imsciences 1.0.9

IMS Data Processing Package

IMS Package Documentation

The Independent Marketing Sciences package is a Python library designed to process incoming data into a format tailored for projects, particularly those utilising weekly time series data. This package offers a suite of functions for efficient data collection, manipulation, visualisation and analysis.

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Key Features

• Seamless data processing for time series workflows.
• Aggregation, filtering, and transformation of time series data.
• Visualising Data
• Integration with external data sources like FRED, Bank of England and ONS.

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Table of Contents

1. Usage
2. Data Processing for Time Series
3. Data Processing for Incrementality Testing
4. Data Visualisations
5. Data Pulling
6. Installation
7. License
8. Roadmap

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Usage

from imsciences import dataprocessing, geoprocessing, datapull, datavis
ims_proc = dataprocessing()
ims_geo = geoprocessing()
ims_pull = datapull()
ims_vis = datavis()

Data Processing for Time Series

1. get_wd_levels

• Description: Get the working directory with the option of moving up parents.
• Usage: get_wd_levels(levels)
• Example: get_wd_levels(0)

2. aggregate_daily_to_wc_long

• Description: Aggregates daily data into weekly data, grouping and summing specified columns, starting on a specified day of the week.
• Usage: aggregate_daily_to_wc_long(df, date_column, group_columns, sum_columns, wc, aggregation='sum')
• Example: aggregate_daily_to_wc_long(df, 'date', ['platform'], ['cost', 'impressions', 'clicks'], 'mon', 'average')

3. convert_monthly_to_daily

• Description: Converts monthly data in a DataFrame to daily data by expanding and dividing the numeric values.
• Usage: convert_monthly_to_daily(df, date_column, divide=True)
• Example: convert_monthly_to_daily(df, 'date')

4. week_of_year_mapping

• Description: Converts a week column in 'yyyy-Www' or 'yyyy-ww' format to week commencing date.
• Usage: week_of_year_mapping(df, week_col, start_day_str)
• Example: week_of_year_mapping(df, 'week', 'mon')

5. rename_cols

• Description: Renames columns in a pandas DataFrame with a specified prefix or format.
• Usage: rename_cols(df, name='ame_')
• Example: rename_cols(df, 'ame_facebook')

6. merge_new_and_old

• Description: Creates a new DataFrame by merging old and new dataframes based on a cutoff date.
• Usage: merge_new_and_old(old_df, old_col, new_df, new_col, cutoff_date, date_col_name='OBS')
• Example: merge_new_and_old(df1, 'old_col', df2, 'new_col', '2023-01-15')

7. merge_dataframes_on_column

• Description: Merge a list of DataFrames on a common column.
• Usage: merge_dataframes_on_column(dataframes, common_column='OBS', merge_how='outer')
• Example: merge_dataframes_on_column([df1, df2, df3], common_column='OBS', merge_how='outer')

8. merge_and_update_dfs

• Description: Merges two dataframes, updating columns from the second dataframe where values are available.
• Usage: merge_and_update_dfs(df1, df2, key_column)
• Example: merge_and_update_dfs(processed_facebook, finalised_meta, 'OBS')

9. convert_us_to_uk_dates

• Description: Convert a DataFrame column with mixed US and UK date formats to datetime.
• Usage: convert_us_to_uk_dates(df, date_col)
• Example: convert_us_to_uk_dates(df, 'date')

10. combine_sheets

• Description: Combines multiple DataFrames from a dictionary into a single DataFrame.
• Usage: combine_sheets(all_sheets)
• Example: combine_sheets({'Sheet1': df1, 'Sheet2': df2})

11. pivot_table

• Description: Dynamically pivots a DataFrame based on specified columns.
• Usage: pivot_table(df, index_col, columns, values_col, filters_dict=None, fill_value=0, aggfunc='sum', margins=False, margins_name='Total', datetime_trans_needed=True, reverse_header_order=False, fill_missing_weekly_dates=False, week_commencing='W-MON')
• Example: pivot_table(df, 'OBS', 'Channel Short Names', 'Value', filters_dict={'Master Include': ' == 1'}, fill_value=0)

