It’s likely that sometime in your work, you have used Excel. Excel is a versatile and simple to use spreadsheet tool. It is relatively easy to learn, can help you produce fantastic analysis, and makes small edits relatively easy. However for larger datasets, Excel begins to slow down and freeze preventing a smooth work flow. This is where a little Python background and Pandas sweep in to save the day. Pandas is a data analysis library of the Python programming language. It is high functioning, fast, and provides all your favorite Excel functions for larger datasets. 

Excel limits you to around 1 million rows of data but after 10,000 rows you’ll notice a significant slowdown. This can cause simple functions such as adding a column or using an equation to turn into a task that can take minutes. Pandas allows you to perform functions on data that you can store within Python. Pandas has no data limit and its speed depends entirely on the power of your computer. Manipulations and data cleaning is much more efficient within Pandas and a much smaller margin of error because of the lack of accidental changes made in a single cell.  

The types of data that can be read into Python are numerous and it is easy to switch between any. This becomes an issue in Excel as each file needs to be converted into the right format not to mention the data that can be lost during the conversion process. Meanwhile automating data analysis or manipulation is much more simplified in Python and can provide for faster clean up, leaving much more time for analysis. Pandas allows for stellar data visuals with much more customizations than Excel. 

Of course, learning a programming language from scratch takes time and thus presents another hurdle. If working with large sets of data, it is worth the trouble to learn the basics of Python. There will be many functions that will be much too difficult to perform in Excel on large datasets, if you’re able to open them at all. 

It is necessary to mention that while Pandas is a great tool for larger data sets, Excel is much more user friendly. If you would like to “browse” data, Excel is the way to go. If you have to edit specific cells with various changes and your dataset is small enough Excel will be the best tool for this. The combination of Python and Excel is a powerful force that will allow Pandas to do the heavy lifting, while you can browse and work on decision making with Excel.

If you’re interested in exploring the world of data analysis in Pandas, start off by downloading Anaconda full installer, which will give you many of the libraries you’ll need. 

Some common libraries, you’ll work with: 

·         Matplotlib – for data visualization 

·         NumPy – for numerical data functionality 

·         Pandas – for data analysis

From there I recommend using Jupyter notebook. Jupyter notebook creates a notebook to store and run your code however you choose to split it. It’s great for beginners and users looking for a way to run their code section by section. To open up jupyter notebook to begin, open up Terminal or Command Prompt and type “jupyter notebook”. This will open up the notebook and allow you to begin writing your first line of code! 

The resources are endless on this topic and you’re guaranteed to get the information you need to successfully analyze a set of data using Pandas. Be patient! Learning a programming language is tough and debugging can be stressful but rewarding!  

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