local variable 'col' referenced before assignment pyspark

Fix "local variable referenced before assignment" in Python

Introduction

If you're a Python developer, you've probably come across a variety of errors, like the "local variable referenced before assignment" error. This error can be a bit puzzling, especially for beginners and when it involves local/global variables.

Today, we'll explain this error, understand why it occurs, and see how you can fix it.

The "local variable referenced before assignment" Error

The "local variable referenced before assignment" error in Python is a common error that occurs when a local variable is referenced before it has been assigned a value. This error is a type of UnboundLocalError , which is raised when a local variable is referenced before it has been assigned in the local scope.

Here's a simple example:

Running this code will throw the "local variable 'x' referenced before assignment" error. This is because the variable x is referenced in the print(x) statement before it is assigned a value in the local scope of the foo function.

Even more confusing is when it involves global variables. For example, the following code also produces the error:

But wait, why does this also produce the error? Isn't x assigned before it's used in the say_hello function? The problem here is that x is a global variable when assigned "Hello ". However, in the say_hello function, it's a different local variable, which has not yet been assigned.

We'll see later in this Byte how you can fix these cases as well.

Fixing the Error: Initialization

One way to fix this error is to initialize the variable before using it. This ensures that the variable exists in the local scope before it is referenced.

Let's correct the error from our first example:

In this revised code, we initialize x with a value of 1 before printing it. Now, when you run the function, it will print 1 without any errors.

Fixing the Error: Global Keyword

Another way to fix this error, depending on your specific scenario, is by using the global keyword. This is especially useful when you want to use a global variable inside a function.

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Here's how:

In this snippet, we declare x as a global variable inside the function foo . This tells Python to look for x in the global scope, not the local one . Now, when you run the function, it will increment the global x by 1 and print 1 .

Similar Error: NameError

An error that's similar to the "local variable referenced before assignment" error is the NameError . This is raised when you try to use a variable or a function name that has not been defined yet.

Running this code will result in a NameError :

In this case, we're trying to print the value of y , but y has not been defined anywhere in the code. Hence, Python raises a NameError . This is similar in that we are trying to use an uninitialized/undefined variable, but the main difference is that we didn't try to initialize y anywhere else in our code.

Variable Scope in Python

Understanding the concept of variable scope can help avoid many common errors in Python, including the main error of interest in this Byte. But what exactly is variable scope?

In Python, variables have two types of scope - global and local. A variable declared inside a function is known as a local variable, while a variable declared outside a function is a global variable.

Consider this example:

In this code, x is a global variable, and y is a local variable. x can be accessed anywhere in the code, but y can only be accessed within my_function . Confusion surrounding this is one of the most common causes for the "variable referenced before assignment" error.

In this Byte, we've taken a look at the "local variable referenced before assignment" error and another similar error, NameError . We also delved into the concept of variable scope in Python, which is an important concept to understand to avoid these errors. If you're seeing one of these errors, check the scope of your variables and make sure they're being assigned before they're being used.

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Spark Reference

• array_contains
• array_distinct
• array_intersect
• array_union
• collect_list
• current_date
• current_timestamp
• date_format
• monotonically_increasing_id
• regexp_extract
• regexp_extract_all
• regexp_replace
• spark_partition_id
• unionByName

data_frame functions

• withColumnRenamed

math functions

Introduction to the col() function.

The col() function in PySpark is a powerful tool that allows you to reference a column in a DataFrame. It is commonly used in data transformations, aggregations, and filtering operations. By using col() , you can easily access and manipulate the values within a specific column of your DataFrame.

The col() function is part of the pyspark.sql.functions module, which provides a wide range of built-in functions for working with structured data. It is a convenient way to refer to a column by name without explicitly referencing the DataFrame it belongs to.

Using col() simplifies your code and makes it more readable, especially when dealing with complex transformations involving multiple columns. It allows you to perform operations on specific columns without the need to reference them by their full names or using indexing.

In the upcoming sections, we will explore the syntax and parameters of the col() function, provide examples demonstrating its usage, discuss common use cases and scenarios, explain how col() works with different data types, and highlight its interaction with other PySpark functions. We will also cover performance considerations, best practices, tips, and tricks for effectively using col() in your data transformations.

Let's dive into the details of this versatile function and discover how it can simplify your PySpark data processing tasks.

Syntax and parameters of the col() function

The col() function in PySpark allows you to reference a column in a DataFrame by name. It has a simple syntax and accepts only one parameter:

The basic syntax of the col() function is as follows:

The column_name parameter is a string that represents the name of the column you want to reference. It can be either a simple column name or a fully qualified column name, depending on the context.

• column_name : This parameter is mandatory and represents the name of the column you want to reference. It can be a string that matches the exact name of the column in the DataFrame.

