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Created Feb 15, 2019
Numpy
View numpy_linear_algebra2.py
 ## Working same as np.dot() np.matmul([[2,3],[3,4]],[[1,2],[5,6]]) #[Output]: #array([[17, 22], # [23, 30]]) ## Here matmul will automatically brodcast if dimensiona are not same np.matmul([[1, 0], [0, 1]],[1,2])
Created Feb 15, 2019
Numpy
View numpy_linear_algebra1.py
 ##importing libraries import numpy.linalg as lnp import numpy as np ## dot product for simple numbers. np.dot(3,4) #[Output]: #12
Created Feb 15, 2019
Numpy
View numpy_copies_views3.py
 ## Creating a new array. e = np.array([21,22,23]) ## Creating a new array by using copy function. f = e.copy() ## Checking the ids of both arrays #3 In this case also both will have different idsIn [13]: print(id(e))
Created Feb 15, 2019
Numpy
View numpy_copies_views2.py
 ## Creating a new array c = np.array([11,12,13]) ## Creating another array using view function d = c.view() ## Checking the value of "d" print(d) #[Output]:
Created Feb 15, 2019
Numpy
View numpy_copies_views1.py
 ## Importing numpy library for creating numpy import numpy as np ## Creating a 1-D array a = np.array([0,2,1]) print(a) #[Output]: #array([0, 2, 1])
Created Feb 15, 2019
Numpy
View numpy_sort_search2.py
 ## Searching import numpy as np ## 1. argmax() a = np.arange(6).reshape(2,3) print(a) #[Output]: #[[0 1 2]
Created Feb 15, 2019
Numpy
View numpy_sort_search1.py
 import numpy as np ## 1. sort() ## Sorting along flattened array a = np.array([[5,4],[3,1]]) np.sort(a) #[Output]: #array([[4, 5], # [1, 3]])
Created Feb 15, 2019
Numpy
View numpy_sort_search1.py
 import numpy as np ## 1. argmax() a = np.arange(6).reshape(2,3) print(a) #[Output]: #[[0 1 2] # [3 4 5]]
Created Feb 15, 2019
Numpy
View numpy_statistical_functions3.py
 y= a.mean(axis=1) print(y) #[Output]: #[0.5 2.5 4.5] y.reshape(3,1) #[Output]:
Created Feb 15, 2019
Numpy
View numpy_statistical_functions2.py
 import numpy as np a = np.arange(6).reshape(3,2) print(a) #[Output]: #array([[0, 1], # [2, 3], # [4, 5]])
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