02_Feature_Engineering.ipynb

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      "name": "02 - Feature Engineering.ipynb",
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        "<a href=\"https://colab.research.google.com/github/statmike/Logistic-Regression/blob/main/02_Feature_Engineering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "## Important Tools for Fitting: Descriptions and Demonstrations\n",
        "\n",
        "The example above is rather simple: 2 classification levels, single continous factor.  No missing data, no rare events, no execess of features, no ability to add more features ....\n",
        "\n",
        "In real-life we need tools to handle complexity ... messiness\n",
        "\n",
        "Feature Engineering is all about making the data useful for machine learning!\n",
        "\n",
        "The following takes common tasks and shows some typical ways of handling them.  This is not a detailed or exhaustive list but is a great starting point for understanding."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eS2-M_2Kxb_d"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn as sk\n",
        "from sklearn import linear_model"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ltqR7-N634E"
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      "source": [
        "### Categorical Factors - call me a dummy!\n",
        "\n",
        "One-hot encoding ...\n",
        "\n",
        "Why one-hot?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGLl4fns-1vu"
      },
      "source": [
        "size_format = {0 : 'Small', 1 : 'Medium', 2 : 'Large'}"
      ],
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "np.random.seed(seed=32259)\n",
        "coffee = pd.DataFrame({'size' : np.random.binomial(1,.5,10)})\n",
        "coffee['size_code'] = coffee['size'].map(size_format)\n",
        "coffee"
      ],
      "execution_count": 3,
      "outputs": [
        {
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            "text/plain": [
              "   size size_code\n",
              "0     1    Medium\n",
              "1     0     Small\n",
              "2     0     Small\n",
              "3     1    Medium\n",
              "4     0     Small\n",
              "5     0     Small\n",
              "6     1    Medium\n",
              "7     1    Medium\n",
              "8     1    Medium\n",
              "9     0     Small"
            ]
          },
          "metadata": {
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          },
          "execution_count": 3
        }
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        "id": "FTkL7C1QBuYZ",
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      },
      "source": [
        "pd.get_dummies(coffee['size_code'])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Medium  Small\n",
              "0       1      0\n",
              "1       0      1\n",
              "2       0      1\n",
              "3       1      0\n",
              "4       0      1\n",
              "5       0      1\n",
              "6       1      0\n",
              "7       1      0\n",
              "8       1      0\n",
              "9       0      1"
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        "np.random.seed(seed=32259)\n",
        "coffee = pd.DataFrame({'size' : np.random.binomial(2,.5,10)})\n",
        "coffee['size_code'] = coffee['size'].map(size_format)\n",
        "coffee"
      ],
      "execution_count": 5,
      "outputs": [
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            "text/plain": [
              "   size size_code\n",
              "0     2     Large\n",
              "1     1    Medium\n",
              "2     1    Medium\n",
              "3     2     Large\n",
              "4     0     Small\n",
              "5     1    Medium\n",
              "6     1    Medium\n",
              "7     2     Large\n",
              "8     1    Medium\n",
              "9     1    Medium"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
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        "id": "b6zQm_LzCM_B",
        "outputId": "c0446420-bbca-4327-ecf3-ec9106ad8c14"
      },
      "source": [
        "pd.get_dummies(coffee['size_code'])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>9</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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            "text/plain": [
              "   Large  Medium  Small\n",
              "0      1       0      0\n",
              "1      0       1      0\n",
              "2      0       1      0\n",
              "3      1       0      0\n",
              "4      0       0      1\n",
              "5      0       1      0\n",
              "6      0       1      0\n",
              "7      1       0      0\n",
              "8      0       1      0\n",
              "9      0       1      0"
            ]
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          "metadata": {
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          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N5RTGspn7yTj"
      },
      "source": [
        "### Continous Factors - stairs can make the hill easier to climb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "7eZ3vRTXEJaF",
        "outputId": "d7f89e86-93b4-4947-c609-56df317f5803"
      },
      "source": [
        "np.random.seed(seed=32259)\n",
        "dist = pd.DataFrame(np.random.normal(loc=(10,20), scale=(4,2), size=(1000,2)),columns=['x1','x2'])\n",
        "dist.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>x2</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.014745</td>\n",
              "      <td>20.001762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.810962</td>\n",
              "      <td>1.968010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.812063</td>\n",
              "      <td>12.509771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.385437</td>\n",
              "      <td>18.680680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.783921</td>\n",
              "      <td>20.020651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12.492201</td>\n",
