Social_Determinants_in_Health.ipynb

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        "## Project Code\n",
        "### Goals:\n",
        "\n",
        "*   to predict heart/stroke problems, they are indicated as variables LAHCA7 and LAHCA8 respectively.\n",
        "*   to identify some variables you think would be relevant and trying out some summary using pandas, for example, get the percentage of categorical variables(ethnicity, marital status), average for continuous variables(age, income, etc), or plotting\n"
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      "source": [
        "#### Import packages and code"
      ]
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import IPython \n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sns\n",
        "sns.set(style=\"white\")\n",
        "sns.set(style=\"whitegrid\", color_codes=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
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        "outputId": "5daa381a-6c45-44fa-c17b-751820ae2dd1"
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      "source": [
        "persons_data = pd.read_csv('https://github.com/raw/MaxShalom/arise/master/personsx1.csv', delimiter = ',')\n",
        "persons_data.head()"
      ],
      "execution_count": 3,
      "outputs": [
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              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 602 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   FPX  AGE_CHG  INTV_QRT  SEX  ...  ARMFTM4P  ARMFTM5P  ARMFTM6P  ENGLANG\n",
              "0    1      NaN         1    1  ...       NaN       NaN       NaN        1\n",
              "1    1      NaN         1    1  ...       NaN       NaN       NaN        1\n",
              "2    1      NaN         1    1  ...       NaN       NaN       NaN        1\n",
              "3    1      NaN         1    2  ...       NaN       NaN       NaN        1\n",
              "4    1      NaN         1    1  ...       1.0       NaN       NaN        1\n",
              "\n",
              "[5 rows x 602 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNQfSuy1X-2n",
        "colab_type": "text"
      },
      "source": [
        "#### Sex count \n",
        "1 = Male, 2 = Female"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jt_AYriGYOw4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "a7867a14-bdc9-4079-c1ff-936ace6b46f2"
      },
      "source": [
        "persons_data.groupby(\"SEX\").count()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FPX</th>\n",
              "      <th>AGE_CHG</th>\n",
              "      <th>INTV_QRT</th>\n",
              "      <th>NOWAF</th>\n",
              "      <th>FSPOUS2</th>\n",
              "      <th>COHAB1</th>\n",
              "      <th>COHAB2</th>\n",
              "      <th>FCOHAB3</th>\n",
              "      <th>ASTATFLG</th>\n",
              "      <th>CSTATFLG</th>\n",
              "      <th>FMX</th>\n",
              "      <th>RRP</th>\n",
              "      <th>FRRP</th>\n",
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              "      <th>HISCODI3</th>\n",
              "      <th>MRACBPI2</th>\n",
              "      <th>AGE_P</th>\n",
              "      <th>HHREFLG</th>\n",
              "      <th>RECTYPE</th>\n",
              "      <th>SRVY_YR</th>\n",
              "      <th>FMREFLG</th>\n",
              "      <th>FMRPFLG</th>\n",
              "      <th>PARENTS</th>\n",
              "      <th>DAD_DEGP</th>\n",
              "      <th>MOM_DEGP</th>\n",
              "      <th>SIB_DEGP</th>\n",
              "      <th>CDCMSTAT</th>\n",
              "      <th>DAD_ED</th>\n",
              "      <th>MOM_ED</th>\n",
              "      <th>FMOTHER1</th>\n",
              "      <th>FFATHER1</th>\n",
              "      <th>HHX</th>\n",
              "      <th>WTIA</th>\n",
              "      <th>...</th>\n",
              "      <th>MAFLG</th>\n",
              "      <th>CHFLG</th>\n",
              "      <th>OPFLG</th>\n",
              "      <th>OGFLG</th>\n",
              "      <th>WHONAM1</th>\n",
              "      <th>WHONAM2</th>\n",
              "      <th>NOTCOV</th>\n",
              "      <th>PRPLPLUS</th>\n",
              "      <th>PWRKBR1</th>\n",
              "      <th>COVER</th>\n",
              "      <th>COVER65O</th>\n",
              "      <th>COVER65</th>\n",
              "      <th>REGIONBR</th>\n",
              "      <th>WHYNOWKP</th>\n",
              "      <th>GEOBRTH</th>\n",
              "      <th>YRSINUS</th>\n",
              "      <th>CITIZENP</th>\n",
              "      <th>DOINGLWP</th>\n",
              "      <th>WRKLYR1</th>\n",
              "      <th>WRKHRS2</th>\n",
              "      <th>PLBORN</th>\n",
              "      <th>HEADST</th>\n",
              "      <th>HEADSTV1</th>\n",
              "      <th>ARMFVER</th>\n",
              "      <th>ARMFEV</th>\n",
              "      <th>ARMFFC</th>\n",
              "      <th>VACOV</th>\n",
              "      <th>WRKFTALL</th>\n",
              "      <th>WRKMYR</th>\n",
              "      <th>HIEMPOF</th>\n",
              "      <th>EDUC1</th>\n",
              "      <th>ERNYR_P</th>\n",
              "      <th>ARMFTM7P</th>\n",
              "      <th>ARMFTM1P</th>\n",
              "      <th>ARMFTM2P</th>\n",
              "      <th>ARMFTM3P</th>\n",
              "      <th>ARMFTM4P</th>\n",
              "      <th>ARMFTM5P</th>\n",
              "      <th>ARMFTM6P</th>\n",
              "      <th>ENGLANG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEX</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>1</th>\n",
              "      <td>4208</td>\n",
              "      <td>0</td>\n",
              "      <td>4208</td>\n",
              "      <td>2356</td>\n",
              "      <td>2020</td>\n",
              "      <td>230</td>\n",
              "      <td>145</td>\n",
              "      <td>230</td>\n",
              "      <td>4208</td>\n",
              "      <td>0</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
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              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>2542</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>2578</td>\n",
              "      <td>2431</td>\n",
              "      <td>4208</td>\n",
              "      <td>279</td>\n",
              "      <td>484</td>\n",
              "      <td>55</td>\n",
              "      <td>4208</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1652</td>\n",
              "      <td>63</td>\n",
              "      <td>4208</td>\n",
              "      <td>1635</td>\n",
              "      <td>203</td>\n",
              "      <td>2356</td>\n",
              "      <td>1852</td>\n",
              "      <td>1852</td>\n",
              "      <td>4208</td>\n",
              "      <td>3225</td>\n",
              "      <td>4208</td>\n",
              "      <td>449</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>4208</td>\n",
              "      <td>975</td>\n",
              "      <td>4208</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>4208</td>\n",
              "      <td>1305</td>\n",
              "      <td>924</td>\n",
              "      <td>380</td>\n",
              "      <td>1143</td>\n",
              "      <td>918</td>\n",
              "      <td>4208</td>\n",
              "      <td>1143</td>\n",
              "      <td>216</td>\n",
              "      <td>1039</td>\n",
              "      <td>1234</td>\n",
              "      <td>1216</td>\n",
              "      <td>1035</td>\n",
              "      <td>698</td>\n",
              "      <td>368</td>\n",
              "      <td>4208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5080</td>\n",
              "      <td>0</td>\n",
              "      <td>5080</td>\n",
              "      <td>2581</td>\n",
              "      <td>1841</td>\n",
              "      <td>243</td>\n",
              "      <td>154</td>\n",
              "      <td>243</td>\n",
              "      <td>5080</td>\n",
              "      <td>0</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>3248</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>3286</td>\n",
              "      <td>3442</td>\n",
              "      <td>5080</td>\n",
              "      <td>152</td>\n",
              "      <td>309</td>\n",
              "      <td>76</td>\n",
              "      <td>5080</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>...</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2086</td>\n",
              "      <td>75</td>\n",
              "      <td>5080</td>\n",
              "      <td>2065</td>\n",
              "      <td>203</td>\n",
              "      <td>2581</td>\n",
