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ChunML/muffy_stata.ipynb

Created October 6, 2019 14:30
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muffy_stata.ipynb
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muffy_stata.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "muffy_stata.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ChunML/f88601a76fdc9f70967b0d6de954cdb1/muffy_stata.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "_kykMyQCPzAV",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd\n",
"import os"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "pTGnYp_rRf36",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"outputId": "6892ca75-217a-4d3c-f645-d965d0e48c1c"
},
"source": [
"!pip install pandas"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (0.25.1)\n",
"Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas) (1.16.5)\n",
"Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.8.0)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.9)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas) (1.12.0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mbkkvNsnQeKk",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "ea0a1c09-a2d3-436a-965a-2ef217ecc25a"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/gdrive')"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "yPMOc6BVQr_e",
"colab_type": "code",
"colab": {}
},
"source": [
"data_dir = '/gdrive/My Drive/stata'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mHXsVVr5Q6TR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "cc703162-ecb0-4455-cd9a-7a1653f2504f"
},
"source": [
"data_1a = pd.read_stata(os.path.join(data_dir, 'Muc1A.dta'))\n",
"data_2ab = pd.read_stata(os.path.join(data_dir, 'Muc2AB.dta'))\n",
"data_3a = pd.read_stata(os.path.join(data_dir, 'Muc3A.dta'))\n",
"data_3b = pd.read_stata(os.path.join(data_dir, 'Muc3B.dta'))\n",
"data_4A = pd.read_stata(os.path.join(data_dir, 'Muc4a.dta'))\n",
"data_16 = pd.read_stata(os.path.join(data_dir, 'wt16.dta'))"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/pandas/io/stata.py:1453: UnicodeWarning: \n",
"One or more strings in the dta file could not be decoded using utf-8, and\n",
"so the fallback encoding of latin-1 is being used. This can happen when a file\n",
"has been incorrectly encoded by Stata or some other software. You should verify\n",
"the string values returned are correct.\n",
" warnings.warn(msg.format(encoding=self._encoding), UnicodeWarning)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1yy0_Qm1ZYQm",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 629
},
"outputId": "b70bb8bd-400f-4b5f-b4c2-e83f1321f684"
},
"source": [
"data_1a.info()"
],
"execution_count": 48,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 35788 entries, 0 to 35787\n",
"Data columns (total 31 columns):\n",
"tinh 35788 non-null category\n",
"huyen 35788 non-null int16\n",
"xa 35788 non-null int32\n",
"diaban 35788 non-null int16\n",
"hoso 35788 non-null int16\n",
"matv 35787 non-null float64\n",
"m1ac2 35787 non-null category\n",
"m1ac3 35787 non-null category\n",
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"m1ac4b 35787 non-null float64\n",
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"m1ac7c 491 non-null category\n",
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"m1ac9 35787 non-null category\n",
"m1ac10 1123 non-null category\n",
"m1ama1 35787 non-null float64\n",
"m1ac11 35787 non-null category\n",
"m1ac12 35787 non-null category\n",
"m1ac13 361 non-null category\n",
"m1ac14a 395 non-null category\n",
"m1ac14b 395 non-null float64\n",
"m1ac15a 27124 non-null category\n",
"m1ac15b 27124 non-null category\n",
"m1ac15c 27124 non-null category\n",
"m1ac15d 27124 non-null category\n",
"m1ac16 32350 non-null category\n",
"agecat 35787 non-null float32\n",
"allage 35787 non-null float32\n",
"dtypes: category(20), float32(2), float64(5), int16(3), int32(1)\n",
"memory usage: 2.9 MB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MW2Jl1ThZg_w",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 357
},
"outputId": "4cb5b5d7-64f1-4ba5-cce9-196d290e0cb4"
},
"source": [
"data_2ab.info()"
],
"execution_count": 53,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 35787 entries, 0 to 35786\n",
"Data columns (total 15 columns):\n",
"tinh 35787 non-null category\n",
"huyen 35787 non-null int16\n",
"xa 35787 non-null int32\n",
"diaban 35787 non-null int16\n",
"hoso 35787 non-null int16\n",
"matv 35787 non-null int8\n",
"m2ac1 35787 non-null object\n",
"m2ac2a 32050 non-null category\n",
"m2ac2b 32050 non-null category\n",
"m2ac3 32050 non-null category\n",
"m2ac4 32050 non-null category\n",
"m2ac5 23266 non-null category\n",
"m2ac6 9010 non-null category\n",
"m2ac7 6438 non-null category\n",
"m2ac8 9010 non-null category\n",
"dtypes: category(9), int16(3), int32(1), int8(1), object(1)\n",
"memory usage: 1.2+ MB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7_pSiZiwZpxL",
