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What is NumPy?
Numpy is an open-source library for working efficiently with arrays. Developed in 2005 by Travis Oliphant, the name stands for Numerical Python. As a critical data science library in Python, many other libraries depend on it.
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- What is NumPy?
- Why is NumPy so popular?
- Best place to get more info
- When should you start using NumPy?
- What's the relationship between NumPy, SciPy, Scikit-learn, and Pandas?
- An Alternative to MATLAB?
- Installation
- List of useful NumPy functions
- Section 1: The basics
- NumPy arrays
- Array data types
- Defining arrays
- Using np.array()
- Defining arrays: np.arange()
- Defining arrays: np.zeros, np.ones, np.full
- Array shape
- Reshaping arrays
- Reading data from a file into an array
- Saving
- Indexing
- Basics of indexing notation
- Examples
- Indexing example 1: Colons and commas
- Indexing example 1: Colons as *all* rows or columns
- Indexing example 3: Subset of columns
- Indexing example 4: Explicitly specifying column numbers
- Indexing example 5: Mask arrays
- Concatenating
- Splitting
- Adding/Removing Elements
- Sorting
- No Copy vs. Shallow Copy vs. Deep Copy
- Section 2: Must-know tools
- Broadcasting
- Broadcasting example 1: Adding a scalar to a matrix
- Broadcasting example 2: Multiplying a matrix by a scalar
- Broadcasting example 3:
- Vectorization
- Vectorization Example 1
- Calculating the speed up
- Vectorization example 2
- Machine Learning context
- Pseudo-random number generation
- Section 3: Putting it all together
- Viewing the data
- Extracting wind energy data
- Mathematical functions
- Fitting
- One last plot...
- Summary
- Course Recommendations
Contents
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