              "      <td>2499</td>\n",
              "      <td>2499</td>\n",
              "      <td>5080</td>\n",
              "      <td>4161</td>\n",
              "      <td>5080</td>\n",
              "      <td>669</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>5080</td>\n",
              "      <td>962</td>\n",
              "      <td>5080</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5080</td>\n",
              "      <td>99</td>\n",
              "      <td>75</td>\n",
              "      <td>452</td>\n",
              "      <td>1095</td>\n",
              "      <td>880</td>\n",
              "      <td>5080</td>\n",
              "      <td>1095</td>\n",
              "      <td>7</td>\n",
              "      <td>90</td>\n",
              "      <td>84</td>\n",
              "      <td>80</td>\n",
              "      <td>47</td>\n",
              "      <td>22</td>\n",
              "      <td>11</td>\n",
              "      <td>5080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 601 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      FPX  AGE_CHG  INTV_QRT  NOWAF  ...  ARMFTM4P  ARMFTM5P  ARMFTM6P  ENGLANG\n",
              "SEX                                  ...                                       \n",
              "1    4208        0      4208   2356  ...      1035       698       368     4208\n",
              "2    5080        0      5080   2581  ...        47        22        11     5080\n",
              "\n",
              "[2 rows x 601 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69v54-qnTMzd",
        "colab_type": "text"
      },
      "source": [
        "#### Value count of disease types\n",
        "`LAHCA7`: Persons 18+ years who have at least one limitation due to a heart problem<br> \n",
        "\n",
        "\n",
        "Heart problem causes limitation:\n",
        "\n",
        "\n",
        "> 1 - Mentioned <br>\n",
        "2 - Not mentioned<br>\n",
        "7 - Refused<br>\n",
        "8 - Not ascertained<br>\n",
        "9 - Don't know\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsYldQdpMolQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "7d36ff24-a7d9-46b0-f6f5-ad859f32d410"
      },
      "source": [
        "persons_data[\"LAHCA7\"].unique()\n",
        "persons_data[\"LAHCA7\"].value_counts()\n",
        "sns.countplot(x=\"LAHCA7\",data=persons_data,palette=\"hls\")\n",
        "plt.show()\n",
        "persons_data_sub=persons_data.loc[persons_data['LAHCA7'] <=2]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5ROHec4ZW1v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "fcd488d9-7eef-4425-a796-f3126a98be7d"
      },
      "source": [
        "persons_data.groupby(\"LAHCA7\").count()"
      ],
      "execution_count": 6,
      "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>FPX</th>\n",
              "      <th>AGE_CHG</th>\n",
              "      <th>INTV_QRT</th>\n",
              "      <th>SEX</th>\n",
              "      <th>NOWAF</th>\n",
              "      <th>FSPOUS2</th>\n",
              "      <th>COHAB1</th>\n",
              "      <th>COHAB2</th>\n",
              "      <th>FCOHAB3</th>\n",
              "      <th>ASTATFLG</th>\n",
              "      <th>CSTATFLG</th>\n",
              "      <th>FMX</th>\n",
              "      <th>RRP</th>\n",
              "      <th>FRRP</th>\n",
              "      <th>ORIGIN_I</th>\n",
              "      <th>HISPAN_I</th>\n",
              "      <th>RACRECI3</th>\n",
              "      <th>QCADULT</th>\n",
              "      <th>QCCHILD</th>\n",
              "      <th>R_MARITL</th>\n",
              "      <th>MRACRPI2</th>\n",
              "      <th>RACERPI2</th>\n",
              "      <th>HISCODI3</th>\n",
              "      <th>MRACBPI2</th>\n",
              "      <th>AGE_P</th>\n",
              "      <th>HHREFLG</th>\n",
              "      <th>RECTYPE</th>\n",
              "      <th>SRVY_YR</th>\n",
              "      <th>FMREFLG</th>\n",
              "      <th>FMRPFLG</th>\n",
              "      <th>PARENTS</th>\n",
              "      <th>DAD_DEGP</th>\n",
              "      <th>MOM_DEGP</th>\n",
              "      <th>SIB_DEGP</th>\n",
              "      <th>CDCMSTAT</th>\n",
              "      <th>DAD_ED</th>\n",
              "      <th>MOM_ED</th>\n",
              "      <th>FMOTHER1</th>\n",
              "      <th>FFATHER1</th>\n",
              "      <th>HHX</th>\n",
              "      <th>...</th>\n",
              "      <th>MAFLG</th>\n",
              "      <th>CHFLG</th>\n",
              "      <th>OPFLG</th>\n",
              "      <th>OGFLG</th>\n",
              "      <th>WHONAM1</th>\n",
              "      <th>WHONAM2</th>\n",
              "      <th>NOTCOV</th>\n",
              "      <th>PRPLPLUS</th>\n",
              "      <th>PWRKBR1</th>\n",
              "      <th>COVER</th>\n",
              "      <th>COVER65O</th>\n",
              "      <th>COVER65</th>\n",
              "      <th>REGIONBR</th>\n",
              "      <th>WHYNOWKP</th>\n",
              "      <th>GEOBRTH</th>\n",
              "      <th>YRSINUS</th>\n",
              "      <th>CITIZENP</th>\n",
              "      <th>DOINGLWP</th>\n",
              "      <th>WRKLYR1</th>\n",
              "      <th>WRKHRS2</th>\n",
              "      <th>PLBORN</th>\n",
              "      <th>HEADST</th>\n",
              "      <th>HEADSTV1</th>\n",
              "      <th>ARMFVER</th>\n",
              "      <th>ARMFEV</th>\n",
              "      <th>ARMFFC</th>\n",
              "      <th>VACOV</th>\n",
              "      <th>WRKFTALL</th>\n",
              "      <th>WRKMYR</th>\n",
              "      <th>HIEMPOF</th>\n",
              "      <th>EDUC1</th>\n",
              "      <th>ERNYR_P</th>\n",
              "      <th>ARMFTM7P</th>\n",
              "      <th>ARMFTM1P</th>\n",
              "      <th>ARMFTM2P</th>\n",
              "      <th>ARMFTM3P</th>\n",
              "      <th>ARMFTM4P</th>\n",
              "      <th>ARMFTM5P</th>\n",
              "      <th>ARMFTM6P</th>\n",
              "      <th>ENGLANG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1103</td>\n",
              "      <td>0</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>414</td>\n",
              "      <td>456</td>\n",
              "      <td>46</td>\n",
              "      <td>36</td>\n",
              "      <td>46</td>\n",
              "      <td>1103</td>\n",
              "      <td>0</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>722</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>727</td>\n",
              "      <td>700</td>\n",
              "      <td>1103</td>\n",
              "      <td>28</td>\n",
              "      <td>50</td>\n",
              "      <td>8</td>\n",
              "      <td>1103</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>412</td>\n",
              "      <td>17</td>\n",
              "      <td>1103</td>\n",
              "      <td>405</td>\n",
              "      <td>33</td>\n",
              "      <td>414</td>\n",
              "      <td>689</td>\n",
              "      <td>689</td>\n",
              "      <td>1103</td>\n",
              "      <td>978</td>\n",
              "      <td>1103</td>\n",
              "      <td>121</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>133</td>\n",
              "      <td>1103</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1103</td>\n",
              "      <td>236</td>\n",
              "      <td>168</td>\n",
              "      <td>69</td>\n",
              "      <td>163</td>\n",
              "      <td>120</td>\n",
              "      <td>1103</td>\n",
              "      <td>163</td>\n",
              "      <td>40</td>\n",
              "      <td>187</td>\n",
              "      <td>230</td>\n",
              "      <td>235</td>\n",
              "      <td>208</td>\n",
              "      <td>146</td>\n",
              "      <td>77</td>\n",
              "      <td>1103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8062</td>\n",
              "      <td>0</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>4453</td>\n",
              "      <td>3351</td>\n",
              "      <td>415</td>\n",
              "      <td>259</td>\n",
              "      <td>415</td>\n",
              "      <td>8062</td>\n",
              "      <td>0</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>4998</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>5065</td>\n",
              "      <td>5101</td>\n",
              "      <td>8062</td>\n",
              "      <td>400</td>\n",
              "      <td>734</td>\n",
              "      <td>118</td>\n",
              "      <td>8062</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>...</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3275</td>\n",
