Feat: isolate category replacements
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@ -78,8 +78,7 @@ def crg(history_path: Path, staging_path: Path, metadatas: dict, writing_func=to
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return wrote_files
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return wrote_files
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def trans_2017_2021(dfs, **kwrds):
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def extract_cat(cat):
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df, cat = dfs
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cat_drop = list(cat[cat["Nouvelles"] == "NE PAS IMPORTER"]["Anciennes"])
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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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cat_trans = cat[cat["Nouvelles"] != "NE PAS IMPORTER"]
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@ -87,6 +86,13 @@ def trans_2017_2021(dfs, **kwrds):
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for _, (old, new) in cat_trans.iterrows():
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for _, (old, new) in cat_trans.iterrows():
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trans[old] = new
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trans[old] = new
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return trans, cat_drop
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def trans_2017_2021(dfs, **kwrds):
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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[~df["Categorie"].isin(cat_drop)]
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df = df.assign(
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df = df.assign(
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@ -97,7 +103,7 @@ def trans_2017_2021(dfs, **kwrds):
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Lot=df["immeuble"].astype(str) + df["porte"].astype("str").str.zfill(2),
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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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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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Mois=df["Date"].astype(str).str.slice(5, 7),
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Catégorie=df["Categorie"].replace(trans),
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Catégorie=df["Categorie"].replace(cat_trans),
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Fournisseur="",
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Fournisseur="",
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)
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)
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