fall20/432_Week_03_InfoVis_Python.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/cs432-websci-fall20/assignments/blob/master/432_Week_03_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",
      "metadata": {
        "id": "hdzLxxc9Q4ES",
        "colab_type": "text"
      },
      "source": [
        "# CS 432/532 InfoVis with Python Tutorial"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfatT9E1l-b7",
        "colab_type": "text"
      },
      "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 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",
      "metadata": {
        "id": "IZeTiREWlOLy",
        "colab_type": "code",
        "colab": {}
      },
      "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": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 300
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        "outputId": "1605b800-e9a4-4fc5-e85b-68ec14cbeed7"
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      "source": [
        "midwest = pd.read_csv('midwest.csv')\n",
        "midwest.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>PID</th>\n",
              "      <th>county</th>\n",
              "      <th>state</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",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>561</td>\n",
              "      <td>ADAMS</td>\n",
              "      <td>IL</td>\n",
              "      <td>0.052</td>\n",
              "      <td>66090</td>\n",
              "      <td>1270.961540</td>\n",
              "      <td>63917</td>\n",
              "      <td>1702</td>\n",
              "      <td>98</td>\n",
              "      <td>249</td>\n",
              "      <td>124</td>\n",
              "      <td>96.712059</td>\n",
              "      <td>2.575276</td>\n",
              "      <td>0.148283</td>\n",
              "      <td>0.376759</td>\n",
              "      <td>0.187623</td>\n",
              "      <td>43298</td>\n",
              "      <td>75.107395</td>\n",
              "      <td>19.631392</td>\n",
              "      <td>4.355859</td>\n",
              "      <td>63628</td>\n",
              "      <td>96.274777</td>\n",
              "      <td>13.151443</td>\n",
              "      <td>18.011717</td>\n",
              "      <td>11.009776</td>\n",
              "      <td>12.443812</td>\n",
              "      <td>0</td>\n",
              "      <td>AAR</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>562</td>\n",
              "      <td>ALEXANDER</td>\n",
              "      <td>IL</td>\n",
              "      <td>0.014</td>\n",
              "      <td>10626</td>\n",
              "      <td>759.000000</td>\n",
              "      <td>7054</td>\n",
              "      <td>3496</td>\n",
              "      <td>19</td>\n",
              "      <td>48</td>\n",
              "      <td>9</td>\n",
              "      <td>66.384340</td>\n",
              "      <td>32.900433</td>\n",
              "      <td>0.178807</td>\n",
              "      <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",
              "      <td>LHR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>563</td>\n",
              "      <td>BOND</td>\n",
              "      <td>IL</td>\n",
              "      <td>0.022</td>\n",
              "      <td>14991</td>\n",
              "      <td>681.409091</td>\n",
              "      <td>14477</td>\n",
              "      <td>429</td>\n",
              "      <td>35</td>\n",
              "      <td>16</td>\n",
              "      <td>34</td>\n",
              "      <td>96.571276</td>\n",
              "      <td>2.861717</td>\n",
              "      <td>0.233473</td>\n",
              "      <td>0.106731</td>\n",
