What is the difference between pandas and Polars?
In the world of data analysis and manipulation, two powerful libraries have emerged:
- pandas
- Polars
Both are designed to handle tabular data effectively, but they have their own unique features and characteristics. In this answer, we’ll explore the key differences between pandas and Polars, shedding light on their strengths and helping you choose the right tool for your data processing needs.
Key differences
pandas | Polars | |
Data Structure | pandas revolves around the DataFrame, a two-dimensional data structure that stores data in rows and columns. It provides a rich set of functions and operations to manipulate and analyze data efficiently. pandas also offers the series data structure for working with one-dimensional labeled data. | Polars, like pandas, utilizes a DataFrame-like structure to manage tabular data. However, Polars introduces its own DataFrame, which is built on Rust, a high-performance programming language. This design choice enables Polars to deliver impressive speed and memory efficiency. |
Performance | pandas is written in Python, which provides ease of use and a large ecosystem. However, it can be relatively slower when dealing with massive datasets or complex operations due to the underlying Python interpretation. | Polars takes advantage of Rust's performance and memory efficiency, making it notably faster than pandas for large-scale data processing tasks. It achieves this by leveraging multi-threading and SIMD (Single Instruction, Multiple Data) parallelism, enabling efficient execution of operations. |
API and Syntax | pandas is known for its expressive and intuitive API, which allows users to perform various data manipulation tasks with relative ease. It offers a wide range of functions and methods, enabling quick data filtering, sorting, grouping, and more. | Polars aims to provide a similar API to Pandas, making it familiar to Pandas users. While the core API shares similarities, Polars also introduces additional functionality inspired by other data processing libraries, such as Apache Spark and dplyr, enhancing its capabilities. |
Scalability | pandas was initially designed for single-machine environments, and while it offers excellent performance for moderate-sized datasets, it can face limitations when dealing with massive datasets that do not fit into memory. | Polars was created with scalability in mind. It can efficiently handle larger-than-memory datasets by utilizing parallel computation and efficient memory management. This makes Polars a suitable choice for big data processing scenarios. |
Example code
Here's an example code that demonstrates the basic usage and key differences between pandas and Polars:
# Importing the librariesimport pandas as pdimport polars as pl# Creating a Pandas DataFramepandas_df = pd.DataFrame({'X': ['a', 'b', 'c', 'd', 'e'], 'Y': [1, 2, 3, 4, 5]})print("Pandas DataFrame:")print(pandas_df)# Creating a Polars DataFramepolars_df = pl.DataFrame({"X": ['a', 'b', 'c', 'd', 'e'], "Y": [1, 2, 3, 4, 5]})print("\nPolars DataFrame:")print(polars_df)# Adding a new columnpandas_df['Z'] = [-1, -2, -3, -4, -5]print("\nPandas DataFrame after adding column:")print(pandas_df)polars_df = polars_df.with_column(pl.Series([-1, -2, -3, -4, -5], dtype=pl.Object).alias('Z'))print("\nPolars DataFrame after adding column:")print(polars_df)# Filtering the DataFramefiltered_pandas_df = pandas_df[pandas_df['Y'] > 2]print("\nFiltered Pandas DataFrame:")print(filtered_pandas_df)filtered_polars_df = polars_df.filter(pl.col("Y") > 2)print("\nFiltered Polars DataFrame:")print(filtered_polars_df)
Explanation
In the above code:
-
Lines 6–13: We create a simple
DataFramewith two columns,'X'and'Y', using both Pandas and Polars libraries. -
Lines 16–22: We then demonstrate adding a new column to the
DataFrameusing the['Z']notation in Pandas and thewith_column()method in Polars. -
Lines 25–31: We showcase filtering the
DataFramebased on a condition using Pandas’ bracket notation and Polars’filter()method.
By running the above code, you can observe the similarities and differences in syntax and usage between Pandas and Polars.
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