The Python Data Analysis Library (pandas) is a data structures and analysis library.
Intro to pandas data structures, working with pandas data frames and Using pandas on the MovieLens dataset is a well-written three-part introduction to pandas blog series that builds on itself as the reader works from the first through the third post.
pandas exercises is a GitHub repository with Jupyter Notebooks that let you practice sorting, filtering, visualizing, grouping, merging and more with pandas.
Modern pandas is the first part in a well-written seven-part introductory series.
A simple way to anonymize data with Python and Pandas is a good tutorial on removing sensitive data from your unfiltered data sets.
Analyzing a photographer's flickr stream using pandas explains how the author grabbed a bunch of Flickr data using the flickr-api library then analyzed the EXIF data in the photos using pandas.
Pandas Crosstab Explained
shows how to use the
crosstab function in pandas so you can summarize
and group data.
This two-part series on loading data into a pandas DataFrame presents what to do when CSV files do not match your expectations and how to handle missing values so you can start performing your analysis rather than getting frustrated with common issues at the beginning of your workflow.
Building a financial model with pandas explains how to create an amortization schedule with corresponding table and charts that show the pay off period broken down by interest and principal.
tabula-py: Extract table from PDF into Python DataFrame presents how to use the Python wrapper for the Tabula library that makes it easier to extract table data from PDF files.
Time Series Forecast Case Study with Python: Monthly Armed Robberies in Boston walks through the data wrangling, analysis and visualization steps with a public data set of murders in Boston from 1966 to 1975. This particular data problem may not be your thing but by going through the process you can learn a lot that can be applied to any data set.
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