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folhesgabriel
added
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test-dev-model
|
@rdahis, |
📝 WalkthroughWalkthroughFive new dbt models added to the Changes
Sequence Diagram(s)sequenceDiagram
participant Script as clean_munic_data.py
participant InputDir as Input Directory<br/>(XLSX/XLS/ZIP)
participant MappingSheet as Mapping Spreadsheet<br/>(sections_mapping.xlsx)
participant MunicipalityDir as Directory CSV<br/>(br_bd_diretorios...)
participant GoogleSheets as Google Sheets<br/>(Section Architectures)
participant Output as Output CSVs<br/>(output/*.csv)
Script->>InputDir: Read MUNIC datasets by year
Script->>MappingSheet: Load section-to-column mapping
Script->>MunicipalityDir: Load municipality ID directory
Script->>GoogleSheets: Fetch architecture per section
Note over Script: For each section & year:
Script->>Script: Detect municipality ID column
Script->>Script: Normalize/map IDs (6→7 digit)
Script->>Script: Derive sigla_uf from id_municipio
Script->>Script: Fuzzy match columns to architecture
Script->>Script: Cast values per bigquery_type
Script->>Script: Clean sentinel/invalid values
Script->>Output: Write standardized CSV per section
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes Poem
🚥 Pre-merge checks | ❌ 3❌ Failed checks (1 warning, 2 inconclusive)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
Comment |
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Actionable comments posted: 5
🧹 Nitpick comments (3)
models/br_ibge_munic/code/clean_munic_data.py (3)
37-44: Add docstrings to public functions.Per coding guidelines, Python functions should have docstrings following Google Style. Key functions like
normalize_text,find_data_file,detect_id_column,build_section_year, andrunwould benefit from brief docstrings explaining their purpose, parameters, and return values.Example for normalize_text
def normalize_text(text: object) -> str: + """Normalize text for fuzzy column matching. + + Args: + text: Input text to normalize. + + Returns: + Lowercase ASCII string with non-alphanumeric chars replaced by spaces. + """ if text is None: return ""🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@models/br_ibge_munic/code/clean_munic_data.py` around lines 37 - 44, Add Google-style docstrings to the public functions normalize_text, find_data_file, detect_id_column, build_section_year, and run: for each function include a one-line summary, Args with parameter names and types, Returns with type and description, and any raised exceptions or side effects; place the docstring directly below the def line and follow existing project tone/formatting (triple-quoted, imperative summary, type hints already present can be mirrored in the docstring).
221-228: Add timeout for external network request.The
pd.read_excel(url)call to fetch architecture from Google Sheets has no timeout. If the network is slow or unavailable, this could hang indefinitely.Proposed fix using requests with timeout
+import requests + def read_architecture(section: str) -> pd.DataFrame: + """Fetch architecture table from Google Sheets for a section.""" sheet_id = ARCH_SHEETS.get(section) if not sheet_id: raise ValueError(f"No architecture sheet configured for: {section}") url = ( f"https://docs.google.com/spreadsheets/d/{sheet_id}/export?format=xlsx" ) - return pd.read_excel(url) + response = requests.get(url, timeout=30) + response.raise_for_status() + return pd.read_excel(io.BytesIO(response.content))🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@models/br_ibge_munic/code/clean_munic_data.py` around lines 221 - 228, The read_architecture function currently calls pd.read_excel(url) with a Google Sheets export URL and no timeout; change it to perform an HTTP GET with a timeout (e.g., using requests.get(..., timeout=...)), check for a successful response, wrap response.content in a BytesIO and pass that to pd.read_excel, and raise/log a clear error if the request fails; update references in read_architecture to use requests and io.BytesIO when loading the sheet.
