Python is a great multi-purpose language with plenty of advantages that could appeal to a data analyst. It is also one of the easiest languages to learn, which is why a lot of beginner data analysts get drawn to this language. But do expert data analysts use Python?
Data analysts use Python and it is incredibly popular among them. It has a lot of great packages for every aspect of data analysis, including data mining, data processing/modeling, and data visualization. It is also easy to dive into and easy to eventually master.
In this article, we will be exploring this subject in detail. We will be discussing all the advantages of Python, and we will also be comparing it to the other popular programming language among data analysts, R. Keep reading to learn more.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is Data Analysis And Where Does Python Fit?
Data analysis is the process of extracting useful information and insights from raw data so that these can be used to optimize a system or a business. Most modern businesses hire data analysts to analyze their data so that they can learn how to improve their business practices in order to maximize profit.
As a data analyst, you will come across three distinct scenarios. First comes data mining. Data mining is the process of retrieving organized data from an unorganized dataset or source. Once the data has been retrieved, it has to be processed. For this, data processing/modeling is employed.
And finally, once the data has been analyzed and the information has been retrieved, it needs to be presented in such a manner that business folks or people with little to no technical knowledge can understand it. This is where data visualization comes in. While the domain of data analysis has a lot of complexities, this is the basic outline.
Python is an incredibly versatile language that can be used to perform each of these tasks. Furthermore, since data analysts do not necessarily have much programming skills, Python’s simplicity and the short learning curve is very appealing to most.
This was one of the reasons why the pioneers of this domain initially picked Python as one of the primary languages of choice. And this trend continues to this day. Today, the data analysis community that works with Python is huge. As such, you will find a large number of tools and libraries that are catered for data analysis purposes. Furthermore, you will also find a very supportive and reliable community of analysts.
In the next section, we will look at some of the main reasons why Python is a perfect language for data analysis.
Why Python Is Perfect for Data Analysis?
Python is a great language overall for several reasons. It is employed both for Rapid Application Development (RAD) and for writing scripts. Also, since it has high readability, it is easier to work with.
But besides the general advantages mentioned above, there are a few specific reasons why data analysts, in particular, prefer Python:
It Is Easy to Learn
The first and most important feature of Python is that it is incredibly easy to learn. There is a reason why most technical courses (and not just data analysis) teach Python as a first language to their students. Python, thanks to its readability and simplicity, is easy to learn for entry-level programmers.
This is of particular significance to aspiring data analysts since they usually have lesser programming experience than software engineering students. With Python, the syntaxes are easier to learn, and even the most unseasoned programmer can learn to implement some pretty complex solutions using this language.
And besides, the simplicity of this language means the solutions can usually be implemented in fewer lines, which makes it ideal for a programmer.
It Has a Lot of Libraries
Another reason why Python is the preferred language for data analysis is that it comes with a huge collection of supported libraries. Libraries are incredibly important in any kind of programming, especially because they can save you the trouble of having to reinvent the wheel every single time.
While it could make sense for a beginner programmer to implement every single part of the solution using basic syntaxes, professionals like to do things more efficiently. So they like to group a set of commonly used mini-solutions in a bundle. These bundles are called libraries and can be used by other programmers as well. This is as relevant for data analysis and data science as it is for the other domains of programming.
Some of the most popular free data science libraries in Python include Pandas, SciPy, and StatsModel. NumPy, while not being a data science specific library, is an incredibly popular choice among data analysts and scientists.
Furthermore, most of these libraries are open source. And thanks to the amazing community with its ever-growing size, the libraries in Python continue to evolve, becoming more efficient and useful over time.
It Is Flexible and Scalable
You may have already inferred this from the previous sections, but Python is an incredibly flexible language, and nowhere is this more relevant than in the domain of data analysis where the data analyst may be required to perform a large range and variety of tasks. And since Python’s flexibility can handle most of these tasks, it is an ideal choice for data analysts.
Data analysts can use this one language for pretty much every task required in data analysis, from organizing data sets and building data models to building web services and visualizations.
Another reason behind the massive popularity of Python in data science is its scalability compared with other popular data science/analysis languages like R or Go. Python is, in general, much faster and also much more scalable. Its scalability is also the reason why so many big companies use Python for their data analysis jobs.
It Supports Analytics and Visualization Tools
This is perhaps the most important aspect for a professional data analyst to choose Python. Ever since Python was first adopted by the pioneers of the data science/analysis domain, numerous libraries and tools particularly catered for data analysis, have been developed. These include libraries/tools for every aspect of data analysis, viz. data mining, data processing & modeling, and data visualization.
The most popular data mining libraries/tools include Scrapy and Beautiful Soup. Either of these libraries can be used to build crawling programs (also known as Spider bots) that crawl web pages and retrieve structured data that can later be used for data analysis purposes.
Popular data processing and modeling libraries/tools include SciPy, Pandas, Keras, PyTorch, TensorFlow, and SciKit-Learn. Most of these languages come with modules for a range of mathematical and statistical analysis. These can be used for anything from simple data analysis to the implementation of complex machine-learning algorithms.
NumPy is another popular library that deserves a special mention. This is because it is unmatched in its usability in terms of simple to complex array operations. The vectorization of mathematical operations using NumPy, in particular, is known to improve the execution speed of models.
Data visualization is just as important a task as any in data analysis. And Python supports several tools that are perfect at this. Matplotlib is perhaps the most popular among them, and it can be used to represent the findings of data analysis in two-dimensional figures of varying complexity, from histograms to non-Cartesian coordinate graphs.
