在python中使用“isin”作为三个过滤器

Question

我有以下数据

# Import pandas library
import pandas as pd
import numpy as np

# initialize list elements
data = ['george',
        'instagram',
        'nick',
        'basketball',
        'tennis']
  
# Create the pandas DataFrame with column name is provided explicitly
df = pd.DataFrame(data, columns=['Unique Words'])
  
# print dataframe.
df

我想根据下面两个列表创建一个新列，如下所示

key_words = ["football", "basketball", "tennis"]
usernames = ["instagram", "facebook", "snapchat"]

Label
-----
0
2
0
1
1

因此，列表中的单词key_words取标签1，在列表中用户名取标签2，所有其他字取标签0。

非常感谢您的时间和帮助！

Answer 1

一种方法是创建一个标签地图，将第一个列表中的所有元素编号为1，另一个元素编号为2。然后，您可以使用熊猫中的.map将值和填充值映射为0。

# Import pandas library
import pandas as pd
import numpy as np

# initialize list elements
data = ['george',
        'instagram',
        'nick',
        'basketball',
        'tennis']
  
# Create the pandas DataFrame with column name is provided explicitly
df = pd.DataFrame(data, columns=['Unique Words'])
  
key_words = ["football", "basketball", "tennis"]
usernames = ["instagram", "facebook", "snapchat"]


label_map = {e: i+1 for i, l in enumerate([key_words,usernames]) for e in l}
print(label_map)

df['Label'] = df['Unique Words'].map(label_map).fillna(0).astype(int)

print(df)

输出

{'football': 1, 'basketball': 1, 'tennis': 1, 'instagram': 2, 'facebook': 2, 'snapchat': 2}

  Unique Words  Label
0       george      0
1    instagram      2
2         nick      0
3   basketball      1
4       tennis      1