python - 在 pandas DataFrame 中使用多个条件会导致 ValueError

Question

我有一个这样的数据表:

       Item Colour    Item Range Item Size
789    COLOUR-BLUE    RANGE-PANT  SIZE-XXL
2507  COLOUR-BLACK   RANGE-OTHER  SIZE-XXL
2376  COLOUR-BLACK  RANGE-JACKET    SIZE-S
1378  COLOUR-WHITE   RANGE-OTHER    SIZE-L
598    COLOUR-BLUE  RANGE-JACKET    SIZE-M
1589   COLOUR-BLUE  RANGE-JACKET    SIZE-L
2580  COLOUR-BLACK   RANGE-SHIRT    SIZE-L
366    COLOUR-BLUE    RANGE-PANT  SIZE-XXL
2320  COLOUR-WHITE   RANGE-OTHER    SIZE-L
1247  COLOUR-GREEN    RANGE-PANT    SIZE-M
2224  COLOUR-BLACK  RANGE-JACKET    SIZE-L
3615  COLOUR-BLACK   RANGE-OTHER    SIZE-S
4176  COLOUR-GREEN    RANGE-PANT   SIZE-XL
1640  COLOUR-BLACK    RANGE-PANT    SIZE-S
1136  COLOUR-WHITE   RANGE-OTHER    SIZE-M
3437  COLOUR-BLACK  RANGE-JACKET    SIZE-S
4448  COLOUR-WHITE   RANGE-OTHER    SIZE-S
1188  COLOUR-WHITE   RANGE-SHIRT  SIZE-XXL
3332  COLOUR-GREEN   RANGE-OTHER    SIZE-M
1080  COLOUR-WHITE   RANGE-OTHER  SIZE-XXL

我只想使用以下掩码获取数据的子选择:

mask = (df['Item Colour'] == 'COLOUR-WHITE') & (df['Item Range'] in ['RANGE-JACKET','RANGE-PANT']) & (df['Item Size'] not in ['SIZE-XXL'])

我尝试了df[mask]，但它给了我错误:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

如何避免错误。

到目前为止我已经这样做了:

import numpy as np
import pandas as pd

df = pd.read_clipboard()
df.drop(['Item','Item.2','Size'], inplace=True,axis=1)
df.columns = ['Item Colour', 'Item Range', 'Item Size']
print(df)

mask = (df['Item Colour'] == 'COLOUR-WHITE') & (df['Item Range'] in ['RANGE-JACKET','RANGE-PANT']) & (df['Item Size'] not in ['SIZE-XXL'])

dff = df[mask]
dff

更新还是不行。

mask =  (df['Item Colour'] == 'COLOUR-WHITE').all()\
      & (df['Item Range'] in ['RANGE-JACKET','RANGE-PANT']).all()\
      & ( ~df['Item Size'].isin(['SIZE-XXL']).all())

df[mask]

Answer 1

问题出在您通过检查项目是否在列表中来构建掩码的方式上。您可以使用 pd.Series.isin([item1, item2, ...]) Series 方法来执行此操作。所以，而不是:

df['Item Range'] in ['RANGE-JACKET','RANGE-PANT'],

做:

df['元素范围'].isin(['RANGE-JACKET','RANGE-PANT'])

否定，对于“不在”:

df['商品尺寸'] 不在 ['SIZE-XXL'] 范围内，

你可以这样做:

~df['商品尺寸'].isin(['SIZE-XXL'])