slides/module_4/data_visualization.ipynb

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      "display_name": "Psych81.09",
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      "name": "data_visualization.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ContextLab/cs-for-psych/blob/master/slides/module_4/data_visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install davos\n",
        "import davos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-yuk-bDe0vF",
        "outputId": "879bcb32-49dd-49ab-cdbd-aad11ef79899"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ni9m-xyzDzCM"
      },
      "source": [
        "# Overview\n",
        "This notebook provides an overview of some different types of plots you can make in Python.  We'll be using several sample datasets that come with the scikit-learn Python toolbox:\n",
        "  - `boston`: Boston house prices dataset\n",
        "  - `digits`: images of hand-drawn numerals\n",
        "  - `wine`: wine characteristics\n",
        "\n",
        "We'll explore these datasets (and some others, introduced later) in a few different ways to illustrate different ways of manipulating and visualizing the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZqaD5sfADzCO"
      },
      "source": [
        "# Import libraries\n",
        "\n",
        "The `smuggle` keyword works like `import`, but it allows us to import packages that haven't yet been installed.  To enable use of the `smuggle` keyword, you need to install and import that `davos` library (see previous cell)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIEw_Lc5DzCP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7a6cb2f-76e6-4080-f256-d9777418b910"
      },
      "source": [
        "#machine learning library\n",
        "from sklearn smuggle datasets, svm, metrics # pip: scikit-learn<1.2\n",
        "                                            #sklearn is a python machine learning library\n",
        "                                            # - datasets has sample datasets\n",
        "                                            # - svm is a pattern classification tool\n",
        "                                            # - metrics is a tool for evaluating the performance \n",
        "                                            #   of machine learning algorithms\n",
        "\n",
        "#visualization tools\n",
        "smuggle matplotlib.pyplot as plt # matplotlib is a basic plotting library\n",
        "smuggle seaborn as sns # seaborn is a library that uses matplotlib to make styled plots\n",
        "smuggle plotly_express as px # plotly express is a library for drawing interactive figures\n",
        "smuggle hypertools as hyp # hypertools is a library for visualizing \"high-dimensional\" data\n",
        "\n",
        "#data wrangling tools\n",
        "smuggle pandas as pd\n",
        "smuggle numpy as np\n",
        "\n",
        "#warning management\n",
        "smuggle warnings"
      ],
      "execution_count": 2,
      "outputs": [
        {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t1uNkMBbDzCR"
      },
      "source": [
        "# Load in datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RfViQMY1DzCS"
      },
      "source": [
