diff --git a/pysus/Notebooks/CNES.ipynb b/pysus/Notebooks/CNES.ipynb new file mode 100644 index 00000000..c8f080d9 --- /dev/null +++ b/pysus/Notebooks/CNES.ipynb @@ -0,0 +1,895 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "tFRs4aDTsdZb" + }, + "source": [ + "# CNES FTP Database\n", + "\n", + "Code to work with CNES (Cadastro Nacional de Estabelecimentos de Saúde) directories and files inside DATASUS FTP\n", + "\r\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pysus.ftp.databases.cnes import CNES\n", + "cnes = CNES()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'long_name': 'Cadastro Nacional de Estabelecimentos de Saúde',\n", + " 'source': 'https://cnes.datasus.gov.br/',\n", + " 'description': 'O Cadastro Nacional de Estabelecimentos de Saúde (CNES) é o sistema de informação oficial de cadastramento de informações de todos os estabelecimentos de saúde no país, independentemente de sua natureza jurídica ou de integrarem o Sistema Único de Saúde (SUS). Trata-se do cadastro oficial do Ministério da Saúde (MS) no tocante à realidade da capacidade instalada e mão-de-obra assistencial de saúde no Brasil em estabelecimentos de saúde públicos ou privados, com convênio SUS ou não.'}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnes.metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CNES FTP Database has lazy loading (also applied to Directories), therefore its content will require explict `load()` to be displayed:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-09-19 11:57:40.905 | INFO | pysus.ftp:content:440 - content is not loaded, use `load()` to load default paths\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnes.content" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CNES - Cadastro Nacional de Estabelecimentos de Saúde" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnes.load() # Loads default CNES content (from cnes.paths)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[/dissemin/publicos/CNES/200508_/Dados/DC,\n", + " /dissemin/publicos/CNES/200508_/Dados/EE,\n", + " /dissemin/publicos/CNES/200508_/Dados/EF,\n", + " /dissemin/publicos/CNES/200508_/Dados/EP,\n", + " /dissemin/publicos/CNES/200508_/Dados/EQ,\n", + " /dissemin/publicos/CNES/200508_/Dados/GM,\n", + " /dissemin/publicos/CNES/200508_/Dados/HB,\n", + " /dissemin/publicos/CNES/200508_/Dados/IN,\n", + " /dissemin/publicos/CNES/200508_/Dados/LT,\n", + " /dissemin/publicos/CNES/200508_/Dados/PF,\n", + " /dissemin/publicos/CNES/200508_/Dados/RC,\n", + " /dissemin/publicos/CNES/200508_/Dados/SR,\n", + " /dissemin/publicos/CNES/200508_/Dados/ST]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default content for (loaded) CNES database \n", + "cnes.content" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CNES Groups" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'DC': 'Dados Complementares',\n", + " 'EE': 'Estabelecimento de Ensino',\n", + " 'EF': 'Estabelecimento Filantrópico',\n", + " 'EP': 'Equipes',\n", + " 'EQ': 'Equipamentos',\n", + " 'GM': 'Gestão e Metas',\n", + " 'HB': 'Habilitação',\n", + " 'IN': 'Incentivos',\n", + " 'LT': 'Leitos',\n", + " 'PF': 'Profissional',\n", + " 'RC': 'Regra Contratual',\n", + " 'SR': 'Serviço Especializado',\n", + " 'ST': 'Estabelecimentos'}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnes.groups" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading groups\n", + "\n", + "To load specific groups into `cnes` content, it's possible to pass them in the `load()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "17838 files loaded\n" + ] + } + ], + "source": [ + "cnes.load(\"DC\")\n", + "cnes.load([\"ST\", \"SR\"])\n", + "print(str(len(cnes.content)) + \" files loaded\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filtering files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### by group (required)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5940" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get files by group\n", + "len(cnes.get_files(\"SR\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11887" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Multiple groups\n", + "len(cnes.get_files([\"ST\", \"SR\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### by UF (state)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SR - São Paulo files: 220\n" + ] + } + ], + "source": [ + "# Get files by UF from group\n", + "print(\"SR - São Paulo files: \" + str(len( cnes.get_files(\"SR\", uf=\"SP\" ))))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "440" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Multiple UFs\n", + "len(cnes.get_files(\"SR\", uf=[\"SP\", \"RJ\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### by year" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "216" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cnes.get_files(\"SR\", year=2023)) # or 23" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1243" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Multiple Years\n", + "len(cnes.get_files(\"SR\", year=[20, 21, 22, 23]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### by month" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1945" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(cnes.get_files(\"SR\", month=[1, 2, 3, 4])) # or single month" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combining filters" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[SRSP2301.dbc, SRSP2302.dbc, SRSP2303.dbc]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files = cnes.get_files(\"SR\", uf=\"SP\", year=2023, month=[1,2,3])\n", + "files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Describing Files" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'group': 'Serviço Especializado',\n", + " 'last_update': '2023-02-17 07:31AM',\n", + " 'month': 'Janeiro',\n", + " 'name': 'SRSP2301.dbc',\n", + " 'size': '1.6 MB',\n", + " 'uf': 'São Paulo',\n", + " 'year': 2023}\n", + "{'group': 'Serviço Especializado',\n", + " 'last_update': '2023-03-14 02:34PM',\n", + " 'month': 'Fevereiro',\n", + " 'name': 'SRSP2302.dbc',\n", + " 'size': '1.6 MB',\n", + " 'uf': 'São Paulo',\n", + " 'year': 2023}\n", + "{'group': 'Serviço Especializado',\n", + " 'last_update': '2023-04-17 07:34AM',\n", + " 'month': 'Março',\n", + " 'name': 'SRSP2303.dbc',\n", + " 'size': '1.6 MB',\n", + " 'uf': 'São Paulo',\n", + " 'year': 2023}\n" + ] + } + ], + "source": [ + "from pprint import pprint\n", + "for file in files:\n", + " pprint(cnes.describe(file))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Downloading Files" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "SRSP2303.parquet: 100%|██████████| 81.4k/81.4k [00:05<00:00, 14.8kB/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[/home/bida/pysus/SRSP2301.parquet,\n", + " /home/bida/pysus/SRSP2302.parquet,\n", + " /home/bida/pysus/SRSP2303.parquet]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parquets = cnes.download(files)\n", + "parquets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading files\n", + "\n", + "PySUS uses Parquets as output, use the method `to_dataframe()` to read the file as pandas DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CNESCODUFMUNSERV_ESPCLASS_SRSRVUNICOREGSAUDEMICR_REGDISTRSANDISTRADMTPGESTAO...CNPJ_MANCARACTERAMB_NSUSAMB_SUSHOSP_NSUSHOSP_SUSCOMPETENCONTSRVUCNESTERCNAT_JUR
027909203514501450080206M...00000000000000110002023012062
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227909203514501450100206M...00000000000000110002023012062
327909203514501450130206M...00000000000000110002023012062
430254893514501110011110206M...461374850001601010020230111244
..................................................................
1448300008885354780141002M...46522942000130101002023011244
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1448340008885354780159005M...46522942000130101002023011244
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144835 rows × 32 columns

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COPAISINF COMUNINF DOENCA_TRA EVOLUCAO DT_OBITO \\\n", + "0 1381 2015-09-05 ... 1 292630 \n", + "1 1385 2015-08-28 ... 1 291360 \n", + "2 1380 2015-09-01 ... 0 \n", + "3 1381 2015-09-04 ... 0 \n", + "4 1381 2015-09-05 ... 0 \n", + "\n", + " DT_ENCERRA CS_FLXRET FLXRECEBI TP_SISTEMA TPUNINOT \n", + "0 20151009 0 2 1 \n", + "1 20151228 0 2 1 \n", + "2 20160111 0 2 1 \n", + "3 20151111 0 2 1 \n", + "4 20151111 0 2 1 \n", + "\n", + "[5 rows x 38 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "casos = casos[casos.ID_AGRAVO=='A920']\n", + "\n", + "casos.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "casos.DT_NOTIFIC = pd.to_datetime(casos.DT_NOTIFIC)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "casos = casos.set_index('DT_NOTIFIC')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "casos.ID_AGRAVO.resample('1W').count().plot();" + ] + } + ], + "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": "3.11.5" + }, + "vscode": { + "interpreter": { + "hash": "2a96a5ccec8dfcba7d06b2e71f6eef3b5dac5716461bf5d73ea1bb7ee462cdaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pysus/Notebooks/Dengue.ipynb b/pysus/Notebooks/Dengue.ipynb new file mode 100644 index 00000000..b4e85f10 --- /dev/null +++ b/pysus/Notebooks/Dengue.ipynb @@ -0,0 +1,497 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Análise exploratória de casos de Dengue notificado pelo SUS\n", + "\n", + "Neste tutorial vamos explorar como começar a entender a descrição de casos do sis tema de notificação de agravos do SUS, o SINAN. Estes dados são bastante ricos e a [documentação](www.sgc.goias.gov.br/upload/arquivos/2013-08/dengue2.pdf) sobre o significado de cada uma das variáveis pode ser encontrada [aqui](www.sgc.goias.gov.br/upload/arquivos/2013-08/dengue2.pdf)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-28T13:18:48.751476Z", + "start_time": "2020-01-28T13:18:48.741691Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n", + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "from pysus.ftp.databases.sinan import SINAN\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "%pylab inline\n", + "\n", + "sinan = SINAN().load()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Primeiro vamos começar pelo carregamento dos dados a partir do servidor do DataSUS. Como o dado está no formato Parquet, nós vamos utilizar a biblioteca pandas para visualizar os dados em um Dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-28T12:43:13.470977Z", + "start_time": "2020-01-28T12:43:03.655395Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████| 30.5M/30.5M [00:00<00:00, 6.93GB/s]\n" + ] + } + ], + "source": [ + "cols = [\n", + " 'ID_AGRAVO', 'DT_NOTIFIC', 'SEM_NOT', 'NU_ANO', 'ID_MUNICIP',\n", + " 'DT_SIN_PRI', 'SEM_PRI', 'NU_IDADE_N', 'CS_SEXO', 'CS_GESTANT',\n", + " 'latitude', 'longitude',\n", + " 'NM_DISEASE'\n", + "]\n", + "casos = sinan.download(sinan.get_files('DENG', 2010)).to_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-28T13:58:05.829780Z", + "start_time": "2020-01-28T13:58:05.129157Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bida/micromamba/envs/pysus/lib/python3.11/site-packages/geopandas/array.py:93: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.\n", + " aout[:] = out\n", + "/home/bida/micromamba/envs/pysus/lib/python3.11/site-packages/geopandas/plotting.py:33: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", + " for poly in geom:\n", + "/home/bida/micromamba/envs/pysus/lib/python3.11/site-packages/descartes/patch.py:62: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", + " vertices = concatenate([\n" + ] + }, + { + "data": { + "image/png": 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TP_NOTID_AGRAVODT_NOTIFICSEM_NOTNU_ANOSG_UF_NOTID_MUNICIPID_REGIONAID_UNIDADEDT_SIN_PRI...PLASMATICOEVIDENCIAPLAQ_MENORCON_FHDCOMPLICAHOSPITALIZDT_INTERNAUFMUNICIPIOTP_SISTEMA
02A902010-04-14201015201035352510134820386412010-04-07...91
12A902010-04-15201015201035352510134820386412010-04-09...91
22A902010-04-13201015201035352510134820386412010-04-09...91
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42A902010-04-20201016201035352510134820386412010-04-15...1
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5 rows × 66 columns

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" + ], + "text/plain": [ + " TP_NOT ID_AGRAVO DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP ID_REGIONA \\\n", + "0 2 A90 2010-04-14 201015 2010 35 352510 1348 \n", + "1 2 A90 2010-04-15 201015 2010 35 352510 1348 \n", + "2 2 A90 2010-04-13 201015 2010 35 352510 1348 \n", + "3 2 A90 2010-04-13 201015 2010 35 352510 1348 \n", + "4 2 A90 2010-04-20 201016 2010 35 352510 1348 \n", + "\n", + " ID_UNIDADE DT_SIN_PRI ... PLASMATICO EVIDENCIA PLAQ_MENOR CON_FHD \\\n", + "0 2038641 2010-04-07 ... \n", + "1 2038641 2010-04-09 ... \n", + "2 2038641 2010-04-09 ... \n", + "3 2038641 2010-04-08 ... \n", + "4 2038641 2010-04-15 ... \n", + "\n", + " COMPLICA HOSPITALIZ DT_INTERNA UF MUNICIPIO TP_SISTEMA \n", + "0 9 1 \n", + "1 9 1 \n", + "2 9 1 \n", + "3 9 1 \n", + "4 1 \n", + "\n", + "[5 rows x 66 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "casos.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estes dados correspondem a todos os casos de dengue notificado ao SUS durante um período. Neste caso de 2015 a 2016. Para podermos tratar adequadamente estes dados para fins de visualização ou análise precisamos corrigir os tipos das colunas. Por exemplo vamos converter as datas." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-28T13:12:53.828452Z", + "start_time": "2020-01-28T13:12:53.817698Z" + } + }, + "outputs": [], + "source": [ + "casos.DT_NOTIFIC = pd.to_datetime(casos.DT_NOTIFIC)\n", + "casos.DT_SIN_PRI = pd.to_datetime(casos.DT_SIN_PRI)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para poder organizar os dados temporalmente, é útil indexar a tabela por alguma variável temporal" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-28T12:43:42.057415Z", + "start_time": "2020-01-28T12:43:42.035459Z" + } + }, + "outputs": [], + "source": [ + "casos = casos.set_index('DT_NOTIFIC')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora podemos plotar o número de casos por semana de dengue na cidade do Rio de Janeiro." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-28T12:44:19.281144Z", + "start_time": "2020-01-28T12:44:19.005624Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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0D5Áreas Urbanizadas[{'id': '8418', 'nome': 'Áreas urbanizadas, Lo...
1CLCadastro Central de Empresas[{'id': '1685', 'nome': 'Unidades locais, empr...
2CACenso Agropecuário[{'id': '1005', 'nome': 'Número de estabelecim...
3MECenso Comum do Mercosul, Bolívia e Chile[{'id': '1221', 'nome': 'População residente, ...
4CDCenso Demográfico[{'id': '102', 'nome': 'Mulheres de 10 anos ou...
............
63VSProdução da Extração Vegetal e da Silvicultura[{'id': '289', 'nome': 'Quantidade produzida e...
64POProdução de Ovos de Galinha[{'id': '6672', 'nome': 'Quantidade de ovos pr...
65IOProduto Interno Bruto dos Municípios[{'id': '21', 'nome': 'Produto interno bruto a...
66XEProjeção da População[{'id': '7358', 'nome': 'População, por sexo e...
67SISistema Nacional de Pesquisa de Custos e Índic...[{'id': '2062', 'nome': 'Preços medianos, por ...
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68 rows × 3 columns