              "      <td>0.226803</td>\n",
              "      <td>9669</td>\n",
              "      <td>69.334988</td>\n",
              "      <td>17.033819</td>\n",
              "      <td>4.488572</td>\n",
              "      <td>14235</td>\n",
              "      <td>94.956974</td>\n",
              "      <td>12.068844</td>\n",
              "      <td>14.036061</td>\n",
              "      <td>10.852090</td>\n",
              "      <td>12.697410</td>\n",
              "      <td>0</td>\n",
              "      <td>AAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>564</td>\n",
              "      <td>BOONE</td>\n",
              "      <td>IL</td>\n",
              "      <td>0.017</td>\n",
              "      <td>30806</td>\n",
              "      <td>1812.117650</td>\n",
              "      <td>29344</td>\n",
              "      <td>127</td>\n",
              "      <td>46</td>\n",
              "      <td>150</td>\n",
              "      <td>1139</td>\n",
              "      <td>95.254171</td>\n",
              "      <td>0.412257</td>\n",
              "      <td>0.149322</td>\n",
              "      <td>0.486918</td>\n",
              "      <td>3.697332</td>\n",
              "      <td>19272</td>\n",
              "      <td>75.472188</td>\n",
              "      <td>17.278954</td>\n",
              "      <td>4.197800</td>\n",
              "      <td>30337</td>\n",
              "      <td>98.477569</td>\n",
              "      <td>7.209019</td>\n",
              "      <td>11.179536</td>\n",
              "      <td>5.536013</td>\n",
              "      <td>6.217047</td>\n",
              "      <td>1</td>\n",
              "      <td>ALU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>565</td>\n",
              "      <td>BROWN</td>\n",
              "      <td>IL</td>\n",
              "      <td>0.018</td>\n",
              "      <td>5836</td>\n",
              "      <td>324.222222</td>\n",
              "      <td>5264</td>\n",
              "      <td>547</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>90.198766</td>\n",
              "      <td>9.372858</td>\n",
              "      <td>0.239890</td>\n",
              "      <td>0.085675</td>\n",
              "      <td>0.102810</td>\n",
              "      <td>3979</td>\n",
              "      <td>68.861523</td>\n",
              "      <td>14.475999</td>\n",
              "      <td>3.367680</td>\n",
              "      <td>4815</td>\n",
              "      <td>82.505140</td>\n",
              "      <td>13.520249</td>\n",
              "      <td>13.022889</td>\n",
              "      <td>11.143211</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>0</td>\n",
              "      <td>AAR</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFMaXjzw5NMk",
        "colab_type": "text"
      },
      "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/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJwbK7t8oysx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "00c7f062-cf66-4ebd-fd90-983da0621e29"
      },
      "source": [
        "economics = pd.read_csv('economics.csv', parse_dates = ['date'])\n",
        "economics.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <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",
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              "  </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": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JFudREYr9dO",
        "colab_type": "text"
      },
      "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",
        "colab_type": "text"
      },
      "source": [
        "## Scatterplot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GB7VbsgQqBB5",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {}