              "      <td>119</td>\n",
              "      <td>8062</td>\n",
              "      <td>3244</td>\n",
              "      <td>371</td>\n",
              "      <td>4453</td>\n",
              "      <td>3609</td>\n",
              "      <td>3609</td>\n",
              "      <td>8062</td>\n",
              "      <td>6323</td>\n",
              "      <td>8062</td>\n",
              "      <td>982</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>8062</td>\n",
              "      <td>1767</td>\n",
              "      <td>8062</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>8062</td>\n",
              "      <td>1149</td>\n",
              "      <td>814</td>\n",
              "      <td>746</td>\n",
              "      <td>2037</td>\n",
              "      <td>1642</td>\n",
              "      <td>8062</td>\n",
              "      <td>2037</td>\n",
              "      <td>179</td>\n",
              "      <td>927</td>\n",
              "      <td>1071</td>\n",
              "      <td>1042</td>\n",
              "      <td>862</td>\n",
              "      <td>565</td>\n",
              "      <td>297</td>\n",
              "      <td>8062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>32</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>34</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>35</td>\n",
              "      <td>32</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>53</td>\n",
              "      <td>37</td>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>15</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>15</td>\n",
              "      <td>53</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>26</td>\n",
              "      <td>20</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>25</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>26</td>\n",
              "      <td>28</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>53</td>\n",
              "      <td>40</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>13</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>53</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 601 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         FPX  AGE_CHG  INTV_QRT   SEX  ...  ARMFTM4P  ARMFTM5P  ARMFTM6P  ENGLANG\n",
              "LAHCA7                                 ...                                       \n",
              "1       1103        0      1103  1103  ...       208       146        77     1103\n",
              "2       8062        0      8062  8062  ...       862       565       297     8062\n",
              "7         53        0        53    53  ...         5         4         2       53\n",
              "8         17        0        17    17  ...         1         0         0       17\n",
              "9         53        0        53    53  ...         6         5         3       53\n",
              "\n",
              "[5 rows x 601 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5T5g_w_WV9gG",
        "colab_type": "text"
      },
      "source": [
        "#### Data.unique "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtvJamK3M8XN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "95b610d9-30e8-4199-b059-dada38ff4812"
      },
      "source": [
        "persons_data_sub.loc[persons_data_sub['LAHCA7'] == 2, 'LAHCA7'] = 0\n",
        "persons_data_sub[\"LAHCA7\"].unique()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:966: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  self.obj[item] = s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ITgmyE3VhAK",
        "colab_type": "text"
      },
      "source": [
        "#### Data groups (mean, describe)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uugbpdsxNVS5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "c011263a-e286-4a70-f578-9936a700216a"
      },
      "source": [
        "persons_data_sub.groupby('LAHCA7').mean()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FPX</th>\n",
              "      <th>AGE_CHG</th>\n",
              "      <th>INTV_QRT</th>\n",
              "      <th>SEX</th>\n",
              "      <th>NOWAF</th>\n",
              "      <th>FSPOUS2</th>\n",
              "      <th>COHAB1</th>\n",
              "      <th>COHAB2</th>\n",
              "      <th>FCOHAB3</th>\n",
              "      <th>ASTATFLG</th>\n",
              "      <th>CSTATFLG</th>\n",
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              "      <th>RRP</th>\n",
              "      <th>FRRP</th>\n",
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              "      <th>HISPAN_I</th>\n",
              "      <th>RACRECI3</th>\n",
              "      <th>QCADULT</th>\n",
              "      <th>QCCHILD</th>\n",
              "      <th>R_MARITL</th>\n",
              "      <th>MRACRPI2</th>\n",
              "      <th>RACERPI2</th>\n",
              "      <th>HISCODI3</th>\n",
              "      <th>MRACBPI2</th>\n",
              "      <th>AGE_P</th>\n",
              "      <th>RECTYPE</th>\n",
              "      <th>SRVY_YR</th>\n",
              "      <th>PARENTS</th>\n",
              "      <th>DAD_DEGP</th>\n",
              "      <th>MOM_DEGP</th>\n",
              "      <th>SIB_DEGP</th>\n",
              "      <th>CDCMSTAT</th>\n",
              "      <th>DAD_ED</th>\n",
              "      <th>MOM_ED</th>\n",
              "      <th>FMOTHER1</th>\n",
              "      <th>FFATHER1</th>\n",
              "      <th>HHX</th>\n",
              "      <th>WTIA</th>\n",
              "      <th>WTFA</th>\n",
              "      <th>INTV_MON</th>\n",
              "      <th>...</th>\n",
              "      <th>MAFLG</th>\n",
              "      <th>CHFLG</th>\n",
              "      <th>OPFLG</th>\n",
              "      <th>OGFLG</th>\n",
              "      <th>WHONAM1</th>\n",
              "      <th>WHONAM2</th>\n",
              "      <th>NOTCOV</th>\n",
              "      <th>PRPLPLUS</th>\n",
              "      <th>PWRKBR1</th>\n",
              "      <th>COVER</th>\n",
              "      <th>COVER65O</th>\n",
              "      <th>COVER65</th>\n",
              "      <th>REGIONBR</th>\n",
              "      <th>WHYNOWKP</th>\n",
              "      <th>GEOBRTH</th>\n",
              "      <th>YRSINUS</th>\n",
              "      <th>CITIZENP</th>\n",
              "      <th>DOINGLWP</th>\n",
              "      <th>WRKLYR1</th>\n",
              "      <th>WRKHRS2</th>\n",
              "      <th>PLBORN</th>\n",
              "      <th>HEADST</th>\n",
              "      <th>HEADSTV1</th>\n",
              "      <th>ARMFVER</th>\n",
              "      <th>ARMFEV</th>\n",
              "      <th>ARMFFC</th>\n",
              "      <th>VACOV</th>\n",
              "      <th>WRKFTALL</th>\n",
              "      <th>WRKMYR</th>\n",
              "      <th>HIEMPOF</th>\n",
              "      <th>EDUC1</th>\n",
              "      <th>ERNYR_P</th>\n",
              "      <th>ARMFTM7P</th>\n",
              "      <th>ARMFTM1P</th>\n",
              "      <th>ARMFTM2P</th>\n",
              "      <th>ARMFTM3P</th>\n",
              "      <th>ARMFTM4P</th>\n",
              "      <th>ARMFTM5P</th>\n",
              "      <th>ARMFTM6P</th>\n",
              "      <th>ENGLANG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.517738</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.355867</td>\n",
              "      <td>1.555073</td>\n",
              "      <td>1.997081</td>\n",
              "      <td>1.592659</td>\n",
              "      <td>1.426506</td>\n",
              "      <td>2.795367</td>\n",
              "      <td>1.674699</td>\n",
              "      <td>1.318035</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.011039</td>\n",
              "      <td>2.217068</td>\n",
              "      <td>2.044158</td>\n",
              "      <td>1.897792</td>\n",
              "      <td>11.080005</td>\n",
              "      <td>1.285413</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.700323</td>\n",
              "      <td>1.678244</td>\n",
              "      <td>1.426197</td>\n",
              "      <td>2.160010</td>\n",
              "      <td>2.153560</td>\n",
              "      <td>60.266807</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>3.789878</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>1.083106</td>\n",
              "      <td>1.389831</td>\n",
              "      <td>3.318903</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.148102</td>\n",
              "      <td>0.080005</td>\n",
              "      <td>27584.910816</td>\n",
              "      <td>3896.152890</td>\n",
              "      <td>4232.544406</td>\n",
