Python熊猫在索引上逐行使用lambda

Question

So I'm trying to apply a function to the index row by row but having some problems

startDate = '2015-05-01 00:00'
endDate = '2015-05-08 00:00'
idx = pd.date_range(startDate, endDate, freq="1min")
df = pd.DataFrame(columns=['F(t)'])
df = df.reindex(idx, fill_value=0)

def circadian_function(T):
    return math.cos(math.pi*(T-delta)/12)

Everything is okay up to here but trying to apply the function I'm not sure what to do

df['F(t)'] = df.index.apply(lambda x: circadian_function x[index].hour, axis=1)

Should I be using a lambda? Or just an apply?

Answer 1

I don't have 50 rep so I can't comment on @Ted Petrou's answer ;-; I just wanted to say a couple things that you should know.

1. If you are going to feed df.index.hour into your carcadian_function, make sure you use numpy instead of math . Otherwise the interpreter will throw a TypeError (I just found out about this).

2. Make sure to define delta .

Example:

import numpy as np

def circadian_function(T, delta):
    return np.cos(np.pi * (T-delta) / 12)

What @Ted Petrou recommends you do in full:

df['F(x)'] = circadian_function(df.index.hour, 0.5) #I picked an arbitrary delta

Numpy will automatically vectorize the function for you. Props to Ted I learned something new as well :>

Answer 2

Use apply only as a last result. This can be easily vectorized. Make sure you define delta.

 import numpy as np
 df['F(t)'] = np.cos(np.pi*(idx.hour-delta)/12)