How to use the swifter.progress_bar function in swifter

def apply_substring_array_filter(df, series, pattern):
    """ Filters dataframe.

        Parameters:
            df(DataFrame): defect reports' file parsed to pandas DataFrame;
            series(str): series name;
            pattern(str): the value you're looking for.

        Returns:
            filtered_df(DataFrame): filtered dataframe.
    """
    filtered_df = df[df[series].swifter.progress_bar(
        enable=False, desc=None).apply(compare_words, args=(pattern,))]
    return filtered_df[filtered_df[series].str.contains(
        pattern, case=False, na=False, regex=False)]

def get_statistical_info(df):
    """ Statistical info calculation.

    Parameters:
        df (DataFrame): defect reports' file parsed to pandas DataFrame.

    Returns:
        dict object filled in calculated statistics.

    """
    comments = df['Comments'].swifter.progress_bar(
        enable=False, desc=None).apply(int)
    attachments = df['Attachments'].swifter.progress_bar(
        enable=False, desc=None).apply(int)
    return {'comments_stat': {
        'min': str(comments.min()),
        'max': str(comments.max()),
        'mean': str(int(math_round(comments.mean()))),
        'std': str(int(math_round(numpy.nan_to_num(comments.std()))))
    },
        'attachments_stat': {
        'min': str(attachments.min()),
        'max': str(attachments.max()),
        'mean': str(int(math_round(attachments.mean()))),
        'std': str(int(math_round(numpy.nan_to_num(attachments.std()))))
    },
        'ttr_stat': {
        'min': str(df['ttr'].min()),
        'max': str(df['ttr'].max()),

def transform_series(df, defect_attributes):
    """ Transforms series to make theirs' data ready for analysis.

    Parameters:
        df (DataFrame): defect reports' file parsed to pandas DataFrame;
        defect_attributes (dict): defect attributes configurations;.

    Returns:
        DataFrame with transformed series appended.

    """
    df['Resolved'] = df['Resolved'].fillna(
        value='').astype(str).swifter.progress_bar(
    enable=False, desc=None).apply(
        convert_date)
    df['Created'] = df['Created'].fillna(
        value='').astype(str).swifter.progress_bar(
    enable=False, desc=None).apply(
        convert_date)
    for group in ['special_attributes', 'mandatory_attributes']:
        for attribute in defect_attributes[group]:
            df[attribute] = apply_datatype(
                df[attribute], attribute, defect_attributes[group][attribute]['type'])     
    pool = Pool()
    df['Description_tr'] = pool.map(clean_description, df['Description'])
    pool.close()
    pool.join()

    df['Resolved_tr'] = df['Resolved'].fillna(
        value=datetime.date.today())
    df['ttr'] = (df['Resolved_tr'] - df['Created']).dt.days
    defect_attributes['special_attributes']['ttr'] = {

series,
        area_of_testing,
        patterns):
    """ Appends binarized series to df.

    Parameters:
        df (DataFrame): defect reports' file parsed to pandas DataFrame;
        series (str): df series name;
        area_of_testing (str):
        patterns (str): searching elements.

    Returns:
        The whole df with binarized series.

    """
    df[area_of_testing] = df[series].swifter.progress_bar(
        enable=False, desc=None).apply(
        binarize_value, args=(
            patterns,))
    return df