Python User-Defined Function

Introduction

In this article, we’ll explore how to create and use a user-defined function (UDF) in Python. A UDF is a reusable block of code that can be applied to various data sets. We’ll delve into the world of pandas DataFrames, where we’ll learn how to write and apply a UDF to manipulate and analyze data.

Pandas DataFrames

A pandas DataFrame is a two-dimensional table of data with columns of potentially different types. It’s a powerful data structure that allows us to easily manipulate and analyze data. In this example, we have a DataFrame df with three columns: what_type, spend, and profit. We’ll focus on the what_type column, which contains categorical values like ‘hotels’, ‘fishing’, and ‘soccer’.

Defining the User-Defined Function

Our UDF will take a value from the what_type column as input and return a calculated value based on that value. Let’s define the function in Python:

def what_type(x):
    if x in ['hotels', 'fishing', 'soccer']:
        return 1.5 * 10
    else:
        return 2.1 * 5

Explanation of the UDF

In this example, we define a function called what_type that takes one argument, x. This argument is expected to be a value from the what_type column.

We use an if-else statement to determine which branch of the code to execute. If the input value x matches any of the specified values (‘hotels’, ‘fishing’, or ‘soccer’), we return the result of multiplying 1.5 by 10, which is 15. Otherwise, we return the result of multiplying 2.1 by 5.

Applying the UDF to the DataFrame

Now that we have our UDF defined, let’s apply it to the df DataFrame using the apply() method:

df.apply(what_type, axis=1)

This code applies the what_type function to each row of the DataFrame, passing the value in the what_type column as input. The axis=1 argument tells pandas to operate on rows (as opposed to columns).

However, we notice that this approach doesn’t produce the expected output. Instead of getting 15 for the ‘hotels’ value, we get 10.5.

Why Isn’t This Working?

The reason why our UDF isn’t producing the expected output is due to how pandas DataFrames work. When you apply a function to a DataFrame using apply(), the function is applied element-wise to each row of the DataFrame. This means that the input value for your UDF is actually the entire row, not just the value in the what_type column.

To fix this issue, we need to modify our UDF to work with individual values within the row, rather than the entire row itself.

Modifying the UDF

Let’s update our UDF to take only the value from the what_type column:

def what_type(x):
    if x == 'hotels':
        return 1.5 * 10
    elif x == 'fishing':
        return 1.5 * 10
    elif x == 'soccer':
        return 1.5 * 10
    else:
        return 2.1 * 5

By making this change, we ensure that our UDF receives only the desired value from the what_type column.

Applying the Modified UDF

Now that we have our updated UDF, let’s reapply it to the df DataFrame:

df['what_type_output'] = df['what_type'].apply(what_type)

In this code, we create a new column called what_type_output in the df DataFrame. We then apply our modified UDF to each value in the what_type column using the apply() method.

This approach produces the expected output for the ‘hotels’ value: 15.

Conclusion

In this article, we explored how to create and use a user-defined function (UDF) in Python. We defined a UDF that takes a value from the what_type column of a pandas DataFrame as input and returns a calculated value based on that value.

We discussed the importance of understanding how pandas DataFrames work when applying functions to manipulate and analyze data. By modifying our UDF to take only the desired value from the what_type column, we were able to produce the expected output.

We also showed how to apply a UDF to a DataFrame using the apply() method. This approach is useful for performing element-wise operations on DataFrames.

Conclusion

In conclusion, user-defined functions are a powerful tool in Python that can be used to manipulate and analyze data. By understanding how pandas DataFrames work and modifying our UDF accordingly, we were able to produce the expected output.

We hope this article has provided valuable insights into using user-defined functions with pandas DataFrames. If you have any questions or need further clarification on any of the concepts discussed in this article, please feel free to ask.

Last modified on 2023-08-31