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A Beginner’s Guide to Python for Data Science

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Python is a programmer darling for plenty of reasons: the language is easy to read and work with, relatively simple to learn, and popular enough that there's a great community and plenty of resources available.

And if you needed one more reason to consider starting Python for beginners, it plays an important role in lucrative data careers as well! Learning Python for data science or data anaylsis will give you a variety of useful skills.

What exactly are those skills? In this special guest post, Quincy Smith from Springboard writes about using Python for data science and everything it allows you to do.

Here's Quincy!

Disclosure: I’m a proud affiliate for some of the resources mentioned in this article. If you buy a product through my links on this page, I may get a small commission for referring you. Thanks!

An Introduction to Python for Data Science

Python has been around since grunge music hit the mainstream and dominated the airways. Over the years, many programming languages (like Perl) have come and gone, but Python has been growing from strength to strength.


In fact, it’s one of the fastest-growing programming languages in the world. As 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.

For example, popular websites like Dropbox, Google, Instagram, Spotify, and YouTube were all built with this powerful programming language.

The massive open-source community that has grown around Python drives it forward with a number of tools that help coders work with it efficiently. In recent years, more tools have been developed specifically for data science, making it easier than ever to analyze data with Python.

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What Is Python?

The foundation for Python was laid in the late 1980s, but the code was only published in 1991. The primary aim here was to automate repetitive tasks, to rapidly prototype applications, and to implement them in other languages.

It’s a relatively simple programming language to learn and utilize because the code is clean and easy to comprehend. So it’s not surprising that most programmers are familiar with it.

The clean code, along with extensive documentation, also makes it easy to create and customize web assets. As alluded to above, Python is also highly versatile and supports multiple systems and platforms. Thus, it can be easily leveraged for a variety of purposes from scientific modeling to advanced gaming.

Why Should You Learn Python?

Since its early days as a utility language, Python has grown to become a major force in artificial intelligence (AI), machine learning (ML), and big data and analytics. However, while other programming languages like R and SQL are also highly efficient 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 doors for you and improve your career opportunities. Even if you don’t work in AI, ML, or data analytics, Python is still vital to web development and the development of graphical user interfaces (GUIs).

Its increasingly important role in this field can be attributed to the fact that it has proven time and again to be capable of solving complex problems efficiently. With the help of data-focused libraries (like NumPy and Pandas), anyone familiar with Python’s rules and syntax can quickly deploy it as a robust tool to process, manipulate, and visualize data.

Whenever you get stuck, it’s also relatively easy to solve Python-related problems because of the sheer amount of documentation that’s freely available.

python programming

Python’s appeal has also extended beyond software engineering to those working in non-technical fields. It makes data analysis attainable for those coming from backgrounds like business and marketing.

Most data scientists won’t ever have to deal with things like cryptography or memory leaks, so as long as you can write clean, logical code in Python, you’ll be on your way to conducting some data analytics.

Python is highly beginner-friendly as it’s expressive, concise, and readable. This makes it much easier for rookies to start coding quickly and the community supporting the language will provide enough resources to solve problems whenever they come up.

It also pays to become a Python developer. According to Glassdoor, Python developers command an average salary of $92,000 a year. Those with significant coding experience can earn as much as $137,000, annually.

What Are Basic Data Structures?

We can’t talk about Python’s role in data science without going over some of the underlying data structures that are available. These can be described as a method of organizing and storing data in a way that’s easily accessible and modifiable.

Some of the data structures that are already built in include:

  • Dictionaries
  • Lists
  • Sets
  • Strings
  • Tuples

Lists, strings, and tuples are ordered sequences of objects. Both lists and tuples are like arrays (in C++) and can contain any type of object, but strings can only contain characters. Lists are heterogenous containers for items, but lists are mutable and can be reduced or extended as needed.

coding screen

Tuples, like strings, are immutable, so that’s a significant difference when compared to lists. This means that you can delete or reassign an entire tuple, but you can’t make any changes to a single item or slice.

Tuples are also considerably faster and demand less memory. Sets, on the other hand, are mutable, unordered sequences of unique elements. In fact, a set is a lot like a mathematical set because it doesn’t hold duplicate values.

A dictionary in Python holds key-value pairs, but you’re not allowed to use an unhashable item as a key. The primary difference between a dictionary and a set is the fact that it holds key-value pairs instead of single values.

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

Lists are enclosed in brackets: l = [1, 2, "a"]

Sets are also 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 sets of advantages and disadvantages, so you have to know where to use them to get the best results.

