Updated on 27th Jun, 22 3903 Views

What is Python?

Python is a high-level, general-purpose programming language with an elegant syntax that allows programmers to focus more on problem-solving than on syntactical errors. One of the primary goals of Python developers is to keep the language fun to use. Python has gained massive buzz in the field of modern software development, infrastructure management, and, especially in, data science and artificial intelligence. Most recently, Python has risen to the top 3 list of the TIOBE index of language popularity.

Why Python for Data Science?

Python programming comes first when we think of data science. Python has rapidly gained popularity in the IT community as a simple yet feature-rich language powering anything from simple web applications to the IoT, game development, and even artificial intelligence.

Big data and data analytics are other sectors in which Python is currently making inroads. In this Python data science tutorial, let us find out why Python is used in big data.

So many programming languages provide the much-needed options to execute data science jobs. This has resulted in it being difficult to handpick a specific language.

But it is data that provides a peep into these languages that are making their way into the world of data science, i.e., nothing can be as compelling as the data itself unveiling the results of the comparison between different data science tools.

Python as a “Leader”

Python is one of the fastest-growing programming languages in the world, and it is quite easy to learn. Being a high-level programming language, Python is widely used in mobile app development, web development, software development, and in the analysis and computing of numeric and scientific data.

Python can run on any platform—Windows, Linux, Macintosh, etc.

Why is Python preferred over others?

Codes in Python are written in a very natural style, which is why it is easy to read and understand.

Some of the features of Python that make it a popular language in data science applications are:

Why Is Python Preferred over Others

Easy to Learn

Easy to Learn

Python is for anyone aspiring to learn because of its ease of learning and understanding.

Python is a popular data science tool with 35 percent of data analysts using it. It follows R in popularity and is ahead of SQL and SAS. 



Python is known to be an extremely scalable language when compared to other languages such as R. Python is also faster to use than MATLAB or Stata.

Python’s scalable nature lies in its flexibility during problem-solving situations because of which even YouTube has migrated to Python.

Python has come to be good for different usages in industries as many data scientists use this language to develop various types of applications successfully.

Availability of Data Science Libraries

Availability of Data Science Libraries

The best answer to the question, Why Python for data science, is the availability of various libraries such as pandas, statsModels, NumPy, SciPy, and scikit-learn.

The constraints that developers faced a year ago are addressed by the Python community; the Python community helps address problems of a specific nature through robust solutions.

Python Community

Python Community

One of the major factors behind the remarkable upsurge of Python in the industry is its ecosystem. Many volunteers are developing Python libraries these days as Python has extended its hands to the data science community, which, in turn, has led the way for creating the most modern tools and processing in Python. The community helps these Python aspirants with relevant solutions to their coding problems.

Graphics and Visualizations

Graphics and Visualizations

Python provides various graphic and visualization options, which are very helpful for generating insights into the data available. Matplotlib is a plotting library in Python that provides a solid base around which other libraries, such as Seaborn, pandas, and ggplot, have been successfully built.

These packages help in getting a good sense of data, creating charts, graphical plots, web-ready interactive plots, and much more.

Data Science using Python vs R

For almost a decade, researchers and developers have been debating the topic, Python or R for data science—which is a better language?

With the adoption of open-source technologies taking over the traditional, closed-source commercial technologies, Python and R have become extremely popular among data scientists and analysts.

But it has been noticed by Maruti Techlabs that “Python’s increase in the share over 2015 rose by 51% demonstrating its influence as a popular Data Science tool.”

Primary UsersResearchers and scholarsProgrammers and developers
Primary ObjectiveStatistics and data analysisDeployment and production of the machine learning and deep learning algorithms
Important Librariesdplyr, ggplot2, caret, and zoopandas, Matplotlib, and scikit-learn
Ease of LearningSteep learning curveEasy to learn
SpeedCan be slow with large datasetsFaster than R in dealing with large datasets

Here’s a video from Intellipaat on Data Science With Python

How to install Python?

There are two ways to install Python:

  • We can download Python directly from its website and install the needed individual components and libraries.
  • Alternatively, we can also download and install a package, which comes with preinstalled libraries such as downloading Anaconda or Enthought Canopy Express

The second method is a more hassle-free installation and is ideal for beginners. However, one has to wait for the entire package to be upgraded, even if they just want the latest version of a single library. Unless there is cutting-edge statistical research involved, this should not be a problem.

The next step is choosing a development environment. Once Python is installed, there are various options to choose an environment. The following are the three most common options:

  • IDLE (default environment)
  • Terminal/Shell-based
  • IPython Notebook

Watch Python Course for Beginners Tutorial:

So let’s ahead in this python data science tutorial and understand the concepts of python libraries for data science.

Python Libraries for Data Science

Python has gained immense popularity as a general-purpose, high-level back-end programming language for creating prototypes and developing applications. Python’s readability, flexibility, and suitability for data science operations have made it one of the most preferred languages among developers.

Check out our blog on why Python is considered one of the best programming languages for data science.

It has been reported that Python is used extensively by developers in the creation of games, standalone PCs, mobile applications, and other enterprise applications.

Python libraries simplify complex tasks and make data integration much easier with fewer codes in lesser time. Python has more than 137,000 libraries, which are very powerful and are vastly used to satisfy the requirements of customers and businesses. These libraries have helped scientists and developers in analyzing large amounts of data, generating insights, engaging in critical decision-making, and much more.

The following are a few Python libraries that are widely used in the fields related to data science.


NumPy is an extensive Python library that is used for scientific computations. It leverages your usage of sophisticated functions, N-dimensional array objects, tools for integrating C/C++ and Fortran code, mathematical concepts, such as linear algebra, random number capabilities, etc. You can use it as a multidimensional container for treating generic data. It allows you to load data into Python and export data from the same.


SciPy is another important library of Python for developers, researchers, and data scientists out there. It includes optimizations, statistics, linear algebra, and integration packages for computation. It can be of great help for someone who has just started their career in data science to guide them through numerical computations.


Matplotlib is a popular plotting library of Python that is extensively used by data scientists for designing numerous figures in multiple formats depending on their compatibility across their respective platforms. For example, with Matplotlib, you can create your own scatter plots, histograms, bar charts, and so on. It provides good quality 2D plotting and basic 3D plotting with limited usage.


Pandas is the most powerful open-source library of Python for data manipulation. It is known as the Python Data Analysis Library. It is developed over the NumPy package. DataFrames are considered as the most used data structures in Python that help in handling and storing data from tables by performing manipulations over rows and columns. pandas is very useful in merging, reshaping, aggregating, splitting, and selecting data.


scikit-learn is a collection of tools for performing mining-related tasks and data analysis. Its foundation is built over SciPy, NumPy, and Matplotlib. It consists of classification models, regression analysis, image recognition, data reduction methods, model selection and tuning, and many other things.


statsmodels for statistical modeling. statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.


Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. Based on Matplotlib, Seaborn aims to make visualization a central part of exploring and understanding data.


Bokeh for creating interactive plots, dashboards, and data applications on modern web browsers. Bokeh empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.


Blaze for extending the capability of Numpy and pandas to distributed and streaming datasets. Blaze can be used to access data from a multitude of sources including bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on large chunks of data.


Scrapy for web crawling. Scrapy is a very useful framework for getting specific patterns of data. It has the capability to start at a website home URL and then dig through web pages in the website to gather information.


SymPy for symbolic computation. SymPy has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics, and quantum physics. Another useful feature of SymPy is its capability of formatting the result of the computations as LaTeX code.


Requests for accessing the web. It works similar to the standard Python library urllib2 but is much easier to code. You will find subtle differences between Requests and urllib2, but for beginners, Requests may be more convenient.

Some additional libraries that one may need are:

  • os for operating system and file operations
  • NetworkX and igraph for graph-based data manipulations
  • Regular Expression for finding patterns in text data
  • Beautiful Soup for performing web scraping by extracting information from just a single web page in a run

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Data Science with Python: Demo

Problem Statement

You are a data scientist at a telecom company Neo whose customers are switching over to its competitors. You have to analyze the data of your company and find insights and stop your customers from switching over to other telecom companies


This is a snapshot of the dataset that you will be working on:

                                                                                               Python for Data Science

Tasks to be Done

  • Data Manipulation: Extracting individual rows and columns from the dataset and find interesting patterns
  • Data Visualization: Understanding individual columns from the dataset by visualizations
  • Model Building: Building a decision-tree model

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Data Manipulation

We will start by loading the required packages:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

Now, let us load the customer_churn dataset:

#reading file
customer_churn = pd.read_csv("customer_churn.csv")

Glancing at the first few rows of the dataset:

#Looking at the first few rows

Python for Data Science

Extracting the 5th column from the entire dataset:

#Extracting 5th column

Extracting male senior citizens with payment method -> Electronic check:

senior_male_electronic=customer_churn[(customer_churn['gender']=='Male') & (customer_churn['SeniorCitizen']==1) & (customer_churn['PaymentMethod']=='Electronic check')]

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Data Visualization

Making a bar-plot for the distribution of the “Internet Service” column:

plt.xlabel('Categories of Internet Service')
plt.ylabel('Count of categories')
plt.title('Distribution of Internet Service')

Making a histogram for the distribution of “tenure” column:

plt.title('Distribution of tenure')

Model Building

Let us build a decision-tree model on top of the “customer_churn” dataset, where “Churn” is the dependent variable and “tenure” is the independent variable.

We will start by extracting “Churn” and “tenure” from the original dataframe:


Now, let us divide our data into “train” and “test” sets:

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20)

We will import the decision-tree classifier and fit the model on top of the train set:

from sklearn.tree import DecisionTreeClassifier 
classifier = DecisionTreeClassifier() 
classifier.fit(x_train, y_train)

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Now that we have fit the model on the “train” set, it is time to predict the values on the “test” set:

y_pred = classifier.predict(x_test)

Let us now go ahead and calculate the accuracy:

from sklearn.metrics import classification_report, confusion_matrix,accuracy_score
print(confusion_matrix(y_test, y_pred)) 
print(accuracy_score(y_test, y_pred))

We see that we get an accuracy of 73.88% for this decision-tree model.

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Companies that Use Python for Data Science


Instagram has about 400 million daily active users who share more than 95 million photos and videos.

Instagram has recently moved to Python 3, and the main reason why Instagram chose Python was its simplicity and popularity.

Instagram claim to have considered different languages over Python but did not get any significant performance improvement.



Spotify trusts Python and uses it for back-end services, as well as for data analysis.

Spotify claims that the speed of development is its priority, and that is the reason why Spotify uses Python to build its music streaming service as Python meets Spotify’s development speed expectations.

For data analysis, Spotify uses Hadoop with Python to process large amounts of data in order to polish its services.



Amazon analyzes customers’ buying habits and search patterns to provide them with accurate recommendations. It is possible due to their Python machine learning engine, which interacts with Hadoop, the company’s database. They work in conjunction to achieve maximum efficiency and accuracy in providing recommendations to customers.

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Amazon prefers Python because it is popular, scalable, and appropriate for dealing with big data.



Facebook deals with large amounts of data, including tons of images, and it uses Python to process the images.

Facebook decided to use Python for its back-end applications connected with image processing, such as image resizing, because of its simplicity and ease of development.



SurveyMonkey  is one of the largest survey companies in the world. It processes more than one million survey responses daily.

At the very beginning, SurveyMonkey’s web app was built on .NET along with C#. There weren’t any issues with the smoothness of the system, but it got relatively slow in testing while deploying new features.

SurveyMonkey rewrote its app in Python and broke the main features into several separate services and these services were communicated through the web APIs. This allowed SurveyMonkey to implement features on smaller codebases that can be managed more easily.

SurveyMonkey chose Python because of its simplicity, availability of tons of libraries to build web apps faster, availability of tools that facilitate deployment, unit testing, etc.

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Python is a great tool and is becoming an increasingly popular language among data scientists as it is easy to learn and integrates well with other databases and tools such as Spark and Hadoop. Python also has great computational intensity and has powerful data analytics libraries.  

So, learn Python to perform the full life cycle of any data science project. It includes reading, analyzing, visualizing, and making predictions. From this Python data science tutorial, you would have learned why python is preferred over any language and python libraries for data science.

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