Pandas - 删除只有 NaN 值的行

Question

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.

Answer 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

Answer 2

df.dropna的可选 thresh 参数允许您为其提供最小数量的非 NA 值以保留该行。

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