Should I learn Excel or Python

You take some time to get familiar with data wrangling and manipulation using code. Its complexity makes it less used by casual users. However, we still need to appreciate that more companies are choosing cloud-based infrastructures like google cloud for data storage and Amazon web services. Cloud storage and big data are trends making more people up skill knowledge in querying and programming skills to work in companies driven by data. 

The good thing about Python is that there are cheap training programs that you can take advantage of and many free resources. So anyone passionate about learning can. 

Excel’s GUI is friendly compared to Python, which is more daunting. However, online courses and tools slowly break down the boundaries between the two. Therefore, the choice between these two at this level depends on what you intend to use them for. 

Big data and scalability

If you need to handle multiple and larger data sets, Python should be your pick as it has some significant advantages you cannot overlook. Excel, on the other hand, can handle little data at a time. Having more open tabs on the workbook means more challenging time management. More open tabs mean slower operations. This causes the program to crash, and you could lose unsaved work. You cannot handle too many entries and tables with Excel. 

With Python, files can be saved separately. You can also write code while another file interacts with data. The advantages here include stability even as you simultaneously handle a lot of data and computational speed. 

It can be frustrating when working with rows of data and tables in Excel. This would mean navigating up and down rows or creating formulas on different sheets. Even with too much work, you cannot see the work done already in the analyses. Furthermore, if a mistake is made, returning to fix it is not possible. 

In Python, columns and row numbers only affect a little. This is because data in the interface is not being interacted with. It is possible to visually check and access sections of the data frame if there are areas that interest you that need to be checked without loading the whole data frame. Dealing with many tables and sheets in Python is easier because you can join or merge data easily. 

Excel is a powerful tool. However, you need Python to upgrade your data analytics workflow because analytics, wrangling, and extracting data can be integrated into a single environment. Your work can be shown in containers, making fixing issues and mistakes much easier. You need Python when handling many data sets and big data. In this case, Python wins. 

Automation capacities

In the present world, automation is essential. Automation can be beneficial in clearing up all those repetitive tasks that require so much work. Many businesses are data-driven today, meaning analysis and reporting are helpful in different roles. Analyses and reports may cover a lot of data and tables, which can take time. 

When you pick Python, you can easily automate work for easy replication. Consider making monthly reports with different sales performance breakdowns. This could mean 10 charts. If we want to remake the performance breakdown using Excel every month, we would need to pull the data and create 10 charts or tables, then copy them to make a presentation on PowerPoint. The main thing here is that this process would have to be repeated every month. Doing this creates room for human errors. 

Python makes it easier since you can connect to a database directly. You can then write your 10 queries or code that would manipulate and aggregate the data to help create all sorts of views needed. Different libraries can help with data visualization. Programs can be coded to get data from the database at a specified time to update the analysis. Once a notebook is created, you can run it, and all the charts and data will be updated and created automatically. Python saves what would have been hours of hard work and improves workers productivity. 

Instead of dealing with Excel reports every month, the job can be automated using Python so that everything gets done and updated with a simple push of a button. You can also share the Python code to make tracking and modifying changes easier for teammates. In this case, Python wins.