[英]Conditional statement / If statement with Dataframes
我正在嘗試根據多個列“類”和“值”為“百分比”列分配一個值
下面是一個包含我的 dataframe 的鏈接: https://filebin.net/fo2wk7crmwf0fycc
這是我要應用的邏輯:
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
下面是我正在尋找的 output 的示例:
Class | 價值 | 百分比 |
---|---|---|
2 | 0.01 | 0 |
2 | 0.6 | 0.25 |
3 | 0.9 | 0.5 |
3 | 3 | 1 |
謝謝
searchsorted
使用searchsorted
時,在這種情況下,您不需要包括0
和1
之類的邊界。
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]
cut
使用pd.cut
時,您確實需要邊界,因為Pandas將創建間隔。
# / 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]
您還可以使用純np.where
如下所示:
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
就像你可以輕松理解的 if-then-else 條件語句。
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]
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