如何从包含 nan 的 pandas 数据帧中删除行

Question

I'm working on python 3.xI have a pandas data frame with only one column, student.At 501th row student contains nan df.at[501,'student'] returns nan To remove this I used following code

df.at['student'].replace('', np.nan, inplace=True)

But after that I'm still getting nan for df.at[501,'student'] I also tried this

df.at['student'].replace('', np.nan, inplace=True)

But I'm using df in for loop to check value of student to apply some business logic but with inplace=True I'm getting key error:501 Can you suggest me how do I remove the nan & use df in for loop to check student value?

Answer 1

Adding another answer since it's completely a different case.
I think you are not looping correctly on the dataframe, seems like you are looping relying on the index of the dataframe when you should probably loop on the items row by row or preferably use df.apply .
If you still want to loop on the items and you don't care about the previous index, you can reset the index with df.reset_index(drop=True)

df['student'].replace('', np.nan, inplace=True)
df['student'].dropna(inplace=True)
df = df.reset_index(drop=True)
# do your loop here

your problem is that you are dropping the item at index 501 then trying to access it, when you drop items pandas doesn't automatically update the index.

Answer 2

the replace function that you use would replace the first parameter with the second.
if you want to replace the np.nan with empty then you have to do

df['student'].replace(np.nan, '', inplace=True)

but this would not remove the row, it'd just replace it with an empty string, what you want is

df['student'].dropna(inplace=True)

but you gotta do this before looping over the elements, don't dropna in the loop.

it'd be helpful to know what exactly you are doing in the loop

Answer 3

One way to remove the rows that contains Nan values in the "student" column is

df = df[~df['student'].isnull()]