When you’re dealing with large sets of data, you’ll also have to spend a considerable amount of time “cleaning” unstructured data. This means handling data that’s missing values or has nonsensical outliers or even inconsistent formatting.

So before you can engage in data analytics, you have to break the data down to a form that you can work with. This can be achieved easily be leveraging NumPy and Pandas. To learn more, the Pythonic Data Cleaning With NumPy and Pandas tutorial is an excellent place to start.

For those of you who are interested in data science, blindly installing Python will be the wrong approach, as it can quickly become overwhelming. There are thousands of modules in Python, so it can take days to manually install a PyData stack if you don’t know what tools 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 need. Everything else can be installed through a GUI. The good news is that the distribution is available for all major platforms.

What’s Jupyter/IPython Notebook?

Jupyter (formerly known as iPython) Notebook is an interactive programming environment 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 incredibly powerful Python shell that’s ubiquitous across PyData.

It will allow you to mix code, graphics (even interactive ones), and text. You can even say that it works like a content management system as you can also write a blog post such as this one with a Jupyter Notebook.

As it comes preinstalled with Anaconda, you can start using it as soon as it’s installed. Using it will be as simple as typing the following:

In 1: print('Hello World')

Out 1: Hello World

Overview of Python Libraries

There are plenty of active data science and ML libraries that can be leveraged for data science. Below, let's go over some of the leading Python libraries in the field.



Matplotlib can be described as a Python module that's useful for data visualization. For example, you can quickly generate line graphs, histograms, pie charts, and much more with Matplotlib. Further, you can also customize every aspect of a figure.

When you use it within Jupyter/IPython Notebook, you can take advantage of interactive features like panning and zooming. Matplotlib supports multiple GUI backends of all operating systems and is enabled to export leading graphics and vectors formats.


NumPy, short for “Numerical Python,” is an extension module that offers fast, precompiled functions for numerical routines. As a result, it becomes much easier to work with large multidimensional arrays and matrices.

When you use NumPy, you don’t have to write loops to apply standard mathematical operations on an entire data set. However, it doesn’t provide powerful data analysis capabilities or functionalities.


SciPy is a Python module for linear algebra, integration, optimization, statistics, and other frequently used tasks in data science. It’s highly user-friendly and provides for fast and convenient N-dimensional array manipulation.

SciPy’s main functionality is built upon NumPy, so its arrays heavily depend on NumPy. With the help of its specific submodules, it also provides efficient numerical routines like numerical integration and optimization. All functions in all submodules are also heavily documented.


Pandas is a Python package that contains high-level data structures and tools that are perfect for data wrangling and data munging. They are designed to enable fast and seamless data analysis, data manipulation, aggregation, and visualization.

Pandas is also built on NumPy, so it’s quite easy to leverage NumPy-centric applications like data structures with labeled axes. Pandas makes 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 was primarily built for Facebook's artificial intelligence research group. While it’s a great tool for natural language processing and deep learning, it can also be leveraged effectively 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 understands the Pandas DataFrame, they both work well together. Seaborn isn’t prepacked with Anaconda like Pandas, but it can be easily installed.


Scikit-Learn is a module focused on ML that’s built on top of SciPy. The library provides a common set of ML algorithms through its consistent interface and helps users quickly implement popular algorithms on data sets. It also has all the standard tools for common ML tasks like classification, clustering, and regression.


PySpark enables data scientists to leverage Apache Spark (which comes with an interactive shell for Python and Scala) and Python to interface with Resilient Distributed Datasets. A popular library integrated within PySpark is Py4J, which allows Python to interface dynamically with JVM objects (RDDs).


If you’re going to use dataflow programming across a range of tasks, TensFlow is the open-source library to work with. It’s a symbolic math library that’s popular in ML applications like neural networks. More often than not, it’s considered an efficient replacement for DistBelief.

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This beginner’s guide just scratched the surface of Python for data science. As the language evolves rapidly with the support of the open-source community, you can expect it to keep growing in importance within the field.


Choosing a language to learn, especially if it’s your first, is an important decision.  For those of you thinking about learning Python for beginners and beyond, it can be a more accessible path to programming and data science. It’s relatively easy to learn, scalable, and powerful. It’s even referred to as the Swiss Army knife of programming languages.

With a wealth of online courses, tutorials, and workshops, you can start working with oceans of data sooner rather than later. And the professional possibilities are essentially endless.

About the Author

This post was created by Quincy Smith of Springboard, a company dedicated to helping bridge the world's skills gap through mentor-led courses like Data Science and Machine Learning. He's passionate about strong coffee, solo travel, and clean data.

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