For data analysis to be useful and accurate, the analyzed data needs to be cleaned and curated. There are copious methods to achieve this in pandas. In this course, you'll learn how to identify and eliminate duplicates in pandas.
You'll start by using the pandas cut method to discretize data into bins, using bins to plot histograms and identify outliers using box-and-whisker plots. You'll parse and work with datetime objects read in from strings and convert string columns to datetime using the dateutils python library.
Moving on, you'll master different pandas methods for aggregating data - including the groupby, pivot, and pivot_table methods. Lastly, you'll perform various joins - inner, left outer, right outer, and full outer - using both the merge and join methods.
Analyzing Data Using Python: Cleaning & Analyzing Data in Pandas