              "      <td>6.051848</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.687939</td>\n",
              "      <td>1.369748</td>\n",
              "      <td>1.978789</td>\n",
              "      <td>2.236436</td>\n",
              "      <td>2.617251</td>\n",
              "      <td>1.980013</td>\n",
              "      <td>2.181214</td>\n",
              "      <td>2.602106</td>\n",
              "      <td>1.586331</td>\n",
              "      <td>5.675154</td>\n",
              "      <td>1.242248</td>\n",
              "      <td>4.821792</td>\n",
              "      <td>1.050112</td>\n",
              "      <td>4.173034</td>\n",
              "      <td>1.767055</td>\n",
              "      <td>34.847765</td>\n",
              "      <td>1.129744</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.307692</td>\n",
              "      <td>1.865666</td>\n",
              "      <td>1.494343</td>\n",
              "      <td>1.653563</td>\n",
              "      <td>1.896783</td>\n",
              "      <td>10.732941</td>\n",
              "      <td>1.566991</td>\n",
              "      <td>15.417514</td>\n",
              "      <td>19.963181</td>\n",
              "      <td>1.77095</td>\n",
              "      <td>1.991370</td>\n",
              "      <td>1.995331</td>\n",
              "      <td>1.860845</td>\n",
              "      <td>1.566125</td>\n",
              "      <td>1.757522</td>\n",
              "      <td>1.723906</td>\n",
              "      <td>1.267924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.473255</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.327289</td>\n",
              "      <td>1.480508</td>\n",
              "      <td>1.997585</td>\n",
              "      <td>1.614035</td>\n",
              "      <td>1.217391</td>\n",
              "      <td>2.777778</td>\n",
              "      <td>1.608696</td>\n",
              "      <td>1.330916</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.006346</td>\n",
              "      <td>2.078876</td>\n",
              "      <td>2.015413</td>\n",
              "      <td>1.919311</td>\n",
              "      <td>11.270172</td>\n",
              "      <td>1.317316</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.475068</td>\n",
              "      <td>1.750680</td>\n",
              "      <td>1.500453</td>\n",
              "      <td>2.216682</td>\n",
              "      <td>2.204896</td>\n",
              "      <td>67.620127</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>3.889393</td>\n",
              "      <td>1.214286</td>\n",
              "      <td>1.160000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>3.293744</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.080689</td>\n",
              "      <td>0.040798</td>\n",
              "      <td>27113.267452</td>\n",
              "      <td>3864.845875</td>\n",
              "      <td>4071.953762</td>\n",
              "      <td>5.943790</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.572816</td>\n",
              "      <td>1.352941</td>\n",
              "      <td>1.970988</td>\n",
              "      <td>2.175309</td>\n",
              "      <td>1.909091</td>\n",
              "      <td>2.099034</td>\n",
              "      <td>2.168360</td>\n",
              "      <td>2.589260</td>\n",
              "      <td>1.435177</td>\n",
              "      <td>5.378323</td>\n",
              "      <td>1.213055</td>\n",
              "      <td>4.900826</td>\n",
              "      <td>1.030825</td>\n",
              "      <td>4.556664</td>\n",
              "      <td>1.870354</td>\n",
              "      <td>34.022556</td>\n",
              "      <td>1.109701</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.786038</td>\n",
              "      <td>1.491525</td>\n",
              "      <td>1.654762</td>\n",
              "      <td>1.884058</td>\n",
              "      <td>12.914110</td>\n",
              "      <td>1.816667</td>\n",
              "      <td>14.697189</td>\n",
              "      <td>18.417178</td>\n",
              "      <td>1.52500</td>\n",
              "      <td>2.117647</td>\n",
              "      <td>2.043478</td>\n",
              "      <td>1.846809</td>\n",
              "      <td>1.581731</td>\n",
              "      <td>1.719178</td>\n",
              "      <td>1.584416</td>\n",
              "      <td>1.261106</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 598 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             FPX  AGE_CHG  INTV_QRT  ...  ARMFTM5P  ARMFTM6P   ENGLANG\n",
              "LAHCA7                               ...                              \n",
              "0       1.517738      NaN  2.355867  ...  1.757522  1.723906  1.267924\n",
              "1       1.473255      NaN  2.327289  ...  1.719178  1.584416  1.261106\n",
              "\n",
              "[2 rows x 598 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYl0yOTlNZmD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "c659f42c-7a5e-4c6d-8103-7ed6601b1ff6"
      },
      "source": [
        "persons_data[[\"LAHCA7\", \"LAHCA8\"]].describe()"
      ],
      "execution_count": 9,
      "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>LAHCA7</th>\n",
              "      <th>LAHCA8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9288.000000</td>\n",
              "      <td>9288.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.960702</td>\n",
              "      <td>2.026055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.778096</td>\n",
              "      <td>0.735485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            LAHCA7       LAHCA8\n",
              "count  9288.000000  9288.000000\n",
              "mean      1.960702     2.026055\n",
              "std       0.778096     0.735485\n",
              "min       1.000000     1.000000\n",
              "25%       2.000000     2.000000\n",
              "50%       2.000000     2.000000\n",
              "75%       2.000000     2.000000\n",
              "max       9.000000     9.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OXRExW6yVvXp",
        "colab_type": "text"
      },
      "source": [
        "#### Value counts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zi3oP1IYNf7g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "15c47b4d-143a-4f43-8b08-aefb18e73ccb"
      },
      "source": [
        "persons_data[\"LAHCA7\"].value_counts()\n",
        "persons_data[\"LAHCA8\"].value_counts()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    8669\n",
              "1     496\n",
              "7      53\n",
              "9      53\n",
              "8      17\n",
              "Name: LAHCA8, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9oHeYH5TeqR",
        "colab_type": "text"
      },
      "source": [
        "#### Marital status for heart diseases\n",
        "\n",
        "Marital Status:\n",
        "\n",
        "> 1 - Married - spouse in household<br>\n",
        "2 - Married - spouse not in household<br>\n",
        "3 - Married - spouse in household unknown<br>\n",
        "4 - Widowed<br>\n",
        "5 - Divorced<br>\n",
        "6 - Separated<br>\n",
        "7 - Never married<br>\n",
        "8 - Living with partner<br>\n",
        "9 - Unknown marital status<br>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KExlld2_O4nq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "9f5fa054-7b62-4722-c43e-c0653118eea2"
      },
      "source": [
        "pd.crosstab(persons_data_sub.R_MARITL,persons_data_sub.LAHCA7).plot(kind='bar')\n",
        "plt.title('Marital status for heart diseases')\n",
        "plt.xlabel('Heart Disease')\n",
        "plt.ylabel('Frequency of Marital Status')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency of Marital Status')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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": "H0H_Q8luVMNa",
        "colab_type": "text"
      },
      "source": [
        "#### Marital data by different disease groups"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mlf_q-f_QiPd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "4e8171cf-e6f7-469f-d7de-2c1d1e1560d4"
      },
      "source": [
        "pd.crosstab(persons_data.LAHCA7, persons_data.R_MARITL).apply(lambda r: r/r.sum(), axis=1)"
      ],
      "execution_count": 13,
      "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>R_MARITL</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.413418</td>\n",
              "      <td>0.009973</td>\n",
              "      <td>0.215775</td>\n",
              "      <td>0.179510</td>\n",
              "      <td>0.031732</td>\n",
              "      <td>0.106981</td>\n",
              "      <td>0.041704</td>\n",
              "      <td>0.000907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.415654</td>\n",
