NumPy Library stands for Numerical Python Library. In this library a NumPy Array object is homogeneous multidimensional array object. It provides many routines / functions for processing these arrays. It is very useful for fast processing on large scale data. It is up to 50x faster than traditional Python lists. Therefore it is one of the most used library in Python by Data Scientists.
We have created a comprehensive list of tutorials of NumPy Library here,
Chapter 1: Creating Numpy Arrays
- Create NumPy Arrays from list, tuple or list of lists
- Create NumPy Arrays from a range of evenly spaced numbers using np.arrange().
- Create NumPy Array of zeros (0's) using np.zeros()
- Create 1D / 2D NumPy Array filled with ones (1's) using np.ones()
- Create NumPy Array of different shapes & initialize with identical values using numpy.full()
- Create NumPy Array with same sized samples over an interval in Python using numpy.linspace()
- Create a NumPy Array of bool value.
Chapter 2: Adding Elements in Numpy Array
Chapter 3: Searching in Numpy Arrays
Chapter 4: Get Metadata of Numpy Array
Chapter 5: Selecting elements from Numpy Array
Chapter 6: Modifying a Numpy Array
- How to append elements to a Numpy Array
- Delete elements, rows or columns from a Numpy Array by index positions using numpy.delete() in Python
- How to sort a Numpy Array in Python ?
- Sorting 2D Numpy Array by column or row in Python
- How to Reverse a 1D & 2D numpy array using np.flip() and  operator in Python
- Append rows or columns to a 2D Numpy Array
- numpy.reshape() Tutorial with examples
- numpy.flatten() – Tutorial with examples
- Numpy: flatten() vs ravel()