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November 16, 2019 21:25
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| import re | |
| import os | |
| FILE_DIR = os.path.abspath(__file__).split(os.sep) | |
| import re | |
| from sqlalchemy import types | |
| from sqlalchemy import create_engine | |
| from sqlalchemy_utils import database_exists, create_database | |
| import pandas as pd | |
| import yaml | |
| def parse_type_sqlalchemy(value): | |
| regex = r"^(?P<type>\w+)\((?P<arg>\d{1,3})\)" | |
| test_str = ["char(1)"] | |
| matches = re.match(regex, value) | |
| type_sql = matches.group('type').lower() | |
| if type_sql in {'char', 'character'}: | |
| n = int(matches.group('arg')) | |
| return types.NCHAR(n) | |
| if type_sql in {'varchar', 'varchar2'}: | |
| n = int(matches.group('arg')) | |
| return types.VARCHAR(n) | |
| if type_sql in {'date'}: | |
| n = int(matches.group('arg')) | |
| return types.DATE() | |
| raise NotImplementedError | |
| datapackage = None | |
| with open(os.path.join('./dags/sus_cnes/', "datapackage.yml"), 'r') as stream: | |
| try: | |
| datapackage = yaml.safe_load(stream) | |
| except yaml.YAMLError as exc: | |
| raise ImportError | |
| fields = datapackage['resources'][0]['schema']['fields'] | |
| dtype_pd = {f['name']: f.get('_type_pandas', f['type']) | |
| for f in fields if not 'date' in f.get('_type_pandas', '') } | |
| dtype_sql = {f['name']: parse_type_sqlalchemy(f['_type_sql']) | |
| for f in fields if '_type_sql' in f} | |
| cols_dates = (f['name'] | |
| for f in fields if ('_type_pandas' in f \ | |
| and 'date' in f.get('_type_pandas'))) | |
| # print(dtype_pd) | |
| # print(dtype_sql) | |
| # print(list(cols_dates)) | |
| # print(converters) | |
| rep = { | |
| "TO_CHAR(": "", | |
| ",'DD/MM/YYYY')": "", | |
| } | |
| rep = dict((re.escape(k), v) for k, v in rep.items()) | |
| pattern = re.compile("|".join(rep.keys())) | |
| def date_parser(value): | |
| return pd.to_datetime(value, format='%d/%m/%Y', errors='coerce') | |
| df = pd.read_csv( | |
| '/home/gustavorps/workspace/datasets/datasus-cnes/BASE_DE_DADOS_CNES_201909/tbEstabelecimento201909.csv', | |
| sep=';', | |
| dtype=dtype_pd, | |
| parse_dates=list(cols_dates), | |
| date_parser=date_parser, | |
| nrows=5000) | |
| # print(df.info(memory_usage='deep')) | |
| df.columns = [pattern.sub(lambda m: rep[re.escape(m.group(0))], c) | |
| for c in df.columns] | |
| # print(df.info(memory_usage='deep')) | |
| # print(df.memory_usage(deep=True)) | |
| engine = create_engine('postgresql://odoo:odoo@localhost:5432/cnes_raw') | |
| if not database_exists(engine.url): | |
| create_database(engine.url) | |
| df.to_sql('tbEstabelecimento', | |
| engine, | |
| dtype=dtype_sql, | |
| if_exists='replace', | |
| chunksize=1000,) | |
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| name: CNES | |
| resources: | |
| - name: TB_ESTABELECIMENTO | |
| description: > | |
| LFCES004 | |
| Estabelecimentos de Saúde | |
| path: "./" | |
| profile: tabular-data-resource | |
| schema: | |
| primaryKey: CO_UNIDADE | |
| fields: | |
| - name: CO_ATIVIDADE | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: CHAR(2) | |
| - name: CO_ATIVIDADE_PRINCIPAL | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(3) | |
| - name: CO_CEP | |
| type: integer | |
| _type_pandas: string_ | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(8) | |
| - name: CO_CLIENTELA | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: CHAR(2) | |
| - name: CO_CNES | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(7) | |
| - name: CO_CPFDIRETORCLN | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(11) | |
| - name: CO_DISTRITO_ADMINISTRATIVO | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(4) | |
| - name: CO_DISTRITO_SANITARIO | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(4) | |
| - name: CO_ESTADO_GESTOR | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: CHAR(2) | |
| - name: CO_MICRO_REGIAO | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(6) | |
