Pandas TypeError: Cannot perform ‘rand_’ with a dtyped [int64] array and scalar of type [bool]’

The Problem

When working with Pandas, you might encounter this error:

TypeError: Cannot perform 'rand_' with a dtyped [int64] array and scalar of type [bool]'

It usually occurs when performing operations in Pandas that expect both operands to be of compatible types, but encounter a mismatch, such as trying to perform bitwise operations (e.g., &, |) between an integer array and a boolean scalar.

Solution 1: Convert Boolean to Integer

One straightforward way to resolve this error is by converting boolean values to integers (0 or 1) before performing the operation. This ensures type compatibility.

1. Identify the boolean scalar involved in the operation.
2. Convert the boolean scalar to an integer (1 for True, 0 for False).
3. Perform the operation again.

Example:

import pandas as pd

# Example DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4]})

# Convert True to 1 and perform operation
df['B'] = df['A'] & int(True)

print(df)

Notes: This method is simple and direct, but requires explicit type conversion for boolean values. It’s beneficial in situations where the logical operation’s intent is clear and numerical representation of boolean is acceptable.

Solution 2: Use .astype() for Type Conversion

Another method involves using the .astype() method to explicitly convert the entire column or array’s data type before performing the operation.

1. Choose the DataFrame column or array involved in the operation.
2. Use .astype(int) to convert boolean values to integers.
3. Perform the desired operation.

Example:

import pandas as pd

# Example DataFrame
df = pd.DataFrame({'A': [False, True, False, True]})

# Convert column to int
df['A'] = df['A'].astype(int)

# Now you can perform any int-compatible operation
df['B'] = df['A'] * 2

print(df)

Notes: This approach is flexible and makes the code more readable by clearly indicating the intended data type. However, it modifies the original data type of the column or array, which might not be desired in all cases.