遍历行，同时一次定位两个列

Question

cols_df 代表 DataFrame 的块，我希望能够在其中执行多个操作，但每次都针对两列和第一列。我在每一轮中将选定的列从小到大排序（例如，第一轮中的“0”、“2”、“3”列，第二轮中的“0”、“4”、“5”列）。在一个新列中，我用 X 标记不包含两个数值的每一行。然后，我将有一个 DataFrame 包含新标记的列以及所有其他列。

输入：

import pandas as pd

cols_dict = {'matr': {0: '18I1', 1: '03I2', 2: '03I3', 3: '18I4', 4: '03I5', 5: '03I6', 6: '03I7', 7: '03I8', 8: '18I9', 9: '18I0'}, 'cat': {0: '3', 1: '3', 2: '3', 3: '3', 4: '3', 5: '18', 6: '3', 7: '3', 8: '3', 9: '3'}, 'Unnamed: 5': {0: 81, 1: 81, 2: 81, 3: 77, 4: None, 5: None, 6: 83, 7: 81, 8: 79, 9: 81}, 'Unnamed: 6': {0: 91, 1: 97, 2: 97, 3: 91, 4: 81, 5: 93, 6: 89, 7: 83, 8: 81, 9: 99}, 'Unnamed: 7': {0: 117.0, 1: 115.0, 2: 115.0, 3: 115.0, 4: 115.0, 5: None, 6: 115.0, 7: 115.0, 8: 115.0, 9: 115.0}, 'Unnamed: 8': {0: 123.0, 1: 115.0, 2: 115.0, 3: 115.0, 4: 123.0, 5: 123.0, 6: 125.0, 7: 123.0, 8: 117.0, 9: None}}  

cols_df = pd.DataFrame.from_dict(cols_dict)

所需的输出：

cols_dict_out = {'matr': {0: '18I1', 1: '03I2', 2: '03I3', 3: '18I4', 4: '03I5', 5: '03I6', 6: '03I7', 7: '03I8', 8: '18I9', 9: '18I0'}, 'xs': {0: None, 1: None, 2: None, 3: None, 4: 'X', 5: 'X', 6: None, 7: None, 8: None, 9: 'X'}, 'cat': {0: '3', 1: '3', 2: '3', 3: '3', 4: '3', 5: '18', 6: '3', 7: '3', 8: '3', 9: '3'}, 'Unnamed: 5': {0: 81, 1: 81, 2: 81, 3: 77, 4: None, 5: None, 6: 83, 7: 81, 8: 79, 9: 81}, 'Unnamed: 6': {0: 91, 1: 97, 2: 97, 3: 91, 4: 81, 5: 93, 6: 89, 7: 83, 8: 81, 9: 99}, 'Unnamed: 7': {0: 117.0, 1: 115.0, 2: 115.0, 3: 115.0, 4: 115.0, 5: None, 6: 115.0, 7: 115.0, 8: 115.0, 9: 115.0}, 'Unnamed: 8': {0: 123.0, 1: 115.0, 2: 115.0, 3: 115.0, 4: 123.0, 5: 123.0, 6: 125.0, 7: 123.0, 8: 117.0, 9: None}}

cols_out_df = pd.DataFrame.from_dict(cols_dict_out)

Answer 1

逻辑并不完全清楚，但看起来你可能想要：

cols_df['xs'] = np.where(cols_df.filter(like='Unnamed').isna().any(axis=1), 'X', None)

或者，如果您想作为第二列插入：

cols_df.insert(1, 'xs', np.where(cols_df.filter(like='Unnamed').isna().any(axis=1), 'X', None))

输出：

   matr    xs cat  Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8
0  18I1  None   3        81.0          91       117.0       123.0
1  03I2  None   3        81.0          97       115.0       115.0
2  03I3  None   3        81.0          97       115.0       115.0
3  18I4  None   3        77.0          91       115.0       115.0
4  03I5     X   3         NaN          81       115.0       123.0
5  03I6     X  18         NaN          93         NaN       123.0
6  03I7  None   3        83.0          89       115.0       125.0
7  03I8  None   3        81.0          83       115.0       123.0
8  18I9  None   3        79.0          81       115.0       117.0
9  18I0     X   3        81.0          99       115.0         NaN