12. apply_lookup_table_for_columns

• Description: Maps substrings in columns to new values based on a dictionary.
• Usage: apply_lookup_table_for_columns(df, col_names, to_find_dict, if_not_in_dict='Other', new_column_name='Mapping')
• Example: apply_lookup_table_for_columns(df, col_names, {'spend': 'spd'}, if_not_in_dict='Other', new_column_name='Metrics Short')

13. aggregate_daily_to_wc_wide

• Description: Aggregates daily data into weekly data and pivots it to wide format.
• Usage: aggregate_daily_to_wc_wide(df, date_column, group_columns, sum_columns, wc='sun', aggregation='sum', include_totals=False)
• Example: aggregate_daily_to_wc_wide(df, 'date', ['platform'], ['cost', 'impressions'], 'mon', 'average', True)

14. merge_cols_with_seperator

• Description: Merges multiple columns in a DataFrame into one column with a specified separator.
• Usage: merge_cols_with_seperator(df, col_names, separator='_', output_column_name='Merged')
• Example: merge_cols_with_seperator(df, ['Campaign', 'Product'], separator='|', output_column_name='Merged Columns')

15. check_sum_of_df_cols_are_equal

• Description: Checks if the sum of two columns in two DataFrames are equal and provides the difference.
• Usage: check_sum_of_df_cols_are_equal(df_1, df_2, cols_1, cols_2)
• Example: check_sum_of_df_cols_are_equal(df_1, df_2, 'Media Cost', 'Spend')

16. convert_2_df_cols_to_dict

• Description: Creates a dictionary from two DataFrame columns.
• Usage: convert_2_df_cols_to_dict(df, key_col, value_col)
• Example: convert_2_df_cols_to_dict(df, 'Campaign', 'Channel')

17. create_FY_and_H_columns

• Description: Adds financial year and half-year columns to a DataFrame based on a start date.
• Usage: create_FY_and_H_columns(df, index_col, start_date, starting_FY, short_format='No', half_years='No', combined_FY_and_H='No')
• Example: create_FY_and_H_columns(df, 'Week', '2022-10-03', 'FY2023', short_format='Yes')

18. keyword_lookup_replacement

• Description: Updates values in a column based on a lookup dictionary with conditional logic.
• Usage: keyword_lookup_replacement(df, col, replacement_rows, cols_to_merge, replacement_lookup_dict, output_column_name='Updated Column')
• Example: keyword_lookup_replacement(df, 'channel', 'Paid Search Generic', ['channel', 'segment'], lookup_dict, output_column_name='Channel New')

19. create_new_version_of_col_using_LUT

• Description: Creates a new column based on a lookup table applied to an existing column.
• Usage: create_new_version_of_col_using_LUT(df, keys_col, value_col, dict_for_specific_changes, new_col_name='New Version of Old Col')
• Example: create_new_version_of_col_using_LUT(df, 'Campaign Name', 'Campaign Type', lookup_dict)

20. convert_df_wide_2_long

• Description: Converts a wide-format DataFrame into a long-format DataFrame.
• Usage: convert_df_wide_2_long(df, value_cols, variable_col_name='Stacked', value_col_name='Value')
• Example: convert_df_wide_2_long(df, ['col1', 'col2'], variable_col_name='Var', value_col_name='Val')

21. manually_edit_data

• Description: Manually updates specified cells in a DataFrame based on filters.
• Usage: manually_edit_data(df, filters_dict, col_to_change, new_value, change_in_existing_df_col='No', new_col_to_change_name='New', manual_edit_col_name=None, add_notes='No', existing_note_col_name=None, note=None)
• Example: manually_edit_data(df, {'col1': '== 1'}, 'col2', 'new_val', add_notes='Yes', note='Manual Update')

22. format_numbers_with_commas

• Description: Formats numerical columns with commas and a specified number of decimal places.
• Usage: format_numbers_with_commas(df, decimal_length_chosen=2)
• Example: format_numbers_with_commas(df, decimal_length_chosen=1)

23. filter_df_on_multiple_conditions

• Description: Filters a DataFrame based on multiple column conditions.
• Usage: filter_df_on_multiple_conditions(df, filters_dict)
• Example: filter_df_on_multiple_conditions(df, {'col1': '>= 5', 'col2': '== 'val''})

24. read_and_concatenate_files

• Description: Reads and concatenates files from a specified folder into a single DataFrame.
• Usage: read_and_concatenate_files(folder_path, file_type='csv')
• Example: read_and_concatenate_files('/path/to/files', file_type='xlsx')

