Pandas and Microsoft Excel can be combined to give you the best of both worlds, optimize your workflow, and allow you to combine the two.
What is pandas?
Pandas, a Python software extension, was released in 2008. It works with Python data to manipulate and analyze it. Python is completely free to download, unlike Excel.
Data scientists and analysts can use the pandas library for tasks that range from very large to very small. Pandas can:
Use BeautifulSoup with BeautifulSup to dump text from a scraper in a database
Convert file formats and clean up data quickly
Handle large datasets
Matplotlib allows you to visualize data

It is a powerful library that anyone can use to quickly get results. The program is more difficult to learn than Excel, and requires basic knowledge of Python and coding.
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Start trainingAnalyze large data sets easily
Pandas is a Python-based program. Pandas is very fast and efficient. Excel slows down significantly once you have more than 10,000 rows. Pandas, however, has no limit and can handle millions of data points seamlessly. Excel limits a single spreadsheet to 1,048,576 rows. Your calculations would take forever to calculate at that point. Excel could crash. Excel could crash if there are more rows than you think. However, data scientists will find that this is just a drop in the bucket.
Pandas does not limit the number of data points that you can have in a DataFrame, which is their version of a dataset. It is limited only by the computing power of the computer it runs on and the memory available.
It is also much easier to create complex equations and calculations using your data. Pandas allows you to instantly apply hundreds of calculations to millions of data points. There are hundreds of libraries that can be used to speed up the time it takes for Python to calculate.
Import datasets in HTML, CSV, or SQL Formats
There are many data formats available in today’s world. It is important for data analysts to be able easily to switch between them. Clients and projects might send data in SQL format to expect an HTML format back. Pandas can convert files from Excel to other formats, and then import them.
Format converters can cause formatting problems when importing data into Excel. This can lead to data corruption and even death.
Clean up and organize data sets
Pandas is faster than Excel and has a smarter machine-learning backbone. Pandas is much more adept at automatically reading and categorizing data with this ML software. It can clean up data faster than Excel and can automate a lot of the process, including repairing duplicates and eliminating data gaps. It would be difficult to search through millions of data points and find missing information when dealing with so many. Pandas can do that in seconds.
Pandas is also a great tool for visualizing data and identifying patterns. While Excel’s interface is simple for creating charts and graphs, pandas is more flexible and can do more. Pandas is much more flexible than Excel’s interface for creating graphs and charts. You can create almost any type of concept with pandas.
Why should you use pandas and excel together
It is best to use both Microsoft Excel and Python pandas together. It is best to use Excel’s simple-to-use interface when working with smaller data sets.
Because pandas is so versatile, even if you start an analysis in Excel, you can import it to Python and carry on. You can also start in pandas, clean up your data and then move to Excel to visualize it.