Examples demonstrating the usage of col()

To better understand the functionality and versatility of the col() function in PySpark, let's explore some practical examples that showcase its usage in various scenarios.

Example 1: Selecting a specific column

One of the most common use cases for col() is to select a specific column from a DataFrame. Let's assume we have a DataFrame called df with the following columns: id , name , age , and salary . We can use the col() function to reference the name column as follows:

In this example, we import the col() function from the pyspark.sql.functions module. We then use col() to reference the name column and assign it to the variable name_col . Finally, we select and display the content of the name column using the select() function.

Example 2: Filtering rows based on a condition

Another powerful use case for col() is to filter rows based on a condition. Let's consider the same DataFrame df from the previous example and filter out the rows where the age is greater than 30:

In this case, we use col() to reference the age column and filter the DataFrame to only include rows where the age is greater than 30.

Example 3: Performing mathematical operations

col() can also be used to perform mathematical operations on columns. Let's assume we have a DataFrame called df with columns num1 and num2 . To calculate the sum of these two columns and create a new column sum , we can utilize col() as follows:

In this example, we use col() to reference the num1 and num2 columns and perform a calculation to create a new column sum that contains the sum of the two columns.

These examples demonstrate just a few of the many ways col() can be utilized in PySpark. By leveraging the flexibility and power of col() , you can efficiently manipulate and transform your data to meet your specific requirements.

Common use cases and scenarios for col()

The col() function in PySpark is a powerful tool that allows you to reference a column in a DataFrame by name. It is commonly used in a variety of scenarios to manipulate and transform data. Let's explore some of the common use cases and scenarios where col() can be applied effectively.

Selecting and Filtering Columns

One of the primary use cases for col() is to select and filter columns from a DataFrame. By using col() in conjunction with other PySpark functions, you can easily extract the desired columns based on specific conditions. For example:

In the above example, we use col() to select the columns "column1" and "column2" from the DataFrame df . We also demonstrate how to filter rows based on a condition using col() .

Renaming Columns

Another common use case for col() is to rename columns in a DataFrame. By using col() in conjunction with the alias() function, you can easily assign new names to columns. Here's an example:

In the above example, we use col() to reference the column "column1" and assign it a new name "new_column_name" using the alias() function. This allows us to easily rename columns in a DataFrame.

Mathematical and Statistical Operations

col() can also be used in mathematical and statistical operations on columns. By combining col() with other PySpark functions, you can perform various calculations and aggregations on your data. Here's an example:

In the above example, we use col() to reference the column "column1" and calculate the sum of its values using the sum() function. This demonstrates how col() can be used in mathematical and statistical operations.

These are just a few examples of the common use cases and scenarios where col() can be applied effectively. By leveraging the power of col() and combining it with other PySpark functions, you can perform a wide range of data transformations and manipulations with ease.

How col() interacts with other PySpark functions

The col() function in PySpark can be combined with other PySpark functions to perform powerful data transformations. Here are some examples of how col() interacts with other commonly used PySpark functions:

Using col() with select()

The select() function is used to select specific columns from a DataFrame. When combined with col() , you can easily reference and select multiple columns by name. Here's an example:

Using col() with filter()

The filter() function is used to filter rows in a DataFrame based on a condition. col() can be used within filter() to reference a specific column and apply filtering operations. Here's an example:

Using col() with withColumn()

The withColumn() function is used to add or replace a column in a DataFrame. col() can be used within withColumn() to reference an existing column and perform calculations or transformations. Here's an example:

Using col() with orderBy()

The orderBy() function is used to sort the rows in a DataFrame based on one or more columns. col() can be used within orderBy() to reference a specific column for sorting. Here's an example:

By combining col() with these and other PySpark functions, you can perform complex data transformations and achieve the desired results efficiently.

Performance considerations and best practices when using col()

When using the col() function in PySpark, it is important to consider performance optimizations and follow best practices to ensure efficient and effective data transformations. Here are some key considerations to keep in mind:

Minimize the usage of col() within transformations

Although col() is a powerful function for referencing and manipulating column data, excessive use of it within transformations can impact performance. Instead, try to minimize the number of times col() is used and consider alternative approaches, such as using column aliases or creating intermediate DataFrames to reduce the reliance on col() .

Leverage predicate pushdown

PySpark optimizes query execution by pushing down predicates to the data sources whenever possible. When using col() in filter conditions or join operations, PySpark can leverage predicate pushdown to reduce the amount of data that needs to be processed. This can significantly improve performance, especially when dealing with large datasets.

Utilize column pruning

Column pruning refers to the optimization technique of eliminating unnecessary columns from the query execution plan. When using col() in transformations, ensure that only the required columns are selected and processed. This helps to minimize the amount of data transferred and processed, leading to improved performance.