              "      <td>21.351225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>24.112145</td>\n",
              "      <td>26.366796</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                x1           x2\n",
              "count  1000.000000  1000.000000\n",
              "mean     10.014745    20.001762\n",
              "std       3.810962     1.968010\n",
              "min      -0.812063    12.509771\n",
              "25%       7.385437    18.680680\n",
              "50%       9.783921    20.020651\n",
              "75%      12.492201    21.351225\n",
              "max      24.112145    26.366796"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 322
        },
        "id": "_AAf2fg6-dBy",
        "outputId": "488a020e-d8c5-4166-fe19-f5c0c189bf6e"
      },
      "source": [
        "plt.xkcd()\n",
        "fig, ax = plt.subplots()\n",
        "dist.plot.kde(ax=ax, legend=True, title='Histogram')\n",
        "ax.set_ylabel('Probability')\n",
        "ax.grid(axis='y')\n",
        "plt.rcdefaults()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 8722 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n"
          ],
          "name": "stderr"
        },
        {
          "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": "7hkxKvl97yGh"
      },
      "source": [
        "#### Scale"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "sBWiEy9w_B_5",
        "outputId": "317bdfde-d662-44b3-8693-dc0377403443"
      },
      "source": [
        "# mean normalization\n",
        "normalized_dist = (dist-dist.mean())/dist.std()\n",
        "\n",
        "plt.xkcd()\n",
        "fig, ax = plt.subplots()\n",
        "normalized_dist.plot.kde(ax=ax, legend=True, title='Histogram')\n",
        "ax.set_ylabel('Probability')\n",
        "ax.grid(axis='y')\n",
        "plt.rcdefaults()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 8722 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "Sw_EIQZS_n2y",
        "outputId": "1032328f-5046-4a1b-8792-55964ec3ae74"
      },
      "source": [
        "# min/max normalization\n",
        "normalized_dist = (dist-dist.min())/(dist.max()-dist.min())\n",
        "\n",
        "plt.xkcd()\n",
        "fig, ax = plt.subplots()\n",
        "normalized_dist.plot.kde(ax=ax, legend=True, title='Histogram')\n",
        "ax.set_ylabel('Probability')\n",
        "ax.grid(axis='y')\n",
        "plt.rcdefaults()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 8722 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62bzI-Zn7xzh"
      },
      "source": [
        "#### Divy-Up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "6DZbFhv5D2Mr",
        "outputId": "2fd04d45-87cc-4182-f321-bc383c18743b"
      },
      "source": [
        "plt.xkcd()\n",
        "fig, ax = plt.subplots()\n",
        "normalized_dist.plot.hist(ax=ax, bins=10, alpha=0.5, legend=True)\n",
        "plt.rcdefaults()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2v121_kl8z5P"
      },
      "source": [
        "### Missing Points? (must be somewhere in [The Middle](https://youtu.be/M3mJkSqZbX4?t=36))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "pg8SJFSOFhoD",
        "outputId": "72ccefd1-47b3-4926-dd98-abecfd39fffe"
      },
      "source": [
        "np.random.seed(seed=32259)\n",
        "coffee['taste'] = pd.DataFrame({'taste' : np.random.normal(10,1,10) * np.random.binomial(1,.8,10)})\n",
        "coffee.loc[coffee['taste'] == 0, 'taste'] = np.nan\n",
        "coffee"
      ],
      "execution_count": 12,
      "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>size</th>\n",
              "      <th>size_code</th>\n",
              "      <th>taste</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>Large</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Medium</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Medium</td>\n",
              "      <td>10.708636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Large</td>\n",
              "      <td>9.740679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Small</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>Medium</td>\n",
              "      <td>8.634119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>Medium</td>\n",
              "      <td>11.520915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "      <td>Large</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>Medium</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>Medium</td>\n",
              "      <td>10.190728</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   size size_code      taste\n",
              "0     2     Large        NaN\n",
              "1     1    Medium        NaN\n",
              "2     1    Medium  10.708636\n",
              "3     2     Large   9.740679\n",
              "4     0     Small        NaN\n",
              "5     1    Medium   8.634119\n",
              "6     1    Medium  11.520915\n",
              "7     2     Large        NaN\n",
              "8     1    Medium        NaN\n",
              "9     1    Medium  10.190728"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "DcBFsFbIKPqH",
        "outputId": "e024016a-b71f-4e6b-f9f2-7543d15915f6"
      },
      "source": [
        "coffee['taste_mean']= coffee['taste'].fillna(coffee['taste'].mean())\n",
        "coffee.style.highlight_null(null_color='red')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "#T_098af62e_7ada_11eb_b836_0242ac1c0002row0_col2,#T_098af62e_7ada_11eb_b836_0242ac1c0002row1_col2,#T_098af62e_7ada_11eb_b836_0242ac1c0002row4_col2,#T_098af62e_7ada_11eb_b836_0242ac1c0002row7_col2,#T_098af62e_7ada_11eb_b836_0242ac1c0002row8_col2{\n",
              "            background-color:  red;\n",
