Pandas - Delete Rows with only NaN values

问题:

I have a DataFrame containing many NaN values. I want to delete rows that contain too many NaN values; specifically: 7 or more.

I tried using the dropna function several ways but it seems clear that it greedily deletes columns or rows that contain any NaN values.

This question (Slice Pandas DataFrame by Row), shows me that if I can just compile a list of the rows that have too many NaN values, I can delete them all with a simple

df.drop(rows)

I know I can count non-null values using the count function which I could them subtract from the total and get the NaN count that way (Is there a direct way to count NaN values in a row?). But even so, I am not sure how to write a loop that goes through a DataFrame row-by-row.

Here's some pseudo-code that I think is on the right track:

### LOOP FOR ADDRESSING EACH row:
    m = total - row.count()
    if (m > 7):
        df.drop(row)

I am still new to Pandas so I'm very open to other ways of solving this problem; whether they're simpler or more complex.

回答1:

Basically the way to do this is determine the number of cols, set the minimum number of non-nan values and drop the rows that don't meet this criteria:

df.dropna(thresh=(len(df) - 7))

See the docs

回答2:

The optional thresh argument of df.dropna lets you give it the minimum number of non-NA values in order to keep the row.

df.dropna(thresh=df.shape[1]-7)