432_InfoVis_Python.ipynb

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        "<a href=\"https://colab.research.google.com/github/odu-cs432-websci/public/blob/main/432_InfoVis_Python.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",
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        "id": "hdzLxxc9Q4ES"
      },
      "source": [
        "# CS 432/532 InfoVis with Python Tutorial"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfatT9E1l-b7"
      },
      "source": [
        "We're going to use [Seaborn](https://seaborn.pydata.org/), which is a high-level interface to the popular [Matplotlib](https://matplotlib.org/).  We'll also be using [Pandas](https://pandas.pydata.org/) to read in data from a CSV file to a dataframe (aka table) and manipulate the data.\n",
        "\n",
        "The goal is to produce plots similar to those from the InfoVis in R tutorial. So first, upload the `midwest.csv` and `economics.csv` datasets that were created in the R tutorial to your notebook.\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "IZeTiREWlOLy"
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      "source": [
        "import matplotlib.pyplot as plt  # will need some Matplotlib functions\n",
        "import seaborn as sns\n",
        "import pandas as pd              # will use Pandas for data manipulation\n",
        "sns.set_style(\"whitegrid\");   # use white grid as default"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "midwest = pd.read_csv('midwest.csv')\n",
        "midwest.head()"
      ],
      "execution_count": null,
      "outputs": [
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              "      <th>poptotal</th>\n",
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              "      <th>percpovertyknown</th>\n",
              "      <th>percbelowpoverty</th>\n",
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              "      <td>0.451722</td>\n",
              "      <td>0.084698</td>\n",
              "      <td>6724</td>\n",
              "      <td>59.726353</td>\n",
              "      <td>11.243308</td>\n",
              "      <td>2.870315</td>\n",
              "      <td>10529</td>\n",
              "      <td>99.087145</td>\n",
              "      <td>32.244278</td>\n",
              "      <td>45.826514</td>\n",
              "      <td>27.385647</td>\n",
              "      <td>25.228976</td>\n",
              "      <td>0</td>\n",
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              "      <td>564</td>\n",
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              "      <td>565</td>\n",
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              "      <td>19.200000</td>\n",
              "      <td>0</td>\n",
              "      <td>AAR</td>\n",
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            "text/plain": [
              "   Unnamed: 0  PID     county  ... percelderlypoverty  inmetro  category\n",
              "0           1  561      ADAMS  ...          12.443812        0       AAR\n",
              "1           2  562  ALEXANDER  ...          25.228976        0       LHR\n",
              "2           3  563       BOND  ...          12.697410        0       AAR\n",
              "3           4  564      BOONE  ...           6.217047        1       ALU\n",
              "4           5  565      BROWN  ...          19.200000        0       AAR\n",
              "\n",
              "[5 rows x 29 columns]"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFMaXjzw5NMk"
      },
      "source": [
        "Read in the \"date\" column as a date.\n",
        "\n",
        "ref: https://www.earthdatascience.org/courses/use-data-open-source-python/use-time-series-data-in-python/date-time-types-in-pandas-python/"
      ]
    },
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        "outputId": "cd1968d6-b374-4493-9e60-4817551dc58e"
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      "source": [
        "economics = pd.read_csv('economics.csv', parse_dates = ['date'])\n",
        "economics.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1967-07-01</td>\n",
              "      <td>506.7</td>\n",
              "      <td>198712.0</td>\n",
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              "      <td>4.5</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1967-08-01</td>\n",
              "      <td>509.8</td>\n",
              "      <td>198911.0</td>\n",
              "      <td>12.6</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
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              "      <td>515.6</td>\n",
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              "      <td>11.9</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2958</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1967-10-01</td>\n",
              "      <td>512.2</td>\n",
              "      <td>199311.0</td>\n",
              "      <td>12.9</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3143</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1967-11-01</td>\n",
              "      <td>517.4</td>\n",
              "      <td>199498.0</td>\n",
              "      <td>12.8</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3066</td>\n",
