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April 9, 2020 11:49
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Overview\n", | |
| "This script reads in the formatted workbook that we recieved from Stop Palu + and produces an import file that can be used in the Import Wizard. The template for this import file is here: Cohort Data Import.xlsx" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Step 1\n", | |
| "Read in file to get the facility names and regions (they don't make this easy)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "cohortfiles = pd.read_excel('../../Other Forms/DONNEES SUIVI DE COHORTE_STOPPALU+_Janv-Juin 2019 vf(1).xlsx', index_col=0, header=None,sheet_name=None)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "region = cohortfiles['CS BINTIMODIA'].loc['DRS/DSVCO'][1] \n", | |
| "district = cohortfiles['CS BINTIMODIA'].loc['DRS/DSVCO'][5] \n", | |
| "facility = cohortfiles['CS BINTIMODIA'].loc['DRS/DSVCO'][10] " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# build a list of health facilities and their districts\n", | |
| "regions = []\n", | |
| "districts = []\n", | |
| "facilities = []\n", | |
| "\n", | |
| "for tabname, df in cohortfiles.items():\n", | |
| "\n", | |
| " if \"total\" not in tabname.lower() and \"dps \" not in tabname.lower(): #don't include these\n", | |
| " #print(tabname)\n", | |
| " regions.append(df.loc['DRS/DSVCO'][1].title())\n", | |
| " districts.append(df.loc['DRS/DSVCO'][5].title() )\n", | |
| " facilities.append(df.loc['DRS/DSVCO'][10].title())\n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "combined = zip(regions, districts,facilities)\n", | |
| "facility_df = pd.DataFrame(combined, columns = ['region', 'district', 'facility'])\n", | |
| "\n", | |
| "# add period\n", | |
| "facility_df[\"period\"] = \"2019Oct\"" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(161, 4)" | |
| ] | |
| }, | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "facility_df.shape" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Step 2\n", | |
| "Initialize the data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# define columns\n", | |
| "columns = [\n", | |
| "\"Cohorte - CPN 1 Cohorte de janvier\",\n", | |
| "\"Cohorte - CPN 1 Cohorte de février\",\n", | |
| "\"Cohorte - CPN 1 Cohorte de mars\",\n", | |
| "\"Cohorte - CPN 1 Cohorte d'avril\",\n", | |
| "\"Cohorte - CPN 1 Cohorte de mai\",\n", | |
| "\"Cohorte - CPN 1 Cohorte de juin\",\n", | |
| "\"Cohorte - CPN 1 Cohorte de juillet\",\n", | |
| "\"Cohorte - CPN 1 Cohorte d'août\",\n", | |
| "\"Cohorte - CPN 1 Cohorte de septembre\",\n", | |
| "\"Cohorte - CPN 1 Cohorte d'octobre\",\n", | |
| "\"Cohorte - CPN 1 Cohorte de novembre\",\n", | |
| "\"Cohorte - CPN 1 Cohorte de décembre\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte de janvier\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte de février\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte de mars\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte d'avril\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte de mai\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte de juin\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte de juillet\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte d'août\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte de septembre\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte d'octobre\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte de novembre\",\n", | |
| "\"Cohorte - CPN1 au 1er trimestre ≤ 12 semaines Cohorte de décembre\",\n", | |
| "\"Cohorte - SP 1 Cohorte de janvier\",\n", | |
| "\"Cohorte - SP 1 Cohorte de février\",\n", | |
| "\"Cohorte - SP 1 Cohorte de mars\",\n", | |
| "\"Cohorte - SP 1 Cohorte d'avril\",\n", | |
| "\"Cohorte - SP 1 Cohorte de mai\",\n", | |
| "\"Cohorte - SP 1 Cohorte de juin\",\n", | |
| "\"Cohorte - SP 1 Cohorte de juillet\",\n", | |
