Skip to main content

Python Pandas: How to Fix "TypeError: Cannot perform 'rand_' / 'ror_' with a dtyped array and scalar of type [bool]"

When filtering Pandas DataFrames using multiple conditions combined with logical operators like & (AND) or | (OR), a common pitfall is to encounter TypeError: Cannot perform 'rand_' with a dtyped [int64] array and scalar of type [bool] (or 'ror_' for the OR operator). This error arises due to Python's operator precedence rules, which, without explicit guidance, can lead to parts of your filtering expression being evaluated in an unintended order, causing a type mismatch during the logical operation.

This guide will clearly explain why this TypeError occurs when combining boolean Series with bitwise logical operators in Pandas, demonstrate how to reproduce it, and provide the straightforward solution: always wrap individual conditions in parentheses () to ensure correct evaluation order.

Understanding the Error: Operator Precedence in Python and Pandas​

In Python, operators have a predefined order of precedence. For example, arithmetic operators (*, /) are typically evaluated before addition (+, -). Similarly, bitwise operators like & (bitwise AND) and | (bitwise OR), which Pandas uses for element-wise logical operations on Series, have a higher precedence than comparison operators (==, >, <, etc.).

When you write an expression like df.A == 'value' & df.C > 10, Python, due to operator precedence, might try to evaluate 'value' & df.C before df.A == ... or ... > 10. This intermediate operation 'value' & df.C (a string AND a Series) is often meaningless and leads to a TypeError because the bitwise & operator is not defined for these operand types in the way intended for combining boolean Series.

The error message Cannot perform 'rand_' with a dtyped [int64] array and scalar of type [bool] (or similar for other dtypes) indicates that Pandas (or NumPy, which Pandas uses under the hood) is trying to perform a bitwise AND (&, which rand_ internally relates to) between an array of one type (e.g., int64 from df.C) and a scalar of another incompatible type (e.g., bool that might result from an intermediate, incorrect comparison like A == 'Bobby').

Reproducing the Error with Logical AND (&)​

Let's create a DataFrame and attempt to filter it using multiple conditions without proper parenthesizing.

import pandas as pd

df = pd.DataFrame({
    'product_name': ['Laptop', 'Laptop', 'Mouse', 'Keyboard', 'Mouse'],
    'brand': ['BrandA', 'BrandB', 'BrandA', 'BrandC', 'BrandB'],
    'stock_qty': [10, 5, 50, 20, 30],
    'unit_price': [1200, 1500, 25, 70, 30]
})
print("Original DataFrame:")
print(df)

try:
    # ⛔️ Incorrect: Missing parentheses around individual conditions
    # Python might interpret `df.brand == 'BrandA' & df.stock_qty` incorrectly due to precedence.
    filtered_df_error = df.loc[df.brand == 'BrandA' & df.stock_qty > 15]
    print(filtered_df_error)
except TypeError as e:
    print(f"Error: {e}")

Output:

Original DataFrame:
  product_name   brand  stock_qty  unit_price
0       Laptop  BrandA         10        1200
1       Laptop  BrandB          5        1500
2        Mouse  BrandA         50          25
3     Keyboard  BrandC         20          70
4        Mouse  BrandB         30          30
Error: Cannot perform 'rand_' with a dtyped [int64] array and scalar of type [bool]

Solution: Wrap Each Condition in Parentheses ()​

To ensure that each comparison (which results in a boolean Series) is fully evaluated before the logical & or | operator is applied, you must enclose each individual condition in parentheses.

Correcting the AND (&) Condition​

The expression (condition1) & (condition2) ensures condition1 and condition2 are evaluated to boolean Series first, and then the bitwise AND is performed element-wise between these two boolean Series.

import pandas as pd

# df defined as before
df = pd.DataFrame({
    'product_name': ['Laptop', 'Laptop', 'Mouse', 'Keyboard', 'Mouse'],
    'brand': ['BrandA', 'BrandB', 'BrandA', 'BrandC', 'BrandB'],
    'stock_qty': [10, 5, 50, 20, 30],
    'unit_price': [1200, 1500, 25, 70, 30]
})

# ✅ Correct: Each condition is wrapped in parentheses
filtered_df_correct_and = df.loc[
    (df['brand'] == 'BrandA') & (df['stock_qty'] > 15)
]

print("Filtered DataFrame (BrandA AND stock > 15):")
print(filtered_df_correct_and)

Output:

Filtered DataFrame (BrandA AND stock > 15):
  product_name   brand  stock_qty  unit_price
2        Mouse  BrandA         50          25

This correctly selects rows where the 'brand' is 'BrandA' AND the 'stock_qty' is greater than 15.