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            "text/plain": [
              "   Unnamed: 0       date    pce       pop  psavert  uempmed  unemploy\n",
              "0           1 1967-07-01  506.7  198712.0     12.6      4.5      2944\n",
              "1           2 1967-08-01  509.8  198911.0     12.6      4.7      2945\n",
              "2           3 1967-09-01  515.6  199113.0     11.9      4.6      2958\n",
              "3           4 1967-10-01  512.2  199311.0     12.9      4.9      3143\n",
              "4           5 1967-11-01  517.4  199498.0     12.8      4.7      3066"
            ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JFudREYr9dO"
      },
      "source": [
        "## Things to Note\n",
        "\n",
        "* If you're running these in a script, you'll need to include `plt.show()` at the end to actually draw the plot.  We don't need this line in the notebook.\n",
        "\n",
        "* In Seaborn, you may need to note if the chart function returns a `FacetGrid` object or an `Axes` object.  I've tried to use the variable `g` for `FacetGrid` and `ax` for `Axes`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PsnBm1atRBuS"
      },
      "source": [
        "## Scatterplot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GB7VbsgQqBB5"
      },
      "source": [
        "Here's a basic scatterplot, showing the percentage of college educated (mapped to the y-axis) vs. the total population (mapped to the x-axis) in each county in the midwest states."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrYiiJNTy2OC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "9556724c-9f62-4b1f-812f-37ef64438da8"
      },
      "source": [
        "ax = sns.scatterplot(x=\"poptotal\", y=\"percollege\", data=midwest)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuTrMBn13HeR"
      },
      "source": [
        "Now we're going to subset this and just show the counties in Ohio (state==OH). `midwest['state']` refers to the `state` column in the midwest dataset.\n",
        "\n",
        "We also have some adjustments to the x-axis labels and tick marks so that the population is printed with commas and everything fits."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0E3ItFN93HCq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "84694648-9749-4114-d864-cfd8caa7395d"
      },
      "source": [
        "ax = sns.scatterplot(x=\"poptotal\", y=\"percollege\", data=midwest[midwest['state']==\"OH\"])\n",
        "\n",
        "ax.set_xlabel ('Population')\n",
        "ax.set_ylabel ('% College Educated')\n",
        "ax.set_title('Ohio counties (source: midwest)')\n",
        "\n",
        "# set x-axis parameters (to look nice)\n",
        "ax.set_xlim(left=0)    # set lowest xtick at 0\n",
        "ax.set_xticks(ax.get_xticks()[::2]) # use every other tick mark\n",
        "ticks = ax.get_xticks()\n",
        "labels = ['{:,.0f}'.format(x) for x in ticks]\n",
        "ax.set_xticklabels(labels);  # using the semicolon at the end won't print output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pzMwxshRDRM"
      },
      "source": [
        "## Bar Chart"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SutVv-6HTnpF"
      },
      "source": [
        "We need to sum the population in each state.  We can use Pandas functions for this.  First, we group the population by (`groupby`) state and `sum()` the values that we're grouping.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZmW5G-pRJpE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "c87fabc0-9e4b-446a-8bf7-266e545785f1"
      },
      "source": [
        "by_state = midwest.groupby('state').sum()\n",
        "by_state"
      ],
      "execution_count": null,
      "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>Unnamed: 0</th>\n",
              "      <th>PID</th>\n",
              "      <th>area</th>\n",
              "      <th>poptotal</th>\n",
              "      <th>popdensity</th>\n",
              "      <th>popwhite</th>\n",
              "      <th>popblack</th>\n",
              "      <th>popamerindian</th>\n",
              "      <th>popasian</th>\n",
              "      <th>popother</th>\n",
              "      <th>percwhite</th>\n",
              "      <th>percblack</th>\n",
              "      <th>percamerindan</th>\n",
              "      <th>percasian</th>\n",
              "      <th>percother</th>\n",
              "      <th>popadults</th>\n",
              "      <th>perchsd</th>\n",
              "      <th>percollege</th>\n",
              "      <th>percprof</th>\n",
              "      <th>poppovertyknown</th>\n",
              "      <th>percpovertyknown</th>\n",
              "      <th>percbelowpoverty</th>\n",
              "      <th>percchildbelowpovert</th>\n",
              "      <th>percadultpoverty</th>\n",
              "      <th>percelderlypoverty</th>\n",
              "      <th>inmetro</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>IL</th>\n",
              "      <td>5253</td>\n",
              "      <td>62373</td>\n",
              "      <td>3.304</td>\n",
              "      <td>11430602</td>\n",
              "      <td>288020.543030</td>\n",
              "      <td>8952978</td>\n",
