- “numpy” is the fundamental package for scientific computing in Python.
- Python library for creating N-Dimensional array
- Great support for Liner-Algebra, Statistics Distribution, Trignometery
- Aabhar : Jose Portilla (Head of Data Science at Pierian Training) @Udemy
- Creating an array in 3 ways, accessing via index, some major methods
- Broadcasting and how to prevent
- 1D np.array([1,2]) 2D np.array([ [1,2], [3,4] ]) 3D np.array([ [ [1,2], [3,4] ] ])
import numpy as np
# #### Create numpy array using
# A) Transforming standard python list
myList = [1,2,3]
myArray = np.array(myList)
print(type(myArray), "---", myArray)
# RES -> <class 'numpy.ndarray'> --- [1 2 3]
# B) Using Built-in functions
print(np.arange(0,11,2)) # o to 10 in steps of 2
# RES -> [ 0 2 4 6 8 10]
print(np.linspace(0,10,3)) # Evenly distributed 3 number bwn 0 to 10
# RES -> [ 0. 5. 10.]
print(np.zeros((1,5))) # 1D array with 5 zeroes, nD possible
# RES -> [[0. 0. 0. 0. 0.]]
print(np.ones(5)) #1D array with 5 ones, only 1D possible
# RES -> [1. 1. 1. 1. 1.]
print(np.eye(3)) #3x3 matrix with diagonal elements as 1
# RES -> [[1. 0. 0.]
# [0. 1. 0.]
# [0. 0. 1.]]
# C) By Generating random data
print(np.random.rand(2,3)) # 2x3 matrix with every number bwn o to 1
# RES -> [[0.08564555 0.5295177 0.82151728]
# [0.83742842 0.96277555 0.84243966]]
print(np.random.randint(5,10,3)) # 3 integer no. bwn 5 to 10
# RES -> [6 6 8]
print(np.random.randint(5,55, (2,5))) # any 12 integers between 5 to 55 in 2x5 format
# RES -> [[35 46 49 16 14]
# [53 43 16 51 23]]
#randn : "standard normal" distribution with mean = 0 and SD = 1
print(np.random.randn(1,5))
# RES -> [[ 0.95835337 -1.70240379 -0.9072812 -0.51675364 1.25098128]]
############# FEW MAJOR METHODS #############
x = np.arange(5,11)
print(x)
# RES -> [ 5 6 7 8 9 10]
print(x.max(), x.min(), x.argmax(), x.argmin() ) #max and min with their position value
# RES -> 10 5 5 0
#1 D array with 6 elements to it
print("X shape : " , x.shape) # shape is property where as max() is method
# RES -> X shape : (6,)
#2 D array, with 3 elements for each 1D array
y = x.reshape(2,3)
print(y)
# RES -> [[ 5 6 7]
# [ 8 9 10]]
print("Y Shape : ", y.shape)
# RES -> Y Shape : (2, 3)
############# INDEXING & SLICING #############
arr = np.arange(1,11)
print(arr)
# RES -> [ 1 2 3 4 5 6 7 8 9 10]
print(arr[5])
# RES -> 6
print(arr[5:9]) #5th to 9th indexed value
# RES -> [6 7 8 9]
print(arr[5:]) # 5th till end
# RES -> [ 6 7 8 9 10]
print(arr[:5]) # From start till 5th
# RES -> [1 2 3 4 5]
############# BROADCASTING #############
myArr = np.arange(11,66,11)
print(myArr)
# RES -> [11 22 33 44 55]
myArr[0:3] = 99 # Value is broadcasted to 1st 3 elements
print(myArr)
# RES -> [99 99 99 44 55]
myArr2 = myArr # This myArr2 is basically a pointer
myArr2[0] = 0 # Modifying via pointer and hence original array will change
print(myArr)
# RES -> [ 0 99 99 44 55]
# To prevent, use copy() method
myArr3 = myArr.copy()
myArr3[0] = -1
print(myArr)
# RES -> [ 0 99 99 44 55]
########### BROADCASTING - Advantage of bool array #####
x = np.arange(1,7)
print(x)
#RES -> [1 2 3 4 5 6]
boolX = x>3
print(boolX)
# RES -> [False False False True True True]
print(x[boolX]) # returns only those values > 3
# RES -> [4 5 6]
########### 2D array selection and indexing ###########
# Array of 1D arrays
arr2d = np.array([ [5,10,15], [20,25,30], [35,40,45]])
print(arr2d)
# RES -> [[ 5 10 15]
# [20 25 30]
# [35 40 45]]
print(arr2d[0]) # 1 row
# RES -> [ 5 10 15]
print(arr2d[0,2]) # 1st row last element - 15
# RES -> 15
print(arr2d[:2]) #Rows upto 2 excluding 2 means leave the 3rd row
# RES -> [[ 5 10 15]
# [20 25 30]]
print(arr2d[:2, 1]) # Now form the extracted, 1st colum from all rows
# RES -> [10 25]
print(arr2d[:2, 1:]) # Now form the extracted, 1st and all the rest colum from all rows
# RES -> [[10 15]
# [25 30]]
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