      },
      "source": [
        "ax = sns.scatterplot(x=\"poptotal\", y=\"percollege\", data=midwest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuTrMBn13HeR",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pzMwxshRDRM",
        "colab_type": "text"
      },
      "source": [
        "## Bar Chart"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SutVv-6HTnpF",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {}
      },
      "source": [
        "by_state = midwest.groupby('state').sum()\n",
        "by_state"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLZh8JsqbizK",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {}
      },
      "source": [
        "state_pop = by_state.reset_index()[['state', 'poptotal']]\n",
        "state_pop"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0natf7WxUupn",
        "colab_type": "text"
      },
      "source": [
        "Then we can sort in descending order"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLgWwfJEUxrl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "state_pop.sort_values(by=['poptotal'], ascending=False, inplace=True)\n",
        "state_pop"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWbRV9xuU328",
        "colab_type": "text"
      },
      "source": [
        "Now we can plot with `catplot()` and `kind=\"bar\"`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-S6JXsZzVEor",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPqq_1DycUnS",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2viXqJtwREmc",
        "colab_type": "text"
      },
      "source": [
        "## Line Chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GN9gkTZdQXg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "7aa00006-6e9f-4a9b-bd5f-885e981ec319"
      },
      "source": [
        "economics.head()"
      ],
      "execution_count": 19,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mb6ZvhQz2Ee3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "80f876bb-d972-4bb8-b4c5-603032f929dd"
      },
      "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": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qM1518i8RGv5",
        "colab_type": "text"
      },
      "source": [
        "## Scatterplot Matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02HbPW-7-yo4",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "outputId": "00d1c78f-2025-4e95-cebc-9c0d835847fc"
      },
      "source": [
        "g = sns.pairplot(midwest, vars=['area', 'poptotal', 'popdensity'])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NneMJx2ZRIL1",
        "colab_type": "text"
      },
      "source": [
        "## Histogram"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0eXhJqf52Nd",
        "colab_type": "text"
      },
      "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",
        "To create the histogram, we use the `distplot()` 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.\n",
        "\n",
        "By default `distplot()` also shows a kernel, so we specify `kde=False` to turn that off."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfKq7f4H5pCl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "61cafe74-8a47-4648-f94d-2ef93a169fc2"
      },
      "source": [
        "ax = sns.distplot(midwest[midwest['poptotal']<1000000]['poptotal'], kde=False) \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": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEiOqNK78bkI",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "372ed58d-dee2-4a54-edd1-dcf3922c7816"
      },
      "source": [
        "ax = sns.distplot(midwest[midwest['poptotal']<1000000]['poptotal'], kde=False, 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": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V2RG95LkRKQe",
