20

I am using Python and Pandas. I have a df that works similar to this:

 +--------+--------+-------+
 |  Col1  |  Col2  | Col3 |
 +--------+--------+-------+
 | Team 1 | High   | Pizza |
 | Team 1 | Medium | Sauce |
 | Team 1 | Low    | Crust |
 +--------+--------+-------+

I would like to filter the df so that I only see High or Medium from Col2.

This is what I have tried with no luck

 df = df.loc[df['Col2'] == 'High' | (df['Col2'] == 'Medium')]

This is the error I am getting

 cannot compare a dtyped [bool] array with a scalar of type [bool]

Any ideas how to make this work and what that error means?

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4 Answers 4

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103

.isin() works as well, more pythonic

country_list = ['brazil', 'poland', 'russia', 'countrydummy', 'usa']

filtered_df = df[df['Country Name'].isin(country_list)]
print(filtered_df)
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1 Comment

this solution is better because you can use dynamic length of country_list
29

You are missing a pair of parentheses to get comparable items on both sides of the | operator - which has higher precedence than ==:

df = df.loc[(df['Col 2'] == 'High') | (df['Col2'] == 'Medium')]
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4

You can also use ( for Pandas >= 0.13 ) :

filtered_df = df.query( '"Country Name" == ["brazil","poland","russia","countrydummy","usa"]' )

print(filtered_df )
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1

I think that df.query is the best way for this kind of things

df = df.query("Col2 == ['High','Medium']")
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