25. upgrade_outdated_packages

• Description: Upgrades all outdated Python packages except specified ones.
• Usage: upgrade_outdated_packages(exclude_packages=['twine'])
• Example: upgrade_outdated_packages(exclude_packages=['pip', 'setuptools'])

26. convert_mixed_formats_dates

• Description: Converts mixed-format date columns into standardized datetime format.
• Usage: convert_mixed_formats_dates(df, column_name)
• Example: convert_mixed_formats_dates(df, 'date_col')

27. fill_weekly_date_range

• Description: Fills in missing weekly dates in a DataFrame with a specified frequency.
• Usage: fill_weekly_date_range(df, date_column, freq='W-MON')
• Example: fill_weekly_date_range(df, 'date_col')

28. add_prefix_and_suffix

• Description: Adds prefixes and/or suffixes to column names, with an option to exclude a date column.
• Usage: add_prefix_and_suffix(df, prefix='', suffix='', date_col=None)
• Example: add_prefix_and_suffix(df, prefix='pre_', suffix='_suf', date_col='date_col')

29. create_dummies

• Description: Creates dummy variables for columns, with an option to add a total dummy column.
• Usage: create_dummies(df, date_col=None, dummy_threshold=0, add_total_dummy_col='No', total_col_name='total')
• Example: create_dummies(df, date_col='date_col', dummy_threshold=1)

30. replace_substrings

• Description: Replaces substrings in a column based on a dictionary, with options for case conversion and new column creation.
• Usage: replace_substrings(df, column, replacements, to_lower=False, new_column=None)
• Example: replace_substrings(df, 'text_col', {'old': 'new'}, to_lower=True, new_column='updated_text')

31. add_total_column

• Description: Adds a total column to a DataFrame by summing values across columns, optionally excluding one.
• Usage: add_total_column(df, exclude_col=None, total_col_name='Total')
• Example: add_total_column(df, exclude_col='date_col')

32. apply_lookup_table_based_on_substring

• Description: Categorizes text in a column using a lookup table based on substrings.
• Usage: apply_lookup_table_based_on_substring(df, column_name, category_dict, new_col_name='Category', other_label='Other')
• Example: apply_lookup_table_based_on_substring(df, 'text_col', {'sub1': 'cat1', 'sub2': 'cat2'})

33. compare_overlap

• Description: Compares overlapping periods between two DataFrames and summarizes differences.
• Usage: compare_overlap(df1, df2, date_col)
• Example: compare_overlap(df1, df2, 'date_col')

34. week_commencing_2_week_commencing_conversion_isoweekday

• Description: Maps dates to the start of the current ISO week based on a specified weekday.
• Usage: week_commencing_2_week_commencing_conversion_isoweekday(df, date_col, week_commencing='mon')
• Example: week_commencing_2_week_commencing_conversion_isoweekday(df, 'date_col', week_commencing='fri')

35. seasonality_feature_extraction

• Description: Splits data into train/test sets, trains XGBoost and Random Forest on all features, extracts top features based on feature importance, merges them, optionally retrains models on top and combined features, and returns a dict of results.
• Usage: seasonality_feature_extraction(df, kpi_var, n_features=10, test_size=0.1, random_state=42, shuffle=False)
• Example: seasonality_feature_extraction(df, 'kpi_total_sales', n_features=5, test_size=0.2, random_state=123, shuffle=True)

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Data Processing for Incrementality Testing

1. pull_ga

• Description: Pull in GA4 data for geo experiments.
• Usage: pull_ga(credentials_file, property_id, start_date, country, metrics)
• Example: pull_ga('GeoExperiment-31c5f5db2c39.json', '111111111', '2023-10-15', 'United Kingdom', ['totalUsers', 'newUsers'])

2. process_itv_analysis

• Description: Processes region-level data for geo experiments by mapping ITV regions, grouping selected metrics, merging with media spend data, and saving the result.
• Usage: process_itv_analysis(self, raw_df, itv_path, cities_path, media_spend_path, output_path, test_group, control_group, columns_to_aggregate, aggregator_list)
• Example: process_itv_analysis(df, 'itv regional mapping.csv', 'Geo_Mappings_with_Coordinates.xlsx', 'IMS.xlsx', 'itv_for_test_analysis_itvx.csv', ['West', 'Westcountry', 'Tyne Tees'], ['Central Scotland', 'North Scotland'], ['newUsers', 'transactions'], ['sum', 'sum'])

3. process_city_analysis

• Description: Processes city-level data for geo experiments by grouping selected metrics, merging with media spend data, and saving the result.
• Usage: process_city_analysis(raw_df, spend_df, output_path, test_group, control_group, columns_to_aggregate, aggregator_list)
• Example: process_city_analysis(df, spend, output, ['Barnsley'], ['Aberdeen'], ['newUsers', 'transactions'], ['sum', 'sum'])

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Data Visualisations

1. plot_one

• Description: Plots a specified column from a DataFrame with white background and black axes.
• Usage: plot_one(df1, col1, date_column)
• Example: plot_one(df, 'sales', 'date')

2. plot_two

• Description: Plots specified columns from two DataFrames, optionally on the same or separate y-axes.
• Usage: plot_two(df1, col1, df2, col2, date_column, same_axis=True)
• Example: plot_two(df1, 'sales', df2, 'revenue', 'date', same_axis=False)

3. plot_chart

• Description: Plots various chart types using Plotly, including line, bar, scatter, area, pie, etc.
• Usage: plot_chart(df, date_col, value_cols, chart_type='line', title='Chart', x_title='Date', y_title='Values')
• Example: plot_chart(df, 'date', ['sales', 'revenue'], chart_type='line', title='Sales and Revenue')

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Data Pulling

1. pull_fred_data

• Description: Fetch data from FRED using series ID tokens.
• Usage: pull_fred_data(week_commencing, series_id_list)
• Example: pull_fred_data('mon', ['GPDIC1', 'Y057RX1Q020SBEA', 'GCEC1', 'ND000333Q', 'Y006RX1Q020SBEA'])

2. pull_boe_data

• Description: Fetch and process Bank of England interest rate data.
• Usage: pull_boe_data(week_commencing)
• Example: pull_boe_data('mon')

3. pull_oecd

• Description: Fetch macroeconomic data from OECD for a specified country.
• Usage: pull_oecd(country='GBR', week_commencing='mon', start_date='2020-01-01')
• Example: pull_oecd('GBR', 'mon', '2000-01-01')

4. get_google_mobility_data

• Description: Fetch Google Mobility data for the specified country.
• Usage: get_google_mobility_data(country, wc)
• Example: get_google_mobility_data('United Kingdom', 'mon')

5. pull_seasonality

• Description: Generate combined dummy variables for seasonality, trends, and COVID lockdowns.
• Usage: pull_seasonality(week_commencing, start_date, countries)
• Example: pull_seasonality('mon', '2020-01-01', ['US', 'GB'])

6. pull_weather

• Description: Fetch and process historical weather data for the specified country.
• Usage: pull_weather(week_commencing, start_date, country)
• Example: pull_weather('mon', '2020-01-01', 'GBR')

7. pull_macro_ons_uk

• Description: Fetch and process time series data from the Beta ONS API.
• Usage: pull_macro_ons_uk(additional_list, week_commencing, sector)
• Example: pull_macro_ons_uk(['HBOI'], 'mon', 'fast_food')

8. pull_yfinance

• Description: Fetch and process time series data from Yahoo Finance.
• Usage: pull_yfinance(tickers, week_start_day)
• Example: pull_yfinance(['^FTMC', '^IXIC'], 'mon')

9. pull_sports_events

• Description: Pull a veriety of sports events primaraly football and rugby.
• Usage: pull_sports_events(start_date, week_commencing)
• Example: pull_sports_events('2020-01-01', 'mon')

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Installation

Install the IMS package via pip:

pip install imsciences

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License

This project is licensed under the MIT License.

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Roadmap

• [Fixes]: Naming conventions are inconsistent/ have changed from previous seasonality tools (eg. 'seas_nyd' is named 'seas_new_years_day', 'week_1' is named 'seas_1')
• [Fixes]: Naming conventions can be inconsistent within the data pull (suffix on some var is 'gb' on some it is 'uk' and for others there is no suffix) - furthermore, there is a lack of consistency for global holidays/events (Christmas, Easter, Halloween, etc) - some have regional suffix and others don't.
• [Additions]: Need to add new data pulls for more macro and seasonal varibles

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