Python Numpy Source Codes
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The document contains various Python code snippets illustrating the use of the NumPy library for array creation, manipulation, and basic operations. It covers topics such as vector addition, multiplication, and operations like square, square root, logarithm, and exponential. Additionally, it demonstrates array characteristics like shape, size, and data types, along with reshaping, stacking, and splitting arrays.
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############################Python Numpy Source Codes######################
Note: Loops are slower than numpy arrays!
###############
import numpy as np
l=[1,2]
nplist = np.array(l)
print(nplist)
RESULT:
[1 2]
##################
import numpy as np
L=[1,2,3]
NA = np.array(L)
for i in L:
print(i)
RESULT:
1
2
3
####################
import numpy as np
L=[1,2,3]
NA = np.array(L)
for i in NA:
print(i)
RESULT:
1
2
3
########################
import numpy as np
L=[1,2,3]
L1=L+[4] #append
print(L1)
RESULT:
[1, 2, 3, 4]](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-1-320.jpg)
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#########################
import numpy as np
import numpy as np
L=[1,2,3,4]
L.append(5)
print(L)
RESULT:
[1, 2, 3, 4, 5]
#########################
import numpy as np
L=[1,2,3]
NA = np.array(L)
print(NA) #NA=[1 2 3]
NA1 = NA + [4] #vector addition, 4 is added to each element
print(NA1) #NA1=[5 6 7]
##########################
import numpy as np
L=[1,2,3]
NA = np.array(L)
print(NA) #NA=[1 2 3]
NA.append(8)
RESULT:
AttributeError: 'numpy.ndarray' object has no attribute 'append'
#############################
import numpy as np
L=[1,2,3]
NA = np.array(L)#[1 2 3]
NA2 = NA + NA
print(NA2) #Vector addition:[1 2 3]+[1 2 3]=[2 4 6]
L2 = L + L
print(L2) #List addition is simply concatenation [1, 2, 3, 1, 2, 3]
##########vector addition in list#########
L= [1,2,3]
L3 = []
for i in L:
L3.append(i+i)
print(L3) #[2,4,6]](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-2-320.jpg)
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#########vector multipication######
import numpy as np
L=[1,2,3]
NA = np.array(L)#[1 2 3]
print(2*NA) # vector multiplication [2 4 6]
print(2*L) #list multiplication :[1, 2, 3, 1, 2, 3]
#############Square operation########
import numpy as np
L=[1,2,3]
NA = np.array(L)#[1 2 3]
print(NA**2) # Square operation: [1 4 9]
print(L**2) #TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int'
############List Square operation#######
L= [1,2,3]
L3 = []
for i in L:
L3=i*i
print(L3) #[1,4,9]
############Square root operation######
import numpy as np
L=[1,2,3]
NA = np.array(L)#[1 2 3]
print(np.sqrt(NA)) # Square operation: [1. 1.41421356 1.73205081]
############log operation############
import numpy as np
L=[1,2,3]
NA = np.array(L)#[1 2 3]
print(np.log(NA))# log operation:[0. 0.69314718 1.09861229]
###########exponential operation######
import numpy as np
L=[1,2,3]
NA = np.array(L)#[1 2 3]
print(np.exp(NA))# exponential operation:[ 2.71828183 7.3890561 20.08553692]
#############################
import numpy as np
a=np.array([1,2,3])
b=np.array([[1,2], [3,4], [5,6]])
print(a[0]) #a[0]=1
print(b[0]) #b[0]=[1 2]](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-3-320.jpg)
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print(b[0][0])# b[0][0]= 1
print(b[0][1])# b[0][1]= 2
M=np.matrix([[1,2], [3,4], [5,6]])
print(M) #Matrix form
print(M[0][0]) #M[0][0]=[[1 2]]
print(M[0,0]) #M[0,0]=1
############################
import numpy as np
a=np.array([1,2,3])
b=np.array([[1,2], [3,4], [5,6]])
print(b)
RESULT:
[[1 2]
[3 4]
[5 6]]
#####################Transpose operation##############
import numpy as np
a=np.array([1,2,3])
b=np.array([[1,2], [3,4], [5,6]])
print(b.T) # Transpose operation
RESULT:
[[1 3 5]
[2 4 6]]
#############No. of rows and cols ##############
import numpy as np
b=np.array([[1,2], [3,4], [5,6]])
print(b.shape) # rows x cols= (3, 2)
c=b.T
print(c.shape) # rows x cols=(2, 3)
###############To check dimension of array#############
import numpy as np
a=np.array([1,2,3])
print(a.ndim) #a is 1 dimensional array
b=np.array([[1,2], [3,4], [5,6]])
print(b.ndim) #b is 2 dimensional array
c=b.T
print(c.ndim) #c is 2 dimensional array](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-4-320.jpg)
