Numpy
By default Python does not have a concept of Arrays. And there is no inbuilt support for multidimensional arrays.
Python Numpy is a library that handles multidimensional arrays with ease. It has a great collection of functions that makes it easy while working with arrays. Especially with the increase in the usage of Python for data analytic and scientific projects, numpy has become an integral part of Python while working with arrays.
Numpy Array Basics
The following are list of numpy tutorials that cover basics of a numpy array.
- NumPy – Create 1D array
- NumPy – Create 2D array
- NumPy – Create 3D array
- NumPy – Create array with random values
- NumPy – Print array
- NumPy – Save array to file and load array from file
- NumPy – Reshape array
- NumPy – Array with zeros
- NumPy – Array with ones
- NumPy – Initialize array with a range of numbers
- NumPy – Access array elements using index
- NumPy – Get specific row
- NumPy – Get array shape
- NumPy – Get array size
- NumPy – Iterate over array
- NumPy – Duplicate or copy array to another array
- NumPy – Concatenate arrays
- NumPy – Reverse array
- NumPy – Stack arrays vertically
- NumPy – Stack arrays horizontally
- NumPy – Split array into smaller arrays
- NumPy – Array Slicing
- NumPy – Array dot product
- NumPy – Array cross product
Mathematical Functions
- Numpy exp() – Exponentiation
- Numpy sqrt() – Square root of each element in array
- Numpy max() – Get maximum value of array
- Numpy amax() – Get maximum value of array along specified axis
- Numpy maximum()
Conversions
- Convert Numpy Array to List
- Convert List into Numpy Array
- Numpy Vectorize
Logic Functions
Summary
In this tutorial of Python Examples, we learned about Python Numpy library and different concepts of Numpy.