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dd0d8af40c
...
2f77206b8f
45
Makefile
45
Makefile
@ -1,49 +1,10 @@
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DATA_BASE=./datas
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PDF_BASE=$(DATA_BASE)/pdfs
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PDF_YEARS=$(wildcard $(PDF_BASE)/*)
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RAW_BASE=$(DATA_BASE)/raw
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RAW_CRG=$(RAW_BASE)/CRG
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RAW_CRG_YEARS=$(subst $(PDF_BASE), $(RAW_CRG), $(PDF_YEARS))
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$(RAW_CRG)/%/: $(wildcard $(PDF_BASE)/%/*)
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echo $(wildcard $(PDF_BASE)/$*/*)
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@echo ----
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ls $(PDF_BASE)/$*/
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@echo ----
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echo $*
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@echo ----
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echo $^
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@echo ----
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echo $?
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#./datas/raw/CRG/%:
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#pdf-oralia extract all --src $$year --dest $$(subst $$PDF_BASE, $$RAW_CRG, $$year)
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# $(RAW_CRG_YEARS): $(PDF_PATHS)
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# for year in $(PDF_PATHS); do \
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# echo $$year; \
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# echo $$(subst $$PDF_BASE, $$RAW_CRG, $$year); \
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# echo "----"; \
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# done;
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extract_pdfs:
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for year in 2021 2022 2023 2024; do \
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mkdir -p $(RAW_CRG)/$$year/extracted;\
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pdf-oralia extract all --src $(PDF_BASE)/$$year/ --dest $(RAW_CRG)/$$year/extracted; \
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pdf-oralia join --src $(RAW_CRG)/$$year/extracted/ --dest $(RAW_CRG)/$$year/; \
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done
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clean_raw:
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rm -rf ./PLESNA Compta SYSTEM/raw/**/*.csv
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clean_built:
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rm -rf $(DATA_BASE)/staging/**/*.csv
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rm -rf $(DATA_BASE)/gold/**/*.csv
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rm -rf $(DATA_BASE)/datamart/**/*.csv
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rm -rf ./PLESNA Compta SYSTEM/staging/**/*.csv
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rm -rf ./PLESNA Compta SYSTEM/gold/**/*.csv
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rm -rf ./PLESNA Compta SYSTEM/datamart/**/*.csv
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clean_all: clean_built clean_raw
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import_nextcloud:
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rsync -a ~/Nextcloud/PLESNA\ Compta\ SYSTEM/Histoire/ ./datas/Histoire
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@ -1,4 +0,0 @@
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jupyter==1.0.0
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pandas==1.5.0
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pdf-oralia==0.3.11
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pydantic==2.6.1
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146
scripts/flux.py
146
scripts/flux.py
@ -1,146 +0,0 @@
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import logging
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from abc import abstractmethod
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from collections.abc import Callable
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from pathlib import Path
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import pandas as pd
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from pydantic import BaseModel
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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class Source(BaseModel):
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filename: str
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@abstractmethod
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def get_df(self) -> pd.DataFrame:
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raise NotImplementedError
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class ExcelSource(Source):
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sheet_name: str
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def get_df(self, base_path: Path) -> pd.DataFrame:
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filepath = base_path / self.filename
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logger.debug(f"Get content of {filepath}")
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return pd.read_excel(filepath, sheet_name=self.sheet_name)
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class CSVSource(Source):
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options: dict = {}
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def get_df(self, base_path: Path) -> pd.DataFrame:
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filepath = base_path / self.filename
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logger.debug(f"Get content of {filepath}")
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return pd.read_csv(filepath, **self.options)
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class Transformation(BaseModel):
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function: Callable
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extra_kwrds: dict = {}
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class Destination(BaseModel):
