Pypolars is an alternative to the Pandas library in Python which is used for working with data while working on a data science task. We use Pandas from reading a dataset to preparing the dataset for a machine learning model. Just like Pandas, Pypolars is another great library that can be used while working with data as it contains most of the functions provided by the Pandas library. In this article, I will introduce you to a tutorial on Pypolars Library in Python.
What is Pypolars in Python?
Pypolars is an alternative to the Pandas library in Python. Without a doubt, the Pandas library is an amazing Python library that we use when working with data. If you come from a non-coding background, you might find Pandas very easy to use because it looks a lot like SQL. It has long been used for working with data. From reading a dataset to analyzing the data, everything can be done using Pandas. So why Pypolars?
Pypolars is just an alternative to the Pandas library in Python. You can use it in almost all the tasks which can be done using Pandas. The best feature about this library is that it is very efficient in terms of memory. So in any case in the future, the industry decided to switch to Pypolars for efficient memory management then you don’t need to worry as this library contains most of the functions in Pandas which works the same way. In the section below, I will take you through a tutorial on the Pypolars library in Python.
Pypolars using Python (Tutorial)
I hope you now know what is Pypolars in Python. In short, it is an alternative to the Pandas library which is more efficient in terms of memory. Now let’s see how to work with this library in Python. If you have never used it before then you can easily install it by using the pip command; pip install py-polars. I will first start by creating a DataFrame:
import pypolars as pl
data = pl.DataFrame({"Name" : ["Aman", "Hritika", "Raj",
"Simran", "Rahul", "Neha"],
"Age" : [22, 21, 25, 23, 30, 27]})
data.head()
The only difference between a DataFrame created using Pandas and Pypolars is that the Pypolars DataFrame shows a datatype of columns at the top before the first row where a Pandas DataFrame lacks this feature. The other feature where I think Pandas is better is that in the above DataFrame you can see the string values in ” ” which looks very Pythonic and generally it doesn’t look good while working with a large dataset.
Now let’s see how to read a dataset with this library:
data = pl.read_csv("class_grades.csv")
data.head()
So reading the dataset is very similar in both the libraries. You can also convert the above DataFrame to a Pandas DataFrame by using the code below:
df = data.to_pandas() df.head()
Summary
The Pypolars library is a great alternative to the Pandas library in Python if you want to save memory while working with a large dataset, but still, it lacks some features as compared to the Pandas library in Python. I hope you liked this article on a tutorial on the Pypolars library in Python. Feel free to ask your valuable questions in the comments section below.