              "        }</style><table id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >size</th>        <th class=\"col_heading level0 col1\" >size_code</th>        <th class=\"col_heading level0 col2\" >taste</th>        <th class=\"col_heading level0 col3\" >taste_mean</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row0_col0\" class=\"data row0 col0\" >2</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row0_col1\" class=\"data row0 col1\" >Large</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row0_col2\" class=\"data row0 col2\" >nan</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row0_col3\" class=\"data row0 col3\" >10.159015</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row1_col1\" class=\"data row1 col1\" >Medium</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row1_col2\" class=\"data row1 col2\" >nan</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row1_col3\" class=\"data row1 col3\" >10.159015</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row2_col1\" class=\"data row2 col1\" >Medium</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row2_col2\" class=\"data row2 col2\" >10.708636</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row2_col3\" class=\"data row2 col3\" >10.708636</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row3_col1\" class=\"data row3 col1\" >Large</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row3_col2\" class=\"data row3 col2\" >9.740679</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row3_col3\" class=\"data row3 col3\" >9.740679</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row4_col1\" class=\"data row4 col1\" >Small</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row4_col2\" class=\"data row4 col2\" >nan</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row4_col3\" class=\"data row4 col3\" >10.159015</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row5_col0\" class=\"data row5 col0\" >1</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row5_col1\" class=\"data row5 col1\" >Medium</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8.634119</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row5_col3\" class=\"data row5 col3\" >8.634119</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row6_col0\" class=\"data row6 col0\" >1</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row6_col1\" class=\"data row6 col1\" >Medium</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row6_col2\" class=\"data row6 col2\" >11.520915</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row6_col3\" class=\"data row6 col3\" >11.520915</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row7_col0\" class=\"data row7 col0\" >2</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row7_col1\" class=\"data row7 col1\" >Large</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row7_col2\" class=\"data row7 col2\" >nan</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row7_col3\" class=\"data row7 col3\" >10.159015</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row8_col0\" class=\"data row8 col0\" >1</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row8_col1\" class=\"data row8 col1\" >Medium</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row8_col2\" class=\"data row8 col2\" >nan</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row8_col3\" class=\"data row8 col3\" >10.159015</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row9_col0\" class=\"data row9 col0\" >1</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row9_col1\" class=\"data row9 col1\" >Medium</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row9_col2\" class=\"data row9 col2\" >10.190728</td>\n",
              "                        <td id=\"T_098af62e_7ada_11eb_b836_0242ac1c0002row9_col3\" class=\"data row9 col3\" >10.190728</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f611bf34990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "Zc3fZyGpLMAv",
        "outputId": "c23b97cb-36c4-401c-c816-8bbcf15d6c6d"
      },
      "source": [
        "coffee['taste_med']= coffee['taste'].fillna(coffee['taste'].median())\n",
        "coffee.style.highlight_null(null_color='red')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "#T_0a501472_7ada_11eb_b836_0242ac1c0002row0_col2,#T_0a501472_7ada_11eb_b836_0242ac1c0002row1_col2,#T_0a501472_7ada_11eb_b836_0242ac1c0002row4_col2,#T_0a501472_7ada_11eb_b836_0242ac1c0002row7_col2,#T_0a501472_7ada_11eb_b836_0242ac1c0002row8_col2{\n",
              "            background-color:  red;\n",
              "        }</style><table id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >size</th>        <th class=\"col_heading level0 col1\" >size_code</th>        <th class=\"col_heading level0 col2\" >taste</th>        <th class=\"col_heading level0 col3\" >taste_mean</th>        <th class=\"col_heading level0 col4\" >taste_med</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row0_col0\" class=\"data row0 col0\" >2</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row0_col1\" class=\"data row0 col1\" >Large</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row0_col2\" class=\"data row0 col2\" >nan</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row0_col3\" class=\"data row0 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row0_col4\" class=\"data row0 col4\" >10.190728</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row1_col1\" class=\"data row1 col1\" >Medium</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row1_col2\" class=\"data row1 col2\" >nan</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row1_col3\" class=\"data row1 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row1_col4\" class=\"data row1 col4\" >10.190728</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row2_col1\" class=\"data row2 col1\" >Medium</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row2_col2\" class=\"data row2 col2\" >10.708636</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row2_col3\" class=\"data row2 col3\" >10.708636</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row2_col4\" class=\"data row2 col4\" >10.708636</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row3_col1\" class=\"data row3 col1\" >Large</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row3_col2\" class=\"data row3 col2\" >9.740679</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row3_col3\" class=\"data row3 col3\" >9.740679</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row3_col4\" class=\"data row3 col4\" >9.740679</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row4_col1\" class=\"data row4 col1\" >Small</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row4_col2\" class=\"data