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" + ], + "text/plain": [ + " id nome \\\n", + "0 D5 Áreas Urbanizadas \n", + "1 CL Cadastro Central de Empresas \n", + "2 CA Censo Agropecuário \n", + "3 ME Censo Comum do Mercosul, Bolívia e Chile \n", + "4 CD Censo Demográfico \n", + ".. .. ... \n", + "63 VS Produção da Extração Vegetal e da Silvicultura \n", + "64 PO Produção de Ovos de Galinha \n", + "65 IO Produto Interno Bruto dos Municípios \n", + "66 XE Projeção da População \n", + "67 SI Sistema Nacional de Pesquisa de Custos e Índic... \n", + "\n", + " agregados \n", + "0 [{'id': '8418', 'nome': 'Áreas urbanizadas, Lo... \n", + "1 [{'id': '1685', 'nome': 'Unidades locais, empr... \n", + "2 [{'id': '1005', 'nome': 'Número de estabelecim... \n", + "3 [{'id': '1221', 'nome': 'População residente, ... \n", + "4 [{'id': '102', 'nome': 'Mulheres de 10 anos ou... \n", + ".. ... \n", + "63 [{'id': '289', 'nome': 'Quantidade produzida e... \n", + "64 [{'id': '6672', 'nome': 'Quantidade de ovos pr... \n", + "65 [{'id': '21', 'nome': 'Produto interno bruto a... \n", + "66 [{'id': '7358', 'nome': 'População, por sexo e... \n", + "67 [{'id': '2062', 'nome': 'Preços medianos, por ... \n", + "\n", + "[68 rows x 3 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ag = IBGE.list_agregados()\n", + "ag" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Let's look at the datasets (called \"agregados\") available within the category of \"Censo Demografico\"." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-05T16:53:10.279217Z", + "start_time": "2022-07-05T16:53:09.972254Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnome
0305População residente em domicílios particulares...
1319Média de moradores por domicílio particular pe...
2472População residente por idade, forma de declar...
3473Pessoas de 4 anos ou mais de idade por grupos ...
4475População residente por grupos de idade, sexo ...
5476Pessoas de 4 anos ou mais de idade por frequên...
6477Pessoas de 4 anos ou mais que frequentam escol...
7478Pessoas não residentes no município de residên...
8479Chefes de domicílios particulares permanentes ...
9480População residente por relação com o chefe do...
10481Média de moradores por domicílio particular pe...
11482Domicílios particulares permanentes por sexo d...
12484Pessoas não residentes no município de residên...
13552Média de moradores por domicílio particular pe...
14553Média de moradores por domicílio particular pe...
15579Domicílios particulares permanentes, População...
16580Domicílios particulares permanentes, População...
17793População residente
18794População recenseada por situação do domicílio...
19797Domicílios recenseados por espécie de domicílio
20798Domicílios particulares ocupados por sexo da p...
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idnome
07358População, por sexo e idade
17360Indicadores implícitos na projeção da população
27362Esperança de vida ao nascer e Taxa de mortalid...
37363Taxa específica de fecundidade, por grupo de i...
47365Proporção de pessoas, por grupo de idade
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" + ], + "text/plain": [ + " id nome\n", + "0 7358 População, por sexo e idade\n", + "1 7360 Indicadores implícitos na projeção da população\n", + "2 7362 Esperança de vida ao nascer e Taxa de mortalid...\n", + "3 7363 Taxa específica de fecundidade, por grupo de i...\n", + "4 7365 Proporção de pessoas, por grupo de idade" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(ag[ag.id=='XE'].agregados.iloc[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Downloading data\n", + "Before downloading the data, it may be useful to look at the metadata of the dataset we are interested in." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2021-02-28T15:14:31.247660Z", + "start_time": "2021-02-28T15:14:18.747976Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4b34f6337bfd460196a6967f849b9866", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Number:', index=4, options=(('População residente em domicílios particulares permanentes…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'id': 475,\n", + " 'nome': 'População residente por grupos de idade, sexo e situação',\n", + " 'URL': 'https://sidra.ibge.gov.br/tabela/475',\n", + " 'pesquisa': 'Contagem da População ',\n", + " 'assunto': 'Pessoas',\n", + " 'periodicidade': {'frequencia': 'anual', 'inicio': 1996, 'fim': 1996},\n", + " 'nivelTerritorial': {'Administrativo': ['N102',\n", + " 'N1',\n", + " 'N10',\n", + " 'N2',\n", + " 'N8',\n", + " 'N9',\n", + " 'N6',\n", + " 'N3'],\n", + " 'Especial': [],\n", + " 'IBGE': []},\n", + " 'variaveis': [{'id': 93,\n", + " 'nome': 'População residente',\n", + " 'unidade': 'Pessoas',\n", + " 'sumarizacao': []},\n", + " {'id': 1000093,\n", + " 'nome': 'População residente - percentual do total geral',\n", + " 'unidade': '%',\n", + " 'sumarizacao': []}],\n", + " 'classificacoes': [{'id': 58,\n", + " 'nome': 'Grupo de idade',\n", + " 'sumarizacao': {'status': True, 'excecao': []},\n", + " 'categorias': [{'id': 0, 'nome': 'Total', 'unidade': None, 'nivel': 0},\n", + " {'id': 1140, 'nome': '0 a 4 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1141, 'nome': '5 a 9 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1142, 'nome': '10 a 14 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1143, 'nome': '15 a 19 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1144, 'nome': '20 a 24 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1145, 'nome': '25 a 29 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1146, 'nome': '30 a 34 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1147, 'nome': '35 a 39 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1148, 'nome': '40 a 44 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1149, 'nome': '45 a 49 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1150, 'nome': '50 a 54 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1151, 'nome': '55 a 59 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1152, 'nome': '60 a 64 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 1153, 'nome': '65 a 69 anos', 'unidade': None, 'nivel': 1},\n", + " {'id': 3244, 'nome': '70 anos ou mais', 'unidade': None, 'nivel': 1},\n", + " {'id': 3245, 'nome': 'Idade ignorada', 'unidade': None, 'nivel': 1}]},\n", + " {'id': 2,\n", + " 'nome': 'Sexo',\n", + " 'sumarizacao': {'status': True, 'excecao': []},\n", + " 'categorias': [{'id': 0, 'nome': 'Total', 'unidade': None, 'nivel': 0},\n", + " {'id': 4, 'nome': 'Homens', 'unidade': None, 'nivel': 1},\n", + " {'id': 5, 'nome': 'Mulheres', 'unidade': None, 'nivel': 1}]},\n", + " {'id': 1,\n", + " 'nome': 'Situação do domicílio',\n", + " 'sumarizacao': {'status': True, 'excecao': []},\n", + " 'categorias': [{'id': 0, 'nome': 'Total', 'unidade': None, 'nivel': 0},\n", + " {'id': 1, 'nome': 'Urbana', 'unidade': None, 'nivel': 1},\n", + " {'id': 2, 'nome': 'Rural', 'unidade': None, 'nivel': 1}]}]}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "opts= [(r.nome, int(r.id)) for r in pd.DataFrame(ag[ag.id=='CM'].agregados.iloc[0]).itertuples()]\n", + "ds = widgets.Dropdown(\n", + " options=opts,\n", + " value=475,\n", + " description='Number:',\n", + ")\n", + "display(ds)\n", + "IBGE.metadados(ds.value)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2021-02-28T15:14:33.313940Z", + "start_time": "2021-02-28T15:14:31.249236Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnomenivel
011Rondônia{'id': 'N3', 'nome': 'Unidade da Federação'}
112Acre{'id': 'N3', 'nome': 'Unidade da Federação'}
213Amazonas{'id': 'N3', 'nome': 'Unidade da Federação'}
314Roraima{'id': 'N3', 'nome': 'Unidade da Federação'}
415Pará{'id': 'N3', 'nome': 'Unidade da Federação'}
516Amapá{'id': 'N3', 'nome': 'Unidade da Federação'}
617Tocantins{'id': 'N3', 'nome': 'Unidade da Federação'}
721Maranhão{'id': 'N3', 'nome': 'Unidade da Federação'}
822Piauí{'id': 'N3', 'nome': 'Unidade da Federação'}
923Ceará{'id': 'N3', 'nome': 'Unidade da Federação'}
1024Rio Grande do Norte{'id': 'N3', 'nome': 'Unidade da Federação'}
1125Paraíba{'id': 'N3', 'nome': 'Unidade da Federação'}
1226Pernambuco{'id': 'N3', 'nome': 'Unidade da Federação'}
1327Alagoas{'id': 'N3', 'nome': 'Unidade da Federação'}
1428Sergipe{'id': 'N3', 'nome': 'Unidade da Federação'}
1529Bahia{'id': 'N3', 'nome': 'Unidade da Federação'}
1631Minas Gerais{'id': 'N3', 'nome': 'Unidade da Federação'}
1732Espírito Santo{'id': 'N3', 'nome': 'Unidade da Federação'}
1833Rio de Janeiro{'id': 'N3', 'nome': 'Unidade da Federação'}
1935São Paulo{'id': 'N3', 'nome': 'Unidade da Federação'}
2041Paraná{'id': 'N3', 'nome': 'Unidade da Federação'}
2142Santa Catarina{'id': 'N3', 'nome': 'Unidade da Federação'}
2243Rio Grande do Sul{'id': 'N3', 'nome': 'Unidade da Federação'}
2350Mato Grosso do Sul{'id': 'N3', 'nome': 'Unidade da Federação'}
2451Mato Grosso{'id': 'N3', 'nome': 'Unidade da Federação'}
2552Goiás{'id': 'N3', 'nome': 'Unidade da Federação'}
2653Distrito Federal{'id': 'N3', 'nome': 'Unidade da Federação'}
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" + ], + "text/plain": [ + " id nome nivel\n", + "0 11 Rondônia {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "1 12 Acre {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "2 13 Amazonas {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "3 14 Roraima {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "4 15 Pará {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "5 16 Amapá {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "6 17 Tocantins {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "7 21 Maranhão {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "8 22 Piauí {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "9 23 Ceará {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "10 24 Rio Grande do Norte {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "11 25 Paraíba {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "12 26 Pernambuco {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "13 27 Alagoas {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "14 28 Sergipe {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "15 29 Bahia {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "16 31 Minas Gerais {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "17 32 Espírito Santo {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "18 33 Rio de Janeiro {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "19 35 São Paulo {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "20 41 Paraná {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "21 42 Santa Catarina {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "22 43 Rio Grande do Sul {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "23 50 Mato Grosso do Sul {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "24 51 Mato Grosso {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "25 52 Goiás {'id': 'N3', 'nome': 'Unidade da Federação'}\n", + "26 53 Distrito Federal {'id': 'N3', 'nome': 'Unidade da Federação'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IBGE.localidades_por_agregado(475, nivel='N3')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "To actually download the data after chosing the dataset, we can use the `FetchData` class, which will fetch the data and make it available both in JSON format and Dataframe as exemplified below." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2021-02-28T15:24:20.894071Z", + "start_time": "2021-02-28T15:24:11.255234Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fetching https://servicodados.ibge.gov.br/api/v3/agregados/475/periodos/1996/variaveis/93?localidades=N3[all]&classificacao=58[all]|2[4,5]|1[all]&view=flat\n" + ] + }, + { + "data": { + "text/plain": [ + "{'NC': 'Nível Territorial (Código)',\n", + " 'NN': 'Nível Territorial',\n", + " 'MC': 'Unidade de Medida (Código)',\n", + " 'MN': 'Unidade de Medida',\n", + " 'V': 'Valor',\n", + " 'D1C': 'Unidade da Federação (Código)',\n", + " 'D1N': 'Unidade da Federação',\n", + " 'D2C': 'Ano (Código)',\n", + " 'D2N': 'Ano',\n", + " 'D3C': 'Variável (Código)',\n", + " 'D3N': 'Variável',\n", + " 'D4C': 'Grupo de idade (Código)',\n", + " 'D4N': 'Grupo de idade',\n", + " 'D5C': 'Sexo (Código)',\n", + " 'D5N': 'Sexo',\n", + " 'D6C': 'Situação do domicílio (Código)',\n", + " 'D6N': 'Situação do domicílio'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = IBGE.FetchData(475,periodos=1996,variavel=93,localidades='N3[all]',\n", + " classificacao='58[all]|2[4,5]|1[all]',view='flat')\n", + "ds.JSON[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2021-02-28T15:21:52.749728Z", + "start_time": "2021-02-28T15:21:52.686123Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NCNNMCMNVD1CD1ND2CD2ND3CD3ND4CD4ND5CD5ND6CD6N
0Nível Territorial (Código)Nível TerritorialUnidade de Medida (Código)Unidade de MedidaValorUnidade da Federação (Código)Unidade da FederaçãoAno (Código)AnoVariável (Código)VariávelGrupo de idade (Código)Grupo de idadeSexo (Código)SexoSituação do domicílio (Código)Situação do domicílio
13Unidade da Federação45Pessoas63359411Rondônia1996199693População residente0Total4Homens0Total
23Unidade da Federação45Pessoas38011911Rondônia1996199693População residente0Total4Homens1Urbana
33Unidade da Federação45Pessoas25347511Rondônia1996199693População residente0Total4Homens2Rural
43Unidade da Federação45Pessoas59571211Rondônia1996199693População residente0Total5Mulheres0Total
......................................................
27503Unidade da Federação45Pessoas107353Distrito Federal1996199693População residente3245Idade ignorada4Homens1Urbana
27513Unidade da Federação45Pessoas9453Distrito Federal1996199693População residente3245Idade ignorada4Homens2Rural
27523Unidade da Federação45Pessoas138153Distrito Federal1996199693População residente3245Idade ignorada5Mulheres0Total
27533Unidade da Federação45Pessoas128653Distrito Federal1996199693População residente3245Idade ignorada5Mulheres1Urbana
27543Unidade da Federação45Pessoas9553Distrito Federal1996199693População residente3245Idade ignorada5Mulheres2Rural
\n", + "

2755 rows × 17 columns

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" + ], + "text/plain": [ + " NC NN \\\n", + "0 Nível Territorial (Código) Nível Territorial \n", + "1 3 Unidade da Federação \n", + "2 3 Unidade da Federação \n", + "3 3 Unidade da Federação \n", + "4 3 Unidade da Federação \n", + "... ... ... \n", + "2750 3 Unidade da Federação \n", + "2751 3 Unidade da Federação \n", + "2752 3 Unidade da Federação \n", + "2753 3 Unidade da Federação \n", + "2754 3 Unidade da Federação \n", + "\n", + " MC MN V \\\n", + "0 Unidade de Medida (Código) Unidade de Medida Valor \n", + "1 45 Pessoas 633594 \n", + "2 45 Pessoas 380119 \n", + "3 45 Pessoas 253475 \n", + "4 45 Pessoas 595712 \n", + "... ... ... ... \n", + "2750 45 Pessoas 1073 \n", + "2751 45 Pessoas 94 \n", + "2752 45 Pessoas 1381 \n", + "2753 45 Pessoas 1286 \n", + "2754 45 Pessoas 95 \n", + "\n", + " D1C D1N D2C D2N \\\n", + "0 Unidade da Federação (Código) Unidade da Federação Ano (Código) Ano \n", + "1 11 Rondônia 1996 1996 \n", + "2 11 Rondônia 1996 1996 \n", + "3 11 Rondônia 1996 1996 \n", + "4 11 Rondônia 1996 1996 \n", + "... ... ... ... ... \n", + "2750 53 Distrito Federal 1996 1996 \n", + "2751 53 Distrito Federal 1996 1996 \n", + "2752 53 Distrito Federal 1996 1996 \n", + "2753 53 Distrito Federal 1996 1996 \n", + "2754 53 Distrito Federal 1996 1996 \n", + "\n", + " D3C D3N D4C \\\n", + "0 Variável (Código) Variável Grupo de idade (Código) \n", + "1 93 População residente 0 \n", + "2 93 População residente 0 \n", + "3 93 População residente 0 \n", + "4 93 População residente 0 \n", + "... ... ... ... \n", + "2750 93 População residente 3245 \n", + "2751 93 População residente 3245 \n", + "2752 93 População residente 3245 \n", + "2753 93 População residente 3245 \n", + "2754 93 População residente 3245 \n", + "\n", + " D4N D5C D5N D6C \\\n", + "0 Grupo de idade Sexo (Código) Sexo Situação do domicílio (Código) \n", + "1 Total 4 Homens 0 \n", + "2 Total 4 Homens 1 \n", + "3 Total 4 Homens 2 \n", + "4 Total 5 Mulheres 0 \n", + "... ... ... ... ... \n", + "2750 Idade ignorada 4 Homens 1 \n", + "2751 Idade ignorada 4 Homens 2 \n", + "2752 Idade ignorada 5 Mulheres 0 \n", + "2753 Idade ignorada 5 Mulheres 1 \n", + "2754 Idade ignorada 5 Mulheres 2 \n", + "\n", + " D6N \n", + "0 Situação do domicílio \n", + "1 Total \n", + "2 Urbana \n", + "3 Rural \n", + "4 Total \n", + "... ... \n", + "2750 Urbana \n", + "2751 Rural \n", + "2752 Total \n", + "2753 Urbana \n", + "2754 Rural \n", + "\n", + "[2755 rows x 17 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.to_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2021-02-28T21:08:15.885959Z", + "start_time": "2021-02-28T21:08:15.882112Z" + }, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Using the SIDRA endpoint\n", + "IBGE also has a simpler API at https://api.sidra.ibge.gov.br that PySUS also gives access through a simple function. Below we have table 200, which is a sample from the resident population. classification `2` is sex, of which I am fetching all categories: `total`, `Homens`, and `Mulheres`. Terrotorial level 6 is municipality." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2021-02-28T21:23:52.116309Z", + "start_time": "2021-02-28T21:22:42.616561Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requesting data from https://apisidra.ibge.gov.br/values/t/200/n6/all/c2/all\n" + ] + }, + { + "data": { + "text/html": [ + "
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NCNNMCMNVD1CD1ND2CD2ND3CD3ND4CD4ND5CD5ND6CD6N
0Nível Territorial (Código)Nível TerritorialUnidade de Medida (Código)Unidade de MedidaValorMunicípio (Código)MunicípioSexo (Código)SexoAno (Código)AnoVariável (Código)VariávelSituação do domicílio (Código)Situação do domicílioGrupo de idade (Código)Grupo de idade
16Município45Pessoas243921100015Alta Floresta D'Oeste - RO0Total2010201093População residente0Total0Total
26Município45Pessoas126561100015Alta Floresta D'Oeste - RO4Homens2010201093População residente0Total0Total
36Município45Pessoas117361100015Alta Floresta D'Oeste - RO5Mulheres2010201093População residente0Total0Total
46Município45Pessoas903531100023Ariquemes - RO0Total2010201093População residente0Total0Total
......................................................
166916Município45Pessoas26895222302Vila Propício - GO4Homens2010201093População residente0Total0Total
166926Município45Pessoas24565222302Vila Propício - GO5Mulheres2010201093População residente0Total0Total
166936Município45Pessoas25701605300108Brasília - DF0Total2010201093População residente0Total0Total
166946Município45Pessoas12288805300108Brasília - DF4Homens2010201093População residente0Total0Total
166956Município45Pessoas13412805300108Brasília - DF5Mulheres2010201093População residente0Total0Total
\n", + "

