A Guide to Polars for Faster Data Analysis

For a long time, Pandas has been the go-to tool for Python data analysis, offering reliability, familiarity, and the ability to handle most tasks. However, as your datasets grow larger, Pandas can begin to slow down. Polars is different. It’s built in Rust, a language known for being safe and fast. This lets Polars use all your computer’s cores at once and plan the most efficient way to process your data. Let’s dive in and see how you can use Polars for faster data analysis.

A Quick Start Guide to Polars for Faster Data Analysis

Let’s ground this in reality. We will analyse a dataset representing the Sensex (Indian Stock Market Index) historical data. This file (sensex.csv) contains daily price information.

We will walk through how to set up Polars, load this data, clean it up, and perform some lightning-fast analysis.

Step 0: The Setup

First, you need to install the library. Open your terminal or command prompt and run:

pip install polars

Once installed, we import it into our Python script:

import polars as pl

Note the abbreviation pl, just like pd for Pandas, pl is the standard shorthand for Polars.

Step 1: Loading the Data

Polars is incredibly fast at reading CSV files. Let’s load our data:

# Read the CSV file
df = pl.read_csv("sensex.csv")

# Take a quick look at the first few rows
print(df.head())

When we inspect this specific dataset, we might notice a quirk. The column headers are Price, Close, High, Low, Open, Volume. However, the first row of actual data contains the word “Date” under Price and nulls elsewhere. Real-world data is rarely perfect!

We need to clean this up immediately.

Step 2: Cleaning and Preparing Data

In Pandas, you might iterate or use complex indexing. In Polars, we chain expressions. It reads almost like a sentence. Here is our plan:

1. Filter out the row where the “Price” column is just the text “Date”.
2. Rename the “Price” column to “Date” so it makes sense.
3. Cast the “Date” column to a proper Date object (so we can extract years/months later).

# Clean the dataframe in one smooth chain
df_clean = (
    df
    .filter(pl.col("Price") != "Date")       # 1. Remove the bad row
    .rename({"Price": "Date"})               # 2. Fix the column name
    .with_columns(                           # 3. Convert types
        pl.col("Date").str.strptime(pl.Date, "%Y-%m-%d"),
        pl.col("Close").cast(pl.Float64)
    )
)

print(df_clean.head())

Note: str.strptime parses the string into a Date object. The format “%Y-%m-%d” matches dates like “1997-07-01”.

Step 3: Filtering and Selecting

Let’s say we only care about the market performance after the year 2020. In Polars, filter and select are your primary tools:

# Filter for data after Jan 1st, 2020
recent_data = df_clean.filter(
    pl.col("Date") > pl.date(2020, 1, 1)
)

# Select only the Date and Close columns to view
view_data = recent_data.select(["Date", "Close"])

print(view_data.head())

Notice the syntax pl.col(“Name”). This is a Polars Expression. It refers to the column abstractly, allowing Polars to optimise the query under the hood before executing it.

Step 4: Aggregation

This is where Polars is best for. Let’s calculate the average closing price for every year in our dataset. To do this, we need to:

1. Extract the year from the Date column.
2. Group by that year.
3. Calculate the mean of the Close price.

yearly_stats = (
    df_clean
    .with_columns(pl.col("Date").dt.year().alias("Year")) # Create a Year column
    .group_by("Year")                                     # Group by it
    .agg(                                                 # Aggregate
        pl.col("Close").mean().alias("Average_Close"),
        pl.col("Volume").sum().alias("Total_Volume")
    )
    .sort("Year")                                         # Sort by Year
)

print(yearly_stats)

In just a few lines, we’ve transformed thousands of daily records into a clean yearly summary.

Step 5: Lazy Execution

The examples above were Eager; they ran immediately. But Polars has a superpower called Lazy API.

By adding .lazy(), you tell Polars not to run this yet, just memorise the plan. Polars then looks at your entire plan, optimises it (e.g., if you only use 2 columns, it won’t load the other 4 from the CSV), and then runs it when you call .collect(). Here’s an example:

# The Lazy Way
q = (
    pl.scan_csv("sensex.csv")                # 'scan' instead of 'read'
    .filter(pl.col("Price") != "Date")
    .rename({"Price": "Date"})
    .with_columns(
        pl.col("Date").str.strptime(pl.Date, "%Y-%m-%d"),
        pl.col("Close").cast(pl.Float64)
    )
    .filter(pl.col("Date").dt.year() > 2020)
    .select(["Date", "Close"])
)

# Nothing has happened yet! 'q' is just a plan.
# Now we execute it:
result = q.collect()
print(result)

For small files like this, the difference is negligible. But for a 10GB file? The Lazy API can be the difference between your code crashing your laptop and running smoothly.

Closing Thoughts

Learning a new tool like Polars isn’t just about syntax; it’s about changing your relationship with data. When your tools are fast, you ask more questions. You experiment more. You can learn more about Polars as a Data Analyst/Scientist here.