              "      <td>0.012404</td>\n",
              "      <td>0.157901</td>\n",
              "      <td>0.150459</td>\n",
              "      <td>0.025056</td>\n",
              "      <td>0.184818</td>\n",
              "      <td>0.051476</td>\n",
              "      <td>0.002233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.471698</td>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.094340</td>\n",
              "      <td>0.132075</td>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.150943</td>\n",
              "      <td>0.075472</td>\n",
              "      <td>0.037736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.529412</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.377358</td>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.075472</td>\n",
              "      <td>0.132075</td>\n",
              "      <td>0.075472</td>\n",
              "      <td>0.188679</td>\n",
              "      <td>0.132075</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "R_MARITL         1         2         4  ...         7         8         9\n",
              "LAHCA7                                  ...                              \n",
              "1         0.413418  0.009973  0.215775  ...  0.106981  0.041704  0.000907\n",
              "2         0.415654  0.012404  0.157901  ...  0.184818  0.051476  0.002233\n",
              "7         0.471698  0.018868  0.094340  ...  0.150943  0.075472  0.037736\n",
              "8         0.529412  0.058824  0.176471  ...  0.176471  0.058824  0.000000\n",
              "9         0.377358  0.018868  0.075472  ...  0.188679  0.132075  0.000000\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "l8Bb6eOPBiZ2"
      },
      "source": [
        "#### Alcohol abuse data by different disease groups"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9wBCWdpXBiZ4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "0213d10b-51a5-4375-cb06-4162cea27a7d"
      },
      "source": [
        "pd.crosstab(persons_data.LAHCA7, persons_data.LAHCA29_).apply(lambda r: r/r.sum(), axis=1)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>LAHCA29_</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.002109</td>\n",
              "      <td>0.997891</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "LAHCA29_         1         2    7    8    9\n",
              "LAHCA7                                     \n",
              "1         0.000000  1.000000  0.0  0.0  0.0\n",
              "2         0.002109  0.997891  0.0  0.0  0.0\n",
              "7         0.000000  0.000000  1.0  0.0  0.0\n",
              "8         0.000000  0.000000  0.0  1.0  0.0\n",
              "9         0.000000  0.000000  0.0  0.0  1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5Fh0uhpCCTn4"
      },
      "source": [
        "#### Health insurance data by different disease groups `<65`\n",
        "Under 65 years old\n",
        "> 1 - Private <br>\n",
        "2 - Medicaid and other public <br>\n",
        "3 - Other coverage<br>\n",
        "4 - Uninsured<br>\n",
        "5 - Don't know "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WzYhn3orFL22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "06428ccc-5917-4fe2-bbbd-23e09ac774e9"
      },
      "source": [
        "pd.crosstab(persons_data.LAHCA7, persons_data.COVER).apply(lambda r: r/r.sum(), axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>COVER</th>\n",
              "      <th>1.0</th>\n",
              "      <th>2.0</th>\n",
              "      <th>3.0</th>\n",
              "      <th>4.0</th>\n",
              "      <th>5.0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.301932</td>\n",
              "      <td>0.391304</td>\n",
              "      <td>0.214976</td>\n",
              "      <td>0.089372</td>\n",
              "      <td>0.002415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.396811</td>\n",
              "      <td>0.338873</td>\n",
              "      <td>0.159668</td>\n",
              "      <td>0.096789</td>\n",
              "      <td>0.007860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.437500</td>\n",
              "      <td>0.281250</td>\n",
              "      <td>0.156250</td>\n",
              "      <td>0.031250</td>\n",
              "      <td>0.093750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.538462</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.076923</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "COVER        1.0       2.0       3.0       4.0       5.0\n",
              "LAHCA7                                                  \n",
              "1       0.301932  0.391304  0.214976  0.089372  0.002415\n",
              "2       0.396811  0.338873  0.159668  0.096789  0.007860\n",
              "7       0.437500  0.281250  0.156250  0.031250  0.093750\n",
              "8       0.750000  0.166667  0.083333  0.000000  0.000000\n",
              "9       0.076923  0.538462  0.153846  0.153846  0.076923"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-r9iOEU3FL2c"
      },
      "source": [
        "#### Health insurance data by different disease groups `>65`\n",
        "65 years old and over\n",
        "> 1 - Private <br>\n",
        "2 - Medicaid and other public <br>\n",
        "3 - Other coverage<br>\n",
        "4 - Uninsured<br>\n",
        "5 - Don't know "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZFzTG-2jCTn8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "6e3ca2d0-e8cf-43c3-9454-243efc842a62"
      },
      "source": [
        "pd.crosstab(persons_data.LAHCA7, persons_data.COVER65).apply(lambda r: r/r.sum(), axis=1)"
      ],
      "execution_count": 41,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>COVER65</th>\n",
              "      <th>1.0</th>\n",
              "      <th>2.0</th>\n",
              "      <th>3.0</th>\n",
              "      <th>4.0</th>\n",
              "      <th>5.0</th>\n",
              "      <th>6.0</th>\n",
              "      <th>7.0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.359942</td>\n",
              "      <td>0.142235</td>\n",
              "      <td>0.185776</td>\n",
              "      <td>0.175617</td>\n",
              "      <td>0.133527</td>\n",
              "      <td>0.002903</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.352452</td>\n",
              "      <td>0.126905</td>\n",
              "      <td>0.226378</td>\n",
              "      <td>0.164589</td>\n",
              "      <td>0.121917</td>\n",
              "      <td>0.005542</td>\n",
              "      <td>0.002217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.523810</td>\n",
              "      <td>0.047619</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.238095</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.095238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.407407</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.148148</td>\n",
              "      <td>0.185185</td>\n",
              "      <td>0.185185</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.037037</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "COVER65       1.0       2.0       3.0       4.0       5.0       6.0       7.0\n",
              "LAHCA7                                                                       \n",
              "1        0.359942  0.142235  0.185776  0.175617  0.133527  0.002903  0.000000\n",
              "2        0.352452  0.126905  0.226378  0.164589  0.121917  0.005542  0.002217\n",
              "7        0.523810  0.047619  0.000000  0.238095  0.095238  0.000000  0.095238\n",
              "8        0.200000  0.000000  0.200000  0.400000  0.200000  0.000000  0.000000\n",
              "9        0.407407  0.037037  0.148148  0.185185  0.185185  0.000000  0.037037"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "joUkjFBkNT1-"
      },
      "source": [
        "#### Private health insurance data by different disease groups \n",
        "Private health insurance recode\n",
        "> 1 - Yes, information<br>\n",
        "2 - Yes, but no information<br>\n",
        "3 - No<br>\n",
        "7 - Refused<br>\n",
        "8 - Not ascertained<br>\n",
        "9 - Don't know<br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "T67xirtgNT2B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "7b4663a5-9bae-4df3-e531-be7970077b03"
      },
      "source": [
        "pd.crosstab(persons_data.LAHCA7, persons_data.HIKINDNA).apply(lambda r: r/r.sum(), axis=1)"
      ],
      "execution_count": 42,
      "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>HIKINDNA</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>7</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.328196</td>\n",
              "      <td>0.670898</td>\n",