289-304: Catch specific exceptions instead of bareException.The static analysis tool flags these bare
Exceptioncatches (Ruff BLE001). Catching specific exceptions improves debuggability and prevents masking unexpected errors.Proposed fix
try: xl, _engine = open_excel_file(file_path) - except Exception as exc: + except (FileNotFoundError, zipfile.BadZipFile, ValueError) as exc: print(f" Year {year}: could not open file ({exc})") return None, {}try: df = xl.parse(sheet_name=sheet) - except Exception as exc: + except (ValueError, KeyError) as exc: print(f" Year {year}: failed reading sheet '{sheet}' ({exc})") continueAlternatively, if you need to catch a broader range of exceptions, use
except Exception as exc:with explicit logging of the exception type for debugging.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@models/br_ibge_munic/code/clean_munic_data.py` around lines 289 - 304, The two bare "except Exception" blocks around open_excel_file(file_path) and xl.parse(sheet_name=sheet) should be replaced with specific exception handlers: catch file/IO errors (e.g., FileNotFoundError, OSError) and Excel/parse-specific exceptions (e.g., pandas.errors.EmptyDataError, pandas.errors.ParserError, xlrd.biffh.XLRDError or the engine-specific error) for the open_excel_file call, and similarly catch parsing/formatting exceptions for xl.parse; for any remaining unexpected errors either re-raise them or log the exception type alongside the message. Update the try/except around open_excel_file, the sheet_names computation that uses is_dictionary_sheet, and the try/except around xl.parse (referencing open_excel_file and xl.parse) to use these specific exception types and include the exception type in the log.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@models/br_ibge_munic/br_ibge_munic__habitacao.sql`:
- Around line 337-339: The column name is misspelled: change every occurrence of
demais_instrumentos_egitimacao_posse_ano_lei to the correct
demais_instrumentos_legitimacao_posse_ano_lei in the SQL select (the safe_cast
expression) and update the corresponding field name in the schema.yml entry (the
schema field referenced near the existing
demais_instrumentos_legitimacao_posse_existencia). Ensure both SQL and
schema.yml use the same corrected identifier so the model and schema remain
consistent.
In `@models/br_ibge_munic/br_ibge_munic__meio_ambiente.sql`:
- Line 70: Fix the consistent typo "licensiamento" -> "licenciamento" across
column aliases: update capacitacao_area_licensiamento to
capacitacao_area_licenciamento, all occurrences of licensiamento_impacto_local
(and any related columns in that block) to licenciamento_impacto_local,
recursos_especificos_meio_ambiente_licensiamento to
recursos_especificos_meio_ambiente_licenciamento, and
recursos_oriundos_orgao_publico_taxa_licensiamento to
recursos_oriundos_orgao_publico_taxa_licenciamento; search the SQL for these
identifiers (e.g., the safe_cast line using capacitacao_area_licensiamento and
the blocks around licensiamento_impacto_local,
recursos_especificos_meio_ambiente_licensiamento,
recursos_oriundos_orgao_publico_taxa_licensiamento) and update each alias/name
consistently.
- Around line 17-19: Fix the misspelled column names coming from the staging
schema: rename secretaria_trata_unicamiente_meio_ambiente to
secretaria_trata_unicamente_meio_ambiente and change all occurrences of
licensiamento to licenciamento (e.g., capacitacao_area_licensiamento,
recursos_especificos_meio_ambiente_licensiamento,
recursos_oriundos_orgao_publico_taxa_licensiamento and any licensiamento_*
columns) in the source column definitions used by the cleaning pipeline; update
the architecture/column mapping that clean_munic_data.py reads
(models/br_ibge_munic/code/clean_munic_data.py) so it fetches the corrected
names from the Google Sheets mapping and ensure the SQL model
(br_ibge_munic__meio_ambiente.sql) selects the corrected column identifiers (use
the corrected symbol names in the safe_casts and aliases).
In `@models/br_ibge_munic/br_ibge_munic__recursos_gestao.sql`:
- Around line 388-396: The three column aliases contain a typo: change
occurrences of articulacao_saude_consorcio_admnistrativo_intermunicipal,
articulacao_saude_consorcio_admnistrativo_estado, and
articulacao_saude_consorcio_admnistrativo_uniao to use "administrativo"
(articulacao_saude_consorcio_administrativo_...) to match the rest of the model
and upstream staging; update the safe_cast lines and any other references in
this model that use the misspelled identifiers, and if the staging table
actually uses the correct names, fix only these aliases here, otherwise correct
the source staging column names to maintain consistency.
In `@models/br_ibge_munic/br_ibge_munic__recursos_humanos.sql`:
- Line 210: The column alias and source field `pesionista` is misspelled; change
all occurrences of `pesionista` to `pensionista` (e.g., update the expression
`safe_cast(pesionista as int64) pesionista` to use the correctly spelled source
and alias), and propagate this fix upstream by renaming the field in the staging
table and in the Google Sheets architecture definition so the corrected
`pensionista` name flows through the pipeline and matches the schema.yml
description.