        "#load the datasets\n",
        "with warnings.catch_warnings():\n",
        "  # ignore depreciation warning\n",
        "  warnings.simplefilter('ignore')\n",
        "  boston = datasets.load_boston()\n",
        "digits = datasets.load_digits()\n",
        "wine = datasets.load_wine()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2yMxkjmDzCV"
      },
      "source": [
        "#convert the datasets into Pandas DataFrames\n",
        "boston = pd.DataFrame(boston['data'], columns=boston['feature_names'], index=boston['target'])\n",
        "digits = pd.DataFrame(digits['data'], index=digits['target'])\n",
        "wine = pd.DataFrame(wine['data'], columns=wine['feature_names'], index=wine['target'])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09aOMOptDzCX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f352bf77-2feb-432b-f1b5-c302e9547a6f"
      },
      "source": [
        "boston.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "         CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "24.0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "21.6  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
              "34.7  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
              "33.4  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "36.2  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "      PTRATIO       B  LSTAT  \n",
              "24.0     15.3  396.90   4.98  \n",
              "21.6     17.8  396.90   9.14  \n",
              "34.7     17.8  392.83   4.03  \n",
              "33.4     18.7  394.63   2.94  \n",
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              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
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              "      <th>LSTAT</th>\n",
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              "      <th>24.0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
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              "      <th>21.6</th>\n",
              "      <td>0.02731</td>\n",
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              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
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              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
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    {
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        "outputId": "5b415d0f-873d-4d06-fd87-5e085a45408f"
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      "source": [
        "wine.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
              "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
              "0    13.20        1.78  2.14               11.2      100.0           2.65   \n",
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              "0    14.37        1.95  2.50               16.8      113.0           3.85   \n",
              "0    13.24        2.59  2.87               21.0      118.0           2.80   \n",
              "\n",
              "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
              "0        3.06                  0.28             2.29             5.64  1.04   \n",