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name: str
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def write(
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self, df: pd.DataFrame, dest_path: Path, writing_func: Callable
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) -> list[Path]:
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dest_basename = dest_path / self.name
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return [writing_func(df, dest_basename)]
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class SplitDestination(Destination):
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split_column: str
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def write(
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self, df: pd.DataFrame, dest_path: Path, writing_func: Callable
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) -> list[Path]:
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wrote_files = []
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for col_value in df[self.split_column].unique():
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filtered_df = df[df[self.split_column] == col_value]
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dest_basename = dest_path / f"{self.name}-{col_value}"
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dest = writing_func(filtered_df, dest_basename)
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wrote_files.append(dest)
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return wrote_files
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class Flux(BaseModel):
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sources: list[Source]
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transformation: Transformation
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destination: Destination
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def to_csv(df, dest_basename: Path) -> Path:
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dest = dest_basename.parent / (dest_basename.stem + ".csv")
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if dest.exists():
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df.to_csv(dest, mode="a", header=False, index=False)
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else:
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df.to_csv(dest, index=False)
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return dest
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def write_split_by(
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df: pd.DataFrame, column: str, dest_path: Path, name: str, writing_func
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) -> list[Path]:
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wrote_files = []
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for col_value in df[column].unique():
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filtered_df = df[df[column] == col_value]
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dest_basename = dest_path / f"{name}-{col_value}"
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dest = writing_func(filtered_df, dest_basename)
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wrote_files.append(dest)
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return wrote_files
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def extract_sources(sources: list[Source], base_path: Path = Path()):
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for src in sources:
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if "*" in src.filename:
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expanded_src = [
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src.model_copy(update={"filename": str(p.relative_to(base_path))})
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for p in base_path.glob(src.filename)
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]
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yield from extract_sources(expanded_src, base_path)
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else:
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filepath = base_path / src.filename
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assert filepath.exists
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yield src.filename, src.get_df(base_path)
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def split_duplicates(
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df, origin: str, duplicated: dict[str, pd.DataFrame]
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) -> [pd.DataFrame, dict[str, pd.DataFrame]]:
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duplicates = df.duplicated()
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no_duplicates = df[~duplicates]
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duplicated[origin] = df[duplicates]
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return no_duplicates, duplicated
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def consume_fluxes(
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fluxes: dict[str, Flux],
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origin_path: Path,
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dest_path: Path,
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writing_func=to_csv,
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):
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duplicated = {}
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wrote_files = []
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for name, flux in fluxes.items():
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print(name)
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logger.info(f"Processing flux {name}")
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src_df = []
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for filename, df in extract_sources(flux.sources, origin_path):
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df, duplicated = split_duplicates(df, str(filename), duplicated)
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src_df.append(df)
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df = flux.transformation.function(src_df, **flux.transformation.extra_kwrds)
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files = flux.destination.write(df, dest_path, writing_func)
