CONTROL\"].map(control_dict)\n",
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4024 entries, 0 to 7114\n",
            "Data columns (total 4 columns):\n",
            "INSTNM           4024 non-null object\n",
            "STABBR           4024 non-null object\n",
            "CONTROL          4024 non-null object\n",
            "TUITIONFEE_IN    4024 non-null float64\n",
            "dtypes: float64(1), object(3)\n",
            "memory usage: 157.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zhjDx0aIcpx",
        "colab_type": "code",
        "colab": {},
        "outputId": "561e3411-d25c-4164-ba9a-015a9e008a79"
      },
      "source": [
        "df.sample(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>INSTNM</th>\n",
              "      <th>STABBR</th>\n",
              "      <th>CONTROL</th>\n",
              "      <th>TUITIONFEE_IN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3963</th>\n",
              "      <td>University of Puerto Rico-Humacao</td>\n",
              "      <td>PR</td>\n",
              "      <td>Public</td>\n",
              "      <td>2117.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4843</th>\n",
              "      <td>MDT College of Health Sciences</td>\n",
              "      <td>OH</td>\n",
              "      <td>Private for-profit</td>\n",
              "      <td>26535.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>856</th>\n",
              "      <td>Savannah Technical College</td>\n",
              "      <td>GA</td>\n",
              "      <td>Public</td>\n",
              "      <td>2744.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4177</th>\n",
              "      <td>Nossi College of Art</td>\n",
              "      <td>TN</td>\n",
              "      <td>Private for-profit</td>\n",
              "      <td>17800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3902</th>\n",
              "      <td>American Samoa Community College</td>\n",
              "      <td>AS</td>\n",
              "      <td>Public</td>\n",
              "      <td>3950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7035</th>\n",
              "      <td>Strayer University-Tampa East Campus</td>\n",
              "      <td>FL</td>\n",
              "      <td>Private for-profit</td>\n",
              "      <td>13857.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5153</th>\n",
              "      <td>W L Bonner College</td>\n",
              "      <td>SC</td>\n",
              "      <td>Private nonprofit</td>\n",
              "      <td>5436.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6872</th>\n",
              "      <td>Columbia College - Fort Drum</td>\n",
              "      <td>NY</td>\n",
              "      <td>Private nonprofit</td>\n",
              "      <td>6840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2852</th>\n",
              "      <td>Oral Roberts University</td>\n",
              "      <td>OK</td>\n",
              "      <td>Private nonprofit</td>\n",
              "      <td>26792.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>975</th>\n",
              "      <td>Illinois State University</td>\n",
              "      <td>IL</td>\n",
              "      <td>Public</td>\n",
              "      <td>14061.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3501</th>\n",
              "      <td>Our Lady of the Lake University</td>\n",
              "      <td>TX</td>\n",
              "      <td>Private nonprofit</td>\n",
              "      <td>28192.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3995</th>\n",
              "      <td>Metro Business College-Jefferson City</td>\n",
              "      <td>MO</td>\n",
              "      <td>Private for-profit</td>\n",
              "      <td>11375.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Northwest Technical Institute</td>\n",
              "      <td>AR</td>\n",
              "      <td>Public</td>\n",
              "      <td>2321.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2472</th>\n",
              "      <td>Appalachian State University</td>\n",
              "      <td>NC</td>\n",
              "      <td>Public</td>\n",
              "      <td>7302.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3803</th>\n",
              "      <td>Meredith Manor International Equestrian Center</td>\n",
              "      <td>WV</td>\n",
              "      <td>Private for-profit</td>\n",
              "      <td>22086.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5508</th>\n",
              "      <td>Vatterott College-Appling Farms</td>\n",
              "      <td>TN</td>\n",
              "      <td>Private for-profit</td>\n",
              "      <td>11093.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3960</th>\n",
              "      <td>University of Puerto Rico-Bayamon</td>\n",
              "      <td>PR</td>\n",
              "      <td>Public</td>\n",
              "      <td>2083.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2495</th>\n",
              "      <td>Craven Community College</td>\n",
              "      <td>NC</td>\n",
              "      <td>Public</td>\n",
              "      <td>2053.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1163</th>\n",
              "      <td>Drake University</td>\n",
              "      <td>IA</td>\n",
              "      <td>Private nonprofit</td>\n",
              "      <td>39062.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5447</th>\n",
              "      <td>Mayfield College</td>\n",
              "      <td>CA</td>\n",
              "      <td>Private for-profit</td>\n",
              "      <td>13079.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              INSTNM STABBR  \\\n",
              "3963               University of Puerto Rico-Humacao     PR   \n",
              "4843                  MDT College of Health Sciences     OH   \n",
              "856                       Savannah Technical College     GA   \n",
              "4177                            Nossi College of Art     TN   \n",
              "3902                American Samoa Community College     AS   \n",
              "7035            Strayer University-Tampa East Campus     FL   \n",
              "5153                              W L Bonner College     SC   \n",
              "6872                    Columbia College - Fort Drum     NY   \n",
              "2852                         Oral Roberts University     OK   \n",
              "975                        Illinois State University     IL   \n",
              "3501                 Our Lady of the Lake University     TX   \n",
              "3995           Metro Business College-Jefferson City     MO   \n",
              "158                    Northwest Technical Institute     AR   \n",
              "2472                    Appalachian State University     NC   \n",
              "3803  Meredith Manor International Equestrian Center     WV   \n",
              "5508                 Vatterott College-Appling Farms     TN   \n",
              "3960               University of Puerto Rico-Bayamon     PR   \n",
              "2495                        Craven Community College     NC   \n",
              "1163                                Drake University     IA   \n",
              "5447                                Mayfield College     CA   \n",
              "\n",
              "                 CONTROL  TUITIONFEE_IN  \n",
              "3963              Public         2117.0  \n",
              "4843  Private for-profit        26535.0  \n",
              "856               Public         2744.0  \n",