Be mindful of data type conversions

When using col() with different data types, be aware of potential data type conversions that may occur. In some cases, implicit conversions might be performed by PySpark, which can impact performance. To avoid unnecessary conversions, ensure that the data types of the columns being operated on are compatible and aligned with the desired transformation.

Consider caching or persisting DataFrames

If you find yourself repeatedly using col() within multiple transformations on the same DataFrame, consider caching or persisting the DataFrame in memory or disk. This can help avoid unnecessary recomputation and improve overall performance, especially when dealing with iterative or complex data processing pipelines.

Optimize shuffle operations

Shuffle operations, such as groupBy or sortBy, can be resource-intensive and impact performance. When using col() in such operations, try to minimize the amount of data being shuffled by carefully selecting the necessary columns. Additionally, consider using appropriate partitioning strategies to optimize the shuffle process and distribute the data evenly across the cluster.

By following these performance considerations and best practices, you can effectively leverage the col() function in PySpark and ensure efficient data transformations in your Spark applications.

Tips and tricks for effectively using col() in data transformations

The col() function in PySpark is a powerful tool for manipulating and transforming data within a DataFrame. Here are some tips and tricks to help you make the most out of this function in your data transformations:

1. Understand the purpose of col()

Before diving into the tips and tricks, it's important to understand the purpose of the col() function. col() is a shorthand for accessing a column in a DataFrame. It allows you to refer to a column by name and perform various operations on it, such as filtering, aggregating, or transforming its values.

2. Import the col() function

To use the col() function, you need to import it from the pyspark.sql.functions module. Make sure to include the following import statement at the beginning of your code:

3. Alias columns

When working with multiple columns in a DataFrame, it's common to alias them to make the code more readable. You can use the alias() function in conjunction with col() to achieve this. Here's an example:

4. Combine col() with other PySpark functions

One of the strengths of col() is its ability to work seamlessly with other PySpark functions. You can combine col() with functions like when() , isNull() , isNotNull() , and many others to perform complex data transformations. Here's an example:

5. Use col() in filtering operations

col() is particularly useful when filtering data based on specific conditions. You can use it in conjunction with comparison operators like == , > , < , and logical operators like and , or , not to create powerful filters. Here's an example:

6. Handle missing or null values

When dealing with missing or null values in your data, you can use col() in combination with other PySpark functions like isNull() and isNotNull() to handle them effectively. Here's an example:

7. Follow best practices

To make your code more readable and maintainable, follow these best practices when using col() :

• Use meaningful aliases when aliasing columns with col() .
• Break down complex transformations into smaller, more manageable steps.
• Leverage other PySpark functions and techniques to optimize your code.
• Comment your code to explain the purpose and logic behind each transformation.

By following these tips and tricks, you can effectively leverage the col() function in your PySpark data transformations and achieve efficient and readable code.

In this blog post, we explored the col() function in PySpark and its various use cases, syntax, and parameters. We learned how col() can be combined with other PySpark functions to perform powerful data transformations and manipulations. We also discussed performance considerations, best practices, and provided tips and tricks for effectively using col() in your PySpark code.

By mastering the col() function and understanding its interactions with other PySpark functions, you can efficiently process and transform your data to meet your specific requirements. Experiment with different scenarios, refer to the official PySpark documentation for further exploration, and continue to enhance your PySpark skills. Happy coding!

How to fix UnboundLocalError: local variable 'x' referenced before assignment in Python

by Nathan Sebhastian

Posted on May 26, 2023

Reading time: 2 minutes

One error you might encounter when running Python code is:

This error commonly occurs when you reference a variable inside a function without first assigning it a value.

You could also see this error when you forget to pass the variable as an argument to your function.

Let me show you an example that causes this error and how I fix it in practice.

How to reproduce this error

Suppose you have a variable called name declared in your Python code as follows:

Next, you created a function that uses the name variable as shown below:

When you execute the code above, you’ll get this error:

This error occurs because you both assign and reference a variable called name inside the function.

Python thinks you’re trying to assign the local variable name to name , which is not the case here because the original name variable we declared is a global variable.

How to fix this error

To resolve this error, you can change the variable’s name inside the function to something else. For example, name_with_title should work:

As an alternative, you can specify a name parameter in the greet() function to indicate that you require a variable to be passed to the function.

When calling the function, you need to pass a variable as follows:

This code allows Python to know that you intend to use the name variable which is passed as an argument to the function as part of the newly declared name variable.

Still, I would say that you need to use a different name when declaring a variable inside the function. Using the same name might confuse you in the future.

Here’s the best solution to the error:

Now it’s clear that we’re using the name variable given to the function as part of the value assigned to name_with_title . Way to go!

The UnboundLocalError: local variable 'x' referenced before assignment occurs when you reference a variable inside a function before declaring that variable.

To resolve this error, you need to use a different variable name when referencing the existing variable, or you can also specify a parameter for the function.

