Why Should I Learn Python for Data Analysis?

Do you actually need Python to become a data analyst? Discover why you should learn Python and how you can benefit from it as a data analyst.

Data is ubiquitous. From retail stores to digital marketing agencies, from sports teams to production sites, numerous industries use data in their operations to improve productivity, efficiency, productivity, or any other metric that’s important for them. They unlock the power of data, which has the potential to offer valuable insights, which are difficult for the human eye to catch.

How Data Powers Business

Let’s take a moment to think about electric cars. They are elegant and stylish. They work so quietly that it’s hard to notice they are actually running – especially if you’re used to a fuel-based car. However, the most important parameter in the perceived quality of an electric car is its power. People mostly care about how long it lasts.

What power means for electric cars is similar to what data means for businesses. It doesn’t matter how well they design things and structure their processes; if they don’t use data properly, they are likely to fall behind.

The first step to leverage data for a business is collecting (and often storing) it. But, raw data does not tell us much. We need to analyze it to extract insights. There are many tools and programming languages for this, but Python stands out.

In this article, we’ll go over the reasons why you should choose Python for data analysis and how Python expedites and simplifies the analysis process. If you are planning to start a career with Python, make sure to take a look at our free Python Basics: Part 1 course. It contains 95 interactive exercises that will help you start your Python programming journey.  Once you understand these fundamentals, we recommend taking our Python for Data Science track as the next step.

Why Learn Python?

Python has become the go-to language for data analysts and data scientists. There are many reasons for this.

First of all, Python is easy to learn, even for complete beginners. Its structure is intuitive and understandable, almost like plain English. You don’t need to be a software developer with years of experience to use Python for data analysis.

People who switch careers to become data analysts or data scientists come from various backgrounds. Some have a technical background, with some experience in programming. But many who start a career in data science don’t have any prior programming experience. Python makes the transition as simple as possible for both groups – which further motivates them to work with data.

Being a beginner-friendly programming language is not Python’s only advantage in data analysis. Python is a highly mature language; here’s a brief history of Python if you’d like to learn more about it. As you might guess, the more people use a tool, the better it gets. Python has a very active community and a rich ecosystem of libraries and frameworks. This combination provides two main advantages.

The first one is the ease of finding information. Whatever issue you face when using Python, someone else has almost certainly already faced it. And since potential problems are usually well known, other developers may have created a solution you can apply.

The second benefit is the vast selection of third-party libraries, especially for data analysis. Python is a general-purpose language that’s used for many things. Libraries are designed to handle specific tasks, such as cleaning or visualizing data. For instance, pandas is a data analysis and manipulation library for tabular data. It’s one of the libraries data analysts most frequently use. The great thing is that you can import these libraries into your code and use their functions without having to re-write all that code yourself – a huge time-saver!

Why Python Stands Out for Data Analysis

If processed and analyzed properly, data gives us extremely valuable insights. These are what the human eye can catch and what we can learn from experience. In general, the more data we have, the better insights we get. On the other hand, as the data size gets bigger, we need more capable tools.

That’s where Python stands out from other programming languages. Python libraries simplify and expedite the typical tasks of a data analyst. Whether it’s cleaning raw data, processing or analyzing it, or visualizing it for demonstration and reporting, there is a Python library for the task. Moreover, these libraries have the advantage of Python’s easy-to-understand syntax.

I’m currently working as a data scientist focusing on retail analytics. What I do at work is not very different from what a data analyst does. In fact, data scientists and data analysts usually do similar things and their responsibilities are intertwined in many organizations.

