utils4text.py
def apply_parallel(grouped_df, func):
    """
    Parallelize the 'build_convs' function by grouping each post and its comments.
    And then concatenate all of them into a complete dataframe.

    Arg:
        grouped_df: A dataframe on which groupby function is applied.
    Return:
        pd.concat(retLst): A complete dataframe with the conversation sets between posts and comments.  
    """
    retLst = Parallel(n_jobs = mp.cpu_count())(delayed(func)(group) for id, group in grouped_df)
    return pd.concat(retLst)

    Parallelize the 'build_convs' function by grouping each post and its comments.
    And then concatenate all of them into a complete dataframe.

    Arg:
        grouped_df: A dataframe on which groupby function is applied.
    Return:
        pd.concat(retLst): A complete dataframe with the conversation sets between posts and comments.  
    
Parallelize the 'build_convs' function by grouping each post and its comments.
And then concatenate all of them into a complete dataframe.

A dataframe on which groupby function is applied.
Return:
pd.concat(retLst): A complete dataframe with the conversation sets between posts and comments.
Parallelize the ' build_convs ' function by grouping each post and its comments . And then concatenate all of them into a complete dataframe . A dataframe on which groupby function is applied . Return : pd . concat ( retLst ) : A complete dataframe with the conversation sets between posts and comments .
def apply_parallel(grouped_df, func):
    retLst = Parallel(n_jobs = mp.cpu_count())(delayed(func)(group) for id, group in grouped_df)
    return pd.concat(retLst)
def apply_parallel ( grouped_df , func ) : retLst = Parallel ( n_jobs = mp . cpu_count ( ) ) ( delayed ( func ) ( group ) for id , group in grouped_df ) return pd . concat ( retLst )
Parallelize the 'build_convs' function by grouping each post and its comments.
Parallelize the ' build_convs ' function by grouping each post and its comments .
"""
    Parallelize the 'build_convs' function by grouping each post and its comments.
    And then concatenate all of them into a complete dataframe.

    Arg:
        grouped_df: A dataframe on which groupby function is applied.
    Return:
        pd.concat(retLst): A complete dataframe with the conversation sets between posts and comments.  
    """
grouped_df
func