# Performing a Flatmap Operation on a selected column
# containing rows of lists this example uses
# Pythons itertools
import pandas as pd
from itertools import chain


def flatmap(f, items):
        return list(chain.from_iterable(list(map(f, items))))


def create_dict(df, headers_ls, f_var, x):
    temp_dict = dict({})
    for h in headers_ls:
        temp_dict[h] = df[h]
    temp_dict[f_var] = x
    return temp_dict


def flatmap_df(df, flatten_col):
    headers_ls = df.columns
    rec_dict = df[headers_ls].to_dict(orient='records')
    return pd.DataFrame(flatmap(lambda rec_dict: [create_dict(rec_dict,
                                        headers_ls, flatten_col,
                                        col_ls) for col_ls in rec_dict[flatten_col]], rec_dict))


##########
# Create a DataFrame with a row list
##########
print("\nOriginal DataFrame:")
episodes = [
    {"id": 1, "topics": [5,7,8], "date": '2018-01-01'},
    {"id": 2, "topics": [65,43,64], "date": '2018-01-02'}
]
df_epi = pd.DataFrame(episodes)
print(df_epi)
print("\nFlattened DataFrame:")
##########
# Flatten the column  lists
##########
flattened_df = flatmap_df(df_epi[['id', 'topics']], 'topics')
print(flattened_df)