75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
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import pandas as pd
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class Comparison:
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@staticmethod
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def normalize_value(val):
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"""Normalize non-numeric values (date, string)."""
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if pd.isna(val):
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return ""
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# Normalize dates
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if isinstance(val, pd.Timestamp):
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return val.date().isoformat()
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# Normalize strings
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return str(val).strip().upper().replace(" ", "")
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@staticmethod
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def round_amount(val, decimals=0):
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"""Round numeric values safely for comparison."""
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if pd.isna(val):
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return ""
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try:
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return round(float(val), decimals)
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except Exception:
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return val
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@staticmethod
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def find_unmatched_rows(sheet1_df, sheet2_df):
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# Clean column names
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sheet1_df.columns = sheet1_df.columns.str.strip()
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sheet2_df.columns = sheet2_df.columns.str.strip()
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comparison_columns = [
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'Date',
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'GSTIN/UIN',
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'Voucher Ref. No.',
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'Total Tax',
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'Total Amount'
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]
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amount_columns = ['Total Tax', 'Total Amount']
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# Validate required columns
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for col in comparison_columns:
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if col not in sheet1_df.columns or col not in sheet2_df.columns:
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raise ValueError(f"Missing column '{col}' in one of the sheets.")
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# Normalize values
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for col in comparison_columns:
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if col in amount_columns:
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sheet1_df[col] = sheet1_df[col].apply(
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lambda x: Comparison.round_amount(x, 0)
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)
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sheet2_df[col] = sheet2_df[col].apply(
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lambda x: Comparison.round_amount(x, 0)
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)
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else:
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sheet1_df[col] = sheet1_df[col].apply(Comparison.normalize_value)
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sheet2_df[col] = sheet2_df[col].apply(Comparison.normalize_value)
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# Create comparison keys
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sheet1_keys = sheet1_df[comparison_columns].apply(tuple, axis=1)
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sheet2_keys = sheet2_df[comparison_columns].apply(tuple, axis=1)
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# Find unmatched rows
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unmatched_sheet1 = sheet1_df[~sheet1_keys.isin(sheet2_keys)].copy()
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unmatched_sheet2 = sheet2_df[~sheet2_keys.isin(sheet1_keys)].copy()
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# Add source column
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unmatched_sheet1["Source"] = "Portal"
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unmatched_sheet2["Source"] = "Tally"
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return unmatched_sheet1, unmatched_sheet2
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