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Features of Pandas

We all know Pandas is a great library. But why is it so different from other libraries? What is its USP? Should one learn it or not? These are a few questions that pop up in the mind of each beginner. Don't worry because we have decided to cure this headache of yours and have done all the hard work.

We bring to you the top features of Pandas that make it a great library. These are comprehensive points and will explain the things one needs to know before beginning with Pandas.

The features of Pandas that make it indispensable:

To be able to harness the true power of something as versatile as the Pandas library, one should know the following features.

1. Great data handling:

The Pandas library provides its users with Series and DataFrames, both of which are efficient and fast ways of managing data and exploring it. They also aid us in representing our data efficiently, allowing us to manipulate it in a variety of ways. This feature is the one that makes Pandas so efficient for data scientists.

2. Handling of missing data:

Data is often complex and very confusing to decipher. But that is just the beginning. The unprocessed nature of data creates many problems, one of which is the frequent occurrence of missing values and data. It is very important to handle all the values which are missing properly otherwise they tend to contaminate the end results of our study.

Pandas have the handling of missing data integrated into its library and some of its features have you covered on this front.

3. Indexing and Alignment:

You may have a lot of data but all of it is useless when you don't know what it depicts or where any of it actually belongs. Therefore it is paramount that the data is labeled. Another important note is to keep it organized. If the organization is not done correctly, the data will be impossible to read.

Pandas have several unique methods of indexing and aligning its data which takes care of the needs for the organizing and labeling of data.

4. Tools for input and output:

Pandas offer you a large variety of tools that are built-in which help you in reading and writing data. When you try to understand your data, you will obviously have to write it into databases, data structures, web services, etc. and read them from these sources too. Pandas' built-in tools have made these tasks very simple.

5. Data clean-up:

Like we discussed, data can be quite unprocessed. This often makes it extremely adulterated and performing any research or analysis on such unprocessed data can lead to results that are far from reality. Thus cleaning our data is very important and Pandas provides this feature to us.

They help in making our code cleaner and the data clean enough for the human eye to see what's happening. Cleaner data gives better results.

6. Support for multiple file formats:

There are various different file formats these days in which data is present. Therefore it becomes important that there are libraries that can read and analyze different file formats. Pandas leave its peers far behind with the number of file formats it supports. Pandas can support JSON, CSV, HDF5, and Excel. This is one of the biggest selling points of Pandas.

7. Multiple features for Time Series:

If you are a beginner, this feature might not make complete sense to you now but you will love it in the days to come. These set of features include frequency conversion and moving window statistics as well. As we learn more about Pandas, we will realize how important these features are for people working in data science.

8. Joining and Merging Datasets:

When we analyse data, there is a constant need to join and merge different datasets to create a final one for analysis. A robust system for this is required because if the datasets don’t get joined or merged properly, our results will get affected and that is not good.

Pandas is extremely efficient in merging various datasets.

9. Support for Python:

This feature just eliminates any opposition that Pandas has. Python, with a scarcely believable number of powerful libraries at its disposal, has become one of the leading programming languages used by data scientists.

Pandas can be a part of Python and give us access to other helpful libraries like MatPlotLib and NumPy.

10. Optimal performance:

Anyone who has worked with Pandas extensively can testify that it is really fast, efficient and suitable for data scientists. The code for Pandas is written in Python or C, which makes it fast and extremely responsive.

11. Grouping of data:

It is necessary to have the ability to group your data after separating it according to your needs.

Pandas have various features, one of them being GroupBy, which helps you to separate data into chosen categories according to criteria given by you. This function splits the data and implements the given function on them. It then combines the results.

12. Visualization of data:

A huge part of data science is the visualization of data. This is what makes the study's results understandable to normal human eyes.

Pandas offer in-build abilities that help you in plotting your data and analyzing the different kinds of graphs which form. If there is no visualization, any analysis of data would make no sense to almost anyone.

13. Data is Unique:

In unprocessed data, there is a lot of repetition. Therefore it is essential that we analyze only those data that have unique values.

Pandas offer us this feature which lets us see all the values in the dataset which are unique. This function is dataset.column.unique() where the keywords column and dataset are the respective names of your column and dataset.

14. Masking data:

There is always unnecessary data present in our datasets which we don't require. Therefore it is essential that we filter these data which we don't want. The mask function which Pandas provides helps us in doing exactly data as it turns any data that meets our given criteria for elimination, it turns it into missing data.

15. Mathematical Operations:

Pandas have a function called apply which allows its users to implement all kinds of mathematical operations on their data. This is of enormous help as one's dataset may or may not be of the correct order. This is taken care of by a simple mathematical operation.


This article has covered the features which are at the very core of Pandas, making it so dynamic and amazing to use. We hope that this helped in clearing any doubts you may have had in your mind about Pandas. If you still have any doubts, fire away in the comments section below.

About the author:
I like writing about Python, and frameworks like Pandas, Numpy, Scikit, etc. I am still learning Python. I like sharing what I learn with others through my content.