              "      <td>1694273</td>\n",
              "      <td>21836</td>\n",
              "      <td>285311</td>\n",
              "      <td>476204</td>\n",
              "      <td>9685.441091</td>\n",
              "      <td>372.717549</td>\n",
              "      <td>17.732086</td>\n",
              "      <td>57.517266</td>\n",
              "      <td>66.592008</td>\n",
              "      <td>7293930</td>\n",
              "      <td>7478.700489</td>\n",
              "      <td>1916.390625</td>\n",
              "      <td>440.111253</td>\n",
              "      <td>11143856</td>\n",
              "      <td>9843.184682</td>\n",
              "      <td>1333.797490</td>\n",
              "      <td>1732.519686</td>\n",
              "      <td>1169.104246</td>\n",
              "      <td>1232.778404</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IN</th>\n",
              "      <td>13662</td>\n",
              "      <td>65182</td>\n",
              "      <td>2.127</td>\n",
              "      <td>5544159</td>\n",
              "      <td>236727.940645</td>\n",
              "      <td>5020700</td>\n",
              "      <td>432092</td>\n",
              "      <td>12720</td>\n",
              "      <td>37617</td>\n",
              "      <td>41030</td>\n",
              "      <td>8943.056699</td>\n",
              "      <td>173.910816</td>\n",
              "      <td>20.409484</td>\n",
              "      <td>35.204495</td>\n",
              "      <td>27.418507</td>\n",
              "      <td>3489470</td>\n",
              "      <td>6749.995874</td>\n",
              "      <td>1529.292153</td>\n",
              "      <td>464.154032</td>\n",
              "      <td>5372388</td>\n",
              "      <td>8953.607679</td>\n",
              "      <td>949.296782</td>\n",
              "      <td>1207.639931</td>\n",
              "      <td>798.248542</td>\n",
              "      <td>1013.487580</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MI</th>\n",
              "      <td>19588</td>\n",
              "      <td>102754</td>\n",
              "      <td>3.357</td>\n",
              "      <td>9295297</td>\n",
              "      <td>249897.137546</td>\n",
              "      <td>7756086</td>\n",
              "      <td>1291706</td>\n",
              "      <td>55638</td>\n",
              "      <td>104983</td>\n",
              "      <td>86884</td>\n",
              "      <td>7838.899137</td>\n",
              "      <td>254.484092</td>\n",
              "      <td>113.314600</td>\n",
              "      <td>42.070796</td>\n",
              "      <td>51.231376</td>\n",
              "      <td>5842642</td>\n",
              "      <td>6214.819863</td>\n",
              "      <td>1611.980825</td>\n",
              "      <td>388.914106</td>\n",
              "      <td>9077016</td>\n",
              "      <td>8073.497524</td>\n",
              "      <td>1180.657470</td>\n",
              "      <td>1566.749003</td>\n",
              "      <td>1062.835227</td>\n",
              "      <td>977.570778</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OH</th>\n",
              "      <td>28292</td>\n",
              "      <td>180620</td>\n",
              "      <td>2.421</td>\n",
              "      <td>10847115</td>\n",
              "      <td>408234.101862</td>\n",
              "      <td>9521756</td>\n",
              "      <td>1154826</td>\n",
              "      <td>20358</td>\n",
              "      <td>91179</td>\n",
              "      <td>58996</td>\n",
              "      <td>8395.012258</td>\n",
              "      <td>309.281861</td>\n",
              "      <td>16.212946</td>\n",
              "      <td>38.086707</td>\n",
              "      <td>41.406228</td>\n",
              "      <td>6924764</td>\n",
              "      <td>6442.069262</td>\n",
              "      <td>1486.359445</td>\n",
              "      <td>359.043689</td>\n",
              "      <td>10574315</td>\n",
              "      <td>8555.322851</td>\n",
              "      <td>1146.910223</td>\n",
              "      <td>1562.569550</td>\n",
              "      <td>989.773297</td>\n",
              "      <td>997.240081</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WI</th>\n",
              "      <td>28908</td>\n",
              "      <td>217188</td>\n",
              "      <td>3.286</td>\n",
              "      <td>4891769</td>\n",
              "      <td>170833.961315</td>\n",
              "      <td>4512523</td>\n",
              "      <td>244539</td>\n",
              "      <td>39387</td>\n",
              "      <td>53583</td>\n",
              "      <td>41737</td>\n",
              "      <td>6896.629559</td>\n",
              "      <td>59.155814</td>\n",
              "      <td>181.466351</td>\n",
              "      <td>40.047318</td>\n",
              "      <td>22.700959</td>\n",
              "      <td>3094226</td>\n",
              "      <td>5437.358016</td>\n",
              "      <td>1441.162412</td>\n",
              "      <td>291.228901</td>\n",
              "      <td>4754103</td>\n",
              "      <td>7011.574033</td>\n",
              "      <td>856.428805</td>\n",