              "4177  Private for-profit        17800.0  \n",
              "3902              Public         3950.0  \n",
              "7035  Private for-profit        13857.0  \n",
              "5153   Private nonprofit         5436.0  \n",
              "6872   Private nonprofit         6840.0  \n",
              "2852   Private nonprofit        26792.0  \n",
              "975               Public        14061.0  \n",
              "3501   Private nonprofit        28192.0  \n",
              "3995  Private for-profit        11375.0  \n",
              "158               Public         2321.0  \n",
              "2472              Public         7302.0  \n",
              "3803  Private for-profit        22086.0  \n",
              "5508  Private for-profit        11093.0  \n",
              "3960              Public         2083.0  \n",
              "2495              Public         2053.0  \n",
              "1163   Private nonprofit        39062.0  \n",
              "5447  Private for-profit        13079.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HibTNdmrIcp3",
        "colab_type": "code",
        "colab": {},
        "outputId": "a240eb6a-cb51-414f-ff57-18727714ad33"
      },
      "source": [
        "df.describe(include=\"all\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>INSTNM</th>\n",
              "      <th>STABBR</th>\n",
              "      <th>CONTROL</th>\n",
              "      <th>TUITIONFEE_IN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4024</td>\n",
              "      <td>4024</td>\n",
              "      <td>4024</td>\n",
              "      <td>4024.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>3966</td>\n",
              "      <td>59</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Stevens-Henager College</td>\n",
              "      <td>CA</td>\n",
              "      <td>Public</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>7</td>\n",
              "      <td>316</td>\n",
              "      <td>1692</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15096.391402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12721.153785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>480.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5255.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11315.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>74514.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         INSTNM STABBR CONTROL  TUITIONFEE_IN\n",
              "count                      4024   4024    4024    4024.000000\n",
              "unique                     3966     59       3            NaN\n",
              "top     Stevens-Henager College     CA  Public            NaN\n",
              "freq                          7    316    1692            NaN\n",
              "mean                        NaN    NaN     NaN   15096.391402\n",
              "std                         NaN    NaN     NaN   12721.153785\n",
              "min                         NaN    NaN     NaN     480.000000\n",
              "25%                         NaN    NaN     NaN    5255.750000\n",
              "50%                         NaN    NaN     NaN   11315.000000\n",
              "75%                         NaN    NaN     NaN   19400.000000\n",
              "max                         NaN    NaN     NaN   74514.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8C2GevxTIcp8",
        "colab_type": "code",
        "colab": {},
        "outputId": "989d5d2f-e54d-46ba-e48f-13583f931a81"
      },
      "source": [
        "# Make sure there is no missing value for the institution type\n",
        "df[\"CONTROL\"].isna().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McKFiyM4IcqF",
        "colab_type": "code",
        "colab": {},
        "outputId": "53be7791-5ef4-4ff6-e774-14979570d6ab"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TUITIONFEE_IN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4024.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>15096.391402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12721.153785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>480.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5255.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11315.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>19400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>74514.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       TUITIONFEE_IN\n",
              "count    4024.000000\n",
              "mean    15096.391402\n",
              "std     12721.153785\n",
              "min       480.000000\n",
              "25%      5255.750000\n",
              "50%     11315.000000\n",
              "75%     19400.000000\n",
              "max     74514.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQ24Pmx0IcqK",
        "colab_type": "code",
        "colab": {},
        "outputId": "c57a9a09-81fe-44b2-cf92-22c28213a5dd"
      },
      "source": [
        "# find out the median tuition\n",
        "# It appears that there is a large difference between the mean and the median\n",
        "statistics.median(df[\"TUITIONFEE_IN\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11315.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIzEI247IcqP",
        "colab_type": "code",
        "colab": {},
        "outputId": "8b73f87f-915c-4f59-f049-972afc07376d"
      },
      "source": [
        "# Make a boxplot\n",
        "# there are many outliers above the maximum point which explains why the mean is much larger than the median\n",
        "# The outliers skewed the mean towards higher tuition\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,16))\n",
        "df.boxplot(ax = ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x29365176c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RkgDnw8IcqY",
        "colab_type": "code",
        "colab": {},
        "outputId": "97d4c789-7813-4b9f-b3ba-b6dbd70c4af9"
      },
      "source": [
        "# MAke a histogram\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "df[\"TUITIONFEE_IN\"].plot.hist(ax=ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x293658a2ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OiPcYQRSIcqe",
        "colab_type": "code",
        "colab": {},
        "outputId": "20760740-3e9e-409c-b961-fafc1dc017a2"
      },
      "source": [
        "# Make boxplots, one for each type of institution\n",
        "# IT makes sense public colleges have lowerest tuition\n",
        "# However, private nonprofit colleges have higher tuition than for-profit colleges. A little counter-intuitive.\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16,8))\n",
        "df.boxplot(ax = ax,by = \"CONTROL\", vert=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x293656f7cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGoam9WCIcqi",
        "colab_type": "code",
        "colab": {},
        "outputId": "a89f037b-6ae4-4d6c-d34f-e2d88cac875f"
      },
      "source": [
        "# Plot the count of colleges within each category\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "df[[\"CONTROL\"]].groupby([\"CONTROL\"]).size().plot.bar(ax=ax, rot=0)\n",
        "ax.set_title(\"College Count by Institution Type\", size=18)\n",
        "ax.legend(\"\")\n",
        "ax.set_ylabel(\"Number of Colleges\", size=14)\n",
        "ax.set_xlabel(\"Institution Type\",size=14)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Institution Type')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elw4G_c_Icq-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The end"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}