16696 rows × 17 columns

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" + ], + "text/plain": [ + " NC NN \\\n", + "0 Nível Territorial (Código) Nível Territorial \n", + "1 6 Município \n", + "2 6 Município \n", + "3 6 Município \n", + "4 6 Município \n", + "... ... ... \n", + "16691 6 Município \n", + "16692 6 Município \n", + "16693 6 Município \n", + "16694 6 Município \n", + "16695 6 Município \n", + "\n", + " MC MN V \\\n", + "0 Unidade de Medida (Código) Unidade de Medida Valor \n", + "1 45 Pessoas 24392 \n", + "2 45 Pessoas 12656 \n", + "3 45 Pessoas 11736 \n", + "4 45 Pessoas 90353 \n", + "... ... ... ... \n", + "16691 45 Pessoas 2689 \n", + "16692 45 Pessoas 2456 \n", + "16693 45 Pessoas 2570160 \n", + "16694 45 Pessoas 1228880 \n", + "16695 45 Pessoas 1341280 \n", + "\n", + " D1C D1N D2C \\\n", + "0 Município (Código) Município Sexo (Código) \n", + "1 1100015 Alta Floresta D'Oeste - RO 0 \n", + "2 1100015 Alta Floresta D'Oeste - RO 4 \n", + "3 1100015 Alta Floresta D'Oeste - RO 5 \n", + "4 1100023 Ariquemes - RO 0 \n", + "... ... ... ... \n", + "16691 5222302 Vila Propício - GO 4 \n", + "16692 5222302 Vila Propício - GO 5 \n", + "16693 5300108 Brasília - DF 0 \n", + "16694 5300108 Brasília - DF 4 \n", + "16695 5300108 Brasília - DF 5 \n", + "\n", + " D2N D3C D3N D4C D4N \\\n", + "0 Sexo Ano (Código) Ano Variável (Código) Variável \n", + "1 Total 2010 2010 93 População residente \n", + "2 Homens 2010 2010 93 População residente \n", + "3 Mulheres 2010 2010 93 População residente \n", + "4 Total 2010 2010 93 População residente \n", + "... ... ... ... ... ... \n", + "16691 Homens 2010 2010 93 População residente \n", + "16692 Mulheres 2010 2010 93 População residente \n", + "16693 Total 2010 2010 93 População residente \n", + "16694 Homens 2010 2010 93 População residente \n", + "16695 Mulheres 2010 2010 93 População residente \n", + "\n", + " D5C D5N \\\n", + "0 Situação do domicílio (Código) Situação do domicílio \n", + "1 0 Total \n", + "2 0 Total \n", + "3 0 Total \n", + "4 0 Total \n", + "... ... ... \n", + "16691 0 Total \n", + "16692 0 Total \n", + "16693 0 Total \n", + "16694 0 Total \n", + "16695 0 Total \n", + "\n", + " D6C D6N \n", + "0 Grupo de idade (Código) Grupo de idade \n", + "1 0 Total \n", + "2 0 Total \n", + "3 0 Total \n", + "4 0 Total \n", + "... ... ... \n", + "16691 0 Total \n", + "16692 0 Total \n", + "16693 0 Total \n", + "16694 0 Total \n", + "16695 0 Total \n", + "\n", + "[16696 rows x 17 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = IBGE.get_sidra_table(200,territorial_level=6,classification=2, categories='all')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Suppose we just wanted a single municipality, `Balneário Rincão (SC)`: Unfortunately for this one there is no data available, thus the `...` in the column `Valor`. Try another one." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-02T16:46:11.486937Z", + "start_time": "2021-03-02T16:46:09.551637Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requesting data from https://apisidra.ibge.gov.br/values/t/200/n6/4220000/p/last/c2/all\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " NC NN MC \\\n", + "0 Nível Territorial (Código) Nível Territorial Unidade de Medida (Código) \n", + "1 6 Município 45 \n", + "2 6 Município 45 \n", + "3 6 Município 45 \n", + "\n", + " MN V D1C D1N \\\n", + "0 Unidade de Medida Valor Município (Código) Município \n", + "1 Pessoas ... 4220000 Balneário Rincão (SC) \n", + "2 Pessoas ... 4220000 Balneário Rincão (SC) \n", + "3 Pessoas ... 4220000 Balneário Rincão (SC) \n", + "\n", + " D2C D2N D3C D3N D4C \\\n", + "0 Ano (Código) Ano Sexo (Código) Sexo Variável (Código) \n", + "1 2010 2010 0 Total 93 \n", + "2 2010 2010 4 Homens 93 \n", + "3 2010 2010 5 Mulheres 93 \n", + "\n", + " D4N D5C D5N \\\n", + "0 Variável Situação do domicílio (Código) Situação do domicílio \n", + "1 População residente 0 Total \n", + "2 População residente 0 Total \n", + "3 População residente 0 Total \n", + "\n", + " D6C D6N \n", + "0 Grupo de idade (Código) Grupo de idade \n", + "1 0 Total \n", + "2 0 Total \n", + "3 0 Total " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "single = IBGE.get_sidra_table(200,territorial_level=6, geocode=4220000,period='last',classification=2, categories='all')\n", + "single" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "## Retrieving population and census data from IBGE" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function get_population in module pysus.online_data.IBGE:\n", + "\n", + "get_population(year: int, source: Literal['POP', 'censo', 'POPTCU', 'projpop'] = 'POPTCU', censo_data: Literal['ALF', 'ESCA', 'ESCB', 'IDOSO', 'RENDA'] = 'ALF') -> pandas.core.frame.DataFrame\n", + " Get population data from IBGE as shared by DATASUS\n", + " :param year: year of the data\n", + " :param source: \n", + " \"POP\" - 1992-presente: Estimativas populacionais estratificadas por \n", + " idade e sexo.\n", + " \"censo\" - 1991, 2000 e 2010: Censos Demográficos\n", + " \"POPTCU\" - 1992-presente: Estimativas populacionais enviadas para o TCU,\n", + " estratificadas por idade e sexo pelo MS/SGEP/Datasus.\n", + " \"projpop\": Estimativas preliminares para os anos intercensitários dos \n", + " totais populacionais, estratificadas por idade e sexo pelo \n", + " MS/SGEP/Datasus.\n", + " :param censo_data: \n", + " \"ALF\": Censo Demográfico\n", + " \"ESCA\": Censo Escolar da Educação Básica\n", + " \"ESCB\": Censo Escolar da Educação Superior\n", + " \"IDOSO\": População de pessoas com 65 anos ou mais\n", + " \"RENDA\": População de pessoas de acordo com a renda familiar\n", + " :return: DataFrame with population data\n", + "\n" + ] + } + ], + "source": [ + "help(IBGE.get_population)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sources:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### POP\n", + "Population estimates stratified by age and sex" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Available years for `POP`:\n", + "def get_available_years(source):\n", + " return sorted(set([zfill_year(f.name[-2:]) for f in IBGE.ibge.get_files(source=source)]))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('The years available for source `POP` are: [1992, 1993, 1994, 1995, 1997, '\n", + " '1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, '\n", + " '2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021]')\n" + ] + } + ], + "source": [ + "from pprint import pprint\n", + "pprint(f\"The years available for source `POP` are: {get_available_years('POP')}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " MUNIC_RES ANO POPULACAO\n", + "0 110001 1992 34768\n", + "1 110002 1992 61737\n", + "2 110003 1992 8633\n", + "3 110004 1992 72462\n", + "4 110005 1992 23280\n", + "... ... ... ...\n", + "4969 522190 1992 3361\n", + "4970 522200 1992 9135\n", + "4971 522205 1992 5384\n", + "4972 522220 1992 2942\n", + "4973 530010 1992 1639035\n", + "\n", + "[4974 rows x 3 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IBGE.get_population(1992, source=\"POP\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### censo\n", + "Demographic Censuses of 1991, 2000 and 2010. `censo` is subdivided in 5 categories in which can be selected using the attribute `censo_data`, please see the help() text for more information about `censo_data`, its default value is `ALF`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " MUNCOD ANO CORRACA SITUACAO NUMRENDA DENRENDA DENCRIREND \\\n", + "0 110001 2010 1 I 6000724.88 10482 2618 \n", + "1 110001 2010 2 I 378314.66 1079 186 \n", + "2 110001 2010 3 I 31515.78 95 0 \n", + "3 110001 2010 4 I 4791228.10 11944 3348 \n", + "4 110001 2010 5 I 68893.15 497 182 \n", + "... ... ... ... ... ... ... ... \n", + "25395 530010 2010 2 I 203004614.58 190267 31396 \n", + "25396 530010 2010 3 I 63474517.86 42380 8308 \n", + "25397 530010 2010 4 I 1409083997.9 1236607 315001 \n", + "25398 530010 2010 5 I 8884195.09 6822 1140 \n", + "25399 530010 2010 0 I 972433.65 853 167 \n", + "\n", + " NUMPOBRES NUMPOBRESX NUMCRIPOB NUMCRIPOBX NUMDESOCUP DENDESOCUP \\\n", + "0 4831 2804 1645 914 181 4451 \n", + "1 539 182 144 46 18 554 \n", + "2 46 12 0 0 0 65 \n", + "3 5802 3048 2096 1160 304 4906 \n", + "4 450 250 165 107 0 67 \n", + "... ... ... ... ... ... ... \n", + "25395 41498 14373 11574 4522 10289 119202 \n", + "25396 6538 1901 2311 550 1669 24301 \n", + "25397 279962 98474 112171 39581 56885 661364 \n", + "25398 1021 297 195 67 345 3892 \n", + "25399 410 330 141 121 0 0 \n", + "\n", + " NUMTRABINF DENTRABINF \n", + "0 153 1127 \n", + "1 16 80 \n", + "2 0 8 \n", + "3 159 1583 \n", + "4 0 108 \n", + "... ... ... \n", + "25395 1817 16766 \n", + "25396 472 4001 \n", + "25397 9720 144469 \n", + "25398 23 589 \n", + "25399 0 101 \n", + "\n", + "[25400 rows x 15 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IBGE.get_population(2010, source=\"censo\", censo_data=\"RENDA\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### POPTCU (default)\n", + "\n", + "Population estimates sent to TCU (Federal Court of Accounts - Brazil), stratified by age and sex by MS/SGEP/Datasus" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('The years available for source `POPTCU` are: [1992, 1993, 1994, 1995, 1997, '\n", + " '1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, '\n", + " '2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021]')\n" + ] + } + ], + "source": [ + "pprint(f\"The years available for source `POPTCU` are: {get_available_years('POPTCU')}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " MUNIC_RES ANO POPULACAO\n", + "0 1100015 2021 22516\n", + "1 1100023 2021 111148\n", + "2 1100031 2021 5067\n", + "3 1100049 2021 86416\n", + "4 1100056 2021 16088\n", + "... ... ... ...\n", + "5565 5222005 2021 14088\n", + "5566 5222054 2021 9002\n", + "5567 5222203 2021 6451\n", + "5568 5222302 2021 5941\n", + "5569 5300108 2021 3094325\n", + "\n", + "[5570 rows x 3 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IBGE.get_population(2021)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### projpop\n", + "\n", + "Preliminary estimates for the intercensal years of population totals, stratified by age and sex by MS/SGEP/Datasus." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('The years available for source `projpop` are: [1925, 1926, 1927, 1928, 1929, '\n", + " '1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, '\n", + " '1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, '\n", + " '1954, 1955, 1956, 1957, 1958, 1959, 1960, 2000, 2001, 2002, 2003, 2004, '\n", + " '2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, '\n", + " '2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024]')\n" + ] + } + ], + "source": [ + "pprint(f\"The years available for source `projpop` are: {get_available_years('projpop')}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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In this tutorial, we will walk through the process of using InfoDengue's API with Python to fetch up-to-date arbovirus data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4936cdc7-ab6b-4659-b7a3-497c80b97e17", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from pysus.online_data.Infodengue import search_string, download" + ] + }, + { + "cell_type": "markdown", + "id": "44fb4cec-c0f9-4754-9fb0-57463df46410", + "metadata": {}, + "source": [ + "Infodengue is a national-wide system, use the `search_string` method to check how the city is found in the API:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "56af134b-fc1f-4b7b-aaee-46aa7ba436ff", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Arroio do Meio': 4301008,\n", + " 'Granjeiro': 2304806,\n", + " 'Jerônimo Monteiro': 3203106,\n", + " 'Minador do Negrão': 2705309,\n", + " 'Rio Branco': 5107206,\n", + " 'Rio Claro': 3304409,\n", + " 'Rio Grande': 4315602,\n", + " 'Rio Largo': 2707701,\n", + " 'Rio Manso': 3155306,\n", + " 'Rio Negrinho': 4215000,\n", + " 'Rio Negro': 4122305,\n", + " 'Rio Pardo': 4315701,\n", + " 'Rio da Conceição': 1718659,\n", + " 'Rio das Antas': 4214409,\n", + " 'Rio de Janeiro': 3304557,\n", + " 'Rio do Antônio': 2926806,\n", + " 'Rio do Pires': 2926905,\n", + " 'Rio dos Cedros': 4214706,\n", + " 'Rodeiro': 3156304,\n", + " 'Roteiro': 2707800}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "search_string('Rio d janeiro')" + ] + }, + { + "cell_type": "markdown", + "id": "7a38a112-5229-4ec7-b57b-af1e93d58305", + "metadata": {}, + "source": [ + "The download method extracts data for a specified range of Epidemiological Weeks (SE in pt) in the format `YYYYWW`. The output is a Pandas DataFrame containing all the EWs within this range." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7be2747e-d54d-464a-aec1-7e4f2f353a00", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df = download('dengue', 202301, 202304, 'Rio de Janeiro')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3eefdfe6-9c28-4614-8ee5-46a345480cc5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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SE202304202303202302202301
data_iniSE2023-01-222023-01-152023-01-082023-01-01
casos_est311.0241.0212.0225.0
casos_est_min311241212225
casos_est_max311241212225
casos311241212225
p_rt10.9999230.9692450.9863860.999998
p_inc100k4.6088993.5715273.1417583.334413
Localidade_id0000
nivel3333
id330455720230419614330455720230319614330455720230219614330455720230119614
versao_modelo2023-09-142023-09-142023-09-142023-09-14
tweet0.00.00.00.0
Rt1.3900051.1947231.2545081.643794
pop6747815.06747815.06747815.06747815.0
tempmin25.14285726.71428623.42857122.428571
umidmax82.14379377.15708489.98082992.587399
receptivo1111
transmissao1111
nivel_inc1111
umidmed82.14379377.15708482.59239578.188093
umidmin82.14379377.15708469.33168263.034302
tempmed25.14285726.71428625.07142924.976191
tempmax25.14285726.71428628.28571428.428571
casprovNaNNaNNaNNaN
casprov_estNaNNaNNaNNaN
casprov_est_minNaNNaNNaNNaN
casprov_est_maxNaNNaNNaNNaN
casconfNaNNaNNaNNaN
notif_accum_year989989989989
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" + ], + "text/plain": [ + "SE 202304 202303 202302 \\\n", + "data_iniSE 2023-01-22 2023-01-15 2023-01-08 \n", + "casos_est 311.0 241.0 212.0 \n", + "casos_est_min 311 241 212 \n", + "casos_est_max 311 241 212 \n", + "casos 311 241 212 \n", + "p_rt1 0.999923 0.969245 0.986386 \n", + "p_inc100k 4.608899 3.571527 3.141758 \n", + "Localidade_id 0 0 0 \n", + "nivel 3 3 3 \n", + "id 330455720230419614 330455720230319614 330455720230219614 \n", + "versao_modelo 2023-09-14 2023-09-14 2023-09-14 \n", + "tweet 0.0 0.0 0.0 \n", + "Rt 1.390005 1.194723 1.254508 \n", + "pop 6747815.0 6747815.0 6747815.0 \n", + "tempmin 25.142857 26.714286 23.428571 \n", + "umidmax 82.143793 77.157084 89.980829 \n", + "receptivo 1 1 1 \n", + "transmissao 1 1 1 \n", + "nivel_inc 1 1 1 \n", + "umidmed 82.143793 77.157084 82.592395 \n", + "umidmin 82.143793 77.157084 69.331682 \n", + "tempmed 25.142857 26.714286 25.071429 \n", + "tempmax 25.142857 26.714286 28.285714 \n", + "casprov NaN NaN NaN \n", + "casprov_est NaN NaN NaN \n", + "casprov_est_min NaN NaN NaN \n", + "casprov_est_max NaN NaN NaN \n", + "casconf NaN NaN NaN \n", + "notif_accum_year 989 989 989 \n", + "\n", + "SE 202301 \n", + "data_iniSE 2023-01-01 \n", + "casos_est 225.0 \n", + "casos_est_min 225 \n", + "casos_est_max 225 \n", + "casos 225 \n", + "p_rt1 0.999998 \n", + "p_inc100k 3.334413 \n", + "Localidade_id 0 \n", + "nivel 3 \n", + "id 330455720230119614 \n", + "versao_modelo 2023-09-14 \n", + "tweet 0.0 \n", + "Rt 1.643794 \n", + "pop 6747815.0 \n", + "tempmin 22.428571 \n", + "umidmax 92.587399 \n", + "receptivo 1 \n", + "transmissao 1 \n", + "nivel_inc 1 \n", + "umidmed 78.188093 \n", + "umidmin 63.034302 \n", + "tempmed 24.976191 \n", + "tempmax 28.428571 \n", + "casprov NaN \n", + "casprov_est NaN \n", + "casprov_est_min NaN \n", + "casprov_est_max NaN \n", + "casconf NaN \n", + "notif_accum_year 989 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "08fd951a-26c0-4814-bf2e-e8b8b72cab3a", + "metadata": {}, + "source": [ + "You can save the dataframe in a CSV file" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "012e70f2-a204-46d9-983c-cd6b971999a7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df.to_csv('rio_se01_04.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "01c98131-d49d-48e8-bc85-e6dbcdca2ecc", + "metadata": {}, + "source": [ + "In order to fetch data with different parameters, it is possible to iterate over a list, for instance:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b4ddf6e9-ad86-438a-add0-8b7068307b98", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from itertools import product\n", + "\n", + "diseases = ['dengue', 'zika']\n", + "cities = ['Rio de Janeiro', 'Rio do Antônio', 'Rio do Pires']\n", + "\n", + "for disease, city in product(diseases, cities):\n", + " df = download(disease, 202301, 202304, city)\n", + " df.to_csv(f'{disease}_{city.lower().replace(\" \", \"_\")}_se01_04.csv')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a84fc3e6-ee12-4dac-907e-3a33bf8f9509", + "metadata": {}, + "source": [ + "Expected files:\n", + "\n", + "- dengue_rio_de_janeiro_se01_04.csv\n", + "- dengue_rio_do_antônio_se01_04.csv\n", + "- dengue_rio_do_pires_se01_04.csv\n", + "- zika_rio_de_janeiro_se01_04.csv\n", + "- zika_rio_do_antônio_se01_04.csv\n", + "- zika_rio_do_pires_se01_04.csv" + ] + } + ], + "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": "3.11.5" + }, + "vscode": { + "interpreter": { + "hash": "2a96a5ccec8dfcba7d06b2e71f6eef3b5dac5716461bf5d73ea1bb7ee462cdaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pysus/Notebooks/Infogripe.ipynb b/pysus/Notebooks/Infogripe.ipynb new file mode 100644 index 00000000..14e7309a --- /dev/null +++ b/pysus/Notebooks/Infogripe.ipynb @@ -0,0 +1,1526 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Working with Infogripe datasets\n", + "[Infogripe](http://info.gripe.fiocruz.br/) is an online platform that tracks epidemiolgical data about influenza-like diseases in Brazil.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "from pysus.online_data.Infogripe import list_datasets, download\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Infogripe makes available different datasets. To findout which ones are available before downloading we can ask PySUS to list them:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Alerta de situação',\n", + " 'Casos por idade, sexo e virus',\n", + " 'Casos Totais e estimativas',\n", + " 'Valores esperados por localidades']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_datasets()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 41.5 ms, sys: 6.24 ms, total: 47.7 ms\n", + "Wall time: 450 ms\n" + ] + }, + { + "data": { + "text/html": [ + "
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UFUnidade da FederaçãodadoescalaAno epidemiológicoSemana epidemiológicacasos estimadosmédia móvelnível semanalnível por média móvel
00Brasilsragincidência2023350.7217640.722381valor epidêmicoAmarelo
10Brasilsragincidência2023360.614818NaNvalor epidêmicoNaN
21Região Nortesragincidência2023350.8396260.876132valor altoVermelho
31Região Nortesragincidência2023360.698819NaNvalor epidêmicoNaN
42Região Nordestesragincidência2023350.6579860.650637valor muito altoVermelho
.................................
691002Regional Lestesragincidência2023360.652379NaNvalor epidêmicoNaN
701003Regional Centrosragincidência2023350.5859270.623618valor epidêmicoAmarelo
711003Regional Centrosragincidência2023360.520824NaNvalor epidêmicoNaN
721004Regional Sulsragincidência2023350.7204270.723445valor epidêmicoAmarelo
731004Regional Sulsragincidência2023360.595660NaNvalor epidêmicoNaN
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data de publicaçãoUFUnidade da FederaçãoTipodadoescalasexoAno epidemiológicoSemana epidemiológicaAno e semana epidemiológica...Vírus sincicial respiratório (VSR)Parainfluenza 1Parainfluenza 2Parainfluenza 3Parainfluenza 4AdenovirusRinovirusBocavirusMetapneumovirusOutros virus
02023-09-1311RondôniaEstadosragincidênciaF200912009W01...0.00.00.00.00.00.00.00.00.00.0
12023-09-1311RondôniaEstadosragincidênciaM200912009W01...0.00.00.00.00.00.00.00.00.00.0
22023-09-1311RondôniaEstadosragincidênciaTotal200912009W01...0.00.00.00.00.00.00.00.00.00.0
32023-09-1311RondôniaEstadosragincidênciaF200922009W02...0.00.00.00.00.00.00.00.00.00.0
42023-09-1311RondôniaEstadosragincidênciaM200922009W02...0.00.00.00.00.00.00.00.00.00.0
..................................................................
8927682023-09-133Região SudesteRegiãoobitocasosTotal2023352023W35...0.00.00.00.00.00.00.00.00.00.0
8927692023-09-133Região SudesteRegiãoobitocasosF2023362023W36...0.00.00.00.00.00.00.00.00.00.0
8927702023-09-133Região SudesteRegiãoobitocasosI2023362023W36...0.00.00.00.00.00.00.00.00.00.0
8927712023-09-133Região SudesteRegiãoobitocasosM2023362023W36...0.00.00.00.00.00.00.00.00.00.0
8927722023-09-133Região SudesteRegiãoobitocasosTotal2023362023W36...0.00.00.00.00.00.00.00.00.00.0
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892773 rows × 42 columns

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" + ], + "text/plain": [ + " data de publicação UF Unidade da Federação Tipo dado escala \\\n", + "0 2023-09-13 11 Rondônia Estado srag incidência \n", + "1 2023-09-13 11 Rondônia Estado srag incidência \n", + "2 2023-09-13 11 Rondônia Estado srag incidência \n", + "3 2023-09-13 11 Rondônia Estado srag incidência \n", + "4 2023-09-13 11 Rondônia Estado srag incidência \n", + "... ... .. ... ... ... ... \n", + "892768 2023-09-13 3 Região Sudeste Região obito casos \n", + "892769 2023-09-13 3 Região Sudeste Região obito casos \n", + "892770 2023-09-13 3 Região Sudeste Região obito casos \n", + "892771 2023-09-13 3 Região Sudeste Região obito casos \n", + "892772 2023-09-13 3 Região Sudeste Região obito casos \n", + "\n", + " sexo Ano epidemiológico Semana epidemiológica \\\n", + "0 F 2009 1 \n", + "1 M 2009 1 \n", + "2 Total 2009 1 \n", + "3 F 2009 2 \n", + "4 M 2009 2 \n", + "... ... ... ... \n", + "892768 Total 2023 35 \n", + "892769 F 2023 36 \n", + "892770 I 2023 36 \n", + "892771 M 2023 36 \n", + "892772 Total 2023 36 \n", + "\n", + " Ano e semana epidemiológica ... Vírus sincicial respiratório (VSR) \\\n", + "0 2009W01 ... 0.0 \n", + "1 2009W01 ... 0.0 \n", + "2 2009W01 ... 0.0 \n", + "3 2009W02 ... 0.0 \n", + "4 2009W02 ... 0.0 \n", + "... ... ... ... \n", + "892768 2023W35 ... 0.0 \n", + "892769 2023W36 ... 0.0 \n", + "892770 2023W36 ... 0.0 \n", + "892771 2023W36 ... 0.0 \n", + "892772 2023W36 ... 0.0 \n", + "\n", + " Parainfluenza 1 Parainfluenza 2 Parainfluenza 3 Parainfluenza 4 \\\n", + "0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "... ... ... ... ... \n", + "892768 0.0 0.0 0.0 0.0 \n", + "892769 0.0 0.0 0.0 0.0 \n", + "892770 0.0 0.0 0.0 0.0 \n", + "892771 0.0 0.0 0.0 0.0 \n", + "892772 0.0 0.0 0.0 0.0 \n", + "\n", + " Adenovirus Rinovirus Bocavirus Metapneumovirus Outros virus \n", + "0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 \n", + "... ... ... ... ... ... \n", + "892768 0.0 0.0 0.0 0.0 0.0 \n", + "892769 0.0 0.0 0.0 0.0 0.0 \n", + "892770 0.0 0.0 0.0 0.0 0.0 \n", + "892771 0.0 0.0 0.0 0.0 0.0 \n", + "892772 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + "[892773 rows x 42 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_age_sex_virus = download(\"Casos por idade, sexo e virus\")\n", + "df_age_sex_virus\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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data de publicaçãoUFUnidade da FederaçãoTipodadoescalaAno epidemiológicoSemana epidemiológicaSituação do dadoCasos semanais reportados até a última atualização...casos estimadosmédia móvellimite superior da estimativaPercentual em relação ao paísPopulaçãolimiar pré-epidêmicointensidade altaintensidade muito altanível semanalnível por média móvel
02023-09-130BrasilPaíssragcasos20091Dado estável. Sujeito a pequenas alterações.0.000000...NaNNaNNaNNaN193543969614.0000001588.0000002056.000000valor baixoNaN
12023-09-130BrasilPaíssragcasos20092Dado estável. Sujeito a pequenas alterações.0.000000...NaN0.000000NaNNaN193543969614.0000001588.0000002056.000000valor baixoVerde
22023-09-130BrasilPaíssragcasos20093Dado estável. Sujeito a pequenas alterações.0.000000...NaN0.000000NaNNaN193543969614.0000001588.0000002056.000000valor baixoVerde
32023-09-130BrasilPaíssragcasos20094Dado estável. Sujeito a pequenas alterações.0.000000...NaN0.000000NaNNaN193543969614.0000001588.0000002056.000000valor baixoVerde
42023-09-130BrasilPaíssragcasos20095Dado estável. Sujeito a pequenas alterações.0.000000...NaN0.000000NaNNaN193543969614.0000001588.0000002056.000000valor baixoVerde
..................................................................
3400992023-09-131004Regional SulRegionalobitocovidincidência202332Estimado. Sujeito a alterações.0.008049...0.0080490.0087200.01106853.333333993855700.0111890.0661150.095612valor baixoVerde
3401002023-09-131004Regional SulRegionalobitocovidincidência202333Estimado. Sujeito a alterações.0.010062...0.0110680.0077140.01308061.111111993855700.0111890.0661150.095612valor baixoVerde
3401012023-09-131004Regional SulRegionalobitocovidincidência202334Estimado. Sujeito a alterações.0.003019...0.0040250.0067080.00804950.000000993855700.0111890.0661150.095612valor baixoVerde
3401022023-09-131004Regional SulRegionalobitocovidincidência202335Estimado. Sujeito a alterações.NaN...0.0050310.0043600.01106855.555556993855700.0111890.0661150.095612valor baixoVerde
3401032023-09-131004Regional SulRegionalobitocovidincidência202336Estimado. Sujeito a alterações.NaN...0.004025NaN0.01207457.142857993855700.0111890.0661150.095612valor baixoNaN
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340104 rows × 21 columns

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UFUnidade da FederaçãoTipodadoescalaSemana epidemiológicacorredor baixocorredor medianocorredor altolimiar pré-epidêmicointensidade altaintensidade muito altatemporadas consideradas regularesPopulação de referência para cálculo de incidência
011RondôniaEstadosragincidência10.00.0000000.0533530.1600580.6935860.9069972010, 2013, 2014, 2016, 2018, 20191874318
111RondôniaEstadosragincidência20.00.0000000.0000000.1600580.6935860.9069972010, 2013, 2014, 2016, 2018, 20191874318
211RondôniaEstadosragincidência30.00.0000000.0533530.1600580.6935860.9069972010, 2013, 2014, 2016, 2018, 20191874318
311RondôniaEstadosragincidência40.00.0533530.0533530.1600580.6935860.9069972010, 2013, 2014, 2016, 2018, 20191874318
411RondôniaEstadosragincidência50.00.0000000.0533530.1600580.6935860.9069972010, 2013, 2014, 2016, 2018, 20191874318
.............................................
230833Região SudesteRegiãoobitocasos485.07.00000010.00000023.00000088.000000124.0000002012, 2013, 2014, 2015, 2017, 2018, 201989107377
230843Região SudesteRegiãoobitocasos496.07.0000009.00000023.00000088.000000124.0000002012, 2013, 2014, 2015, 2017, 2018, 201989107377
230853Região SudesteRegiãoobitocasos505.07.00000011.00000023.00000088.000000124.0000002012, 2013, 2014, 2015, 2017, 2018, 201989107377
230863Região SudesteRegiãoobitocasos514.07.00000011.00000023.00000088.000000124.0000002012, 2013, 2014, 2015, 2017, 2018, 201989107377
230873Região SudesteRegiãoobitocasos524.06.0000008.00000023.00000088.000000124.0000002012, 2013, 2014, 2015, 2017, 2018, 201989107377
\n", + "

