210 lines
8.6 KiB
Plaintext
210 lines
8.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"df = pd.read_excel(\"G:\\Mon Drive\\Ziwig-Health\\Data\\Extract_Prof_Patient_List.xlsx\", header=3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "(7728, 9)"
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"timedDf = df.set_index('createdAt')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"timedDf['count']=True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"timedDf['NonStarted']=1-timedDf['isStartMedicalRecord'].astype(int)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"timedDf['NonFinished']=1-timedDf['isFinishMedicalRecord'].astype(int)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"timedDf=timedDf.loc[:, ['isStartMedicalRecord','isFinishMedicalRecord','count','NonStarted','NonFinished']]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"timedDfMonthly = timedDf.resample('M')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"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 ",
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"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>isStartMedicalRecord</th>\n <th>isFinishMedicalRecord</th>\n <th>count</th>\n <th>NonStarted</th>\n <th>NonFinished</th>\n </tr>\n <tr>\n <th>createdAt</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>2020-11-30</th>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2020-12-31</th>\n <td>3</td>\n <td>3</td>\n <td>3</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2021-01-31</th>\n <td>21</td>\n <td>21</td>\n <td>21</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2021-02-28</th>\n <td>10</td>\n <td>10</td>\n <td>10</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2021-03-31</th>\n <td>348</td>\n <td>323</td>\n <td>404</td>\n <td>56</td>\n <td>81</td>\n </tr>\n <tr>\n <th>2021-04-30</th>\n <td>602</td>\n <td>559</td>\n <td>712</td>\n <td>110</td>\n <td>153</td>\n </tr>\n <tr>\n <th>2021-05-31</th>\n <td>511</td>\n <td>465</td>\n <td>622</td>\n <td>111</td>\n <td>157</td>\n </tr>\n <tr>\n <th>2021-06-30</th>\n <td>406</td>\n <td>372</td>\n <td>503</td>\n <td>97</td>\n <td>131</td>\n </tr>\n <tr>\n <th>2021-07-31</th>\n <td>426</td>\n <td>398</td>\n <td>498</td>\n <td>72</td>\n <td>100</td>\n </tr>\n <tr>\n <th>2021-08-31</th>\n <td>429</td>\n <td>393</td>\n <td>528</td>\n <td>99</td>\n <td>135</td>\n </tr>\n <tr>\n <th>2021-09-30</th>\n <td>561</td>\n <td>517</td>\n <td>677</td>\n <td>116</td>\n <td>160</td>\n </tr>\n <tr>\n <th>2021-10-31</th>\n <td>580</td>\n <td>539</td>\n <td>696</td>\n <td>116</td>\n <td>157</td>\n </tr>\n <tr>\n <th>2021-11-30</th>\n <td>453</td>\n <td>416</td>\n <td>557</td>\n <td>104</td>\n <td>141</td>\n </tr>\n <tr>\n <th>2021-12-31</th>\n <td>480</td>\n <td>447</td>\n <td>608</td>\n <td>128</td>\n <td>161</td>\n </tr>\n <tr>\n <th>2022-01-31</th>\n <td>608</td>\n <td>562</td>\n <td>786</td>\n <td>178</td>\n <td>224</td>\n </tr>\n <tr>\n <th>2022-02-28</th>\n <td>544</td>\n <td>502</td>\n <td>704</td>\n <td>160</td>\n <td>202</td>\n </tr>\n <tr>\n <th>2022-03-31</th>\n <td>286</td>\n <td>255</td>\n <td>397</td>\n <td>111</td>\n <td>142</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"timedDfMonthly.sum()"
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]
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}
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],
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"codemirror_mode": {
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"name": "ipython",
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"file_extension": ".py",
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"name": "python",
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