Reorder Columns in a Specific Order Using Python Polars

Polars is a powerful DataFrame library in Rust and Python that is known for its speed and efficiency. It's designed to handle large datasets with ease, making it an excellent choice for data analysis and manipulation. One common task in data manipulation is reordering the columns of a data frame. This article will guide you through three different methods to reorder columns in a specific order using Polars.

Prerequisites

Before we dive into the methods, ensure you have Polars installed. You can install it using pip:

pip install polars

Loading Data into Polars DataFrame

To illustrate the methods of reordering columns, let's create a sample DataFrame. This DataFrame will contain some fictional data for demonstration purposes.

import polars as pl

# Create a sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago'],
    'Salary': [70000, 80000, 90000]
}

df = pl.DataFrame(data)
print(df)

Output

shape: (3, 4)
┌─────────┬─────┬─────────────┬────────┐
│ Name    ┆ Age ┆ City        ┆ Salary │
│ ---     ┆ --- ┆ ---         ┆ ---    │
│ str     ┆ i64 ┆ str         ┆ i64    │
╞═════════╪═════╪═════════════╪════════╡
│ Alice   ┆ 25  ┆ New York    ┆ 70000  │
│ Bob     ┆ 30  ┆ Los Angeles ┆ 80000  │
│ Charlie ┆ 35  ┆ Chicago     ┆ 90000  │
└─────────┴─────┴─────────────┴────────┘

Reorder Columns in a Specific Order Using Polars

1. Using the select Method

The select method allows you to specify the columns in the order you want and select method allows us to specify the order of columns explicitly.

import polars as pl

# Create a sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago'],
    'Salary': [70000, 80000, 90000]
}

# Reorder columns using the select method
df_reordered = df.select(['City', 'Name', 'Salary', 'Age'])
print(df_reordered)

Output

shape: (3, 4)
┌─────────────┬─────────┬────────┬─────┐
│ City        ┆ Name    ┆ Salary ┆ Age │
│ ---         ┆ ---     ┆ ---    ┆ --- │
│ str         ┆ str     ┆ i64    ┆ i64 │
╞═════════════╪═════════╪════════╪═════╡
│ New York    ┆ Alice   ┆ 70000  ┆ 25  │
│ Los Angeles ┆ Bob     ┆ 80000  ┆ 30  │
│ Chicago     ┆ Charlie ┆ 90000  ┆ 35  │
└─────────────┴─────────┴────────┴─────┘

2. Using the with_columns Method

The with_columns method can also be used to reorder columns by specifying the columns in the desired order.

import polars as pl

# Create a sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago'],
    'Salary': [70000, 80000, 90000]
}

# Reorder columns using the with_columns method
df_reordered = df.with_columns([df['City'], df['Name'], df['Salary'], df['Age']])
print(df_reordered)

Output

shape: (3, 4)
┌─────────┬─────┬─────────────┬────────┐
│ Name    ┆ Age ┆ City        ┆ Salary │
│ ---     ┆ --- ┆ ---         ┆ ---    │
│ str     ┆ i64 ┆ str         ┆ i64    │
╞═════════╪═════╪═════════════╪════════╡
│ Alice   ┆ 25  ┆ New York    ┆ 70000  │
│ Bob     ┆ 30  ┆ Los Angeles ┆ 80000  │
│ Charlie ┆ 35  ┆ Chicago     ┆ 90000  │
└─────────┴─────┴─────────────┴────────┘

3. Using Column Indexing

Another method is to use column indexing to reorder the columns.

# Reorder columns
df_reordered = df[:, ["Salary", "Age", "Name", "City"]]
print(df_reordered)

Output

shape: (3, 4)
┌────────┬─────┬─────────┬─────────────┐
│ Salary ┆ Age ┆ Name    ┆ City        │
│ ---    ┆ --- ┆ ---     ┆ ---         │
│ i64    ┆ i64 ┆ str     ┆ str         │
╞════════╪═════╪═════════╪═════════════╡
│ 70000  ┆ 25  ┆ Alice   ┆ New York    │
│ 80000  ┆ 30  ┆ Bob     ┆ Los Angeles │
│ 90000  ┆ 35  ┆ Charlie ┆ Chicago     │
└────────┴─────┴─────────┴─────────────┘

Conclusion

Reordering columns in a Polars DataFrame is a straightforward task that can be achieved using various methods. The select method offers explicit control over the column order, while column indexing provides a concise way to achieve the same result. The with_columns method allows for reconstructing the DataFrame with the desired column order. Each method has its use cases, and understanding them enhances your ability to manipulate data efficiently in Polars.

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