I hope this tutorial is useful. See you in other tutorials.

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Local variable referenced before assignment in Python

Last updated: Apr 8, 2024 Reading time · 4 min

# Local variable referenced before assignment in Python

The Python "UnboundLocalError: Local variable referenced before assignment" occurs when we reference a local variable before assigning a value to it in a function.

To solve the error, mark the variable as global in the function definition, e.g. global my_var .

Here is an example of how the error occurs.

We assign a value to the name variable in the function.

# Mark the variable as global to solve the error

To solve the error, mark the variable as global in your function definition.

If a variable is assigned a value in a function's body, it is a local variable unless explicitly declared as global .

# Local variables shadow global ones with the same name

You could reference the global name variable from inside the function but if you assign a value to the variable in the function's body, the local variable shadows the global one.

Accessing the name variable in the function is perfectly fine.

On the other hand, variables declared in a function cannot be accessed from the global scope.

The name variable is declared in the function, so trying to access it from outside causes an error.

Make sure you don't try to access the variable before using the global keyword, otherwise, you'd get the SyntaxError: name 'X' is used prior to global declaration error.

# Returning a value from the function instead

An alternative solution to using the global keyword is to return a value from the function and use the value to reassign the global variable.

We simply return the value that we eventually use to assign to the name global variable.

# Passing the global variable as an argument to the function

You should also consider passing the global variable as an argument to the function.

We passed the name global variable as an argument to the function.

If we assign a value to a variable in a function, the variable is assumed to be local unless explicitly declared as global .

# Assigning a value to a local variable from an outer scope

If you have a nested function and are trying to assign a value to the local variables from the outer function, use the nonlocal keyword.

The nonlocal keyword allows us to work with the local variables of enclosing functions.

Had we not used the nonlocal statement, the call to the print() function would have returned an empty string.

Printing the message variable on the last line of the function shows an empty string because the inner() function has its own scope.

Changing the value of the variable in the inner scope is not possible unless we use the nonlocal keyword.

Instead, the message variable in the inner function simply shadows the variable with the same name from the outer scope.

# Discussion

As shown in this section of the documentation, when you assign a value to a variable inside a function, the variable:

• Becomes local to the scope.
• Shadows any variables from the outer scope that have the same name.

The last line in the example function assigns a value to the name variable, marking it as a local variable and shadowing the name variable from the outer scope.

At the time the print(name) line runs, the name variable is not yet initialized, which causes the error.

The most intuitive way to solve the error is to use the global keyword.

The global keyword is used to indicate to Python that we are actually modifying the value of the name variable from the outer scope.

• If a variable is only referenced inside a function, it is implicitly global.
• If a variable is assigned a value inside a function's body, it is assumed to be local, unless explicitly marked as global .

If you want to read more about why this error occurs, check out [this section] ( this section ) of the docs.

# Additional Resources

You can learn more about the related topics by checking out the following tutorials:

• SyntaxError: name 'X' is used prior to global declaration

Borislav Hadzhiev

Web Developer

Copyright © 2024 Borislav Hadzhiev

Book version 2022.6

Reference columns by name: F.col()

Reference columns by name: f.col() #.

There are several different ways to reference columns in a PySpark DataFrame df , e.g. in a .filter() operation:

df.filter(F.col("column_name") == value) : references column by name; the recommended method, used throughout this book

df.filter(df.column_name == value) : references column directly from the DF

df.flter(df["column_name"] == value) : pandas style, less commonly used in PySpark

The preferred method is using F.col() from the pyspark.sql.functions module and is used throughout this book. Although all three methods above will work in some circumstances, only F.col() will always have the desired outcome. This is because it references the column by name rather than directly from the DF, which means columns not yet assigned to the DF can be used, e.g. when chaining several operations on the same DF together.

There are several cases where F.col() will work but one of the other methods may not:

Filter the DataFrame when reading in

Filter on a new column

Ensuring you are using the latest values

Columns with special characters or spaces

Example 1: Filter the DataFrame when reading in #

First, import the modules and create a Spark session:

We can filter on columns when reading in the DataFrame. For instance to only read "Cat" from the animal rescue data:

This cannot be done using cats.animal_group as we have not defined cats when referencing the DataFrame. To use the other notation we need to define rescue then filter on cats.animal_group :

Example 2: Filter on a new column #

Read in the animal rescue data:

Create a new column, animal_group_upper , which consists of the animal_group in uppercase.

If we try and immediately filter on this column using rescue.animal_group_upper , it will not work. This is because we have yet to define the column in rescue . Error handling is being used here; for more information see the article on Handling Errors in PySpark .

