替换 dataframe 列的值

Question

i have the current dataframe:

df = pd.DataFrame({"A":[1,2,-3,-4,5], 
                   "B":[1,-2,3,-4,5]})

i want to replace, just in column A ,
all positive values with 1 and all negative values with 0.

i tried to do it this way:

df[df["A"]>0]["A"] = 1
df[df["A"]<0]["A"] = 0

but that didnt work (dataframe didnt change at all).

however the below code did work:

df["A"][df["A"]>0] = 1
df["A"][df["A"]<0] = 0

can anyone tell me what the difference between the two?
why the first one didnt work, while the second one did?

thanks!

Answer 1

To put it simply:

df[df["A"]>0]["A"] gives you a copy of the dataframe

and df["A"][df["A"]>0] gives you a view of it.

The copy isnt linked to the dataframe so changing it wont do anything to the original.

Go to this link for more info:

https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

Answer 2

As an alternative method to above, you can use np.where and do:

import numpy as np
df['A'] = np.where(df['A'] >0 ,1,0) # 0's will be given 0 here.

which will essentially replace all +ve's with 1, and everything else with 0.