Polars read_csv(): Load CSV Data Fast in Python

If you regularly use Python for data analysis, just as I do, you soon notice that reading CSV files is one of the most common tasks. However, as your datasets grow, this method can become slow or memory-intensive.

Polars is a fast, modern DataFrame library for Python designed as a high-performance alternative to Pandas. It can handle large datasets much more smoothly than traditional tools since it’s built with a focus on speed and low memory usage.

Polars pl.read_csv() core function provides the simple solution to load CSV files into a DataFrame, with built-in options to control parsing, data types, and memory usage.

In this guide, I will show you how to read CSV files, control parsing, and handle large datasets using the Polars  pl.read_csv() function. If you are just getting started, check out our Introduction to Polars course to learn how to manipulate data and extract insights with Polars.

Basic Usage of pl.read_csv()

Before we go further, check out our Python Polars tutorial to learn how to set your environment. 

Now, let’s look at how the pl.read_csv() function works: 

# import the module
import polars as pl

# Load CSV into a Polars DataFrame
fuel_data = pl.read_csv("Fuel_Consumption_2000-2022.csv")  

# Preview first few rows
print(fuel_data.head())  

In the above example, Polars reads the file and loads it into a DataFrame.The function returns a Polars DataFrame, which is a tabular data structure, similar to what you’d get in pandas.

By default, pl.read_csv():

• Assumes the first row contains column names (has_header=True)

• Automatically infers column data types

• Uses a comma (,) as the delimiter

• Reads the entire file into memory

Common Parameters in pl.read_csv()

Now that you have learned how pl.read_csv() loads data, let’s look at how you can use the following parameters to tailor how Polars parses your data.

File path and source

Polars allows you to load data from different sources. You can pass a local string path, a Pathlib object, or even a URL. For example, the following code reads a large csv file from the web containing the GDP of different countries in various years.

# Reading from a URL
url = "https://raw.githubusercontent.com/datasets/gdp/master/data/gdp.csv"
gdp_data = pl.read_csv(url)

Delimiters and separators

Not all CSV files use commas. You can use the separator argument to handle tabs, semicolons, pipes, or other characters. The example below shows how to specify separators when reading files

# Reading a Semicolon-separated file
sales_data = pl.read_csv("2024_sales.csv", separator=";")

# Reading a Tab-separated file (TSV)
orders_data = pl.read_csv("all_orders.tsv", separator="\t")

Header handling

As we saw earlier, Polars assumes the first row of the data contains column names. If your file does not have a header row, use the parameter has_header=False as shown below. Polars will automatically assign column names like column_1, column_2, column_3, and so on.

# Load file without a header
orders_data = pl.read_csv("all_orders.csv", has_header=False)

You can also rename columns by providing the specific column names. For example:

# Providing specific column names
sales_data = pl.read_csv("sales_April.csv", new_columns=[
    "OrderDate", "OrderNumber", "ProductKey", "SalespersonKey", "Salesperson"])

Encoding

Different CSV files may use different text encodings. If you run into strange characters or errors, specify the encoding:

# Load CSV into a Polars DataFrame
fuel_data = pl.read_csv("Fuel_Consumption_2000-2022.csv", encoding="utf8")  

Other common encoding options include ”latin1” and ”utf8-lossy”, which handle invalid characters appropriately.

How to Select Columns When Reading CSV in Polars

When working with large CSV files, you often don’t need every column. Polars allows you to load only the columns you require using the columns parameter.

# Load only specific columns
fuel_data = pl.read_csv("Fuel_Consumption_2000-2022.csv",
    columns=["YEAR", "MAKE", "MODEL"]
)

When you select the required columns when loading the file, it reduces memory usage by not loading unnecessary data. This method also speeds up file reading and improves overall pipeline performance.

How to Handle Data Types (Schema) in Polars CSV

Polars is a strongly typed library, meaning every column must have a consistent data type, like all integers or all strings.

Automatic type inference

By default, Polars inspects your data and automatically determines column types. The default method works well in most cases but can sometimes misinterpret columns, such as treating IDs as integers instead of strings.

Specifying schema manually

When you want consistency in your DataFrame, you should specify the data schema manually. This ensures consistency across multiple files and helps you avoid type-related errors in downstream processing. 

For most cases, use schema_overrides to specify the data type of particular columns while allowing Polars to infer the remaining columns

import polars as pl

# Manually overriding specific data types
empl_data = pl.read_csv(
    "all_employees.csv",
    schema_overrides={
        "id": pl.Int64,
        "name": pl.String,
        "age": pl.Int64,
        "salary": pl.Float64
    }
)

Or use schema to define the entire structure of the DataFrame.

# Manually define the full schema
empl_data = pl.read_csv(
    "all_employees.csv",
    schema={
        "id": pl.Int64,
        "name": pl.String,
        "age": pl.Int64,
        "salary": pl.Float64
    }
)

How to Handle Missing Values in Polars CSV

By default, Polars automatically detects these missing values and represents them as null. Sometimes these missing values are represented by specific strings, such as ”NA”, ”N/A”, or ”missing”. You can define these using null_values to treat them as null. 

