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""" |
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Data Manipulation class |
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""" |
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# package to add support for multi-language (i18n) |
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import gettext |
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# package to handle files/folders and related metadata/operations |
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import os |
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class DataManipulator: |
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lcl = None |
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def __init__(self, default_language='en_US'): |
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current_script = os.path.basename(__file__).replace('.py', '') |
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lang_folder = os.path.join(os.path.dirname(__file__), current_script + '_Locale') |
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self.lcl = gettext.translation(current_script, lang_folder, languages=[default_language]) |
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def fn_add_and_shift_column(self, local_logger, timmer, input_data_frame, input_details): |
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for in_dt in input_details: |
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timmer.start() |
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input_data_frame[in_dt['New Column']] = input_data_frame[in_dt['Original Column']] |
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offset_sign = (lambda x: 1 if x == 'down' else -1) |
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col_offset = offset_sign(in_dt['Direction']) * in_dt['Deviation'] |
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input_data_frame[in_dt['New Column']] = input_data_frame[in_dt['New Column']]\ |
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.shift(col_offset) |
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input_data_frame[in_dt['New Column']] = input_data_frame[in_dt['New Column']]\ |
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.apply(lambda x: str(x).replace('.0', ''))\ |
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.apply(lambda x: str(x).replace('nan', str(in_dt['Empty Values Replacement']))) |
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local_logger.info(self.lcl.gettext( |
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'A new column named "{new_column_name}" as copy from "{original_column}" ' |
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+ 'then shifted by {shifting_rows} to relevant data frame ' |
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+ '(filling any empty value as {empty_values_replacement})') |
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.replace('{new_column_name}', in_dt['New Column']) |
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.replace('{original_column}', in_dt['Original Column']) |
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.replace('{shifting_rows}', str(col_offset)) |
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.replace('{empty_values_replacement}', |
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str(in_dt['Empty Values Replacement']))) |
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timmer.stop() |
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return input_data_frame |
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@staticmethod |
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def fn_add_minimum_and_maximum_columns_to_data_frame(input_data_frame, dict_expression): |
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grouped_df = input_data_frame.groupby(dict_expression['group_by']) \ |
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.agg({dict_expression['calculation']: ['min', 'max']}) |
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grouped_df.columns = ['_'.join(col).strip() for col in grouped_df.columns.values] |
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grouped_df = grouped_df.reset_index() |
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if 'map' in dict_expression: |
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grouped_df.rename(columns=dict_expression['map'], inplace=True) |
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return grouped_df |
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def fn_apply_query_to_data_frame(self, local_logger, timmer, input_data_frame, extract_params): |
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timmer.start() |
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query_expression = '' |
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generic_pre_feedback = self.lcl.gettext('Will retain only values {filter_type} ' |
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+ '"{filter_values}" within the field ' |
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+ '"{column_to_filter}"') \ |
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.replace('{column_to_filter}', extract_params['column_to_filter']) |
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if extract_params['filter_to_apply'] == 'equal': |
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local_logger.debug(generic_pre_feedback |
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.replace('{filter_type}', self.lcl.gettext('equal with')) |
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.replace('{filter_values}', extract_params['filter_values'])) |
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query_expression = '`' + extract_params['column_to_filter'] + '` == "' \ |
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+ extract_params['filter_values'] + '"' |
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elif extract_params['filter_to_apply'] == 'different': |
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local_logger.debug(generic_pre_feedback |
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.replace('{filter_type}', self.lcl.gettext('different than')) |
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.replace('{filter_values}', extract_params['filter_values'])) |
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query_expression = '`' + extract_params['column_to_filter'] + '` != "' \ |
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+ extract_params['filter_values'] + '"' |
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elif extract_params['filter_to_apply'] == 'multiple_match': |
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multiple_values = '["' + '", "'.join(extract_params['filter_values'].values()) + '"]' |
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local_logger.debug(generic_pre_feedback |
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.replace('{filter_type}', |
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self.lcl.gettext('matching any of these values')) |
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.replace('{filter_values}', multiple_values)) |
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query_expression = '`' + extract_params['column_to_filter'] + '` in ' + multiple_values |
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local_logger.debug(self.lcl.gettext('Query expression to apply is: {query_expression}') |
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.replace('{query_expression}', query_expression)) |
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input_data_frame.query(query_expression, inplace=True) |
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timmer.stop() |
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return input_data_frame |
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def fn_filter_data_frame_by_index(self, local_logger, in_data_frame, filter_rule): |
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reference_expression = filter_rule['Query Expression for Reference Index'] |
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index_current = in_data_frame.query(reference_expression, inplace=False) |
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local_logger.info(self.lcl.gettext( |
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'Current index has been determined to be {index_current_value}') |
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.replace('{index_current_value}', str(index_current.index))) |
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if str(index_current.index) != "Int64Index([], dtype='int64')" \ |
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and 'Deviation' in filter_rule: |
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for deviation_type in filter_rule['Deviation']: |
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deviation_number = filter_rule['Deviation'][deviation_type] |
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index_to_apply = index_current.index |
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if deviation_type == 'Lower': |
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index_to_apply = index_current.index - deviation_number |
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in_data_frame = in_data_frame[in_data_frame.index >= index_to_apply[0]] |
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elif deviation_type == 'Upper': |
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index_to_apply = index_current.index + deviation_number |
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in_data_frame = in_data_frame[in_data_frame.index <= index_to_apply[0]] |
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local_logger.info(self.lcl.gettext( |
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'{deviation_type} Deviation Number is {deviation_number} ' |
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+ 'to be applied to Current index, became {index_to_apply}') |
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.replace('{deviation_type}', deviation_type) |
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.replace('{deviation_number}', str(deviation_number)) |
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.replace('{index_to_apply}', str(index_to_apply))) |
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return in_data_frame |
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@staticmethod |
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def fn_get_column_index_from_dataframe(data_frame_columns, column_name_to_identify): |
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column_index_to_return = 0 |
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for ndx, column_name in enumerate(data_frame_columns): |
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if column_name == column_name_to_identify: |
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column_index_to_return = ndx |
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return column_index_to_return |
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@staticmethod |
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def fn_get_first_and_last_column_value_from_data_frame(in_data_frame, in_column_name): |
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return { |
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'first': in_data_frame.iloc[0][in_column_name], |
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'last': in_data_frame.iloc[(len(in_data_frame) - 1)][in_column_name], |
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} |
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danielgp /
db-extractor
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