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‎.editorconfig‎

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[*.md]
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indent_size = 2
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# Sobreescrever Geral
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[*.yml]
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indent_size = 2

‎docs/.readthedocs.yaml‎

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# Optionally, but recommended,
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# declare the Python requirements required to build your documentation
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# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
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# python:
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# install:
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# - requirements: docs/requirements.txt
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python:
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install:
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- requirements: docs/requirements.txt

‎docs/scripts/listas.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"id": "d037b267",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"id": "8a20998f",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>SG_UF</th>\n",
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" <th>DS_UF</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>AC</td>\n",
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" <td>ACRE</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>AL</td>\n",
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" <td>ALAGOAS</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>AM</td>\n",
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" <td>AMAZONAS</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>AP</td>\n",
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" <td>AMAPÁ</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>BA</td>\n",
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" <td>BAHIA</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" SG_UF DS_UF\n",
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"0 AC ACRE\n",
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"1 AL ALAGOAS\n",
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"2 AM AMAZONAS\n",
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"3 AP AMAPÁ\n",
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"4 BA BAHIA"
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]
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},
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"execution_count": 2,
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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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"lista = cfm.Lista(lista='estado')\n",
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"lista.as_dataframe.head()"
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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": null,
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"execution_count": 3,
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"id": "9b676c47",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'DS_UF': 'ACRE', 'SG_UF': 'AC'},\n",
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" {'DS_UF': 'ALAGOAS', 'SG_UF': 'AL'},\n",
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" {'DS_UF': 'AMAZONAS', 'SG_UF': 'AM'},\n",
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" {'DS_UF': 'AMAPÁ', 'SG_UF': 'AP'},\n",
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" {'DS_UF': 'BAHIA', 'SG_UF': 'BA'}]\n"
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]
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}
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],
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"source": [
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"pprint.pprint(lista.as_json[:5])"
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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": null,
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"execution_count": 4,
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"id": "53397f61",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>ID_ESPECIALIDADE</th>\n",
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" <th>DS_ESPECIALIDADE</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>66</td>\n",
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" <td>ACUPUNTURA</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>97</td>\n",
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" <td>ADMINISTRAÇÃO EM SAÚDE</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1</td>\n",
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" <td>ADMINISTRAÇÃO HOSPITALAR</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>2</td>\n",
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" <td>ALERGIA E IMUNOLOGIA</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>98</td>\n",
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" <td>ALERGIA E IMUNOPATOLOGIA</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" ID_ESPECIALIDADE DS_ESPECIALIDADE\n",
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"0 66 ACUPUNTURA\n",
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"1 97 ADMINISTRAÇÃO EM SAÚDE\n",
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"2 1 ADMINISTRAÇÃO HOSPITALAR\n",
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"3 2 ALERGIA E IMUNOLOGIA\n",
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"4 98 ALERGIA E IMUNOPATOLOGIA"
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]
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},
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"execution_count": 4,
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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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"lista = cfm.Lista(lista='especialidade')\n",
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"lista.as_dataframe.head()"
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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": null,
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"execution_count": 5,
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"id": "636b5dbb",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'DS_ESPECIALIDADE': 'ACUPUNTURA', 'ID_ESPECIALIDADE': '66'},\n",
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" {'DS_ESPECIALIDADE': 'ADMINISTRAÇÃO EM SAÚDE', 'ID_ESPECIALIDADE': '97'},\n",
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" {'DS_ESPECIALIDADE': 'ADMINISTRAÇÃO HOSPITALAR', 'ID_ESPECIALIDADE': '1'},\n",
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" {'DS_ESPECIALIDADE': 'ALERGIA E IMUNOLOGIA', 'ID_ESPECIALIDADE': '2'},\n",
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" {'DS_ESPECIALIDADE': 'ALERGIA E IMUNOPATOLOGIA', 'ID_ESPECIALIDADE': '98'}]\n"
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]
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}
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],
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"source": [
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"pprint.pprint(lista.as_json[:5])"
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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": null,
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"execution_count": 6,
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"id": "73eb64fc",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>ID_AREA_ATUACAO</th>\n",
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" <th>DS_AREA_ATUACAO</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2</td>\n",
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" <td>Administração Hospitalar</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>Administração em Saúde</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>81</td>\n",
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" <td>Adolescência</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3</td>\n",
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" <td>Alergia e Imunologia Pediátrica</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4</td>\n",
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" <td>Andrologia</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" ID_AREA_ATUACAO DS_AREA_ATUACAO\n",
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"0 2 Administração Hospitalar\n",
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"1 1 Administração em Saúde\n",
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"2 81 Adolescência\n",
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"3 3 Alergia e Imunologia Pediátrica\n",
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"4 4 Andrologia"
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]
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},
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"execution_count": 6,
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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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"lista = cfm.Lista(lista='atuação')\n",
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"lista.as_dataframe.head()"
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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": null,
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"execution_count": 7,
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"id": "0a9339c6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'DS_AREA_ATUACAO': 'Administração Hospitalar', 'ID_AREA_ATUACAO': '2'},\n",
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" {'DS_AREA_ATUACAO': 'Administração em Saúde', 'ID_AREA_ATUACAO': '1'},\n",
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" {'DS_AREA_ATUACAO': 'Adolescência', 'ID_AREA_ATUACAO': '81'},\n",
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" {'DS_AREA_ATUACAO': 'Alergia e Imunologia Pediátrica', 'ID_AREA_ATUACAO': '3'},\n",
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" {'DS_AREA_ATUACAO': 'Andrologia', 'ID_AREA_ATUACAO': '4'}]\n"
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]
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}
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],
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"source": [
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"pprint.pprint(lista.as_json[:5])"
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]

‎mkdocs.yml‎

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- mkdocs-jupyter:
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ignore_h1_titles: True
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include_source: True
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execute: true
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execute: false
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show_input: True # Default True
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no_input: False # Default False
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#ignore: [ '1*.ipynb' ]

‎pyproject.toml‎

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[project]
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name = 'pycfm'
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version = '0.4.2'
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version = '0.4.3'
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description = 'Obtem dados de médicos acessando informações do Conselho Federal de Medicina'
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authors = [{ name = 'michelmetran', email = 'michelmetran@gmail.com' }]
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keywords = ['CRM', 'CFM', 'github', 'médicos']

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