We could split this statement up over two different lines:

Using F.col() is instead is much neater:

Example 3: Ensuring you are using the latest values #

Using df.column_name can also result in bugs when you think you are referencing the latest values, but are actually using the original ones. Here, the values in animal_group are changed, but rescue is yet to be redefined, and so the old values are used. As such no data is returned:

Changing to F.col("animal_group") gives the correct result:

Example 4: Columns with special characters or spaces #

One final use case for this method is when your source data has column names with spaces or special characters in them. This can happen if reading in from a CSV file rather than parquet or Hive table. The animal rescue CSV has a column called IncidentNotionalCost(£) . You cannot refer to the column using rescue.IncidentNotionalCost(£) , instead, use F.col("IncidentNotionalCost(£)") :

You can use the pandas style rescue["IncidentNotionalCost(£)"] but this notation is not encouraged in PySpark:

Of course, the best idea is to rename the column something sensible, which is easier to reference:

If your data is stored as CSV with non-standard column names you may want to create a data cleansing stage, which reads in the CSV and renames the columns, then write this out as a parquet file or Hive table . Parquet files and Hive tables also have the advantage of being far quicker for Spark to process

Further Resources #

Spark at the ONS Articles:

Handling Errors in PySpark

Writing Data

Writing Data to a Parquet File

Writing Data to a Hive Table

PySpark Documentation:

How to Fix Local Variable Referenced Before Assignment Error in Python

Table of Contents

Fixing local variable referenced before assignment error.

In Python , when you try to reference a variable that hasn't yet been given a value (assigned), it will throw an error.

That error will look like this:

In this post, we'll see examples of what causes this and how to fix it.

Let's begin by looking at an example of this error:

If you run this code, you'll get

The issue is that in this line:

We are defining a local variable called value and then trying to use it before it has been assigned a value, instead of using the variable that we defined in the first line.

If we want to refer the variable that was defined in the first line, we can make use of the global keyword.

The global keyword is used to refer to a variable that is defined outside of a function.

Let's look at how using global can fix our issue here:

Global variables have global scope, so you can referenced them anywhere in your code, thus avoiding the error.

If you run this code, you'll get this output:

In this post, we learned at how to avoid the local variable referenced before assignment error in Python.

The error stems from trying to refer to a variable without an assigned value, so either make use of a global variable using the global keyword, or assign the variable a value before using it.

Thanks for reading!

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pyspark.sql.functions.col ¶

Returns a Column based on the given column name.

New in version 1.3.0.

Changed in version 3.4.0: Supports Spark Connect.

the name for the column

the corresponding column instance.

pyspark.sql.functions.column

How to Solve Error - Local Variable Referenced Before Assignment in Python

• Python How-To's
• How to Solve Error - Local Variable …

Check the Variable Scope to Fix the local variable referenced before assignment Error in Python

Initialize the variable before use to fix the local variable referenced before assignment error in python, use conditional assignment to fix the local variable referenced before assignment error in python.

This article delves into various strategies to resolve the common local variable referenced before assignment error. By exploring methods such as checking variable scope, initializing variables before use, conditional assignments, and more, we aim to equip both novice and seasoned programmers with practical solutions.

Each method is dissected with examples, demonstrating how subtle changes in code can prevent this frequent error, enhancing the robustness and readability of your Python projects.

The local variable referenced before assignment occurs when some variable is referenced before assignment within a function’s body. The error usually occurs when the code is trying to access the global variable.

The primary purpose of managing variable scope is to ensure that variables are accessible where they are needed while maintaining code modularity and preventing unexpected modifications to global variables.

We can declare the variable as global using the global keyword in Python. Once the variable is declared global, the program can access the variable within a function, and no error will occur.

The below example code demonstrates the code scenario where the program will end up with the local variable referenced before assignment error.

In this example, my_var is a global variable. Inside update_var , we attempt to modify it without declaring its scope, leading to the Local Variable Referenced Before Assignment error.

We need to declare the my_var variable as global using the global keyword to resolve this error. The below example code demonstrates how the error can be resolved using the global keyword in the above code scenario.

In the corrected code, we use the global keyword to inform Python that my_var references the global variable.

When we first print my_var , it displays the original value from the global scope.

After assigning a new value to my_var , it updates the global variable, not a local one. This way, we effectively tell Python the scope of our variable, thus avoiding any conflicts between local and global variables with the same name.

Ensure that the variable is initialized with some value before using it. This can be done by assigning a default value to the variable at the beginning of the function or code block.

The main purpose of initializing variables before use is to ensure that they have a defined state before any operations are performed on them. This practice is not only crucial for avoiding the aforementioned error but also promotes writing clear and predictable code, which is essential in both simple scripts and complex applications.

In this example, the variable total is used in the function calculate_total without prior initialization, leading to the Local Variable Referenced Before Assignment error. The below example code demonstrates how the error can be resolved in the above code scenario.

In our corrected code, we initialize the variable total with 0 before using it in the loop. This ensures that when we start adding item values to total , it already has a defined state (in this case, 0).