"colab_type": "code",
"colab": {}
},
"source": [
"keys = ['tinh', 'huyen', 'xa', 'diaban', 'hoso', 'matv']\n",
"data_1a_2ab = data_1a.merge(data_2ab, left_on=keys, right_on=keys)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nhmuQJ0ka0NU",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 479
},
"outputId": "710932f7-403a-4785-d188-deafce9e67ee"
},
"source": [
"data_1a_3a = data_1a_2ab.merge(data_3a, left_on=keys, right_on=keys)\n",
"data_1a_3a.head()"
],
"execution_count": 58,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <td>8</td>\n",
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" <td>Không</td>\n",
" <td>Không</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Không</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>??i h?c</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>K0 b?ng c?p</td>\n",
" <td>Không</td>\n",
" <td>Công l?p</td>\n",
" <td>Có</td>\n",
" <td>NaN</td>\n",
" <td>Ti?u h?c</td>\n",
" <td>3.0</td>\n",
" <td>Công l?p</td>\n",
" <td>Không</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>Có</td>\n",
" <td>H?c sinh t? nguy?n</td>\n",
" <td>Khong co</td>\n",
" <td>457</td>\n",
" <td>Không</td>\n",
" <td>Không</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" tinh huyen xa diaban hoso ... m3c12a m3c12b m3c13 m3c14 m3c15\n",
"0 Thành ph? Hà N?i 1 4 8 14 ... Có Không 200.0 0.0 0.0\n",
"1 Thành ph? Hà N?i 1 4 8 14 ... Không Không NaN NaN NaN\n",
"2 Thành ph? Hà N?i 1 4 8 15 ... Không Không 670.0 200.0 0.0\n",
"3 Thành ph? Hà N?i 1 4 8 15 ... Không Không NaN NaN NaN\n",
"4 Thành ph? Hà N?i 1 4 8 15 ... Không Không NaN NaN NaN\n",
"\n",
"[5 rows x 57 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 58
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QIz5OLgIbHte",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"outputId": "af95338c-67ec-425f-ecc9-cdd2458fd308"
},
"source": [
"id_vars = ['tinh', 'huyen', 'xa', 'diaban', 'hoso', 'matv']\n",
"value_vars = ['m3c2', 'm3c3a', 'm3c3b', 'm3c4', 'm3c5a', 'm3c5b', 'm3c6a', 'm3c6b', 'm3c7', 'm3c8']\n",
"data_3b.melt(id_vars=id_vars)"
],
"execution_count": 67,
"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>tinh</th>\n",
" <th>huyen</th>\n",
" <th>xa</th>\n",
" <th>diaban</th>\n",
" <th>hoso</th>\n",
" <th>matv</th>\n",
" <th>variable</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Thành ph? Hà N?i</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>m3c2</td>\n",
" <td>h?nh</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Thành ph? Hà N?i</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>m3c2</td>\n",
" <td>kh?i</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Thành ph? Hà N?i</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>19</td>\n",
" <td>2</td>\n",
" <td>m3c2</td>\n",
" <td>phuong</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Thành ph? Hà N?i</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>m3c2</td>\n",
" <td>nga</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Thành ph? Hà N?i</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>14</td>\n",
" <td>5</td>\n",
" <td>m3c2</td>\n",
" <td>ng?c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>179065</td>\n",
" <td>T?nh Cà Mau</td>\n",
" <td>973</td>\n",
" <td>32242</td>\n",
" <td>18</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>m3c8</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>179066</td>\n",
" <td>T?nh Cà Mau</td>\n",
" <td>973</td>\n",
" <td>32248</td>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>4</td>\n",
" <td>m3c8</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>179067</td>\n",
" <td>T?nh Cà Mau</td>\n",
" <td>973</td>\n",
" <td>32248</td>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>m3c8</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>179068</td>\n",
" <td>T?nh Cà Mau</td>\n",
" <td>973</td>\n",
" <td>32248</td>\n",
" <td>9</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>m3c8</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>179069</td>\n",
" <td>T?nh Cà Mau</td>\n",
" <td>973</td>\n",
" <td>32248</td>\n",
" <td>9</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>m3c8</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>179070 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" tinh huyen xa diaban hoso matv variable value\n",
"0 Thành ph? Hà N?i 1 4 8 14 1 m3c2 h?nh\n",
"1 Thành ph? Hà N?i 1 4 8 19 1 m3c2 kh?i\n",
"2 Thành ph? Hà N?i 1 4 8 19 2 m3c2 phuong\n",
"3 Thành ph? Hà N?i 1 7 6 14 2 m3c2 nga\n",
"4 Thành ph? Hà N?i 1 7 6 14 5 m3c2 ng?c\n",
"... ... ... ... ... ... ... ... ...\n",
"179065 T?nh Cà Mau 973 32242 18 15 1 m3c8 NaN\n",
"179066 T?nh Cà Mau 973 32248 9 13 4 m3c8 NaN\n",
"179067 T?nh Cà Mau 973 32248 9 13 1 m3c8 NaN\n",
"179068 T?nh Cà Mau 973 32248 9 14 1 m3c8 NaN\n",
"179069 T?nh Cà Mau 973 32248 9 19 1 m3c8 NaN\n",
"\n",
"[179070 rows x 8 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 67
}
]
}
]
}
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