              "0        2.76                  0.26             1.28             4.38  1.05   \n",
              "0        3.24                  0.30             2.81             5.68  1.03   \n",
              "0        3.49                  0.24             2.18             7.80  0.86   \n",
              "0        2.69                  0.39             1.82             4.32  1.04   \n",
              "\n",
              "   od280/od315_of_diluted_wines  proline  \n",
              "0                          3.92   1065.0  \n",
              "0                          3.40   1050.0  \n",
              "0                          3.17   1185.0  \n",
              "0                          3.45   1480.0  \n",
              "0                          2.93    735.0  "
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              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f23fee6-494c-4643-877c-e01131cb9667')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3f23fee6-494c-4643-877c-e01131cb9667 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3f23fee6-494c-4643-877c-e01131cb9667');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nTk8t4lDzCf"
      },
      "source": [
        "# Hypertools\n",
        "\n",
        "HyperTools is a nice tool for starting to explore a dataset.  Plotting a dataset with HyperTools entails projecting the data onto a 3-dimensional space, where similarly valued datapoints are shown close together.  Documentation for Hypertools may be found [here](https://hypertools.readthedocs.io/en/latest/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMG-gcS4DzCf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "outputId": "3e06d4bb-6d53-4170-95a2-f15635f23dbb"
      },
      "source": [
        "#Boston dataset\n",
        "hyp.plot(boston, 'o', hue=np.array(boston.index), reduce='FastICA'); #each dot is a home, colored by sale price"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/decomposition/_fastica.py:494: FutureWarning: Starting in v1.3, whiten='unit-variance' will be used by default.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/hypertools/plot/plot.py:508: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  kwargs[kwarg]=np.array(kwargs[kwarg])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDCIKNK1DzCh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "dfcec257-7bbd-4084-85dc-133a855507e7"
      },
      "source": [
        "#Digits dataset\n",
        "hyp.plot(digits, 'o', hue=np.array(digits.index)); #each digit is a drawn digit, colored by the number it refers to"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/hypertools/plot/plot.py:508: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  kwargs[kwarg]=np.array(kwargs[kwarg])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5ym_Ot0DzCk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "59710cb1-6705-4219-871e-7ac9e4c78112"
      },
      "source": [
        "#Wine dataset\n",
        "hyp.plot(wine, 'o', hue=np.array(wine.index), reduce='SpectralEmbedding'); #each dot is a wine, colored by "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/hypertools/plot/plot.py:508: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  kwargs[kwarg]=np.array(kwargs[kwarg])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egxboi6EDzCm"