This initialization is crucial because it provides a starting point for accumulation within the loop. Without this step, Python does not know the initial state of total , leading to the error.

Conditional assignment allows variables to be assigned values based on certain conditions or logical expressions. This method is particularly useful when a variable’s value depends on certain prerequisites or states, ensuring that a variable is always initialized before it’s used, thereby avoiding the common error.

In this example, message is only assigned within the if and elif blocks. If neither condition is met (as with guest ), the variable message remains uninitialized, leading to the Local Variable Referenced Before Assignment error when trying to print it.

The below example code demonstrates how the error can be resolved in the above code scenario.

In the revised code, we’ve included an else statement as part of our conditional logic. This guarantees that no matter what value user_type holds, the variable message will be assigned some value before it is used in the print function.

This conditional assignment ensures that the message is always initialized, thereby eliminating the possibility of encountering the Local Variable Referenced Before Assignment error.

Throughout this article, we have explored multiple approaches to address the Local Variable Referenced Before Assignment error in Python. From the nuances of variable scope to the effectiveness of initializations and conditional assignments, these strategies are instrumental in developing error-free code.

The key takeaway is the importance of understanding variable scope and initialization in Python. By applying these methods appropriately, programmers can not only resolve this specific error but also enhance the overall quality and maintainability of their code, making their programming journey smoother and more rewarding.

Python UnboundLocalError: local variable referenced before assignment

by Suf | Programming , Python , Tips

If you try to reference a local variable before assigning a value to it within the body of a function, you will encounter the UnboundLocalError: local variable referenced before assignment.

The preferable way to solve this error is to pass parameters to your function, for example:

Alternatively, you can declare the variable as global to access it while inside a function. For example,

This tutorial will go through the error in detail and how to solve it with code examples .

Table of contents

What is scope in python, unboundlocalerror: local variable referenced before assignment, solution #1: passing parameters to the function, solution #2: use global keyword, solution #1: include else statement, solution #2: use global keyword.

Scope refers to a variable being only available inside the region where it was created. A variable created inside a function belongs to the local scope of that function, and we can only use that variable inside that function.

A variable created in the main body of the Python code is a global variable and belongs to the global scope. Global variables are available within any scope, global and local.

UnboundLocalError occurs when we try to modify a variable defined as local before creating it. If we only need to read a variable within a function, we can do so without using the global keyword. Consider the following example that demonstrates a variable var created with global scope and accessed from test_func :

If we try to assign a value to var within test_func , the Python interpreter will raise the UnboundLocalError:

This error occurs because when we make an assignment to a variable in a scope, that variable becomes local to that scope and overrides any variable with the same name in the global or outer scope.

var +=1 is similar to var = var + 1 , therefore the Python interpreter should first read var , perform the addition and assign the value back to var .

var is a variable local to test_func , so the variable is read or referenced before we have assigned it. As a result, the Python interpreter raises the UnboundLocalError.

Example #1: Accessing a Local Variable

Let’s look at an example where we define a global variable number. We will use the increment_func to increase the numerical value of number by 1.

Let’s run the code to see what happens:

The error occurs because we tried to read a local variable before assigning a value to it.

We can solve this error by passing a parameter to increment_func . This solution is the preferred approach. Typically Python developers avoid declaring global variables unless they are necessary. Let’s look at the revised code:

We have assigned a value to number and passed it to the increment_func , which will resolve the UnboundLocalError. Let’s run the code to see the result:

We successfully printed the value to the console.

We also can solve this error by using the global keyword. The global statement tells the Python interpreter that inside increment_func , the variable number is a global variable even if we assign to it in increment_func . Let’s look at the revised code:

Let’s run the code to see the result:

Example #2: Function with if-elif statements

Let’s look at an example where we collect a score from a player of a game to rank their level of expertise. The variable we will use is called score and the calculate_level function takes in score as a parameter and returns a string containing the player’s level .

In the above code, we have a series of if-elif statements for assigning a string to the level variable. Let’s run the code to see what happens:

The error occurs because we input a score equal to 40 . The conditional statements in the function do not account for a value below 55 , therefore when we call the calculate_level function, Python will attempt to return level without any value assigned to it.

We can solve this error by completing the set of conditions with an else statement. The else statement will provide an assignment to level for all scores lower than 55 . Let’s look at the revised code:

In the above code, all scores below 55 are given the beginner level. Let’s run the code to see what happens:

We can also create a global variable level and then use the global keyword inside calculate_level . Using the global keyword will ensure that the variable is available in the local scope of the calculate_level function. Let’s look at the revised code.

In the above code, we put the global statement inside the function and at the beginning. Note that the “default” value of level is beginner and we do not include the else statement in the function. Let’s run the code to see the result:

Congratulations on reading to the end of this tutorial! The UnboundLocalError: local variable referenced before assignment occurs when you try to reference a local variable before assigning a value to it. Preferably, you can solve this error by passing parameters to your function. Alternatively, you can use the global keyword.

If you have if-elif statements in your code where you assign a value to a local variable and do not account for all outcomes, you may encounter this error. In which case, you must include an else statement to account for the missing outcome.

For further reading on Python code blocks and structure, go to the article: How to Solve Python IndentationError: unindent does not match any outer indentation level .

Go to the  online courses page on Python  to learn more about Python for data science and machine learning.

Have fun and happy researching!

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[SOLVED] Local Variable Referenced Before Assignment

Python treats variables referenced only inside a function as global variables. Any variable assigned to a function’s body is assumed to be a local variable unless explicitly declared as global.

Why Does This Error Occur?

Unboundlocalerror: local variable referenced before assignment occurs when a variable is used before its created. Python does not have the concept of variable declarations. Hence it searches for the variable whenever used. When not found, it throws the error.

Before we hop into the solutions, let’s have a look at what is the global and local variables.

Local Variable Declarations vs. Global Variable Declarations

Local Variable Referenced Before Assignment Error with Explanation

Try these examples yourself using our Online Compiler.

Let’s look at the following function:

Explanation

The variable myVar has been assigned a value twice. Once before the declaration of myFunction and within myFunction itself.

Using Global Variables

Passing the variable as global allows the function to recognize the variable outside the function.

Create Functions that Take in Parameters

Instead of initializing myVar as a global or local variable, it can be passed to the function as a parameter. This removes the need to create a variable in memory.

UnboundLocalError: local variable ‘DISTRO_NAME’

This error may occur when trying to launch the Anaconda Navigator in Linux Systems.

Upon launching Anaconda Navigator, the opening screen freezes and doesn’t proceed to load.

Try and update your Anaconda Navigator with the following command.

If solution one doesn’t work, you have to edit a file located at

After finding and opening the Python file, make the following changes:

In the function on line 159, simply add the line:

DISTRO_NAME = None

Save the file and re-launch Anaconda Navigator.

DJANGO – Local Variable Referenced Before Assignment [Form]

The program takes information from a form filled out by a user. Accordingly, an email is sent using the information.

Upon running you get the following error:

We have created a class myForm that creates instances of Django forms. It extracts the user’s name, email, and message to be sent.

A function GetContact is created to use the information from the Django form and produce an email. It takes one request parameter. Prior to sending the email, the function verifies the validity of the form. Upon True , .get() function is passed to fetch the name, email, and message. Finally, the email sent via the send_mail function

Why does the error occur?

We are initializing form under the if request.method == “POST” condition statement. Using the GET request, our variable form doesn’t get defined.

Local variable Referenced before assignment but it is global

This is a common error that happens when we don’t provide a value to a variable and reference it. This can happen with local variables. Global variables can’t be assigned.

This error message is raised when a variable is referenced before it has been assigned a value within the local scope of a function, even though it is a global variable.

Here’s an example to help illustrate the problem:

In this example, x is a global variable that is defined outside of the function my_func(). However, when we try to print the value of x inside the function, we get a UnboundLocalError with the message “local variable ‘x’ referenced before assignment”.

This is because the += operator implicitly creates a local variable within the function’s scope, which shadows the global variable of the same name. Since we’re trying to access the value of x before it’s been assigned a value within the local scope, the interpreter raises an error.

To fix this, you can use the global keyword to explicitly refer to the global variable within the function’s scope:

However, in the above example, the global keyword tells Python that we want to modify the value of the global variable x, rather than creating a new local variable. This allows us to access and modify the global variable within the function’s scope, without causing any errors.

Local variable ‘version’ referenced before assignment ubuntu-drivers

This error occurs with Ubuntu version drivers. To solve this error, you can re-specify the version information and give a split as 2 –

Here, p_name means package name.

With the help of the threading module, you can avoid using global variables in multi-threading. Make sure you lock and release your threads correctly to avoid the race condition.

When a variable that is created locally is called before assigning, it results in Unbound Local Error in Python. The interpreter can’t track the variable.

Therefore, we have examined the local variable referenced before the assignment Exception in Python. The differences between a local and global variable declaration have been explained, and multiple solutions regarding the issue have been provided.

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4 ways to fix local variable referenced before assignment error in python, resolving the local variable referenced before assignment error in python.

Python is one of the world’s most popular programming languages due to its simplicity, readability, and versatility. Despite its many advantages, when coding in Python, one may encounter various errors, with the most common being the “local variable referenced before assignment” error.

Even the most experienced Python developers have encountered this error at some point in their programming career. In this article, we will look at four effective strategies for resolving the local variable referenced before assignment error in Python.