row4 col2\" >nan</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row4_col3\" class=\"data row4 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row4_col4\" class=\"data row4 col4\" >10.190728</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row5_col0\" class=\"data row5 col0\" >1</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row5_col1\" class=\"data row5 col1\" >Medium</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8.634119</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row5_col3\" class=\"data row5 col3\" >8.634119</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row5_col4\" class=\"data row5 col4\" >8.634119</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row6_col0\" class=\"data row6 col0\" >1</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row6_col1\" class=\"data row6 col1\" >Medium</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row6_col2\" class=\"data row6 col2\" >11.520915</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row6_col3\" class=\"data row6 col3\" >11.520915</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row6_col4\" class=\"data row6 col4\" >11.520915</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row7_col0\" class=\"data row7 col0\" >2</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row7_col1\" class=\"data row7 col1\" >Large</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row7_col2\" class=\"data row7 col2\" >nan</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row7_col3\" class=\"data row7 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row7_col4\" class=\"data row7 col4\" >10.190728</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row8_col0\" class=\"data row8 col0\" >1</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row8_col1\" class=\"data row8 col1\" >Medium</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row8_col2\" class=\"data row8 col2\" >nan</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row8_col3\" class=\"data row8 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row8_col4\" class=\"data row8 col4\" >10.190728</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row9_col0\" class=\"data row9 col0\" >1</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row9_col1\" class=\"data row9 col1\" >Medium</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row9_col2\" class=\"data row9 col2\" >10.190728</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row9_col3\" class=\"data row9 col3\" >10.190728</td>\n",
              "                        <td id=\"T_0a501472_7ada_11eb_b836_0242ac1c0002row9_col4\" class=\"data row9 col4\" >10.190728</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f611bc1a750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "eseQXacT6DgL",
        "outputId": "404ea0e2-f774-4f70-fe98-4bf303d6ae92"
      },
      "source": [
        "coffee['size_code2'] = coffee['size_code']\n",
        "coffee['size_code2'][5] = np.nan\n",
        "coffee.style.highlight_null(null_color='red')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "#T_0b115d3a_7ada_11eb_b836_0242ac1c0002row0_col2,#T_0b115d3a_7ada_11eb_b836_0242ac1c0002row1_col2,#T_0b115d3a_7ada_11eb_b836_0242ac1c0002row4_col2,#T_0b115d3a_7ada_11eb_b836_0242ac1c0002row5_col5,#T_0b115d3a_7ada_11eb_b836_0242ac1c0002row7_col2,#T_0b115d3a_7ada_11eb_b836_0242ac1c0002row8_col2{\n",
              "            background-color:  red;\n",
              "        }</style><table id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >size</th>        <th class=\"col_heading level0 col1\" >size_code</th>        <th class=\"col_heading level0 col2\" >taste</th>        <th class=\"col_heading level0 col3\" >taste_mean</th>        <th class=\"col_heading level0 col4\" >taste_med</th>        <th class=\"col_heading level0 col5\" >size_code2</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row0_col0\" class=\"data row0 col0\" >2</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row0_col1\" class=\"data row0 col1\" >Large</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row0_col2\" class=\"data row0 col2\" >nan</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row0_col3\" class=\"data row0 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row0_col4\" class=\"data row0 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row0_col5\" class=\"data row0 col5\" >Large</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row1_col1\" class=\"data row1 col1\" >Medium</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row1_col2\" class=\"data row1 col2\" >nan</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row1_col3\" class=\"data row1 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row1_col4\" class=\"data row1 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row1_col5\" class=\"data row1 col5\" >Medium</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row2_col1\" class=\"data row2 col1\" >Medium</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row2_col2\" class=\"data row2 col2\" >10.708636</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row2_col3\" class=\"data row2 col3\" >10.708636</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row2_col4\" class=\"data row2 col4\" >10.708636</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row2_col5\" class=\"data row2 col5\" >Medium</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row3_col1\" class=\"data row3 col1\" >Large</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row3_col2\" class=\"data row3 col2\" >9.740679</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row3_col3\" class=\"data row3 col3\" >9.740679</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row3_col4\" class=\"data row3 col4\" >9.740679</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row3_col5\" class=\"data row3 col5\" >Large</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row4_col1\" class=\"data row4 col1\" >Small</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row4_col2\" class=\"data row4 col2\" >nan</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row4_col3\" class=\"data row4 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row4_col4\" class=\"data row4 