Polars vs Pandas: A Brief Tale of Two DataFrame Libraries 🐼⚡🐻
Tags: python, datascience, programming
Published: 2023-06-15 15:34:24

## {% details Outlines %}
- [Introduction 🌟](#introduction)
- [Installation and Setup 🔧](#installation-and-setup)
  - [Installing Polars 🐻](#installing-polars)
  - [Installing Pandas 🐼](#installing-pandas)
- [Special Note on Dependencies 📚](#special-note-on-dependencies)
  - [Polars 🐻](#polars)
  - [Pandas 🐼](#pandas)
{% enddetails %}

## Introduction 🌟

Hello, fellow data enthusiasts! 🚀 In this new series of posts, we'll embark on a comparative journey between two popular DataFrame libraries - [**Pandas**](https://pandas.pydata.org/docs/getting_started/overview.html) and [**Polars**](https://pola-rs.github.io/polars-book/). Whether you're interested in transitioning from one to the other or simply curious about their differences and similarities, this series will guide you through all you need to know. We aim to make this transition smooth 💫. In each post, we'll be doing hands-on comparisons on how to perform various data manipulations using both libraries. So let's dive in! 🏊‍♂️

To kick-start our journey, let's get a high-level overview of both these libraries:

| | Pandas | Polars |
|---|---|---|
| **Overview** | A Python package designed for efficient relational or labeled data manipulation, making it a fundamental tool for real-world data analysis. | A high-performance DataFrame library, available in Python, Rust & NodeJS. It's known for speed, user-friendly queries, out-of-core data transformation, parallelization, and its vectorized query engine.
| **Best Suited For** | Working with tabular, time-series, matrix, and observational/statistical data sets, especially when dealing with missing data, data alignment, group by operations, reshaping, pivoting, merging, and joining data sets. | Fast and memory-efficient manipulation of large datasets, even those not fitting into memory. Extensive support for I/O operations, query writing, and SIMD optimized computations.
| **Programming Language** | Python | Python, Rust, NodeJS |
| **Key Features** | High-level data structures that are easy to use and flexible, robust I/O tools. | Speed and efficiency, owing to its design close to the machine. Also, I/O support, parallelization, and out-of-core data transformation capabilities. |
| **Common Use Cases** | General-purpose data manipulation, with particular utility in fields such as finance, statistics, social science, and engineering. | Manipulating structured data in a way that fully utilizes CPU power by dividing the workload among available cores. |
| **Notable Capabilities** | Handling missing data, mutability of data size, powerful group by functionality, merging, joining, reshaping, and pivoting of data sets. | Extensive I/O support, efficient query optimizer, out-of-core data transformation, and a vectorized query engine built upon Apache Arrow. |
| **Built On** | NumPy | Rust and Apache Arrow |

Comparing these two libraries is essential because while they serve similar purposes, they differ significantly in their design philosophies, performance characteristics, and specific functionalities. The comparison between these two libraries is relevant due to several reasons:
- They serve similar purposes but offer different features, performance characteristics, and usage styles.
- Understanding the differences can help you choose the right tool for your particular use case.
- As the data science field evolves, it's essential to stay updated with the latest tools and libraries, and how they stack up against each other.

## Installation and Setup 🔧

Before we can dive into code comparisons, let's get our systems set up with both libraries.

### Installing Polars 🐻

```bash
pip install polars
```

### Installing Pandas 🐼

```bash
pip install pandas
```

To get started with these libraries, you import them as follows:

```python
# Importing Polars
import polars as pl

# Importing Pandas
import pandas as pd
```

## Special Note on Dependencies 📚

### Polars 🐻

To leverage additional Polars functionalities, we might need to install optional dependencies. Some of these include support for different file formats, database connectors, and specific operations. Below are some commands to install these dependencies:

```bash
pip install 'polars[all]'  # Install all optional dependencies
pip install 'polars[numpy,pandas,pyarrow]'  # Install a subset of optional dependencies
```

**Polars Dependencies**

| Tag | Description |
|---|---|
| all | Install all optional dependencies (all of the following) |
| pandas | Install with Pandas for converting data to and from Pandas DataFrames/Series |
| numpy | Install with numpy for converting data to and from numpy arrays |
| pyarrow | Reading data formats using PyArrow |
| fsspec | Support for reading from remote file systems |
| connectorx | Support for reading from SQL databases |
| xlsx2csv | Support for reading from Excel files |
| deltalake | Support for reading from Delta Lake Tables |
| timezone | Timezone support, only needed if you are on Python<3.9 or you are on Windows |

Regularly updating Polars can also help you access new features and bug fixes, considering its active development. 

For Rust users, you can take the latest release from crates.io, or use the main branch of this repo for the latest features and performance improvements.

```rust
polars = { git = "https://github.com/pola-rs/polars", rev = "<optional git tag>" }
```

> _The required Rust version is >=1.62._

For a more complex installation, including optional dependencies and utilizing conda, check out the [Polars GitHub](https://github.com/pola-rs/polars).

### Pandas 🐼

Also, Pandas has various optional dependencies that unlock additional functionalities. Below are some commands to install these dependencies:

```bash
pip install "pandas[excel]" # Install Excel file reading/writing
pip install "pandas[performance]" # Include speed improvements, especially when working with large data sets
```

**Pandas Dependencies**

| Tag | Description |
|---|---|
| all | All optional dependencies can be installed with pandas[all] |
| performance | Includes numexpr, bottleneck, and numba for speed improvements |
| plot, output_formatting | Includes matplotlib, Jinja2, tabulate for visualization and formatting |
| computation | Includes SciPy and xarray for computation |
| excel | Includes xlrd, xlsxwriter, openpyxl, pyxlsb for Excel file reading/writing |
| html | Includes BeautifulSoup4, html5lib, lxml for HTML parsing |
| xml | Includes lxml for XML parsing |
| postgresql, mysql, sql-other | Includes SQLAlchemy, psycopg2, pymysql for SQL database access |
| hdf5, parquet, feather, spss, excel | Includes PyTables, blosc, zlib, fastparquet, pyarrow, pyreadstat, odfpy for various data sources |
| fss, aws, gcp | Includes fsspec, gcsfs, pandas-gbq, s3fs for cloud data access |
| clipboard | Includes PyQt4/PyQt5, qtpy for Clipboard I/O |
| compression | Includes brotli, python-snappy, Zstandard for compression |

For a more complex installation, including optional dependencies and utilizing conda, check out the [Pandas installation guide](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html).

---

That's it for our brief introduction to Polars and Pandas. Next up in this series, we'll delve into the world of **Series** in both **Polars** and **Pandas**. Until then, happy coding! 🎉🚀

If you find these posts useful and enjoy the content, don't hesitate to share it on your social media platforms! Also, feel free to connect with me for more such content on my [Beacons page](https://beacons.ai/ranggakd). Spread the knowledge and keep the learning spirit alive! Cheers! 🥳🚀

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