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refactoring

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frankknoll
2022-02-05 19:32:27 +01:00
parent f75546355c
commit c4f1ac726c
1 changed files with 9 additions and 18 deletions
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27
HowBadIsMyBatch.ipynb
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@@ -261,21 +261,20 @@
" \n",
" @staticmethod\n",
" def getDoseTable(dataFrame):\n",
" doseTable = DoseAnalysis._getDoseTable(dataFrame.groupby('VAX_DOSE_SERIES'))\n",
" doseTable.index.set_names('Dose', inplace = True)\n",
" return doseTable\n",
" return DoseAnalysis._getDoseTable(\n",
" dataFrame.groupby(\n",
" dataFrame['VAX_DOSE_SERIES'].rename('Dose')))\n",
"\n",
" @staticmethod\n",
" def getDoseByMonthTable(dataFrame):\n",
" # https://stackoverflow.com/questions/61879166/pandas-groupby-month-and-year-date-as-datetime64ns-and-summarized-by-count\n",
" doseByMonthTable = DoseAnalysis._getDoseTable(\n",
" return DoseAnalysis._getDoseTable(\n",
" dataFrame.groupby(\n",
" [\n",
" dataFrame['RECVDATE'].dt.year.rename('Year'),\n",
" dataFrame['RECVDATE'].dt.month.rename('Month'),\n",
" dataFrame['VAX_DOSE_SERIES'].rename('Dose')\n",
" ]))\n",
" return doseByMonthTable\n",
" [\n",
" dataFrame['RECVDATE'].dt.year.rename('Year'),\n",
" dataFrame['RECVDATE'].dt.month.rename('Month'),\n",
" dataFrame['VAX_DOSE_SERIES'].rename('Dose')\n",
" ]))\n",
"\n",
" @staticmethod\n",
" def _getDoseTable(dataFrame):\n",
@@ -791,14 +790,6 @@
"doseByMonthTable.to_excel('results/doseByMonthTable.xlsx')\n",
"doseByMonthTable"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f915532",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {