Polars is an emerging DataFrame library in Python, designed to handle large datasets efficiently. Its performance often surpasses that of pandas due to its design and optimizations. Plotly is a versatile graphing library that creates interactive plots and visualizations. While Plotly primarily supports pandas DataFrames, it is possible to use Polars without converting to pandas. This article will guide you through the steps to achieve this.

Prerequisites

Before we dive into the integration, ensure you have the following installed:

• Python 3.7 or higher
• Polars library
• Plotly library
  • You can install the required libraries using pip:
  • pip install polars plotly

Loading Data into Polars DataFrame

Let’s start by creating a sample Polars DataFrame. We’ll generate some synthetic data for demonstration purposes.

We begin by importing the necessary libraries, Polars and NumPy. Using NumPy, we generate random data for three columns: x, y, and category. We then create a Polars DataFrame from this synthetic data.

import polars as pl
import numpy as np

# Generate sample data
np.random.seed(0)
data = {
    "x": np.random.rand(100),
    "y": np.random.rand(100),
    "category": np.random.choice(['A', 'B', 'C'], 100)
}

# Create Polars DataFrame
df = pl.DataFrame(data)

print(df)

Output

shape: (100, 3)
┌──────────┬──────────┬──────────┐
│ x        ┆ y        ┆ category │
│ ---      ┆ ---      ┆ ---      │
│ f64      ┆ f64      ┆ str      │
╞══════════╪══════════╪══════════╡
│ 0.548814 ┆ 0.677817 ┆ C        │
│ 0.715189 ┆ 0.270008 ┆ C        │
│ 0.602763 ┆ 0.735194 ┆ A        │
│ 0.544883 ┆ 0.962189 ┆ B        │
│ …        ┆ …        ┆ …        │
│ 0.586513 ┆ 0.227415 ┆ A        │
│ 0.020108 ┆ 0.254356 ┆ A        │
│ 0.82894  ┆ 0.058029 ┆ A        │
│ 0.004695 ┆ 0.434417 ┆ C        │
└──────────┴──────────┴──────────┘

Examples of Using Polars with Plotly

1. Scatter Plot

To create a scatter plot, we first convert the Polars DataFrame into a dictionary. Using Plotly Express, we create a scatter plot with x and y as the axes and category as the color dimension, and then display it.

import plotly.express as px

# Convert Polars DataFrame to dictionary
data_dict = df.to_dict(as_series=False)

# Create scatter plot
fig = px.scatter(data_dict, x='x', y='y', color='category', title='Scatter Plot')
fig.show()

Output

2. Histogram

Similar to the scatter plot, we convert the Polars DataFrame to a dictionary. We then use Plotly Express to create a histogram of the x variable, which shows the distribution of this data, and display the plot.

# Convert Polars DataFrame to dictionary
data_dict = df.to_dict(as_series=False)

# Create histogram
fig = px.histogram(data_dict, x='x', title='Histogram of X')
fig.show()

Output

3. Box Plot

We convert the Polars DataFrame into a dictionary again. Using Plotly Express, we create a box plot to visualize the distribution and outliers of the y variable across different categories, and display the plot.

# Convert Polars DataFrame to dictionary
data_dict = df.to_dict(as_series=False)

# Create box plot
fig = px.box(data_dict, x='category', y='y', title='Box Plot by Category')
fig.show()

Output

Conclusion

Using Polars with Plotly without converting to pandas is straightforward. By leveraging Polars’ efficient data handling and Plotly’s robust visualization capabilities, you can create powerful and interactive visualizations for your data. This approach allows you to maintain performance benefits while taking advantage of Plotly’s extensive plotting functions