---
Nitpick comments:
In `@models/br_ibge_munic/code/clean_munic_data.py`:
- Around line 37-44: Add Google-style docstrings to the public functions
normalize_text, find_data_file, detect_id_column, build_section_year, and run:
for each function include a one-line summary, Args with parameter names and
types, Returns with type and description, and any raised exceptions or side
effects; place the docstring directly below the def line and follow existing
project tone/formatting (triple-quoted, imperative summary, type hints already
present can be mirrored in the docstring).
- Around line 221-228: The read_architecture function currently calls
pd.read_excel(url) with a Google Sheets export URL and no timeout; change it to
perform an HTTP GET with a timeout (e.g., using requests.get(..., timeout=...)),
check for a successful response, wrap response.content in a BytesIO and pass
that to pd.read_excel, and raise/log a clear error if the request fails; update
references in read_architecture to use requests and io.BytesIO when loading the
sheet.
- Around line 289-304: The two bare "except Exception" blocks around
open_excel_file(file_path) and xl.parse(sheet_name=sheet) should be replaced
with specific exception handlers: catch file/IO errors (e.g., FileNotFoundError,
OSError) and Excel/parse-specific exceptions (e.g.,
pandas.errors.EmptyDataError, pandas.errors.ParserError, xlrd.biffh.XLRDError or
the engine-specific error) for the open_excel_file call, and similarly catch
parsing/formatting exceptions for xl.parse; for any remaining unexpected errors
either re-raise them or log the exception type alongside the message. Update the
try/except around open_excel_file, the sheet_names computation that uses
is_dictionary_sheet, and the try/except around xl.parse (referencing
open_excel_file and xl.parse) to use these specific exception types and include
the exception type in the log.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
⚙️ Run configuration
Configuration used: defaults
Review profile: CHILL
Plan: Pro
Run ID: c31aa09e-017a-4100-86c9-5c3e0a39c6ff
📒 Files selected for processing (7)
models/br_ibge_munic/br_ibge_munic__atual_prefeito.sqlmodels/br_ibge_munic/br_ibge_munic__habitacao.sqlmodels/br_ibge_munic/br_ibge_munic__meio_ambiente.sqlmodels/br_ibge_munic/br_ibge_munic__recursos_gestao.sqlmodels/br_ibge_munic/br_ibge_munic__recursos_humanos.sqlmodels/br_ibge_munic/code/clean_munic_data.pymodels/br_ibge_munic/schema.yml
| safe_cast( | ||
| demais_instrumentos_egitimacao_posse_ano_lei as int64 | ||
| ) demais_instrumentos_egitimacao_posse_ano_lei, |
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Typo in column name: egitimacao → legitimacao
The column demais_instrumentos_egitimacao_posse_ano_lei is missing the leading "l" and should be demais_instrumentos_legitimacao_posse_ano_lei, matching the correct spelling in demais_instrumentos_legitimacao_posse_existencia on line 335.
Update both the SQL file and schema.yml (line 485) to use the correct spelling.
Proposed fix
safe_cast(
- demais_instrumentos_egitimacao_posse_ano_lei as int64
- ) demais_instrumentos_egitimacao_posse_ano_lei,
+ demais_instrumentos_legitimacao_posse_ano_lei as int64
+ ) demais_instrumentos_legitimacao_posse_ano_lei,📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| safe_cast( | |
| demais_instrumentos_egitimacao_posse_ano_lei as int64 | |
| ) demais_instrumentos_egitimacao_posse_ano_lei, | |
| safe_cast( | |
| demais_instrumentos_legitimacao_posse_ano_lei as int64 | |
| ) demais_instrumentos_legitimacao_posse_ano_lei, |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@models/br_ibge_munic/br_ibge_munic__habitacao.sql` around lines 337 - 339,
The column name is misspelled: change every occurrence of
demais_instrumentos_egitimacao_posse_ano_lei to the correct
demais_instrumentos_legitimacao_posse_ano_lei in the SQL select (the safe_cast
expression) and update the corresponding field name in the schema.yml entry (the
schema field referenced near the existing
demais_instrumentos_legitimacao_posse_existencia). Ensure both SQL and
schema.yml use the same corrected identifier so the model and schema remain
consistent.
| safe_cast( | ||
| secretaria_trata_unicamiente_meio_ambiente as string | ||
| ) secretaria_trata_unicamiente_meio_ambiente, |
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Fix typo in column name: unicamiente → unicamente
Line 18 contains the misspelled column secretaria_trata_unicamiente_meio_ambiente. The correct Portuguese spelling is unicamente.
