根据条件替换熊猫数据框列中的数据，如果不满足条件则跳过

Question

I have a dataframe with a column of numbers. If the number is less than 0, I would like to add 3.14 to that number. If not, I would like to skip that number and leave it as is. I am using pandas and numpy but cannot find out how to just skip the numbers not meeting the condition while leaving them in the dataframe as is.

When trying this:

df['rad'].apply(lambda x: [y if y>=0 else y+3.14 for y in x])

I get TypeError: 'int' object is not iterable

Even though the numbers in 'rad' column are all int64.

Answer 1

那是np.where

np.where(df['rad']>0,df['rad'],df['rad']+3.14)