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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()
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