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wrote_files += files
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return wrote_files
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@ -1,75 +0,0 @@
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import logging
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from collections.abc import Callable
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from pathlib import Path
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import pandas as pd
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from scripts.flux import (
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CSVSource,
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Destination,
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Flux,
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SplitDestination,
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Transformation,
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consume_fluxes,
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)
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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def build_lots(dfs: list[pd.DataFrame]) -> pd.DataFrame:
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df = dfs[0]
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df = df.assign(
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Impact=df["Crédit"] - df["Débit"],
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)
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return df
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def build_pnl(dfs: list[pd.DataFrame], year: int) -> pd.DataFrame:
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df = pd.concat(dfs)
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df = df[df["Année"] == year]
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pt = df.groupby(["Catégorie", "Mois"]).agg("sum").unstack().fillna(0)
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pt.columns = [c[1] for c in pt.columns]
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pt.reset_index(["Catégorie"])
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return pt
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def build_pnl_flux(year: int) -> Flux:
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return Flux(
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sources=[
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CSVSource(filename=f"CRG/crg-{year}.csv"),
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CSVSource(filename=f"banque/banque-{year}.csv"),
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],
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transformation=build_pnl,
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extra_kwrds={"year": year},
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destination=Destination(name=f"pnl/{year}"),
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)
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if __name__ == "__main__":
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data_path = Path("datas/")
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assert data_path.exists()
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gold_path = data_path / "gold"
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assert gold_path.exists()
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gold_crg_path = gold_path / "CRG"
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assert gold_crg_path.exists()
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mart_path = data_path / "datamart"
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assert mart_path.exists()
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lot_fluxes = {
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"Lots": Flux(
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sources=[CSVSource(filename="CRG/crg-*.csv")],
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transformation=Transformation(function=build_lots),
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destination=SplitDestination(name="Lot/lot", split_column="Lot"),
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),
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}
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years = list(range(2017, 2024))
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# pnl_fluxes = {f"pnl-{year}": build_pnl_flux(year) for year in years}
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pnl_fluxes = {}
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files = consume_fluxes(
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fluxes={**lot_fluxes, **pnl_fluxes}, origin_path=gold_path, dest_path=mart_path
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)
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print(files)
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@ -1,186 +0,0 @@
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import logging
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from pathlib import Path
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import pandas as pd
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from scripts.flux import consume_fluxes
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from .flux import Destination, ExcelSource, Flux, SplitDestination, Transformation
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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def extract_cat(cat: pd.DataFrame):
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cat_drop = list(cat[cat["Nouvelles"] == "NE PAS IMPORTER"]["Anciennes"])
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cat_trans = cat[cat["Nouvelles"] != "NE PAS IMPORTER"]
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trans = {}
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for _, (old, new) in cat_trans.iterrows():
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trans[old] = new
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return trans, cat_drop
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def trans_2017_2021(
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dfs: list[pd.DataFrame], stagging_columns: list[str], **kwrds
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) -> pd.DataFrame:
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df, cat = dfs
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cat_trans, cat_drop = extract_cat(cat)
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df = df[~df["Categorie"].isin(cat_drop)]
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df = df.assign(
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Immeuble=df["immeuble"],
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Porte=df["porte"],
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Débit=df["Débit"].fillna(0),
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Crédit=df["Crédit"].fillna(0),
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Lot=df["immeuble"].astype(str) + df["porte"].astype("str").str.zfill(2),
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Année=df["Date"].astype(str).str.slice(0, 4),
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Mois=df["Date"].astype(str).str.slice(5, 7),
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Catégorie=df["Categorie"].replace(cat_trans),
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Fournisseur="",
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)
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return df[stagging_columns]
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def trans_2022_charge(
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dfs: list[pd.DataFrame], stagging_columns: list[str], **kwrds
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) -> pd.DataFrame:
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df = dfs[0]
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df = df.assign(
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Immeuble=df["immeuble"],
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Porte=df["lot"],
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Débit=df["Débits"].fillna(0),
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Crédit=df["Crédits"].fillna(0),
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Lot=df["immeuble"].astype(str)[0] + df["lot"].astype("str").str.zfill(2),
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Année=df["annee"],
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Mois=df["mois"],
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Catégorie=df["Catégorie Charge"],
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# Catégorie=df["Catégorie Charge"].replace(trans),
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Fournisseur="",
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Régie="Oralia - Gelas",
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Libellé="",
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)
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return df[stagging_columns]
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def trans_2022_loc(
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dfs: list[pd.DataFrame], stagging_columns: list[str], **kwrds
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) -> pd.DataFrame:
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df = dfs[0]
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df = df.assign(
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Immeuble=df["immeuble"],
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Porte=df["lot"],
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Débit=0,
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Crédit=df["Réglés"].fillna(0),
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Lot=df["immeuble"].astype(str)[0] + df["lot"].astype("str").str.zfill(2),
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Année=df["annee"],
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Mois=df["mois"],
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Catégorie="Loyer Charge",
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Fournisseur="",
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Régie="Oralia - Gelas",
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Libellé="",
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)
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return df[stagging_columns]
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|
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def trans_2023(
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dfs: list[pd.DataFrame], year: str, stagging_columns: list[str], **kwrds
|
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) -> pd.DataFrame:
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df = dfs[0]
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df = df.assign(
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Débit=df["Débit"].fillna(0),
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Crédit=df["Crédit"].fillna(0),
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Lot=df["Immeuble"].astype(str) + df["Porte"].astype("str").str.zfill(2),
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Année=year,
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)
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return df[stagging_columns]
|
||||
|
||||
|
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STAGGING_COLUMNS = [
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"Régie",
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"Immeuble",
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"Porte",
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"Lot",
|
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"Année",
|
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"Mois",
|
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"Catégorie",
|
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"Fournisseur",
|
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"Libellé",
|
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"Débit",
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"Crédit",
|
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]
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FLUXES = {
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"2017 2021 - charge et locataire.xlsx": Flux(
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sources=[
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ExcelSource(
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filename="2017 2021 - charge et locataire.xlsx", sheet_name="DB CRG"