| "\"Cohorte - SP 1 Cohorte d'août\",\n", | |
| "\"Cohorte - SP 1 Cohorte de septembre\",\n", | |
| "\"Cohorte - SP 1 Cohorte d'octobre\",\n", | |
| "\"Cohorte - SP 1 Cohorte de novembre\",\n", | |
| "\"Cohorte - SP 1 Cohorte de décembre\",\n", | |
| "\"Cohorte - SP 2 Cohorte de janvier\",\n", | |
| "\"Cohorte - SP 2 Cohorte de février\",\n", | |
| "\"Cohorte - SP 2 Cohorte de mars\",\n", | |
| "\"Cohorte - SP 2 Cohorte d'avril\",\n", | |
| "\"Cohorte - SP 2 Cohorte de mai\",\n", | |
| "\"Cohorte - SP 2 Cohorte de juin\",\n", | |
| "\"Cohorte - SP 2 Cohorte de juillet\",\n", | |
| "\"Cohorte - SP 2 Cohorte d'août\",\n", | |
| "\"Cohorte - SP 2 Cohorte de septembre\",\n", | |
| "\"Cohorte - SP 2 Cohorte d'octobre\",\n", | |
| "\"Cohorte - SP 2 Cohorte de novembre\",\n", | |
| "\"Cohorte - SP 2 Cohorte de décembre\",\n", | |
| "\"Cohorte - SP 3 Cohorte de janvier\",\n", | |
| "\"Cohorte - SP 3 Cohorte de février\",\n", | |
| "\"Cohorte - SP 3 Cohorte de mars\",\n", | |
| "\"Cohorte - SP 3 Cohorte d'avril\",\n", | |
| "\"Cohorte - SP 3 Cohorte de mai\",\n", | |
| "\"Cohorte - SP 3 Cohorte de juin\",\n", | |
| "\"Cohorte - SP 3 Cohorte de juillet\",\n", | |
| "\"Cohorte - SP 3 Cohorte d'août\",\n", | |
| "\"Cohorte - SP 3 Cohorte de septembre\",\n", | |
| "\"Cohorte - SP 3 Cohorte d'octobre\",\n", | |
| "\"Cohorte - SP 3 Cohorte de novembre\",\n", | |
| "\"Cohorte - SP 3 Cohorte de décembre\",\n", | |
| "\"Cohorte - SP 4+ Cohorte de janvier\",\n", | |
| "\"Cohorte - SP 4+ Cohorte de février\",\n", | |
| "\"Cohorte - SP 4+ Cohorte de mars\",\n", | |
| "\"Cohorte - SP 4+ Cohorte d'avril\",\n", | |
| "\"Cohorte - SP 4+ Cohorte de mai\",\n", | |
| "\"Cohorte - SP 4+ Cohorte de juin\",\n", | |
| "\"Cohorte - SP 4+ Cohorte de juillet\",\n", | |
| "\"Cohorte - SP 4+ Cohorte d'août\",\n", | |
| "\"Cohorte - SP 4+ Cohorte de septembre\",\n", | |
| "\"Cohorte - SP 4+ Cohorte d'octobre\",\n", | |
| "\"Cohorte - SP 4+ Cohorte de novembre\",\n", | |
| "\"Cohorte - SP 4+ Cohorte de décembre\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte de janvier\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte de février\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte de mars\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte d'avril\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte de mai\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte de juin\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte de juillet\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte d'août\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte de septembre\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte d'octobre\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte de novembre\",\n", | |
| "\"Cohorte - Accouch au CS Cohorte de décembre\"\n", | |
| "]\n", | |
| "\n", | |
| "cohort_data = {}\n", | |
| "for n in columns:\n", | |
| " #print(n)\n", | |
| " cohort_data[n]=[]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# whats in here again (optional)\n", | |
| "#cohort_data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Step 3\n", | |
| "Read in file to get the cohort data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "cohorts = pd.read_excel('../../Other Forms/DONNEES SUIVI DE COHORTE_STOPPALU+_Janv-Juin 2019 vf(1).xlsx', header=6, index_col=0, sheet_name=None)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "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>CPN 1</th>\n", | |
| " <th>CPN1 au 1er trimestre ≤ 12 semaines</th>\n", | |
| " <th>SP 1</th>\n", | |
| " <th>SP 1 à 13 SA</th>\n", | |
| " <th>SP 2</th>\n", | |
| " <th>SP 3</th>\n", | |
| " <th>SP 4+</th>\n", | |
| " <th>Accouch au CS</th>\n", | |
| " <th>FE ayant reçu au moins 3 doses de SP avec respect d'1 mois d'interval entre 2 prises</th>\n", | |
| " <th>Couverture en TPI-3</th>\n", | |
| " <th>Couverture effective en TPI-3</th>\n", | |
