Python Tools and Libraries: Why Data Analysts should consider gaining experience in Python

Most of you have probably heard about Python. No, not the snake, the programming language. Python has many uses, and is a perfect language for beginners who have never programmed or coded before. It's easy to understand and interpret the language, and once you familiarize yourself with the language and different functions, you can do almost anything.

But what about data analytics? Can Python be used for data analytics? In what way would it benefit data analysis? How could I use it to help with data? Is there any way to learn how to use it in a way that would benefit me?

The answer is yes. Yes, Python can be used in data analytics. As a data analyst who has experience in using Python, I'll go over a few reasons why you should, and how you can do so.

Data Analysts and Python

Why Data Analysts use Python

Data analysts often work with large sets of data. The issue with working with a ton of data is that it is very difficult to work with. Data is messy, and often needs to be cleaned before it can be analyzed, interpreted, and be used in visuals. With a lot of data, it can be difficult to clean. This is why when working with large amounts of data, analysts will use tools to help make the process easier, as it helps save time.

Another reason is the language's simplicity. As mentioned earlier, Python is a language that most beginners first learn how to code in. If you take a look at the code, you can get a general idea of what the code is trying to do, even if you have no programming experience. Therefore, it's not a difficult language to learn, which is why analysts often use it.

Here's a snippet of Python code: see if you can understand the general idea of this code

How Data Analysts use Python

I've talked a bit about this already, but data analysts who use Python for analyzing data often will import different libraries into their program in order to access different functions.

One of these libraries that analysts might use is called "Pandas". And no, in case you're wondering, it's not the animal. Pandas is a built in library that when imported into your code, focuses on analyzing and comparing two different data sets. It can handle spreadsheets of data, and is often used for data cleaning and analysis.

Here are some of the functions that Pandas can perform on data:

Merge and group data together based on similar qualities
Read and interpret data from different sources including .xlsx and .txt files
Can select certain data points based on a specific criteria
Perform complex operations involving binary and boolean data
Provide descriptive statistics for a dataset

Another library that can be used is "NumPy". This library contains mathematic functions that can be used for data analysis. While Python already has mathematic functions, NumPy is focused more on complicated mathematic operations, such as working with arrays, matrices, and vectors. NumPy focuses on data analysis techniques involving linear algebra, which differ from the standard, regular mathematical operations used in Python. “Data Analyst in Ottawa with Experience in Python”

For data visualization, libraries such as "Matplotlib" allow for data visualization that can be used in data analysis. Instead of writing scripts to clean and manipulate data to analyze data, in some instances, it may be more useful to create visualizations to get a better idea of what is going on. Some of the visualizations that can be created using Matplotlib are mostly plots, such as histograms, bar charts, and scatterplots.

How can I learn Python?

Here are some resources that can help you get started on using Python for data analysis, or whether you want to practice your Python programming skills:

Online courses and certifications are a great way to start! Check out websites like DataCamp, which not only offer courses teaching you how to use Python, but also offer courses on how to implement these Python Libraries, such as Pandas!
Check out Google Colab to get started with coding! It's completely free, all you need is a Google Account. You can import libraries directly into your script, and you can import data files from your desktop into your code as well! That way, you can begin practicing data analysis using Python.
Once you get familiar with programming, try and challenge yourself! There are lots of online coding challenges involving Python and Pandas. If you're feeling confident in your abilities, it's worth giving one a try! Here's an example of where you can look to get started.

Takeaways

Python is a valuable asset for data analytics. It's a versatile language, with many different libraries that cover many different functions, and it's an easy language for beginners to learn.

Whether you're a student interested in data analytics, or are an individual with years of experience in the field, I hope that you consider incorporating Python as one of your into your data analysis. If you are not familiar with how to code, I encourage you to give it a shot! It's not that hard, and if I can do it, I'm sure you can as well.

If you have any questions or want more resources, please reach out to me on my Contact page, or connect with me on LinkedIn!

Good luck!