Python and Excel are among the most popular tools for data manipulation. Still, some companies prefer Excel spreadsheets because they are simple to deal with. Other businesses find it easier in Python because they can deal with both data science and data analysis. In the following article, we will go through several criteria to understand who is better at data analytics: Excel or Python?
Automation is a very useful activity to reduce the number of repetitive tasks and focus on core responsibilities. Python helps to automate a lot of work. For example, if your company has a monthly sales report you can break it down into 10 charts. Whereas in Excel, you will have to pull in the data, create tables and charts, and then copy them on a PowerPoint slide.
With Python, you can connect your database with Jupyter Notebook and manipulate it from there. Once you create it, you can Run the whole notebook with charts. It saves hours and improves productivity. However, with Excel, you will have to make those reports from scratch every month.
In order to understand if the tool is good enough for analytics, we need to ensure that it gives access to the data we need. Python has an advantage if your company works with cloud-based data. You can write blocks of code and analyze them within the cloud at your convenience.
Whereas, in Excel, this option could work but it is a bit more difficult. Excel was created way before the cloud data platforms and it was designed for financial reporting. Therefore, businesses cannot use Excel to handle modern data. If you are working with simple reports such as the ones for Shopify, Excel is better at handling those. But you can always import the files into Python and move from there.
Python has a great advantage when working with large datasets. Excel can handle only the data that can fit into the tabs of your workbook. The more data you insert, the slower the system becomes and can, sometimes, crash. That being said, Excel is not meant for large scale of data.
On the other hand, using Python allows you to save your data and write the code as another file that will interact with the data. It makes a difference when you have thousands of rows of data. Moreover, you can use Jupyter Notebook that has the ability to handle data really quickly.
Excel is one of the easiest data management tools that companies use to create timelines and perform basic calculations. It requires limited Excel knowledge that you can pick up fast. You can download the software quickly and after a few tutorials, dive into the spreadsheets.
If you don’t have any previous programming experience, Python will have a steep learning curve. Starting from the pip installs and Terminal window to juggling with data via coding makes this tool comprehensive. Companies are moving to cloud-based data infrastructures to encourage people to learn more coding and get familiar with the system. These solutions along with other free resources should make the learning process easier. But still, Excel is a more simple tool for data analysis.
To sum up, we love both tools but depending on your company’s needs, you should make the right decision. Python is difficult to learn but with more effort and time, it is better in working with big data, automation, and data visualization. Excel is easy to handle when you need a few simple and quick analyses. Either Excel or Python you choose the right tool for you and your business and start learning through our educational games.