Additionally, multiple columns use the misspelled term licensiamento (should be licenciamento):
capacitacao_area_licensiamentolicensiamento_*(multiple columns)recursos_especificos_meio_ambiente_licensiamentorecursos_oriundos_orgao_publico_taxa_licensiamento
These typos originate from the staging data. Fix them in the architecture definition referenced by the Python cleaning script (models/br_ibge_munic/code/clean_munic_data.py), which fetches column definitions from Google Sheets.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@models/br_ibge_munic/br_ibge_munic__meio_ambiente.sql` around lines 17 - 19,
Fix the misspelled column names coming from the staging schema: rename
secretaria_trata_unicamiente_meio_ambiente to
secretaria_trata_unicamente_meio_ambiente and change all occurrences of
licensiamento to licenciamento (e.g., capacitacao_area_licensiamento,
recursos_especificos_meio_ambiente_licensiamento,
recursos_oriundos_orgao_publico_taxa_licensiamento and any licensiamento_*
columns) in the source column definitions used by the cleaning pipeline; update
the architecture/column mapping that clean_munic_data.py reads
(models/br_ibge_munic/code/clean_munic_data.py) so it fetches the corrected
names from the Google Sheets mapping and ensure the SQL model
(br_ibge_munic__meio_ambiente.sql) selects the corrected column identifiers (use
the corrected symbol names in the safe_casts and aliases).
| safe_cast( | ||
| capacitacao_area_estruturacao_gestao as string | ||
| ) capacitacao_area_estruturacao_gestao, | ||
| safe_cast(capacitacao_area_licensiamento as string) capacitacao_area_licensiamento, |
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Consistent typo: licensiamento → licenciamento
Multiple columns use the incorrect spelling licensiamento instead of the correct Portuguese licenciamento (licensing):
capacitacao_area_licensiamento(line 70)licensiamento_impacto_localand related columns (lines 150–165)recursos_especificos_meio_ambiente_licensiamento(lines 390–391)recursos_oriundos_orgao_publico_taxa_licensiamento(lines 410–411)
These typos should be corrected to maintain data quality consistency.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@models/br_ibge_munic/br_ibge_munic__meio_ambiente.sql` at line 70, Fix the
consistent typo "licensiamento" -> "licenciamento" across column aliases: update
capacitacao_area_licensiamento to capacitacao_area_licenciamento, all
occurrences of licensiamento_impacto_local (and any related columns in that
block) to licenciamento_impacto_local,
recursos_especificos_meio_ambiente_licensiamento to
recursos_especificos_meio_ambiente_licenciamento, and
recursos_oriundos_orgao_publico_taxa_licensiamento to
recursos_oriundos_orgao_publico_taxa_licenciamento; search the SQL for these
identifiers (e.g., the safe_cast line using capacitacao_area_licensiamento and
the blocks around licensiamento_impacto_local,
recursos_especificos_meio_ambiente_licensiamento,
recursos_oriundos_orgao_publico_taxa_licensiamento) and update each alias/name
consistently.
| safe_cast( | ||
| articulacao_saude_consorcio_admnistrativo_intermunicipal as string | ||
| ) articulacao_saude_consorcio_admnistrativo_intermunicipal, | ||
| safe_cast( | ||
| articulacao_saude_consorcio_admnistrativo_estado as string | ||
| ) articulacao_saude_consorcio_admnistrativo_estado, | ||
| safe_cast( | ||
| articulacao_saude_consorcio_admnistrativo_uniao as string | ||
| ) articulacao_saude_consorcio_admnistrativo_uniao, |
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Fix typos in column names: admnistrativo → administrativo
Three columns at lines 389-396 use admnistrativo instead of administrativo:
articulacao_saude_consorcio_admnistrativo_intermunicipalarticulacao_saude_consorcio_admnistrativo_estadoarticulacao_saude_consorcio_admnistrativo_uniao
This creates inconsistency with other articulacao_*_consorcio_administrativo_* columns throughout the file. Verify the staging table column names and fix at the source if needed.
