In this article, we'll discuss two of the most popular tools for data analytics, Excel and Python. We'll walk through how each of these tools fares across a variety of dimensions to help you with your next steps in professional development and learning.
Python vs Excel for Data Analytics
Excel spreadsheets are the standard in the business world for all kinds of data analysis tasks. While Excel’s simplicity makes it so commonplace, it also brings about some limitations. Python, on the other hand, is a programming language that is commonly used for data analysis and data science. We’ll go head-to-head on Python vs. Excel across a couple of important dimensions.
1. Ease of Use
Excel is the most widely used data management and analysis tool and its ease of use is likely a large contributor to that. Most people in the business world have some baseline level of comfort using Excel as they are often using Excel files for various tasks. Excel can be used for simple things like managing contact lists, creating schedules or timelines, and basic calculations, all of which require minimal knowledge of Excel and can be picked up fairly quickly. It is not uncommon for users to go from zero Excel knowledge to intermediate or advanced level knowledge within just days.
You can download Excel in minutes and start writing your first formulas with a quick tutorial. With just a couple minutes of an intro, you can start doing a handful of things like adding numbers, finding averages, and even making charts. Excel is the perfect tool for someone who dabbles with data but doesn't necessarily want to go deep into data analytics.
Python, on the other hand, has a fairly steep learning curve if you have little or no programming experience. Getting set up in the right environment for a programming language and starting to get into the data can take a bit of time to become comfortable. Python’s steeper learning curve makes it a little bit less mainstream as a data analysis tool. That being said, more and more companies are moving to cloud-based data infrastructure and demanding employees have some programming/querying skills to work with cloud-based data. With the handful of free resources and relatively cheap tools out there, Python is becoming even easier to learn.
Since Excel is a GUI (graphical user interface) you can pretty much just open a sheet and play around with your mouse. Python doesn't really have that friendly interface that makes it less daunting, but more and more tools are breaking down those boundaries. For example, learnpython.org takes out all the setup challenges with Python and allows you to start writing simple queries early on and see the outputs right there.
All in all, Excel is much easier to get started in and is much more user friendly, despite the handful of free tools out there meant to make Python easy to learn.
2. Scalability & Big Data
When it comes to scaling up your work to work with larger and multiple datasets, Python has some big advantages. Excel can only handle so much data and the more data and tabs you have in your workbook, the more difficult it becomes to manage and the slower the file will be. Excel is not really meant to act as a full data warehouse with many tables and millions of entries.
As you scale up in the data you are using, using programming or querying languages becomes more and more beneficial. Python is equipped to handle lots of data really quickly and is the preferred solution when working with big data.
Let's say you are working with millions of rows of data or a variety of tables. In Excel, it would be a nightmare to go up and down all those rows or create formulas across multiple sheets.
In Python, the number of rows or columns of data doesn't really change anything because you are not interacting with the data in an interface by using a coding language (Python) to interact with the data. In the case of working across multiple sheets or tables, Python also makes this a lot easier because you can easily merge/join data from different tables on specific fields.
Excel is powerful, but Python is a whole new level, and working in a programming language basically becomes a necessity when working with big data or lots of datasets.
3. Automation Capabilities
One of the big buzzwords these days is “automation,” and rightfully so. Automation can be extremely helpful to clear up repetitive tasks that take up lots of valuable time. More and more businesses are largely data-driven which means reporting and analysis are the core of lots of roles. These reports and analyses can span lots of tables and data and can require a lot of time.
Thankfully, using Python can help automate a lot of your work so it can easily be replicated or automated. Say we need to make a monthly report with various breakdowns of our sales performance, 10 different charts. If we wanted to remake this in Excel each month, we’d have to pull in our data, make 10 pivot tables or charts, and then copy them over to a PowerPoint presentation. The key point is we’d have to do that process every month.
With Python, connecting directly to our database, we could write 10 or so queries or chunks of code that would aggregate and manipulate our data to create the view we wanted and then we could use a library like matplotlib to create our visualizations. Once we create this notebook of code, we can simply hit “Run” and the entire notebook of data and charts will be created or updated for us automatically.
All of the steps we’d need to go through in Excel each month would be automated with Python.
Instead of making those Excel reports from scratch each month, you can automate your job with Python and just hit run each month.
4. Data Connectivity
Lastly, our analysis tools can only be useful if we can access the data we need to analyze. Depending on the type of work you do, one tool might be better than another. If you work with cloud-based data, then connecting that data with Python is a big advantage. As alluded to in the automation section, you can write blocks of code to connect and analyze data in the cloud and use the same scripts to repeat those processes instantly.
Connecting to cloud-based data is also possible with Excel, but it is a bit more difficult and Excel is not really meant to stream large amounts of data. Excel has made strides in this category which makes it a lot better of a tool, but when compared to Python it doesn't have the same level of connectivity.
On the flip side, if we are working with basic reports, say exports from an email client or Shopify site, then Excel will probably be the easier option since that is usually the default format for these exports. That being said, Excel files can also be imported into a Python notebook from your local machine if you’d prefer to use Python for all your analyses.
While Excel has the large advantage of simplicity and easy ramp-up, Python is a higher-powered solution for data science and analytics.
Excel is best when doing small analyses or creating basic visualizations quickly
Python provides a big leg up when working with big data and creating repeatable, automatable analyses and visualizations
If the power of Python for analytics and data science excites you and you want to learn more, see our Python courses and certificates. If you feel like starting with Excel is the right next step for you then see our Excel classes offered by our affiliate school, NYIM. Also, see more Python free resources and articles.