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row4_col5\" class=\"data row4 col5\" >Small</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row5_col0\" class=\"data row5 col0\" >1</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row5_col1\" class=\"data row5 col1\" >Medium</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8.634119</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row5_col3\" class=\"data row5 col3\" >8.634119</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row5_col4\" class=\"data row5 col4\" >8.634119</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row5_col5\" class=\"data row5 col5\" >nan</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row6_col0\" class=\"data row6 col0\" >1</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row6_col1\" class=\"data row6 col1\" >Medium</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row6_col2\" class=\"data row6 col2\" >11.520915</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row6_col3\" class=\"data row6 col3\" >11.520915</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row6_col4\" class=\"data row6 col4\" >11.520915</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row6_col5\" class=\"data row6 col5\" >Medium</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row7_col0\" class=\"data row7 col0\" >2</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row7_col1\" class=\"data row7 col1\" >Large</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row7_col2\" class=\"data row7 col2\" >nan</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row7_col3\" class=\"data row7 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row7_col4\" class=\"data row7 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row7_col5\" class=\"data row7 col5\" >Large</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row8_col0\" class=\"data row8 col0\" >1</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row8_col1\" class=\"data row8 col1\" >Medium</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row8_col2\" class=\"data row8 col2\" >nan</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row8_col3\" class=\"data row8 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row8_col4\" class=\"data row8 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row8_col5\" class=\"data row8 col5\" >Medium</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row9_col0\" class=\"data row9 col0\" >1</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row9_col1\" class=\"data row9 col1\" >Medium</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row9_col2\" class=\"data row9 col2\" >10.190728</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row9_col3\" class=\"data row9 col3\" >10.190728</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row9_col4\" class=\"data row9 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0b115d3a_7ada_11eb_b836_0242ac1c0002row9_col5\" class=\"data row9 col5\" >Medium</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f611bbf1210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "0r13moBo66bn",
        "outputId": "bb8d0324-7a59-4c9b-957a-954b9e9eca36"
      },
      "source": [
        "coffee['size_code2_mode']= coffee['size_code2'].fillna(coffee['size_code2'].mode()[0])\n",
        "coffee.style.highlight_null(null_color='red')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "#T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row0_col2,#T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row1_col2,#T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row4_col2,#T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row5_col5,#T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row7_col2,#T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row8_col2{\n",
              "            background-color:  red;\n",
              "        }</style><table id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >size</th>        <th class=\"col_heading level0 col1\" >size_code</th>        <th class=\"col_heading level0 col2\" >taste</th>        <th class=\"col_heading level0 col3\" >taste_mean</th>        <th class=\"col_heading level0 col4\" >taste_med</th>        <th class=\"col_heading level0 col5\" >size_code2</th>        <th class=\"col_heading level0 col6\" >size_code2_mode</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row0_col0\" class=\"data row0 col0\" >2</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row0_col1\" class=\"data row0 col1\" >Large</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row0_col2\" class=\"data row0 col2\" >nan</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row0_col3\" class=\"data row0 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row0_col4\" class=\"data row0 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row0_col5\" class=\"data row0 col5\" >Large</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row0_col6\" class=\"data row0 col6\" >Large</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row1_col1\" class=\"data row1 col1\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row1_col2\" class=\"data row1 col2\" >nan</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row1_col3\" class=\"data row1 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row1_col4\" class=\"data row1 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row1_col5\" class=\"data row1 col5\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row1_col6\" class=\"data row1 col6\" >Medium</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row2_col1\" class=\"data row2 col1\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row2_col2\" class=\"data row2 col2\" >10.708636</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row2_col3\" class=\"data row2 col3\" >10.708636</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row2_col4\" class=\"data row2 col4\" >10.708636</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row2_col5\" class=\"data row2 col5\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row2_col6\" class=\"data row2 col6\" >Medium</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row3_col1\" class=\"data row3 col1\" >Large</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row3_col2\" class=\"data