Fix #3: replace empty string with np.nan #5
@ -83,6 +83,5 @@ def table2df(tables):
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df = pd.concat(dfs)
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df = pd.concat(dfs)
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df["immeuble"] = df["immeuble"].apply(lambda x: x[0].capitalize())
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df["immeuble"] = df["immeuble"].apply(lambda x: x[0].capitalize())
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print(df.columns)
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df["lot"] = df["RECAPITULATIF DES OPERATIONS"].apply(get_lot)
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df["lot"] = df["RECAPITULATIF DES OPERATIONS"].apply(get_lot)
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return df.astype(DF_TYPES, errors="ignore")
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return df.astype(DF_TYPES)
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@ -1,3 +1,4 @@
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import numpy as np
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import pandas as pd
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import pandas as pd
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DF_TYPES = {
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DF_TYPES = {
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@ -33,7 +34,7 @@ def is_drop(row):
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def extract(table, additionnal_fields: dict = {}):
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def extract(table, additionnal_fields: dict = {}):
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"""Turn table to dictionary with additionnal fields"""
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"""Turn table to dictionary with additional fields"""
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extracted = []
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extracted = []
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header = table[0]
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header = table[0]
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for row in table[1:]:
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for row in table[1:]:
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@ -159,4 +160,7 @@ def table2df(tables):
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df["immeuble"] = df["immeuble"].apply(lambda x: x[0].capitalize())
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df["immeuble"] = df["immeuble"].apply(lambda x: x[0].capitalize())
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df["Type"] = df["Type"].apply(clean_type)
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df["Type"] = df["Type"].apply(clean_type)
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return df.astype(DF_TYPES, errors="ignore")
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numeric_cols = [k for k, v in DF_TYPES.items() if v == float]
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df[numeric_cols] = df[numeric_cols].replace("", np.nan)
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return df.astype(DF_TYPES)
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