              "      <td>1118.063669</td>\n",
              "      <td>751.553428</td>\n",
              "      <td>755.935035</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Unnamed: 0     PID   area  ...  percadultpoverty  percelderlypoverty  inmetro\n",
              "state                             ...                                               \n",
              "IL           5253   62373  3.304  ...       1169.104246         1232.778404       28\n",
              "IN          13662   65182  2.127  ...        798.248542         1013.487580       37\n",
              "MI          19588  102754  3.357  ...       1062.835227          977.570778       25\n",
              "OH          28292  180620  2.421  ...        989.773297          997.240081       40\n",
              "WI          28908  217188  3.286  ...        751.553428          755.935035       20\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLZh8JsqbizK"
      },
      "source": [
        "Then we have to `reset_index()` to move 'state' back to a column instead of an index and then choose just 'state' and 'poptotal' columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8kRM954be63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e9a0fa24-2ce5-4bf8-cc13-21149e8feb4e"
      },
      "source": [
        "state_pop = by_state.reset_index()[['state', 'poptotal']]\n",
        "state_pop"
      ],
      "execution_count": null,
      "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>state</th>\n",
              "      <th>poptotal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IL</td>\n",
              "      <td>11430602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>IN</td>\n",
              "      <td>5544159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MI</td>\n",
              "      <td>9295297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>10847115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>WI</td>\n",
              "      <td>4891769</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state  poptotal\n",
              "0    IL  11430602\n",
              "1    IN   5544159\n",
              "2    MI   9295297\n",
              "3    OH  10847115\n",
              "4    WI   4891769"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0natf7WxUupn"
      },
      "source": [
        "Then we can sort in descending order"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLgWwfJEUxrl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "23c52ad3-37b7-4daa-acb6-b8cb970234b1"
      },
      "source": [
        "state_pop.sort_values(by=['poptotal'], ascending=False, inplace=True)\n",
        "state_pop"
      ],
      "execution_count": null,
      "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>state</th>\n",
              "      <th>poptotal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IL</td>\n",
              "      <td>11430602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>10847115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MI</td>\n",
              "      <td>9295297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>IN</td>\n",
              "      <td>5544159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>WI</td>\n",
              "      <td>4891769</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state  poptotal\n",
              "0    IL  11430602\n",
              "3    OH  10847115\n",
              "2    MI   9295297\n",
              "1    IN   5544159\n",
              "4    WI   4891769"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWbRV9xuU328"
      },
      "source": [
        "Now we can plot with `catplot()` and `kind=\"bar\"`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-S6JXsZzVEor",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "38860708-11f9-4733-bd31-8d218f598dac"
      },
      "source": [
        "g = sns.catplot(x=\"state\", y=\"poptotal\", kind=\"bar\", data=state_pop, color=\"steelblue\")\n",
        "\n",
        "g = (g.set_axis_labels ('State', 'Total Population'))\n",
        "plt.title('(source: midwest)')\n",
        "\n",
        "# format commas in ticklabels\n",
        "ticks = g.axes[0][0].get_yticks()\n",
        "labels = ['{:,.0f}'.format(x) for x in ticks]\n",
        "g.set_yticklabels(labels);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPqq_1DycUnS"
      },
      "source": [
        "Let's turn it sideways.  Just switch x and y axes and make the chart wider to accomodate the labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOVGNIUecXDX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "f13bb076-f770-4a69-8eab-f31f33bd9cdb"
      },
      "source": [
        "g = sns.catplot(y=\"state\", x=\"poptotal\", kind=\"bar\", data=state_pop, color=\"steelblue\",\n",
        "                height=5, # make the plot 5 units high\n",
        "                aspect=1.5) # width should be 1.5 times height)\n",
        "\n",
        "g = (g.set_axis_labels ('Total Population', 'State' ))\n",
        "plt.title('(source: midwest)')\n",
        "\n",
        "# format commas in ticklabels\n",
        "ticks = g.axes[0][0].get_xticks()\n",
        "labels = ['{:,.0f}'.format(x) for x in ticks]\n",
        "g.set_xticklabels(labels);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2viXqJtwREmc"