              "      <td>0.000907</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.374969</td>\n",
              "      <td>0.619449</td>\n",
              "      <td>0.001364</td>\n",
              "      <td>0.004217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.509434</td>\n",
              "      <td>0.377358</td>\n",
              "      <td>0.075472</td>\n",
              "      <td>0.037736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.411765</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.264151</td>\n",
              "      <td>0.679245</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.056604</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "HIKINDNA         1         2         7         9\n",
              "LAHCA7                                          \n",
              "1         0.328196  0.670898  0.000907  0.000000\n",
              "2         0.374969  0.619449  0.001364  0.004217\n",
              "7         0.509434  0.377358  0.075472  0.037736\n",
              "8         0.588235  0.411765  0.000000  0.000000\n",
              "9         0.264151  0.679245  0.000000  0.056604"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xXHPEp5JSuMN"
      },
      "source": [
        "#### Born in the US data by different disease groups \n",
        "Born in the United States\n",
        "> 1 - Yes <br>\n",
        "2 - No<br>\n",
        "7 - Refused<br>\n",
        "8 - Not ascertained<br>\n",
        "9 - Don't know "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "La3G-hhOSuMs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "2a999a2a-58e0-45cc-8799-d225e8bea7a1"
      },
      "source": [
        "pd.crosstab(persons_data.LAHCA7, persons_data.PLBORN).apply(lambda r: r/r.sum(), axis=1)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>PLBORN</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>7</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.890299</td>\n",
              "      <td>0.109701</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.877078</td>\n",
              "      <td>0.121806</td>\n",
              "      <td>0.000496</td>\n",
              "      <td>0.00062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.943396</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.941176</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.773585</td>\n",
              "      <td>0.226415</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "PLBORN         1         2         7        9\n",
              "LAHCA7                                       \n",
              "1       0.890299  0.109701  0.000000  0.00000\n",
              "2       0.877078  0.121806  0.000496  0.00062\n",
              "7       0.943396  0.037736  0.018868  0.00000\n",
              "8       0.941176  0.058824  0.000000  0.00000\n",
              "9       0.773585  0.226415  0.000000  0.00000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6HM4GFuCWgmZ"
      },
      "source": [
        "#### Education data by different disease groups \n",
        "Highest education level\n",
        "> 00-14 - High School and below<br>15-18 - College<br>19-21 - Graduate and Above"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "L7U9BKOtWgmo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "a817100c-8485-41c1-f208-a707c1dd156a"
      },
      "source": [
        "pd.crosstab(persons_data.LAHCA7, persons_data.EDUC1).apply(lambda r: r/r.sum(), axis=1)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>EDUC1</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>97</th>\n",
              "      <th>99</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.004533</td>\n",
              "      <td>0.002720</td>\n",
              "      <td>0.001813</td>\n",
              "      <td>0.006346</td>\n",
              "      <td>0.007253</td>\n",
              "      <td>0.00544</td>\n",
              "      <td>0.014506</td>\n",
              "      <td>0.008160</td>\n",
              "      <td>0.033545</td>\n",
              "      <td>0.033545</td>\n",
              "      <td>0.032638</td>\n",
              "      <td>0.038078</td>\n",
              "      <td>0.028105</td>\n",
              "      <td>0.050771</td>\n",
              "      <td>0.265639</td>\n",
              "      <td>0.198549</td>\n",
              "      <td>0.073436</td>\n",
              "      <td>0.034451</td>\n",
              "      <td>0.093382</td>\n",
              "      <td>0.043518</td>\n",
              "      <td>0.009066</td>\n",
              "      <td>0.005440</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.008683</td>\n",
              "      <td>0.001116</td>\n",
              "      <td>0.002853</td>\n",
              "      <td>0.004341</td>\n",
              "      <td>0.003845</td>\n",
              "      <td>0.00707</td>\n",
              "      <td>0.012280</td>\n",
              "      <td>0.007938</td>\n",
              "      <td>0.022947</td>\n",
              "      <td>0.024312</td>\n",
              "      <td>0.030265</td>\n",
              "      <td>0.038452</td>\n",
              "      <td>0.027164</td>\n",
              "      <td>0.043165</td>\n",
              "      <td>0.269784</td>\n",
              "      <td>0.180848</td>\n",
              "      <td>0.076284</td>\n",
              "      <td>0.037212</td>\n",
              "      <td>0.121558</td>\n",
              "      <td>0.046763</td>\n",
              "      <td>0.008559</td>\n",
              "      <td>0.010419</td>\n",
              "      <td>0.003349</td>\n",
              "      <td>0.010791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.056604</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.226415</td>\n",
              "      <td>0.169811</td>\n",
              "      <td>0.094340</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>0.113208</td>\n",
              "      <td>0.113208</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.056604</td>\n",
              "      <td>0.056604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.411765</td>\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>9</th>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>0.056604</td>\n",
              "      <td>0.283019</td>\n",
              "      <td>0.207547</td>\n",
              "      <td>0.056604</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>0.075472</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018868</td>\n",
              "      <td>0.037736</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "EDUC1         0         1         2   ...        21        97        99\n",
              "LAHCA7                                ...                              \n",
              "1       0.004533  0.002720  0.001813  ...  0.005440  0.000000  0.009066\n",
              "2       0.008683  0.001116  0.002853  ...  0.010419  0.003349  0.010791\n",
              "7       0.018868  0.000000  0.000000  ...  0.000000  0.056604  0.056604\n",
              "8       0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "9       0.018868  0.000000  0.000000  ...  0.000000  0.018868  0.037736\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "abk0Km9Qdj5m"
      },
      "source": [
        "#### Full-time working data by different disease groups \n",
        "Usually work full time\n",
        "> 1 - Yes <br>\n",
        "2 - No <br>\n",
        "7 - Refused <br>\n",
        "8 - Not ascertained <br>\n",
        "9 - Don't know <br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9oyqLwvidj6D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "6ec53b66-8630-4d74-8fba-6d2e640ab4a7"
      },
      "source": [
        "pd.crosstab(persons_data.LAHCA7, persons_data.WRKFTALL).apply(lambda r: r/r.sum(), axis=1)"
      ],
      "execution_count": 49,
      "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>WRKFTALL</th>\n",
              "      <th>1.0</th>\n",
              "      <th>2.0</th>\n",
              "      <th>9.0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAHCA7</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.115942</td>\n",
              "      <td>0.884058</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.131367</td>\n",
              "      <td>0.864611</td>\n",
              "      <td>0.004021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "WRKFTALL       1.0       2.0       9.0\n",
              "LAHCA7                                \n",
              "1         0.115942  0.884058  0.000000\n",
              "2         0.131367  0.864611  0.004021\n",
              "7         0.000000  1.000000  0.000000\n",
              "8         0.666667  0.333333  0.000000\n",
              "9         0.222222  0.777778  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yl2vHl6yUTxX",
        "colab_type": "text"
      },
      "source": [