# Treating "N/A" and "EMPTY" as nulls
survey_data = pl.read_csv(
    "survey_results.csv",
    null_values=["N/A", "EMPTY", "null"]
)

When you handle missing values correctly, you ensure consistent data types and prevent incorrect calculations, reducing errors during analysis and modeling.

Reading Large CSV Files in Polars

When working with large datasets that exceed your available RAM, Polars really shines by processing data without loading the entire file at once.

Lazy Loading with scan_csv()

Instead of immediately loading data into memory like read_csv(), Polars provides a lazy alternative, scan_csv(), which builds an optimized query plan and executes only needed operations.

In the example below, pl.scan_csv scans the CSV file without loading it, builds a query to grab only the “YEAR”, “MAKE”, and “MODEL” columns where the car brand is “ACURA”, then executes that query. This action will load only the filtered slice of data into memory, rather than the entire file.

# Lazily reference the file (no data loaded yet)
fuel_consumption = pl.scan_csv("Fuel_Consumption_2000-2022.csv")  

fuel_consumption_filtered = fuel_consumption.select(
    ["YEAR", "MAKE", "MODEL"]).filter(pl.col("MAKE") == "ACURA")

# Execute the query and load only the required data
fuel_consumption_acura = fuel_consumption_filtered.collect()  

I recommend you use scan_csv() when:

• Working with large files that don’t fit comfortably in memory
• Applying multiple transformations, such as filtering, selecting, and aggregating data
• You want query optimization before execution

Streaming and memory efficiency

Polars also allows streaming processes to data in chunks, keeping peak memory constant regardless of file size.

For large CSV files, start with scan_csv() to create a lazy query. The file is not fully loaded when scan_csv() is called.

# Read data in chunks
fuel_consumption = pl.scan_csv("Fuel_Consumption_2000-2022.csv",
 low_memory=True)

Since the above method still uses read_csv, the best approach is to combine lazy execution with streaming. 

In the example below, Polars processes the file in a pipeline, row-by-row in batches, keeping only the filtered rows (where “CYLINDERS” > 3) in memory at any given time. 

# Lazily reference the file (no data loaded yet)
fuel_consumption = pl.scan_csv("Fuel_Consumption_2000-2022.csv")  

result = fuel_consumption.filter(pl.col("CYLINDERS") > 3)

# Stream execution instead of loading everything at once
fuel_consumption_high = result.collect(engine="streaming")

Performance advantage vs. Pandas

Compared to pandas, Polars often performs better for CSV reading and processing, as we will see in the next section.

Polars read_csv vs Pandas read_csv

The table below summarizes the difference between Polars and Pandas when reading csv in Python.

I recommend reading our article on the differences between Pandas vs. Polars to determine which tool best suits your analytics needs.

Reading CSV from Different Sources

As we had seen earlier, you can use Polars to read csv file from different sources. From the above examples, I have shown you how to read csv from local files and URLs.

In addition, Polars can automatically handle compressed CSV files. For example, the following code reads data from a .gzip file.

# Read gzip-compressed CSV
census_data = pl.read_csv("2018_census.csv.gz")

Common Errors and Troubleshooting

Even with a high-performance engine like Polars, you can still encounter some issues, especially when reading a csv file. The following are some of the common problems I have encountered and how to fix them.

• Encoding errors: You may see an error like UnicodeDecodeError, which means the file isn't using UTF-8 encoding, a problem common with older files or CSVs exported from certain versions of Excel. To fix this, set the appropriate encoding or use “utf8-lossy” if the file has inconsistent or broken characters.

• Delimiter issues: If your DataFrame loads but all the data is crammed into a single column, Polars didn't recognize the separator. To solve this problem, explicitly set the correct delimiter using the separator argument, such as semicolon (;), tab (\t), or pipe (|).

• Incorrect data types: Sometimes, Polars may infer column types incorrectly, such as reading numeric IDs as strings or encountering parsing errors. When this happens, you can use schema_overrides to specify the data type for specific columns. If you want to define the types for every column, use schema. You can also increase infer_schema_length, so Polars examines more rows before inferring the schema.

• Large file memory issues: If your Python process crashes when loading a large file, it means you've exhausted your RAM. To fix this, switch from read_csv() to scan_csv() for lazy loading to reduce memory usage and improve performance. You can also load fewer columns of the dataset or use stream execution to load the data in chunks.

Conclusion

The read_csv() function in Polars is simple to use but powerful enough for real-world tasks. In my opinion, Polars has a clear advantage when working with growing datasets.

As a next step, check out our blog on the Polars GPU engine to learn more about its various applications. If you are ready to get hands-on, our Super Bowl Analytics with Polars code-along to solve real-world analytical questions, apply calculations, and apply basic ML techniques to sports analytics problems.