      },
      "source": [
        "## Line Chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GN9gkTZdQXg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "68f63c4e-dab4-451d-8c96-a0b552323827"
      },
      "source": [
        "economics.head()"
      ],
      "execution_count": null,
      "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>Unnamed: 0</th>\n",
              "      <th>date</th>\n",
              "      <th>pce</th>\n",
              "      <th>pop</th>\n",
              "      <th>psavert</th>\n",
              "      <th>uempmed</th>\n",
              "      <th>unemploy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1967-07-01</td>\n",
              "      <td>506.7</td>\n",
              "      <td>198712.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1967-08-01</td>\n",
              "      <td>509.8</td>\n",
              "      <td>198911.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>4.7</td>\n",
              "      <td>2945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1967-09-01</td>\n",
              "      <td>515.6</td>\n",
              "      <td>199113.0</td>\n",
              "      <td>11.9</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1967-10-01</td>\n",
              "      <td>512.2</td>\n",
              "      <td>199311.0</td>\n",
              "      <td>12.9</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1967-11-01</td>\n",
              "      <td>517.4</td>\n",
              "      <td>199498.0</td>\n",
              "      <td>12.8</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3066</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0       date    pce       pop  psavert  uempmed  unemploy\n",
              "0           1 1967-07-01  506.7  198712.0     12.6      4.5      2944\n",
              "1           2 1967-08-01  509.8  198911.0     12.6      4.7      2945\n",
              "2           3 1967-09-01  515.6  199113.0     11.9      4.6      2958\n",
              "3           4 1967-10-01  512.2  199311.0     12.9      4.9      3143\n",
              "4           5 1967-11-01  517.4  199498.0     12.8      4.7      3066"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mb6ZvhQz2Ee3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "b06b3320-2afc-4971-be1c-d9d9827400aa"
      },
      "source": [
        "ax = sns.lineplot(x=\"date\", y=\"unemploy\", data=economics)\n",
        "\n",
        "ax.set_xlabel ('Date')\n",
        "ax.set_ylabel ('Number Unemployed (thousands)')\n",
        "ax.set_title('(source: economics)')\n",
        "\n",
        "# set ticks every 5 years, show just the year\n",
        "import matplotlib.dates as mdates\n",
        "ax.xaxis.set_major_locator(mdates.YearLocator(5))\n",
        "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
        "\n",
        "# set y-axis parameters (to look nice)\n",
        "ticks = ax.get_yticks()\n",
        "labels = ['{:,.0f}'.format(x) for x in ticks]\n",
        "ax.set_yticklabels(labels);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qM1518i8RGv5"
      },
      "source": [
        "## Scatterplot Matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02HbPW-7-yo4"
      },
      "source": [
        "We can use the `pairplot()` function to plot a scatterplot matrix.  Instead of plotting the data in the diagonals, it plots a histogram of that attribute.\n",
        "\n",
        "The example here goes back to the midwest dataset, selects only columns for 'area', 'poptotal', and 'popdensity'."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tLsxW0Z-YQ6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "41930d59-3ae5-4296-8da3-62d9707b3b1f"
      },
      "source": [
        "g = sns.pairplot(midwest, vars=['area', 'poptotal', 'popdensity'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NneMJx2ZRIL1"
      },
      "source": [
        "## Histogram"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0eXhJqf52Nd"
      },
      "source": [
        "For the histogram, we show the distribution of population per county. Note that we're limiting this to counties that have less than 1 M people (in particular, Cook County, IL includes Chicago and has > 5 M people), so that skews the histogram.\n",
        "\n",
        "*Updated Feb 2021:* The `distplot()`, which was previously used, has been deprecated.  The new function is `histplot()` and by default has `kde=False`, so I've removed that note from the explanation below.\n",
        "\n",
        "To create the histogram, we use the `histplot()` function. We have to pass just a simple array, so we've taken the subset with population < 1M and then returned just the `poptotal` column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfKq7f4H5pCl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "441e96c7-12ae-4f39-b06f-ae6452708e90"
      },
      "source": [
        "ax = sns.histplot(midwest[midwest['poptotal']<1000000]['poptotal'])\n",
        "\n",
        "ax.set_xlabel ('Population')\n",
        "ax.set_ylabel ('Number of Counties')\n",
        "ax.set_title('(source: midwest)')\n",
        "\n",
        "# format commas in ticklabels\n",
        "ticks = ax.get_xticks()\n",
