10. Data Analytics with pandas and NumPy
Overview
By the end of this chapter, you will be able, use pandas to view, create, analyze, and modify DataFrames; use NumPy to perform statistics and speed up matrix computations; organize and modify data using read, transpose, loc, iloc, and concatenate; clean data by deleting or manipulating NaN values and coercing column types; visualize data by constructing, modifying, and interpreting histograms and scatter plots; generate and interpret statistical models using pandas and statsmodels and solve real-world problems using data analytics techniques.
Introduction
In Chapter 9, Practical Python – Advanced Topics, you looked at how to use GitHub to collaborate with team members. You also used conda ...
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