通过遍历字典列表并基于熊猫中的特定日期条件来创建新列

Question

我有一个df和字典列表，如下所示。

df：

Date                t_factor     
2020-02-01             5             
2020-02-02             23              
2020-02-03             14           
2020-02-04             23
2020-02-05             23  
2020-02-06             23          
2020-02-07             30            
2020-02-08             29            
2020-02-09             100
2020-02-10             38
2020-02-11             38               
2020-02-12             38                    
2020-02-13             70           
2020-02-14             70

param_list：

param_obj_list = [{'type': 'df_first',
  'from': '2020-02-01T20:00:00.000Z',
  'to': '2020-02-03T20:00:00.000Z',
  'days': 0,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
 {'type': 'quadratic',
  'from': '2020-02-03T20:00:00.000Z',
  'to': '2020-02-06T20:00:00.000Z',
  'days': 3,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
 {'type': 'linear',
  'from': '2020-02-06T20:00:00.000Z',
  'to': '2020-02-10T20:00:00.000Z',
  'days': 3,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
 {'type': 'polynomial',
  'from': '2020-02-10T20:00:00.000Z',
  'to': '2020-02-14T20:00:00.000Z',
  'days': 3,
  'coef': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]}]

从以上我想基于字典指定的“类型”和日期列在df中创建一个新列。

我从下面的代码开始

import pandas as pd
import numpy as np
import datetime as DT


def time_objective(df, param_obj_list)
    for params_obj in param_obj_list:
        # Do the data processing
        start_date, end_date, label, coef, n_days = params_obj['from'], params_obj['to'], params_obj['type'], params_obj['coef'], params_obj['days']
        print(start_date, end_date, label, coef, n_days)

        start_date = DT.datetime.strptime(start_date, "%Y-%m-%dT%H:%M:%S.%fZ")
        print(start_date)
        if (start_date == 0) | (end_date == 0):
            return df
        elif:
            if len(coef) == 6:
                # Coefficients Index Initializations
                a0 = coef[0]
                a1 = coef[1]
                a2 = coef[2]
                a3 = coef[3]
                a4 = coef[4]
                a5 = coef[5]
                if label == 'df_first':
                    df['Date'] = pd.to_datetime(df['Date'])
                    m = df['Date'].between(start_date, end_date, inclusive=True)
                    df.loc[m, 't_factor'] =

说明：

if "type" == df_first:
    df['new_col'] = df['t_factor'] (duration only from the "from" and "to" date specified in that dictionary)

elif "type" == "quadratic":
     df['new_col'] = a0 + a1*(T) + a2*(T)**2 + previous value of df['new_col']
     where T = 1 for one day after the "from" date of that dictionary and T counted in days based Date value

elif "type" == "linear":
     df['new_col'] = a0 + a1*(T) + previous value of df['new_col']
     where T = 1 for one day after the "from" date of that dictionary.

elif "type" == "polynomial":
     df['new_col'] = a0 + a1*(T) + a2*(T)**2  + a3*(T)**3  + a4*(T)**4  + a5*(T)**5 + previous value of df['new_col']
     where T = 1 for start_date of that dictionary.

预期产量：

Date                t_factor      new_col  
2020-02-01             5          5      
2020-02-02             23         23      
2020-02-03             14         14  
2020-02-04             23         14 + 0.1 + 0.1*(1) + 0.1*(1)**2
2020-02-05             23         14 + 0.1 + 0.1*(2) + 0.1*(2)**2
2020-02-06             23         14 + 0.1 + 0.1*(3) + 0.1*(3)**2 = 15.3     
2020-02-07             30         15.3 + 0.1 + 0.1*(1)    
2020-02-08             29         15.3 + 0.1 + 0.1*(2)   
2020-02-09             100        15.3 + 0.1 + 0.1*(3)
2020-02-10             38         15.3 + 0.1 + 0.1*(4) = 15.8
2020-02-11             38         15.8 +0.1+0.1*(1)+0.1*(1)**2+0.1*(1)**3+0.1*(1)**4+0.1*(1)**5     
2020-02-12             38         15.8 +0.1+0.1*(2)+0.1*(2)**2+0.1*(2)**3+0.1*(2)**4+0.1*(2)**5             
2020-02-13             70         15.8 +0.1+0.1*(3)+0.1*(3)**2+0.1*(3)**3+0.1*(3)**4+0.1*(3)**5      
2020-02-14             70         15.8 +0.1+0.1*(4)+0.1*(4)**2+0.1*(4)**3+0.1*(4)**4+0.1*(4)**5

Answer 1

定义一个函数time_objective，该函数接受dataframe和作为参数，param_obj_list并返回添加了新列的数据框。在这里，我们已经使用Series.between来创建一个boolean mask和并使用boolean indexing此掩码，根据要求填充值：

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

结果：

         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