23088 rows × 14 columns

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" + ], + "text/plain": [ + " UF Unidade da Federação Tipo dado escala \\\n", + "0 11 Rondônia Estado srag incidência \n", + "1 11 Rondônia Estado srag incidência \n", + "2 11 Rondônia Estado srag incidência \n", + "3 11 Rondônia Estado srag incidência \n", + "4 11 Rondônia Estado srag incidência \n", + "... .. ... ... ... ... \n", + "23083 3 Região Sudeste Região obito casos \n", + "23084 3 Região Sudeste Região obito casos \n", + "23085 3 Região Sudeste Região obito casos \n", + "23086 3 Região Sudeste Região obito casos \n", + "23087 3 Região Sudeste Região obito casos \n", + "\n", + " Semana epidemiológica corredor baixo corredor mediano corredor alto \\\n", + "0 1 0.0 0.000000 0.053353 \n", + "1 2 0.0 0.000000 0.000000 \n", + "2 3 0.0 0.000000 0.053353 \n", + "3 4 0.0 0.053353 0.053353 \n", + "4 5 0.0 0.000000 0.053353 \n", + "... ... ... ... ... \n", + "23083 48 5.0 7.000000 10.000000 \n", + "23084 49 6.0 7.000000 9.000000 \n", + "23085 50 5.0 7.000000 11.000000 \n", + "23086 51 4.0 7.000000 11.000000 \n", + "23087 52 4.0 6.000000 8.000000 \n", + "\n", + " limiar pré-epidêmico intensidade alta intensidade muito alta \\\n", + "0 0.160058 0.693586 0.906997 \n", + "1 0.160058 0.693586 0.906997 \n", + "2 0.160058 0.693586 0.906997 \n", + "3 0.160058 0.693586 0.906997 \n", + "4 0.160058 0.693586 0.906997 \n", + "... ... ... ... \n", + "23083 23.000000 88.000000 124.000000 \n", + "23084 23.000000 88.000000 124.000000 \n", + "23085 23.000000 88.000000 124.000000 \n", + "23086 23.000000 88.000000 124.000000 \n", + "23087 23.000000 88.000000 124.000000 \n", + "\n", + " temporadas consideradas regulares \\\n", + "0 2010, 2013, 2014, 2016, 2018, 2019 \n", + "1 2010, 2013, 2014, 2016, 2018, 2019 \n", + "2 2010, 2013, 2014, 2016, 2018, 2019 \n", + "3 2010, 2013, 2014, 2016, 2018, 2019 \n", + "4 2010, 2013, 2014, 2016, 2018, 2019 \n", + "... ... \n", + "23083 2012, 2013, 2014, 2015, 2017, 2018, 2019 \n", + "23084 2012, 2013, 2014, 2015, 2017, 2018, 2019 \n", + "23085 2012, 2013, 2014, 2015, 2017, 2018, 2019 \n", + "23086 2012, 2013, 2014, 2015, 2017, 2018, 2019 \n", + "23087 2012, 2013, 2014, 2015, 2017, 2018, 2019 \n", + "\n", + " População de referência para cálculo de incidência \n", + "0 1874318 \n", + "1 1874318 \n", + "2 1874318 \n", + "3 1874318 \n", + "4 1874318 \n", + "... ... \n", + "23083 89107377 \n", + "23084 89107377 \n", + "23085 89107377 \n", + "23086 89107377 \n", + "23087 89107377 \n", + "\n", + "[23088 rows x 14 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_localities = download(\"Valores esperados por localidades\")\n", + "df_localities\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [] + } + ], + "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": "3.11.5" + }, + "vscode": { + "interpreter": { + "hash": "2a96a5ccec8dfcba7d06b2e71f6eef3b5dac5716461bf5d73ea1bb7ee462cdaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pysus/Notebooks/Preprocessing SIM with municipality.ipynb b/pysus/Notebooks/Preprocessing SIM with municipality.ipynb new file mode 100644 index 00000000..7b87af1d --- /dev/null +++ b/pysus/Notebooks/Preprocessing SIM with municipality.ipynb @@ -0,0 +1,173 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pysus.preprocessing.geodata import add_data_to_municipality\n", + "from pysus.preprocessing.SIM import group_and_count, redistribute_missing, redistribute_cid_chapter\n", + "from pysus.preprocessing.decoders import translate_variables_SIM\n", + "\n", + "from pysus.ftp.databases.sim import SIM\n", + "\n", + "sim = SIM().load()\n", + "\n", + "states = [\"AC\", \"AM\", \"AP\", \"PA\", \"RO\", \"RR\", \"TO\"]\n", + "variables = ['CODMUNRES','SEXO','IDADE_ANOS','CID10_CHAPTER']\n", + "filter_variables = variables[:3]\n", + "geo_df = pd.DataFrame()\n", + "ages_args = {\n", + " \"start\": 0,\n", + " \"end\": 90,\n", + " \"freq\": 5\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 330k/330k [00:00<00:00, 167MB/s]\n", + "2023-09-19 13:53:06.107 | DEBUG | pysus.online_data.SIM:get_municipios:185 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:06.128 | DEBUG | pysus.online_data.SIM:get_municipios:189 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:06.129 | INFO | pysus.online_data.SIM:get_municipios:199 - Local parquet file found at /home/bida/pysus/SIM_CADMUN_.parquet\n", + "2023-09-19 13:53:06.371 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:47 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:06.392 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:51 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:06.393 | INFO | pysus.online_data.SIM:get_CID10_chapters_table:61 - Local parquet file found at /home/bida/pysus/SIM_CIDCAP10_.parquet\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 1.42M/1.42M [00:00<00:00, 1.07GB/s]\n", + "2023-09-19 13:53:08.409 | DEBUG | pysus.online_data.SIM:get_municipios:185 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:08.431 | DEBUG | pysus.online_data.SIM:get_municipios:189 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:08.432 | INFO | pysus.online_data.SIM:get_municipios:199 - Local parquet file found at /home/bida/pysus/SIM_CADMUN_.parquet\n", + "2023-09-19 13:53:08.879 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:47 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:08.899 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:51 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:08.899 | INFO | pysus.online_data.SIM:get_CID10_chapters_table:61 - Local parquet file found at /home/bida/pysus/SIM_CIDCAP10_.parquet\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 264k/264k [00:00<00:00, 173MB/s]\n", + "2023-09-19 13:53:11.067 | DEBUG | pysus.online_data.SIM:get_municipios:185 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:11.088 | DEBUG | pysus.online_data.SIM:get_municipios:189 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:11.089 | INFO | pysus.online_data.SIM:get_municipios:199 - Local parquet file found at /home/bida/pysus/SIM_CADMUN_.parquet\n", + "2023-09-19 13:53:11.311 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:47 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:11.332 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:51 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:11.333 | INFO | pysus.online_data.SIM:get_CID10_chapters_table:61 - Local parquet file found at /home/bida/pysus/SIM_CIDCAP10_.parquet\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 3.22M/3.22M [00:00<00:00, 4.17GB/s]\n", + "2023-09-19 13:53:13.178 | DEBUG | pysus.online_data.SIM:get_municipios:185 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:13.199 | DEBUG | pysus.online_data.SIM:get_municipios:189 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:13.200 | INFO | pysus.online_data.SIM:get_municipios:199 - Local parquet file found at /home/bida/pysus/SIM_CADMUN_.parquet\n", + "2023-09-19 13:53:14.100 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:47 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:14.122 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:51 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:14.123 | INFO | pysus.online_data.SIM:get_CID10_chapters_table:61 - Local parquet file found at /home/bida/pysus/SIM_CIDCAP10_.parquet\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 665k/665k [00:00<00:00, 509MB/s]\n", + "2023-09-19 13:53:19.139 | DEBUG | pysus.online_data.SIM:get_municipios:185 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:19.160 | DEBUG | pysus.online_data.SIM:get_municipios:189 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:19.161 | INFO | pysus.online_data.SIM:get_municipios:199 - Local parquet file found at /home/bida/pysus/SIM_CADMUN_.parquet\n", + "2023-09-19 13:53:19.459 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:47 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:19.481 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:51 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:19.482 | INFO | pysus.online_data.SIM:get_CID10_chapters_table:61 - Local parquet file found at /home/bida/pysus/SIM_CIDCAP10_.parquet\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 230k/230k [00:00<00:00, 146MB/s]\n", + "2023-09-19 13:53:21.358 | DEBUG | pysus.online_data.SIM:get_municipios:185 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:21.379 | DEBUG | pysus.online_data.SIM:get_municipios:189 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:21.381 | INFO | pysus.online_data.SIM:get_municipios:199 - Local parquet file found at /home/bida/pysus/SIM_CADMUN_.parquet\n", + "2023-09-19 13:53:21.597 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:47 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:21.618 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:51 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:21.619 | INFO | pysus.online_data.SIM:get_CID10_chapters_table:61 - Local parquet file found at /home/bida/pysus/SIM_CIDCAP10_.parquet\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 659k/659k [00:00<00:00, 554MB/s]\n", + "2023-09-19 13:53:22.812 | DEBUG | pysus.online_data.SIM:get_municipios:185 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:22.833 | DEBUG | pysus.online_data.SIM:get_municipios:189 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:22.834 | INFO | pysus.online_data.SIM:get_municipios:199 - Local parquet file found at /home/bida/pysus/SIM_CADMUN_.parquet\n", + "2023-09-19 13:53:23.138 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:47 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-19 13:53:23.159 | DEBUG | pysus.online_data.SIM:get_CID10_chapters_table:51 - Changing FTP work dir to: /dissemin/publicos/SIM/CID10/TABELAS\n", + "2023-09-19 13:53:23.161 | INFO | pysus.online_data.SIM:get_CID10_chapters_table:61 - Local parquet file found at /home/bida/pysus/SIM_CIDCAP10_.parquet\n" + ] + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "for state in states:\n", + " df = sim.download(sim.get_files(\"CID10\",state,2018)).to_dataframe()\n", + " df = translate_variables_SIM(df,age_classes=True,classify_args=ages_args,classify_cid10_chapters=True)\n", + " df = df[variables]\n", + " counts = group_and_count(df,variables)\n", + " counts = redistribute_missing(counts,filter_variables)\n", + " counts = redistribute_cid_chapter(counts,filter_variables)\n", + " geo_df = geo_df.append(add_data_to_municipality(counts,title_cols=['SEXO','IDADE_ANOS','CID10_CHAPTER'], nan_string='NA'))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['code_muni', 'name_muni', 'code_state', 'abbrev_state', 'name_state',\n", + " 'code_region', 'name_region', 'geometry', 'Feminino-[0.0, 5.0)-1',\n", + " 'Feminino-[0.0, 5.0)-2',\n", + " ...\n", + " 'NA-[50.0, 55.0)-8', 'NA-[55.0, 60.0)-8', 'NA-[60.0, 65.0)-8',\n", + " 'NA-[65.0, 70.0)-8', 'NA-[70.0, 75.0)-8', 'NA-[75.0, 80.0)-8',\n", + " 'NA-[80.0, 85.0)-8', 'NA-[85.0, 90.0)-8', 'NA-[90.0, inf)-8',\n", + " 'NA-NA-8'],\n", + " dtype='object', length=1088)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geo_df.columns" + ] + } + ], + "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": "3.11.5" + }, + "vscode": { + "interpreter": { + "hash": "2a96a5ccec8dfcba7d06b2e71f6eef3b5dac5716461bf5d73ea1bb7ee462cdaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pysus/Notebooks/Preprocessing SIM.ipynb b/pysus/Notebooks/Preprocessing SIM.ipynb new file mode 100644 index 00000000..173bbe25 --- /dev/null +++ b/pysus/Notebooks/Preprocessing SIM.ipynb @@ -0,0 +1,1713 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SIM Metadata Information" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pysus.online_data.SIM import get_CID9_table, get_CID10_table, get_municipios, get_ocupations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-09-15 18:51:51.424 | DEBUG | pysus.online_data.SIM:get_CID9_table:139 - Stablishing connection with ftp.datasus.gov.br.\n", + "220 Microsoft FTP Service\n", + "2023-09-15 18:51:51.448 | DEBUG | pysus.online_data.SIM:get_CID9_table:143 - Changing FTP work dir to: /dissemin/publicos/SIM/CID9/TABELAS\n", + "2023-09-15 18:51:51.450 | INFO | pysus.online_data.SIM:get_CID9_table:153 - Local parquet file found at /home/bida/pysus/SIM_CID9_.parquet\n" + ] + }, + { + "data": { + "text/html": [ + "
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CODIGODESCRICAO
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1759ABASTECEDOR DE ESPULAS
2893ABASTECEDOR DE FORNO (CERAMICA)
3999ABASTECEDOR DE LINHA DE PRODUCAO
4739ABASTECEDOR DE MAQUINAS (MAD. E PASTA PAPEL)
.........
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" + ], + "text/plain": [ + " CODIGO DESCRICAO\n", + "0 639 ABALIZADOR (AREAS AGRICOLAS)\n", + "1 759 ABASTECEDOR DE ESPULAS\n", + "2 893 ABASTECEDOR DE FORNO (CERAMICA)\n", + "3 999 ABASTECEDOR DE LINHA DE PRODUCAO\n", + "4 739 ABASTECEDOR DE MAQUINAS (MAD. E PASTA PAPEL)\n", + "... ... ...\n", + "3559 540 ZELADOR DE VESTIARIOS\n", + "3560 551 ZELADOR SEM ESPECIFICACAO\n", + "3561 728 ZINCADOR\n", + "3562 051 ZOOLOGO\n", + "3563 065 ZOOTECNISTA\n", + "\n", + "[3564 rows x 2 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_ocupations()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessing SIM data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pysus.ftp.databases.sim import SIM\n", + "from pysus.preprocessing.decoders import translate_variables_SIM\n", + "from pysus.preprocessing.SIM import group_and_count, redistribute_missing, redistribute_cid_chapter\n", + "\n", + "sim = SIM().load()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DOAC2010.parquet: 0%| | 0.00/7.37k [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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CODMUNRESSEXOIDADE_ANOSCID10_CHAPTERCOUNTSCOUNTS_ORIGINAL
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6348 rows × 6 columns

