Data scientists look for a language that is convenient, extensive, and **open-source. **Due to such brownie points**, Python** has brought together a thriving, close-bound community of contributors and users, over time. It has a **clean syntax** which is even simpler than C, C++, and Java. It facilitates users to carry out **functional and Object-Oriented programming language.**

Python is generic and it has a large set of libraries to implement a wide variety of tasks like building websites, backend API’s, scripting, and much more. As there are so many people who program in Python, hence it is easy for new programmers to reach out to other experienced programmers whenever they need help. This demand led to the coming up of libraries to do tasks required for data science and with that, it has leaps and bounds in the last few years. As a result of this, Python has actually overtaken R, which had ruled data science for several decades.

**The capability of python has increased with a growing number of Data Analytics Libraries-**

- NumPy
- SciPy
- Pandas
- Stats Model
- SciKit Learn

These libraries come with their respective benefits which have been explained below:

NumPy contributes as a library of Python. **Matrix multiplications** and mathematical objectives for computation on arrays are some of its **fast operations** which led this library to be widely used among Data Scientists.

**SciPy**

**Pandas**

Pandas provide** easy-to-use data analysis tools **and contain function design to make data analysis fast and easy. The two important data structures of Pandas library are-

1. **Dimensional -**The panda series is a uni-dimensional array that can** store multiple data types, such as **strings, integers, and floats. Moreover, what makes it different from other standard elements is its ability to index all its elements.

2. **Dimensional series - **Used for indexes in rows and columns-

It is essential for** **running various operations, while simultaneously **extracting data from Excel and SQL tables** into the user interface.

The Pandas library provides a lot of functions that can be executed on series and data frames like average, sum, concatenate, groupby, and orderby among others. Pandas make combining data from databases, spreadsheets into Python easy and efficient.

**Stats model**

**Statistics functions** are performed by the **Stats model **which is a python model to conduct statistical tests and statistical data exploration. We can explore data, estimate statistical models and perform statistical tests through the stats model. An extensive list of **descriptive statistics, Statistical tests, Plotting functions, **and **Result Statistics **are available in the Stats model for different types of data. Stats Model is built on top of mathematical libraries like **NumPy** and it integrates really well with **Pandas** as well.

**SciKit Learn**

Scikit Learn is an exclusive **machine learning package** for python. Scikit Learn includes support for many machine learning algorithms. Implementing simple and complex machine algorithms quickly can be used as a primary option. It is compatible with other python libraries like **NumPy, Pandas, and SciPy **which makes it easy to understand and use. Its functions help in making machine learning models such as **clustering,** **support vector machines (SVM’s)**, **regression,** etc. It has functions that enhance the calculation of the accuracy of a model.

**Visualising Data with Python**

Data Visualisation is now easier than ever, with Python. Matplotlib and Seaborn are the most common libraries that deal with providing options for representing data graphically in Python.

**Matplotlib**

It is popular with users for its open range of** 2d **and** 3d graphics**. It is used to produce publication figures like **Histograms, Power Spectra, Bar charts, Box Plots, Pie Charts and scatter plots** by using few lines of code. Matplotlib easily integrates with Python Dataframes to make visualisation quick and convenient.

The sole drawback of this library is that if used for Advanced visualisations, it is not completely user-friendly.

**Seaborn**

Seaborn is a data visualisation library, supported by Matplotlib. Seaborn with the help of matplotlib introduces additional plot types. It also makes normal Matplotlib visualisations look more elegant. Seaborn is mostly used to create complicated plots with ease. Heatmap is a commonly used visualisation that can be created using one line of code.

**Integrated Development Editor (IDE)**

IDE has changed the way python programmers use code with documentation and live outputs with Notebook, previously known as** Ipython**. Notebooks are used by Data Scientists and Analysts to present reports in the form of story-telling, with multiple blocks of code running with each block, and the output displayed below it. Data Scientists can use it as a magical organizer. One may **write code in multiple programming languages** like Python, R, and Scala.

The IDE has made the workflow simple yet dynamic. The Notebook documents are created by the **Jupyter Notebook** app. The application provides the user with both computer code and rich text elements like paragraphs, equations, figures, and links, etc. One must be curious to see the stage of python libraries in the area of future Data Science and Deep Learning. Python’s development in Deep learning libraries like Google’s TensorFlow and Frameworks like Theano and TF Learn have enabled the Data Scientists to build Artificial Neural Networks. This has also contributed towards making python a popular language of choice for Machine Learning Enthusiasts.

Due to its evident popularity, Python overshadowed R, as shown in the survey conducted on ** Kaggle** for Data Scientists.

*“Every Data Scientist has an opinion on what language you should learn first. As it turns out, people who solely use Python or R feel like they made the right choice. But if you ask people who have used both Python and R, they are twice as likely to recommend Python”*

*- Kaggle Survey*