      },
      "source": [
        "# Visualizing some raw data\n",
        "\n",
        "The `digits` dataset contains 8x8 drawings of different digits.  The hypertools plot above suggests that drawings of different digits are \"clustered\" in different ways.  Let's visualize a few example digits using seaborn heatmaps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GguSDPZ4DzCn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "15939981-88bf-4449-a1e4-825ffef0a419"
      },
      "source": [
        "#plot the first 10 example digits from the dataset\n",
        "for i in range(10):\n",
        "    print(f'Digit identity: {digits.index[i]}')\n",
        "    sns.heatmap(np.reshape(digits.iloc[i].values, [8, 8]), cmap='gray')\n",
        "    plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digit identity: 9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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MUElJibp06eJoHcIfAAAbr8J/8uTJ5107FArpvvvu03333demdbjnDwBAwND5AwBg4/cv9iH8AQCw8Xv4s+0PAEDA0PkDAGDj986f8AcAwMbv4c+2PwAAAUPnDwCAjd87f8IfAAAbv4c/2/4AAAQMnT8AADZ+7/wJfwAAbAh/AAACxu/hzz1/AAACxpPO3zRNmabpxdIAAFwQnb/Na6+9ps2bN+v111+XJL3++uu6/fbbtWjRIr3wwgstmiMWiykcDscdAAAkCsuyXDsSkaPwLykp0ahRo3T33Xdr9OjRKikp0cSJE1VdXa2jR4/qxhtvbNEvAJFIRLW1tXEHAADoGI7C/7777tPy5cv117/+VZs3b9Z//Md/aPHixSotLdWOHTu0fPlyrV69+oLzGIah7t27xx0AACQKOv9P+MMf/qAFCxZIkubMmaMPP/xQX/ziF5vOz58/X7///e9dLRAAgI5G+NuEQqH//4OXXKIuXbrE3a9PSUlhCx8AgATnKPwzMzN15MiRptf79u1Tenp60+tjx44pNTXVveoAAPCA3zt/R3/qd/vtt6uhoaHp9fDhw+POP/vss7rhhhvcqQwAAI8kami7xVH433bbbec9v2rVqjYVAwAA2h8f7wsAgA2dPwAAAUP4AwAQMH4Pf77YBwCAgKHzBwDAxu+dP+EPAICN38OfbX8AAAKGzh8AABu/d/6EPwAANn4Pf7b9AQAIGDp/AABs/N75E/4AANj4PfzZ9gcAIEE0NDRo5cqVGjhwoLp27apPf/rT+s53vuP6LyN0/gAA2HjV+a9Zs0YbNmzQY489pmHDhqmsrEwLFy5UOBzWnXfe6do6hD8AADZuhr9pmjJNM27MMAwZhtHsvS+++KJmzpyp6dOnS5IyMzP1xBNP6MCBA67VIxH+rvrggw+8LsEVr7zyitcluOL999/3uoQ2e+ihh7wuwRWjRo3yuoQ2y8zM9LoEV7z11ltel3BRcDP8Y7GYioqK4sai0agKCwubvfe6667Tpk2bdPjwYQ0aNEivvPKK9u7dqwcffNC1eiTCHwCAdhWJRFRQUBA3drauX5Luuece1dXVafDgwUpKSlJDQ4Puv/9+zZ8/39WaCH8AAGzc7PzPtcV/Nk8++aR+8Ytf6PHHH9ewYcNUWVmpZcuWKS0tTXl5ea7VRPgDAGDj1QN/y5cv1z333KN58+ZJkkaMGKGjR48qFou5Gv78qR8AAAni73//uy65JD6ak5KS1NjY6Oo6dP4AANh41fnn5ubq/vvvV3p6uoYNG6aXX35ZDz74oBYtWuTqOoQ/AAA2XoX/D3/4Q61cuVJf//rXdfLkSaWlpelrX/uavv3tb7u6DuEPAECCSElJUXFxsYqLi9t1HcIfAAAbv3+2P+EPAICN38Ofp/0BAAgYOn8AAGz83vkT/gAA2BD+AAAEjN/Dn3v+AAAEDJ0/AAA2fu/8CX8AAGz8Hv5s+wMAEDB0/gAA2Pi98yf8AQCw8Xv4s+0PAEDAuNL5W5alUCjkxlQAAHiOzr8FDMPQa6+95sZUAAB4zrIs145E5KjzLygoOOt4Q0ODVq9erd69e0uSHnzwwfPOY5qmTNN0sjQAAHCJo/AvLi5WVlaWevToETduWZZee+01devWrUXb/7FYTEVFRY4KBQCgoyRqx+4WR+G/atUqbdq0SQ888IBuuOGGpvFOnTppy5YtGjp0aIvmiUQizXYRwuGwk1IAAGg3hP8n3HPPPZo6dapuueUW5ebmKhaLqVOnTo4XNQxDhmE4/jkAADqC38Pf8QN/Y8aMUXl5uf7yl78oOztbhw4d4kl/AAAuIq36U79LL71Ujz32mLZu3app06apoaHB7boAAPCM3zv/Nv2d/7x58zRhwgSVl5crIyPDrZoAAPAU4X8BAwYM0IABA9yoBQAAdAA+2x8AABs6fwAAAsbv4c8X+wAAEDB0/gAA2Pi98yf8AQCw8Xv4s+0PAEDA0PkDAGDj986f8AcAwIbwBwAgYPwe/tzzBwAgYOj8AQCw8XvnT/gDAGDj9/Bn2x8AgATyzjvv6JZbblHv3r3VtWtXjRgxQmVlZa6uQecPAICNV53/+++/r/Hjx2vKlCl69tlnddlll+nIkSPq2bOnq+sQ/gAA2HgV/mvWrNEVV1yhzZs3N40NHDjQ9XXY9gcAoB2Zpqm6urq4wzTNs773qaeeUnZ2tmbPnq2+fftq9OjReuSRR1yvKWQlyFMNoVDI6xLgM6NGjfK6hDarrKz0ugRXFBcXe11Cm2VmZnpdgitmzZrldQmuaO/oWrBggWtzZWZmqqioKG4sGo2qsLCw2Xu7dOkiSSooKNDs2bN18OBBLV26VBs3blReXp5rNbHtDwCAjZu/XEQiERUUFMSNGYZx1vc2NjYqOztbq1atkiSNHj1ahw4dIvwBALiYGIZxzrC3S01N1dChQ+PGhgwZov/6r/9ytSbCHwAAG6/uiI8fP15VVVVxY4cPH1ZGRoar6xD+AADYeBX+d911l6677jqtWrVKc+bM0YEDB7Rp0yZt2rTJ1XV42h8AABvLslw7nBgzZoy2bdumJ554QsOHD9d3vvMdFRcXa/78+a5eH50/AAAJ5POf/7w+//nPt+sahD8AADYJ8lfw7YbwBwDAxu/hzz1/AAAChs4fAAAbv3f+hD8AADZ+D3+2/QEACBg6fwAAbPze+RP+AADY+D382fYHACBg6PwBALDxe+dP+AMAYEP4AwAQMH4Pf+75AwAQMHT+AADY+L3zJ/wBALAh/M+jvr5eTz75pKqrq5Wamqqbb75ZvXv3vuDPmaYp0zTbsjQAAGglR/f8hw4dqr/97W+SpLffflvDhw/XXXfdpdLSUkWjUQ0dOlQ1NTUXnCcWiykcDscdAAAkCsuyXDsSkaPwf/311/XPf/5TkhSJRJSWlqajR4/qwIEDOnr0qEaOHKl77733gvNEIhHV1tbGHQAAJAq/h3+rt/337dunjRs3NnXtl156qYqKijRv3rwL/qxhGDIMo7VLAwCANnAc/qFQSJJ0+vRppaamxp27/PLL9Ze//MWdygAA8EiiduxucRz+U6dOVXJysurq6lRVVaXhw4c3nTt69GiLHvgDACCREf6fEI1G415feumlca+ffvppXX/99W2vCgAAtJs2hb/d97///TYVAwBAIqDzBwAgYAh/AAACxu/hzxf7AAAQMHT+AADY+L3zJ/wBALDxe/iz7Q8AQMDQ+QMAYOP3zp/wBwDAxu/hz7Y/AAABQ+cPAICN3zt/wh8AABu/hz/b/gAABAzhDwCAjWVZrh2ttXr1aoVCIS1btsy9C/sXtv0BALDxetv/4MGD+tGPfqSRI0e2y/x0/gAA2HjZ+Z86dUrz58/XI488op49e7bD1RH+AAC0K9M0VVdXF3eYpnnO9+fn52v69OmaNm1au9XEtj98q7Ky0usS2qy4uNjrElyxYMECr0tos1mzZnldAjqQm9v+sVhMRUVFcWPRaFSFhYXN3rt161ZVVFTo4MGDrq1/NoQ/AAA2boZ/JBJRQUFB3JhhGM3e9/bbb2vp0qUqLS1Vly5dXFv/bAh/AADakWEYZw17u/Lycp08eVKf+cxnmsYaGhq0Z88erVu3TqZpKikpyZWaCH8AAGy8eNp/6tSpevXVV+PGFi5cqMGDB2vFihWuBb9E+AMA0IwX4Z+SkqLhw4fHjXXr1k29e/duNt5WPO0PAEDA0PkDAGDj9Yf8fGzXrl3tMi/hDwCATaKEf3th2x8AgICh8wcAwMbvnT/hDwCADeEPAEDA+D38uecPAEDA0PkDAGDj986f8AcAwMbv4c+2PwAAAUPnDwCAjd87f8IfAAAbv4c/2/4AAAQMnT8AADZ+7/wJfwAAbPwe/mz7AwAQMI7Cv6KiQjU1NU2vf/azn2n8+PG64oorNGHCBG3durVF85imqbq6urgDAIBEYVmWa0cichT+Cxcu1BtvvCFJ+vGPf6yvfe1rys7O1r333qsxY8Zo8eLFevTRRy84TywWUzgcjjsAAEgUfg9/R/f8jxw5oquuukqS9PDDD+uhhx7S4sWLm86PGTNG999/vxYtWnTeeSKRiAoKCuLG+AUAAJAoEjW03eIo/D/1qU/pvffeU0ZGht555x2NHTs27vy4cePibguci2EYMgzDWaUAAMAVjrb9c3JytGHDBknSpEmT9Ktf/Sru/JNPPqkrr7zSveoAAPAA2/6fsGbNGo0fP16TJk1Sdna2HnjgAe3atUtDhgxRVVWV9u/fr23btrVXrQAAdIhEDW23OOr809LS9PLLL+vaa69VSUmJLMvSgQMH9Pzzz2vAgAH63//9X33uc59rr1oBAIALHH/IT48ePbR69WqtXr26PeoBAMBzfu/8+YQ/AABs/B7+fMIfAAABQ+cPAICN3zt/wh8AABu/hz/b/gAABAydPwAANn7v/Al/AABsCH8AAALG7+HPPX8AAAKGzh8AABu/d/6EPwAANn4Pf7b9AQAIGMIfAAAby7JcO5yIxWIaM2aMUlJS1LdvX82aNUtVVVWuXx/hDwCAjVfhv3v3buXn52v//v0qLS3VmTNndOONN6q+vt7V6+OePwAACaKkpCTu9ZYtW9S3b1+Vl5dr4sSJrq1D+AMAYOPmA3+maco0zbgxwzBkGMYFf7a2tlaS1KtXL9fqkaSQlSCPNIZCIa9LwL8UFxd7XYIrRo0a5XUJbdajRw+vS3DFggULvC6hzSorK70uAZ/Q3tGVmZnp2lwLFixQUVFR3Fg0GlVhYeF5f66xsVEzZszQBx98oL1797pWj0TnDwBAu4pEIiooKIgba0nXn5+fr0OHDrke/BLhDwBAM27uLLR0i/+T7rjjDj3zzDPas2ePBgwY4FotHyP8AQCw8eqOuGVZWrJkibZt26Zdu3Zp4MCB7bIO4Q8AgI1X4Z+fn6/HH39cv/nNb5SSkqLjx49LksLhsLp27eraOvydPwAACWLDhg2qra3V5MmTlZqa2nT88pe/dHUdOn8AAGy83PbvCIQ/AAA2CfJX8O2GbX8AAAKGzh8AABu/d/6EPwAANn4Pf7b9AQAIGDp/AABs/N75E/4AANj4PfzZ9gcAIGDo/AEAsPF750/4AwBgQ/gDABAwfg9/7vkDABAwdP4AANj4vfMn/AEAsPF7+LPtDwBAwDgK/yVLluh3v/tdmxc1TVN1dXVxBwAAicKyLNeOROQo/NevX6/Jkydr0KBBWrNmjY4fP96qRWOxmMLhcNwBAECiIPxtnn/+eX3uc5/TD37wA6Wnp2vmzJl65pln1NjY2OI5IpGIamtr4w4AANAxHIf/iBEjVFxcrHfffVc///nPZZqmZs2apSuuuEL33nuvqqurLziHYRjq3r173AEAQKKg8z+HTp06ac6cOSopKdGbb76pxYsX6xe/+IWuvvpqN+sDAKDDEf4tkJ6ersLCQtXU1KikpMSNKQEAQDtx9Hf+GRkZSkpKOuf5UCikz372s20uCgAALyVqx+4WR+FfU1PTXnUAAJAwCH8AAALG7+HPJ/wBABAwdP4AANj4vfMn/AEAsPF7+LPtDwBAwND5AwBg4/fOn/AHAMDG7+HPtj8AAAFD5w8AgI3fO3/CHwAAG7+