Some of the common exceptions in OOP are as follows:
RuntimeException
NullPointerException
ArithmeticException
You can learn about other exceptions in Java here.
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
andexcept
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.
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 codeexcept:// execute this code when exception occurs
We’ll go through the different exceptions provided by the Polars library.
polars.exceptions
Here is the list of exceptions that occur when Polars face any error that can’t be fixed.
Exceptions | Description |
| When a column is not found that is specified |
| When Polars cannot finish the computation due to an issue or error |
| When there are two or more columns with the same name |
| When an operation or action being performed is not valid or allowed on a certain data type |
| When an operation can not be performed on a data structure that is expected to contain data but is, in fact, empty or uninitialized |
| When at least one row is expected, but no rows are returned |
| When a severe or unrecoverable error causes a panic in the underlying Rust library, and the program cannot continue to run safely |
| When the count of rows returned does not match expectations |
| When the expected schema of the data does not match the actual schema |
| When a field specified in the schema is not found in the data |
| When there’s an inconsistency between the expected and actual shape of the data |
| When a specified schema field is not found |
| When more rows than expected are returned |
| A generic exception raised for various errors encountered while using Polars, including issues related to data processing, operations, or invalid inputs |
Let’s see the examples of a few of them:
import polars as pl# DataFramedata = {"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: ColumnNotFoundErrortry:df["new_column"] # Trying to access a non-existent columnexcept pl.ColumnNotFoundError as e:print(f"ColumnNotFoundError: The column does not exist in the DataFrame: {e}")# Example 2: ComputeErrortry: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: InvalidOperationErrortry: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.
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.
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