[英]Pandas remove rows where several columns are not nan
我有一個看起來像這樣的 dataframe:
A B C D E
0 P 10 NaN 5.0 9.0
1 Q 19 NaN NaN 4.0
2 R 8 NaN 3.0 7.0
3 S 20 NaN 3.0 7.0
4 T 4 NaN 2.0 NaN
我有一個列表: [['A', 'B', 'D', 'E'], ['A', 'B', 'D'], ['A', 'B', 'E']]
我正在遍歷列表並僅從 dataframe 中獲取那些行,其中列表指定的列不為空。
我嘗試過使用以下代碼:
test_df = pd.DataFrame([['P', 10, np.nan, 5, 9], ['Q', 19, np.nan, np.nan, 4], ['R', 8, np.nan, 3, 7],
['S', 20, np.nan, 3, 7], ['T', 4, np.nan, 2, np.nan]], columns=list('ABCDE'))
priority_list = [list('ABDE'), list('ABD'), list('ABE')]
for elem in priority_list:
test_df = test_df.loc[test_df[elem].notna()]
print(test_df)
但這會引發以下錯誤:
File "C:\Python37\lib\site-packages\pandas\core\indexing.py", line 879, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Python37\lib\site-packages\pandas\core\indexing.py", line 1097, in _getitem_axis
raise ValueError("Cannot index with multidimensional key")
ValueError: Cannot index with multidimensional key
如何克服此問題並檢查 dataframe 中的non-na
值的多列?
使用DataFrame.all
進行測試,如果所有選定的值都是True
s:
priority_list = [list('ABDE'), list('ABD'), list('ABE')]
for elem in priority_list:
test_df = test_df.loc[test_df[elem].notna().all(axis=1)]
print(test_df)
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