HP9v+AAAEDJ0/AAA2fu/8CX8AAGz8Hv5s+wMAYGNZlmuHU+vXr1dmZqa6dOmicePG6cCBA65fH+EPAECC+OUvf6mCggJFo1FVVFQoKytLN910k06ePOnqOoQ/AAA2bnb+pmmqrq4u7jBN86zrPvjgg1q8eLEWLlyooUOHauPGjfrUpz6lRx991PULDITTp09b0WjUOn36tNeltJofrsGy/HEdfrgGy+I6EokfrsGy/HMdbopGo5akuCMajTZ7n2maVlJSkrVt27a48S996UvWjBkzXK0pZFk+f6rhX+rq6hQOh1VbW6vu3bt7XU6r+OEaJH9chx+uQeI6EokfrkHyz3W4yTTNZp2+YRgyDCNu7N1339Xll1+uF198Uddee23T+De/+U3t3r1bL730kms18bQ/AADt6GxB7zXu+QMAkAD69OmjpKQknThxIm78xIkT6t+/v6trEf4AACSAzp0765prrtGOHTuaxhobG7Vjx4642wBuCMy2v2EYikajCbf14oQfrkHyx3X44RokriOR+OEaJP9ch1cKCgqUl5en7OxsjR07VsXFxaqvr9fChQtdXScwD/wBAHAxWLdunb7//e/r+PHjGjVqlNauXatx48a5ugbhDwBAwHDPHwCAgCH8AQAIGMIfAICAIfwBAAiYQIR/R3w9Ynvas2ePcnNzlZaWplAopO3bt3tdkmOxWExjxoxRSkqK+vbtq1mzZqmqqsrrshzbsGGDRo4cqe7du6t79+669tpr9eyzz3pdVpusXr1aoVBIy5Yt87oURwoLCxUKheKOwYMHe11Wq7zzzju65ZZb1Lt3b3Xt2lUjRoxQWVmZ12W1WGZmZrN/F6FQSPn5+V6XhnPwffh31Ncjtqf6+nplZWVp/fr1XpfSart371Z+fr7279+v0tJSnTlzRjfeeKPq6+u9Ls2RAQMGaPXq1SovL1dZWZluuOEGzZw5U3/4wx+8Lq1VDh48qB/96EcaOXKk16W0yrBhw/TnP/+56di7d6/XJTn2/vvva/z48erUqZOeffZZ/fGPf9QDDzygnj17el1aix08eDDu30Npaakkafbs2R5XhnNy9WuCEtDYsWOt/Pz8ptcNDQ1WWlqaFYvFPKyq9SQ1+8ani9HJkyctSdbu3bu9LqXNevbsaf34xz/2ugzHPvzwQ+uqq66ySktLrUmTJllLly71uiRHotGolZWV5XUZbbZixQprwoQJXpfhqqVLl1qf/vSnrcbGRq9LwTn4uvP/xz/+ofLyck2bNq1p7JJLLtG0adO0b98+DytDbW2tJKlXr14eV9J6DQ0N2rp1q+rr613/6M2OkJ+fr+nTp8f993GxOXLkiNLS0vRv//Zvmj9/vo4dO+Z1SY499dRTys7O1uzZs9W3b1+NHj1ajzzyiNdltdo//vEP/fznP9eiRYsUCoW8Lgfn4Ovwf++999TQ0KB+/frFjffr10/Hjx/3qCo0NjZq2bJlGj9+vIYPH+51OY69+uqruvTSS2UYhm677TZt27ZNQ4cO9bosR7Zu3aqKigrFYjGvS2m1cePGacuWLSopKdGGDRtUU1Oj66+/Xh9++KHXpTny5ptvasOGDbrqqqv03HPP6fbbb9edd96pxx57zOvSWmX79u364IMPtGDBAq9LwXkE5rP9kTjy8/N16NChi/L+rCRdffXVqqysVG1trX71q18pLy9Pu3fvvmh+AXj77be1dOlSlZaWqkuXLl6X02o5OTlN/3vkyJEaN26cMjIy9OSTT+rLX/6yh5U509jYqOzsbK1atUqSNHr0aB06dEgbN25UXl6ex9U595Of/EQ5OTlKS0vzuhSch687/478ekS0zB133KFnnnlGO3fu1IABA7wup1U6d+6sK6+8Utdcc41isZiysrL00EMPeV1Wi5WXl+vkyZP6zGc+o+TkZCUnJ2v37t1au3atkpOT1dDQ4HWJrdKjRw8NGjRI1dXVXpfiSGpqarNfHIcMGXJR3sI4evSofvvb3+orX/mK16XgAnwd/h359Yg4P8uydMcdd2jbtm164YUXNHDgQK9Lck1jY6NM0/S6jBabOnWqXn31VVVWVjYd2dnZmj9/viorK5WUlOR1ia1y6tQpvfHGG0pNTfW6FEfGjx/f7M9eDx8+rIyMDI8qar3Nmzerb9++mj59utel4AJ8v+3fUV+P2J5OnToV183U1NSosrJSvXr1Unp6uoeVtVx+fr4ef/xx/eY3v1FKSkrTMxfhcFhdu3b1uLqWi0QiysnJUXp6uj788EM9/vjj2rVrl5577jmvS2uxlJSUZs9adOvWTb17976onsG4++67lZubq4yMDL377ruKRqNKSkrSzTff7HVpjtx111267rrrtGrVKs2ZM0cHDhzQpk2btGnTJq9Lc6SxsVGbN29WXl6ekpN9Hy0XP6//3KAj/PCHP7TS09Otzp07W2PHjrX279/vdUmO7Ny505LU7MjLy/O6tBY7W/2SrM2bN3tdmiOLFi2yMjIyrM6dO1uXXXaZNXXqVOv555/3uqw2uxj/1G/u3LlWamqq1blzZ+vyyy+35s6da1VXV3tdVqs8/fTT1vDhwy3DMKzBgwdbmzZt8rokx5577jlLklVVVeV1KWgBvtIXAICA8fU9fwAA0BzhDwBAwBD+AAAEDOEPAEDAEP4AAAQM4Q8AQMAQ/gAABAzhDwBAwBD+AAAEDOEPAEDAEP4AAATM/wP9dvnuhYUXPwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCGgdLn7DzCp"
      },
      "source": [
        "# Scatterplots with plotly express"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "N5v-0_0iDzCq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "e2115aa9-37f1-48d2-a4f2-0a4a9fe5b17b"
      },
      "source": [
        "px.scatter(boston, x='AGE', y='TAX')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "                        })                };                            </script>        </div>\n",
