根据特定条件和输入字典生成数据框- pandas [英] Generate dataframe based on specific condition and input dictionary - pandas

问题描述

我有一个字典，如下所示.

I have a dictionary as shown below.

d1 = { 'start_date' : '2020-10-01T20:00:00.000Z',
       'end_date'  : '2020-10-05T20:00:00.000Z',
       'n_days'    : 6,
       'type'      : 'linear',
       "coef": [0.1,0.1,0.1,0.1,0.1,0.1]    
     }

从上面的字典作为函数的输入，我想在df以下生成作为输出.

From the above dictionary as input to the function, I would like to generate below df as output.

预期输出:

Date                Day           function_type         function_value
2020-10-01          1             linear                (0.1*1)+0.1 = 0.2
2020-10-02          2             linear                (0.1*2)+0.1 = 0.3
2020-10-03          3             linear                (0.1*3)+0.1 = 0.4
2020-10-04          4             linear                (0.1*4)+0.1 = 0.5
2020-10-05          5             linear                (0.1*5)+0.1 = 0.6

注意:

类型可以是线性，常数，多项式和指数.

The type can be linear, constant, polynomial and exponential.

a0, a1, a2, a3, a4, a5 = d1['coef']

If constant:
funtion_value = a0

If exponential: 
funtion_value = e**(a0+a1T)

if polynomial:
funtion_value = a0+a1T+a2(T**2)+a3(T**3)+a4(T**4)+a5(T**5)

T: value of Day column

推荐答案

定义函数 funcValue ，该函数从给定的输入字典 d计算函数值列和天列 T 根据字典中的 type 类型:

Define a function funcValue that computes the function value column from the given input dictionary d and days column T based on the type in dictionary:

def funcValue(d, T):
    a0, a1, a2, a3, a4, a5 = d['coef']
    func = {
        'constant': a0,
        'linear': a0 + a1*T,
        'polynomial': a0 + a1*T + a2*(T**2)+ a3 * (T**3) + a4*(T**4) + a5*(T**5),
        'exponential':  np.power(np.e, a0 + a1*T)
    }

    return func[d['type']]

然后定义一个函数 getDF ，该函数根据用户定义的词典 d 中提供的信息生成所需的数据框:

Then define a function getDF that generates a required dataframe based on the information provided in the user defined dictionary d:

def getDF(d):
    date = pd.date_range(d['start_date'], d['end_date'], freq='D').tz_localize(None).floor('D')
    days = (date - date[0]).days + 1
    return pd.DataFrame({'Date': date, 'Day': days, 'function_type': d['type'],
                         'function_value': funcValue(d, days)})

结果:

print(getDF(d1))

        Date  Day function_type  function_value
0 2020-10-01    1        linear             0.2
1 2020-10-02    2        linear             0.3
2 2020-10-03    3        linear             0.4
3 2020-10-04    4        linear             0.5
4 2020-10-05    5        linear             0.6

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