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Pros and Cons of using Pandas

When you start off with a new library it is a very complex and difficult decision to make. There is a whole lot of homework and research involved and there are numerous roads one can take. This is where you need to jot down the pros and cons of the library which will help you decide whether or not you want to learn it.

It will not only aid you in your decision but also provide you with a clear idea of the exact capabilities of the framework and library and whether it will be able to fulfill your expectations or not.

To help you to know advantages and disadvantages of Pandas library, we bring you this tutorial which contains a list of its pros and cons.

Pros of the Pandas Library

The Pandas library is a robust piece of software and is full of advantages. If we go about listing all of them, it would take way more time than actually going out there and learning the library. Here we have listed the advantages that are at the very core of the Pandas Library:

1. Excellent representation of data:

The Pandas library is the perfect tool for anyone who wants to get into data science or data analysis because of the different ways it can represent and organize data. This is a very important function that cannot be disregarded because one can't possibly analyze or read any data unless it is represented well enough.

A clean set of data organized well is essential when the data is confusing to analyze and read.

2. Less coding done, more work accomplished:

By writing 1-2 lines of code in Pandas, you can easily accomplish tasks that would require about 10-15 lines of code in C++ or Java, maybe even more. This encapsulates the efficiency that Pandas provides you with. In data science, there is so much to practice and hence it is a very useful ability for people just getting into the field.

Reducing the unnecessary burden of coding helps enthusiasts and professionals of data science alike in saving a lot of time which is very crucial for the studies they conduct.

3. Efficient handling of huge data:

As previously discussed, time is very important when it comes to data science. Therefore it becomes extremely important for the library being used to be very efficient in time. This is the front that Pandas excels in. Wes McKinney made this library for the sole purpose of being able to perform on large amounts of data faster and better than each and every other library in the world. This makes it extremely important in analyzing copious amounts of data.

4. Extensive feature set:

Pandas is really robust. This library provides the user with a large set of commands and amazing features that can be used to analyze the given data with the utmost ease.

Pandas have helped data analysis reach an entirely new level. It helps you in filtering the data according to the conditions you have set in place as well as segregating and segmenting your data according to your own preference.

5. Built for Python:

Python has swiftly grown to be the one of the most used programming languages across the world. It provides its users with an insane number of features and provides them with a lot of productivity. Thus, when someone is able to code in Pandas for Python, it enables them to tap into the true power of its long list of features and libraries which are available for use due to Python.

The most popular libraries are NumPy, MatPlotLib, and SciPy among others.

6. The flexibility of data and easy customization:

Pandas provide its users with a huge list of features that they can apply on the data they have in order to edit and customize it, while pivoting it according to their own free will and wants. This helps them get the most out of their data and helps them to analyze all of the information they have available in their hands.

Cons of the Pandas Library:

Everything has two sides like a coin. With a list of advantages, Pandas also has its own limitations and disadvantages which are equally important to know. Here we have listed the disadvantages of the Pandas library.

1. A complex syntax which is not always in line with Python:

When you are using Pandas, knowing it is a part of Python, some of its syntax can be complex. This is a trouble as many users are not able to switch efficiently and seamlessly between the normal python code and Pandas. However, such a problem arises only when you are using advanced levels of Pandas and hence we should not dishearten any newcomers.

2. Learning curve:

Pandas have a very steep learning curve. While it may seem very easy to use and navigate through in the beginning, it is just the tip of the iceberg.

As you advance and go deeper into the framework of pandas, you might have a really hard time to get acquainted with the way the library functions. However, if you have enough determination and good resources, this is an obstacle which can easily be overcome. So do give this library a try.

3. Poor documentation:

This is a big problem with pandas, especially for beginners. When any programming language or library is well documented, it helps people understand its own true potential and gives them an idea of all its features and applications. Because of a lack of good documentation pandas has, in one way, made itself exclusive to its own users and people who want to learn it or try it. Good documentation promotes more and more users into learning the library or language.

4. Poor 3D matrix compatibility:

This is one of the most visible drawbacks that the Pandas library suffers from. If your work deals with two dimensional (2D) matrices, then there could be nothing better than pandas for you. However, once you upgrade your data to a three dimensional (3D) matric, pandas will not be of much use and you will have to take the help of other libraries like NumPy for help.


Though the Pandas library has a few disadvantages which might seem cumbersome, its advantages will always outweigh them as you can see from the points mentioned above. Instead of letting the cons discourage you, let the pros drive you. Harness the unbridled potential that is stored within this gem of a library. If you have any queries regarding this article ask them in the comments section down 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.