Strategy 1: Assigning a Value before Referencing

The first strategy is to assign a value to a variable before referencing it. The error occurs when the variable is referenced before it is assigned a value.

This problem can be avoided by initializing the variable before referencing it. For example, let us consider the snippet below:

“`python

add_numbers():

print(x + y)

add_numbers()

In the snippet above, the variables `x` and `y` are not assigned values before they are referenced in the `print` statement. Therefore, we will get a local variable “referenced before assignment” error.

To resolve this error, we must initialize the variables before referencing them. We can avoid this error by assigning a value to `x` and `y` before they are referenced, as shown below:

Strategy 2: Using the Global Keyword

In Python, variables declared inside a function are considered local variables. Thus, they are separate from other variables declared outside of the function.

If we want to use a variable outside of the function, we must use the global keyword. Using the global keyword tells Python that you want to use the variable that was defined globally, not locally.

For example:

In the code snippet above, the `global` keyword tells Python to use the variable `x` defined outside of the function rather than a local variable named `x`. Thus, Python will output 30.

Strategy 3: Adding Input Parameters for Functions

Another way to avoid the local variable referenced before assignment error is by adding input parameters to functions.

def add_numbers(x, y):

add_numbers(10, 20)

In the code snippet above, `x` and `y` are variables that are passed into the `add_numbers` function as arguments.

This approach allows us to avoid the local variable referenced before assignment error because the variables are being passed into the function as input parameters. Strategy 4: Initializing Variables before Loops or Conditionals

Finally, it’s also a good practice to initialize the variables before loops or conditionals.

If you are defining a variable within a loop, you must initialize it before the loop starts. This way, the variable already exists, and we can update the value inside the loop.

my_list = [1, 2, 3, 4, 5]

for number in my_list:

sum += number

In the code snippet above, the variable `sum` has been initialized with the value of 0 before the loop runs. Thus, we can update and use the variable inside the loop.

In conclusion, the “local variable referenced before assignment” error is a common issue in Python. However, with the strategies discussed in this article, you can avoid the error and write clean Python code.

Remember to initialize your variables, use the global keyword, add input parameters in functions, and initialize variables before loops or conditionals. By following these techniques, your Python code will be error-free and much easier to manage.

In essence, this article has provided four key strategies for resolving the “local variable referenced before assignment” error that is common in Python. These strategies include initializing variables before referencing, using the global keyword, adding input parameters to functions, and initializing variables before loops or conditionals.

These techniques help to ensure clean code that is free from errors. By implementing these strategies, developers can improve their code quality and avoid time-wasting errors that can occur in their work.

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UnboundLocalError Local variable Referenced Before Assignment in Python

Handling errors is an integral part of writing robust and reliable Python code. One common stumbling block that developers often encounter is the “UnboundLocalError” raised within a try-except block. This error can be perplexing for those unfamiliar with its nuances but fear not – in this article, we will delve into the intricacies of the UnboundLocalError and provide a comprehensive guide on how to effectively use try-except statements to resolve it.

What is UnboundLocalError Local variable Referenced Before Assignment in Python?

The UnboundLocalError occurs when a local variable is referenced before it has been assigned a value within a function or method. This error typically surfaces when utilizing try-except blocks to handle exceptions, creating a puzzle for developers trying to comprehend its origins and find a solution.

Why does UnboundLocalError: Local variable Referenced Before Assignment Occur?

below, are the reasons of occurring “Unboundlocalerror: Try Except Statements” in Python :

Variable Assignment Inside Try Block

Reassigning a global variable inside except block.

• Accessing a Variable Defined Inside an If Block

In the below code, example_function attempts to execute some_operation within a try-except block. If an exception occurs, it prints an error message. However, if no exception occurs, it prints the value of the variable result outside the try block, leading to an UnboundLocalError since result might not be defined if an exception was caught.

In below code , modify_global function attempts to increment the global variable global_var within a try block, but it raises an UnboundLocalError. This error occurs because the function treats global_var as a local variable due to the assignment operation within the try block.

Solution for UnboundLocalError Local variable Referenced Before Assignment

Below, are the approaches to solve “Unboundlocalerror: Try Except Statements”.

Initialize Variables Outside the Try Block

Avoid reassignment of global variables.

In modification to the example_function is correct. Initializing the variable result before the try block ensures that it exists even if an exception occurs within the try block. This helps prevent UnboundLocalError when trying to access result in the print statement outside the try block.

Below, code calculates a new value ( local_var ) based on the global variable and then prints both the local and global variables separately. It demonstrates that the global variable is accessed directly without being reassigned within the function.

In conclusion , To fix “UnboundLocalError” related to try-except statements, ensure that variables used within the try block are initialized before the try block starts. This can be achieved by declaring the variables with default values or assigning them None outside the try block. Additionally, when modifying global variables within a try block, use the `global` keyword to explicitly declare them.

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