| " <th>% TPI-1 à 13 SA</th>\n", | |
| " <th>%FE ayant accouché au CS</th>\n", | |
| " <th>Taux d'abandon SP1-SP3</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>COHORTES</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>2019-01-01 00:00:00</th>\n", | |
| " <td>141.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>38.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>52.0</td>\n", | |
| " <td>32.0</td>\n", | |
| " <td>19.0</td>\n", | |
| " <td>22.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.22695</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.156028</td>\n", | |
| " <td>0.157895</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-02-01 00:00:00</th>\n", | |
| " <td>138.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>43.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>39.0</td>\n", | |
| " <td>34.0</td>\n", | |
| " <td>22.0</td>\n", | |
| " <td>26.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.246377</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.188406</td>\n", | |
| " <td>0.209302</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-03-01 00:00:00</th>\n", | |
| " <td>145.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>33.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>50.0</td>\n", | |
| " <td>39.0</td>\n", | |
| " <td>23.0</td>\n", | |
| " <td>37.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.268966</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.255172</td>\n", | |
| " <td>-0.181818</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-04-01 00:00:00</th>\n", | |
| " <td>18.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>16.0</td>\n", | |
| " <td>12.0</td>\n", | |
| " <td>15.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.888889</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.833333</td>\n", | |
| " <td>0.111111</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-01 00:00:00</th>\n", | |
| " <td>26.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>26.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>22.0</td>\n", | |
| " <td>14.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.538462</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.192308</td>\n", | |
| " <td>0.461538</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " CPN 1 CPN1 au 1er trimestre ≤ 12 semaines SP 1 \\\n", | |
| "COHORTES \n", | |
| "2019-01-01 00:00:00 141.0 NaN 38.0 \n", | |
| "2019-02-01 00:00:00 138.0 NaN 43.0 \n", | |
| "2019-03-01 00:00:00 145.0 NaN 33.0 \n", | |
| "2019-04-01 00:00:00 18.0 NaN 18.0 \n", | |
| "2019-05-01 00:00:00 26.0 NaN 26.0 \n", | |
| "\n", | |
| " SP 1 à 13 SA SP 2 SP 3 SP 4+ Accouch au CS \\\n", | |
| "COHORTES \n", | |
| "2019-01-01 00:00:00 NaN 52.0 32.0 19.0 22.0 \n", | |
| "2019-02-01 00:00:00 NaN 39.0 34.0 22.0 26.0 \n", | |
| "2019-03-01 00:00:00 NaN 50.0 39.0 23.0 37.0 \n", | |
| "2019-04-01 00:00:00 NaN 18.0 16.0 12.0 15.0 \n", | |
| "2019-05-01 00:00:00 NaN 22.0 14.0 9.0 5.0 \n", | |
| "\n", | |
| " FE ayant reçu au moins 3 doses de SP avec respect d'1 mois d'interval entre 2 prises \\\n", | |
| "COHORTES \n", | |
| "2019-01-01 00:00:00 NaN \n", | |
| "2019-02-01 00:00:00 NaN \n", | |
| "2019-03-01 00:00:00 NaN \n", | |
| "2019-04-01 00:00:00 NaN \n", | |
| "2019-05-01 00:00:00 NaN \n", | |
| "\n", | |
| " Couverture en TPI-3 Couverture effective en TPI-3 \\\n", | |
| "COHORTES \n", | |
| "2019-01-01 00:00:00 0.22695 0 \n", | |
| "2019-02-01 00:00:00 0.246377 0 \n", | |
| "2019-03-01 00:00:00 0.268966 0 \n", | |
| "2019-04-01 00:00:00 0.888889 0 \n", | |
| "2019-05-01 00:00:00 0.538462 0 \n", | |
| "\n", | |
| " % TPI-1 à 13 SA %FE ayant accouché au CS \\\n", | |
| "COHORTES \n", | |
| "2019-01-01 00:00:00 0 0.156028 \n", | |
| "2019-02-01 00:00:00 0 0.188406 \n", | |
| "2019-03-01 00:00:00 0 0.255172 \n", | |
| "2019-04-01 00:00:00 0 0.833333 \n", | |
