{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "df = pd.read_excel(\"G:\\Mon Drive\\Ziwig-Health\\Data\\Extract_Prof_Patient_List.xlsx\", header=3)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": "(7728, 9)" }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "timedDf = df.set_index('createdAt')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "timedDf['count']=True" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "timedDf['NonStarted']=1-timedDf['isStartMedicalRecord'].astype(int)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "timedDf['NonFinished']=1-timedDf['isFinishMedicalRecord'].astype(int)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "timedDf=timedDf.loc[:, ['isStartMedicalRecord','isFinishMedicalRecord','count','NonStarted','NonFinished']]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "timedDfMonthly = timedDf.resample('M')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": " isStartMedicalRecord isFinishMedicalRecord count NonStarted \\\ncreatedAt \n2020-11-30 2 2 2 0 \n2020-12-31 3 3 3 0 \n2021-01-31 21 21 21 0 \n2021-02-28 10 10 10 0 \n2021-03-31 348 323 404 56 \n2021-04-30 602 559 712 110 \n2021-05-31 511 465 622 111 \n2021-06-30 406 372 503 97 \n2021-07-31 426 398 498 72 \n2021-08-31 429 393 528 99 \n2021-09-30 561 517 677 116 \n2021-10-31 580 539 696 116 \n2021-11-30 453 416 557 104 \n2021-12-31 480 447 608 128 \n2022-01-31 608 562 786 178 \n2022-02-28 544 502 704 160 \n2022-03-31 286 255 397 111 \n\n NonFinished \ncreatedAt \n2020-11-30 0 \n2020-12-31 0 \n2021-01-31 0 \n2021-02-28 0 \n2021-03-31 81 \n2021-04-30 153 \n2021-05-31 157 \n2021-06-30 131 \n2021-07-31 100 \n2021-08-31 135 \n2021-09-30 160 \n2021-10-31 157 \n2021-11-30 141 \n2021-12-31 161 \n2022-01-31 224 \n2022-02-28 202 \n2022-03-31 142 ", "text/html": "
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" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timedDfMonthly.sum()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }