create a new column by looping over list of dictionary and based on specific date condition in pandas

Question

I have a df and list of dictionary as shown below.

df:

Date                t_factor     
2020-02-01             5             
2020-02-02             2         


        

Answer 1

Define a function time_objective which takes arguments as dataframe and param_obj_list and returns the dataframe with new column added. Here we have used Series.between to create a boolean mask and using boolean indexing with this mask, fill the values according to the requirements:

def time_objective(df, param_obj_list):
    df['new_col'] = np.nan
    for d in param_obj_list:
        if 'from' not in d or 'to' not in d \
             or d['from'] == 0 or d['to'] == 0:
            continue

        if len(d['coef']) != 6:
            print('Exception: Coefficients index do not match')
            return df

        a0, a1, a2, a3, a4, a5 = d['coef']

        start = pd.Timestamp(d['from']).strftime('%Y-%m-%d')
        end = pd.Timestamp(d['to']).strftime('%Y-%m-%d')

        T = df['Date'].sub(pd.Timestamp(start)).dt.days
        mask = df['Date'].between(start, end, inclusive=True)

        if d['type'] == 'df_first':
            df.loc[mask, 'new_col'] = df['t_factor']
        elif d['type'] == 'quadratic':
            df.loc[mask, 'new_col'] = a0 + a1 * T + a2 * (T)**2 + df['new_col'].ffill()
        elif d['type'] == 'linear':
            df.loc[mask, 'new_col'] = a0 + a1 * T + df['new_col'].ffill()
        elif d['type'] == 'polynomial':
            df.loc[mask, 'new_col'] = a0 + a1*(T) + a2*(T)**2 + a3 * \
                (T)**3 + a4*(T)**4 + a5*(T)**5 + df['new_col'].ffill()
    return df

Result:

         Date  t_factor  new_col
0  2020-02-01         5      5.0
1  2020-02-02        23     23.0
2  2020-02-03        14     14.1
3  2020-02-04        23     14.3
4  2020-02-05        23     14.7
5  2020-02-06        23     15.4
6  2020-02-07        30     15.5
7  2020-02-08        29     15.6
8  2020-02-09       100     15.7
9  2020-02-10        38     15.9
10 2020-02-11        38     16.4
11 2020-02-12        38     22.1
12 2020-02-13        70     52.2
13 2020-02-14        70    152.3