| "2019-05-01 00:00:00 0 0.192308 \n", | |
| "\n", | |
| " Taux d'abandon SP1-SP3 \n", | |
| "COHORTES \n", | |
| "2019-01-01 00:00:00 0.157895 \n", | |
| "2019-02-01 00:00:00 0.209302 \n", | |
| "2019-03-01 00:00:00 -0.181818 \n", | |
| "2019-04-01 00:00:00 0.111111 \n", | |
| "2019-05-01 00:00:00 0.461538 " | |
| ] | |
| }, | |
| "execution_count": 34, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "cohorts['CS BINTIMODIA'].head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 35, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# go through each tab and put data in the main dictionary\n", | |
| "\n", | |
| "for tabname, df in cohorts.items():\n", | |
| " #print(\"processing \"+ tabname)\n", | |
| " if \"total\" not in tabname.lower() and \"dps \" not in tabname.lower(): #don't include these\n", | |
| "\n", | |
| " df.reset_index(inplace=True) # the index needs to be numeric for this\n", | |
| "\n", | |
| " # load data into main dictionary by column\n", | |
| " \n", | |
| " for index, row in df['CPN 1'].items():\n", | |
| " if index < 12: # don't include totals row\n", | |
| " data = cohort_data[columns[index]]\n", | |
| " data.append(row)\n", | |
| "\n", | |
| " # this indicator starts at row 12\n", | |
| " for index, row in df['CPN1 au 1er trimestre ≤ 12 semaines'].items():\n", | |
| " if index < 12: # don't include totals row\n", | |
| " data = cohort_data[columns[index+12]]\n", | |
| " data.append(row)\n", | |
| " \n", | |
| " # this indicator starts at row 24\n", | |
| " for index, row in df['SP 1'].items():\n", | |
| " if index < 12: # don't include totals row\n", | |
| " data = cohort_data[columns[index+24]]\n", | |
| " data.append(row)\n", | |
| " \n", | |
| " # this indicator starts at row 36\n", | |
| " for index, row in df['SP 2'].items():\n", | |
| " if index < 12: # don't include totals row\n", | |
| " data = cohort_data[columns[index+36]]\n", | |
| " data.append(row)\n", | |
| " \n", | |
| " # this indicator starts at row 48\n", | |
| " for index, row in df['SP 3'].items():\n", | |
| " if index < 12: # don't include totals row\n", | |
| " data = cohort_data[columns[index+48]]\n", | |
| " data.append(row)\n", | |
| " \n", | |
| " # this indicator starts at row 60\n", | |
| " for index, row in df['SP 4+'].items():\n", | |
| " if index < 12: # don't include totals row\n", | |
| " data = cohort_data[columns[index+60]]\n", | |
| " data.append(row)\n", | |
| " \n", | |
| " # this indicator starts at row 72\n", | |
| " for index, row in df['Accouch au CS'].items():\n", | |
| " if index < 12: # don't include totals row\n", | |
| " data = cohort_data[columns[index+72]]\n", | |
| " data.append(row)\n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Step 4\n", | |
| "Put everything together" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 36, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# probably loop here\n", | |
| "main_df = facility_df\n", | |
| "\n", | |
| "\n", | |
| "#for i in range(36): # onsey twosie, there are 12 * 7 = 84 of these\n", | |
| "for i, n in enumerate(columns): #everything\n", | |
| " main_df[columns[i]] = cohort_data[columns[i]]\n", | |
| " #print(i)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(161, 88)" | |
| ] | |
| }, | |
| "execution_count": 37, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "main_df.shape" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Step 5\n", | |
| "Write out the file. This will be what you import" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 38, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "main_df.to_excel(\"cohort import.xlsx\", encoding='utf-8', index=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "rti", | |
| "language": "python", | |
| "name": "rti" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.10" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 4 | |
| } |
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