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" + ], + "text/plain": [ + " CODMUNRES SEXO IDADE_ANOS CID10_CHAPTER COUNTS \\\n", + "0 1200013.0 Feminino [0.0, 1.0) 1 1.0 \n", + "17 1200013.0 Feminino [1.0, 2.0) 1 0.0 \n", + "34 1200013.0 Feminino [2.0, 3.0) 1 0.0 \n", + "51 1200013.0 Feminino [3.0, 4.0) 1 0.0 \n", + "68 1200013.0 Feminino [4.0, 5.0) 1 0.0 \n", + "... ... ... ... ... ... \n", + "107831 NA NA [87.0, 88.0) 1 0.0 \n", + "107848 NA NA [88.0, 89.0) 1 0.0 \n", + "107865 NA NA [89.0, 90.0) 1 0.0 \n", + "107882 NA NA [90.0, inf) 1 0.0 \n", + "107899 NA NA NA 1 0.0 \n", + "\n", + " COUNTS_ORIGINAL \n", + "0 1.0 \n", + "17 0.0 \n", + "34 0.0 \n", + "51 0.0 \n", + "68 0.0 \n", + "... ... \n", + "107831 0.0 \n", + "107848 0.0 \n", + "107865 0.0 \n", + "107882 0.0 \n", + "107899 0.0 \n", + "\n", + "[6348 rows x 6 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counts = redistribute_cid_chapter(counts,['CODMUNRES','SEXO','IDADE_ANOS'])\n", + "counts[counts['CID10_CHAPTER'] == 1]" + ] + } + ], + "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": "3.11.5" + }, + "vscode": { + "interpreter": { + "hash": "2a96a5ccec8dfcba7d06b2e71f6eef3b5dac5716461bf5d73ea1bb7ee462cdaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pysus/Notebooks/Preprocessing SINAN.ipynb b/pysus/Notebooks/Preprocessing SINAN.ipynb new file mode 100644 index 00000000..d2337f73 --- /dev/null +++ b/pysus/Notebooks/Preprocessing SINAN.ipynb @@ -0,0 +1,2559 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SINAN Metadata Information" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pysus.online_data import SINAN" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Nome do campoCampoTipoCategoriaDescriçãoCaracterísticasDBF
02-Agravo/doençaNaNVARCHAR(1)1-Dengue\\n2-ChikungunyaEspecifica a suspeição do agravo.Campo Obrigatório\\nPreenchendo o código,\\na de...ID_AGRAVO
131-Data da Investigaçãodt_investigacaoDATENaNInformar a data da investigaçãoCampo obrigatório\\n- Não aceitar Data da\\ninve...DT_INVEST
232- Ocupação/ramo de atividade econômicaco_cbo_ocupacaoVARCHAR(6)NaNInformar a atividade exercida pelo paciente no...NaNID_OCUPA_N
333- Sinais clínicos (Febre)NaNVARCHAR(1)1 – Sim\\n2 – NãoInformar qual sinal clínico ou achados\\nlabora...Campo obrigatórioFEBRE
433- Sinais clínicos (Mialgia)NaNVARCHAR(1)1 – Sim\\n2 – NãoInformar qual sinal clínico ou achados\\nlabora...Campo obrigatórioMIALGIA
........................
8370- Dengue grave (Alteração da consciência)NaNVARCHAR(1)1 – Sim\\n2 – NãoInformar qual sinal de gravidade foi\\nencontradoCampo obrigatório\\nSe campo 62-\\nClassificação...GRAV_CONSC
8470- Dengue grave (Outros órgãos)NaNNaNNaNInformar qual sinal de gravidade foi\\nencontradoCampo obrigatório\\nSe campo 62-\\nClassificação...GRAV_ORGAO
8571- Dengue grave Data de início dos sintomasNaNDATENaNInformar data de início dos sinais de alarme.\\...Campo obrigatório\\nSe campo 62-\\nClassificação...DT_GRAV
86Informações complementares e observações adici...ds_observacaoVARCHAR(255)NaNInformações complementares e observações\\nadic...Habilitado para todas\\nnotificaçõesDS_OBS
87NaNNaNNaNNaNNaNNaNNaN
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88 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " Nome do campo Campo \\\n", + "0 2-Agravo/doença NaN \n", + "1 31-Data da Investigação dt_investigacao \n", + "2 32- Ocupação/ramo de atividade econômica co_cbo_ocupacao \n", + "3 33- Sinais clínicos (Febre) NaN \n", + "4 33- Sinais clínicos (Mialgia) NaN \n", + ".. ... ... \n", + "83 70- Dengue grave (Alteração da consciência) NaN \n", + "84 70- Dengue grave (Outros órgãos) NaN \n", + "85 71- Dengue grave Data de início dos sintomas NaN \n", + "86 Informações complementares e observações adici... ds_observacao \n", + "87 NaN NaN \n", + "\n", + " Tipo Categoria \\\n", + "0 VARCHAR(1) 1-Dengue\\n2-Chikungunya \n", + "1 DATE NaN \n", + "2 VARCHAR(6) NaN \n", + "3 VARCHAR(1) 1 – Sim\\n2 – Não \n", + "4 VARCHAR(1) 1 – Sim\\n2 – Não \n", + ".. ... ... \n", + "83 VARCHAR(1) 1 – Sim\\n2 – Não \n", + "84 NaN NaN \n", + "85 DATE NaN \n", + "86 VARCHAR(255) NaN \n", + "87 NaN NaN \n", + "\n", + " Descrição \\\n", + "0 Especifica a suspeição do agravo. \n", + "1 Informar a data da investigação \n", + "2 Informar a atividade exercida pelo paciente no... \n", + "3 Informar qual sinal clínico ou achados\\nlabora... \n", + "4 Informar qual sinal clínico ou achados\\nlabora... \n", + ".. ... \n", + "83 Informar qual sinal de gravidade foi\\nencontrado \n", + "84 Informar qual sinal de gravidade foi\\nencontrado \n", + "85 Informar data de início dos sinais de alarme.\\... \n", + "86 Informações complementares e observações\\nadic... \n", + "87 NaN \n", + "\n", + " Características DBF \n", + "0 Campo Obrigatório\\nPreenchendo o código,\\na de... ID_AGRAVO \n", + "1 Campo obrigatório\\n- Não aceitar Data da\\ninve... DT_INVEST \n", + "2 NaN ID_OCUPA_N \n", + "3 Campo obrigatório FEBRE \n", + "4 Campo obrigatório MIALGIA \n", + ".. ... ... \n", + "83 Campo obrigatório\\nSe campo 62-\\nClassificação... GRAV_CONSC \n", + "84 Campo obrigatório\\nSe campo 62-\\nClassificação... GRAV_ORGAO \n", + "85 Campo obrigatório\\nSe campo 62-\\nClassificação... DT_GRAV \n", + "86 Habilitado para todas\\nnotificações DS_OBS \n", + "87 NaN NaN \n", + "\n", + "[88 rows x 7 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metadata = SINAN.metadata_df('DENG')\n", + "metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ID_AGRAVO': '2-Agravo/doença',\n", + " 'DT_INVEST': '31-Data da Investigação',\n", + " 'ID_OCUPA_N': '32- Ocupação/ramo de atividade econômica',\n", + " 'FEBRE': '33- Sinais clínicos (Febre)',\n", + " 'MIALGIA': '33- Sinais clínicos (Mialgia)',\n", + " 'CEFALEIA': '33- Sinais clínicos (Cefaleia)',\n", + " 'EXANTEMA': '33- Sinais clínicos (Exantema)',\n", + " 'VOMITO': '33- Sinais clínicos (Vômito)',\n", + " 'NAUSEA': '33- Sinais clínicos (Náusea)',\n", + " 'DOR_COSTAS': '33- Sinais clínicos (Dor nas costas)',\n", + " 'CONJUNTVIT': '33- Sinais clínicos (Conjuntivite)',\n", + " 'ARTRITE': '33- Sinais clínicos (Artrite)',\n", + " 'ARTRALGIA': '33- Sinais clínicos (Artralgia intensa)',\n", + " 'PETÉQUIA_N': '33- Sinais clínicos (Petéquias)',\n", + " 'LEUCOPENIA': '33- Sinais clínicos (Leucopenia)',\n", + " 'LACO': '33- Sinais clínicos (Prova do laço)',\n", + " 'DOR_RETRO': '33- Sinais clínicos (Dor retroorbital)',\n", + " 'DIABETES': '34- Doenças pré- existentes (Diabetes)',\n", + " 'HEMATOLOG': '34- Doenças pré- existentes (Doenças hematológicas)',\n", + " 'HEPATOPAT': '34- Doenças pré- existentes (Hepatopatias)',\n", + " 'RENAL': '34- Doenças pré- existentes (Doença renal crônica)',\n", + " 'HIPERTENSA': '34- Doenças pré- existentes (Hipertensão arterial)',\n", + " 'ÁCIDO_PEPT': '34- Doenças pré- existentes (Doença ácido-péptica)',\n", + " 'AUTO_IMUNE': '34- Doenças pré- existentes (Doenças auto-imunes)',\n", + " 'DT_CHIK_S1': '35-Exame sorológico (IgM) Chikungunya Soro 1 Data da Coleta',\n", + " 'DT_CHIK_S2': '36-Exame sorológico (IgM) Chikungunya Soro 2 Data da Coleta',\n", + " 'DT_PRNT': '37-Exame PRNT Data da Coleta',\n", + " 'RES_CHIKS1': '38-Exame sorológico (IgM) Chikungunya Soro 1 Resultado',\n", + " 'RES_CHIKS2': '38-Exame sorológico (IgM) Chikungunya Soro 2 Resultado',\n", + " 'RESUL_PRNT': '38-Exame PRNT Resultado',\n", + " 'DT_SORO': '39-Exame sorológico (IgM) Dengue Data da Coleta',\n", + " 'RESUL_SORO': '40-Exame sorológico (IgM) Dengue Resultado',\n", + " 'DT_NS1': '41- Exame NS1 Data da Coleta',\n", + " 'RESUL_NS1': '42- Exame NS1 Resultado',\n", + " 'DT_VIRAL': '43-Isolamento Viral Data da Coleta',\n", + " 'RESUL_VI_N': '43-Isolamento Viral Resultado',\n", + " 'DT_PCR': '45-RT-PCR Data da Coleta',\n", + " 'RESUL_PCR_': '46-RT-PCR Resultado',\n", + " 'SOROTIPO': '47-Sorotipo',\n", + " 'HISTOPA_N': '48-Histopatologia Resultado',\n", + " 'IMUNOH_N': '49-Imunohistoquímica Resultado',\n", + " 'HOSPITALIZ': '50-Ocorreu Hospitalização?',\n", + " 'DT_INTERNA': '51-Data Da Internação',\n", + " 'UF': '52-UF De Hospitalização',\n", + " 'MUNICIPIO': '53-Municipio Do Hospital',\n", + " 'HOSPITAL': '54-Nome Do Hospital',\n", + " 'DDD_HOSP': '55-(DDD)',\n", + " 'TEL_HOSP': '55-Telefone',\n", + " 'TPAUTOCTO': '56-O caso é Autóctone de residência?',\n", + " 'COUFINF': '57-UF (provável da fonte de infecção)',\n", + " 'COPAISINF': '58-País (provável da fonte de infecção)',\n", + " 'COMUNINF': '59-Município (provável da fonte de infecção)',\n", + " 'CODISINF': '60-Distrito (provável de infecção)',\n", + " 'CO_BAINF\\nNOBAIINF': '61-Bairro (provável de infecção)',\n", + " 'CLASSI_FIN': '62-Classificação',\n", + " 'CRITERIO': '63-Critério De Confirmação/Descarte',\n", + " 'CLINIC_CHIK': '64- Apresentação clínica Chikungunya',\n", + " 'EVOLUCAO': '65-Evolução Do Caso',\n", + " 'DT_OBITO': '66-Data do Óbito',\n", + " 'DT_ENCERRA': '67-Data Do Encerramento',\n", + " 'ALRM_HIPOT': '68- Dengue com sinais de alarme (Hipotensão)',\n", + " 'ALRM_PLAQ': '68- Dengue com sinais de alarme (Queda abrupta de plaquetas)',\n", + " 'ALRM_VOM': '68- Dengue com sinais de alarme (Vômitos persistentes)',\n", + " 'ALRM_ABDOM': '68- Dengue com sinais de alarme (Dor abdominal)',\n", + " 'ALRM_LETAR': '68- Dengue com sinais de alarme (Letargia ou irritabilidade)',\n", + " 'ALRM_SANG': '68- Dengue com sinais de alarme (Sangramento de mucosa/outras hemorragias)',\n", + " 'ALRM_HEMAT': '68- Dengue com sinais de alarme (Aumento hematócrito)',\n", + " 'ALRM_HEPAT': '68- Dengue com sinais de alarme (Hepatomegalia)',\n", + " 'ALRM_LIQ': '68- Dengue com sinais de alarme (Acúmulo de líquidos)',\n", + " 'DT_ALRM': '69- Dengue com sinais de alarme Data de início dos sintomas',\n", + " 'GRAV_PULSO': '70- Dengue grave (Pulso débil ou indectável)',\n", + " 'GRAV_CONV': '70- Dengue grave (PA convergente)',\n", + " 'GRAV_ENCH': '70- Dengue grave (Tempo de enchimento capilar)',\n", + " 'GRAV_INSUF': '70- Dengue grave (Acúmulo de líquidos com insuficiência respiratória)',\n", + " 'GRAV_TAQUI': '70- Dengue grave (Taquicardia)',\n", + " 'GRAV_EXTRE': '70- Dengue grave (Extremidade frias)',\n", + " 'GRAV_HIPOT': '70- Dengue grave (Hipotensão arterial em fase tardia)',\n", + " 'GRAV_HEMAT': '70- Dengue grave (Hematêmese)',\n", + " 'GRAV_MELEN': '70- Dengue grave (Melena)',\n", + " 'GRAV_METRO': '70- Dengue grave (Metrorragia volumosa)',\n", + " 'GRAV_SANG': '70- Dengue grave (Sangramento do SNC)',\n", + " 'GRAV_AST': '70- Dengue grave (AST/ALT >1 .000)',\n", + " 'GRAV_MIOC': '70- Dengue grave (Miocardite)',\n", + " 'GRAV_CONSC': '70- Dengue grave (Alteração da consciência)',\n", + " 'GRAV_ORGAO': '70- Dengue grave (Outros órgãos)',\n", + " 'DT_GRAV': '71- Dengue grave Data de início dos sintomas',\n", + " 'DS_OBS': 'Informações complementares e observações adicionais',\n", + " nan: nan}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict(zip(metadata['DBF'], metadata['Nome do campo']))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Downloading data from the SINAN database" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-05T17:20:45.781320Z", + "start_time": "2022-09-05T17:20:45.256619Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from pysus.ftp.databases.sinan import SINAN\n", + "import pandas as pd\n", + "\n", + "sinan = SINAN().load()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "SINAN is a database of reported cases of certain diseases that Brazilian law requires to be reported. Unfortunately the data available for free download, corresponds only to the investigated cases not the totality of the reported cases. Nevertheless it's an interesting dataset.\n", + "\n", + "To find out what are these diseases, we can use PySUS:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-05T17:20:46.623422Z", + "start_time": "2022-09-05T17:20:46.605579Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ACBI': 'Acidente de trabalho com material biológico',\n", + " 'ACGR': 'Acidente de trabalho',\n", + " 'ANIM': 'Acidente por Animais Peçonhentos',\n", + " 'ANTR': 'Atendimento Antirrabico',\n", + " 'BOTU': 'Botulismo',\n", + " 'CANC': 'Cancêr relacionado ao trabalho',\n", + " 'CHAG': 'Doença de Chagas Aguda',\n", + " 'CHIK': 'Febre de Chikungunya',\n", + " 'COLE': 'Cólera',\n", + " 'COQU': 'Coqueluche',\n", + " 'DENG': 'Dengue',\n", + " 'DERM': 'Dermatoses ocupacionais',\n", + " 'DIFT': 'Difteria',\n", + " 'ESQU': 'Esquistossomose',\n", + " 'EXAN': 'Doença exantemáticas',\n", + " 'FMAC': 'Febre Maculosa',\n", + " 'FTIF': 'Febre Tifóide',\n", + " 'HANS': 'Hanseníase',\n", + " 'HANT': 'Hantavirose',\n", + " 'HEPA': 'Hepatites Virais',\n", + " 'IEXO': 'Intoxicação Exógena',\n", + " 'INFL': 'Influenza Pandêmica',\n", + " 'LEIV': 'Leishmaniose Visceral',\n", + " 'LEPT': 'Leptospirose',\n", + " 'LERD': 'LER/Dort',\n", + " 'LTAN': 'Leishmaniose Tegumentar Americana',\n", + " 'MALA': 'Malária',\n", + " 'MENI': 'Meningite',\n", + " 'MENT': 'Transtornos mentais relacionados ao trabalho',\n", + " 'NTRA': 'Notificação de Tracoma',\n", + " 'PAIR': 'Perda auditiva por ruído relacionado ao trabalho',\n", + " 'PEST': 'Peste',\n", + " 'PFAN': 'Paralisia Flácida Aguda',\n", + " 'PNEU': 'Pneumoconioses realacionadas ao trabalho',\n", + " 'RAIV': 'Raiva',\n", + " 'SDTA': 'Surto Doenças Transmitidas por Alimentos',\n", + " 'SIFA': 'Sífilis Adquirida',\n", + " 'SIFC': 'Sífilis Congênita',\n", + " 'SIFG': 'Sífilis em Gestante',\n", + " 'SRC': 'Síndrome da Rubéola Congênia',\n", + " 'TETA': 'Tétano Acidental',\n", + " 'TETN': 'Tétano Neonatal',\n", + " 'TOXC': 'Toxoplasmose Congênita',\n", + " 'TOXG': 'Toxoplasmose Gestacional',\n", + " 'TRAC': 'Inquérito de Tracoma',\n", + " 'TUBE': 'Tuberculose',\n", + " 'VARC': 'Varicela',\n", + " 'VIOL': 'Violência doméstica, sexual e/ou outras violências',\n", + " 'ZIKA': 'Zika Vírus'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.diseases" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2020-11-12T15:39:24.871171Z", + "start_time": "2020-11-12T15:39:24.860483Z" + }, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "These diseases are available in countrywide tables, so if we want to see the cases of `Chagas` disease in the state of Minas Gerais, first we can check which years are available:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-05T17:20:50.094901Z", + "start_time": "2022-09-05T17:20:49.365812Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[CHAGBR00.dbc,\n", + " CHAGBR01.dbc,\n", + " CHAGBR02.dbc,\n", + " CHAGBR03.dbc,\n", + " CHAGBR04.dbc,\n", + " CHAGBR05.dbc,\n", + " CHAGBR06.dbc,\n", + " CHAGBR07.dbc,\n", + " CHAGBR08.dbc,\n", + " CHAGBR09.dbc,\n", + " CHAGBR10.dbc,\n", + " CHAGBR11.dbc,\n", + " CHAGBR12.dbc,\n", + " CHAGBR13.dbc,\n", + " CHAGBR14.dbc,\n", + " CHAGBR15.dbc,\n", + " CHAGBR16.dbc,\n", + " CHAGBR17.dbc,\n", + " CHAGBR18.dbc,\n", + " CHAGBR19.dbc,\n", + " CHAGBR20.dbc,\n", + " CHAGBR21.dbc,\n", + " CHAGBR22.dbc]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.get_files('CHAG')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We can see, that we have data from 2000 to present. Now we can download it:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-05T17:21:11.840622Z", + "start_time": "2022-09-05T17:21:10.951857Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████| 286k/286k [00:00<00:00, 266MB/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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TP_NOTID_AGRAVODT_NOTIFICSEM_NOTNU_ANOSG_UF_NOTID_MUNICIPID_REGIONAID_UNIDADEDT_SIN_PRI...DT_ENCERRADT_DIGITADT_TRANSUSDT_TRANSDMDT_TRANSSMDT_TRANSRMDT_TRANSRSDT_TRANSSENU_LOTE_VNU_LOTE_H
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" + ], + "text/plain": [ + " TP_NOT ID_AGRAVO DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP \\\n", + "0 2 B571 2019-04-10 201915 2019 16 160030 \n", + "1 2 B571 2019-09-16 201938 2019 16 160030 \n", + "2 2 B571 2019-03-07 201910 2019 16 160030 \n", + "3 2 B571 2019-10-22 201943 2019 16 160030 \n", + "4 2 B571 2019-09-10 201937 2019 16 160060 \n", + "... ... ... ... ... ... ... ... \n", + "4477 2 B571 2019-09-05 201936 2019 26 260120 \n", + "4478 2 B571 2019-09-26 201939 2019 26 261390 \n", + "4479 2 B571 2019-01-17 201903 2019 26 260120 \n", + "4480 2 B571 2019-07-03 201927 2019 26 260820 \n", + "4481 2 B571 2019-11-08 201945 2019 26 261220 \n", + "\n", + " ID_REGIONA ID_UNIDADE DT_SIN_PRI ... DT_ENCERRA DT_DIGITA DT_TRANSUS \\\n", + "0 2019639 2019-03-01 ... 20190513 \n", + "1 2022192 2019-08-18 ... 20191002 \n", + "2 2022192 2019-02-28 ... 20190325 \n", + "3 2020653 2019-09-09 ... 20191107 \n", + "4 2020971 2019-08-28 ... 20191108 \n", + "... ... ... ... ... ... ... ... \n", + "4477 1501 5740592 2019-09-04 ... 20191022 \n", + "4478 1506 2348489 2019-08-26 ... 20191008 \n", + "4479 1501 2711443 2019-01-05 ... 20190212 \n", + "4480 1498 5276403 2019-07-03 ... 20190903 \n", + "4481 1502 5844916 2019-08-04 ... 20200124 \n", + "\n", + " DT_TRANSDM DT_TRANSSM DT_TRANSRM DT_TRANSRS DT_TRANSSE NU_LOTE_V \\\n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "... ... ... ... ... ... ... \n", + "4477 \n", + "4478 \n", + "4479 \n", + "4480 \n", + "4481 \n", + "\n", + " NU_LOTE_H \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "... ... \n", + "4477 \n", + "4478 \n", + "4479 \n", + "4480 \n", + "4481 \n", + "\n", + "[4482 rows x 108 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = sinan.download(sinan.get_files('CHAG', 2019)).to_dataframe()\n", + "df" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Downloading large files\n", + "Some SINAN files can be quite large and can take a bit longer to download and convert. As the default behavior is to download data in chunks, some folders may contain lots of parquet chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████| 73.8M/73.8M [00:00<00:00, 71.6GB/s]\n" + ] + } + ], + "source": [ + "fn = sinan.download(sinan.get_files('DENG', 2020))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The cases of dengue were downloaded to multiple chunks to the directory above" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "len(os.listdir(str(fn)))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1495117" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = fn.to_dataframe()\n", + "len(df2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Decoding the age in SINAN tables\n", + "In SINAN the age comes encoded. PySUS can decode the age column `NU_IDADE_N` into any of these units: years, months, days, or hours." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-05T17:21:42.746132Z", + "start_time": "2022-09-05T17:21:42.660877Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mdecodifica_idade_SINAN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mType:\u001b[0m vectorize\n", + "\u001b[0;31mString form:\u001b[0m \n", + "\u001b[0;31mFile:\u001b[0m ~/micromamba/envs/pysus/lib/python3.11/site-packages/numpy/__init__.py\n", + "\u001b[0;31mDocstring:\u001b[0m \n", + "Em tabelas do SINAN frequentemente a idade é representada como um inteiro que precisa ser parseado\n", + "para retornar a idade em uma unidade cronológica padrão.\n", + ":param unidade: unidade da idade: 'Y': anos, 'M' meses, 'D': dias, 'H': horas\n", + ":param idade: inteiro ou sequencia de inteiros codificados.\n", + ":return:\n", + "\u001b[0;31mClass docstring:\u001b[0m\n", + "vectorize(pyfunc=np._NoValue, otypes=None, doc=None, excluded=None,\n", + "cache=False, signature=None)\n", + "\n", + "Returns an object that acts like pyfunc, but takes arrays as input.\n", + "\n", + "Define a vectorized function which takes a nested sequence of objects or\n", + "numpy arrays as inputs and returns a single numpy array or a tuple of numpy\n", + "arrays. The vectorized function evaluates `pyfunc` over successive tuples\n", + "of the input arrays like the python map function, except it uses the\n", + "broadcasting rules of numpy.\n", + "\n", + "The data type of the output of `vectorized` is determined by calling\n", + "the function with the first element of the input. This can be avoided\n", + "by specifying the `otypes` argument.\n", + "\n", + "Parameters\n", + "----------\n", + "pyfunc : callable, optional\n", + " A python function or method.\n", + " Can be omitted to produce a decorator with keyword arguments.\n", + "otypes : str or list of dtypes, optional\n", + " The output data type. It must be specified as either a string of\n", + " typecode characters or a list of data type specifiers. There should\n", + " be one data type specifier for each output.\n", + "doc : str, optional\n", + " The docstring for the function. If None, the docstring will be the\n", + " ``pyfunc.__doc__``.\n", + "excluded : set, optional\n", + " Set of strings or integers representing the positional or keyword\n", + " arguments for which the function will not be vectorized. These will be\n", + " passed directly to `pyfunc` unmodified.\n", + "\n", + " .. versionadded:: 1.7.0\n", + "\n", + "cache : bool, optional\n", + " If `True`, then cache the first function call that determines the number\n", + " of outputs if `otypes` is not provided.\n", + "\n", + " .. versionadded:: 1.7.0\n", + "\n", + "signature : string, optional\n", + " Generalized universal function signature, e.g., ``(m,n),(n)->(m)`` for\n", + " vectorized matrix-vector multiplication. If provided, ``pyfunc`` will\n", + " be called with (and expected to return) arrays with shapes given by the\n", + " size of corresponding core dimensions. By default, ``pyfunc`` is\n", + " assumed to take scalars as input and output.\n", + "\n", + " .. versionadded:: 1.12.0\n", + "\n", + "Returns\n", + "-------\n", + "out : callable\n", + " A vectorized function if ``pyfunc`` was provided,\n", + " a decorator otherwise.\n", + "\n", + "See Also\n", + "--------\n", + "frompyfunc : Takes an arbitrary Python function and returns a ufunc\n", + "\n", + "Notes\n", + "-----\n", + "The `vectorize` function is provided primarily for convenience, not for\n", + "performance. The implementation is essentially a for loop.\n", + "\n", + "If `otypes` is not specified, then a call to the function with the\n", + "first argument will be used to determine the number of outputs. The\n", + "results of this call will be cached if `cache` is `True` to prevent\n", + "calling the function twice. However, to implement the cache, the\n", + "original function must be wrapped which will slow down subsequent\n", + "calls, so only do this if your function is expensive.\n", + "\n", + "The new keyword argument interface and `excluded` argument support\n", + "further degrades performance.\n", + "\n", + "References\n", + "----------\n", + ".. [1] :doc:`/reference/c-api/generalized-ufuncs`\n", + "\n", + "Examples\n", + "--------\n", + ">>> def myfunc(a, b):\n", + "... \"Return a-b if a>b, otherwise return a+b\"\n", + "... if a > b:\n", + "... return a - b\n", + "... else:\n", + "... return a + b\n", + "\n", + ">>> vfunc = np.vectorize(myfunc)\n", + ">>> vfunc([1, 2, 3, 4], 2)\n", + "array([3, 4, 1, 2])\n", + "\n", + "The docstring is taken from the input function to `vectorize` unless it\n", + "is specified:\n", + "\n", + ">>> vfunc.__doc__\n", + "'Return a-b if a>b, otherwise return a+b'\n", + ">>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`')\n", + ">>> vfunc.__doc__\n", + "'Vectorized `myfunc`'\n", + "\n", + "The output type is determined by evaluating the first element of the input,\n", + "unless it is specified:\n", + "\n", + ">>> out = vfunc([1, 2, 3, 4], 2)\n", + ">>> type(out[0])\n", + "\n", + ">>> vfunc = np.vectorize(myfunc, otypes=[float])\n", + ">>> out = vfunc([1, 2, 3, 4], 2)\n", + ">>> type(out[0])\n", + "\n", + "\n", + "The `excluded` argument can be used to prevent vectorizing over certain\n", + "arguments. This can be useful for array-like arguments of a fixed length\n", + "such as the coefficients for a polynomial as in `polyval`:\n", + "\n", + ">>> def mypolyval(p, x):\n", + "... _p = list(p)\n", + "... res = _p.pop(0)\n", + "... while _p:\n", + "... res = res*x + _p.pop(0)\n", + "... return res\n", + ">>> vpolyval = np.vectorize(mypolyval, excluded=['p'])\n", + ">>> vpolyval(p=[1, 2, 3], x=[0, 1])\n", + "array([3, 6])\n", + "\n", + "Positional arguments may also be excluded by specifying their position:\n", + "\n", + ">>> vpolyval.excluded.add(0)\n", + ">>> vpolyval([1, 2, 3], x=[0, 1])\n", + "array([3, 6])\n", + "\n", + "The `signature` argument allows for vectorizing functions that act on\n", + "non-scalar arrays of fixed length. For example, you can use it for a\n", + "vectorized calculation of Pearson correlation coefficient and its p-value:\n", + "\n", + ">>> import scipy.stats\n", + ">>> pearsonr = np.vectorize(scipy.stats.pearsonr,\n", + "... signature='(n),(n)->(),()')\n", + ">>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])\n", + "(array([ 1., -1.]), array([ 0., 0.]))\n", + "\n", + "Or for a vectorized convolution:\n", + "\n", + ">>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)')\n", + ">>> convolve(np.eye(4), [1, 2, 1])\n", + "array([[1., 2., 1., 0., 0., 0.],\n", + " [0., 1., 2., 1., 0., 0.],\n", + " [0., 0., 1., 2., 1., 0.],\n", + " [0., 0., 0., 1., 2., 1.]])\n", + "\n", + "Decorator syntax is supported. The decorator can be called as\n", + "a function to provide keyword arguments.\n", + ">>>@np.vectorize\n", + "...def identity(x):\n", + "... return x\n", + "...\n", + ">>>identity([0, 1, 2])\n", + "array([0, 1, 2])\n", + ">>>@np.vectorize(otypes=[float])\n", + "...def as_float(x):\n", + "... return x\n", + "...\n", + ">>>as_float([0, 1, 2])\n", + "array([0., 1., 2.])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pysus.preprocessing.decoders import decodifica_idade_SINAN\n", + "decodifica_idade_SINAN?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We can easily convert dates and numerical fields in the dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-05T17:21:57.041703Z", + "start_time": "2022-09-05T17:21:56.805000Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 4482 entries, 0 to 4481\n", + "Columns: 108 entries, TP_NOT to NU_LOTE_H\n", + "dtypes: Float64(2), Int64(4), datetime64[ns](16), string(86)\n", + "memory usage: 3.7 MB\n" + ] + } + ], + "source": [ + "for cname in df.columns:\n", + " if cname.startswith('DT_'):\n", + " df[cname] = pd.to_datetime(df[cname], errors='coerce')\n", + " elif cname.startswith('ID_'):\n", + " try:\n", + " df[cname] = pd.to_numeric(df[cname])\n", + " except ValueError:\n", + " continue\n", + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Let's convert the age to years and save it on a different column." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-05T17:21:59.409288Z", + "start_time": "2022-09-05T17:21:59.384411Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NU_IDADE_Nidade_anos
