imsciences 0.4.6

IMS Data Processing Package

IMS Package Documentation

The IMS package is a python library for processing incoming data into a format that can be used for projects. IMS processing offers a variety of functions to manipulate and analyze data efficiently. Here are the functionalities provided by the package:

1. get_wd_levels(levels)

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

2. remove_rows(data_frame, num_rows_to_remove)

• Description: Removes a specified number of rows from a pandas DataFrame.
• Usage: remove_rows(data_frame, num_rows_to_remove)

3. aggregate_daily_to_wc(df, date_column, group_columns, sum_columns, wc, aggregation='sum', include_totals=False)

• 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(df, date_column, group_columns, sum_columns, wc, aggregation='sum', include_totals=False)

4. convert_monthly_to_daily(df, date_column)

• 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)

5. plot_two(df1, col1, df2, col2, date_column, same_axis=True)

• Description: Plots specified columns from two different DataFrames using a shared date column. Useful for comparing data.
• Usage: plot_two(df1, col1, df2, col2, date_column, same_axis=True)

6. remove_nan_rows(df, col_to_remove_rows)

• Description: Removes rows from a DataFrame where the specified column has NaN values.
• Usage: remove_nan_rows(df, col_to_remove_rows)

7. filter_rows(df, col_to_filter, list_of_filters)

• Description: Filters the DataFrame based on whether the values in a specified column are in a provided list.
• Usage: filter_rows(df, col_to_filter, list_of_filters)

8. plot_one(df1, col1, date_column)

• Description: Plots a specified column from a DataFrame.
• Usage: plot_one(df1, col1, date_column)

9. week_of_year_mapping(df, week_col, start_day_str)

• 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)

10. exclude_rows(df, col_to_filter, list_of_filters)

• Description: Removes rows from a DataFrame based on whether the values in a specified column are not in a provided list.
• Usage: exclude_rows(df, col_to_filter, list_of_filters)

11. rename_cols(df, cols_to_rename)

• Description: Renames columns in a pandas DataFrame.
• Usage: rename_cols(df, cols_to_rename)

12. merge_new_and_old(old_df, old_col, new_df, new_col, cutoff_date, date_col_name='OBS')

• Description: Creates a new DataFrame with two columns: one for dates and one for merged numeric values.
• Usage: merge_new_and_old(old_df, old_col, new_df, new_col, cutoff_date, date_col_name='OBS')

13. merge_dataframes_on_column(dataframes, common_column='OBS', merge_how='inner')

• Description: Merge a list of DataFrames on a common column.
• Usage: merge_dataframes_on_column(dataframes, common_column='OBS', merge_how='inner')

14. merge_and_update_dfs(df1, df2, key_column)

• Description: Merges two dataframes on a key column, updates the first dataframe's columns with the second's where available, and returns a dataframe sorted by the key column.
• Usage: merge_and_update_dfs(df1, df2, key_column)

15. convert_us_to_uk_dates(df, date_col)

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

16. combine_sheets(all_sheets)

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

17. dynamic_pivot(data_frame, index_col, columns, values_col, fill_value=0)

• Description: Dynamically pivots a DataFrame based on specified columns.
• Usage: dynamic_pivot(df, 'Date', ['Category1', 'Category2'], ['Value1'])

18. classify_within_column(df, col_name, to_find_dict, default_value = 'other')

• Description: Allows you to map a dictionary of substrings within a column.
• Usage: classify_within_column(df, 'campaign', {'uk_': 'uk'}, 'other')