Is pandas more powerful than excel?

Using pandas with Microsoft Excel can give you the best of both worlds and optimize your workflow.

What is pandas?

Released in 2008, pandas is a software library extension of Python. It works with data stored in Python to manipulate and analyze data. As opposed to Excel, Python is completely free to download and use.

The pandas library is used by data scientists and analysts for tasks ranging from the very big to very small. Pandas can:

• Combine with BeautifulSoup to dump text from a scraper into a database

• Quickly clean data and convert file formats

• Handle large datasets

• Visualize data with Matplotlib

It's a powerful library for anyone who needs to get results quickly. There is a steeper learning curve to the program than Excel and it does require basic knowledge about Python and coding.

Analyze Large Data Sets Easily

Pandas operates right on the back of Python. As a result, is extremely fast and efficient. In Excel, once you exceed 10,000 rows, it starts to slow down — considerably. Pandas, on the other hand, has no real limit and handles millions of data points seamlessly. In terms of pure space, Excel caps a single spreadsheet at 1,048,576 rows exactly. At that point, your calculations would take forever to compute. More likely, Excel would just crash. A million rows may seem like a lot of data, but for data scientists, this is but a drop in a bucket.

Pandas, however, has no limitation to the number of data points you can have in a DataFrame (their version of a data set). It's limited only by the computing power and memory of the computer it is running on.

It is also easier to create and use complex equations and calculations on your data. You can apply hundreds of computations to millions of data points instantly with pandas. Since Python is open source, there are already hundreds of libraries created that could streamline the length of time it takes to calculate.

Import Datasets in Either HTML, CSV, and SQL Formats

In this day and age, there are many data formats and it is important, especially for data analysts, to be able to switch between them easily. Clients or projects could provide data in a SQL format, and expect an HTML format back. For Excel, you would have to spend time converting file formats before importing them, whereas pandas can handle over 15 different formats and switch between them with ease.

In addition, when using format converters to import data into Excel, the formatting often gets ruined and may result in Kills in the data.

Clean Up and Organize Data Sets

In addition to pandas being much faster than Excel, it contains a much smarter machine learning backbone. With this ML software in place, pandas is better at automatically reading and categorizing data. It can clean up data much easier than Excel and is capable of automating a lot of the process including repairing data holes and eliminating duplicates. When dealing with millions of data points, it would be extremely difficult to comb through data looking for missing information. pandas can help with that and do it all in seconds.

Pandas is also very effective for visualizing data to see trends and patterns. Although Excel's interface for making graphs and charts is easy to use, pandas is much more malleable and can do much more. Graphs are much more customizable and you can create pretty much any concept you want with pandas.  

Why Should you use pandas and excel together?

The best course of action is actually to use Microsoft Excel and Python pandas together. When working with smaller data sets it is best to stick with Excel's easy-to-use interface.

Since pandas is so versatile — even if you start your analysis in Excel — you can easily import it over to Python and continue. You can also always start in pandas and use the software to clean up and organize your data, and then move over to Excel to visualize it easier. Pandas' adaptability makes a switch between the two a breeze. You can perform the easy calculations in Excel, then use pandas' more complex programs to dive in deep.