import pandas as pd

# Example 1 year of quarterly expenditure series that is downloaded from ONS.
d = {
    'date': ['1970-01-01', '1970-04-01', '1970-07-01', '1970-10-01'],
    'value': [30, 60, 120, 240],
    'fin_quarter': [1, 2, 3, 4]}
df = pd.DataFrame(data=d)

df['quarter_date'] = pd.to_datetime(df['date'])
df['fin_quarter'] = df['quarter_date'].dt.quarter.astype(str)
df['value'] = pd.to_numeric(df['value'], downcast='float')

quarters = {'1': ['01-01','02-01','03-01'], '2': ['04-01','05-01','06-01'],
            '3': ['07-01','08-01','09-01'], '4': ['10-01', '11-01', '12-01']
           }

df_monthly = (df
    .assign(month_value=lambda x: x['value'] / 3,
            month=lambda x: x['fin_quarter'].map(quarters),
            year=lambda x: x['quarter_date'].dt.year.astype(str)
           )
    .explode('month')
)

df_monthly['month_date'] = df_monthly['year'] + '-' + df_monthly['month']

df_monthly = df_monthly.reset_index(drop=True)
df_monthly['quarter_date'] = df_monthly['quarter_date'].astype(str)

df_monthly = df_monthly.filter(items=['quarter_date', 'value', 'month_value', 'month_date'])