              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ldIXCOzDzCv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "b3a8a284-10ed-41bd-8cc5-1567227c2de6"
      },
      "source": [
        "px.scatter_3d(wine, x='alcohol', y='magnesium', z='ash', color='hue', size='color_intensity')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
              "var gd = document.getElementById('525dcc60-7a1e-478b-8be9-e5533d0c189c');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRuq5jaMDzCx"
      },
      "source": [
        "# Simple line plot with matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhJilmjCDzCy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "b0afd714-9a28-43e7-9288-b6a0658a305c"
      },
      "source": [
        "plt.plot(np.sort(boston.AGE), 'k-');\n",
        "plt.xlabel('House age rank');\n",
        "plt.ylabel('Age');"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vSXIDI_IDzC0"
      },
      "source": [
        "# Grouped bar plots with seaborn\n",
        "We'll use a new example dataset that works well for this example:\n",
        "  - `titanic`: list of Titanic passengers and their attributes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoZYFrT1DzC1"
      },
      "source": [
        "titanic = sns.load_dataset(\"titanic\")"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "parNnyy8DzC3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b78ced7a-6445-44f0-ec70-cb0192350654"
      },
      "source": [
        "titanic.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True  NaN  Southampton    no   True  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3218d8d2-2aa2-40d6-84f5-11dd9e539723\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3218d8d2-2aa2-40d6-84f5-11dd9e539723')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3218d8d2-2aa2-40d6-84f5-11dd9e539723 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3218d8d2-2aa2-40d6-84f5-11dd9e539723');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msyMKytrDzC4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "366885ee-faa5-45d5-a18a-3bec8f36afde"
      },
      "source": [
        "#source: https://seaborn.pydata.org/examples/grouped_barplot.html\n",
        "g = sns.catplot(x=\"class\", y=\"survived\", hue=\"sex\", data=titanic,\n",
        "                height=6, kind=\"bar\", palette=\"muted\")\n",
        "g.despine(left=True)\n",
        "g.set_ylabels(\"survival probability\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f56409c6e50>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 698x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9P8noStcDzC6"
      },
      "source": [
        "# Many other example plots using plotly express\n",
        "We'll use a new dataset for these examples:\n",
        "  - `gapminder`: a dataset of life expectancy information by year and location"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pofuuRzxDzC7"
      },
      "source": [
        "#source: https://nbviewer.jupyter.org/github/plotly/plotly_express/blob/master/walkthrough.ipynb\n",
        "gapminder = px.data.gapminder()\n",
        "gapminder2007 = gapminder.query(\"year == 2007\")"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VeSnHdiDzC9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9dad951b-40ef-48b2-c2ef-385c4b91c111"