|
||||
),
|
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ExcelSource(
|
||||
filename="2017 2021 - charge et locataire.xlsx",
|
||||
sheet_name="Catégories",
|
||||
),
|
||||
],
|
||||
transformation=Transformation(
|
||||
function=trans_2017_2021,
|
||||
extra_kwrds={"stagging_columns": STAGGING_COLUMNS},
|
||||
),
|
||||
destination=SplitDestination(name="crg", split_column="Année"),
|
||||
),
|
||||
"2022 - charge.xlsx": Flux(
|
||||
sources=[
|
||||
ExcelSource(filename="2022 - charge.xlsx", sheet_name="Sheet1"),
|
||||
],
|
||||
transformation=Transformation(
|
||||
function=trans_2022_charge,
|
||||
extra_kwrds={"stagging_columns": STAGGING_COLUMNS},
|
||||
),
|
||||
destination=SplitDestination(name="crg", split_column="Année"),
|
||||
),
|
||||
"2022 - locataire.xlsx": Flux(
|
||||
sources=[
|
||||
ExcelSource(filename="2022 - locataire.xlsx", sheet_name="Sheet1"),
|
||||
],
|
||||
transformation=Transformation(
|
||||
function=trans_2022_loc,
|
||||
extra_kwrds={"stagging_columns": STAGGING_COLUMNS},
|
||||
),
|
||||
destination=SplitDestination(name="crg", split_column="Année"),
|
||||
),
|
||||
"2023 - charge et locataire.xlsx": Flux(
|
||||
sources=[
|
||||
ExcelSource(
|
||||
filename="2023 - charge et locataire.xlsx",
|
||||
sheet_name="DB CRG 2023 ...",
|
||||
),
|
||||
],
|
||||
transformation=Transformation(
|
||||
function=trans_2023,
|
||||
extra_kwrds={"year": 2023, "stagging_columns": STAGGING_COLUMNS},
|
||||
),
|
||||
destination=SplitDestination(name="crg", split_column="Année"),
|
||||
),
|
||||
}
|
||||
|
||||
if __name__ == "__main__":
|
||||
data_path = Path("datas/")
|
||||
assert data_path.exists()
|
||||
history_path = data_path / "Histoire"
|
||||
assert history_path.exists()
|
||||
history_crg_path = history_path / "CRG"
|
||||
assert history_crg_path.exists()
|
||||
|
||||
staging_path = data_path / "staging"
|
||||
assert staging_path.exists()
|
||||
staging_crg_path = staging_path / "CRG"
|
||||
assert staging_crg_path.exists()
|
||||
|
||||
crg_files = consume_fluxes(
|
||||
fluxes=FLUXES,
|
||||
origin_path=history_crg_path,
|
||||
dest_path=staging_crg_path,
|
||||
)
|
||||
print(crg_files)
|
||||
@ -4,7 +4,7 @@ import pandas as pd
|
||||
from pydantic import BaseModel, ValidationError
|
||||
|
||||
|
||||
class ValidationInterseptor:
|
||||
class Interseptor:
|
||||
def __init__(self, model: BaseModel):
|
||||
self.model = model
|
||||
self.not_valid_rows = []
|
||||
@ -18,10 +18,8 @@ class ValidationInterseptor:
|
||||
try:
|
||||
self.model(**r)
|
||||
except ValidationError:
|
||||
r["ValidationInterseptorFunc"] = func.__name__
|
||||
r["ValidationInterseptorArgs"] = args
|
||||
r["ValidationInterseptorKwrds"] = kwrds
|
||||
r["ValidationInterseptorIndex"] = i
|
||||
r["InterseptorOrigin"] = func.__name__
|
||||
r["InterseptorIndex"] = i
|
||||
self.not_valid_rows.append(r)
|
||||
else:
|
||||
valid_rows.append(r)
|
||||
|
||||
@ -1,70 +0,0 @@
|
||||
import logging
|
||||
from collections.abc import Callable
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from scripts.flux import CSVSource, Destination, Flux, Transformation, consume_fluxes
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
|
||||
def feature_crg(dfs: list[pd.DataFrame]) -> pd.DataFrame:
|
||||
df = dfs[0]
|
||||
df = df.assign(
|
||||
Impact=df["Crédit"] - df["Débit"],
|
||||
)
|
||||
return df
|
||||
|
||||
|
||||
GOLD_COLUMNS = [
|
||||
"Régie",
|
||||
"Immeuble",
|
||||
"Porte",
|
||||
"Lot",
|
||||
"Année",
|
||||
"Mois",
|
||||
"Catégorie",
|
||||
"Fournisseur",
|
||||
"Libellé",
|
||||
"Débit",
|
||||
"Crédit",
|
||||
"Impact",
|
||||
]
|
||||
|
||||
|
||||
def build_crg_fluxes(
|
||||
crg_path: Path, pattern: str, transformation: Callable, csv_options: dict = {}
|
||||
) -> dict[str, Flux]:
|
||||
fluxes = {}
|
||||
for file in crg_path.glob(pattern):
|
||||
fluxes[f"CRG - {file.name}"] = Flux(
|
||||
sources=[CSVSource(filename=file.name, options=csv_options)],
|
||||
transformation=Transformation(function=transformation),
|
||||
destination=Destination(name=file.name),
|
||||
)
|
||||
return fluxes
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
data_path = Path("datas/")
|
||||
assert data_path.exists()
|
||||
|
||||
staging_path = data_path / "staging"
|
||||
assert staging_path.exists()
|
||||
staging_crg_path = staging_path / "CRG"
|
||||
assert staging_crg_path.exists()
|
||||
|
||||
gold_path = data_path / "gold"
|
||||
assert gold_path.exists()
|
||||
gold_crg_path = gold_path / "CRG"
|
||||
assert gold_crg_path.exists()
|
||||
|
||||
fluxes = build_crg_fluxes(
|
||||
crg_path=staging_crg_path, pattern="*.csv", transformation=feature_crg
|
||||
)
|
||||
crg_files = consume_fluxes(
|
||||
fluxes=fluxes, origin_path=staging_crg_path, dest_path=gold_crg_path
|
||||
)
|
||||
print(crg_files)
|
||||
@ -4,7 +4,7 @@ import pandas as pd
|
||||
import pytest
|
||||
from pydantic import BaseModel
|
||||
|
||||
from scripts.intersept_not_valid import ValidationInterseptor
|
||||
from scripts.intersept_not_valid import Interseptor
|
||||
|
||||
|
||||
class FakeModel(BaseModel):
|
||||
@ -13,7 +13,7 @@ class FakeModel(BaseModel):
|
||||
|
||||
|
||||
def test_init_composed():
|
||||
interceptor = ValidationInterseptor(FakeModel)
|
||||
interceptor = Interseptor(FakeModel)
|
||||
|
||||
def df_generator(nrows=3):
|
||||
records = [{"name": "plop", "age": random.randint(1, 50)} for _ in range(nrows)]
|
||||
@ -27,7 +27,7 @@ def test_init_composed():
|
||||
|
||||
|
||||
def test_init_decorator():
|
||||
interceptor = ValidationInterseptor(FakeModel)
|
||||
interceptor = Interseptor(FakeModel)
|
||||
|
||||
@interceptor
|
||||
def df_generator(nrows=3):
|
||||
@ -40,7 +40,7 @@ def test_init_decorator():
|
||||
|
||||
|
||||
def test_intersept_not_valid():
|
||||
interceptor = ValidationInterseptor(FakeModel)
|
||||
interceptor = Interseptor(FakeModel)
|
||||
|
||||
@interceptor
|
||||
def df_generator():
|
||||
@ -57,9 +57,7 @@ def test_intersept_not_valid():
|
||||
{
|
||||
"name": "hop",
|
||||
"age": "ui",
|
||||
"ValidationInterseptorFunc": "df_generator",
|
||||
"ValidationInterseptorArgs": (),
|
||||
"ValidationInterseptorKwrds": {},
|
||||
"ValidationInterseptorIndex": 1,
|
||||
"InterseptorOrigin": "df_generator",
|
||||
"InterseptorIndex": 1,
|
||||
}
|
||||
]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user