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##############To check total no. of elements in an array###########
import numpy as np
a=np.array([1,2,3])
print(a.size) #a has 3 elements
b=np.array([[1,2], [3,4], [5,6]])
print(b.size) #b has 6 elements
c=b.T
print(c.size) #c has 6 elements
##############To check data type of an array###########
import numpy as np
a=np.array([1,2,3])
print(a.dtype) #int32
b=np.array([[1,2], [3,4], [5,6]])
print(b.dtype) #int32
c=b.T
print(c.dtype) #int32
##########data type conversion########
import numpy as np
a=np.array([1,2,3])
b=np.array([1,2,3], dtype=np.float32)
print(b) #[1. 2. 3.]
##########Check each elemenet size#########
import numpy as np
a=np.array([1,2,3])
print(a.itemsize) #4 bytes
b=np.array([1,2,3], dtype=np.float64)
print(b.itemsize) #8 bytes
##########Check minimum and maximum elemenet #######
import numpy as np
a=np.array([1,2,3])
print(a.min()) #minimum element is 1
print(a.max()) #maximum element is 3
##########Check sum of elemenets#######
import numpy as np
a=np.array([1,2,3])
print(a.sum()) #sum of all elements is 6](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-5-320.jpg)
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##########axis sum##########
import numpy as np
b=np.array([[1,2], [3,4], [5,6]])
print(b.sum(axis=0)) #1+3+5=9, 2+4+6=12,
print(b.sum(axis=1)) #1+2=3, 3+4=7, 5+6=11
###########
import numpy as np
a=np.zeros((2,3)) #2 rows and 3 cols
print(a)
RESULT:
[[0. 0. 0.]
[0. 0. 0.]]
###############
import numpy as np
b=np.ones((3,2))
print(b)
RESULT:
[[1. 1.]
[1. 1.]
[1. 1.]]
###########
import numpy as np
b=np.ones((3,2), dtype=np.int16)
print(b)
RESULT:
[[1 1]
[1 1]
[1 1]]
##########Crete random data###########
import numpy as np
print(np.empty((3,3)))
RESULT:
[[0.00000000e+000 0.00000000e+000 0.00000000e+000]
[0.00000000e+000 0.00000000e+000 1.91697471e-321]
[1.93101617e-312 1.93101617e-312 0.00000000e+000]]](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-6-320.jpg)
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#######################
import numpy as np
print(np.empty([3,3]))
RESULT:
[[6.23042070e-307 3.56043053e-307 1.60219306e-306]
[7.56571288e-307 1.89146896e-307 1.37961302e-306]
[1.05699242e-307 8.01097889e-307 0.00000000e+000]]
########################
import numpy as np
print(np.empty([3,3], dtype=np.int16))
RESULT:
After first execution:
[[4 0 0]
[0 4 0]
[0 0 0]]
After second execution:
[[0 0 0]
[0 0 0]
[0 0 0]]
#################
import numpy as np
print(np.arange(0,5)) #[0 1 2 3 4]
print(np.arange(0,5, 0.5)) #[0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5]
##################
import numpy as np
print(np.linspace(0,5)) #Linearly space the value in range 0 to 5
#By default it takes 50 values
RESULT:
[0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408
0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898
1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388
1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878
2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367
3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857
3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347
4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837
4.89795918 5.]](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-7-320.jpg)
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##############
import numpy as np
print(np.linspace(1,5,10))
RESULT:
[1. 1.44444444 1.88888889 2.33333333 2.77777778 3.22222222
3.66666667 4.11111111 4.55555556 5. ]
############Create random numbers#############
import numpy as np
print(np.random.random((2,3))) #dimension is 2 rows X 3 cols
RESULT:
[[0.13945931 0.16273058 0.56452845]
[0.61644482 0.18447141 0.17318377]]
############Reshaping array dimension#############
import numpy as np
c=np.zeros((2,3))
print(c)
print(c.reshape(6,1))
print(c.reshape(3,2))
############Reshaping array dimension#############
import numpy as np
c=np.zeros((2,5))
print(c)
print(c.reshape(5,-1)) # here 5 rows are created while cols created automatically by using '-1'
#############Stack vertically####################
import numpy as np
c=np.zeros((2,5))
d=np.ones((1,5))
e=np.vstack((c,d))
print(e)
RESULT;
[[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[1. 1. 1. 1. 1.]]