row3 col2\" >9.740679</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row3_col3\" class=\"data row3 col3\" >9.740679</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row3_col4\" class=\"data row3 col4\" >9.740679</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row3_col5\" class=\"data row3 col5\" >Large</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row3_col6\" class=\"data row3 col6\" >Large</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row4_col1\" class=\"data row4 col1\" >Small</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row4_col2\" class=\"data row4 col2\" >nan</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row4_col3\" class=\"data row4 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row4_col4\" class=\"data row4 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row4_col5\" class=\"data row4 col5\" >Small</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row4_col6\" class=\"data row4 col6\" >Small</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row5_col0\" class=\"data row5 col0\" >1</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row5_col1\" class=\"data row5 col1\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8.634119</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row5_col3\" class=\"data row5 col3\" >8.634119</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row5_col4\" class=\"data row5 col4\" >8.634119</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row5_col5\" class=\"data row5 col5\" >nan</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row5_col6\" class=\"data row5 col6\" >Medium</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row6_col0\" class=\"data row6 col0\" >1</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row6_col1\" class=\"data row6 col1\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row6_col2\" class=\"data row6 col2\" >11.520915</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row6_col3\" class=\"data row6 col3\" >11.520915</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row6_col4\" class=\"data row6 col4\" >11.520915</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row6_col5\" class=\"data row6 col5\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row6_col6\" class=\"data row6 col6\" >Medium</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row7_col0\" class=\"data row7 col0\" >2</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row7_col1\" class=\"data row7 col1\" >Large</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row7_col2\" class=\"data row7 col2\" >nan</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row7_col3\" class=\"data row7 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row7_col4\" class=\"data row7 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row7_col5\" class=\"data row7 col5\" >Large</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row7_col6\" class=\"data row7 col6\" >Large</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row8_col0\" class=\"data row8 col0\" >1</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row8_col1\" class=\"data row8 col1\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row8_col2\" class=\"data row8 col2\" >nan</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row8_col3\" class=\"data row8 col3\" >10.159015</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row8_col4\" class=\"data row8 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row8_col5\" class=\"data row8 col5\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row8_col6\" class=\"data row8 col6\" >Medium</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row9_col0\" class=\"data row9 col0\" >1</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row9_col1\" class=\"data row9 col1\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row9_col2\" class=\"data row9 col2\" >10.190728</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row9_col3\" class=\"data row9 col3\" >10.190728</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row9_col4\" class=\"data row9 col4\" >10.190728</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row9_col5\" class=\"data row9 col5\" >Medium</td>\n",
              "                        <td id=\"T_0d9dcf66_7ada_11eb_b836_0242ac1c0002row9_col6\" class=\"data row9 col6\" >Medium</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f611bbf1d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPnXVGg78574"
      },
      "source": [
        "### Get Fit\n",
        "\n",
        "[![title here](https://www.betterthanpants.com/media/catalog/product/i/m/im-into-fitness-fitness-taco-in-my-mouth-funny-shirt-large.png)](https://www.betterthanpants.com/i-m-into-fitness-fit-ness-taco-in-my-mouth-funny-shirt#)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uud6uCfSTrAV"
      },
      "source": [
        "HA!  A better example (click it for article):\n",
        "\n",
        "[![title here](https://miro.medium.com/max/1400/1*_7OPgojau8hkiPUiHoGK_w.png)](https://medium.com/greyatom/what-is-underfitting-and-overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCuzLoHn9gdG"
      },
      "source": [
        "#### Regulate Bad Behavior to get a \"Good Fit\"\n",
        "It's like putting some of your weight in the penalty box:\n",
        "\n",
        "![title here](https://images.pexels.com/photos/793111/pexels-photo-793111.jpeg?cs=srgb&dl=pexels-francesco-paggiaro-793111.jpg&fm=jpg)\n",
        "Photo by Francesco Paggiaro from Pexels\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZqF1mlt8fxzh"
      },
      "source": [
        "Types of penalties....\n",
        "\n",
        "Regulate = build a less complex model\n",
        "\n",
        "**Common**\n",
        "\n",
        "Why L1 and L2?\n",
        "- names come from [vector norm calculations](https://simple.wikipedia.org/wiki/Norm_(mathematics))\n",
        "- both introduce a hyperparameter that is the multiplier of a penalty\n",
        "- L1 is Lasso\n",
        "  - Lasso Regression\n",
        "  - Lasso = LASSO = Least Aboslute Shrinkage and Selection Operator\n",
        "  - penalty is on absolute value of magnitude of the coefficient (how far it is from zero)\n",
