What are the different exceptions in Polars library?

Key takeaways:

  • Polars is a fast DataFrame library implemented in Rust with Python bindings. It is optimized for performance and parallel processing, making it suitable for large datasets.

  • Polars can handle data from various formats, including CSV, Parquet, and Arrow.

  • Exception handling ensures smooth program execution by using try and except blocks, preventing crashes when errors occur.

  • The library has several specialized exceptions to handle errors, including:

    • ColumnNotFoundError: Raised when a specified column is missing.

    • ComputeError: Raised when an issue prevents computation from being completed.

    • DuplicateError: Raised when there are duplicate column names.

    • InvalidOperationError: Raised for invalid operations on certain data types.

    • NoDataError: Raised when an operation is attempted on empty or uninitialized data.

    • NoRowsReturnedError: Raised when no rows are returned, but rows are expected.

    • PolarsPanicError: Raised for severe, unrecoverable errors in the underlying Rust library.

    • RowsError: Raised when the number of rows returned doesn’t meet expectations.

    • SchemaError: Raised when the actual schema doesn’t match the expected schema.

    • SchemaFieldNotFoundError: Raised when a field specified in the schema is missing.

    • ShapeError: Raised when there's a shape mismatch in the data.

    • StructFieldNotFoundError: Raised when a specified schema field is missing.

    • TooManyRowsReturnedError: Raised when more rows than expected are returned.

    • PolarsError: A generic error for various issues encountered during data operations.

Polars is a fast DataFrame library implemented in Rust with bindings for Python. It is a data manipulation library used for processing large datasets. It is similar to pandas but optimized for performance and parallel processing of operations, making it well-suited for big data processing tasks. The Polars library supports data from various sources, including CSV, Parquet, Arrow, and more. Polars’ efficiency in handling large datasets comes with its own set of nuances, particularly when dealing with potential errors or unexpected data. To ensure smooth execution, it’s important to understand how to handle exceptions effectively in Polars.

Exception handling

When an exception occurs, there are two possible behaviors of a program:

  • It crashes the program and terminates it immediately.

  • It handles the exception using the try and except block and completes the execution.

So, exception handling plays an important role in the proper and successful execution of a program. Here is the basic syntax of try and except block for handling exceptions:

try:
// run this code
except:
// execute this code when exception occurs

We’ll go through the different exceptions provided by the Polars library.

Exceptions in polars.exceptions

Here is the list of exceptions that occur when Polars face any error that can’t be fixed.

Exceptions

Description

ColumnNotFoundError

When a column is not found that is specified

ComputeError

When Polars cannot finish the computation due to an issue or error

DuplicateError

When there are two or more columns with the same name

InvalidOperationError

When an operation or action being performed is not valid or allowed on a certain data type

NoDataError

When an operation can not be performed on a data structure that is expected to contain data but is, in fact, empty or uninitialized

NoRowsReturnedError

When at least one row is expected, but no rows are returned

PolarsPanicError

When a severe or unrecoverable error causes a panic in the underlying Rust library, and the program cannot continue to run safely

RowsError

When the count of rows returned does not match expectations

SchemaError

When the expected schema of the data does not match the actual schema

SchemaFieldNotFoundError

When a field specified in the schema is not found in the data

ShapeError

When there’s an inconsistency between the expected and actual shape of the data

StructFieldNotFoundError

When a specified schema field is not found

TooManyRowsReturnedError

When more rows than expected are returned

PolarsError

A generic exception raised for various errors encountered while using Polars, including issues related to data processing, operations, or invalid inputs

Code example

Let’s see the examples of a few of them:

import polars as pl
# DataFrame
data = {
"x": [10, 20, 30, 40, 50, 60],
"y": [0.1, 0.2, 0.5, 1.0, 2.0, 3.0],
"z": [False, True, False, False, True, True],
"w": ["Red", "Blue", "Red", "Green", "Green", "Blue"]
}
df = pl.DataFrame(data)
# Example 1: ColumnNotFoundError
try:
df["new_column"] # Trying to access a non-existent column
except pl.ColumnNotFoundError as e:
print(f"ColumnNotFoundError: The column does not exist in the DataFrame: {e}")
# Example 2: ComputeError
try:
df["w"] / 0 # Invalid operation on column 'w'
except pl.ComputeError as e:
print(f"ComputeError: A compute error occurred: {e}")
# Example 3: DuplicateError (Artificially introduce duplicate column names)
try:
df_two = pl.DataFrame({"a": [1, 1, 1]})
pl.concat([df_two, df_two], how="horizontal")
except pl.DuplicateError as e:
print(f"DuplicateError: Duplicate column names found: {e}")
# Example 4: InvalidOperationError
try:
df["x"].is_in(["x", "y"])
except pl.InvalidOperationError as e:
print(f"InvalidOperationError: Invalid operation performed: {e}")

We are using version 3.8 of Python.

Code explanation

  • Lines 4–9: We define a DataFrame with the columns as x, y, z, and w.

  • Line 10: We create a Polars DataFrame using the data.

  • Lines 13–16: We use try and except block to access a column new_column. Since there is no column with this name, the ColumnNotFoundError exception will be raised, and the except block will handle this.

  • Lines 19–22: We use try and except block to perform an operation on column w. Since the operation is not valid, ComputeError exception will be raised, and the except block will handle this.

  • Lines 25–29: We use try and except block to add a duplicate column to the df_two DataFrame. Since there already exists a series in this DataFrame, DuplicateError exception will be raised, and the except block will handle this.

  • Lines 32–36: We use try and except block to check if the series of integers has elements in a series of strings. Since the operation is not valid, InvalidOperationError exception will be raised, and the except block will handle this.

In conclusion, the Polars library provides us with a robust set of exceptions to handle various scenarios and errors that may occur. By leveraging exception handling, we can write more resilient and error-tolerant code, leading to more stable and efficient data workflows.

Frequently asked questions

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What are the different types of exceptions in OOP?

Some of the common exceptions in OOP are as follows:

  • RuntimeException
  • NullPointerException
  • ArithmeticException

You can learn about other exceptions in Java here.


What are the different types of exceptions in Python?

Some of the common exceptions in Python are as follows:

  • AssertionError
  • EOFError
  • AttributeError

What are exception methods?

Exception methods are used to handle errors in programming. Common methods include try-catch, throws, and custom exceptions.


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