#############Stack vertically####################](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-8-320.jpg)
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import numpy as np
c=np.zeros((2,5))
d=np.ones((1,5))
e=np.vstack((d,c))
print(e)
RESULT:
[[1. 1. 1. 1. 1.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]
#############Stack horizontally####################
import numpy as np
c=np.zeros((1,5))
d=np.ones((1,5))
e=np.hstack((d,c))
print(e)
RESULT:
[[1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]]
#########Vertical array Split###########
import numpy as np
b=np.array([[1,2], [3,4], [5,6]])
print(b)
e=np.vsplit(b , 3) #vertical split in 3 parts
print(e)
RESULT:
[[1 2]
[3 4]
[5 6]]
[array([[1, 2]]), array([[3, 4]]), array([[5, 6]])]
#########Horizontal array Split###########
import numpy as np
b=np.array([[1,2], [3,4], [5,6]])
print(b)
e=np.hsplit(b , 2)
print(e)](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-9-320.jpg)
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RESULT:
[[1 2]
[3 4]
[5 6]]
[array([[1],
[3],
[5]]), array([[2],
[4],
[6]])]](https://image.slidesharecdn.com/pythonnumpysourcecodes-200603093005/85/Python-Numpy-Source-Codes-10-320.jpg)
Python Numpy Source Codes
- 1. 1 | P a g e ############################Python Numpy Source Codes###################### Note: Loops are slower than numpy arrays! ############### import numpy as np l=[1,2] nplist = np.array(l) print(nplist) RESULT: [1 2] ################## import numpy as np L=[1,2,3] NA = np.array(L) for i in L: print(i) RESULT: 1 2 3 #################### import numpy as np L=[1,2,3] NA = np.array(L) for i in NA: print(i) RESULT: 1 2 3 ######################## import numpy as np L=[1,2,3] L1=L+[4] #append print(L1) RESULT: [1, 2, 3, 4]
- 2. 2 | P a g e ######################### import numpy as np import numpy as np L=[1,2,3,4] L.append(5) print(L) RESULT: [1, 2, 3, 4, 5] ######################### import numpy as np L=[1,2,3] NA = np.array(L) print(NA) #NA=[1 2 3] NA1 = NA + [4] #vector addition, 4 is added to each element print(NA1) #NA1=[5 6 7] ########################## import numpy as np L=[1,2,3] NA = np.array(L) print(NA) #NA=[1 2 3] NA.append(8) RESULT: AttributeError: 'numpy.ndarray' object has no attribute 'append' ############################# import numpy as np L=[1,2,3] NA = np.array(L)#[1 2 3] NA2 = NA + NA print(NA2) #Vector addition:[1 2 3]+[1 2 3]=[2 4 6] L2 = L + L print(L2) #List addition is simply concatenation [1, 2, 3, 1, 2, 3] ##########vector addition in list######### L= [1,2,3] L3 = [] for i in L: L3.append(i+i) print(L3) #[2,4,6]
