Turn pandas dataframe list into boolean column

Question

I have run into some unexpected error while try .isin() Here's the problem. I've scrapped web, turned into dataframe. Now I'd like to make changes to make the data more usable for the project. From scrapped data, one column contains all the features, it's a list in json, but in pd, it's a "non-null object":

"feature": ["Wi-Fi", "LAN", "LED"]

I'd like to create new Boolean column base on each feature, which will be helpful down the road. It should look like this

Product    Wifi    LAN   LED
1          True    True  True
2          True    False False

I've tried both str.contains and .isin(), but only got errors. Such as

TypeError: only list-like objects are allowed to be passed to isin(), you passed a [str]
ValueError: Length of values does not match length of index

What is a better way to tackle this problem?

Also, the original data is in Japanese, I've loaded dataframe with "encoding="utf-8" How to best coding when with utf8 in pandas? I'm using notepad++ as editor.

Answer 1

Use apply with in if need check value in list:

df = pd.read_json('sample.json', lines=True, encoding="utf-8")
print (df)
   access  address                     feature        hour        name offday  \
0      30  5-17-62  [Wi-Fi, LAN1, Non-smoking]  9:00〜22:00  CHEZ MADU       -   
1      30  5-17-62  [Wi-Fi, LAN2, Non-smoking]  9:00〜22:00  CHEZ MADU       -   
2      30  5-17-62  [Wi-Fi, LAN3, Non-smoking]  9:00〜22:00  CHEZ MADU       -   

            tel                                     web  
0  042-465-3533  http://www.hakka-group.co.jp/shoplist/  
1  042-465-3533  http://www.hakka-group.co.jp/shoplist/  
2  042-465-3533  http://www.hakka-group.co.jp/shoplist/  

mask = df['feature'].apply(lambda x: 'LAN1' in x)
print (mask)
0     True
1    False
2    False
Name: feature, dtype: bool