| - name: CO_MOTIVO_DESAB | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(2) | |
| - name: CO_MUNICIPIO_GESTOR | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(7) | |
| - name: CO_NATUREZA_JUR | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(04) | |
| - name: CO_REGIAO_SAUDE | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(4) | |
| - name: CO_TIPO_ESTABELECIMENTO | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(04) | |
| - name: CO_TIPO_UNIDADE | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: CHAR(2) | |
| - name: CO_TURNO_ATENDIMENTO | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: CHAR(2) | |
| - name: CO_UNIDADE | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(31) | |
| - name: CO_USUARIO | |
| type: string | |
| _type_pandas: category | |
| # _type_pandas: Int64 | |
| _type_sql: VARCHAR2(12) | |
| - name: TO_CHAR(DT_ATU_GEO,'DD/MM/YYYY') | |
| _rename: DT_ATU_GEO | |
| type: date | |
| format: "%d/%m/%Y" | |
| _type_pandas: datetime64[D] | |
| # TODO: remote parenteses | |
| _type_sql: date(5) | |
| - name: TO_CHAR(DT_ATUALIZACAO,'DD/MM/YYYY') | |
| _rename: DT_ATUALIZACAO | |
| type: date | |
| format: "%d/%m/%Y" | |
| _type_pandas: datetime64[D] | |
| # TODO: remote parenteses | |
| _type_sql: date(5) | |
| - name: TO_CHAR(DT_ATUALIZACAO_ORIGEM,'DD/MM/YYYY') | |
| _rename: DT_ATUALIZACAO_ORIGEM | |
| type: date | |
| format: "%d/%m/%Y" | |
| _type_pandas: datetime64[D] | |
| # TODO: remote parenteses | |
| _type_sql: date(5) | |
| - name: TO_CHAR(DT_EXPEDICAO,'DD/MM/YYYY') | |
| _rename: DT_ATUALIZACAO_ORIGEM | |
| type: date | |
| format: "%d/%m/%Y" | |
| _type_pandas: datetime64[D] | |
| # TODO: remote parenteses | |
| _type_sql: date(5) | |
| - name: TO_CHAR(DT_VAL_LIC_SANI,'DD/MM/YYYY') | |
| _rename: DT_VAL_LIC_SANI | |
| type: date | |
| format: "%d/%m/%Y" | |
| _type_pandas: datetime64[D] | |
| # TODO: remote parenteses | |
| _type_sql: date(5) | |
| - name: NIVEL_DEP | |
| type: Int64 | |
| # _type_sql: | |
| - name: NO_BAIRRO | |
| type: category | |
| # _type_sql: | |
| - name: NO_COMPLEMENTO | |
| type: string_ | |
| # _type_sql: | |
| - name: NO_EMAIL | |
| type: category | |
| # _type_sql: | |
| - name: NO_FANTASIA | |
| type: category | |
| # _type_sql: | |
| - name: NO_FANTASIA_ABREV | |
| type: string_ | |
| # _type_sql: | |
| - name: NO_LOGRADOURO | |
| type: string_ | |
| # _type_sql: | |
| - name: NO_RAZAO_SOCIAL | |
| type: string_ | |
| # _type_sql: | |
| - name: NO_URL | |
| type: string_ | |
| # _type_sql: | |
| - name: NO_USUARIO_GEO | |
| type: category | |
| # _type_sql: | |
| - name: NU_ALVARA | |
| type: string_ | |
| # _type_sql: | |
| - name: NU_CNPJ | |
| type: category | |
| # _type_sql: | |
| - name: NU_CNPJ_MANTENEDORA | |
| type: string | |
| _type_pandas: U | |
| _type_sql: char(15) | |
| - name: NU_CPF | |
| type: category | |
| # _type_sql: | |
| - name: NU_ENDERECO | |
| type: string_ | |
| # _type_sql: | |
| - name: NU_FAX | |
| type: string_ | |
| # _type_sql: | |
| - name: NU_LATITUDE | |
| type: string_ | |
| # _type_sql: | |
| - name: NU_LONGITUDE | |
| type: string_ | |
| # _type_sql: | |
| - name: NU_TELEFONE | |
| type: string_ | |
| # _type_sql: | |
| - name: REG_DIRETORCLN | |
| type: string_ | |
| # _type_sql: | |
| - name: ST_ADESAO_FILANTROP | |
| type: string_ | |
| # _type_sql: | |
| - name: ST_CONEXAO_INTERNET | |
| type: string_ | |
| # _type_sql: | |
| - name: ST_CONTRATO_FORMALIZADO | |
| type: string_ | |
| # _type_sql: | |
| - name: ST_GERACREDITO_GERENTE_SGIF | |
| type: string_ | |
| # _type_sql: | |
| - name: TP_ESTAB_SEMPRE_ABERTO | |
| type: category | |
| # _type_sql: | |
| - name: TP_GESTAO | |
| type: category | |
| # _type_sql: | |
| - name: TP_LIC_SANI | |
| type: category | |
| # _type_sql: | |
| - name: TP_ORGAO_EXPEDIDOR | |
| type: category | |
| # _type_sql: | |
| - name: TP_PFPJ | |
| type: category | |
| # _type_sql: | |
| - name: TP_UNIDADE | |
| type: category | |
| # _type_sql: |
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