Pandas 中的 IF 语句：使用一列 (A) 中的值从不同列 (B 或 C) 中选择值并将其存储在一个单独的列 (D) [重复]

Question

我的问题是：我怎样才能用 Pandas 简化我的表，只得到一个包含选定值的列（三列应该是一个）。

Name    Selection   Active  Inactive
A       active      0       0.9
B       active      1       0.8
C       inactive    2       0.7
D       inactive    3       0.6
E       active      4       0.5

喜欢

IF Selection = 'active' THEN Active ELSE Inactive as Selected_Value

得到以下结果：

Name    Selected_Value
A       0
B       1
C       0.7
D       0.6
E       4 

Answer 1

下面的代码应该为您提供您正在寻找的东西。

df.loc[df['Selection'] == 'active','Selected_Value'] = df['Active']
df.loc[df['Selection'] == 'unactive','Selected_Value'] = df['Unactive']

或

idx,cols = pd.factorize(df['Selection'].str.title())
df.assign(Selected_Value = df.reindex(cols,axis=1).to_numpy()[range(len(df)),idx])

输出：

  Name Selection  Active  Inactive  Selected_Value
0    A    active       0       0.9             0.0
1    B    active       1       0.8             1.0
2    C  inactive       2       0.7             0.7
3    D  inactive       3       0.6             0.6
4    E    active       4       0.5             4.0

Answer 2

她是你如何使用

numpy.where()

：

import pandas as pd
import numpy as np
df = pd.DataFrame({'Name': ['A', 'B', 'C', 'D', 'E'],
                   'Selection': ['active', 'active', 'unactive', 'unactive', 'active'],
                   'Active': [0, 1, 2, 3, 4],
                   'Unactive': [0.9, 0.8, 0.7, 0.6, 0.5]})

df['Selected_Value'] = np.where(df['Selection']=='active', # If the element for the Selection column is active
                                df['Active'], # The element of the Selected_Value column of that index will be the element from the Active column
                                df['Unactive']) # Else, the element of the Selected_Value column of that index will be the element from the Unactive column
                           
print(df['Selected_Value'])

输出：

0    0.0
1    1.0
2    0.7
3    0.6
4    4.0
Name: Selected_Value, dtype: float64