        "    - zero is like no regularization\n",
        "    - large is like over-weighting, ... so underfitting\n",
        "  - It reins in less important features! Even removes them altogether sometimes. \n",
        "  - Feature selection!\n",
        "- L2 is Ridge\n",
        "  - Ridge Regression\n",
        "  - here\n",
        "  - penalty is on the squared magnitude of the coefficient\n",
        "    - zero is like no regularization\n",
        "    - large is like over-weighting, ... so underfitting \n",
        "- elastic net?\n",
        "  - Combination of L1 and L2\n",
        "  - and it adds another hyperparmeter that adjusts the mixture of L1 and L2\n",
        "  \n",
        "\n",
        "\n",
        "Is there something else?\n",
        "Of Course! It's math so there is always more!\n",
        "For instance, Neural networks have\n",
        "- L1 and L2!!!\n",
        "- and\n",
        "  - dropout\n",
        "  - batch-normalization\n",
        "  - data augmentation\n",
        "  - early stopping\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsVf1RHd9m35",
        "outputId": "b7c46c7b-03c0-4c4d-df8f-28e05ea62313"
      },
      "source": [
        "from sklearn import datasets\n",
        "digits = datasets.load_digits()\n",
        "digits_df = pd.DataFrame(data=digits.data)\n",
        "digits_df['target'] = digits.target\n",
        "digits_df['target_OE'] = digits_df['target'].apply(lambda x : 'Odd' if x%2==1 else ('Even' if x%2==0 else ''))\n",
        "\n",
        "x_train, x_test, y_train, y_test = sk.model_selection.train_test_split(digits_df.iloc[:,:64], digits_df.target_OE, test_size=0.25, random_state=32259)\n",
        "\n",
        "lr_none = sk.linear_model.LogisticRegression(max_iter=3000, solver='saga', penalty='none') # bump up  from default of max_iter=100\n",
        "lr_none.fit(x_train, y_train)\n",
        "\n",
        "lr_l1 = sk.linear_model.LogisticRegression(max_iter=3000, solver='saga', penalty='l1') # bump up  from default of max_iter=100\n",
        "lr_l1.fit(x_train, y_train)\n",
        "\n",
        "lr_l2 = sk.linear_model.LogisticRegression(max_iter=3000, solver='saga', penalty='l2') # bump up  from default of max_iter=100\n",
        "lr_l2.fit(x_train, y_train)\n",
        "\n",
        "lr_en = sk.linear_model.LogisticRegression(max_iter=3000, solver='saga', penalty='elasticnet', l1_ratio=.5) # bump up  from default of max_iter=100\n",
        "lr_en.fit(x_train, y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=0.5, max_iter=3000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='elasticnet',\n",
              "                   random_state=None, solver='saga', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "YNOnex_SYEfh",
        "outputId": "19b036f1-2398-40e0-93b3-e38c99365e03"
      },
      "source": [
        "coef = pd.DataFrame(data=lr_none.coef_.T,columns=['No Regularization'])\n",
        "coef['L1 Regularization'] = pd.DataFrame(lr_l1.coef_.T)\n",
        "coef['L2 Regularization'] = pd.DataFrame(lr_l2.coef_.T)\n",
        "coef['Elastic Net Regularization'] = pd.DataFrame(lr_en.coef_.T)\n",
        "coef"
      ],
      "execution_count": 18,
      "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>No Regularization</th>\n",
              "      <th>L1 Regularization</th>\n",
              "      <th>L2 Regularization</th>\n",
              "      <th>Elastic Net Regularization</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.073589</td>\n",
              "      <td>0.003852</td>\n",
              "      <td>0.072027</td>\n",
              "      <td>0.039288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.128753</td>\n",
              "      <td>-0.099927</td>\n",
              "      <td>-0.126909</td>\n",
              "      <td>-0.113198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.225855</td>\n",
              "      <td>0.216577</td>\n",
              "      <td>0.222765</td>\n",
              "      <td>0.219791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.050512</td>\n",
              "      <td>-0.047461</td>\n",
              "      <td>-0.051272</td>\n",
              "      <td>-0.049384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>-0.022060</td>\n",
              "      <td>-0.017676</td>\n",
              "      <td>-0.021330</td>\n",
              "      <td>-0.019846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>-0.149221</td>\n",
              "      <td>-0.145784</td>\n",
              "      <td>-0.148963</td>\n",
              "      <td>-0.147147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>0.192187</td>\n",
              "      <td>0.175499</td>\n",
              "      <td>0.187217</td>\n",
              "      <td>0.180861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>-0.238008</td>\n",
              "      <td>-0.224750</td>\n",
              "      <td>-0.239600</td>\n",
              "      <td>-0.231708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0.126986</td>\n",
              "      <td>0.129442</td>\n",
              "      <td>0.139771</td>\n",
              "      <td>0.134637</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>64 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    No Regularization  ...  Elastic Net Regularization\n",
              "0            0.000000  ...                    0.000000\n",
              "1            0.073589  ...                    0.039288\n",
              "2           -0.128753  ...                   -0.113198\n",
              "3            0.225855  ...                    0.219791\n",
              "4           -0.050512  ...                   -0.049384\n",
              "..                ...  ...                         ...\n",
              "59          -0.022060  ...                   -0.019846\n",
              "60          -0.149221  ...                   -0.147147\n",
              "61           0.192187  ...                    0.180861\n",
              "62          -0.238008  ...                   -0.231708\n",
              "63           0.126986  ...                    0.134637\n",
              "\n",
              "[64 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "IE0hd71tewwh",