        "#### Statistical variables of dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIrVCWENPIl5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "1a4513b5-80d6-4d22-823a-da2b885662ad"
      },
      "source": [
        "persons_data_analysis = persons_data_sub[['ENGLANG', 'PSSRR', 'EDUC1',\n",
        "                      'CITIZENP','PLBORN', 'HIKINDNA', 'LAHCA29_', 'R_MARITL', 'AGE_P', \n",
        "                      'MRACRPI2', 'SEX', 'WTFA','LAHCA7']]\n",
        "percent_missing = persons_data_analysis.isnull().sum() * 100 / len(persons_data_analysis)\n",
        "missing_value_persons_data = pd.DataFrame({'column_name': persons_data_analysis.columns,\n",
        "                                 'percent_missing': percent_missing})\n",
        "missing_value_persons_data.sort_values('percent_missing', inplace=True)\n",
        "print(missing_value_persons_data)\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         column_name  percent_missing\n",
            "ENGLANG      ENGLANG              0.0\n",
            "PSSRR          PSSRR              0.0\n",
            "EDUC1          EDUC1              0.0\n",
            "CITIZENP    CITIZENP              0.0\n",
            "PLBORN        PLBORN              0.0\n",
            "HIKINDNA    HIKINDNA              0.0\n",
            "LAHCA29_    LAHCA29_              0.0\n",
            "R_MARITL    R_MARITL              0.0\n",
            "AGE_P          AGE_P              0.0\n",
            "MRACRPI2    MRACRPI2              0.0\n",
            "SEX              SEX              0.0\n",
            "WTFA            WTFA              0.0\n",
            "LAHCA7        LAHCA7              0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUG941FePdAs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = persons_data_analysis.loc[:, persons_data_analysis.columns != 'LAHCA7']\n",
        "y = persons_data_analysis.loc[:, persons_data_analysis.columns == 'LAHCA7']"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJKYP_fiPmFS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "64067404-a63c-4748-d944-44df5feae803"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "logit_model=sm.Logit(y,X)\n",
        "result=logit_model.fit()\n",
        "print(result.summary2())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.354418\n",
            "         Iterations 7\n",
            "                         Results: Logit\n",
            "=================================================================\n",
            "Model:              Logit            Pseudo R-squared: 0.036     \n",
            "Dependent Variable: LAHCA7           AIC:              6520.4878 \n",
            "Date:               2020-08-09 16:10 BIC:              6605.9656 \n",
            "No. Observations:   9165             Log-Likelihood:   -3248.2   \n",
            "Df Model:           11               LL-Null:          -3369.2   \n",
            "Df Residuals:       9153             LLR p-value:      1.3268e-45\n",
            "Converged:          1.0000           Scale:            1.0000    \n",
            "No. Iterations:     7.0000                                       \n",
            "------------------------------------------------------------------\n",
            "               Coef.   Std.Err.     z     P>|z|    [0.025   0.975]\n",
            "------------------------------------------------------------------\n",
            "ENGLANG        0.0202    0.0474   0.4267  0.6696  -0.0727   0.1132\n",
            "PSSRR         -0.0723    0.0499  -1.4491  0.1473  -0.1701   0.0255\n",
            "EDUC1         -0.0090    0.0040  -2.2592  0.0239  -0.0168  -0.0012\n",
            "CITIZENP      -0.0913    0.1415  -0.6456  0.5185  -0.3687   0.1860\n",
            "PLBORN        -0.2753    0.1290  -2.1336  0.0329  -0.5281  -0.0224\n",
            "HIKINDNA       0.0629    0.0490   1.2850  0.1988  -0.0330   0.1589\n",
            "LAHCA29_      -1.3984    0.1428  -9.7949  0.0000  -1.6782  -1.1185\n",
            "R_MARITL       0.0184    0.0143   1.2878  0.1978  -0.0096   0.0464\n",
            "AGE_P          0.0290    0.0024  11.9857  0.0000   0.0243   0.0338\n",
            "MRACRPI2       0.0306    0.0135   2.2579  0.0240   0.0040   0.0572\n",
            "SEX           -0.3917    0.0658  -5.9491  0.0000  -0.5207  -0.2627\n",
            "WTFA          -0.0000    0.0000  -0.8577  0.3911  -0.0001   0.0000\n",
            "=================================================================\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZTBs5nJVEw9",
        "colab_type": "text"
      },
      "source": [
        "#### Logistic regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ab0cvlxLQBjO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "6306de4e-5b6f-41ab-9703-6fdb079ce196"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y.values.ravel(), test_size=0.1, random_state=0)\n",
        "logreg = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
        "logreg.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=None, max_iter=1000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2Yc5ZM0QMyw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ba5c1d1c-a23a-4e37-e19c-99662eb52f4f"
      },
      "source": [
        "y_pred = logreg.predict(X_test)\n",
        "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of logistic regression classifier on test set: 0.89\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pmxjq3PrQd6R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "191c6e73-b848-4655-b7da-4bb5316a8e71"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      1.00      0.94       817\n",
            "           1       0.00      0.00      0.00       100\n",
            "\n",
            "    accuracy                           0.89       917\n",
            "   macro avg       0.45      0.50      0.47       917\n",
            "weighted avg       0.79      0.89      0.84       917\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsrB3QEVS1c4",
        "colab_type": "text"
      },
      "source": [
        "#### Calculated mean by different groups and age\n",
        "`LAHCA7`: Persons 18+ years who have at least one limitation due to a heart problem<br> \n",
        "\n",
        "\n",
        "Heart problem causes limitation:\n",
        "\n",
        "\n",
        "> 1 - Mentioned <br>\n",
        "2 - Not mentioned<br>\n",
        "7 - Refused<br>\n",
        "8 - Not ascertained<br>\n",
        "9 - Don't know\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vM1r3IhbQxsn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "177ab7cd-83a2-4eb8-c029-97bb3a4bd997"
      },
      "source": [
        "persons_data.groupby('LAHCA7')['AGE_P'].mean()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LAHCA7\n",
              "1    67.620127\n",
              "2    60.266807\n",
              "7    57.905660\n",
              "8    59.705882\n",
              "9    62.056604\n",
              "Name: AGE_P, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "v2VytVWERLCy"
      },
      "source": [
        "#### Calculated mean by different incomes\n",
        "Total earnings last year\n",
        "> 01 - $01-$4,999 <br>\n",
        "02 - $5,000-$9,999 <br>\n",
        "03 - $10,000-$14,999 <br>\n",
        "04 - $15,000-$19,999 <br>\n",
        "05 - $20,000-$24,999 <br>\n",
        "06 - $25,000-$34,999 <br>\n",
        "07 - $35,000-$44,999 <br>\n",
        "08 - $45,000-$54,999 <br>\n",
        "09 - $55,000-$64,999 <br>\n",
        "10 - $65,000-$74,999 <br>\n",
        "11 - $75,000 and over <br>\n",
        "97 - Refused <br>\n",
        "98 - Not ascertained <br>\n",
        "99 - Don't know  <br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sxQO47UVRLC2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "389fa797-5955-4a76-fcd9-a4aaaedde617"
      },
      "source": [
        "persons_data.groupby('LAHCA7')['ERNYR_P'].median()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LAHCA7\n",
              "1     6.0\n",
              "2     6.0\n",
              "7    97.0\n",
              "8     8.0\n",
              "9     6.0\n",
              "Name: ERNYR_P, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MJYOvlxQc9-J"
      },
      "source": [
        "#### Calculated mean by different language levels\n",
        "How well English is spoken\n",
        "> 1 - Very well <br>\n",
        "2 - Well<br>\n",
        "3 - Not well<br>\n",
        "4 - Not at all<br>\n",
        "7 - Refused<br>\n",
        "8-  Not ascertained<br>\n",
        "9 - Don’t know <br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_vU0dOw2c9-o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "defc298b-f305-4037-ae1e-933e0b5b5122"
      },
      "source": [
        "persons_data.groupby('LAHCA7')['ENGLANG'].median()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LAHCA7\n",
