UNIQUENESS OF PANDAS

Pandas is a great library in Python, but what makes it different from the rest? What are its selling points? Why should one learn it? These are questions that will be in your mind. But don’t worry, because we have brought to you the Best 15 features that make Pandas an excellent package. These Pandas features are comprehensive and tell you in detail about the aspects you should know before starting off with Pandas.

Essential Python Pandas Features

Given below are the best Python Pandas Features, that one should know. So that they can harness the true power of the Pandas Library.

1. Handling of data

The Pandas library provides a really fast and efficient way to manage and explore data. It does that by providing us with Series and DataFrames, which help us not only to represent data efficiently but also manipulate it in various ways. These features of Pandas are exactly what makes it such an attractive library for data scientists.

2. Alignment and indexing

Having data is useless if you don’t know where it belongs and what it tells us about. Therefore, labeling data is of utmost importance. Another important factor is an organization, without which data would be impossible to read. These two needs: Organization and labeling of data are perfectly taken care of by the intelligent methods of alignment and indexing, which can be found within Pandas.

3. Handling missing data

As discussed above, data can be quite confusing to read. But that is not even one of the major problems. Data is very crude in nature and one of the many problems associated with data is the occurrence of missing data or value. Therefore, it is pertinent to handle the missing values properly so that they do not adulterate our study results. Some Pandas features have you covered on this end because handling missing values is integrated within the library.

4. Cleaning up data

Like we just said, Data can be very crude. Therefore it is really messy, so much so that performing any analysis over such data would lead to severely wrong results. Thus it is of extreme importance that we clean our data up, and these Pandas feature is easily provided. They help a lot to not only make the code clean but also tidies up the data so that even the normal eye can decipher parts of the data. The cleaner the data, the better the result.

5. Input and output tools

Pandas provide a wide array of built-in tools for the purpose of reading and writing data. While analyzing you will obviously need to read and write data into data structures, web services, databases, etc. This has been made extremely simple with the help of Pandas’ inbuilt tools. In other languages, it would probably take a lot of code to generate the same results, which would only slow down the process of analyzing.

6. Multiple file formats supported

Data these days can be found in so many different file formats, that it becomes crucial that libraries used for data analysis can read various file formats. Pandas aces this sector with a huge scope of file formats supported. Whether it is a JSON or CSV, Pandas can support it all, including Excel and HDF5. This can be considered as one of the most appealing Python Pandas features.

7. Merging and joining of datasets

While analyzing data we constantly need to merge and join multiple datasets to create a final dataset to be able to properly analyze it. This is important because if the datasets aren’t merged or joined properly, then it is going to affect the results adversely and we do not want that. Pandas can help to merge various datasets, with extreme efficiency so that we don’t face any problems while analyzing the data.

8. A lot of time series

These Pandas features won’t make sense to beginners right away, but they will be of great use in the future. These features include the likes of moving window statistics and frequency conversion. So, as we go deeper into learning Pandas we will see how essential and useful these features are, for a data scientist.

9. Optimized performance

Pandas is said to have a really optimized performance, which makes it really fast and suitable for data science. The critical code for Pandas is written in C or Cython, which makes it extremely responsive and fast.

10. Python support

This feature of Pandas is the deal closer. With an insane amount of helpful libraries at your, disposal Python has become one of the most sought-after programming languages for data analysis. Thus Pandas being a part of Python and allowing us to access the other libraries like NumPy and Matplotlib.

11. Visualize

Visualizing the data is an important part of data science. It is what makes the results of the study understandable by human eyes. Pandas have an in-built ability to help you plot your data and see the various kinds of graphs formed. Without visualization, data analysis would make no sense to most of the population.

12. Grouping

Having the ability to separate your data and group it according to the criteria you want, is pretty essential. With the help of the features of Pandas like GroupBy, you can split data into categories of your choice, according to the criteria you set. The GroupBy function splits the data, implements a function, and then combines the results.

13. Mask data

Sometimes, certain data is not needed for the analysis of data and thus it is important that you filter your data according to the things you want from it. Using the mask function in Pandas allows you exactly to do that. It is extremely useful since whenever it finds data that meets the criteria you set for elimination, it turns the data into a missing value.

14. Unique data

Data always has a lot of repetition, therefore it is important that you are able to analyze data that has only unique values. This is present in the Python Pandas features and lets the user see the unique values in the dataset with the function dataset.column.unique(). Where “dataset” and “column” are the names of your dataset and column, respectively.

15. Perform mathematical operations on the data

The apply function in Pandas allows you to implement a mathematical operation on the data. This helps enormously, because sometimes the dataset you have, is just not of the correct order. This will be correct by simply using a mathematical operation on the dataset. This is one of the most attractive features of Pandas.

Advantages of Pandas Library

There are many benefits of the python Pandas library, listing them all would probably take more time than what it takes to learn the library. Therefore, these are the core advantages of using the Pandas library:

Data representation

Pandas provide extremely streamlined forms of data representation. This helps to analyze and understand data better. Simpler data representation facilitates better results for data science projects.

Less writing and more work done

It is one of the best advantages of Pandas. What would have taken multiple lines in Python without any support libraries, can simply be achieved through 1–2 lines with the use of Pandas. Thus, using Pandas helps to shorten the procedure of handling data. With the time saved, we can focus more on data analysis algorithms.

An extensive set of features

Pandas are really powerful. They provide you with a huge set of important commands and features which are used to easily analyze your data. We can use Pandas to perform various tasks like filtering your data according to certain conditions, or segmenting and segregating the data according to preference, etc.

Efficiently handles large data

Wes McKinney, the creator of Pandas, made the python library to mainly handle large datasets efficiently. Pandas help to save a lot of time by importing large amounts of data very fast.

Makes data flexible and customizable

Pandas provide a huge feature set to apply on the data you have so that you can customize, edit and pivot it according to your own will and desire. This helps to bring the most out of your data.

Made for Python

Python programming has become one of the most sought-after programming languages in the world, with its extensive amount of features and the sheer amount of productivity it provides. Therefore, being able to code Pandas in Python, enables you to tap into the power of the various other features and libraries which will use with Python. Some of these libraries are NumPy, Scipy, MatPlotLib, etc.

Disadvantages of Pandas Library

Everything has its disadvantages as well, and it is important to know them, so, here are the disadvantages of using Pandas.

Steep learning curve

Pandas initially have a mild learning slope. But as you go deeper into the library, the learning slope becomes steeper. The functionality becomes extremely confusing and can cause beginners some problems. However, with determination, it can be overcome.

Difficult syntax

While, being a part of Python, Pandas can become really tedious with respect to syntax. The code syntax of Pandas becomes really different when compared to the Python code, therefore people might have problems switching back and forth.

Poor compatibility for 3D matrices

It is one of the biggest drawbacks of Pandas. If you plan to work with two-dimensional or 2D matrices then Pandas are a Godsend. But once you go for a 3D matrix, Pandas will no longer be your go-to choice, and you will have to resort to Numpy or some other library.

Bad documentation

Without good documentation, it becomes difficult to learn a new library. Pandas documentation isn’t much help to understand the harder functions of the library. Thus it slows down the learning procedure.

So, this was all about the important Advantages and Disadvantages of Pandas. I hope, you liked my explanation.

Summary

In this article, we have gone through the core features, advantages, and disadvantages of Pandas, which make the library popular. Hopefully, this tutorial has cleared up all the queries that you might be having about Pandas.

Nevertheless, if you still have some queries related to Python Pandas Features, then please go ahead and ask them in the comments section.

Happy learning.