I use Python and its libraries for almost anything I do at work. It starts with cleaning raw data. Real world data is usually messy and requires cleaning and processing before it can be analyzed. Let’s say we have an address column that contains the following values:

>>> austin, TX
>>>  Houston, tx
>>> Hostoun, Tx
>>> Atlanta, Ga
>>> atlanta, GA

These values are obviously not in a standard format, so we can’t use them for analysis. We need to clean and process them first. The dataset has millions of rows with these values, so it’s not an option to manually correct them. In addition to standardizing these values, we also want to separate city and state names into two different columns. What we need to get is:

# city column
>>> austin
>>> houston
>>> houston
>>> atlanta
>>> atlanta

# state column
>>> tx
>>> tx
>>> tx
>>> ga
>>> ga

This operation requires the following steps:

• Convert all the letters to lowercase. Python counts “GA”, “Ga”, and “ga” as three different values.
• Trim any whitespace at the beginning. Otherwise, “ houston” and “houston” are regarded as different values.
• Split the values at the “,” to separate city and state names
• Replace “hostoun” with “houston”.

Thanks to pandas, all these operations can be done in two lines of code:

>>> df[["city", "state"]] = df["address"].str.lower().str.lstrip().str.split(",", expand=True)
>>> df.loc[:, "city"] = df.loc[:, "city"].replace("hostoun", "houston")

You don’t need to understand the syntax now; as you start learning Python, you’ll understand how simple it is. Moreover, pandas performs the task as a vectorized operation that takes a few seconds to process millions of rows.

After we clean the data, we need to analyze it. In a typical retail dataset, we look for answers to the following questions:

• What are the sales trends?
• How do products respond to discount or price increases?
• Which products are frequently purchased together?
• What features are important to predict the demand?

There are, of course, a lot more questions we need to search for answers depending on the task. For all of them, we can use a Python library such as Pandas, NumPy, and Scikit-learn.

Once we are done analyzing data or creating models, we should report the results. The other stakeholders may not be as involved with the data as a data analyst, so we need to present data in a way that’s easy to understand. The best option is data visualization. Not surprisingly, Python has many different options for data visualization libraries, such as Matplotlib and Seaborn.

As a data analyst, you often need to do statistical analysis to investigate patterns, relationships and correlations between variables, and more. NumPy, SciPy, and Statsmodels are three common statistical analysis libraries for Python.

Long story short, Python covers the entire process from cleaning the raw data to report your findings to other stakeholders.

How to Learn Python for Data Analysts

Python is a general purpose programming language, so it’s not only used for data science. We can’t immediately start learning its data analysis libraries without first understanding Python’s core principles. If you’re learning Python from scratch, you should start with learning the basics. Our Learn Programming with Python track is a great first step.

After you learn the basics – including variables, functions, loops, and built-in data structures – you can narrow your focus down to Python’s data analysis libraries. The best way to learn them is by practicing. You take a dataset (preferably one that requires cleaning) and start working on it. Try to clean, analyze, and visualize it.

When you get stuck, the library’s documentation should be the first place you look. Most Python data analysis libraries have clean and detailed documentation. If you can’t find the answer in the documentation, search online. But make sure you try and verify any solution you find before using it on your dataset!

When you feel comfortable getting your hands dirty with data, it’s time to do a project. Solving simple problems gets you to a certain point. However, you need to do more to showcase your skills and knowledge. If you want to land a job as a data analyst, I’d recommend completing a project. It doesn’t have to be super complex, but make sure it demonstrates your data analysis skills. A typical project involves collecting raw data, cleaning and processing it, drawing insights by analyzing it, and creating visualizations or a dashboard for reporting.

Python for Data Analysis in the Future

Python is not the only tool available to data analysts. There are many alternatives, and new tools are continually introduced. However, none of them have been able to surpass the popularity of Python in the data science ecosystem. When a new technology is introduced, it almost always comes with Python support first.

For instance, when it comes to dealing with very large datasets (i.e. billions of rows), standard Python libraries like pandas do not perform well. In such cases, a better option is parallel computing. The most commonly used tool in that area is Spark, which is an analytics engine that spreads both data and computations over clusters to achieve a substantial performance increase.

We can use Spark with Python. PySpark is a Python API for Spark that combines the simplicity of Python with the efficiency of Spark. This is just an example to demonstrate that Python will remain dominant in the data science ecosystem.

Considering the ever-increasing use of data across a wide range of industries, learning Python for data analysis is a great way to invest in yourself!