              "1    1\n",
              "2    1\n",
              "7    1\n",
              "8    1\n",
              "9    1\n",
              "Name: ENGLANG, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiPVGPHkSr6I",
        "colab_type": "text"
      },
      "source": [
        "#### Scatterplot by group and compare differences of age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3byTB2NQ4_u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "61ef8f51-9ae9-4783-ef4b-efc099a8dc8c"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(8,6))\n",
        "ax.scatter(persons_data.LAHCA7, persons_data.AGE_P, cmap='tab20b')\n",
        "plt.title('Scatterplot by group and compare differences of age')\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90DFonYcSkxr",
        "colab_type": "text"
      },
      "source": [
        "#### Histogram of Ages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxLrt5IhRL9X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "e9688eae-0729-4639-e8d8-02d421ea7f45"
      },
      "source": [
        "persons_data['AGE_P'].hist(by=persons_data['LAHCA7'])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc3c65850f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc3d3e8ee48>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc3d3ed4908>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc3c6220390>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc3c678da90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc3c617f588>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pdKKqqgoA0NDQAI/Ho3A69VB7XWihJlgPg5DlOGSMZWRkiIqKin6PP//882L+/PkKJBqcy+USBw8eFI899phYsWKF0nH6cblcoqCgQCQkJIh77rlHzJs3TyxcuFCkp6eLpqYmpeMJIYQ4ffq0WLp0qVi2bJn47LPPxKZNm8S6detEfHy8KCsrUzqeamilLtRcE6yHgWnirKT29nbodDrcdttt/bbV1dVh7ty5CqQaWlVVFSoqKrB161alowzI6XSiqakJXq8XRqNRlfeG6eXxeFBTU4PIyEjcfvvtSsdRDa3VhZprgvUgpcrG4PV60dnZiZCQENUdepJ2CSHgdruh1+sRFKSJWVTWAsliuFpQ5eJzZ2cnamtrlY5B45TJZOo73z1QEhMTERoairCwMABAVlYWVqxYMew41gLJabBaUGVjCAkJAdATuvfSebvdDovFomSsEWPWsTcWObu7u1FbW9v3+gq0oqIimEwmn8YMVAvAxPq5BcpEyjpcLaiyMfQeMt/4DguA5O9qx6xjb6xyamlKZrBaACbezy0QJlrWwWpBlY2B1Kvb7UFoSHDAxo0nWVlZEEIgPj4e27dv9+l2Bna7vd9jlZWVYxlPNoHMOd+8EPopk30e1+nsAqCd7ykgb1Y2BvJJaEgw7ttR6vO4sudTZUijHW+99RaMRiO6u7vxu9/9Dnv37kVhYeGIx1ssFsk7xMrKSsTHx8sRdUwpkXM0r08tfE+B0X9fXS7XgG82emnj1AwijTMajQB6poTS0tJw9uxZhRMRDY6NgUhmTqcT165dA9BzmuCJEydgNpsVTkU0OE4lEcmsra0N27Ztg8fjgdfrRWxsLKxWq9KxiAbFxkAkszvuuAPHjh1TOgbRiHEqSUW63f7dEMvfcUREA+ERg4rwjB8iUgMeMRARkQQbAxERSbAxEBGRBBvDOODP4rMWFqxvzOjLVZ5a+L8RqRkXn8cBfxattbBgzcV4ImXwiIGIiCTYGIiISIKNgYiIJNgYiIhIgo1hguo9c0cr958nosDhWUkTFM/4IaLB8IiBiIgkZD1iSExMlHyIeVZWFlasWCHnLomIfNbt9vg8rRrozzG/cX8jzepvRtmnkoqKimAymeTeDRGR37RwkWggM3IqiYiIJGQ/YsjKyoIQAvHx8di+fTvCw8NHPNZut0u+rqysxHzzQuinTPY5R6ezC1/XfOXzOH9VVlb6PGa8nyHk6/dkNN8Pf77/RNRD1sbw1ltvwWg0oru7G7/73e+wd+9eFBYWjni8xWLpW5+orKzs+0Xh79k0gfrFe2NW+lEgvycD7cvlcvV7s0FE/ck6lWQ0GgEAoaGhSEtLw9mzZ+XcHRERjQHZGoPT6cS1a9cAAEIInDhxAmazWa7dERHRGJFtKqmtrQ3btm2Dx+OB1+tFbGwsrFarXLsjIqIxIltjuOOOO3Ds2DG5/nlVm29eqHQE1Qn0Od9E5D/eEkMG+imTebuJm2jhPHEi6sHrGIiISIKNgUjDJsLnfVPgcSqJSMPG6xQd7/6rLB4xEBGRxIRpDP4emvKQlogmmgkzlcRDUyKikZkwRwxERDQybAxERCTBxkBERBJsDEREJMHGQEREEmwMRER+8PdUdpcGToGfMKerEhGNpdGcAq/2U+d5xEBERBJsDEREJMHGQEREEmwMREQkwcZAREQSsjaGhoYGrF+/HsnJyVi/fj0aGxvl3B2RarEWSEtkbQxWqxVpaWk4efIk0tLSkJeXJ+fuiFSLtUBaItt1DG1tbaiursaRI0cAACkpKSgoKIDD4UBERMSQY4UQAIDu7m7J4y6XCwBg0Af7nMflcvk9zh+BzujruEDuy99xY/0z63099b6+AkWOWgD8rwd/X9P+Yg0pN87fWtAJmarEbrdj586dOH78eN9j9957Lw4ePIiFCxcOOfbatWuora2VIxYRTCYTpk6dGrD9sRZIrQarBVVe+azX62EymRASEgKdTqd0HBonhBBwu93Q6/VKRxkx1gLJYbhakK0xGI1GtLS0wOPxIDg4GB6PB62trTAajcOODQoKCug7Opo4Jk+eHPB9shZIjYaqBdkWn6dPnw6z2Yzy8nIAQHl5Ocxm87BzqkTjDWuBtEa2NQYAqK+vR05ODjo6OhAeHg6bzYaYmBi5dkekWqwF0hJZGwMREWkPr3wmIiIJNgYiIpJgYyAiIgk2BiIikmBjICIiCTYGIiKSYGMgIiIJVd4r6UYdHR0AgPDwcIWTDE2JnPX19cjPz8dXX32FiIgIZGdnIykpacgxV69exeXLlwEAkZGRmDZtWiCi+kVLWQNBK7UAaCerVl5jgc6pygvcHA4HCgsL8cEHHwDoueFTUFAQ1q5di6ysLNXcSkDJnNevX8e6deuwYcMGbNy4ERUVFXjiiSfw3nvvITo6ut/zm5qakJubi+rqasyYMQMA0NraigULFiA/Px9RUVGyZfWVlrLKTSu1AGgrq1ZeY4rlFCqUnp4uiouLhcPh6Husra1NHD58WKSnpyuYTErJnN98841YvHix8Hq9fY9t3rxZHDp0aMDnr1+/XpSWlgqPx9P3mMfjEceOHRMPPfSQrFl9paWsctNKLQihraxaeY0plVOVjSE5OXnQbWvWrAlgkqEpmXOgxrBp0ybx5JNPDvj8obIOtU0JWsoqN63UghDjJ6uaXmNK5VTl4nNYWBjOnTvX7/GzZ88iNDRUgUQDUzJndHQ0IiIi8Prrr8PtduPMmTP44osv0NXVNeDzDQYDysvLJZ/YJITA+++/r7p5YC1llZtWagHQVlatvMaUyqnKNYaqqipkZ2cjLCwMs2fPBgBcunQJLpcLNpsNS5YsUThhj6FyHjhwAIsXL5Z1/19//TX27duHCxcuwGKxYNq0aQgNDcXvf//7fs9tbGyE1WpFTU0NZs6cCSEEWltbMX/+fOzZs0dVd/rszVpdXY3IyEgAQEtLiyqzyk0rtQAoXw++0Eo9KFULqmwMQE9XtNvtaG5uBtDzYScWi0V1n2J1c86mpiZs2bJFkZwbNmzAAw88gA0bNgz6HIfDgebmZjidToSFhSEmJga33nprAFOO3M1Zo6KiVPVuLlC0UguAuuphJLRSDwGvBdkmqUbB4XCIZ599VmzevFkcPXpUsi0zM1OhVP1duHBB8qe2tlasXLlS1NXViQsXLsi+/5qaGtHV1SWcTqd4/fXXxapVq4TL5Rrwubm5uaKtrU0IIcSXX34pli9fLtatWyd++tOfik8++UT2rL44deqUWLJkiUhOThbnz58XP/vZz8TPf/5zcdddd4mPPvpI6XgBpZVaEEL5evCFVupBqVpQ5XUMVqsVc+bMwcqVK1FSUoLPP/8cL774IiZNmoRvv/1W6Xh9UlJSMHv2bMn835UrV5CRkQGdToePPvpI1v2Xlpbir3/9K65fv474+HgcOXJk0LncqqqqvtMFX3rpJbzyyitYtGgRGhoasGPHDtx9992yZvXF4cOHUVJSgo6ODmRkZODll19GXFwc6uvrsWPHDiQmJiodMWC0UguA8vXgC63Ug1K1oMrG0NjYiKKiIgBAUlIS9u7di8cffxzFxcUKJ5PKzMzE+fPnkZ+fj1mzZgEAEhMTcfr06YDsf+fOndi5c+eInutyufr+3tnZiUWLFgHoWcR2u92y5BuNefPmAQD0ej3i4uIAALGxsUpGUoRWagFQvh58oaV6UKIWVHlW0o0/GJ1OB6vVCpPJhK1bt0p+oErLzMzE008/je3bt6OkpAQAVDuXunz5cuzfvx8//PADli1bhhMnTgAAPv30UxgMBoXTSel0OtTX1+PcuXNwOp2oqqoCADQ0NMDj8SicLrC0UgsA60EOitWCbJNUo5CRkSEqKir6Pf7888+L+fPnK5BoaC6XSxw8eFA89thjYsWKFUrHGZDL5RIFBQUiISFB3HPPPWLevHli4cKFIj09XTQ1NSkdT+L06dNi6dKlYtmyZeKzzz4TmzZtEuvWrRPx8fGirKxM6XgBpbVaEIL1MJaUqgVVnpXU3t4OnU6H2267rd+2uro6zJ07V4FUw6uqqkJFRQW2bt2qdJRBOZ1ONDU1wev1wmg0qvbeMDfyeDyoqalBZGQkbr/9dqXjBJRWawFgPcghULWgysbg9XrR2dmJkJAQ1R6KkvYIIeB2u6HX6xEUpMpZVCJVGNXis81mw8mTJ3Hp0iWUlZXBZDIB6Jn/ysnJQXt7OwwGA2w2m083e+rs7ERtbe1oohENymQyYerUqUrHIFKtUTWG1atXY+PGjXj44Yclj1utVqSlpSE1NRWlpaXIy8vDG2+8MeJ/NyQkBEBPAQ92+qXdbofFYvE/fIBoIacWMgKjz9nd3Y3a2tq+1xcRDWxUjSEhIaHfY21tbaiursaRI0cA9JzbXFBQAIfDMeLb7vZOH4WGhiIsLGzQ5w21TU20kFMLGYGxycnpSaKhjfl1DM3NzZg5cyaCg4MBAMHBwZgxYwaam5t9vh+73W4fcntlZaXfOQNJ7pzzzQuhnzLZpzGdzi58XfNV39cjzejPvgbanz/7io+Pl21fRPQjVV7g1stisQz6DrGysnLEvyiUFKic9+0o9en5Zc+n9uXyNaOv+7p5f74Yy325XK5h32wQkQwXuBmNRrS0tPRdfOHxeNDa2gqj0TjWuyIiIhmMeWOYPn06zGYzysvLAQDl5eUwm82q+lg/IiIa3Kimkvbt24dTp07hypUr2Lx5MwwGA44fP449e/YgJycHxcXFCA8Ph81mG6u8REQks1E1ht27d2P37t39Ho+NjcW77747mn+aiIgUwss/iYhIgo2BiIgk2BiIiEiCjYGIiCTYGCaobvePH/KhhQsFiShwVH3lM8knNCTY76uKiWh84xEDERFJsDEQEZEEGwMREUmwMRARkQQbAxERSbAxEBGRBBsDERFJsDEQEZEEGwMREUmwMRARkQQbAxERSbAxEBGRBBsDERFJsDEQEZEEGwMFxI2f/0BE6sbPY6CA8OfzH/jZD0TK4BEDERFJsDEQEZEEGwMREUmwMQzDn0VTLrQSkZZx8XkYXDQloomGRwxERCTBxkBERBJsDEREJMHGoCJctCYiNeDis4r4s9ANcLGbiMYWjxiIiEiCjYGIiCTYGIiISEJzjaF3gTY+Pt6vcURENDTNLT5zgZaISF6aO2IgIiJ5sTEQEZEEGwMREUlMmMbAxWciopHR3OKzv7hoTUQ0MrIeMTQ0NGD9+vVITk7G+vXr0djYKOfuiIhoDMjaGKxWK9LS0nDy5EmkpaUhLy9Pzt0REdEYkG0qqa2tDdXV1Thy5AgAICUlBQUFBXA4HIiIiBhyrBACANDd3T3gdoM+2Oc8LpcrYONcLteQXw8lkBm18H30d18D6X099b6+iGhgOiFTldjtduzcuRPHjx/ve+zee+/FwYMHsXDhwiHHXrt2DbW1tXLEIoLJZMLUqVOVjkGkWqpcfNbr9TCZTAgJCYFOp1M6Do0TQgi43W7o9XqloxCpmmyNwWg0oqWlBR6PB8HBwfB4PGhtbYXRaBx2bFBQEN/RkSwmT56sdAQi1ZNt8Xn69Okwm80oLy8HAJSXl8NsNg+7vkBERMqSbY0BAOrr65GTk4OOjg6Eh4fDZrMhJiZGrt0REdEYkLUxEBGR9kyYW2IQEdHIsDEQEZEEGwMREUmwMRARkQQbAxERSbAxEBGRBBsDERFJqPJeSUPp6OgAAISHhyucZHBayHj16lVcvnwZABAZGYlp06YpnKg/LWQkGo800RgcDgcKCwvxwQcfAOi5GVpQUBDWrl2LrKwsVdxmQwsZAaCpqQm5ubmorq7GjBkzAACtra1YsGAB8vPzERUVpWxAaCMj0XimiSuft2zZgoSEBGzYsKHvXaPD4cA777yDyspK/PnPf1Y4oTYyAsCGDRuQlpaGlJQUBAX1zCR6vV6UlZXh7bffxl/+8heFE2ojI9F4pok1hkuXLuGJJ56QTCVERETgySefxHfffadgsh9pISMAtLe34/777+/7hQv03M02NTUV//vf/xRM9iMtZCQazzTRGMLCwnDu3Ll+j589exahoaEKJOpPCxkBwGAwoLy8XPIpZkIIvP/++6pZE9FCRqLxTBNTSVVVVcjOzkZYWEmOByIAAANWSURBVBhmz54NoOcdusvlgs1mw5IlSxROOHTGAwcOYPHixQon7NHY2Air1YqamhrMnDkTQgi0trZi/vz52LNnjyruftubsbq6GpGRkQCAlpYWVWUkGs800RiAnneMdrsdzc3NAHo+CMhisajqE95uztjU1IQtW7aoKmMvh8OB5uZmOJ1OhIWFISYmBrfeeqvSsSRuzhgVFcUjBqIA0MRU0tWrV5Gbm4tDhw6htbUVa9aswZ133gmdTodt27YpHQ8AUFdXh/r6etxyyy2IiYlBdHQ0jh49iosXL6Kurk7peH3y8vLgcDgQERGBrq4uPPXUU9i1axeSkpJw5swZpeMBAD788EPExcUhLS0NHo8H2dnZyMnJQVJSEk6fPq10PKJxTxOnq1qtVsyZMwcrV65ESUkJPv/8c7z44ouYNGkSvv32W6XjAQBSUlIwe/Zsybz4lStXkJGRAZ1Oh48++kjBdD+qqqrqO3X2pZdewiuvvIJFixahoaEBO3bswN13361wQuDw4cMoKSlBR0cHMjIy8PLLLyMuLg719fXYsWMHEhMTlY5INK5pojE0NjaiqKgIAJCUlIS9e/fi8ccfR3FxscLJfpSZmYnz588jPz8fs2bNAgAkJiaq7h2uy+Xq+3tnZycWLVoEAIiOjobb7VYqVj/z5s0DAOj1esTFxQEAYmNjlYxENGFoYirpxl9YOp0OVqsVJpMJW7dulfyiU1JmZiaefvppbN++HSUlJQCgyrWF5cuXY//+/fjhhx+wbNkynDhxAgDw6aefwmAwKJyuh06nQ319Pc6dOwen04mqqioAQENDAzwej8LpiMY/TSw+b926FRkZGVi6dKnk8RdeeAGvvfYaampqFErWX3d3N4qKimC323Hx4kX861//UjqSRHd3Nw4cOIDS0lIYDAZ8++23mDRpEpYtW4Y9e/bgjjvuUDoi/vGPf2Dnzp0ICgrCoUOH8Oqrr+L777/H5cuXsWfPHqSkpCgdkWhc00RjaG9vh06nw2233dZvW11dHebOnatAqqFVVVWhoqICW7duVTrKgJxOJ5qamuD1emE0GlV9HyKPx4OamhpERkbi9ttvVzoO0binicZARESBo4k1BiIiChw2BiIikmBjICIiCTYGIiKS+H/JeUkzQnKnwQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}