OPEN
Anaconda Navigator
Launch
spyder
PROGRAM 1
import numpy as np
arr = np.array([1,2,3,4,5,6,7])
print (arr)
OUTPUT
[1 2 3 4 5 6 7] //displays the array
PROGRAM
2
import numpy as np
arr = np.array([[1, 2,3], [3, 4, 5],[5,6, 7]])
print (arr)
OUTPUT
[[1 2 3] // displays the 3x3 matrix
[3 4 5]
[5 6 7]]
PROGRAM
3
import numpy as np
arr = np.array([5,6,7,8], dtype = complex)
print (arr)
OUTPUT
[5.+0.j 6.+0.j 7.+0.j 8.+0.j] //displays complex numbers
PROGRAM
4
import numpy as np
employee = np.dtype([('ename','S10'), ('eage', 'i1'), ('esalary',
'f4')])
print (employee)
OUTPUT
[('ename', 'S10'), ('eage', 'i1'), ('esalary', '<f4')] //S is string,
i is integer and f is float and the numbers adjacent to it are the
lengths
PROGRAM
5
import numpy as np
employee = np.dtype([('ename','S10'), ('eage', 'i1'), ('esalary',
'f4')])
print (employee)
arr = np.array([('Harsha', 21, 500),('Sobi', 18, 759)], dtype =
employee)
print (arr)
OUTPUT
[('ename', 'S10'), ('eage', 'i1'), ('esalary', '<f4')]
[(b'Harsha', 21, 500.) (b'Sobi', 18, 759.)]
PROGRAM
6
import numpy as np
arr = np.array([[0,0,1],[1,0,0]])
print (arr.shape)
OUTPUT
(2, 3) //returns (2,3) because the matrix has 2 rows and 3 columns
PROGRAM
7
import numpy as np
arr = np.array([[0,0,1],[1,0,0],[2,3,4]])
print (arr.shape)
OUTPUT
(3, 3) //returns (3,3) because the matrix has 3 rows and 3 columns
PROGRAM 8
import numpy as np
arr = np.arange(20)
print (arr)
OUTPUT
[ 0 1 2 3 4 5 6 7
8 9 10 11 12 13 14 15 16 17 18 19]
PROGRAM 9
import numpy as np
x = np.array([1,2,3], dtype = np.int8)
print (x.itemsize)
OUTPUT
1
PROGRAM 10
import numpy as np
x = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype = np.int8)
print (x.itemsize)
OUTPUT
1
PROGRAM 11
import numpy as np
x = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype = np.float32)
print (x.itemsize)
OUTPUT
4
PROGRAM 12
import numpy as np
arr = np.array([1,2,3,4,5,6,7,8,9])
print (arr.flags)
OUTPUT
C_CONTIGUOUS : True
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
PROGRAM 13
import numpy as np
arr = np.zeros(10)
print (arr)
OUTPUT
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
PROGRAM 14
import numpy as np
arr = np.zeros(10,dtype = np.int)
print (arr)
OUTPUT
[0 0 0 0 0 0 0 0 0 0]
PROGRAM 15
import numpy as np
arr = np.zeros((3,3), dtype = [('x', 'i4'), ('y',
'i4'),('z','f4')])
print (arr)
OUTPUT
[[(0, 0, 0.) (0, 0, 0.) (0, 0, 0.)]
[(0, 0, 0.) (0, 0, 0.) (0, 0, 0.)]
[(0, 0, 0.) (0, 0, 0.) (0, 0, 0.)]]
PROGRAM 16
import numpy as np
arr = np.zeros(10)
print (arr)
OUTPUT
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
PROGRAM 17
import numpy as np
arr = np.ones(10,dtype = np.int)
print (arr)
OUTPUT
[1 1 1 1 1 1 1 1 1 1]
PROGRAM 18
import numpy as np
arr = np.ones((3,3), dtype = [('x', 'i4'), ('y',
'i4'),('z','f4')])
print (arr)
OUTPUT
[[(1, 1, 1.) (1, 1, 1.) (1, 1, 1.)]
[(1, 1, 1.) (1, 1, 1.) (1, 1, 1.)]
[(1, 1, 1.) (1, 1, 1.) (1, 1, 1.)]]
PROGRAM 19
import numpy as np
arr = np.zeros((3,3), dtype = [('x', 'i4'), ('y',
'i4'),('z','i4')])
print (arr)
OUTPUT
[[(0, 0, 0) (0, 0, 0) (0, 0, 0)]
[(0, 0, 0) (0, 0, 0) (0, 0, 0)]
[(0, 0, 0) (0, 0, 0) (0, 0, 0)]]
PROGRAM
20
import numpy as np
arr = np.ones((3,3), dtype = [('x', 'i4'), ('y',
'i4'),('z','i4')])
print (arr)
OUTPUT
[[(1, 1, 1) (1, 1, 1) (1, 1, 1)]
[(1, 1, 1) (1, 1, 1) (1, 1, 1)]
[(1, 1, 1) (1, 1, 1) (1, 1, 1)]]
ALSO CHECK
NumPy Basic tutorial 2 - Python Programming TUTORIAL 2
NumPy Basic tutorial 3 - Python Programming TUTORIAL 3
NumPy Basic tutorial 4 - Python Programming TUTORIAL 4