Question

我想根据另一列（'col_search'）中的值更改pd.DataFrame中列（'col_change'）的值（转换为'新值'）。对于单个更改，我有一个解决方案，但我正在寻找多个搜索值的解决方案。

预期单值的示例：

import numpy as np
import pandas as pd
my_array = np.array([[1,2,3,4,5,6,7,8,9,10],[11,22,33,44,55,66,77,88,99,100]])
my_df = pd.DataFrame(my_array, columns = ['col_change', 'col_search'])
my_df.col_change[my_df.col_search == 22] = 'new value'
print(my_df)

多值的示例不能像预期的那样工作：“in”运算符在这里不起作用。

import numpy as np
import pandas as pd
my_array = np.array([[1,2,3,4,5,6,7,8,9,10],[11,22,33,44,55,66,77,88,99,100]])
my_df = pd.DataFrame(my_array, columns = ['col_change', 'col_search'])
list_of_search = [33, 44, 55]
my_df.col_change[my_df.col_search in list_of_search] = 'new value'
print(my_df)

Answer 1

使用df.columns.isin

In [1083]: my_df.loc[my_df.col_search.isin([33, 44, 55]), 'col_change'] = 'new value'

In [1084]: my_df
Out[1084]: 
  col_change  col_search
0          1          11
1          2          22
2  new value          33
3  new value          44
4  new value          55
5          6          66
6          7          77
7          8          88
8          9          99
9         10         100

按列表在pandas.DataFrame中建立索引

1 个答案: