Python for Data Science


Python is an interpreted, object-oriented, significant-level programming language with dynamic semantics. Its elevated level inherent data structures, joined with dynamic composing and dynamic binding, make it exceptionally appealing for Rapid Application Development, just as for use as a scripting or glue language to associate existing parts together.

Python’s basic, simple to learn syntax emphasizes readability and subsequently diminishes the expense of program maintenance. Python Support Modules and packages, which empowers program modularity and code reuse. The Python interpreter and extensive standard library are accessible in source or binary form without charge for every single significant platform and can be freely distributed. 

Checkout this Article: Python Basics for Data Science

An Introduction To Python for Data Science

Python has been around since grunge music hit the standard and ruled the airways. Throughout the years, many programming languages have come and gone, however, Python has been developing from solidarity to quality. 

Actually, it’s one of the quickest developing programming dialects all over the world. As a significant-level programming language, Python is broadly utilized in software development, mobile app development, web advancement, software development, and in the analysis and registering of numeric and scientific data.

For instance, well-known sites like Dropbox, Google, Instagram, Spotify, and YouTube were all together working with this amazing and powerful programming language. 

The gigantic open-source network that has developed around Python drives it forward with various tools that assist coders working with it productively. Lately, more tools have been grown explicitly for Data Science, making it simpler than at any other time to analyze the data with Python. 

The establishment for Python was laid in the late 1980s, however, the code was just distributed in 1991. The essential point here was to robotize repetitive tasks, to quickly prototype applications, and to actualize them in different languages. 

It’s a moderately basic programming language to learn and utilize because the code is perfect and simple to comprehend. So it’s not astonishing that most software engineers know about it. 

The perfect code, alongside broad documentation, additionally makes it simple to make and modify web assets. As implied above, Python is also profoundly adaptable and supports different frameworks and platforms. Thus, it tends to be effectively utilized for an assortment of purposes from scientific modeling to advanced gaming. 

Why you should learn Python?

Since its initial days as a utility language, Python has developed to turn into a significant power in Artificial Intelligence(AI), Machine Learning(ML), and Big Data and Analytics. But, while other programming dialects like R and SQL are also profoundly proficient to use in the field of Data Science, Python has become the go-to language for Data Scientists. 

So if you learn Python, it can open a lot of ways for you and improve your career opportunities. Whether you don’t work in AI, ML, or Data Analytics, Python is still vital to web development and the advancement of the Graphical User Interface (GUIs). 

Its inexorably significant role in this field can be ascribed to the fact that it has proven time to be equipped for taking care of complex issues effectively. With the assistance of data-focused libraries (like NumPy and Pandas), anybody acquainted with Python’s rules and syntax can rapidly convey it as a powerful tool to process, control, and visualize data. 

Python’s intrigue has also stretched out beyond software engineering to those working in non-technical fields. It makes Data Analysis achievable for those coming from backgrounds like business and marketing. 

Most Data Scientists won’t ever need to manage things like cryptography or memory releases, so as long as you can compose perfect, logical code in Python, you’ll be en route to leading a few Data Analytics

Python is exceptionally beginner’s friendly as it’s expressive and readable. This makes it a lot simpler for beginners to begin coding rapidly and the network supporting the language will give enough resources to take care of issues at whatever point they come up. 


These can be portrayed as a method of organizing and storing data in a manner that is effectively accessible and modifiable. 

A portion of the data structures that are already built-in include: 

  • Dictionaries
  • Lists
  • Sets 
  • Strings 
  • Tuples 

List, strings, and tuples are ordered sequences of objects. The two lists and tuples resemble arrays (in C++) and can contain any type of object, but strings can only contain characters. Lists are heterogeneous containers for items, however, lists are changeable and can be decreased or increased as required. 

Tuples, similar to strings, are changeless, so that’s a significant difference when compared to lists. This implies you can erase or reassign a whole tuple, however you can’t make any changes to a single item or slice.

Tuples are also significantly faster and demand less memory. Sets on the other side are mutable, unordered successions of unique components. In fact, a set is a lot like a mathematical set since it doesn’t hold duplicate values.

A dictionary in Python holds key-value pairs, however, you’re not permitted to utilize an unhashable thing as a key. The essential contrast between a dictionary and a set is the way that it holds key-value combines rather than single values. 

Dictionaries are enclosed in curly brackets: d = {“a”:1, “b”:2} 

List are enclosed in brackets : l = [1, 2, “a”] 

Sets are enclosed in curly brackets: s = {1, 2, 3} 

Tuples are enclosed in parentheses: t = (1, 2, “a”) 

(Source: Thomas Cokelaer)

All of the above have their own merits and demerits, so you need to realize where to utilize them to get the best outcomes. 

At the point when you’re managing huge arrangements of data, you’ll also need to invest a lot of time “cleaning” unstructured data. This implies dealing with data that are missing values or has nonsensical outliers or even inconsistent formatting. 

So before you can take part in Data Analytics, you need to separate the data into a form that you can work with. This can be accomplished effectively by utilizing NumPy and Pandas

For those of you who are keen on Data Science, blindly installing Python will be an inappropriate methodology, as it can immediately get overwhelming. There are a large number of modules in Python, so it can take days to manually install a PyData stack if you don’t have a clue what tool you’ll need to engage in data analytics. 

The best way around this is to go with the Anaconda Python Distribution, which will install most of what you’ll require. Everything else can be installed through a GUI. Fortunately, the distribution is accessible for every single significant platform. 


Jupyter (some time ago known as iPython) Notebook is an intelligent programming condition that allows for coding, data exploration, and debugging in the web browser. The Jupyter Notebook, which can be accessed via a web browser, is an unbelievably powerful Python shell that is ubiquitous across PyData

It will enable you to mix code, text, and graphics. You can even say that it works like a content management system as you can also compose a blog post, like this one with a Jupyter Notebook. 

As it comes pre-installed with Anaconda, you can start utilizing it when it’s installed. Utilizing it will be as basic as typing the following:

In 1: print(‘Hello World’) 

Out 1: Hello World 

Overview of python libraries

There are a lot of dynamic Data Science and ML libraries that can be utilized for Data Science. Below, we should turn out a portion of the main Python libraries in the field. 


Matplotlib can be described as a Python module that is valuable for data visualization. For instance, you can rapidly create line graphs, histograms, pie charts, and much more with Matplotlib. Further, you can also change each part of a figure. 

When you use it inside Jupyter/IPython Notebook, you can take advantage of interactive features like panning and zooming. Matplotlib supports various GUI backends of all supporting systems and is empowered to export leading graphics and vectors’ designs. 

Checkout the Article: 5 Steps to Learn Python for Data Science


NumPy, another way to say “Numerical Python,” is an extension module that offers fast, precompiled capacities for numerical routines. Subsequently, it turns out to be a lot simpler to work with huge multi-dimensional arrays and matrices.

At the point when you use NumPy, you don’t need to write loops to apply standard numerical activities on a whole data set. Nonetheless, it doesn’t provide powerful data analysis abilities or functionalities. 


SciPy is a Python module for linear algebra, optimization, integration, measurements, and other much of the time utilized assignments in Data Science. It’s profoundly easy to use and accommodates quick and helpful N-dimensional array manipulation.

SciPy’s fundamental functionality is based upon NumPy, so its array vigorously relies upon NumPy. With the assistance of its particular submodules, it additionally gives effective numerical schedules like numerical integration and improvement. All functions in all submodules are additionally intensely documented. 


Pandas is a Python bundle that contains elevated level data structures and tools that are ideal for data wrangling and data munging. They are intended to empower quick and consistent data analysis, data manipulation, accumulation, and visualization.

Pandas are also built on NumPy, so it’s quite easy to leverage NumPy-centric applications like data structures with labeled axes. Pandas make it easy to handle missing data by using Python and prevents common errors resulting from misaligned data derived from a variety of sources.


PyTorch, based on Torch, is an open-source ML library that principally worked for Facebook’s Artificial Intelligence research group. While it’s an extraordinary tool for Natural  Language Processing and Deep Learning, it can also be utilized viably for Data Science.


Seaborn is highly focused on the visualization of statistical models and essentially treats Matplotlib as a core library (like Pandas with NumPy). Whether you’re trying to create heat maps, statistically meaningful plots, or aesthetically pleasing plots, Seaborn does it all by default.

As it comprehends the Pandas DataFrame, the two of them function admirably together. Seaborn isn’t packaged with Anaconda like Pandas, however, it tends to be easily installed.


Scikit-Learn is a module focused on ML that is based on SciPy. The library gives a common set of ML algorithms through its consistent interface and helps clients rapidly implement well-known algorithms on data sets. It additionally has all the standard tools for common ML assignments like classification, clustering, and regression.


PySpark empowers Data Scientists to use Apache Spark (which accompanies an intuitive shell for Python and Scala) and Python to interface with Resilient Distributed Datasets. A well-known library coordinated inside PySpark is Py4J, which enables Python to interface powerfully with JVM objects (RDDs).


In case you’re going to utilize dataflow programming over a range of tasks, TensorFlow is the open-source library to work with. It’s a symbolic math library that is famous in ML applications like neural networks. As a general rule, it’s viewed as an effective replacement for DistBelief.


This article just scratched the surface of Python for Data Science. As the language advances quickly with the help of the open-source network, you can expect that it should continue developing in significance inside the field. 

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