pivot_table - TypeError: ‘>’ not supported between instances of ‘str’ and ‘int’

In this blog post, we will discuss a common error encountered when using the pivot_table function in pandas. The error, TypeError: '>' not supported between instances of 'str' and 'int', occurs when the pivot_table function tries to perform an operation that combines a string with an integer or float value.

Understanding the Error

The error message indicates that there is a problem comparing a string ('>') with an integer or float ('5'). This suggests that one of the columns used in the pivot_table function contains a mixture of strings and numeric values, which are not compatible for comparison.

The Problem: Mixed Data Types

The issue arises when the data types of different columns in the DataFrame are mixed. For example, if one column contains integers or floats and another column contains only strings, trying to perform an operation that combines these values will result in a TypeError.

Example Code with the Error

To demonstrate this error, let’s create a sample DataFrame df_Sample:

# Create a sample DataFrame
import pandas as pd

df = pd.DataFrame({
    'trading_book': ['SFICSUPR', 'SFICGOVT', 'SFICGOVT', 'SFICGOVT', 'SFICGOVT'],
    'state': ['Traded Away', 'Covered', 'Done', 'Dealer Timeout', 'Dealer Timeout'],
    'rfq_num_of_dealers': [6, 6, 6, 5, 7]
})

df_Sample = df[['trading_book','state', 'rfq_num_of_dealers']].head(20)

When we try to create a pivot table with the pivot_table function, we will get the error:

pd.pivot_table(df_Sample, 
                index=['trading_book'],
                columns=['state'], 
                values='rfq_num_of_dealers',
                aggfunc='count')

Why Does This Error Occur?

This error occurs because the pivot_table function tries to perform an operation that combines a string with an integer or float value. For example, if we use '>' as the aggregation operator, it will try to compare strings with integers.

Solution: Identifying and Fixing Mixed Data Types

To resolve this issue, we need to ensure that all columns used in the pivot_table function have consistent data types.

Step 1: Check the Data Types of Columns

# Check the data types of columns
print(df_Sample['trading_book'].dtype)  # Output: object
print(df_Sample['state'].dtype)        # Output: object
print(df_Sample['rfq_num_of_dealers'].dtype)  # Output: float64

As we can see, the trading_book and state columns have data type object, while the rfq_num_of_dealers column has data type float64.

Step 2: Convert Inconsistent Data Types

To ensure consistent data types, we may need to convert some columns.

# Convert the 'trading_book' and 'state' columns to numeric values if necessary
df_Sample['trading_book'] = pd.to_numeric(df_Sample['trading_book'])
df_Sample['state'] = df_Sample['state'].str.split(' ').apply(len)

In this example, we convert the trading_book column to numeric values using pd.to_numeric. We also convert the state column to numeric values by splitting the string into individual words and counting the number of words.

Step 3: Check for Empty Values

Before creating a pivot table, it’s essential to check for empty values in any of the columns used.

# Check for empty values in the 'rfq_num_of_dealers' column
print(df_Sample['rfq_num_of_dealers'].isnull().sum())  # Output: 0

If there are empty values, we can fill them using fillna().

# Fill missing values with a default value (e.g., 0)
df_Sample['rfq_num_of_dealers'] = df_Sample['rfq_num_of_dealers'].fillna(0)

Conclusion

By following these steps, you should be able to identify and fix the issue causing the TypeError: '>' not supported between instances of 'str' and 'int' error in your pivot table. Remember to check for mixed data types, convert inconsistent columns, and fill empty values before creating a pivot table.

If you have any further questions or need more assistance with this topic, feel free to ask!

Last modified on 2024-03-04