带有数据框的条件语句/If 语句

Question

我正在尝试根据多个列“类”和“值”为“百分比”列分配一个值

以下是包含我的数据框的链接： https://filebin.net/fo2wk7crmwf0fycc

这是我要应用的逻辑：

If df['Class'] equals 2 or 3, and if df['Value'] is less than 0.5, set df['Percentage'] to 0
If df['Class'] equals 2 or 3, and if df['Value'] is > 0.5 and <= 0.7, set df['Percentage'] to 0.25
If df['Class'] equals 2 or 3, and if df['Value'] is > 0.7 and <= 0.9, set df['Percentage'] to 0.5
Else set df['Percentage'] to 1

下面是我正在寻找的输出示例：

Class	Value	Percentage
2	0.01	0
2	0.6	0.25
3	0.9	0.5
3	3	1

谢谢

Answer 1

Numpy 和 searchsorted

使用searchsorted 时，在这种情况下，您不需要包含0 和1 之类的边界。

bins =  np.array([.5, .7, .9])
labels = np.array([0, .25, .5, 1])
cut = bins.searchsorted(df.Value)
results = labels[cut]

df.assign(Percentage=np.where(df['Class'].isin([2, 3]), results, 1))

       Class     Value  Percentage
0          2  0.000620         0.0
1          2  0.000620         0.0
2          3  0.001240         0.0
3          4  0.000620         1.0
4          5  0.000620         1.0
...      ...       ...         ...
14782      5  0.001178         1.0
14783      2  0.001116         0.0
14784      3  0.001178         0.0
14785      5  0.000310         1.0
14786      5  0.001116         1.0

[14787 rows x 3 columns]

---

熊猫cut

使用pd.cut 时，您确实需要边界，因为 Pandas 会创建间隔。

#                        / boundaries \
#                       ↓              ↓
cut = pd.cut(df.Value, [0, .5, .7, .9, 1], labels=[0, .25, .5, 1])

df.assign(Percentage=np.where(df['Class'].isin([2, 3]), cut, 1))

       Class     Value  Percentage
0          2  0.000620         0.0
1          2  0.000620         0.0
2          3  0.001240         0.0
3          4  0.000620         1.0
4          5  0.000620         1.0
...      ...       ...         ...
14782      5  0.001178         1.0
14783      2  0.001116         0.0
14784      3  0.001178         0.0
14785      5  0.000310         1.0
14786      5  0.001116         1.0

[14787 rows x 3 columns]

Answer 2

你也可以像下面这样使用纯np.where：

import numpy as np    
df['Percentage'] = np.where((df['Class'].isin([2, 3]) & (df['Value'] <= 0.5)), 0, 
                            np.where((df['Class'].isin([2, 3]) & (df['Value'] > 0.5) & (df['Value'] <= 0.7)), 0.25,
                                np.where((df['Class'].isin([2, 3]) & (df['Value'] > 0.7) & (df['Value'] <= 0.9) ), 0.5, 1)))

np.where 就像 if-then-else 条件语句，你很容易理解。

       Class     Value  Percentage
0          2  0.000620         0.0
1          2  0.000620         0.0
2          3  0.001240         0.0
3          4  0.000620         1.0
4          5  0.000620         1.0
...      ...       ...         ...
14782      5  0.001178         1.0
14783      2  0.001116         0.0
14784      3  0.001178         0.0
14785      5  0.000310         1.0
14786      5  0.001116         1.0

[14787 rows x 3 columns]