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        "author": "Victor Popov",
        "author_email": "victorvtf@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Build Tools"
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        "description": "This module provides a handful of functions to simplify the typical data processing operations and simplifying data verification procedures.\n\n# Dependencies\n* `numpy 1.17.1`\n* `pandas 0.25.1`\n\n# Installation Guide\n\n* `pip install helper-funcs`\n\n# Usage\n\nImport class \"HF\" from module \"helper_funcs\":\n\n* `from helper_funcs import HF`\n\nAnd then call any of the methods described below.\n\n# Methods\n\n---\n\n* `df_preview(df, n_samples)`\n\n    ***Description***\n    \n    Creates a nice summary table of your DataFrame.\n    \n    ***Parameters***\n    \n    * **`df`: pandas.DataFrame**\n    \n        The DataFrame you want to create a preview for.\n           \n    * **`n_samples`: int, optional (default = 2)**\n    \n        Number of unique values from each column to be displayed.\n    \n    ***Returns***\n    \n    * pandas.DataFrame containing the summary information about the passed DataFrame.\n    \n---\n    \n* `rename_col(df, old_name, new_name)`\n\n    ***Description***\n    \n    Renames the specified column.\n    \n    ***Parameters***\n    \n    * **`df`: pandas.DataFrame**\n    \n        The DataFrame you want to create a preview for.\n           \n    * **`old_name`: str**\n    \n        Name of existing `df` column to be renamed.\n        \n    * **`new_name`: str**\n    \n        Name which will replace the `old_name` column name.\n    \n    ***Returns***\n    \n    * pandas.DataFrame with the renamed column.\n    \n---\n    \n* `columns_mismatch(col_1, col_2)`\n\n    ***Description***\n    \n    Extracts values that are present in `col_1`, but not in `col_2`.\n    \n    ***Parameters***\n    \n    * **`col_1`: pandas.Series**\n    \n        The Series you want to subtract values from.\n           \n    * **`col_2`: pandas.Series**\n    \n        The Series which is subtracted from `col_1`.\n        \n    Note: The word \"subtract\" is used not in arithmetical sense, but in a set difference sense.\n    \n    ***Returns***\n    \n    * Set with values which `col_1` contains and `col_2` does not contain.\n    \n---\n\n* `df_difference(df_1, df_2)`\n\n    ***Description***\n    \n    Extracts rows that are present in `df_1`, but not in `df_2`. \n    \n    Note: `df_1` and `df_2` can have different column names, but number of columns should match.\n    \n    ***Parameters***\n    \n    * **`df_1`: pandas.DataFrame**\n    \n        The DataFrame you want to subtract values from.\n           \n    * **`df_2`: pandas.DataFrame**\n    \n        The DataFrame which is subtracted from `df_1`.\n        \n    Note: The word \"subtract\" is used not in arithmetical sense, but in a set difference sense.\n    \n    ***Returns***\n    \n    * pandas.DataFrame with rows which `df_1` contains and `df_2` does not contain.\n    \n---\n    \n* `verify_dates_integity(df, date_col)`\n\n    ***Description***\n    \n    Checks whether there are any missing dates between earliest and latest dates from `df[date_col]`\n    \n    ***Parameters***\n    \n    * **`df`: pandas.DataFrame**\n    \n        The DataFrame which after selecting values from `date_col` will be verified for integrity\n           \n    * **`date_col`: str**\n    \n        Name of `df` column that will be verified for integrity\n        \n---\n       \n* `duplicate(df, how, n_times)`\n\n    ***Description***\n    \n    Extends the specified DataFrame by repeating its rows.\n    \n    ***Parameters***\n    \n    * **`df`: pandas.DataFrame**\n    \n        The DataFrame which rows you want to repeat\n           \n    * **`how`: str**\n    \n        Strategy for repeating. Should be either 'whole' (then [1,2] -> [1,2,1,2]) or\n        'element_wise' (then [1,2] -> [1,1,2,2])\n        \n    * **`n_times`: int**\n    \n        Number of repetitions of each row\n    \n    ***Returns***\n    \n    * Extended pandas.DataFrame with repeated rows\n    \n---\n    \n* `groupby_to_list(df, by_cols, col_to_list)`\n\n    ***Description***\n    \n    Extracts values of `col_to_list` column that correspond to the same values in \n    `by_cols` column(s) and put them to list.\n    \n    ***Parameters***\n    \n    * **`df`: pandas.DataFrame**\n    \n        The DataFrame which you want to use\n           \n    * **`by_cols`: list of str**\n    \n        Column names that will be used as keys in `df`\n        \n    * **`col_to_list`: str**\n    \n        Column name which values will be put to lists\n    \n    ***Returns***\n    \n    * pandas.DataFrame with columns [`by_cols`, `col_to_list`] so that all the values in `col_to_list` column are lists.\n    \n---\n    \n* `chunkenize(data_to_split, num_chunks, df_indices, copy)`\n\n    ***Description***\n    \n    Splits the `data_to_split` into list with `num_chunks` chunks. Can be helpful when preparing \n    data for parallel processing.\n    \n    ***Parameters***\n    \n    * **`data_to_split`: pandas.DataFrame or list**\n    \n        The DataFrame which you want to split in chunks\n        \n    * **`num_chunks`: int**\n    \n        Number of chunks that your data will be split in\n        \n    * **`df_indices`: list of str, optional (default = [])**\n    \n        This can be used when `data_to_split` is pandas.DataFrame. These column will be used\n        as DataFrame index before splitting and will be reset afterwards.\n        \n    * **`copy`: bool, optional (default = True)**\n    \n        Determines whether you want to perform splitting on a copy of `data_to_split`.\n    \n    ***Returns***\n    \n    * List of `num_chunks` chunks that have same type as `data_to_split`.\n    \n---\n\n* `filter_df(df, col_name, l_bound, r_bound, inclusive)`\n\n    ***Description***\n    \n    Filters the `df` DataFrame `col_name` column so that it contains only records\n    that corresponds to `df`[`col_name`] values in the range between `l_bound` and `r_bound`.\n    \n    ***Parameters***\n    \n    * **`df`: pandas.DataFrame**\n    \n        The DataFrame which column `col_name` you want to filter\n           \n    * **`col_name`: str**\n    \n        Column name from `df` which values you want to filter `df` on\n        \n    * **`l_bound`: same type as values of `df`[`col_name`]**\n    \n        Left bound of the filtered values range. Can be omitted if `r_bound` is specified\n        \n    * **`r_bound`: same type as values of `df`[`col_name`]**\n    \n        Right bound of the filtered values range. Can be omitted if `l_bound` is specified\n        \n    * **`inclusive`: bool, optional (default = True)**\n    \n        Determines whether you want range to be inclusive (True) or exclusive (False)\n    \n    ***Returns***\n    \n    * Filtered pandas.DataFrame\n        \n---\n\n* `prepare_str_cols(df, make_uppercase)`\n\n    ***Description***\n    \n    Strips leading and trailing spaces in str columns of `df` and makes those values to either upper-case or lower-case.\n    \n    ***Parameters***\n    \n    * **`df`: pandas.DataFrame**\n    \n        The DataFrame you want to prepare str columns for.\n           \n    * **`make_uppercase`: bool**\n    \n        Determines whether you want str values to be upper-cased or lower-cased.\n    \n    ***Returns***\n    \n    * pandas.DataFrame where all strings are either upper-cased or lower-cased with all leading and trailing spaces removed.\n---",
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