0401313.0
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2403131.0
3406363.0
4403636.0
.........
4477407979.0
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" + ], + "text/plain": [ + " NU_IDADE_N idade_anos\n", + "0 4013 13.0\n", + "1 4054 54.0\n", + "2 4031 31.0\n", + "3 4063 63.0\n", + "4 4036 36.0\n", + "... ... ...\n", + "4477 4079 79.0\n", + "4478 4063 63.0\n", + "4479 4039 39.0\n", + "4480 4036 36.0\n", + "4481 4052 52.0\n", + "\n", + "[4482 rows x 2 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['idade_anos'] = decodifica_idade_SINAN(df.NU_IDADE_N, 'Y')\n", + "df[['NU_IDADE_N', 'idade_anos']]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Saving the Modified data\n", + "We can save our dataframe in any format we wish to avoid having to redo this analysis next time. If we want to keep only the data from the state of Minas Gerais we need to filter the table using the UF code `31`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-05T17:22:02.591827Z", + "start_time": "2022-09-05T17:22:02.512493Z" + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "df['SG_UF_NOT'] = df.SG_UF_NOT.astype(int)\n", + "df[df.SG_UF_NOT==31].to_csv('chagas_SP_2018_mod.csv',sep=';',compression='zip')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combining SINAN and IBGE databases\n", + "\n", + "We could also use different sources to enrich the analysis. In the example below, population data will be used to calculate how many dengue cases per 100k habitants there are in each geocode:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from pysus.online_data import IBGE" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MUNIC_RESANOPOPULACAO
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" + ], + "text/plain": [ + " MUNIC_RES ANO POPULACAO\n", + "0 110001 2010 24422\n", + "1 110002 2010 90354\n", + "2 110003 2010 6309\n", + "3 110004 2010 78601\n", + "4 110005 2010 17030\n", + "... ... ... ...\n", + "5560 522200 2010 12549\n", + "5561 522205 2010 7371\n", + "5562 522220 2010 4742\n", + "5563 522230 2010 5145\n", + "5564 530010 2010 2562963\n", + "\n", + "[5565 rows x 3 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop = IBGE.get_population(source=\"POP\", year=2010)\n", + "pop" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████| 30.5M/30.5M [00:00<00:00, 31.1GB/s]\n" + ] + } + ], + "source": [ + "df = sinan.download(sinan.get_files('DENG', 2010)).to_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TP_NOTID_AGRAVODT_NOTIFICSEM_NOTNU_ANOSG_UF_NOTID_MUNICIPID_REGIONAID_UNIDADEDT_SIN_PRI...PLASMATICOEVIDENCIAPLAQ_MENORCON_FHDCOMPLICAHOSPITALIZDT_INTERNAUFMUNICIPIOTP_SISTEMA
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32A902010-03-23201012201051510760157927016262010-03-18...1
42A902010-03-12201010201051510760157927016262010-03-07...1
..................................................................
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ID_MUNICIPTOTAL_CASES
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" + ], + "text/plain": [ + " ID_MUNICIP TOTAL_CASES\n", + "1914 310620 71056\n", + "4158 520870 48397\n", + "3867 500270 42761\n", + "61 120040 38333\n", + "3147 354340 36107\n", + "... ... ...\n", + "3639 420810 1\n", + "3972 510395 1\n", + "2421 316020 1\n", + "1533 290515 1\n", + "3789 431480 1\n", + "\n", + "[4307 rows x 2 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Grouping all the cases by geocode\n", + "tot_cases = df[['ID_MUNICIP']].groupby('ID_MUNICIP').size().reset_index(name='TOTAL_CASES').sort_values(by='TOTAL_CASES', ascending=False)\n", + "tot_cases" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ID_MUNICIPTOTAL_CASESPOPULACAO
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" + ], + "text/plain": [ + " ID_MUNICIP TOTAL_CASES POPULACAO\n", + "0 310620 71056 2375444\n", + "1 520870 48397 1301892\n", + "2 500270 42761 787204\n", + "3 120040 38333 335796\n", + "4 354340 36107 605114\n", + "... ... ... ...\n", + "4302 420810 1 20315\n", + "4303 510395 1 3125\n", + "4304 316020 1 4135\n", + "4305 290515 1 13666\n", + "4306 431480 1 30881\n", + "\n", + "[4307 rows x 3 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.merge(tot_cases, pop, left_on='ID_MUNICIP', right_on='MUNIC_RES')\n", + "df = df[['ID_MUNICIP', 'TOTAL_CASES', 'POPULACAO']]\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ID_MUNICIPTOTAL_CASESPOPULACAOCASES_PER_100K
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" + ], + "text/plain": [ + " ID_MUNICIP TOTAL_CASES POPULACAO CASES_PER_100K\n", + "340 411380 630 4592 13719.512195\n", + "708 510719 255 2199 11596.180082\n", + "3 120040 38333 335796 11415.561829\n", + "54 311330 3617 32321 11190.866619\n", + "27 270630 7272 70434 10324.559162\n", + "... ... ... ... ...\n", + "3884 411915 2 117166 1.706980\n", + "4167 431240 1 59436 1.682482\n", + "4179 432250 1 61345 1.630125\n", + "3963 411950 1 93279 1.072053\n", + "4180 432240 1 125507 0.796768\n", + "\n", + "[4307 rows x 4 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['CASES_PER_100K'] = (df['TOTAL_CASES'].astype(int) / df['POPULACAO'].astype(int)) * 100000\n", + "df = df.sort_values(by='CASES_PER_100K', ascending=False)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pysus.utilities.brasil import get_city_name_by_geocode\n", + "from pysus.preprocessing.decoders import calculate_digit\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = df.sort_values(by='CASES_PER_100K', ascending=True).head(20)\n", + "# Get city name\n", + "df['MUNICIP_NAME'] = df['ID_MUNICIP'].apply(lambda x: get_city_name_by_geocode(int(str(x) + str(calculate_digit(x)))))\n", + "\n", + "plt.figure(figsize=(10, 12))\n", + "bars = plt.barh(df['MUNICIP_NAME'], df['CASES_PER_100K'], color='skyblue')\n", + "plt.ylabel('City')\n", + "plt.xlabel('Cases per 100k inhabitants')\n", + "plt.title('Cases per 100k inhabitants by city')\n", + "\n", + "# Adding labels with CASES_PER_100K values\n", + "for bar in bars:\n", + " plt.text(bar.get_width(), bar.get_y() + bar.get_height()/2, f'{int(bar.get_width())}', \n", + " va='center', ha='left', color='black')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "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": "3.11.8" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "position": { + "height": "249.85px", + "left": "1104.8px", + "right": "20px", + "top": "120px", + "width": "318.4px" + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + }, + "vscode": { + "interpreter": { + "hash": "2a96a5ccec8dfcba7d06b2e71f6eef3b5dac5716461bf5d73ea1bb7ee462cdaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pysus/Notebooks/SIA.ipynb b/pysus/Notebooks/SIA.ipynb new file mode 100644 index 00000000..dac1ae2f --- /dev/null +++ b/pysus/Notebooks/SIA.ipynb @@ -0,0 +1,694 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a73920ee-3902-4270-a17d-b7907d8561d7", + "metadata": {}, + "source": [ + "# SIA FTP Database\n", + "##### Sistema de Informações Ambulatoriais" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8d619b0d-300c-4bc2-a738-bf96d650311d", + "metadata": {}, + "outputs": [], + "source": [ + "from pysus.ftp.databases.sia import SIA\n", + "sia = SIA().load() # Loads the files from DATASUS" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4d50b674-b5bd-4ec5-a812-15c680841879", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SIA - Sistema de Informações Ambulatoriais" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sia" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5a74172b-841e-4ba7-bcc6-41ec9a216423", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'long_name': 'Sistema de Informações Ambulatoriais',\n", + " 'source': 'http://sia.datasus.gov.br/principal/index.php',\n", + " 'description': 'O Sistema de Informação Ambulatorial (SIA) foi instituído pela Portaria GM/MS n.º 896 de 29 de junho de 1990. Originalmente, o SIA foi concebido a partir do projeto SICAPS (Sistema de Informação e Controle Ambulatorial da Previdência Social), em que os conceitos, os objetivos e as diretrizes criados para o desenvolvimento do SICAPS foram extremamente importantes e amplamente utilizados para o desenvolvimento do SIA, tais como: (i) o acompanhamento das programações físicas e orçamentárias; (ii) o acompanhamento das ações de saúde produzidas; (iii) a agilização do pagamento e controle orçamentário e financeiro; e (iv) a formação de banco de dados para contribuir com a construção do SUS.'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sia.metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "10e69ba8-baa1-4718-b53e-40af23084324", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'AB': 'APAC de Cirurgia Bariátrica',\n", + " 'ABO': 'APAC de Acompanhamento Pós Cirurgia Bariátrica',\n", + " 'ACF': 'APAC de Confecção de Fístula',\n", + " 'AD': 'APAC de Laudos Diversos',\n", + " 'AM': 'APAC de Medicamentos',\n", + " 'AMP': 'APAC de Acompanhamento Multiprofissional',\n", + " 'AN': 'APAC de Nefrologia',\n", + " 'AQ': 'APAC de Quimioterapia',\n", + " 'AR': 'APAC de Radioterapia',\n", + " 'ATD': 'APAC de Tratamento Dialítico',\n", + " 'BI': 'Boletim de Produção Ambulatorial individualizado',\n", + " 'IMPBO': '',\n", + " 'PA': 'Produção Ambulatorial',\n", + " 'PAM': '',\n", + " 'PAR': '',\n", + " 'PAS': '',\n", + " 'PS': 'RAAS Psicossocial',\n", + " 'SAD': 'RAAS de Atenção Domiciliar'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sia.groups" + ] + }, + { + "cell_type": "markdown", + "id": "ff0b298c-69cf-4884-b7c8-2936de2c3508", + "metadata": {}, + "source": [ + "### Getting specific files" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3910f87e-965b-4a5e-8fb2-9c9ad257d0f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14300 files\n" + ] + } + ], + "source": [ + "print(str(len(sia.get_files([\"PA\", \"BI\"]))) + \" files\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c3cd4449-55fd-418d-a6e0-53ff38cd9258", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[PASP0001.dbc,\n", + " PASP0002.dbc,\n", + " PASP0004.dbc,\n", + " PASP0005.dbc,\n", + " Pasp0006.dbc,\n", + " pasp0003.dbc,\n", + " pasp0007.dbc,\n", + " pasp0008.dbc,\n", + " pasp0009.dbc,\n", + " pasp0010.dbc,\n", + " pasp0011.dbc,\n", + " pasp0012.dbc]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sia.get_files(\"PA\", uf=\"SP\", year=2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3883f518-c21e-4334-b18e-7fd9127aa83f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[PASP0001.dbc, PASP0002.dbc]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files = sia.get_files(\"PA\", uf=\"SP\", year=2000, month=[1,2])\n", + "files" + ] + }, + { + "cell_type": "markdown", + "id": "c065668a-0157-40cb-8662-0daf9db2937b", + "metadata": {}, + "source": [ + "### Describing files inside DATASUS server" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "017d4445-f172-4376-ba85-19c06f9d1de4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'PASP0001.dbc',\n", + " 'group': 'Produção Ambulatorial',\n", + " 'uf': 'São Paulo',\n", + " 'month': 'Janeiro',\n", + " 'year': 2000,\n", + " 'size': '7.2 MB',\n", + " 'last_update': '2013-10-24 04:18PM'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sia.describe(files[0])" + ] + }, + { + "cell_type": "markdown", + "id": "59039f48-9c9b-4807-81f6-24d2139a70b8", + "metadata": {}, + "source": [ + "### Downloading files" + ] + }, + { + "cell_type": "markdown", + "id": "dc671449-d4d3-4e32-9afd-68f0d844a104", + "metadata": {}, + "source": [ + "You can rather download multiple files or download them individually:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8f53a375-266b-4290-8705-408d236fd6d1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PASP0002.parquet: 100%|████████████| 447k/447k [00:20<00:00, 21.6kB/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[/home/bida/pysus/PASP0001.parquet, /home/bida/pysus/PASP0002.parquet]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sia.download(files) # or specify a directory with `local_dir=` " + ] + }, + { + "cell_type": "markdown", + "id": "70e10d61-d2a7-49ed-a409-38175321df04", + "metadata": {}, + "source": [ + "@Note: If the file has been downloaded already, it's required to delete it in order to download the lastest updated file from DATASUS. " + ] + }, + { + "cell_type": "markdown", + "id": "886389c2-5c26-43c2-9820-0c3fa9d85021", + "metadata": {}, + "source": [ + "### Reading files\n", + "\n", + "PySUS uses Parquets as output, use the method `to_dataframe()` to read the file as pandas DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4d2ddb3e-0d04-4b3c-952f-12ddd658751b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████| 7.73M/7.73M [00:00<00:00, 4.84GB/s]\n" + ] + } + ], + "source": [ + "parquet = sia.download(files)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0ef3fb2b-af49-4744-9692-410c4c4820b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " PA_CONDIC PA_GESTAO PA_CODUNI PA_DATREF PA_CODPRO PA_DOCORIG PA_CODESP \\\n", + "0 EC 359999 000867 200001 0701223 B 27 \n", + "1 EC 359999 000867 200001 0701223 B 27 \n", + "2 EC 359999 000867 200001 0701223 B 27 \n", + "3 EC 359999 000867 200001 0701223 B 27 \n", + "4 EC 359999 000867 200001 0701223 B 27 \n", + "... ... ... ... ... ... ... ... \n", + "725999 EA 359999 016127 200001 0102302 B 77 \n", + "726000 EA 359999 016127 200001 0302206 B 30 \n", + "726001 EA 359999 016127 200001 0302207 B 30 \n", + "726002 EA 359999 016127 200001 0304103 B 30 \n", + "726003 EA 359999 016127 200001 0401103 B 01 \n", + "\n", + " PA_TIPPRO PA_TIPATE PA_FXETAR ... PA_DATPR PA_VALPRO PA_VALAPR \\\n", + "0 02 99 ... 200001 58.65 58.65 \n", + "1 02 99 ... 200001 15.30 15.30 \n", + "2 02 99 ... 200001 12.75 12.75 \n", + "3 02 99 ... 200001 12.75 12.75 \n", + "4 02 99 ... 200001 5.10 5.10 \n", + "... ... ... ... ... ... ... ... \n", + "725999 00 00 ... 200001 3972.00 3972.00 \n", + "726000 00 00 ... 200001 2.48 2.48 \n", + "726001 00 00 ... 200001 6.39 6.39 \n", + "726002 00 00 ... 200001 7.92 7.92 \n", + "726003 00 27 ... 200001 2.00 2.00 \n", + "\n", + " PA_UFMUN PA_MUNAT PA_NUMAPA PA_CODOCO PA_CIDPRI PA_CIDSEC PA_MORFOL \n", + "0 355030 355030 00000000000 S01 \n", + "1 355030 355030 00000000000 S01 \n", + "2 355030 355030 00000000000 S01 \n", + "3 355030 355030 00000000000 S01 \n", + "4 355030 355030 00000000000 S01 \n", + "... ... ... ... ... ... ... ... \n", + "725999 355030 355030 00000000000 S01 \n", + "726000 355030 355030 00000000000 S01 \n", + "726001 355030 355030 00000000000 S01 \n", + "726002 355030 355030 00000000000 S01 \n", + "726003 355030 355030 00000000000 S01 \n", + "\n", + "[726004 rows x 24 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parquet.to_dataframe()" + ] + } + ], + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pysus/Notebooks/SIH.ipynb b/pysus/Notebooks/SIH.ipynb new file mode 100644 index 00000000..44dd0178 --- /dev/null +++ b/pysus/Notebooks/SIH.ipynb @@ -0,0 +1,685 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a73920ee-3902-4270-a17d-b7907d8561d7", + "metadata": {}, + "source": [ + "# SIH FTP Database\n", + "##### Sistema de Informações Hospitalares" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8d619b0d-300c-4bc2-a738-bf96d650311d", + "metadata": {}, + "outputs": [], + "source": [ + "from pysus.ftp.databases.sih import SIH\n", + "sih = SIH().load() # Loads the files from DATASUS" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4d50b674-b5bd-4ec5-a812-15c680841879", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SIH - Sistema de Informações Hospitalares" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sih" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5a74172b-841e-4ba7-bcc6-41ec9a216423", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'long_name': 'Sistema de Informações Hospitalares',\n", + " 'source': ('https://datasus.saude.gov.br/acesso-a-informacao/morbidade-hospitalar-do-sus-sih-sus/',\n", + " 'https://datasus.saude.gov.br/acesso-a-informacao/producao-hospitalar-sih-sus/'),\n", + " 'description': 'A finalidade do AIH (Sistema SIHSUS) é a de transcrever todos os atendimentos que provenientes de internações hospitalares que foram financiadas pelo SUS, e após o processamento, gerarem relatórios para os gestores que lhes possibilitem fazer os pagamentos dos estabelecimentos de saúde. Além disso, o nível Federal recebe mensalmente uma base de dados de todas as internações autorizadas (aprovadas ou não para pagamento) para que possam ser repassados às Secretarias de Saúde os valores de Produção de Média e Alta complexidade além dos valores de CNRAC, FAEC e de Hospitais Universitários – em suas variadas formas de contrato de gestão.'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sih.metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "10e69ba8-baa1-4718-b53e-40af23084324", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'RD': 'AIH Reduzida',\n", + " 'RJ': 'AIH Rejeitada',\n", + " 'ER': 'AIH Rejeitada com erro',\n", + " 'SP': 'Serviços Profissionais',\n", + " 'CH': 'Cadastro Hospitalar',\n", + " 'CM': ''}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sih.groups" + ] + }, + { + "cell_type": "markdown", + "id": "ff0b298c-69cf-4884-b7c8-2936de2c3508", + "metadata": {}, + "source": [ + "### Getting specific files" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3910f87e-965b-4a5e-8fb2-9c9ad257d0f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15649 files\n" + ] + } + ], + "source": [ + "print(str(len(sih.get_files([\"RD\", \"RJ\"]))) + \" files\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c3cd4449-55fd-418d-a6e0-53ff38cd9258", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[RDSP0001.dbc,\n", + " RDSP0002.dbc,\n", + " RDSP0003.dbc,\n", + " RDSP0004.dbc,\n", + " RDSP0005.dbc,\n", + " RDSP0006.dbc,\n", + " RDSP0007.dbc,\n", + " RDSP0008.dbc,\n", + " RDSP0009.dbc,\n", + " RDSP0010.dbc,\n", + " RDSP0011.dbc,\n", + " RDSP0012.dbc]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sih.get_files(\"RD\", uf=\"SP\", year=2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3883f518-c21e-4334-b18e-7fd9127aa83f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[RDSP0001.dbc, RDSP0002.dbc, RDSP0003.dbc]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files = sih.get_files(\"RD\", uf=\"SP\", year=2000, month=[1,2,3])\n", + "files" + ] + }, + { + "cell_type": "markdown", + "id": "c065668a-0157-40cb-8662-0daf9db2937b", + "metadata": {}, + "source": [ + "### Describing files inside DATASUS server" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "017d4445-f172-4376-ba85-19c06f9d1de4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'RDSP0001.dbc',\n", + " 'group': 'AIH Reduzida',\n", + " 'uf': 'São Paulo',\n", + " 'month': 'Janeiro',\n", + " 'year': 2000,\n", + " 'size': '10.1 MB',\n", + " 'last_update': '2013-10-31 01:14PM'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sih.describe(files[0])" + ] + }, + { + "cell_type": "markdown", + "id": "59039f48-9c9b-4807-81f6-24d2139a70b8", + "metadata": {}, + "source": [ + "### Downloading files" + ] + }, + { + "cell_type": "markdown", + "id": "dc671449-d4d3-4e32-9afd-68f0d844a104", + "metadata": {}, + "source": [ + "You can rather download multiple files or download them individually:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8f53a375-266b-4290-8705-408d236fd6d1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDSP0003.parquet: 100%|████████████| 340k/340k [00:12<00:00, 28.0kB/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[/home/bida/pysus/RDSP0001.parquet,\n", + " /home/bida/pysus/RDSP0002.parquet,\n", + " /home/bida/pysus/RDSP0003.parquet]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sih.download(files) # Specify a directory with `local_dir=`" + ] + }, + { + "cell_type": "markdown", + "id": "70e10d61-d2a7-49ed-a409-38175321df04", + "metadata": {}, + "source": [ + "@Note: If the file has been downloaded already, it's required to delete it in order to download the lastest updated file from DATASUS. " + ] + }, + { + "cell_type": "markdown", + "id": "886389c2-5c26-43c2-9820-0c3fa9d85021", + "metadata": {}, + "source": [ + "### Reading files\n", + "\n", + "PySUS uses Parquets as output, use the method `to_dataframe()` to read the file as pandas DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5882ddc1-9dfb-4181-baed-2adb09bf66e8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████| 8.46M/8.46M [00:00<00:00, 4.04GB/s]\n" + ] + } + ], + "source": [ + "parquet = sih.download(files)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "70db53ae-7105-48d8-9a42-868385dc3982", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UF_ZIANO_CMPTMES_CMPTESPECCGC_HOSPN_AIHIDENTCEPMUNIC_RESNASC...CAR_INTTOT_PT_SPCPF_AUTHOMONIMONUM_FILHOSINSTRUCID_NOTIFCONTRACEP1CONTRACEP2GESTRISCO
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3352000010246523171000287217938086310601002035344019710106...051760000000
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..................................................................
193819352000010246523171000287217938078610603609035344019740804...057370000000
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\n", + "