- 3. 3 | P a g e #########vector multipication###### import numpy as np L=[1,2,3] NA = np.array(L)#[1 2 3] print(2*NA) # vector multiplication [2 4 6] print(2*L) #list multiplication :[1, 2, 3, 1, 2, 3] #############Square operation######## import numpy as np L=[1,2,3] NA = np.array(L)#[1 2 3] print(NA**2) # Square operation: [1 4 9] print(L**2) #TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int' ############List Square operation####### L= [1,2,3] L3 = [] for i in L: L3=i*i print(L3) #[1,4,9] ############Square root operation###### import numpy as np L=[1,2,3] NA = np.array(L)#[1 2 3] print(np.sqrt(NA)) # Square operation: [1. 1.41421356 1.73205081] ############log operation############ import numpy as np L=[1,2,3] NA = np.array(L)#[1 2 3] print(np.log(NA))# log operation:[0. 0.69314718 1.09861229] ###########exponential operation###### import numpy as np L=[1,2,3] NA = np.array(L)#[1 2 3] print(np.exp(NA))# exponential operation:[ 2.71828183 7.3890561 20.08553692] ############################# import numpy as np a=np.array([1,2,3]) b=np.array([[1,2], [3,4], [5,6]]) print(a[0]) #a[0]=1 print(b[0]) #b[0]=[1 2]
- 4. 4 | P a g e print(b[0][0])# b[0][0]= 1 print(b[0][1])# b[0][1]= 2 M=np.matrix([[1,2], [3,4], [5,6]]) print(M) #Matrix form print(M[0][0]) #M[0][0]=[[1 2]] print(M[0,0]) #M[0,0]=1 ############################ import numpy as np a=np.array([1,2,3]) b=np.array([[1,2], [3,4], [5,6]]) print(b) RESULT: [[1 2] [3 4] [5 6]] #####################Transpose operation############## import numpy as np a=np.array([1,2,3]) b=np.array([[1,2], [3,4], [5,6]]) print(b.T) # Transpose operation RESULT: [[1 3 5] [2 4 6]] #############No. of rows and cols ############## import numpy as np b=np.array([[1,2], [3,4], [5,6]]) print(b.shape) # rows x cols= (3, 2) c=b.T print(c.shape) # rows x cols=(2, 3) ###############To check dimension of array############# import numpy as np a=np.array([1,2,3]) print(a.ndim) #a is 1 dimensional array b=np.array([[1,2], [3,4], [5,6]]) print(b.ndim) #b is 2 dimensional array c=b.T print(c.ndim) #c is 2 dimensional array
- 5. 5 | P a g e ##############To check total no. of elements in an array########### import numpy as np a=np.array([1,2,3]) print(a.size) #a has 3 elements b=np.array([[1,2], [3,4], [5,6]]) print(b.size) #b has 6 elements c=b.T print(c.size) #c has 6 elements ##############To check data type of an array########### import numpy as np a=np.array([1,2,3]) print(a.dtype) #int32 b=np.array([[1,2], [3,4], [5,6]]) print(b.dtype) #int32 c=b.T print(c.dtype) #int32 ##########data type conversion######## import numpy as np a=np.array([1,2,3]) b=np.array([1,2,3], dtype=np.float32) print(b) #[1. 2. 3.] ##########Check each elemenet size######### import numpy as np a=np.array([1,2,3]) print(a.itemsize) #4 bytes b=np.array([1,2,3], dtype=np.float64) print(b.itemsize) #8 bytes ##########Check minimum and maximum elemenet ####### import numpy as np a=np.array([1,2,3]) print(a.min()) #minimum element is 1 print(a.max()) #maximum element is 3 ##########Check sum of elemenets####### import numpy as np a=np.array([1,2,3]) print(a.sum()) #sum of all elements is 6