        "colab_type": "text"
      },
      "source": [
        "## Boxplot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NWJOTNE5PKx",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "bc3ef322-5580-4779-ef21-62b819cb5a78"
      },
      "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": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "psypbAGmRMCv",
        "colab_type": "text"
      },
      "source": [
        "## Empirical CDF (ECDF)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMPrnJJMtYkG",
        "colab_type": "text"
      },
      "source": [
        "Currently, there's no ecdf plotting function for Seaborn, but it's coming soon. (See https://github.com/mwaskom/seaborn/pull/2141).  It will be `ecdfplot()`\n",
        "\n",
        "But there's code showing how to compute an ECDF and plot it with scatter().  See https://cmdlinetips.com/2019/05/empirical-cumulative-distribution-function-ecdf-in-python/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5dAxHNlxtp0",
        "colab_type": "text"
      },
      "source": [
        "*Preparing the data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMHTYp3TxmgP",
        "colab_type": "code",
        "colab": {}
      },
      "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": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-neScQasxoIL",
        "colab_type": "text"
      },
      "source": [
        "Drawing the chart with `scatter()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-qzyV4stQtI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "f686d50b-56ad-4d70-e420-6ed87b881597"
      },
      "source": [
        "plt.scatter(x=x, y=y);\n",
        "plt.xlabel('Population')\n",
        "plt.ylabel('Percentage of Counties');"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkFtVT4fxfQr",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a66e7b01-bbd3-4c7b-fb59-f1b220905a3f"
      },
      "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": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3fm5cX6+/d3Pxu2WhMzIy0L9/f1+H0SL4ulLRNIGiChsqbCpUIRBoNCDSbDptk5Or81QngxfH98EjH/5cr2JadMMALPn6D7293lb1065qsGsCJWXlMCgmODRR1d5f077/yrWX4Ol1e+qds6FmnP/NGoLrlmxzuV1twz2X49e8EoQEKujaNghT3vlBP/9bUwbig505mHbpefWaPHpEh51xn82pFairBOzrz8Wpmhp/U8996h0rAH2fareic7s2flUpe4qvPxd+N0CAvK/2/9BmkwEOTUBoAiVWB46VWPHgmkyXTU6LbhyASruKYyU2WB0qKu0aKu0qKqt+tzpUjOwVjTn/dXb0RocHNtjkEhygYM+RYmfziuJsY69udgo2yAgUCiLbhECpulsw1WqO6dTG3OA5z28XjLenxiLIZIDZZECQSUFwgKHOt/CiCnuD38ojgk1IuaSjXj61v01/sDMHcxIuwmtpvzVLk4cvv1E3B0/GL8sS2ofWX6+mel9W1gHIURFn/T509phsqJ7aySU4wIDDRZW47b2daB8SgIeu6o4H12Ri3uieMBnkOiN/Gmpyqu4Ivus/uxp8rwBFRsLFFv1vDJLUYOUeaJSx6YErXMZ8um9trppx2gSZ0M1Stx2/ocSx+KaBdRLoqc0yDVWmEWZjszZ5uKpU/YW/x09nj8mGANQ0idkdGo6V2nD7CmdyeeXaS/T+k3mje+p3MW3MRgCo11fhqiP4i3suR6BRRqDRgADF+dNkcA7/rJ0Mcosr8beJffX36Rxhxt8m9oXZZDjja2tKx66rxOHuW3lDlSkrV6IaTDatVO3+FlkGyqwqjpVYUWnX9LuVeaN7oqDMpieQ2smkqMIOk0FuVJNTSIAR7du5rnhrJ4OXP/8VT13TE8+M6Y0gkwHlNhWWsEC0MZ95B29Tm3GYOIiaH2d9Pkedbg4nh0PDH8dL8WtuCa5bsg2Zh4qRU1CBB9dk1nlOofaoKqAmmQDA4i37ERFsxN8m9tX3VTc5VW83diRQ7WTwxg39ERMZhF6dwtA5wozencLRtW3wWbfvVyeQThFBaB8a4Df9HUTnCt7ZnANOHRUWGmjAkSJrg8OHNU3gyMkK5BRU6HcwtZvEat+dFFXY8cHOHP1BssVb9utNXBk5RVi0eR8WXNML/5s1BKoAAo0yIs2mM+oIZps+0bmNycYP1H4GRa2aMsSkGBAQGAhNE8g+UYa84ko8uCazXj8LUPPE+YJresGhCbQNNtW5g6ndJLZ4y/46yeWhq7pj6bd/6qOqOrUJxId3XAq7qrlMJkwaRHQqJpsWzOFwzh2VX2LFa2m/1Xtu480b+uHoyQocOFHusp+l2qHCCljCAiFLEnIKy+v0tyzesh9PXtNTv2v5+xe/4pkxvXFeu2CEmg14dlwf2B2ukwsRkTvss2mBNE3gZIUVWXkl+OVIMW5fsRMTBsboiQZwJo87/vMjKuzqaftZqnWOMMMSFoj2oQH4YGdOnf6W6iaxrm2D8L9ZQ7Bwcn/06hSGv0QGoW1wIKJCA9nXQURnhXc2LcCpz7UcL7UhQDHgjhU78cqkS/R+lYbuVkyKoc58UKf2szQ0YV/1HEvTh52H92YMhixJMBsNaBfCZEJEnsFk4yPVnfqaJpBXbMVttZ5rySmoQJe2QXU67F0