Other popular data visualization tools in Python include Bokeh, Plotly, Pydot, and Seaborn. Each of these can be used to implement varying complexities of data visualization.
It Is Well Supported and Has a Great Community
The fifth and final reason behind the massive popularity of Python among data analysts is its massive community and well-established support system.
Python uses a community-based development model. This means that the most valuable knowledge and experience of the people who work in Python across the world are factored in when improving the language. And owing to its massive popularity among data analysts, all the libraries and tools we mentioned in the previous section have their own community that implements their own set of improvements.
This makes Python the most up-to-date language to use. Furthermore, the massive community also means that you will have tons of incredibly helpful people who will always be willing to help you if you come across a problem.
Python vs R
There is one other programming language that data analysts usually use besides Python. For a professional data analyst, both Python and R have their respective strengths and weaknesses. The right thing to do is master both languages and know when to use which for the most effective results.
Python is preferred by programmers who either want to create an application or build a quick model for data analysis. It is a programming language that covers pretty much every aspect of the data analysis workflow.
R, on the other hand, is usually restricted to research work and academia. It is great for scientists or engineers who want to analyze data but lack programming skills.
In this section, we shall attempt to compare the two languages.
Which Is Easier to Learn?
Perhaps the most important difference worth considering is the respective learning curve of these two languages. Python is the easier of the two for those with little to no prior experience. Thanks to its simplicity, beginners can start implementing complex solutions in no time. Also, the learning curve is pretty linear, meaning you will keep on progressing as long as you maintain a pace.
R, on the other hand, is a lot more complicated for those with no prior programming experience to master. It is not that hard when you are starting off, but the learning curve turns extremely steep from thereon.
Simply put, both languages are easy to dive into, but R is a lot harder to master.
When to Use Which?
Python is, in general, much more flexible, so it can be used to create a model or something else that has not been tried or built before. You would use R when you want to implement complex functions or test a model with relative ease.
Of course, most professional data analysts come across both these use cases. So if you want to become an expert data analyst, you will have to learn both.
Which Is More Equipped for Data Analysis?
R comes with a range of data analysis tools as a part of it. That means you can perform basic data analysis tasks on simple datasets without having to install any external packages. And if you are dealing with larger datasets, R does, of course, support plenty of packages like data.table or dplyr that you can install. R is also great at running tests and using formulas.
Python does not come with as many pre-installed data analysis functions as R. But, that does not necessarily mean it cannot compete with R. Python supports plenty of great libraries and packages that are perfect for data analysis. These include packages for data mining, data analysis, and data visualization.
Pros of Python
- Python is revered for its simplicity and readability, making it great for beginners and experts alike.
- Python is a general-purpose language with usability beyond the scope of data analysis.
- Python is generally considered a very fast language. As such, it is perfect for all the mathematical computations that need to be carried out as part of data analysis.
Cons of Python
- Python does not have as many data analysis libraries as R. While this may not affect a lot of data analysts, experts can feel the disadvantage.
- Python is relatively more error-prone, which is why it requires plenty of testing.
- Python does not do as good a job with data visualization as R does. The results are often less informative and less visually appealing.
Pros of R
- R is hands down the best tool for creating informative and visually appealing visualizations and graphs.
- R is perfect for data analysis, thanks to the tones of in-built and external tools and packages it supports. In many ways, it has been developed specifically for statistical data analysis, unlike Python, which is more of a general-purpose language.
- RStudio, which is an environment that most data analysts use to program in R, does a pretty good job of addressing the complexities of this language. It comes with a data editor, a graphics window, and support for debugging.
Cons of R
- R is generally considered much more difficult compared to Python. For people with no programming experience, it can be quite challenging to dive into and then master. The learning curve gets particularly steeper as you learn more about this language.
- Owing to its complexities, many users have to depend on packages to make the most of R. But finding the right packages can be a challenge in and of itself. This is particularly apparent with all the dependency issues between the various R Libraries.
- Unless the code is extremely well written, R programs tend to run slower than Python codes.
The Success of Python in the Domain of Data Analysis
While there are plenty of languages that are better catered for data analysis and statistics (R, for instance), Python is the most prevalent language in the domain right now. And it all comes down to a cycle that the field of data analysis seems to be caught in.
Early data analysts adopted Python because it was one of the easier languages to learn. This suited them well as they lacked a strong programming background. Later, universities started offering data analysis courses in Python since that would likely be the language the students would have to use after they graduate.
Companies hiring data analysts, thus, continued working with Python as most of the fresh recruits were well trained in it. And thus, the cycle has continued.
Today, Python has some incredibly popular and successful data analysis packages (such as SciKit, Keras, or Pandas) that help it rival any other programming language. And it does not look as though this trend is going to change anytime soon.
Furthermore, being a regular programming language as well, Python offers a lot more flexibility compared to languages purely focused on data analysis. This means you get to be a lot more creative with your solutions.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
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- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
Python is a popular choice among data analysts. First and foremost, it is incredibly easy to dive into, and beginners can expect to start using its simple syntaxes to implement complex solutions in no time. It is also considered to be an incredibly fast language, which makes it perfect for the computational tasks that are an integral part of data analysis.
Furthermore, it supports a range of libraries that are perfect for data analysis. These libraries include those of data mining such as Scrapy & Beautiful Soup, Data Processing/Modeling libraries such as NumPy, SciPy, Pandas, Keras, PyTorch, TensorFlow & SciKit-Learn, and those of data visualization such as Mathplotlib, Bokeh, Plotly, Pydot & Seaborn.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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