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Application of Pandas in Data Science Industry

When doing anything in life, having knowledge is as important as knowing when, where, and how to use that knowledge. A concrete idea of the application of knowledge is essential in making you good at anything. In this tutorial, we have made a list of the different fields in which Pandas can be applied, and the various places in the real world where you will find yourself using Pandas. This list will help you identify the different industries and their sectors where you can apply Pandas. Let’s begin.

Different Applications of Pandas

This list will cover quite a few industries and the sectors where Pandas finds its applications.

1. Recommendation systems on Online Platforms:

While streaming on any media channel like Netflix or Spotify, we have been surprised by the recommendations these sites provide which are almost always close to heart. Well, the magic behind all of this is Deep Learning.

There are various models set which take different criteria into account and provide recommendations. These systems are one of the most widespread applications of Pandas. In most of these cases, the models are coded in python and pandas is the best library to use when you have handled such data in these models.

Pandas can handle a large amount of data which helps it in learning the tonnes of data it requires to build a good recommendation system. Functions like mapping and GroupBy are instrumental in making these recommendation systems workable.

2. Economics:

Data analysis is a constant need for Economics because it's always changing. The data needs to be analysed to create patterns and trends which are then analysed and interpreted to understand how the economy in various sectors is faring, something which is of extreme importance to economists.

A lot of modern-day economists have taken to pandas and python to handle huge datasets and interpret that data. Pandas have an extensive list of inbuilt tools and functions that make it the efficient package it is.

Tools like file-handling and data frames are extremely useful to access data and manipulate it to get the results we want. These applications of Pandas has helped Economists all over the world to make breakthroughs and studies they could not do earlier.

3. Neuroscience:

Understanding how our brains (and consequently the entire nervous system) work has been an unfulfilled wish of doctors since time immemorial because they believe that it will help discover answers to many mysteries that our body holds.

Machine Learning, driven by the numerous applications of pandas, has helped this field a lot. Once again, pandas' great capability of data manipulation has played a huge role. They have helped compile copious amounts of data and analysed it to produce results which are quite significant for neuroscientists.

4. Prediction of stocks:

Few things in the world are as volatile as the stock market. This is literally one of the biggest datasets in the world. Although it is almost impossible to predict the changes in stock market, with the correct application of Pandas and some help from a few libraries like MatPlotLib and NumPy, you can easily make models which can predict (to a large extent) how the stock market will turn out to be.

This is only possible because of the huge datasets we have in terms of the previous behaviour of the stocks which inform the system about their behaviour both individual ad codependently. By learning these behaviours and movements, a good model will be able to predict with quite a bit of accuracy as to what your next move should be.

5. Advertising:

With the advent of the 21st century, no field has progressed more than marketing has. Advertising has become more and more personalized. This change has once again been driven by Deep Learning and Machine Learning.

6. Statistical analysis:

Statistics itself means dealing with loads of data. Hence a library like pandas is able to help in various ways because it basically deals with various forms of data handling and its functions. The basic functions of mean, median, and mode are quite helpful while performing calculations based on statistics. There are loads of other functions in pandas which help you with getting better results in Statistics.

7. Natural Language Processing (NLP):

A very common buzzword right now, Natural Language Processing or NLP has announced its arrival to the world in style. The main purpose of this concept is to help computers understand the normal human language and the various nuances it possesses. This is quite a difficult task to accomplish, but with pandas at your service along with Scikit-Learn, you can create a basic model for NLP which you can enhance later on. You can use various functions and other libraries to help you out.

8. Analytics:

Analytics is everywhere. Whether you want to analyse a website or analyse any other platform, pandas helps you in doing it all. Its efficient data handling capabilities and dynamic data manipulation combined with its amazing visualization capabilities play a huge role in helping it ace this field.

9. Data science:

Most of the examples we have discussed are a part and parcel of data science. It is a huge umbrella which covers any and every field that deals with the analysis, handling, and manipulation of data. Therefore almost all of pandas applications fall under it.

10. Big Data:

Python has a great link with Spark and Hadoop. Therefore Pandas can work with Big data too.


We hope that by reading the examples given above, you have now come to know about the various fields in which pandas can be applied in our day to day lives. You now know where to use all of your knowledge in Pandas and also have a good understanding of its true power. If you still have any queries, fire them down below in the comments section.

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.