PKzagS9sgvQmsoX6WU59e9/c5lojI/zDZeFl1P0xxpR1lVgdCA424bcXOOv0tQSYDVE3U6bB/97s/692tLL6xP9qYTc5/QUZ9VFhwgHzafhaO/CIib2Oy8SK7XcXe/FIcL7HW7FNFvf4Wk0HGpqyDWHTjANy5chf+/sWvmJPQDV3aBmHVbUP10Wglx49Alp3zPkUGBwDBPrksIiK3OEDASxwODXmlVtyxYieCTAb936kPTVZ33l9xsQWpPx3G0psH4ZVrL0E3SwgsIQHo2Masd9af7bK6RETewmTjBXa7iqPFlXBU3cUUVTjXUCm3qXpnfnWT2fRh52HR5n0AgOv/2gUmRUZIgIKO4WYYjWc+PxgRkS+xGc3DbDYHjpZYYVeda7FXJ5Unr+kJCcD0YefVe2jy2bF9TvvQJBGRv+GdjYdUPyuTX2bDsRIrDBJQXGHHohsH4FipFQs+2QObQ6Bru2DMT+mFHh1C8Ze2QbCEmREVxudaiOjcwjsbD3A4NGQXlCFAcY4qO1FmgxCASZEQaJSxbPpgyBKgCcCuaujcJohJhYjOabyzaWaaJnC0uBI5BRWwqxoMsoQPdubAqDiTSXGFCoMECOFcOKxj2Jmt3khE5E+YbJrZ8VIrrA5Nf1amuMKO2fHdsGjzPpTbVLQLdfbBBAcY0KmNGYrC/wREdO5jTdeM7HYVFXbnnUu5TcWaHQdhVCQEKBIeuqoHLGGBUDVAloCo0EDe0RBRq8Fk0wwcDg2FZZU4UlwJgyTheKkNkVXPynywIweBRgUBigyDLCEsUEEn9tEQUSvDZHOW7HYVfxaUodSqOUedGSSYqvpnFFnG9YO7wKE6V8nsFO4cacZEQ0StDZPNWaieFeBQ1WCAE2U2FJTaYDYZYHMItAsxQROAUZEQZjawf4aIWi3WfmeoetSZQxX6YIAPdubArmkQQiAi2AQBQACQAIQGmHwcMRGR7zDZnKHqUWcGWdIHA1SPOisos8Mgg300RERV+FDnGdA0oY86K66wo1NEoD5x5oMjL4ZBlmA0yLCEBHA+MyIiMNmcEef0MxLyS60wKRIUIaNThBk3DT0PqhBQZAnRoYHsoyEiqsLasAmq5zurdKh1Rp0VV6hQVQGbQ4NBYqIhIjoVa8RGcjg0/HG81DndjCxx1BkRUROwVmwEh0OrN9+Zo4FRZwBHnRERNcSjySY9PR0jR45EYmIilixZUu/1I0eOYMqUKRg7dixSUlLw1VdfeTKcM1I9xLn2fGdf/HwEQQFKnUk1TYqM6BAuCUBE1BCPJRtVVfH000/j7bffRmpqKtavX499+/bVOebNN9/EqFGjsHbtWrz66qtYsGCBp8I5Y8dKqoY415rvbEDXtvj2t3xYwp2zAQQaZViCTTCZON6CiKghHks2mZmZ6NKlC2JiYmAymZCcnIy0tLQ6x0iShNLSUgBASUkJoqKiPBXOGbHZHM7BABLqzHeW+tNhDOsWBUdVk1pUSAACAphoiIhc8VgNmZeXh+joaH3bYrEgMzOzzjGzZ8/GjBkzsGLFClRUVGDp0qVuz6tpGrKyspo93lOFhIVBDoqAXGuIM1Az35kmAKNBhigvxL7cYo/H05DKykqvlIU/YFnUYFnUYFm0HD79Op6amopx48bhlltuQUZGBh566CGsX78esuz6hkuWZfTo0cOjcVUPCHBozm7/6kRTe+SZSZHRISwQSmQnoFMnj8bjSlZWlsfLwl+wLGqwLGqwLGr4Oul6rBnNYrEgNzdX387Ly4PFYqlzzJo1azBq1CgAQP/+/WG1WlFYWOipkBql9oAAgyzhP9uyGxjiLCM6NIBDnImIGqlJteXJkyexd+/eRh3bp08fZGdnIycnBzabDampqYiPj69zTIcOHbB161YAwP79+2G1WhEZGdmUkJqdPueZBFTaVYzu1xkf7MhBmyAjBIAAo4yIIAOnoSEiagK3zWhTpkzBm2++CYfDgfHjx6Nt27YYMGAAHn300dOfWFEwf/58zJw5E6qqYsKECejWrRsWLlyI3r17IyEhAY888gieeOIJLFu2DJIk4cUXX4Qk+W7ocO05z46X2mBSJISZFdw09DxoQkCWJBhkINjEZ2mIiJrCbbIpKSlBSEgIVq9ejbFjx2LOnDlISUlp1Mnj4uIQFxdXZ9/cuXP13y+88EK8//77TQzZc46VWmGQJeQX1wwIOF5iR7sQqaqfRkL7YC5+RkTUVG6b0VRVRX5+Pj777DNcccUVXgjJNxwODZV2FYosnX5AAPtpiIiazG3Neeedd2LGjBmIiYlB3759kZOTg65du3ohNO/KL7VCliQIiAbnPAsJkJloiIjOkNtmtFGjRukjxgAgJiYGr7/+ukeD8gW7qgEAiiscCDAa6sx5JsA5z4iIzobbr+p//vknpk2bhtGjRwMA9u7di0WLFnk8MG+yVzWf/WdbNiRJQlmligBFhqFqKpoOIRzmTER0NtzWoPPmzcP9998PRXHeBF188cX49NNPPR6YNzlnCJCRfEknfLAjByZF1md3DgmQOecZEdFZcluLVlRUoG/fvnX2GQznzjMmdrsKhyYgqRrCzAomDeoCWQI0ATg0DarGOxoiorPltiaNiIjAwYMH9edfPv/8c7Rv397jgXmDpgnklVqhyBLe/fbPeq9LkoTwQPbVEBGdLbd3Nk8++STmzZuHP/74A5dddhk6d+6Mv/3tb96IzeOOl1rhUAWCTDJG9+uM/247gImxfwEkZ19NWCBHoBERNQe3ySYmJgbLli1DeXk5NE1DSEiIN+Lyigq7c4nnd77+A1OHda0zU4AsA0FG3tUQETUHl8nm448/xpgxY1xO+z99+nSPBeUNmiZgkCWcLLcj+ZJOWP5tNibG/gUGWYJBkRASYOBdDRFRM3GZbCoqKgAAZWVlXgvGm46XWmGUJTg0DYFGuc7AAE0IhHD+MyKiZuMy2Vx//fUAgKFDh2LgwIF1Xtu5c6dno/KCCruKIJMBZpMBJ8sdaBdi0Oc/CwkwcP4zIqJm5Lad6Nlnn23UPn9S3YTm0DSYFLnObAGaEBDC1xESEZ1bXN7ZZGRkICMjAwUFBXX6bUpLS6GqqleC85RjVU1oRRU2BJyyLg2HOxMRNT+XycZut6O8vByqqtbptwkJCcFrr73mleA8oXp2Z7PJoE9NE2Y2QhMCJpkTbhIReYLLZDN48GAMHjwY48aNQ6dOnbwZk0fll1phkCQcK7YiONAAWXJOwqkJQEAAOHdmRyAiaincPmdjs9kwb948HD58GA6HQ9+/fPlyjwbmKXbV2U/j0DRY7YBBrkkusiQhjE1oRETNzm2ymTt3Lq6//npMmjQJsuzfzUuaJqDIEgpKbQ2OQgs3KxyFRkTkAW6TjaIouOGGG7wRi8cdL7UiQJFhVCQIIeqMQgO4Zg0Rkae4vVW58sorsXLlSuTn56OoqEj/5280TaDCrkKDc4lns0lBgCJDrlqzhs/WEBF5jts7m48++ggA8M477+j7JElCWlqa56LygOOnDAywOQRkSUATACAgSxwYQETkKW6TzaZNm7wRh8dV2FUODCAi8hG3yWbt2rUN7h87dmyzB+MpDodz1U0ODCAi8g23yebnn3/Wf7dardi6dSt69erlV8kmv9akm4qQOTCAiMjL3CabefPm1dkuLi7Gvffe67GAPMGualCMMoICFBSV2Wvd1cgID5R5V0NE5GFNfnDGbDbj0KFDnojFY5SqBdKCTDKiwwMhyxJMigzFAARzKQEiIo9ze2dz++23679