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Introduction to Python


Data scientists are looking for a language that is easy to use, extensible, and open-source. Python’s many benefits have helped create a vibrant, connected community of users and contributors over the years. It uses clean syntax, which is simpler than C++, Java, and C++. It allows users to use functional programming languages and Object-Oriented programming languages.

Python is a generic language with many libraries to help you with various tasks, such as building websites, backend APIs, scripting and more. Because many programmers use Python, it is easy for novice programmers to get help from more experienced programmers. This increased demand led to creation of libraries that can perform data science tasks. It has grown exponentially over the past few years. This has led to Python surpassing R, which was the leader in data science for many decades.

Python Features

Python’s capabilities have increased thanks to many Data Analytics Libraries.

  • NumPy
  • SciPy
  • Pandas
  • Stats Model
  • SciKit Learn

These libraries have their benefits, which are explained below.


NumPy is a Python library. Matrix multiplications and mathematical objectives for computations on arrays are just a few of the fast operations this library has provided. Data Scientists widely use this library.


It is a scientific computing library that adds a variety of algorithms and high-level commands to manipulate and visualize data. It includes modules for integration, optimization, fast Fourier transforms, signal and image processing, and many other useful functions.


Pandas offer simple-to-use data analytics tools, including function design, to make data analysis quick and easy. Pandas library has two key data structures:

1. Dimensional The panda series can store multiple data types, including strings and integers. Its ability to index all elements makes it unique from standard elements.

2. Dimensional Series –Used to index in columns and rows-

This is necessary for running multiple operations and extracting data from Excel.

The Pandas library offers many functions that can execute on series and data frames, such as average, sum and concatenate. Pandas make it easy to combine data from spreadsheets and databases into Python.

Stats model

The stats model can perform statistics functions, a Python model that allows for statistical data exploration and statistical tests. The stats model allows us to explore data and estimate statistical models. We can also perform statistical tests using this model. The Stats model offers a wide range of descriptive statistics and Result Statistics. This is available for all types of data. Stats Model is built upon mathematical libraries and integrates well with Pandas.

SciKit Learn

Scikit Learn is a machine-learning package that’s exclusive to Python. Scikit Learn supports many machine learning algorithms. You can quickly implement simple or complex machine algorithms as a primary option. It can be used with other Python libraries such as NumPy and Pandas. This makes it simple to use and understand. Its functions are useful in creating machine learning models, such as clustering and Support Vector Machines (SVM’s), regression and. It includes functions that improve the accuracy of a model’s calculation.

Visualizing Data With Python

Python makes data visualization easy than ever. Seaborn and Matplotlib are two of the most popular libraries for providing options to represent data in Python.


It’s popular for its wide range of 2d and 3d graphics. It can be used to create publication figures such as Histograms and Power Spectra. Matplotlib integrates easily with Python Dataframes, making visualization easy and quick.

This library has one drawback: it’s not user-friendly if you use it for Advanced visualizations.

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