Pandas Basics

NumPy is great when every value in the array is the same type and the columns don't need names. Real data isn't usually like that. A list of orders has a string customer name, a float total, an integer quantity, a timestamp, and a category code, all in the same row. Pandas is the library that sits on top of NumPy and adds labeled rows, named columns, mixed types per column, and a pile of methods for loading, cleaning, filtering, grouping, and saving tabular data. Anything that starts life as a CSV, an Excel file, or a SQL query usually fits into a pandas DataFrame.