rmob9+/dj1KhRHg2qOTkcGgIUucEF0oKMnAeNiMgb3CabW265Rf/dYDCgU6dOiI6O9mhQzSm/1AqTQUKYWamzQJpD0+BQmWiIiLzBbW07ePBgnH/++SgrK0NxcTGMRqM34mo2dlWD5usgiIhaObfJ5tNPP8WkSZPw+eef47PPPtN/9wfVc6F9+tNhSFLdQQDOdWv8K3ESEfkrt81oixcvxpo1a9C2bVsAQEFBAW6++WZcddVVbk+enp6O5557DpqmYdKkSZg1a1a9Yz799FO88cYbkCQJF198MV555ZUzuIyGVc+FNqBrW2zek4v4nh0ghECALCEkUIbRyFkDiIi8wW2yEULoiQYA2rRpA9GIdZNVVcXTTz+NpUuXwmKxYOLEiYiPj8eFF16oH5OdnY0lS5bgv//9L8LDw3HixIkzvIyGVVQtkhZmVjD8IgsctdatsTvYX0NE5C1uk83w4cMxY8YMJCcnA3DeiVx22WVuT5yZmYkuXbogJiYGAJCcnIy0tLQ6yWbVqlW48cYbER4eDgB1klpzMMg1c6GdSjHw2RoiIm9xm2wefvhhbNiwATt37gQAXHfddUhMTHR74ry8vDqj1iwWCzIzM+sck52dDQC4/vrroWkaZs+ejcsvv/y059U0DVlZWW7fHwAiO3Z1ORdaQd4RHK2sbNR5WqrKyspGl8W5jkKIje8AAB7TSURBVGVRg2VRg2XRcrhMNgcOHMDx48cxcOBAJCUlISkpCQCwY8cOHDx4EH/5y1/O+s1VVcWBAwfw3nvvITc3FzfddBPWrVuHsLAwl38jyzJ69Ojh9tyaJlBUYW1w1oCwQBltIs876/h9LSsrq1Fl0RqwLGqwLGqwLGr4Oum67Lh4/vnnERISUm9/aGgonn/+ebcntlgsyM3N1bfz8vJgsVjqHRMfHw+j0YiYmBh07dpVv9s5W8dLrdA01Js1wCALONRmeQsiImokl8nm+PHj6N69e7393bt3x+HDh92euE+fPsjOzkZOTg5sNhtSU1MRHx9f55gRI0bghx9+AOAc5Zadna338Zyt6oXS7KqA1aFB1Zw/7ar7wQ1ERNS8XDajlZSUuPyjykb0dSiKgvnz52PmzJlQVRUTJkxAt27dsHDhQvTu3RsJCQm47LLL8O233+Lqq6+GwWDAQw89hIiIiDO7klNwcAARUcvhMtn07t0bq1atwrXXXltn/+rVq9GrV69GnTwuLg5xcXF19s2dO1f/XZIkPProo3j00UebEnOjVC8pwIXSiIh8z2WyeeyxxzB79mysW7dOTy67d++G3W7HG2+84bUAz5RBlhpcKC0kwMAlBYiIvMxlsmnXrh3ef/99bNu2Db///jsA553K0KFDvRbc2ZBlIMBoQESwrC+Uxt4aIiLfcPuczZAhQzBkyBBvxNKsNA1QZEBWZGhCwChJkGUBjbNyEhF5ndtk48/sqoBDFZAlwC4EFAMQwOnQiIi8zuXQZ5vN5s04mpWmscGMiKglcZlsrrvuOgDAgw8+6LVgmsvxUquvQyAiolpcNqPZ7XasW7cOGRkZ2LBhQ73Xq6evaYkq7CpCAg1Va9jU3OVIkgSZkz0TEXmdy2Tz1FNPYd26dSgpKcHmzZvrvd6Sk41BlpBbZEWbYKXeAAFZ4rBnIiJvc5lsYmNjERsbi969e2PSpEnejOmsVT/QWVrpgEE26AMETAof6CQi8gW3o9HGjBmD5cuXY8eOHQCAQYMG4frrr4fR2HKXVOYDnURELYvbHowFCxbgl19+weTJkzF58mTs2bMHTz31lBdCOzsmRUZEsEl/mFMTfMaGiMhX3N7Z/Pzzz/jkk0/07aFDh+Kaa67xaFBERHRucXtnYzAYcPDgQX07JycHBkPLfjJSllE1Eq0GR6IREfmO2zubhx56CFOnTkVMTAyEEDhy5EijFk/zJdXFVDUqm9GIiHzCbbIZOnQoNmzYgD/++AMAcP7558NkatkjujRN1BuJpmoq2ph5a0NE5AuNmhvNZDLh4osv9nQszcYgS5AkCTkF5QgyGVBuU9EpIhAGjkQjIvKJc3YiTpMiIyYyCLIEaAJQWnY3ExHROe2ca1dyODQOECAiamHcVr9CCHz88cf66pxHjhxBZmamxwM7U/mlVn2AQIAiwyBLCFBkKDL4nA0RkY+4TTZPPfUUfvzxR6SmpgIAgoODsWDBAo8Hdqbsqlb1T8Dq0KBqzp92VYDTohER+YbbZJOZmYknn3wSAQEBAIDw8HDY7XaPB3amFFnCu9/+WW+/JEkI57xoREQ+4TbZKIoCVVX1PpCCggLILbjzI0CRkXxJJ/x32wEI4VxeINAoIzzQAEVpuXETEZ3L3I5GmzJlCu666y6cOHECr776Kj7//HPcc8893oityTRNQJaB8CAjJg3qoo9EU4UAF+8kIvIdt8nmmmuuQa9evbBt2zYIIbBo0SJccMEF3oityY6XWiFLDc8ewMEBRES+4zbZFBUVoW3btkhOTtb32e32FrnEQIVdhdlkgF0VcKhCnz1AMQABfM6GiMhn3Cab8ePH4+jRowgLCwMAFBcXo127dmjXrh2eeeYZ9O7d2+NBNpZBlnCs2IrgwPqZRTFwKBoRka+4TTaXXnopRo4cicsuuwwA8M0332DDhg0YP348FixYgNWrV3s8yMaqXqHTagcMck3CkSWu0ElE5Etuh2f99NNPeqIBgOHDhyMjIwP9+vWDzWbzaHBNVb1CZ3GFCoMECK7QSUTUIri9s2nfvj2WLFmi99l8+umnaNeuHVRVbXFDoGUZCDAaEBEs6yt0chAaEZHvuU02f//73/Gvf/0Ld911FwBgwIABeOWVV6CqKv75z396PMCm0FysY8ORaEREvuU22URGRmLevHkNvtalS5dmD+hscSQaEVHL4zbZFBQU4N///jf27dsHq9Wq71++fLnbk6enp+O5556DpmmYNGkSZs2a1eBxX3zxBebMmYM1a9agT58+TQi/hsanNomIWiy3nS4PPPAAzj//fBw6dAizZ89Gp06dGpUQVFXF008/jbfffhupqalYv3499u3bV++40tJSLF++HJdccsmZXUGV46VW9wcREZFPuE02RUVFmDRpEhRFweDBg/HCCy9g27Ztbk+cmZmJLl26ICYmBiaTCcnJyUhLS6t33MKFC3HrrbfqE32eqQq7Conr2BARtUhum9EUxXlIVFQUtmzZgqioKJw8edLtifPy8hAdHa1vWyyWeuvg/PLLL8jNzcUVV1yBd955p1EBa5qGrKysevtDo7sgt8iKNsFKvQECDocdWVn1Z4L2d5WVlQ2WRWvEsqjBsqjBsmg53CabO+64AyUlJXj44YfxzDPPoKysDI899thZv7GmaXjxxRfxwgsvNOnvZFlGjx496u3PO1kBh6ahtNIBg2zQBwiYFAkd2oQgqoG/8XdZWVkNlkVrxLKowbKowbKo4euk6zbZhIWFITQ0FKGhoXjvvfcAADt37nR7YovFgtzcXH07Ly8PFotF3y4rK8Nvv/2GqVOnAgCOHTuGO+64A2+++eYZDRKofqDzZLkD7UIM0PhAJxFRi+G2N+PZZ59t1L5T9enTB9nZ2cjJyYHNZkNqairi4+P110NDQ/H9999j06ZN2LRpE/r163fGiaaaSZEREWzSH+bUBJ+xISJqCVze2WRkZCAjIwMFBQVYunSpvr+0tBSqqro/saJg/vz5mDlzJlRVxYQJE9CtWzcsXLgQvXv3RkJCQvNcARERtXguk43dbkd5eTlUVUVZWZm+PyQkBK+99lqjTh4XF4e4uLg6++bOndvgsdVNdGdK1kei1Txvw5FoREQtg8tkM3jwYAwePBjjxo1Dp06dvBlTk2ma4FQ1REQtmNsBAjabDfPmzcPhw4fhcDj0/Y2ZQcBbqlfo5FQ1REQtk9tkM3fuXFx//fWYNGlSi5vluVqFXUWQiVmFiKilatRDnTfccIM3YjljhqpF0xoicdQzEZHPub1VufLKK7Fy5Urk5+ejqKhI/9eSGGUJxRX2evslSUI4V+gkIvI5t3c2H330EQDUmU5GkqQG5znzFYMsQZIklFWqCDMboQkBkywhJECGorTMpj8iotbEbbLZtGmTN+I4ayZFrhokoEETgICAQ2WiISJqCdzWxhUVFVi0aJG+gFp2djY2b97s8cCIiOjc4TbZPProozAajcjIyADgnPOsJS0HzUXTiIhaPrfJ5uDBg7j11lv1pQbMZjOEaDkV/PFSa63ZA2pw9gAiopbDbZ+NyWRCZWWlXpkfPHgQJlPLGeFVYVdhlgycPYCIqAVzm2zuvvtuzJw5E0ePHsX999+PjIyMJq9B40kGWcKxYiuCAw31Zg8INvEhGyKilsBtshk2bBh69uyJn376CUIIPP7444iMjPRGbI1irHqg02oHDHLNLAKyJCGMz9gQEbUIbns1vvzySyiKgiuuuAJXXnklFEXBxo0bvRFbo1QvmlZcocIgAYKLphERtThuk80bb7yB0NBQfTssLAxvvPGGR4NqKi6aRkTUsrlNNloDtXZjFk/zFo5EIyJq+dz22fTu3RsvvPACbrzxRgDAypUr0atXL48H1lhcx4aIqOVz+/1/3rx5MBqNuOeee3DvvfciICAA8+fP90ZsjWZXBawODarm/GlXW85zQERE5ObORlVV3HbbbWe9ZDMREbVup72zMRgMkGUZJSUl3oqnSRwOtpUREfkDt302QUFBSElJwaWXXoqgoCB9/xNPPOHRwBojv9SKAKNUNUCgpumMAwSIiFoWt8kmKSkJSUlJ3oilyeyqBrsKaEJDgGLQBwg4NBUOlctEExG1FG6Tzbhx41BZWYkjR47g/PPP90ZMjabIEpZ/9ycmxMYg+3gpgkwGlNtUdI40IzKIswcQEbUUbhubNm3ahDFjxmDmzJkAgKysLNx+++0eD6wxAhQZyZd0wgc7ctA5woz2oQHoZglB+xAjV+gkImpBGjWDwJo1axAWFgYA6NGjBw4dOuTxwBorzKxg0qAuMMgSNAE4NA2OlvPMKRERoRHJRlGUOtPVAPWf2PcVzh5AROQf3PbZXHjhhVi3bh1UVUV2djbee+899O/f3xuxucXZA4iI/EOjZhDYt28fTCYT7r//foSEhODxxx/3RmyNwtkDiIhaPpd3NlarFf/9739x8OBBXHTRRfjf//6nLw1NRETUFC7vbB5++GHs3r0bF110EdLT0/HSSy95My4iIjqHuLxV2b9/P9atWwcAmDhxIiZNmuS1oIiI6Nzi8s6mdpPZmTafpaenY+TIkUhMTMSSJUvqvb506VJcffXVSElJwbRp03D48OEmnZ+j0YiI/IPLLLJ3714MGDAAACCEgNVqxYABAyCEgCRJ2LVr12lPrKoqnn76aSxduhQWiwUTJ05EfHw8LrzwQv2YHj164IMPPoDZbMZ//vMf/O1vf8M///nPRgfP0WhERP7BZbLJyso6qxNnZmaiS5cuiImJAQAkJycjLS2tTrIZMmSI/nu/fv3wySefNPr81TM+21UBhyogS4BdCCgGIIDTohERtSgeG16Wl5eH6OhofdtisSAzM9Pl8WvWrMHll1/u9ryapiErKwth0V0QcJopac42WfqDysrKVnGdjcGyqMGyqMGyaDlaxFjmjz/+GLt378aKFSvcHivLMnr06IEDJ8pgPM1xPXr0aL4AW6isrKxWcZ2NwbKowbKowbKo4euk67GudIvFgtzcXH07Ly8PFoul3nHfffcdFi9ejDfffBMmU+NnalZkCZ/+dLjBAQLhgadLQ0RE5G0eSzZ9+vRBdnY2cnJyYLPZkJqaivj4+DrH7NmzB/Pnz8ebb76Jtm3bNun8AYqMAV3bYvOeXJgUGQZZgtkoo43ZAKORnTZERC2Jx5rRFEXB/PnzMXPmTKiqigkTJqBbt25YuHAhevfujYSEBLz88ssoLy/H3LlzAQAdOnTA4sWLG/0eYWYFwy+ywKFq0AQgIGB3cNwzEVFL49E+m7i4OMTFxdXZV51YAGDZsmWefHsiImoheBtAREQex2RDREQe57fJhlPVEBH5jxbxnM2Z4FQ1RET+w2+TDcCpaoiI/AUbnYiIyOOYbIiIyOP8MtlomuAAASIiP+KXfTbHS62QJQ4QICLyF355H1BhV6HBOUDA6tCgas6fdlX4OjQiImqAXyYbgyzhWLG1wdcUg9TgfiIi8h2/TDZGWYJD02C1q3X2y5KEsMDGL1NARETe4Z/JRpEQFKCguEKFQQKEAEyKjLBAA2SZdzZERC2NXw4Q0DQgyCQjUAmEJgSUqsEBDtX93xIRkff5ZbIBOHsAEZE/8ctmNCIi8i9+l2xkPrVJROR3/K/mlv225Y+IqNXyv2QDQBOiwalqDH55NURE5z6/rJ7f+foPKDIQoMgwyBICFBlGg4TQAD5jQ0TUEvldspElIPmSTlj+bTYq7SpUTUCSgCCjDEXxu8shImoV/LIDJMysYNKgLpAlQBOAQ9PgUJloiIhaKtbQRETkcf6XbCSuY0NE5G/8rxlNcB0bIiJ/43/JBpyqhojI3/hd4xOXRyMi8j9+l2wcLlbj5KJpREQtl98lGwHBRdOIiPyM3yUbWZK4aBoRkZ/xaLJJT0/HyJEjkZiYiCVLltR73Waz4Z577kFiYiImTZqEQ4cOuT2nLEuIDg+ELEswKTIMXDSNiKjF81iyUVUVTz/9NN5++22kpqZi/fr12LdvX51jVq9ejbCwMHz55Ze4+eab8fe//93teYUQsDo0qJrzp91FHw4REbUcHks2mZmZ6NKlC2JiYmAymZCcnIy0tLQ6x2zatAnjxo0DAIwcORJbt26FEEweRETnGo89Z5OXl4fo6Gh922KxIDMzs94xHTp0cAaiKAgNDUVhYSEiIyOb9F4CAllZWWcftJ+prKxsldfdEJZFDZZFDZZFy+GXD3XWJkkSIswBaN+jh69D8bqsrCz0aIXX3RCWRQ2WRQ2WRQ1fJ12PNaNZLBbk5ubq23l5ebBYLPWOOXr0KADA4XCgpKQEERERpz2vptWsYxNolNExNJBLCxARtXAeq6X79OmD7Oxs5OTkwGazITU1FfHx8XWOiY+Px0cffQQA+OKLLzBkyJB6k2yeyiA5p6kJMinoGG6G0ch5aoiIWjqPNaMpioL58+dj5syZUFUVEyZMQLdu3bBw4UL07t0bCQkJmDhxIh588EEkJiYiPDwcr776qtvzaqoDnSKCPBU2ERF5gEf7bOLi4hAXF1dn39y5c/XfAwIC8Nprr3kyBCIiagHY2UFERB7HZENERB7HZENERB7HZENERB4nCT+bH+bHH39EQECAr8MgIvIrVqsV/fr189n7+12yISIi/8NmNCIi8jgmGyIi8jgmGyIi8jgmGyIi8jgmGyIi8jgmGyIi8ji/Sjbp6ekYOXIkEhMTsWTJEl+H02RHjx7FlClTcPXVVyM5ORnvvvsuAKCoqAjTp09HUlISpk+fjpMnTwIAhBB49tlnkZiYiJSUFPzyyy8NntdVueTk5GDSpElITEzEPffcA5vNBgCw2Wy45557