Summary
This chapter covers basic data operations using Polars, including loading a CSV into a DataFrame, selecting specific columns with select(), and filtering rows based on conditions.

General domain of usage
Data analysis

This video introduces you to the main syntax of the `polars` library for Python. You will see how to import polars, read a CSV file, select columns, filter rows, and display data. The instructor walks through each operation slowly, explaining what each line of code does and how the output looks. Short code examples are shown on screen, each with the expected output, so you can clearly see the effect of every command. By the end, you will understand how to perform basic data operations in polars and how its syntax compares to other data libraries.

When you work with large datasets, efficient data manipulation is essential. The `polars` library is designed for high-performance data operations, making it a popular choice for handling large data in Python. In this chapter, you will learn how to load data, select specific columns, and filter rows using `polars`. These basic actions form the foundation for more complex data transformations.

The table below summarizes the main functions in `polars` for performing these basic operations.

Displays the first/last rows of the DataFrame

import polars as pl

# Read data from a CSV file
df = pl.read_csv("data/people.csv")

# Display the first 5 rows
print(df.head())

In this code, you import the `polars` library and use the `pl.read_csv()` function to load data from a file named `"data/people.csv"`. The resulting **DataFrame** is stored in the variable `df`. By calling `df.head()`, you can view the first five rows of the DataFrame, which is useful to quickly inspect your data after loading.

import polars as pl

# Load the dataset
df = pl.read_csv("data/people.csv")

# Select the "name" and "age" columns
selected = df.select(["name", "age"])

print(selected)

Here, you use the `select()` method to choose only the `"name"` and `"age"` columns from the DataFrame. This creates a new DataFrame called **selected** containing just those columns. Selecting columns is a common operation when you want to focus on specific parts of your data for further analysis.

Which method is used to read a CSV file in polars?

A practical, hands-on course for aspiring data scientists ready to tackle real-world large data challenges. Learn to efficiently process, sample, and analyze massive datasets using Python and essential libraries. Each section features engaging video explanations and interactive challenges to build your expertise.

Learn foundational strategies for handling datasets too large to fit in memory, including chunking and streaming techniques.

Explore methods to balance and sample large datasets, including oversampling and undersampling.

Learn to use the polars library for fast, memory-efficient data processing.

Basic Data Operations in Polars