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linqu
linqu

Reputation: 11970

Appending Boolean Column in Panda Dataframe

I am learning pandas and got stuck with this problem here.

I created a dataframe that tracks all users and the number of times they did something.

To better understand the problem I created this example:

import pandas as pd
data = [
    {'username': 'me',  'bought_apples': 2, 'bought_pears': 0},
    {'username': 'you', 'bought_apples': 1, 'bought_pears': 1}
]
df = pd.DataFrame(data)
df['bought_something'] = df['bought_apples'] > 0 or df['bought_pears'] > 0

In the last line I want to add a column that indicates if they user has bought something at all.

This error pops up:

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

I understand the point of ambiguity in panda's Series (also explained here) but I could not relate it to the problem.

Interestingly this works

df['bought_something'] = df['bought_apples'] > 0

Can anyone help me?

Upvotes: 11

Views: 34570

Answers (2)

Jianxun Li
Jianxun Li

Reputation: 24742

The reason for that error is you use 'or' to 'join' two boolean vectors instead of boolean scalar. That's why it says it is ambiguous.

Upvotes: 2

EdChum
EdChum

Reputation: 394041

You can call sum row-wise and compare if this is greater than 0:

In [105]:
df['bought_something'] = df[['bought_apples','bought_pears']].sum(axis=1) > 0
df

Out[105]:
   bought_apples  bought_pears username bought_something
0              2             0       me             True
1              1             1      you             True

Regarding your original attempt, the error message is telling you that it's ambiguous to compare a scalar with an array, if you want to or boolean conditions then you need to use the bit-wise operator | and wrap the conditions in parentheses due to operator precedence:

In [111]:
df['bought_something'] = ((df['bought_apples'] > 0) | (df['bought_pears'] > 0))
df

Out[111]:
   bought_apples  bought_pears username bought_something
0              2             0       me             True
1              1             1      you             True

Upvotes: 20

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