Pandas Boolean Indexing: Why Use '&' Instead of 'and'?

Logical Operators for Boolean Indexing in Pandas

When working with Boolean indexing in Pandas, it's important to understand the difference between the logical operators "&" and "and".

Question: Why does the following statement work without error:

a[(a['some_column']==some_number) & (a['some_other_column']==some_other_number)]

but the following statement exits with an error:

a[(a['some_column']==some_number) and (a['some_other_column']==some_other_number)]

Answer:

The "and" operator in Python implicitly converts its operands to Boolean values. However, when dealing with NumPy arrays (and Pandas Series, which are based on NumPy arrays), this conversion can lead to ambiguities.

When evaluating the truth value of an array containing multiple elements, it's unclear whether it should be considered True if:

• All of its elements are True
• Any of its elements are True
• It has a non-zero length

To avoid this ambiguity, NumPy and Pandas require explicit Boolean evaluation using the "any()", "all()", or "empty()" methods.

In the case of Boolean indexing, we don't want Boolean evaluation but rather element-wise logical operations. This is where the "&" operator comes into play.

The "&" operator performs an element-wise logical AND operation. It returns a Boolean array where each element is the result of the logical AND of the corresponding elements in the input arrays.

Example:

import pandas as pd

a = pd.DataFrame({'x':[1,1],'y':[10,20]})

print(a[(a['x']==1) & (a['y']==10)])

Output:

   x   y
0  1  10

In this example, the "&" operator is used to find rows where both the "x" column and "y" column meet the specified criteria.

The above is the detailed content of Pandas Boolean Indexing: Why Use '&' Instead of 'and'?. For more information, please follow other related articles on the PHP Chinese website!