July 2019
Beginner to intermediate
740 pages
16h 52m
English
Content preview from Hands-On Data Analysis with Pandas
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Why pandas?
When it comes to data science in Python, the pandas library is pretty much ubiquitous. It is built on top of the NumPy library, which allows us to perform mathematical operations on arrays of single-type data efficiently. Pandas expands this to dataframes, which can be thought of as tables of data. We will get a more formal introduction to dataframes in Chapter 2, Working with Pandas DataFrames.
Aside from efficient operations, pandas also provides wrappers around the matplotlib plotting library, making it very easy to create a variety of plots without needing to write many lines of matplotlib code. We can always tweak our plots using matplotlib, but for quickly visualizing our data, we only need one line of code in pandas. We ...
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