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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()

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