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Pandas has so many uses that it might make sense to list the things it can't do instead of what it can do.

This tool is essentially your data’s home. Through pandas, you get acquainted with your data by cleaning, transforming, and analyzing it.

For example, say you want to explore a dataset stored in a CSV on your computer. Pandas will extract the data from that CSV into a DataFrame — a table, basically — then let you do things like:

• Calculate statistics and answer questions about the data, like
  • What's the average, median, max, or min of each column?
  • Does column A correlate with column B?
  • What does the distribution of data in column C look like?
• Clean the data by doing things like removing missing values and filtering rows or columns by some criteria
• Visualize the data with help from Matplotlib. Plot bars, lines, histograms, bubbles, and more.
• Store the cleaned, transformed data back into a CSV, other file or database

Before you jump into the modeling or the complex visualizations you need to have a good understanding of the nature of your dataset and pandas is the best avenue through which to do that.

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