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| import pandas as pd | |
| CAT_COLUMNS = ['currency', 'operation_kind', 'card_type', | |
| 'operation_type', 'operation_type_group', 'ecommerce_flag', | |
| 'payment_system', 'income_flag', 'mcc', 'country', 'city', | |
| 'mcc_category', 'day_of_week', 'hour','weekofyear'] | |
| NUMERIC_COLUMNS = ['days_before', 'hour_diff'] | |
| REAL_COLUMNS = ['amnt'] | |
| def __amnt_pivot_table_by_column_as_frame(frame, column, agg_funcs=None) -> pd.DataFrame: | |
| """ | |
| Generates pivot table for `app_id` and a specified column by aggregating `amnt` column | |
| :param frame: pd.DataFrame containing card transactions | |
| :param column: column with keys to group by on the pivot table column | |
| :param agg_funcs: list of aggregation functions, default is ['sum', 'mean', 'count'] | |
| :return: pd.DataFrame pivot table | |
| """ | |
| if agg_funcs is None: | |
| agg_funcs = ['sum', 'mean', 'count'] | |
| aggs = pd.pivot_table(frame, values='amnt', | |
| index=['app_id'], columns=[column], | |
| aggfunc={'amnt': agg_funcs}, | |
| fill_value=0) | |
| aggs.columns = [f'amnt_{col[0]}_{column}_{col[1]}' for col in aggs.columns.values] | |
| return aggs | |
| def extract_basic_aggregations(transactions_frame: pd.DataFrame, cat_columns=None, agg_funcs=None) -> pd.DataFrame: | |
| """ | |
| Extracts basic features from a card transaction dataframe | |
| :param transactions_frame: pd.DataFrame containing card transactions | |
| :param cat_columns: list of categorical columns for which we want to aggregate `amnt`, default is all | |
| :param agg_funcs: list of aggregation functions for cat_columns, default is | |
| ['sum', 'mean', 'count'] | |
| :return: pd.DataFrame with extracted features | |
| """ | |
| if not cat_columns: | |
| cat_columns = CAT_COLUMNS | |
| if not agg_funcs: | |
| agg_funcs = ['sum', 'mean', 'count'] | |
| pivot_tables = [] | |
| for col in cat_columns: | |
| pivot_tables.append(__amnt_pivot_table_by_column_as_frame(transactions_frame, column=col, | |
| agg_funcs=agg_funcs)) | |
| pivot_tables = pd.concat(pivot_tables, axis=1) | |
| # we will also generate total statistics grouped by app_id | |
| aggs = { | |
| # transation amount | |
| 'amnt': ['max', 'min', 'mean', 'median', 'sum', 'std'], | |
| # time difference between transactions | |
| 'hour_diff': ['max', 'mean', 'median', 'var', 'std'], | |
| # days left before application at the moment when transaction took place | |
| 'days_before': ['min', 'max', 'median']} | |
| numeric_stats = transactions_frame.groupby(['app_id']).agg(aggs) | |
| numeric_stats.columns = numeric_stats.columns.map('_'.join) | |
| return pd.concat([pivot_tables, numeric_stats], axis=1).reset_index() | |