Sort Data in CSV/Excel Files with Polars: A Step-by-Step Guide

Introduction: Sorting Data with Polars

Sorting data is one of the most basic yet essential operations when analyzing or preparing data for further processing. Polars, a high-performance data manipulation library, allows you to efficiently sort CSV and Excel files using simple and intuitive syntax.

In this guide, we’ll show you how to sort data in both CSV and Excel files using Polars. Whether you’re working with small or large datasets, Polars’ fast and memory-efficient operations will help you organize your data quickly.

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1. Install Polars

Before you can start sorting data, you’ll need to install the Polars library. If you haven’t done that already, you can install it using pip:

pip install polars

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2. Sorting Data in a CSV File with Polars

Let’s say we have a CSV file named employee_data.csv with the following data:

Name	Age	Salary
Alice	30	60000
Bob	25	70000
Charlie	35	80000
David	28	55000

We want to sort the data by Age in ascending order.

Code to Sort CSV Data by Age:

import polars as pl

# Read the CSV file into a Polars DataFrame
df = pl.read_csv("employee_data.csv")

# Sort the data by the 'Age' column in ascending order
sorted_df = df.sort("Age")

# Display the sorted DataFrame
print(sorted_df)

Output:

shape: (4, 3)
┌─────────┬─────┬────────┐
│ Name    │ Age │ Salary │
│ ---     │ --- │ ---    │
│ str     │ i64 │ i64    │
├─────────┼─────┼────────┤
│ Bob     │ 25  │ 70000  │
│ David   │ 28  │ 55000  │
│ Alice   │ 30  │ 60000  │
│ Charlie │ 35  │ 80000  │
└─────────┴─────┴────────┘

In this example, we used the sort() method on the Age column. By default, the sorting is in ascending order. If you wanted to sort it in descending order, you can add the reverse=True parameter:

sorted_df_desc = df.sort("Age", reverse=True)
print(sorted_df_desc)

Output (Descending Order by Age):

shape: (4, 3)
┌─────────┬─────┬────────┐
│ Name    │ Age │ Salary │
│ ---     │ --- │ ---    │
│ str     │ i64 │ i64    │
├─────────┼─────┼────────┤
│ Charlie │ 35  │ 80000  │
│ Alice   │ 30  │ 60000  │
│ David   │ 28  │ 55000  │
│ Bob     │ 25  │ 70000  │
└─────────┴─────┴────────┘

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3. Sorting Data in an Excel File with Polars

Polars doesn’t natively support reading Excel files, but you can read the Excel data with Pandas and then convert it to a Polars DataFrame. Let’s use the same example, but this time the data is stored in an Excel file.

Code to Sort Excel Data by Salary:

import pandas as pd
import polars as pl

# Read the Excel file into a pandas DataFrame
df_pandas = pd.read_excel("employee_data.xlsx")

# Convert the pandas DataFrame to a Polars DataFrame
df_polars = pl.from_pandas(df_pandas)

# Sort the data by the 'Salary' column in descending order
sorted_df = df_polars.sort("Salary", reverse=True)

# Display the sorted DataFrame
print(sorted_df)

Output (Descending Order by Salary):

shape: (4, 3)
┌─────────┬─────┬────────┐
│ Name    │ Age │ Salary │
│ ---     │ --- │ ---    │
│ str     │ i64 │ i64    │
├─────────┼─────┼────────┤
│ Charlie │ 35  │ 80000  │
│ Bob     │ 25  │ 70000  │
│ Alice   │ 30  │ 60000  │
│ David   │ 28  │ 55000  │
└─────────┴─────┴────────┘

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4. How to Group Data with Polars

Another essential operation for data analysis is grouping. Grouping allows you to summarize your data by specific categories (e.g., summing values, calculating averages). In Polars, grouping is done using the groupby() method.

Example: Grouping Data by a Column

Let’s use the following dataset in a CSV file named sales_data.csv:

Region	Sales	Employees
North	1000	10
South	1500	15
North	1200	12
South	1600	18
West	2000	20

We want to group by the Region and calculate the total Sales and the average number of Employees for each region.

Code to Group by Region and Aggregate Sales/Employees:

import polars as pl

# Read the CSV file into a Polars DataFrame
df = pl.read_csv("sales_data.csv")

# Group by 'Region' and aggregate total sales and average number of employees
grouped_df = df.groupby("Region").agg([
    pl.col("Sales").sum().alias("Total_Sales"),
    pl.col("Employees").mean().alias("Avg_Employees")
])

# Display the grouped DataFrame
print(grouped_df)

Output:

shape: (3, 3)
┌─────────┬──────────┬────────────┐
│ Region  │ Total_Sales │ Avg_Employees │
│ ---     │ ---          │ ---            │
│ str     │ i64          │ f64            │
├─────────┼──────────────┼────────────────┤
│ North   │ 2200         │ 11.0           │
│ South   │ 3100         │ 16.5           │
│ West    │ 2000         │ 20.0           │
└─────────┴──────────────┴────────────────┘

In this example, we:

• Grouped the data by the Region column.
• Aggregated the total sales using the sum() function.
• Calculated the average number of employees using the mean() function.

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Conclusion: Master Data Sorting and Grouping with Polars

Polars provides an intuitive and efficient way to sort and group data, whether it’s in CSV or Excel format. By taking advantage of Polars’ high-performance features, you can sort and group large datasets quickly, with minimal memory usage.

If you’re working with big data and need fast operations for sorting and aggregating, Polars is an excellent choice. It allows you to perform common data analysis tasks like sorting and grouping with ease and efficiency, even for large-scale datasets.

Start using Polars today to speed up your data processing workflows, and handle even the largest datasets effortlessly!

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Get Started with Polars for Fast Data Sorting and Grouping

Polars makes it easy to sort and group data in both CSV and Excel files. Install Polars now and start optimizing your data processing tasks!