193824 rows × 60 columns

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" + ], + "text/plain": [ + " UF_ZI ANO_CMPT MES_CMPT ESPEC CGC_HOSP N_AIH IDENT \\\n", + "0 35 2000 01 02 46523171000287 2179380830 1 \n", + "1 35 2000 01 02 46523171000287 2179380841 1 \n", + "2 35 2000 01 02 46523171000287 2179380852 1 \n", + "3 35 2000 01 02 46523171000287 2179380863 1 \n", + "4 35 2000 01 02 46523171000287 2179380874 1 \n", + "... ... ... ... ... ... ... ... \n", + "193819 35 2000 01 02 46523171000287 2179380786 1 \n", + "193820 35 2000 01 02 46523171000287 2179380797 1 \n", + "193821 35 2000 01 02 46523171000287 2179380808 1 \n", + "193822 35 2000 01 02 46523171000287 2179380819 1 \n", + "193823 35 2000 01 02 46523171000287 2179380820 1 \n", + "\n", + " CEP MUNIC_RES NASC ... CAR_INT TOT_PT_SP CPF_AUT HOMONIMO \\\n", + "0 06040090 353440 19631229 ... 05 719 \n", + "1 06110000 353440 19620609 ... 05 176 \n", + "2 06184080 353440 19781207 ... 05 36 \n", + "3 06010020 353440 19710106 ... 05 176 \n", + "4 06112010 353440 19710717 ... 05 158 \n", + "... ... ... ... ... ... ... ... ... \n", + "193819 06036090 353440 19740804 ... 05 737 \n", + "193820 06240120 353440 19670403 ... 05 737 \n", + "193821 06140080 353440 19741001 ... 05 737 \n", + "193822 06240090 353440 19721028 ... 05 719 \n", + "193823 06260030 353440 19721122 ... 05 737 \n", + "\n", + " NUM_FILHOS INSTRU CID_NOTIF CONTRACEP1 CONTRACEP2 GESTRISCO \n", + "0 0 0 00 00 0 \n", + "1 0 0 00 00 0 \n", + "2 0 0 00 00 0 \n", + "3 0 0 00 00 0 \n", + "4 0 0 00 00 0 \n", + "... ... ... ... ... ... ... \n", + "193819 0 0 00 00 0 \n", + "193820 0 0 00 00 0 \n", + "193821 0 0 00 00 0 \n", + "193822 0 0 00 00 0 \n", + "193823 0 0 00 00 0 \n", + "\n", + "[193824 rows x 60 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parquet.to_dataframe()" + ] + } + ], + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pysus/Notebooks/SIM.ipynb b/pysus/Notebooks/SIM.ipynb new file mode 100644 index 00000000..007ae26c --- /dev/null +++ b/pysus/Notebooks/SIM.ipynb @@ -0,0 +1,705 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# SIM FTP Database\n", + "##### Sistema de Informação sobre Mortalidade\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from pysus.ftp.databases.sim import SIM\n", + "sim = SIM().load() # Loads the files from DATASUS" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'long_name': 'Sistema de Informação sobre Mortalidade',\n", + " 'source': 'http://sim.saude.gov.br',\n", + " 'description': ''}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CID10': 'DO', 'CID9': 'DOR'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.groups" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[/dissemin/publicos/SIM/CID10/DORES, /dissemin/publicos/SIM/CID9/DORES]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.paths" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more information about CID9 and CID10, visit http://tabnet.saude.es.gov.br/cgi/tabnet/sim/sim96/obtdescr.htm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting specific files " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[DORSP95.DBC]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.get_files(\"CID9\", uf=\"SP\", year=1995)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[DORJ2019.dbc,\n", + " DORJ2020.dbc,\n", + " DORJ2021.dbc,\n", + " DOSP2019.dbc,\n", + " DOSP2020.dbc,\n", + " DOSP2021.dbc]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.get_files(\"CID10\", uf=[\"SP\", \"RJ\"], year=[2019, 2020, 2021])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "files = sim.get_files([\"CID9\", \"CID10\"], uf=[\"SP\"], year=[1995, 2020])\n", + "sp_cid9, sp_cid10 = files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Describing a file inside DATASUS server" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'DORSP95.DBC',\n", + " 'uf': 'São Paulo',\n", + " 'year': 1995,\n", + " 'group': 'CID9',\n", + " 'size': '8.2 MB',\n", + " 'last_update': '2020-01-31 02:48PM'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.describe(sp_cid9)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'DOSP2020.dbc',\n", + " 'uf': 'São Paulo',\n", + " 'year': 2020,\n", + " 'group': 'CID10',\n", + " 'size': '28.7 MB',\n", + " 'last_update': '2022-03-31 04:19PM'}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.describe(sp_cid10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Downloading files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can rather download multiple files or download them individually:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DORSP95.parquet: 100%|█████████████| 434k/434k [00:12<00:00, 36.0kB/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[/home/bida/pysus/DORSP95.parquet]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.download(sp_cid9) # Downloads to default directory" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "/home/bida/pysus/DORSP95.parquet" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parquet = sp_cid9.download() # Or in a custom directory with `local_dir=`\n", + "parquet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "@Note: If the file has been downloaded already, it's required to delete it in order to download the lastest updated file from DATASUS. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading files\n", + "\n", + "PySUS uses Parquets as output, use the method `to_dataframe()` to read the file as pandas DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " contador CARTORIO REGISTRO DATAREG TIPOBITO DATAOBITO ESTCIVIL \\\n", + "0 180001 951006 2 951002 2 \n", + "1 180002 951006 2 951002 3 \n", + "2 180003 951006 2 951003 2 \n", + "3 180004 951006 2 951003 3 \n", + "4 180005 951006 2 951004 1 \n", + "... ... ... ... ... ... ... ... \n", + "227832 179996 951004 2 951001 4 \n", + "227833 179997 951004 2 951001 2 \n", + "227834 179998 951004 2 951001 3 \n", + "227835 179999 951006 2 951001 1 \n", + "227836 180000 951006 2 951001 1 \n", + "\n", + " SEXO DATANASC IDADE ... FONTINFO ACIDTRAB LOCACID CRITICA \\\n", + "0 1 19291003 465 ... 0 0 \n", + "1 2 18980317 497 ... 0 0 \n", + "2 2 19281002 467 ... 0 0 \n", + "3 1 19110613 484 ... 0 0 \n", + "4 1 19610914 434 ... 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "227832 1 19380423 457 ... 0 0 \n", + "227833 1 19470130 448 ... 0 0 \n", + "227834 2 19160113 479 ... 0 0 \n", + "227835 1 19550901 440 ... 0 0 \n", + "227836 1 19700510 425 ... 0 0 \n", + "\n", + " NUMEXPORT CRSOCOR CRSRES RACACOR ETNIA UFINFORM \n", + "0 35 \n", + "1 35 \n", + "2 35 \n", + "3 35 \n", + "4 35 \n", + "... ... ... ... ... ... ... \n", + "227832 35 \n", + "227833 35 \n", + "227834 35 \n", + "227835 35 \n", + "227836 35 \n", + "\n", + "[227837 rows x 50 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parquet.to_dataframe()" + ] + } + ], + "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": "3.11.5" + }, + "vscode": { + "interpreter": { + "hash": "2a96a5ccec8dfcba7d06b2e71f6eef3b5dac5716461bf5d73ea1bb7ee462cdaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pysus/Notebooks/SINAN.ipynb b/pysus/Notebooks/SINAN.ipynb new file mode 100644 index 00000000..d94abe23 --- /dev/null +++ b/pysus/Notebooks/SINAN.ipynb @@ -0,0 +1,1222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# SINAN FTP Database \n", + "##### Available diseases and years to download\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from pysus.ftp.databases.sinan import SINAN\n", + "sinan = SINAN().load() # Loads the files from DATASUS" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'long_name': 'Doenças e Agravos de Notificação',\n", + " 'source': 'https://portalsinan.saude.gov.br/',\n", + " 'description': 'The Notifiable Diseases Information System - Sinan is primarilyfed by the notification and investigation of cases of diseases and conditions listed in the national list of compulsorily notifiable diseases (Consolidation Ordinance No. 4, September 28, 2017, Annex).However, states and municipalities are allowed to include other important health problems in their region, such as difilobotriasis in the municipality of São Paulo. Its effective use enables the dynamic diagnosis of the occurrence of an event in the population, providing evidence for causal explanations of compulsorily notifiable diseases and indicating risks to which people are exposed. This contributes to identifying the epidemiological reality of a specific geographical area. Its systematic, decentralized use contributes to the democratization of information, allowing all healthcare professionals to access and make it available to the community. Therefore, it is a relevant tool to assist in health planning, define intervention priorities, and evaluate the impact of interventions.'}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Listing SINAN Codes & Diseases" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ACBI': 'Acidente de trabalho com material biológico',\n", + " 'ACGR': 'Acidente de trabalho',\n", + " 'ANIM': 'Acidente por Animais Peçonhentos',\n", + " 'ANTR': 'Atendimento Antirrabico',\n", + " 'BOTU': 'Botulismo',\n", + " 'CANC': 'Cancêr relacionado ao trabalho',\n", + " 'CHAG': 'Doença de Chagas Aguda',\n", + " 'CHIK': 'Febre de Chikungunya',\n", + " 'COLE': 'Cólera',\n", + " 'COQU': 'Coqueluche',\n", + " 'DENG': 'Dengue',\n", + " 'DERM': 'Dermatoses ocupacionais',\n", + " 'DIFT': 'Difteria',\n", + " 'ESQU': 'Esquistossomose',\n", + " 'EXAN': 'Doença exantemáticas',\n", + " 'FMAC': 'Febre Maculosa',\n", + " 'FTIF': 'Febre Tifóide',\n", + " 'HANS': 'Hanseníase',\n", + " 'HANT': 'Hantavirose',\n", + " 'HEPA': 'Hepatites Virais',\n", + " 'IEXO': 'Intoxicação Exógena',\n", + " 'INFL': 'Influenza Pandêmica',\n", + " 'LEIV': 'Leishmaniose Visceral',\n", + " 'LEPT': 'Leptospirose',\n", + " 'LERD': 'LER/Dort',\n", + " 'LTAN': 'Leishmaniose Tegumentar Americana',\n", + " 'MALA': 'Malária',\n", + " 'MENI': 'Meningite',\n", + " 'MENT': 'Transtornos mentais relacionados ao trabalho',\n", + " 'NTRA': 'Notificação de Tracoma',\n", + " 'PAIR': 'Perda auditiva por ruído relacionado ao trabalho',\n", + " 'PEST': 'Peste',\n", + " 'PFAN': 'Paralisia Flácida Aguda',\n", + " 'PNEU': 'Pneumoconioses realacionadas ao trabalho',\n", + " 'RAIV': 'Raiva',\n", + " 'SDTA': 'Surto Doenças Transmitidas por Alimentos',\n", + " 'SIFA': 'Sífilis Adquirida',\n", + " 'SIFC': 'Sífilis Congênita',\n", + " 'SIFG': 'Sífilis em Gestante',\n", + " 'SRC': 'Síndrome da Rubéola Congênia',\n", + " 'TETA': 'Tétano Acidental',\n", + " 'TETN': 'Tétano Neonatal',\n", + " 'TOXC': 'Toxoplasmose Congênita',\n", + " 'TOXG': 'Toxoplasmose Gestacional',\n", + " 'TRAC': 'Inquérito de Tracoma',\n", + " 'TUBE': 'Tuberculose',\n", + " 'VARC': 'Varicela',\n", + " 'VIOL': 'Violência doméstica, sexual e/ou outras violências',\n", + " 'ZIKA': 'Zika Vírus'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.diseases" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting specific files " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[CHIKBR15.dbc,\n", + " CHIKBR16.dbc,\n", + " CHIKBR17.dbc,\n", + " CHIKBR18.dbc,\n", + " CHIKBR19.dbc,\n", + " CHIKBR20.dbc,\n", + " CHIKBR21.dbc,\n", + " CHIKBR22.dbc,\n", + " CHIKBR23.dbc,\n", + " ZIKABR16.dbc,\n", + " ZIKABR17.dbc,\n", + " ZIKABR18.dbc,\n", + " ZIKABR19.dbc,\n", + " ZIKABR20.dbc,\n", + " ZIKABR21.dbc,\n", + " ZIKABR22.dbc,\n", + " ZIKABR23.dbc]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.get_files(dis_code=[\"ZIKA\", \"CHIK\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[BOTUBR23.dbc,\n", + " CHIKBR23.dbc,\n", + " DENGBR23.dbc,\n", + " ESQUBR23.dbc,\n", + " FTIFBR23.dbc,\n", + " HANSBR23.dbc,\n", + " MENIBR23.dbc,\n", + " TOXCBR23.dbc,\n", + " TOXGBR23.dbc,\n", + " TUBEBR23.dbc,\n", + " VARCBR23.dbc,\n", + " ZIKABR23.dbc]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.get_files(year=2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[DENGBR22.dbc, DENGBR23.dbc, ZIKABR22.dbc, ZIKABR23.dbc]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.get_files(dis_code=[\"DENG\", \"ZIKA\"], year=[2022, 2023])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "files = sinan.get_files(dis_code=\"BOTU\", year=[2007, 2008])\n", + "botu_2007, botu_2008 = files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Describing a file inside DATASUS server" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'BOTUBR07.dbc',\n", + " 'disease': 'Botulismo',\n", + " 'year': 2007,\n", + " 'size': '7.5 kB',\n", + " 'last_update': '2021-11-23 11:55AM'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.describe(botu_2007)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'BOTUBR08.dbc',\n", + " 'disease': 'Botulismo',\n", + " 'year': 2008,\n", + " 'size': '8.3 kB',\n", + " 'last_update': '2021-11-23 11:56AM'}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.describe(botu_2008)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Downloading files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can rather download multiple files or download them individually:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████| 8.35k/8.35k [00:00<00:00, 6.12MB/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[/home/bida/pysus/BOTUBR07.parquet, /home/bida/pysus/BOTUBR08.parquet]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinan.download(files) # Downloads to default directory" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "/home/bida/pysus/BOTUBR07.parquet" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "botu_2007.download() # or specify a custom directory with `local_dir=`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "@Note: If the file has been downloaded already, it's required to delete it in order to download the lastest updated file from DATASUS. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading files\n", + "\n", + "PySUS uses Parquets as output, use the method `to_dataframe()` to read the file as pandas DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "parquet = botu_2007.download()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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12A0512007-01-12200702200735355030133120774852007-01-10...TORTA DE PALMITO/ FRANGO .20070323
22A0512007-01-03200701200752521180179123815322006-11-16...20070515
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142A0512007-08-28200735200735354850134920797202007-08-17...SALGADO TORTA PAO COM QUEIJO CCASA DE MASSAS ROMANA EM SANTO20070903
152A0512007-08-24200734200735354850134920797202007-08-17...2007081920071025
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182A0512007-08-23200734200723230440151924821692007-08-19...MORTADELADOMICILIO20071023
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252A0512007-11-01200744200752520870177925184062007-10-02...11120071112
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26 rows × 140 columns

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" + ], + "text/plain": [ + " TP_NOT ID_AGRAVO DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP \\\n", + "0 2 A051 2007-01-12 200702 2007 41 410690 \n", + "1 2 A051 2007-01-12 200702 2007 35 355030 \n", + "2 2 A051 2007-01-03 200701 2007 52 521180 \n", + "3 2 A051 2007-01-08 200702 2007 43 431410 \n", + "4 2 A051 2007-02-27 200709 2007 50 500830 \n", + "5 2 A051 2007-02-16 200707 2007 35 354340 \n", + "6 2 A051 2007-02-15 200707 2007 33 330170 \n", + "7 2 A051 2007-02-28 200709 2007 22 220800 \n", + "8 2 A051 2007-05-12 200719 2007 27 270710 \n", + "9 2 A051 2007-06-11 200724 2007 35 354850 \n", + "10 2 A051 2007-06-22 200725 2007 35 352340 \n", + "11 2 A051 2007-06-27 200726 2007 29 292740 \n", + "12 2 A051 2007-07-30 200731 2007 53 530010 \n", + "13 2 A051 2007-07-10 200728 2007 52 520870 \n", + "14 2 A051 2007-08-28 200735 2007 35 354850 \n", + "15 2 A051 2007-08-24 200734 2007 35 354850 \n", + "16 2 A051 2007-08-17 200733 2007 35 355030 \n", + "17 2 A051 2007-08-20 200734 2007 51 510340 \n", + "18 2 A051 2007-08-23 200734 2007 23 230440 \n", + "19 2 A051 2007-09-16 200738 2007 31 317010 \n", + "20 2 A051 2007-09-06 200736 2007 35 354340 \n", + "21 2 A051 2007-09-04 200736 2007 35 355220 \n", + "22 2 A051 2007-10-08 200741 2007 35 355220 \n", + "23 2 A051 2007-10-02 200740 2007 31 314330 \n", + "24 2 A051 2007-11-06 200745 2007 35 355220 \n", + "25 2 A051 2007-11-01 200744 2007 52 520870 \n", + "\n", + " ID_REGIONA ID_UNIDADE DT_SIN_PRI ... NU_PROTEI DT_LIQUOR TP_SENSITI \\\n", + "0 1356 2384299 2006-12-31 ... 53 20070112 2 \n", + "1 1331 2077485 2007-01-10 ... \n", + "2 1791 2381532 2006-11-16 ... \n", + "3 1611 2246988 2007-01-03 ... 31 20070107 \n", + "4 1973 2757206 2007-02-27 ... \n", + "5 1348 2082187 2006-12-12 ... 1 \n", + "6 2290227 2007-02-12 ... 27 20070214 \n", + "7 1888 4009622 2007-02-28 ... \n", + "8 1539 2721643 2007-05-09 ... \n", + "9 1349 2025752 2007-06-11 ... \n", + "10 1342 2023709 2007-06-12 ... 40 20070616 \n", + "11 1380 0004057 2007-06-25 ... 39 20070625 2 \n", + "12 0010456 2007-07-26 ... 83 20070728 1 \n", + "13 1779 2338262 2007-05-08 ... \n", + "14 1349 2079720 2007-08-17 ... \n", + "15 1349 2079720 2007-08-17 ... 20070819 \n", + "16 1331 2077574 2007-08-12 ... 40 20070813 \n", + "17 1578 2495015 2007-08-18 ... \n", + "18 1519 2482169 2007-08-19 ... \n", + "19 1461 2206595 2007-09-08 ... 20070915 \n", + "20 1348 2082187 2007-08-31 ... \n", + "21 1353 2081695 2007-08-31 ... \n", + "22 1353 2081695 2007-10-08 ... \n", + "23 1473 2149990 2007-09-28 ... 19 20070930 \n", + "24 1353 2078732 2007-11-03 ... 20071105 \n", + "25 1779 2518406 2007-10-02 ... 1 \n", + "\n", + " TP_MOTORA TP_REPETE DS_ALI1 DS_ALI2 \\\n", + "0 2 3 \n", + "1 TORTA DE PALMITO/ FRANGO . \n", + "2 \n", + "3 SALAME \n", + "4 \n", + "5 2 2 \n", + "6 \n", + "7 \n", + "8 \n", + "9 \n", + "10 \n", + "11 2 \n", + "12 2 2 \n", + "13 \n", + "14 SALGADO TORTA PAO COM QUEIJO C \n", + "15 \n", + "16 \n", + "17 \n", + "18 MORTADELA \n", + "19 \n", + "20 \n", + "21 \n", + "22 \n", + "23 \n", + "24 \n", + "25 1 1 \n", + "\n", + " DS_LOCAL1 DS_LOCAL2 DT_ENCERRA \n", + "0 20070222 \n", + "1 20070323 \n", + "2 20070515 \n", + "3 DOMICILIO \n", + "4 20070816 \n", + "5 \n", + "6 20070724 \n", + "7 20070503 \n", + "8 20070716 \n", + "9 20071120 \n", + "10 20070704 \n", + "11 20070730 \n", + "12 20070730 \n", + "13 20070814 \n", + "14 CASA DE MASSAS ROMANA EM SANTO 20070903 \n", + "15 20071025 \n", + "16 \n", + "17 20070918 \n", + "18 DOMICILIO 20071023 \n", + "19 20071017 \n", + "20 \n", + "21 \n", + "22 \n", + "23 20080601 \n", + "24 20080108 \n", + "25 20071112 \n", + "\n", + "[26 rows x 140 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parquet.to_dataframe()" + ] + } + ], + "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": "3.11.8" + }, + "vscode": { + "interpreter": { + "hash": "2a96a5ccec8dfcba7d06b2e71f6eef3b5dac5716461bf5d73ea1bb7ee462cdaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pysus/Notebooks/SINASC.ipynb b/pysus/Notebooks/SINASC.ipynb new file mode 100644 index 00000000..fb78cc02 --- /dev/null +++ b/pysus/Notebooks/SINASC.ipynb @@ -0,0 +1,693 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# SINASC FTP Database\n", + "##### Sistema de Informações sobre Nascidos Vivos\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from pysus.ftp.databases.sinasc import SINASC\n", + "sinasc = SINASC().load() # Loads the files from DATASUS" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SINASC - Sistema de Informações sobre Nascidos Vivos" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinasc" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'long_name': 'Sistema de Informações sobre Nascidos Vivos',\n", + " 'source': 'http://sinasc.saude.gov.br/',\n", + " 'description': ''}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinasc.metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Listing codes & groups" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'DN': 'Declarações de Nascidos Vivos',\n", + " 'DNR': 'Dados dos Nascidos Vivos por UF de residência'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinasc.groups" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting specific files " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[DNSP1996.DBC,\n", + " DNSP1997.DBC,\n", + " DNSP1998.DBC,\n", + " DNSP1999.DBC,\n", + " DNSP2000.DBC,\n", + " DNSP2001.DBC,\n", + " DNSP2002.DBC,\n", + " DNSP2003.DBC,\n", + " DNSP2004.DBC,\n", + " DNSP2005.dbc,\n", + " DNSP2006.DBC,\n", + " DNSP2007.dbc,\n", + " DNSP2008.dbc,\n", + " DNSP2009.dbc,\n", + " DNSP2010.DBC,\n", + " DNSP2011.DBC,\n", + " DNSP2012.DBC,\n", + " DNSP2013.dbc,\n", + " DNSP2014.dbc,\n", + " DNSP2015.dbc,\n", + " DNSP2016.dbc,\n", + " DNSP2017.dbc,\n", + " DNSP2018.dbc,\n", + " DNSP2019.dbc,\n", + " DNSP2020.dbc,\n", + " DNSP2021.dbc]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinasc.get_files(\"DN\", uf=\"SP\") # or multiple [\"SP\", ...]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[DNSP1999.DBC, DNSP2000.DBC]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files = sinasc.get_files(\"DN\", uf=\"SP\", year=[1999, 2000])\n", + "files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Describing files inside DATASUS server" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'group': 'Declarações de Nascidos Vivos',\n", + " 'last_update': '2020-01-27 12:12PM',\n", + " 'name': 'DNSP1999.DBC',\n", + " 'size': '14.3 MB',\n", + " 'uf': 'São Paulo',\n", + " 'year': 1999}\n", + "{'group': 'Declarações de Nascidos Vivos',\n", + " 'last_update': '2020-01-27 12:12PM',\n", + " 'name': 'DNSP2000.DBC',\n", + " 'size': '14.3 MB',\n", + " 'uf': 'São Paulo',\n", + " 'year': 2000}\n" + ] + } + ], + "source": [ + "from pprint import pprint\n", + "for file in files:\n", + " pprint(sinasc.describe(file))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Downloading files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can rather download multiple files or download them individually:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DNSP2000.parquet: 100%|████████████| 523k/523k [00:15<00:00, 34.3kB/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[/home/bida/pysus/DNSP1999.parquet, /home/bida/pysus/DNSP2000.parquet]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sinasc.download(files) # Downloads to default directory or specify with `local_dir=`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "@Note: If the file has been downloaded already, it's required to delete it in order to download the lastest updated file from DATASUS. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading files\n", + "\n", + "PySUS uses Parquets as output, use the method `to_dataframe()` to read the file as pandas DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████| 14.3M/14.3M [00:00<00:00, 6.32GB/s]\n" + ] + } + ], + "source": [ + "parquet = sinasc.download(files)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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714428 rows × 21 columns

