Today we will deal with two techniques we need to cover before moving to more advanced Pandas topics: Reindexing and element deletion.
It will be a bit shorter than the first two articles in the series, but that doesn't mean it's not important. Both techniques are very useful, and you
In the last article, we learned about the two basic pandas data structures: Series and DataFrames. We also built a couple of them on our own and learned the basics of indexing and selection.
Today we will learn a bit more about selecting and filtering elements from Pandas data structures.
In a previous series we covered the fundamentals of NumPy, now it's time to deal with another important tool frequently used in data analysis: Pandas.
Pandas is a library for data manipulation and analysis that lets you manipulate heterogeneous data in tabular form (in contrast to NumPy, designed to work
Linear algebra has many useful applications in science and engineering. If you are doing scientific computing, it's very likely that sooner or later you will need to use linear algebra to solve problems.
If your linear algebra is a bit rusty, you can take a look at Khan Academy's linear
Reductions (or aggregations) are a family of NumPy functions that operate over an array returning a result with fewer dimensions.
Many of these functions perform typical statistical operations on arrays, while others perform dimensionality-reductions.
In this article, we will learn about some of the most common aggregations, but before we