3 Aspects to Consider When Using Python for Data Analysis

There are many interesting professions out there, and if you like numbers, data analysis is probably going to be your favorite. According to Investopedia, this is a career that also pays relatively well, with average total pay being around $90,000. (Inferred data from the U.S. Bureau of Labor Statistics.)

These days, getting into data analysis tends to require knowledge of certain programming languages, with Python being a common requirement. It is one of the most popular languages and is heavily used by data scientists and researchers in almost every field under the sun.

So, it doesn’t really matter if your use case lies in business or even something like law, Python can help. Today, let’s find out what you need to know if you’re going to be using the language for professional data analysis.

1. Understand the Importance of Clean Data

Good data analysis can lead to big wins for any firm and in almost every niche. For instance, MIT researchers found that basketball teams that hired more analysts saw more wins. To be specific, with each extra analyst hired, the teams had one extra win per season. Meanwhile, if these teams simply focused on athletes, they would need to spend $9.6 million more on salaries to gain that same single win.

That said, if you’re new to using Python, you may be forgiven for thinking that it’s easy and you can get started right away. If only it were that simple. Data analysis is messy work, and it is highly likely that the data you will be given needs to be cleaned up.

Raw data will often come with a bunch of elements that will prevent you from working with it. These include typos and values that are missing. Sometimes, you’ll have to watch out for duplicate entries, which can be a little frustrating. This process has a name and is called ‘data preprocessing.’

Thankfully, it doesn’t have to be done completely manually. You can use Python’s very own pandas library to remove rows with missing values, fill in blank areas with defaults, and standardize other formats. However, it doesn’t end there, which brings us to our next point.

2. Verify Data Integrity as Thoroughly as Possible

So, we’ve established that data analysis is only as good as your data; however, even with clean data, mistakes can happen. This is simply one of the quirks of data analysis. All it takes is a single error, and your outcome can be completely thrown off. What makes this dangerous are two factors. One, you may not even notice the error once you finish the analysis, and two, the consequences can be severe.

Let’s say that you’re an analyst hired by a law firm that’s working on the firefighter foam lawsuit situation. Large companies like 3M, which sold fire extinguishers with toxic chemicals in them, are involved in these lawsuits. The implications are serious here, as reports of cancer have followed from exposure to them.

There are a total of 10,520 active cases pending in this legal drama. Each case will undoubtedly have several specific data points that need careful attention and handling. Make a mistake, and who knows? Maybe it’ll throw off the expected settlement amounts that the firm advertises. Or maybe, something about the timeline might be misrepresented. You may never know until it’s too late.

Last year, Walmart-backed Symbotic, a warehouse automation firm, saw a 35% slump from data-related errors. The company postponed its annual report after discovering “material weaknesses” that stemmed from errors in key metrics of gross profit and net income.

As you can see, this was a revenue recognition error. It wasn’t gross fraud or anything highly illegal. Yet, it was enough to wipe out $7.6 billion from their market value. Thus, never underestimate the importance of data integrity and verification.

3. Remember to Set Realistic Limits or Use Alternatives for Large Data Sets

While you can definitely use tools like pandas for data analysis, they are not exactly ideal for huge datasets (think millions of rows). That sounds rare, but you would be surprised at how much data even a medium-sized company can accumulate.

If the company computer isn’t up to spec, things can slow down to a crawl since these tools tend to load everything into memory. If you believe this is likely, it’s important to set limits and try your best to categorize and conduct analysis in chunks.

That said, sometimes, there’s no alternative but to deal with large data. Thankfully, there are solutions in the form of Dask and PySpark that you can use to analyze large data sets.

Of course, some analysts also try to optimize code by using vectorized operations instead of loops. You could even drop unused columns early and really focus on filtering data before loading. Most of the time, you will end up needing to keep your head on a swivel to know which approach the data you are dealing with requires.

At the end of the day, if you’re analyzing data, you just have to remember that most of the problems stem from minor errors. If something big goes wrong, it’s pretty obvious and instantly recognizable. However, it’s the small things like wrongly imputed data that don’t immediately break things that are scary.

Your results might go out to the rest of the firm and get summarized and used to make big decisions. That’s what you want to be afraid of and prepare against.