        "outputId": "ea73caed-92e2-4ac0-cc73-c9ccf373b583"
      },
      "source": [
        "coef[coef['L1 Regularization']==0]"
      ],
      "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>No Regularization</th>\n",
              "      <th>L1 Regularization</th>\n",
              "      <th>L2 Regularization</th>\n",
              "      <th>Elastic Net Regularization</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.058252</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030249</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-0.394225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.227361</td>\n",
              "      <td>-0.053649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.444519</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.249970</td>\n",
              "      <td>0.077627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>-0.057160</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.016117</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>-0.001112</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000684</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>-0.000023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    No Regularization  ...  Elastic Net Regularization\n",
              "0            0.000000  ...                    0.000000\n",
              "7            0.058252  ...                    0.000000\n",
              "8           -0.394225  ...                   -0.053649\n",
              "16           0.444519  ...                    0.077627\n",
              "24          -0.057160  ...                    0.000000\n",
              "31           0.000000  ...                    0.000000\n",
              "32           0.000000  ...                    0.000000\n",
              "39           0.000000  ...                    0.000000\n",
              "40          -0.001112  ...                    0.000000\n",
              "48          -0.000023  ...                    0.000000\n",
              "56           0.000000  ...                    0.000000\n",
              "\n",
              "[11 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "4BKAb8QuaH7n",
        "outputId": "357e0348-1b4d-4cae-e115-fc1ce8910126"
      },
      "source": [
        "plt.xkcd()\n",
        "plt.axhline(0,color='black')\n",
        "plt.scatter(coef.index, coef['No Regularization'], alpha=0.5, label='No Regularization')\n",
        "plt.scatter(coef.index, coef['L1 Regularization'], alpha=0.5, label='L1 Regularization')\n",
        "plt.scatter(coef.index, coef['L2 Regularization'], alpha=0.5, label='L2 Regularization')\n",
        "plt.scatter(coef.index, coef['Elastic Net Regularization'], alpha=0.5, label='Elastic Net Regularization')\n",
        "plt.xlabel(\"Weight #\")\n",
        "plt.legend()\n",
        "plt.ylabel(\"Estimate for Weight\")\n",
        "plt.rcdefaults()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 8722 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTRKvgbRS0ab",
        "outputId": "6b0cc341-7d12-4f74-c62b-7db612c8422b"
      },
      "source": [
        "print(\"No Regularization: \\n\",sk.metrics.classification_report(y_test,lr_none.predict(x_test)))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No Regularization: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "        Even       0.91      0.90      0.91       220\n",
            "         Odd       0.91      0.91      0.91       230\n",
            "\n",
            "    accuracy                           0.91       450\n",
            "   macro avg       0.91      0.91      0.91       450\n",
            "weighted avg       0.91      0.91      0.91       450\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Li6G-wmQS5D3",
        "outputId": "4aa90367-755f-415b-9874-3d5bc1f6e33b"
      },
      "source": [
        "print(\"L1 Regularization: \\n\",sk.metrics.classification_report(y_test,lr_l1.predict(x_test)))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "L1 Regularization: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "        Even       0.91      0.90      0.91       220\n",
            "         Odd       0.91      0.92      0.91       230\n",
            "\n",
            "    accuracy                           0.91       450\n",
            "   macro avg       0.91      0.91      0.91       450\n",
            "weighted avg       0.91      0.91      0.91       450\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_jyVGH0S-_d",
        "outputId": "e9962bee-2660-4909-fe95-0b29098af35e"
      },
      "source": [
        "print(\"L2 Regularization: \\n\",sk.metrics.classification_report(y_test,lr_l2.predict(x_test)))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "L2 Regularization: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "        Even       0.91      0.90      0.91       220\n",
            "         Odd       0.91      0.91      0.91       230\n",
            "\n",
            "    accuracy                           0.91       450\n",
            "   macro avg       0.91      0.91      0.91       450\n",
            "weighted avg       0.91      0.91      0.91       450\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OZWmR0nmcav",
        "outputId": "373937c1-a6b2-4311-98df-93e5a3b18993"
      },
      "source": [
        "print(\"Elastic Net Regularization:\",sk.metrics.classification_report(y_test,lr_en.predict(x_test)))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Elastic Net Regularization:               precision    recall  f1-score   support\n",
            "\n",
            "        Even       0.91      0.90      0.91       220\n",
            "         Odd       0.91      0.92      0.91       230\n",
            "\n",
            "    accuracy                           0.91       450\n",
            "   macro avg       0.91      0.91      0.91       450\n",
            "weighted avg       0.91      0.91      0.91       450\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_L1zREQ01Jv"
      },
      "source": [
        "4 Fits, Three types of regularization, 4 seperate sets of parameter estimates, all have the same accuracy.  "
      ]
    }
  ]
}