kJiYiEmTJuHQoUMevnLXVFXF2LFjcdtttzVLzB999BGSkpKQlJSkL18BALt370ZKSgoSExPx7LPP6suPuyp7bysuLsacOXNw1VVXYdSoUcjIyGi1n4tly5YhOTkZo0ePxn333Qer1doqPhePPvoohg4ditGjRzf4+umu9a233kJiYiJGjhyJr7/+Wt/fmDrzTD5nrsrztISfcDgcIiEhQRw8eFBYrVaRkpIifv/9d1+H1SR5eXli9+7dQgghSkpKRFJSkvj999/FSy+9JN566y0hhBBvvfWWePnll4UQQmzZskXMmDFDaJomMjIyxMSJE+ud83TlMmfOHLF+/XohhBDz5s0TK1euFEIIsWLFCjFv3jwhhBDr168Xc+fO9eyFn8b//d//ifvuu0/MmjVLCHF2MRcWFor4+HhRWFgoioqKRHx8vCgqKhJCCDFhwgSRkZEhNE0TM2bMEFu2bBFCCJdl720PPfSQWLVqlRBCCKvVKk6ePNkqPxe5ubniyiuvFBUVFXqsH3zwQav4XPzwww9i9+7dIjk5ucHXXV3r77//LlJSUoTVahUHDx4UCQkJwuFwNLrObOrn7HTleTp+c2eTmZmJLl26ICYmBiaTCcnJyUhLS/N1WE0SFRWFXr16AQBCQkJw/vnnIy8vD2lpaRg7diwAYOzYsdi4cSMA6PslSUK/fv1QXFyM/Pz8Oud0VS5CCGzbtg0jR44EAIwbN04vr02bNmHcuHEAgJEjR2Lr1q36Nzpvys3NxZYtWzBx4kQAOOuYv/nmGwwbNgxt2rRBeHg4hg0bhq+//hr5+fkoLS1Fv379IEkSxo4dq5/XVdl7U0lJCbZv366Xg8lkQlhYWKv9XKiqisrKSjgcDlRWVqJ9+/at4nMxaNAghIeHu3zd1bWmpaUhOTkZJpMJMTEx6NKlCzIzMxtdZzb1c+aqPN3xm2STl5eH6OhofdtisSAvL8+HEZ2dQ4cOISsrC5dccglOnDiBqKgoAED79u1x4sQJAPWvOTo6ut41uyqXwsJChIWFQVGUen+bl5eHDh06AHAuchcaGorCwkLPXawLzz//PB588EHIsvNjeLYxuyqL05Wjq7L3pkOHDiEyMhKPPvooxo4di8cffxzl5eWt8nNhsVhwyy234Morr8Tw4cMREhKCXr16tcrPxalcXWtjr89VndnUz9mZ1sV+k2zOJWVlZZgzZw4ee+wxhISE1HlNkiS3S2OfCzZv3ozIyEj07t3b16HofFX2DocDe/bsweTJk7F27VqYzeZ67eut5XNx8uRJpKWlIS0tDV9//TUqKioa9a3Zk1pL2QOevVa/STYWiwW5ubn6dl5eHiwWiw8jOjN2ux1z5sxBSkoKkpKSAABt27bVm0Hy8/MRGRkJoP415+bm1rtmV+USERGB4uJiOByOen9rsVhw9OhRAM6KrqSkBBERER664obt2rULmzZtQnx8PO677z5s27YNzz333FnF7KosTleOrsrem6KjoxEdHY1LLrkEAHDVVVdhz549rfJz8d1336Fz586IjIyE0WhEUlISdu3a1So/F6dyda2NvT5XdWZTP2dnWhf7TbLp06cPsrOzkZOTA5vNhtTUVMTHx/s6rCYRQuDxxx/H+eefj+nTp+v74+PjsXbtWgDA2rVrkZCQUGe/EAI//vgjQkND9dvdq666CoDrcpEkCX/961/xxRdfAHCOHqkur/j4eH0EyRdffIEhQ4Z4/Zvb/fffj/T0dGzatAn/+Mc/MGTIELzyyitNjjkvLw/Tpk0DAAwfPhzffPMNTp48iZMnT+Kbb77B8OHDERUVhZCQEPz4448QQjRYxkDdsvem9u3bIzo6Gn/88QcAYOvWrbjgggta5eeiY8eO+Omnn1BRUQEhBLZu3YoLL7ywVX4uAGDFihVYsWKFHlND1xofH4/U1FTYbDbk5OQgOzsbffv2PW2d+corr+DLL7/Uz9uUz5mr8nSrEYMkWowtW7aIpKQkkZCQIBYtWuTrcJps+/bt4qKLLhKjR48W11xzjbjmmmvEli1bREFBgZg6dapITEwU06ZNE4WFhUIIITRNE0899ZRISEgQo0ePFpmZmUIIIU6cOCGSkpL087oql4MHD4oJEyaIESNGiLvvvltYrVYhhBCVlZXi7rvvFiNGjBATJkwQBw8e9GIp1Ldt2zZ9NFpTY87MzBS33HKLfq7Vq1eLESNGiBEjRog1a9bo+zMzM0VycrJISEgQCxYsEJqmCSGEy7L3tj179ohx48aJ0aNHizvuuEMUFRW12s/FwoULxciRI0VycrJ44IEH9FFW5/rn4t577xXDhg0TPXv2FJdddplYtWqVWLBggVi3bt1pr1UIIRYtWiQSEhJEUlKSPqJOCNefgVmzZoldu3ad9lpdfc6EcF2ep8MlBvzQ5s2bkZOTg6lTp/o6FJ9bsWIFOnTo4LNvni0JPxc1zpXPxW233YbXX38dJpOpWc87Y8YMvPPOO816TneYbIiIyOP8ps+GiIj8F5MNERF5HJMNERF5HJMNERF5HJMNtQo9evTAmDFjMHr0aMyZMwcVFRXNev4pU6bg559/Pu0xy5Ytq/O+t956K4qLi5s1DqKWismGWoXAwEB8/PHHWL9+PYxGI95//32vx7B8+fI6yebf//43wsLCvB4HkS8w2VCrExsbiwMHDqCoqAh33nknUlJScO2112Lv3r0AgNdffx0PPvggrrvuOiQlJWHVqlUAgO+//15fdwcAnn76aXz44Yf1zv/kk09i/PjxSE5OxmuvvQbAmWjy8/Mxbdo0TJkyBYDzCe2CggIAwNKlSzF69GiMHj0ay5YtA+CcoHPUqFF44oknkJycjFtuuQWVlZUeKxciT2KyoVbF4XAgPT0dF110EV5//XX07NkT69atw7333ouHH35YP+7XX3/Fu+++i/fffx//+te/mjTD+L333osPP/wQn3zyCbZv3469e/di6tSpiIqKwrvvvov33nuvzvG7d+/Ghx9+iFWrVuF///sfVq9ejT179gAADhw4gBtvvBGpqakIDQ3Vp2wh8jdMNtQqVFZWYsyYMZgwYQI6duyIiRMnYufOnRgzZgwAYOjQoSgqKkJpaSkAICEhAYGBgYiMjMRf//pXt/0xtX322WcYN24cxo4di99//x379+8/7fE7d+7EiBEjEBQUhODgYCQmJmLHjh0AgM6dO6NHjx4AgF69euHw4cNncvlEPqf4OgAib6jus2mshiagNBgM0DRN37ZarfWOycnJwf/93/9hzZo1CA8PxyOPPNLgcY1Ve5oSg8FwVuci8iXe2VCrFRsbi08++QSAsz8mIiJCX18oLS0NVqsVhYWF+OGHH9CnTx906tQJ+/fvh81mQ3FxMbZu3VrvnGVlZTCbzQgNDcXx48eRnp6uvxYcHIyysrIG49i4cSMqKipQXl6OjRs3IjY21kNXTeQbvLOhVmv27Nl47LHHkJKSArPZjBdffFF/rXv37pg6dSoKCwtx55136ut1XHXVVRg9ejQ6d+6Mnj171jvnxRdfjJ49e2LUqFGIjo7GgAED9NeuvfZazJw5E1FRUXX6bXr16oXx48dj0qRJAICJEyeiZ8+eOHTokKcuncjrOBEn0Slef/11BAUFYcaMGb4OheicwWY0IiLyON7ZEBGRx/HOhoiIPI7JhoiIPI7JhoiIPI7JhoiIPI7JhoiIPO7/AecydEOCPq89AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}