      },
      "source": [
        "gapminder2007.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        country continent  year  lifeExp       pop     gdpPercap iso_alpha  \\\n",
              "11  Afghanistan      Asia  2007   43.828  31889923    974.580338       AFG   \n",
              "23      Albania    Europe  2007   76.423   3600523   5937.029526       ALB   \n",
              "35      Algeria    Africa  2007   72.301  33333216   6223.367465       DZA   \n",
              "47       Angola    Africa  2007   42.731  12420476   4797.231267       AGO   \n",
              "59    Argentina  Americas  2007   75.320  40301927  12779.379640       ARG   \n",
              "\n",
              "    iso_num  \n",
              "11        4  \n",
              "23        8  \n",
              "35       12  \n",
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    {
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      ],
      "execution_count": 20,
      "outputs": [
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              "                            \n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHBny1--DzDB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "bec2893a-4480-4c20-d552-9a584e038d2f"
      },
      "source": [
        "px.scatter(gapminder2007, x=\"gdpPercap\", y=\"lifeExp\", color=\"continent\", size=\"pop\", size_max=60,\n",
        "          hover_name=\"country\", facet_col=\"continent\", log_x=True)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tZ6UG86DzDD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "165a983b-443c-415b-87c5-a1f63dd624a7"
      },
      "source": [
        "#neat animation\n",
        "px.scatter(gapminder, x=\"gdpPercap\", y=\"lifeExp\",size=\"pop\", size_max=60, color=\"continent\", hover_name=\"country\", \n",
        "           animation_frame=\"year\", animation_group=\"country\", log_x=True, range_x=[100,100000], range_y=[25,90],\n",
        "           labels=dict(pop=\"Population\", gdpPercap=\"GDP per Capita\", lifeExp=\"Life Expectancy\"))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJaRjVlXDzDF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "outputId": "7d4f1cf7-3813-45c1-f230-81d069c6f2ab"
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        "              color_continuous_scale=px.colors.sequential.Plasma, projection=\"natural earth\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
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      "source": [
        "# Where do you go from here?\n",
        "\n",
        "Useful tutorials from each of the libraries we've explored here:\n",
        "  - [Matplotlib tutorials](https://matplotlib.org/tutorials/index.html)\n",
        "  - [Seaborn tutorials](https://seaborn.pydata.org/tutorial.html)\n",
        "  - [Hypertools tutorials](https://hypertools.readthedocs.io/en/latest/tutorials.html)\n",
        "  - [Plotly Express tutorials](https://www.plotly.express/)\n",
        "\n",
        "Use the course textbooks for inspiration!\n",
        "\n",
        "For inspiration, also search for things like:\n",
        "  - data visualization\n",
        "  - storytelling with data\n",
        "  - infographic"
      ]
    }
  ]
}