Correcting the OR (|) Condition​

The same rule applies when using the logical OR | operator.

import pandas as pd

# df defined as before
df = pd.DataFrame({
    'product_name': ['Laptop', 'Laptop', 'Mouse', 'Keyboard', 'Mouse'],
    'brand': ['BrandA', 'BrandB', 'BrandA', 'BrandC', 'BrandB'],
    'stock_qty': [10, 5, 50, 20, 30],
    'unit_price': [1200, 1500, 25, 70, 30]
})

# Example of OR without parentheses (will cause 'ror_' TypeError)
try:
    filtered_df_or_error = df.loc[df.brand == 'BrandB' | df.unit_price < 50]
    print(filtered_df_or_error)
except TypeError as e:
    print(f"Error with OR without parentheses: {e}")

# ✅ Correct: Each condition wrapped in parentheses for OR
filtered_df_correct_or = df.loc[
    (df['brand'] == 'BrandB') | (df['unit_price'] < 50)
]

print("Filtered DataFrame (BrandB OR unit_price < 50):")
print(filtered_df_correct_or)

Output:

Error with OR without parentheses: Cannot perform 'ror_' with a dtyped [int64] array and scalar of type [bool]
Filtered DataFrame (BrandB OR unit_price < 50):
  product_name   brand  stock_qty  unit_price
1       Laptop  BrandB          5        1500
2        Mouse  BrandA         50          25
4        Mouse  BrandB         30          30

Let's re-run the OR condition with the provided df to be precise:

import pandas as pd

df = pd.DataFrame({
    'product_name': ['Laptop', 'Laptop', 'Mouse', 'Keyboard', 'Mouse'],
    'brand': ['BrandA', 'BrandB', 'BrandA', 'BrandC', 'BrandB'],
    'stock_qty': [10, 5, 50, 20, 30],
    'unit_price': [1200, 1500, 25, 70, 30]
})

filtered_df_correct_or = df.loc[
    (df['brand'] == 'BrandB') | (df['unit_price'] < 50)
]

print("Filtered DataFrame (BrandB OR unit_price < 50) - Corrected Output:")
print(filtered_df_correct_or)

Output:

Filtered DataFrame (BrandB OR unit_price < 50) - Corrected Output:
  product_name   brand  stock_qty  unit_price
1       Laptop  BrandB          5        1500
2        Mouse  BrandA         50          25
4        Mouse  BrandB         30          30

Applying Parentheses in Conditional Assignments with .loc​

The requirement for parentheses around individual conditions also applies when you are using .loc to conditionally assign new values.

import pandas as pd

# df defined as before
df = pd.DataFrame({
    'product_name': ['Laptop', 'Laptop', 'Mouse', 'Keyboard', 'Mouse'],
    'brand': ['BrandA', 'BrandB', 'BrandA', 'BrandC', 'BrandB'],
    'stock_qty': [10, 5, 50, 20, 30],
    'unit_price': [1200, 1500, 25, 70, 30]
})

df_assignment = df.copy()

# ✅ Correct: Assign a new 'status' based on multiple conditions
# Set 'status' to 'High_Value_Low_Stock' if brand is 'BrandA' AND stock_qty < 15
df_assignment.loc[
    (df_assignment['brand'] == 'BrandA') & (df_assignment['stock_qty'] < 15),
    'status'
] = 'High_Value_Low_Stock'

print("DataFrame after conditional assignment:")
print(df_assignment)

Output:

DataFrame after conditional assignment:
  product_name   brand  stock_qty  unit_price                status
0       Laptop  BrandA         10        1200  High_Value_Low_Stock
1       Laptop  BrandB          5        1500                   NaN
2        Mouse  BrandA         50          25                   NaN
3     Keyboard  BrandC         20          70                   NaN
4        Mouse  BrandB         30          30                   NaN

Why Parentheses Are Necessary: A Deeper Look at Precedence​

As stated by the Pandas documentation and Python's language reference, bitwise operators (&, |, ^) have higher precedence than comparison operators (<, >, ==, !=).

  • Without parentheses, df.A == 'X' & df.C > Y is effectively interpreted by Python as df.A == ('X' & df.C) > Y (or something similarly nonsensical due to the types involved with 'X' & df.C). The intermediate ('X' & df.C) part is where the type conflict usually occurs before the comparisons can even produce boolean Series.
  • With parentheses, (df.A == 'X') & (df.C > Y) ensures that:
    1. df.A == 'X' is evaluated first, producing a boolean Series.
    2. df.C > Y is evaluated next, producing another boolean Series.
    3. Finally, the bitwise & (element-wise AND) is applied between these two resulting boolean Series, which is the intended operation.

Conclusion​

The TypeError: Cannot perform 'rand_' (or 'ror_') with a dtyped [...] array and scalar of type [bool] when filtering Pandas DataFrames with multiple conditions is a direct result of Python's operator precedence rules interfering with the intended element-wise logical combination of boolean Series. The solution is simple yet crucial: Always enclose each individual comparison or condition within its own set of parentheses () before combining them with logical operators & (for AND) or | (for OR). df.loc[(condition1) & (condition2)] df.loc[(condition1) | (condition2)]

This practice ensures that each condition is evaluated to a boolean Series first, allowing Pandas to then correctly perform the element-wise logical AND or OR operation between these Series.

Table of Contents