CODEWITHSUNDEEP
pythonpandasdataframe
John Nann
John Nann

Reputation: 37

Check to see if value is in DataFrame series ("The truth value of a Series is ambiguous" error)

I'm trying to check to see if the value in a DataFrame column is contained in a series in a separate column. I'm receiving the "ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."

I've researched this, but do not quite understand why I'm receiving this error in this specific instance.

I've tried using both the .contains functions.

A simplified version of the DataFrame structure is as follows:

df

index     id       id_list           in_series (desired return column]
1         23       [1,2,34,56,75]    False
2         14       [1,5,14,23,45]    True
3         2        [1,2,4,25,37]     True
4         14       [2,4,34,26,77]    False
5         27       [1,6,19,27,50]    True

a = df['id']
b = df['id_list]
df['in_series'] = b.str.contains(a, regex=False)

Is there a better way of going about this?

Upvotes: 1

Views: 98

Answers (3)

Kingindanord
Kingindanord

Reputation: 2036

you still can use a loop

id_list=[[1,2,34,56,75],[1,5,14,23,45],[1,2,4,25,37],[2,4,34,26,77],[1,6,19,27,50]]
id=[23,14,2,14,27]
df=pd.DataFrame([id,id_list]).T
df.columns=["id","id_list"]

boo=list()
for i in range(len(df)):
    boo.append(df.iloc[i,0] in df.iloc[i,1])

df["in_series (desired return column]"]=boo

in this case you don't change the type of your data

Upvotes: 1

Erfan
Erfan

Reputation: 42886

One of the few cases we can use apply to check presence of id in id_list:

df['in_series'] = df.apply(lambda x: str(x['id']) in ', '.join(str(y) for y in x['id_list']),axis=1)

   id             id_list  in_series
0  23  [1, 2, 34, 56, 75]      False
1  14  [1, 5, 14, 23, 45]       True
2   2   [1, 2, 4, 25, 37]       True
3  14  [2, 4, 34, 26, 77]      False
4  27  [1, 6, 19, 27, 50]       True

Upvotes: 1

Umar.H
Umar.H

Reputation: 23099

a little list comprehension magic should work : 

df['in_series (desired return column'] = ([df.id[i].astype(str) in df.id_list[i] 
                                          for i in range(len(df))])



   print(df)
        index   id  id_list in_series (desired return column)
0   1   23  [1,2,34,56,75]  False
1   2   14  [1,5,14,23,45]  True
2   3   2   [1,2,4,25,37]   True
3   4   14  [2,4,34,26,77]  False
4   5   27  [1,6,19,27,50]  True

Upvotes: 0

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