Numpy - Multi-dimensional arrays and functions to manipulate them

Numpy offers very efficient functions to manipulate data in multi-dimensional arrays. Here are a few common ones. Use them instead of regular python functions to reduce code length as well as produce efficient and fast programs.

import numpy as np

a = np.array([(1,2,3,4),(3,4,5,6),(7,8,9,10)])

c = np.array([(10,9,8,7),(6,5,4,3),(4,3,2,1)])

print(a)

print("find dimension of array")

print(a.ndim)

print("find byte size of array")

print(a.itemsize)

print("size of entire array")

print(a.size)

print("find data type of elements")

print(a.dtype)

print("shape of array")

print(a.shape)

print("reshape")

b = a.reshape(4,3)

print(b)

print("get value from a place")

print(a[1,2])

print("get values from same column of first two rows")

print(a[0:2,2])

print("equal spacing between a range")

b = np.linspace(0,100,11)

print(b)

print("find max in array")

print(a.max())

print("find min in array")

print(a.min())

print("find total of all elements in array")

print(a.sum())

print("find sum or rows and columns | axis 0 = columns, axis 1 = rows")

print(a.sum(axis=0))

print(a.sum(axis=1))

print("find sqr root of each element")

print(np.sqrt(a))

print("find sqr of each element | matrix multiplication")

print(a*a)

print("find standard deviation")

print(np.std(a))

print("matrix addition")

print(a+a)

print("matrix multiplication")

print(a*5)

print("matrix aubtraction")

print(a-1)

print("matrix division")

print(a/2)

print("stacking horizontally")

print(np.hstack((a,c)))

print("stacking vertically")

print(np.vstack((a,c)))

print("multi-dimensional array to single column | ravel")

print(np.ravel(a))

print("Calculate Exponential")

print(np.exp(a))

print("Calculate Natural Log (log base e)")

print(np.log(a))

print("Calculate Log base 10")

print(np.log10(a))

import matplotlib.pyplot as plt

x = np.arange(0, 3*np.pi, 0.1) # np.pi is 3.14...

y = np.tan(x) # np.sin(x) np.cos(x)

plt.plot(x,y)

plt.show()