Looking for an alternative to pandas

Polaris is a Python data science library that provides a simple and intuitive interface for performing common data analysis tasks. In this article, we will explore the basics of Polaris, its methods, and provide a code example of its use.

Polaris is built on top of NumPy, Pandas, and Scikit-learn, making it a powerful and versatile tool for data analysis. The library includes a wide range of methods for data manipulation, visualization, and machine learning.

To illustrate the use of Polaris, we will provide a code example that demonstrates how to load and explore a dataset, perform some basic data manipulation, and visualize the results.

First, we will import the Polaris library and load a dataset of car sales data:

import polaris as pl
import pandas as pd

# load the dataset
df = pd.read_csv('car_sales_data.csv')

# create a Polaris dataframe
data = pl.DataFrame(df)

Next, we will filter the data to include only cars sold in the year 2019 and sort the results by the price of the car:

# filter the data
data_filtered = data[data.year == 2019]

# sort the results by price
data_sorted = data_filtered.sort_values('price')

Finally, we will create a scatter plot of the data, with the x-axis representing the car's price and the y-axis representing the car's mileage:

# create a scatter plot
pl.scatter(data_sorted.price, data_sorted.mileage)

This code will produce a scatter plot of the car sales data, showing the relationship between price and mileage.

Conclusion

Polaris is a powerful and versatile data science library that simplifies the data analysis process by providing an easy-to-use interface for data manipulation, visualization, and machine learning. Its efficient handling of large datasets, comprehensive visualization tools, and support for machine learning make it a valuable tool in a wide range of data science applications.