        "labels = ['{:,.0f}'.format(x) for x in ticks]\n",
        "ax.set_xticklabels(labels);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEiOqNK78bkI"
      },
      "source": [
        "We can use the `bins` option to change the number of bins in the histogram.  Note this is number of bins, not the binwidth (as we had in the R example)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phpm155a9o01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "7de5033f-cc41-4163-87de-57192774cd10"
      },
      "source": [
        "ax = sns.histplot(midwest[midwest['poptotal']<1000000]['poptotal'], bins=100)\n",
        "\n",
        "ax.set_xlabel ('Population')\n",
        "ax.set_ylabel ('Number of Counties')\n",
        "ax.set_title('(source: midwest)')\n",
        "\n",
        "# format commas in ticklabels\n",
        "ticks = ax.get_xticks()\n",
        "labels = ['{:,.0f}'.format(x) for x in ticks]\n",
        "ax.set_xticklabels(labels);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V2RG95LkRKQe"
      },
      "source": [
        "## Boxplot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NWJOTNE5PKx"
      },
      "source": [
        "We're again looking at the total population by county in the midwest (and again, only for counties with < 1M people). This time, we'll use boxplots and create a separate boxplot for each state.\n",
        "\n",
        "The code is very similar to the bar chart.  We use `catplot()`, specify `kind=\"box\"`, and use the midwest dataset (filtered to counties with < 1M people) instead of the state_pop dataset that we'd created for the bar chart."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAZ2z22C4qmY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "22479725-d702-438b-f97b-0d3b89c2a39a"
      },
      "source": [
        "g = sns.catplot(x=\"state\", y=\"poptotal\", kind=\"box\", data=midwest[midwest['poptotal']<1000000])\n",
        "\n",
        "g = (g.set_axis_labels ('State', 'Total Population'))\n",
        "plt.title('(source: midwest)')\n",
        "\n",
        "# format commas in ticklabels\n",
        "ticks = g.axes[0][0].get_yticks()\n",
        "labels = ['{:,.0f}'.format(x) for x in ticks]\n",
        "g.set_yticklabels(labels);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "psypbAGmRMCv"
      },
      "source": [
        "## Empirical CDF (ECDF)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMPrnJJMtYkG"
      },
      "source": [
        "*Updated Feb 2021:* Seaborn has added a `ecdfplot()` function. I've added this example, along with leaving in the `scatter()` and `sns.lineplot()` examples below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3zDGF3_WmUcG",
        "outputId": "f4fca6ae-7378-4d89-fc44-2882ab3c9b12"
      },
      "source": [
        "ax = sns.ecdfplot(midwest[midwest['poptotal']<1000000]['poptotal'])\n",
        "\n",
        "ax.set_xlabel ('Population')\n",
        "ax.set_ylabel ('Percentage of Counties')\n",
        "ax.set_title('(source: midwest)')\n",
        "\n",
        "# format commas in ticklabels\n",
        "ticks = ax.get_xticks()\n",
        "labels = ['{:,.0f}'.format(x) for x in ticks]\n",
        "ax.set_xticklabels(labels);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5dAxHNlxtp0"
      },
      "source": [
        "Code showing how to compute an ECDF and plot it with scatter() is available at https://cmdlinetips.com/2019/05/empirical-cumulative-distribution-function-ecdf-in-python/\n",
        "\n",
        "*Preparing the data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMHTYp3TxmgP"
      },
      "source": [
        "import numpy as np  # use for sort() and arrange() to build the ECDF\n",
        "pop_data = midwest[midwest['poptotal']<1000000]['poptotal']\n",
        "x = np.sort(pop_data)\n",
        "n = x.size\n",
        "y = np.arange(1, n+1) / n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-neScQasxoIL"
      },
      "source": [
        "Drawing the chart with `scatter()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-qzyV4stQtI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "e95d2caa-367e-4267-e43a-2b76320b4982"
      },
      "source": [
        "plt.scatter(x=x, y=y);\n",
        "plt.xlabel('Population')\n",
        "plt.ylabel('Percentage of Counties');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkFtVT4fxfQr"
      },
      "source": [
        "Here's an example of how to do the same thing with Seaborn's `lineplot()` to include the line and points."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgrYKOX8wHoA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "08a31778-ff29-4381-917e-a212edd77524"
      },
      "source": [
        "ax = sns.lineplot(x=x, y=y, marker=\"o\")\n",
        "\n",
        "ax.set_xlabel ('Population')\n",
        "ax.set_ylabel ('Percentage of Counties')\n",
        "ax.set_title('(source: midwest)')\n",
        "\n",
        "# set x-axis parameters (to look nice)\n",
        "ax.set_xlim(left=0, right=1000000)    # set lowest xtick at 0, max at 1M\n",
        "ticks = ax.get_xticks()\n",
        "labels = ['{:,.0f}'.format(x) for x in ticks]\n",
        "ax.set_xticklabels(labels);  # using the semicolon at the end won't print output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}