Polars is a fast DataFrame library implemented in Rust and designed to process large data sets efficiently. One of the common tasks while handling data frames is retrieving row numbers (indices) for various operations. This article explores the recommended ways to retrieve row numbers in Polars, providing clear examples for each method.

Prerequisites

Before diving into the examples, ensure you have the following prerequisites:

• Python installed on your system.
• Polars library installed.
  • You can install it using pip install polars.

Loading Data into Polars DataFrame

Let’s start by loading some sample data into a Polars DataFrame. For this article, we’ll use a small dataset to keep the examples simple and clear.

import polars as pl

# Sample data
data = {
    "name": ["Alice", "Bob", "Charlie", "David"],
    "age": [25, 30, 35, 40],
    "city": ["New York", "Los Angeles", "Chicago", "Houston"]
}

# Creating a Polars DataFrame
df = pl.DataFrame(data)
print(df)

Output

shape: (4, 3)
┌─────────┬─────┬─────────────┐
│ name    ┆ age ┆ city        │
│ ---     ┆ --- ┆ ---         │
│ str     ┆ i64 ┆ str         │
╞═════════╪═════╪═════════════╡
│ Alice   ┆ 25  ┆ New York    │
│ Bob     ┆ 30  ┆ Los Angeles │
│ Charlie ┆ 35  ┆ Chicago     │
│ David   ┆ 40  ┆ Houston     │
└─────────┴─────┴─────────────┘

Retrieving Row Numbers (Index) for Polars

1. Using .with_row_count()

The .with_row_count() method adds a new column to the DataFrame that contains the row numbers. This method creates a new column, row_number, with the index of each row, making it straightforward to reference rows by their index.

# Adding row numbers
df_with_row_count = df.with_row_count("row_number")
print(df_with_row_count)

Output

shape: (4, 4)
┌────────────┬─────────┬─────┬─────────────┐
│ row_number ┆ name    ┆ age ┆ city        │
│ ---        ┆ ---     ┆ --- ┆ ---         │
│ u32        ┆ str     ┆ i64 ┆ str         │
╞════════════╪═════════╪═════╪═════════════╡
│ 0          ┆ Alice   ┆ 25  ┆ New York    │
│ 1          ┆ Bob     ┆ 30  ┆ Los Angeles │
│ 2          ┆ Charlie ┆ 35  ┆ Chicago     │
│ 3          ┆ David   ┆ 40  ┆ Houston     │
└────────────┴─────────┴─────┴─────────────┘

2. Using .enumerate()

The .enumerate() method returns an iterator of tuples, where each tuple contains the row number and the row data. This approach is useful when you need to process each row individually along with its index, providing more flexibility in handling the DataFrame.

# Creating a Polars DataFrame
df = pl.DataFrame(data)

# Adding row numbers
df_with_row_count = df.with_row_count("row_number")

# Enumerating rows
for row in df_with_row_count.iter_rows(named=True):
    print(f"Row {row['row_number']}: {row}")

Output

Row 0: {'row_number': 0, 'name': 'Alice', 'age': 25, 'city': 'New York'}
Row 1: {'row_number': 1, 'name': 'Bob', 'age': 30, 'city': 'Los Angeles'}
Row 2: {'row_number': 2, 'name': 'Charlie', 'age': 35, 'city': 'Chicago'}
Row 3: {'row_number': 3, 'name': 'David', 'age': 40, 'city': 'Houston'}

3. Using a Custom Index Column

Another method is to manually create an index column. This approach gives you full control over the naming and content of the index column. By creating a custom index column, you can ensure the row numbers are always accessible and clearly labeled, fitting seamlessly into your data manipulation workflow.

# Creating a Polars DataFrame
df = pl.DataFrame(data)

# Creating a custom index column
df = df.with_columns(pl.Series(name="index", values=range(len(df))))

print(df)

Output

shape: (4, 4)
┌─────────┬─────┬─────────────┬───────┐
│ name    ┆ age ┆ city        ┆ index │
│ ---     ┆ --- ┆ ---         ┆ ---   │
│ str     ┆ i64 ┆ str         ┆ i64   │
╞═════════╪═════╪═════════════╪═══════╡
│ Alice   ┆ 25  ┆ New York    ┆ 0     │
│ Bob     ┆ 30  ┆ Los Angeles ┆ 1     │
│ Charlie ┆ 35  ┆ Chicago     ┆ 2     │
│ David   ┆ 40  ┆ Houston     ┆ 3     │
└─────────┴─────┴─────────────┴───────┘

Conclusion

Retrieving row numbers (indices) in Polars can be done efficiently using several methods. The .with_row_count() method is recommended for straightforward use cases, while .enumerate() offers more flexibility for row-wise processing. Creating a custom index column provides additional control and customization. Choose the method that best fits your specific needs and workflow to leverage Polars’ powerful data processing capabilities effectively.