Skip to content

Latest commit

 

History

History
352 lines (240 loc) · 6.02 KB

CreationOfNumpy.md

File metadata and controls

352 lines (240 loc) · 6.02 KB
Raw

Numpy Array Create

Import Required Module Numpy and Rename

import numpy as np

Numpy Array Using List

#Define Simple List
lst=[10,23,2,3,7,8]

#Create numpy array using lst
np_array=np.array(lst)

#print numpy array
print("Create Numpy Array Using List:\n",np_array)

Output:

Create Numpy Array Using List:
 [10 23  2  3  7  8]

Numpy Array Using Tuple

#Define Simple Tuple
tple=(10,23,2,3,7,8)

#Create numpy array using tuple
np_array=np.array(tple)

#print numpy array
print("Create a Numpy Array Using Tuple:\n",np_array)

Output:

Create a Numpy Array Using Tuple:
 [10 23  2  3  7  8]

Numpy String Array

tple=np.array(['P','Y','T','H','O','N'])
#Create numpy array using tuple
np_array=np.array(tple)

#print numpy array
print("Create Numpy Array Using Characters :\n",np_array)

Output:

Create Numpy Array Using Characters :
 ['P' 'Y' 'T' 'H' 'O' 'N']

Create Numpy array using arange() function

#Simple Array Here Only End is Specified
arr=np.arange(6)
print("Array With stop Parameter: ",arr)
print("Type of an Array: ",arr.dtype)


#Create Float Array
arr=np.arange(6.0)
print("\nArray With stop Parameter: ",arr)
print("Type of an Array: ",arr.dtype)


#Numpy Array From Specific Range
arr=np.arange(4,9) #it will include 4 and exclude 9
print("\nArray With Specified Range: ",arr)
print("Type of an Array: ",arr.dtype)


#Simple Numpy array with Step counter(print 5 table)
arr=np.arange(5,51,5)
print("\nArray of 5 Table: ",arr)
print("Type of an Array: ",arr.dtype)

Output:

Array With stop Parameter:  [0 1 2 3 4 5]
Type of an Array:  int32

Array With stop Parameter:  [0. 1. 2. 3. 4. 5.]
Type of an Array:  float64

Array With Specified Range:  [4 5 6 7 8]
Type of an Array:  int32

Array of 5 Table:  [ 5 10 15 20 25 30 35 40 45 50]
Type of an Array:  int32

important Terminologies Used In Numpy array

#Simple list
lst=[1,2,3]
np_ar=np.array(lst)

Print The Numpy Array

# print Normal numpy array
print(np_ar)

Output:

[1 2 3]

Get Shape of Numpy array

Use the following syntax to get the shape of numpy array numpy_array.shape

#This is 1d Array So Only 
shape=np_ar.shape
print("Shape Of an Array(row,columns):",shape)

Output:

Shape Of an Array(row, columns): (3,)

Get element data types in numpy array

numpy_array.dtype

data_type=np_ar.dtype
print("Data Type Of Numpy Array:",data_type)

Output:

Data Type Of Numpy Array: int32

Get Dimension Of Numpy Array

  • To get the Dimension of numpy array simple call ndim with numpy array numpy_array.ndim
dim=np_ar.ndim
print("The dimension of the Numpy Array:",dim)

Output:

The dimension of the Numpy Array: 1

Get Element Count(Size How many Element in numpy array)

  • To simply get the Count of the present element in the numpy array use size as follows numpy_array.size
elem_count=np_ar.size
print("Element Count:",elem_count)

Output:

Element Count: 3

Get the element Size in Bytes

size=np_ar.itemsize
print("Item Size in Bytes:",size)

Output:

Item Size in Bytes: 4

Type Of Numpy Array

1. Single Dimensional
2. Two Dimensional
3. Three Dimensional

1. Single Dimensional

1d

import numpy as np
#Simple list
lst=[1,2,3,4]

np_1d=np.array(lst)
print("1D Numpy Array:\n",np_1d)


#dimensssions
print("\nDimenssion of np_1d: ",np_1d.ndim)

#Second value from tuple is blank for shape becaue it is 1d array
print("Shape Of Array(row,colums) :",np_1d.shape)

#Get Type of Array Elements
print("Type Of Numpy Array Elements : ",np_1d.dtype)

#get Item Size 
print("Item Size: ",np_1d.itemsize)

#get objects Size 
print("Object Size: ",np_1d.size)

Output:

1D Numpy Array:
 [1 2 3 4]

Dimenssion of np_1d:  1
Shape Of Array(row,colums) : (4,)
Type Of Numpy Array Elements :  int32
Item Size:  4
Object Size:  4

2.Two Dimensssional

2d

import numpy as np
#2d List
lst2=[
    [1,2],
    [3,4],
    [5,6]
]

np_2d=np.array(lst2)
print("2D Numpy Array:\n",np_2d)

#dimensssions
print("\nDimenssion of np_2d: ",np_2d.ndim)

#three rows 2 columns
print("Shape Of Array(row,colums) :",np_2d.shape)

#Get Type of Array Elements
print("Type Of Numpy Array Elements : ",np_2d.dtype)

#get Item Size 
print("Size of Object Size: ",np_2d.itemsize)

#get objects Size 
print("element Count : ",np_2d.size)

Output:

2D Numpy Array:
 [[1 2]
 [3 4]
 [5 6]]

Dimenssion of np_2d:  2
Shape Of Array(row,colums) : (3, 2)
Type Of Numpy Array Elements :  int32
Size of Object Size:  4
element Count :  6

3. Three dimensional

3d

import numpy as np

#3d List
lst3=[
    [
        [3,4,5],
        [6,7,8],
        [9,10,11]
    ],
    [
        [0,1,2],
        [12,13,14],
        [15,16,17]
    ],
    [
        [18,19,20],
        [21,22,23],
        [24,25,27]
    ]
]

np_3d=np.array(lst3)
print(np_3d)

#dimensssions
print("\nDimenssion of np_3d: ",np_3d.ndim)

#three rows 2 columns
print("Shape Of Array(row,colums) :",np_3d.shape)

#Get Type of Array Elements
print("Type Of Numpy Array Elements : ",np_3d.dtype)

#get Item Size 
print("Size of Object Size: ",np_3d.itemsize)

#get objects Size 
print("Element Count: ",np_3d.size)

Output

[[[ 3  4  5]
  [ 6  7  8]
  [ 9 10 11]]

 [[ 0  1  2]
  [12 13 14]
  [15 16 17]]

 [[18 19 20]
  [21 22 23]
  [24 25 27]]]

Dimenssion of np_3d:  3
Shape Of Array(row,colums) : (3, 3, 3)
Type Of Numpy Array Elements :  int32
Size of Object Size:  4
Element Count:  27