Proposed fix
safe_cast(
- articulacao_saude_consorcio_admnistrativo_intermunicipal as string
- ) articulacao_saude_consorcio_admnistrativo_intermunicipal,
+ articulacao_saude_consorcio_administrativo_intermunicipal as string
+ ) articulacao_saude_consorcio_administrativo_intermunicipal,
safe_cast(
- articulacao_saude_consorcio_admnistrativo_estado as string
- ) articulacao_saude_consorcio_admnistrativo_estado,
+ articulacao_saude_consorcio_administrativo_estado as string
+ ) articulacao_saude_consorcio_administrativo_estado,
safe_cast(
- articulacao_saude_consorcio_admnistrativo_uniao as string
- ) articulacao_saude_consorcio_admnistrativo_uniao,
+ articulacao_saude_consorcio_administrativo_uniao as string
+ ) articulacao_saude_consorcio_administrativo_uniao,📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| safe_cast( | |
| articulacao_saude_consorcio_admnistrativo_intermunicipal as string | |
| ) articulacao_saude_consorcio_admnistrativo_intermunicipal, | |
| safe_cast( | |
| articulacao_saude_consorcio_admnistrativo_estado as string | |
| ) articulacao_saude_consorcio_admnistrativo_estado, | |
| safe_cast( | |
| articulacao_saude_consorcio_admnistrativo_uniao as string | |
| ) articulacao_saude_consorcio_admnistrativo_uniao, | |
| safe_cast( | |
| articulacao_saude_consorcio_administrativo_intermunicipal as string | |
| ) articulacao_saude_consorcio_administrativo_intermunicipal, | |
| safe_cast( | |
| articulacao_saude_consorcio_administrativo_estado as string | |
| ) articulacao_saude_consorcio_administrativo_estado, | |
| safe_cast( | |
| articulacao_saude_consorcio_administrativo_uniao as string | |
| ) articulacao_saude_consorcio_administrativo_uniao, |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@models/br_ibge_munic/br_ibge_munic__recursos_gestao.sql` around lines 388 -
396, The three column aliases contain a typo: change occurrences of
articulacao_saude_consorcio_admnistrativo_intermunicipal,
articulacao_saude_consorcio_admnistrativo_estado, and
articulacao_saude_consorcio_admnistrativo_uniao to use "administrativo"
(articulacao_saude_consorcio_administrativo_...) to match the rest of the model
and upstream staging; update the safe_cast lines and any other references in
this model that use the misspelled identifiers, and if the staging table
actually uses the correct names, fix only these aliases here, otherwise correct
the source staging column names to maintain consistency.
| existencia_fundo_municipal_previdencia as int64 | ||
| ) existencia_fundo_municipal_previdencia, | ||
| safe_cast(aposentado as int64) aposentado, | ||
| safe_cast(pesionista as int64) pesionista |
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Fix spelling of column name: pesionista → pensionista
The column name pesionista is misspelled. The correct Portuguese spelling for "pensioner" is pensionista. This is documented in schema.yml (line 957) with description "Total de pensionistas", confirming the intent. The typo is consistent throughout the pipeline and should be corrected in the upstream staging table and Google Sheets architecture definition to propagate the fix properly.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@models/br_ibge_munic/br_ibge_munic__recursos_humanos.sql` at line 210, The
column alias and source field `pesionista` is misspelled; change all occurrences
of `pesionista` to `pensionista` (e.g., update the expression
`safe_cast(pesionista as int64) pesionista` to use the correctly spelled source
and alias), and propagate this fix upstream by renaming the field in the staging
table and in the Google Sheets architecture definition so the corrected
`pensionista` name flows through the pipeline and matches the schema.yml
description.
2 participants
Descrição do PR:
Subindo a versão harmonizada da pesquisa MUNIC do IBGE. Já está mapeada em https://basedosdados.org/dataset/218ae306-29ac-4a83-836d-95bfdb9683fe.
O principal trabalho é de criação das tabelas de arquitetura. Segue a lista de tabelas de arquitetura:
Teste e Validações:
Summary by CodeRabbit