- 6. 6 | P a g e ##########axis sum########## import numpy as np b=np.array([[1,2], [3,4], [5,6]]) print(b.sum(axis=0)) #1+3+5=9, 2+4+6=12, print(b.sum(axis=1)) #1+2=3, 3+4=7, 5+6=11 ########### import numpy as np a=np.zeros((2,3)) #2 rows and 3 cols print(a) RESULT: [[0. 0. 0.] [0. 0. 0.]] ############### import numpy as np b=np.ones((3,2)) print(b) RESULT: [[1. 1.] [1. 1.] [1. 1.]] ########### import numpy as np b=np.ones((3,2), dtype=np.int16) print(b) RESULT: [[1 1] [1 1] [1 1]] ##########Crete random data########### import numpy as np print(np.empty((3,3))) RESULT: [[0.00000000e+000 0.00000000e+000 0.00000000e+000] [0.00000000e+000 0.00000000e+000 1.91697471e-321] [1.93101617e-312 1.93101617e-312 0.00000000e+000]]
- 7. 7 | P a g e ####################### import numpy as np print(np.empty([3,3])) RESULT: [[6.23042070e-307 3.56043053e-307 1.60219306e-306] [7.56571288e-307 1.89146896e-307 1.37961302e-306] [1.05699242e-307 8.01097889e-307 0.00000000e+000]] ######################## import numpy as np print(np.empty([3,3], dtype=np.int16)) RESULT: After first execution: [[4 0 0] [0 4 0] [0 0 0]] After second execution: [[0 0 0] [0 0 0] [0 0 0]] ################# import numpy as np print(np.arange(0,5)) #[0 1 2 3 4] print(np.arange(0,5, 0.5)) #[0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5] ################## import numpy as np print(np.linspace(0,5)) #Linearly space the value in range 0 to 5 #By default it takes 50 values RESULT: [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5.]
- 8. 8 | P a g e ############## import numpy as np print(np.linspace(1,5,10)) RESULT: [1. 1.44444444 1.88888889 2.33333333 2.77777778 3.22222222 3.66666667 4.11111111 4.55555556 5. ] ############Create random numbers############# import numpy as np print(np.random.random((2,3))) #dimension is 2 rows X 3 cols RESULT: [[0.13945931 0.16273058 0.56452845] [0.61644482 0.18447141 0.17318377]] ############Reshaping array dimension############# import numpy as np c=np.zeros((2,3)) print(c) print(c.reshape(6,1)) print(c.reshape(3,2)) ############Reshaping array dimension############# import numpy as np c=np.zeros((2,5)) print(c) print(c.reshape(5,-1)) # here 5 rows are created while cols created automatically by using '-1' #############Stack vertically#################### import numpy as np c=np.zeros((2,5)) d=np.ones((1,5)) e=np.vstack((c,d)) print(e) RESULT; [[0. 0. 0. 0. 0.] [0. 0. 0. 0. 0.] [1. 1. 1. 1. 1.]] #############Stack vertically####################
- 9. 9 | P a g e import numpy as np c=np.zeros((2,5)) d=np.ones((1,5)) e=np.vstack((d,c)) print(e) RESULT: [[1. 1. 1. 1. 1.] [0. 0. 0. 0. 0.] [0. 0. 0. 0. 0.]] #############Stack horizontally#################### import numpy as np c=np.zeros((1,5)) d=np.ones((1,5)) e=np.hstack((d,c)) print(e) RESULT: [[1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]] #########Vertical array Split########### import numpy as np b=np.array([[1,2], [3,4], [5,6]]) print(b) e=np.vsplit(b , 3) #vertical split in 3 parts print(e) RESULT: [[1 2] [3 4] [5 6]] [array([[1, 2]]), array([[3, 4]]), array([[5, 6]])] #########Horizontal array Split########### import numpy as np b=np.array([[1,2], [3,4], [5,6]]) print(b) e=np.hsplit(b , 2) print(e)
- 10. 10 | P a g e RESULT: [[1 2] [3 4] [5 6]] [array([[1], [3], [5]]), array([[2], [4], [6]])]