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" + ], + "text/plain": [ + " contador LOCNASC CODMUNNASC IDADEMAE ESTCIVMAE ESCMAE CODOCUPMAE \\\n", + "0 90001 1 3550308 21 9 2 \n", + "1 90002 1 3550308 23 9 3 \n", + "2 90003 1 3550308 19 9 2 \n", + "3 90004 1 3550308 16 9 2 \n", + "4 90005 1 3550308 33 9 2 \n", + "... ... ... ... ... ... ... ... \n", + "714423 299996 1 3522604 18 9 3 \n", + "714424 299997 1 3522604 37 2 2 \n", + "714425 299998 1 3522604 21 9 4 \n", + "714426 299999 1 3522604 34 2 4 \n", + "714427 300000 1 3522604 37 9 2 \n", + "\n", + " QTDFILVIVO QTDFILMORT CODMUNRES ... GRAVIDEZ PARTO CONSULTAS \\\n", + "0 03 00 3550308 ... 1 1 8 \n", + "1 01 00 3550308 ... 1 1 8 \n", + "2 01 00 3550308 ... 1 1 8 \n", + "3 00 00 3550308 ... 1 1 8 \n", + "4 00 00 3550308 ... 1 2 8 \n", + "... ... ... ... ... ... ... ... \n", + "714423 01 00 3522604 ... 1 1 4 \n", + "714424 01 00 3522604 ... 1 2 4 \n", + "714425 00 00 3522604 ... 1 1 4 \n", + "714426 00 00 3522604 ... 1 2 3 \n", + "714427 05 00 3522604 ... 1 1 8 \n", + "\n", + " DTNASC SEXO APGAR1 APGAR5 RACACOR PESO CODANOMAL \n", + "0 20021999 2 09 10 3300 \n", + "1 19031999 2 09 10 2300 \n", + "2 09031999 1 08 09 4 3600 \n", + "3 15031999 2 09 10 1 2600 \n", + "4 17031999 2 09 09 2700 \n", + "... ... ... ... ... ... ... ... \n", + "714423 20051999 1 08 10 1 3400 \n", + "714424 29051999 1 09 10 1 3800 \n", + "714425 04061999 2 09 10 1 3500 \n", + "714426 04061999 2 09 10 1 3800 \n", + "714427 29051999 2 10 10 1 3100 \n", + "\n", + "[714428 rows x 21 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parquet.to_dataframe()" + ] + } + ], + "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": "3.11.5" + }, + "vscode": { + "interpreter": { + "hash": "2a96a5ccec8dfcba7d06b2e71f6eef3b5dac5716461bf5d73ea1bb7ee462cdaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pysus/Notebooks/Utilities.ipynb b/pysus/Notebooks/Utilities.ipynb new file mode 100644 index 00000000..95bf8951 --- /dev/null +++ b/pysus/Notebooks/Utilities.ipynb @@ -0,0 +1,156 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c153a255-ad53-4b27-b689-4c119ea8cc52", + "metadata": {}, + "source": [ + "## Utilities module\n", + "\n", + "Some helper functions that are used throughout the package: " + ] + }, + { + "cell_type": "markdown", + "id": "c5c639e6-fa54-482a-a91d-20a8bbe05206", + "metadata": {}, + "source": [ + "### brasil" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "451830fc-04af-4003-8e70-c71d61a57ac5", + "metadata": {}, + "outputs": [], + "source": [ + "from pysus.utilities import brasil" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "76a37da8-7b41-4565-83e2-e23bfbeae5bb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'BR': 'Brasil',\n", + " 'AC': 'Acre',\n", + " 'AL': 'Alagoas',\n", + " 'AP': 'Amapá',\n", + " 'AM': 'Amazonas',\n", + " 'BA': 'Bahia',\n", + " 'CE': 'Ceará',\n", + " 'ES': 'Espírito Santo',\n", + " 'GO': 'Goiás',\n", + " 'MA': 'Maranhão',\n", + " 'MT': 'Mato Grosso',\n", + " 'MS': 'Mato Grosso do Sul',\n", + " 'MG': 'Minas Gerais',\n", + " 'PA': 'Pará',\n", + " 'PB': 'Paraíba',\n", + " 'PR': 'Paraná',\n", + " 'PE': 'Pernambuco',\n", + " 'PI': 'Piauí',\n", + " 'RJ': 'Rio de Janeiro',\n", + " 'RN': 'Rio Grande do Norte',\n", + " 'RS': 'Rio Grande do Sul',\n", + " 'RO': 'Rondônia',\n", + " 'RR': 'Roraima',\n", + " 'SC': 'Santa Catarina',\n", + " 'SP': 'São Paulo',\n", + " 'SE': 'Sergipe',\n", + " 'TO': 'Tocantins',\n", + " 'DF': 'Distrito Federal'}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brasil.UFs" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "965a2323-066c-45af-83f7-b20ece735089", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 'Janeiro',\n", + " 2: 'Fevereiro',\n", + " 3: 'Março',\n", + " 4: 'Abril',\n", + " 5: 'Maio',\n", + " 6: 'Junho',\n", + " 7: 'Julho',\n", + " 8: 'Agosto',\n", + " 9: 'Setembro',\n", + " 10: 'Outubro',\n", + " 11: 'Novembro',\n", + " 12: 'Dezembro'}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brasil.MONTHS" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "573f2f20-f038-4384-b6f2-558bad80f276", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Rio de Janeiro'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get municipality name by IBGE's geocode \n", + "# https://www.ibge.gov.br/explica/codigos-dos-municipios.php\n", + "brasil.get_city_name_by_geocode(3304557)" + ] + } + ], + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pysus/Notebooks/Zika.ipynb b/pysus/Notebooks/Zika.ipynb new file mode 100644 index 00000000..bf35fdd2 --- /dev/null +++ b/pysus/Notebooks/Zika.ipynb @@ -0,0 +1,628590 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Análise exploratória de casos de Zika notificado pelo SUS\n", + "\n", + "Neste tutorial vamos explorar como começar a entender a descrição de casos do sistema de notificação de agravos do SUS, o SINAN. Estes dados são bastante ricos e a [documentação](www.sgc.goias.gov.br/upload/arquivos/2013-08/dengue2.pdf) sobre o significado de cada uma das variáveis pode ser encontrada [aqui](www.sgc.goias.gov.br/upload/arquivos/2013-08/dengue2.pdf)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-28T11:29:24.900905Z", + "start_time": "2020-01-28T11:29:24.888820Z" + } + }, + "outputs": [], + "source": [ + "from pysus.ftp.databases.sinan import SINAN\n", + "\n", + "sinan = SINAN().load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Primeiro vamos começar pelo carregamento dos dados a partir do site do InfoDengue. Como o dado está no formato parquet, nós vamos utilizar a biblioteca pandas para carregar os dados em um Dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-27T19:34:28.480536Z", + "start_time": "2020-01-27T19:34:18.668094Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 4.14M/4.14M [00:00<00:00, 3.47GB/s]\n" + ] + } + ], + "source": [ + "cols = [\n", + " 'ID_AGRAVO', 'DT_NOTIFIC', 'SEM_NOT', 'NU_ANO', 'ID_MUNICIP',\n", + " 'DT_SIN_PRI', 'SEM_PRI', 'NU_IDADE_N', 'CS_SEXO', 'CS_GESTANT',\n", + " 'latitude', 'longitude',\n", + " 'NM_DISEASE'\n", + "]\n", + "casos = sinan.download(sinan.get_files('ZIKA', 2016)).to_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vejamos os nomes da variáveis" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-27T19:34:28.839518Z", + "start_time": "2020-01-27T19:34:28.834865Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['TP_NOT', 'ID_AGRAVO', 'CS_SUSPEIT', 'DT_NOTIFIC', 'SEM_NOT', 'NU_ANO',\n", + " 'SG_UF_NOT', 'ID_MUNICIP', 'ID_REGIONA', 'DT_SIN_PRI', 'SEM_PRI',\n", + " 'NU_IDADE_N', 'CS_SEXO', 'CS_GESTANT', 'CS_RACA', 'CS_ESCOL_N', 'SG_UF',\n", + " 'ID_MN_RESI', 'ID_RG_RESI', 'ID_PAIS', 'NDUPLIC_N', 'IN_VINCULA',\n", + " 'DT_INVEST', 'ID_OCUPA_N', 'CLASSI_FIN', 'CRITERIO', 'TPAUTOCTO',\n", + " 'COUFINF', 'COPAISINF', 'COMUNINF', 'DOENCA_TRA', 'EVOLUCAO',\n", + " 'DT_OBITO', 'DT_ENCERRA', 'CS_FLXRET', 'FLXRECEBI', 'TP_SISTEMA',\n", + " 'TPUNINOT'],\n", + " dtype='object')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "casos.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-27T19:12:59.015819Z", + "start_time": "2020-01-27T19:12:58.757569Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TP_NOTID_AGRAVOCS_SUSPEITDT_NOTIFICSEM_NOTNU_ANOSG_UF_NOTID_MUNICIPID_REGIONADT_SIN_PRI...COPAISINFCOMUNINFDOENCA_TRAEVOLUCAODT_OBITODT_ENCERRACS_FLXRETFLXRECEBITP_SISTEMATPUNINOT
02A9282016-01-262016042016333304902016-01-19...0120160331021
12A9282016-01-262016042016333304902016-01-20...020160331021
22A9282016-01-262016042016333304902016-01-18...09120160326021
32A9282016-01-262016042016333304902016-01-19...09120160331021
42A9282016-01-262016042016333304902016-01-19...09120160331021
\n", + "

5 rows × 38 columns

\n", + "
" + ], + "text/plain": [ + " TP_NOT ID_AGRAVO CS_SUSPEIT DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP \\\n", + "0 2 A928 2016-01-26 201604 2016 33 330490 \n", + "1 2 A928 2016-01-26 201604 2016 33 330490 \n", + "2 2 A928 2016-01-26 201604 2016 33 330490 \n", + "3 2 A928 2016-01-26 201604 2016 33 330490 \n", + "4 2 A928 2016-01-26 201604 2016 33 330490 \n", + "\n", + " ID_REGIONA DT_SIN_PRI ... COPAISINF COMUNINF DOENCA_TRA EVOLUCAO DT_OBITO \\\n", + "0 2016-01-19 ... 0 1 \n", + "1 2016-01-20 ... 0 \n", + "2 2016-01-18 ... 0 9 1 \n", + "3 2016-01-19 ... 0 9 1 \n", + "4 2016-01-19 ... 0 9 1 \n", + "\n", + " DT_ENCERRA CS_FLXRET FLXRECEBI TP_SISTEMA TPUNINOT \n", + "0 20160331 0 2 1 \n", + "1 20160331 0 2 1 \n", + "2 20160326 0 2 1 \n", + "3 20160331 0 2 1 \n", + "4 20160331 0 2 1 \n", + "\n", + "[5 rows x 38 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "casos = casos[casos.ID_AGRAVO=='A928']\n", + "casos.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estes dados correspondem a todos os casos de Zika notificados ao SUS durante um período. Neste caso de 2015 a 2016. Para podermos tratar adequadamente estes dados para fins de visualização ou análise precisamos corrigir os tipos das colunas. Por exemplo vamos converter as datas." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-27T19:34:32.817449Z", + "start_time": "2020-01-27T19:34:32.778916Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "casos['DT_INDEX'] = pd.to_datetime(casos.DT_NOTIFIC)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para poder organizar os dados temporalmente, é útil indexar a tabela por alguma variável temporal. Vamos usar a data de notifiacão de cada caso como índice" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-27T19:34:36.298456Z", + "start_time": "2020-01-27T19:34:36.281839Z" + } + }, + "outputs": [], + "source": [ + "casos = casos.set_index('DT_INDEX')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora podemos plotar o número de casos por semana de dengue na cidade do Rio de Janeiro." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-27T19:34:41.003314Z", + "start_time": "2020-01-27T19:34:40.695986Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[02-base_territorio_fev24.zip,\n", + " 03-base_territorial_mar.zip,\n", + " 04-base_territorial_abr.zip,\n", + " 05-base_territorial_mai24.zip,\n", + " 06-base_territorial_jun24.zip,\n", + " 07-base_territorial_jul24.zip,\n", + " 08-base_territorial_ago24.zip,\n", + " 09-base_territorial_set24.zip,\n", + " 10-base_territorial_out24.zip,\n", + " base_territorial_2023.zip]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "territory.list_tables()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ac_mapas_2013.zip,\n", + " al_mapas_2013.zip,\n", + " am_mapas_1991.zip,\n", + " am_mapas_1994.zip,\n", + " am_mapas_1997.zip,\n", + " am_mapas_2001.zip,\n", + " am_mapas_2005.zip,\n", + " am_mapas_2013.zip,\n", + " ap_mapas_2013.zip,\n", + " ba_mapas_2013.zip,\n", + " br_mapas_1991.zip,\n", + " br_mapas_1994.zip,\n", + " br_mapas_1997.zip,\n", + " br_mapas_2001.zip,\n", + " br_mapas_2005.zip,\n", + " br_mapas_2013.zip,\n", + " ce_mapas_1991.zip,\n", + " ce_mapas_1994.zip,\n", + " ce_mapas_1997.zip,\n", + " ce_mapas_2001.zip,\n", + " ce_mapas_2005.zip,\n", + " ce_mapas_2013.zip,\n", + " df_mapas_2013.zip,\n", + " es_mapas_2013.zip,\n", + " go_mapas_1991.zip,\n", + " go_mapas_1994.zip,\n", + " go_mapas_1997.zip,\n", + " go_mapas_2001.zip,\n", + " go_mapas_2005.zip,\n", + " go_mapas_2013.zip,\n", + " ma_mapas_1991.zip,\n", + " ma_mapas_1994.zip,\n", + " ma_mapas_1997.zip,\n", + " ma_mapas_2001.zip,\n", + " ma_mapas_2005.zip,\n", + " ma_mapas_2013.zip,\n", + " mg_mapas_2013.zip,\n", + " mrco_mapas_1991.zip,\n", + " mrco_mapas_1994.zip,\n", + " mrco_mapas_1997.zip,\n", + " mrco_mapas_2001.zip,\n", + " mrco_mapas_2005.zip,\n", + " mrco_mapas_2013.zip,\n", + " mrne_mapas_1991.zip,\n", + " mrne_mapas_1994.zip,\n", + " mrne_mapas_1997.zip,\n", + " mrne_mapas_2001.zip,\n", + " mrne_mapas_2005.zip,\n", + " mrne_mapas_2013.zip,\n", + " mrno_mapas_1991.zip,\n", + " mrno_mapas_1994.zip,\n", + " mrno_mapas_1997.zip,\n", + " mrno_mapas_2001.zip,\n", + " mrno_mapas_2005.zip,\n", + " mrno_mapas_2013.zip,\n", + " mrse_mapas_1991.zip,\n", + " mrse_mapas_1994.zip,\n", + " mrse_mapas_1997.zip,\n", + " mrse_mapas_2001.zip,\n", + " mrse_mapas_2005.zip,\n", + " mrse_mapas_2013.zip,\n", + " mrsu_mapas_1991.zip,\n", + " mrsu_mapas_1994.zip,\n", + " mrsu_mapas_1997.zip,\n", + " mrsu_mapas_2001.zip,\n", + " mrsu_mapas_2005.zip,\n", + " mrsu_mapas_2013.zip,\n", + " ms_mapas_2013.zip,\n", + " mt_mapas_1991.zip,\n", + " mt_mapas_1994.zip,\n", + " mt_mapas_1997.zip,\n", + " mt_mapas_2001.zip,\n", + " mt_mapas_2005.zip,\n", + " mt_mapas_2013.zip,\n", + " pa_mapas_2013.zip,\n", + " pb_mapas_1991.zip,\n", + " pb_mapas_1994.zip,\n", + " pb_mapas_1997.zip,\n", + " pb_mapas_2001.zip,\n", + " pb_mapas_2005.zip,\n", + " pb_mapas_2013.zip,\n", + " pe_mapas_2013.zip,\n", + " pi_mapas_2013.zip,\n", + " pr_mapas_1991.zip,\n", + " pr_mapas_1994.zip,\n", + " pr_mapas_1997.zip,\n", + " pr_mapas_2001.zip,\n", + " pr_mapas_2005.zip,\n", + " pr_mapas_2013.zip,\n", + " rj_mapas_2013.zip,\n", + " rn_mapas_2013.zip,\n", + " ro_mapas_2013.zip,\n", + " rr_mapas_2013.zip,\n", + " rs_mapas_1991.zip,\n", + " rs_mapas_1994.zip,\n", + " rs_mapas_1997.zip,\n", + " rs_mapas_2001.zip,\n", + " rs_mapas_2005.zip,\n", + " rs_mapas_2013.zip,\n", + " sc_mapas_1991.zip,\n", + " sc_mapas_1994.zip,\n", + " sc_mapas_1997.zip,\n", + " sc_mapas_2001.zip,\n", + " sc_mapas_2005.zip,\n", + " sc_mapas_2013.zip,\n", + " se_mapas_2013.zip,\n", + " sp_mapas_1991.zip,\n", + " sp_mapas_1994.zip,\n", + " sp_mapas_1997.zip,\n", + " sp_mapas_2001.zip,\n", + " sp_mapas_2005.zip,\n", + " sp_mapas_2013.zip,\n", + " to_mapas_2013.zip,\n", + " todos_mapas_1991.zip,\n", + " todos_mapas_1994.zip,\n", + " todos_mapas_1997.zip,\n", + " todos_mapas_2001.zip,\n", + " todos_mapas_2005.zip,\n", + " todos_mapas_2013.zip]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "territory.list_maps()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/20 [00:00