Python NumPy Module
NumPy (Numerical Python) is the fundamental package for scientific computing in Python. It provides a high-performance multidimensional array object and tools for working with these arrays.
Installation
pip install numpyImporting the Module
Standard convention is to import it as np.
import numpy as npCreating Arrays
NumPy arrays are faster and more compact than Python lists.
# From a list
arr = np.array([1, 2, 3])
# 2D Array (Matrix)
matrix = np.array(<a href='/1%2C%202%2C%203%5D%2C%20%5B4%2C%205%2C%206'>1, 2, 3], [4, 5, 6</a>)
# Range of values (start, stop, step)
r = np.arange(0, 10, 2) # [0, 2, 4, 6, 8]
# Linear space (start, stop, number of items)
l = np.linspace(0, 1, 5) # [0., 0.25, 0.5, 0.75, 1.]
# Zeros and Ones
z = np.zeros((2, 3)) # 2x3 matrix of zeros
o = np.ones((2, 3)) # 2x3 matrix of onesArray Inspection
a = np.array(<a href='/1%2C%202%2C%203%5D%2C%20%5B4%2C%205%2C%206'>1, 2, 3], [4, 5, 6</a>)
print(a.ndim) # 2 (Dimensions)
print(a.shape) # (2, 3) (Rows, Columns)
print(a.size) # 6 (Total elements)
print(a.dtype) # int64 (Data type)Operations
Operations are element-wise by default.
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b) # [5, 7, 9]
print(a * b) # [4, 10, 18]
print(a * 2) # [2, 4, 6] (Broadcasting)Indexing and Slicing
a = np.array(<a href='/1%2C%202%2C%203%5D%2C%20%5B4%2C%205%2C%206'>1, 2, 3], [4, 5, 6</a>)
# Element at row 0, column 1
print(a[0, 1]) # 2
# Slice: All rows, column 1
print(a[:, 1]) # [2, 5]Basic Statistics
a = np.array(<a href='/1%2C%202%2C%203%5D%2C%20%5B4%2C%205%2C%206'>1, 2, 3], [4, 5, 6</a>)
print(np.mean(a)) # 3.5
print(np.max(a)) # 6
print(np.sum(a, axis=0)) # [5, 7, 9] (Sum columns)