I decided to go through this webdoc completely and just code up the examples in a single running piece of python code, I named it "np_big_examples.py".

 

Here is the source for np_big_examples.py:  
 
#!/usr/bin/env python

# Following exmaples frpm //stackabuse.com/numpy-tutorial-a-simple-example-based-guide/#numpyoperations

def anykey(): 
 rr = raw_input("\nEnter any key to continue = ")
 return rr

#===========================================================================
# Here is the start of my "main" program 
# This early stuff, including making the function above, are from reading 
# articles in the very useful:   https://stackabuse.com/
#===========================================================================

import numpy as np
from datetime import datetime

print "\nOutput from my test code:   /home/sco/NumPy/np_big_examples.py\n"
print "To run this code:     % python /home/sco/NumPy/np_big_examples.py \n"

#--------------------------------------------------------------------------
# I get the current time                    DEMO of TIME stuff 
current_datetime = datetime.now()
print type(current_datetime)
print "\ndate and time at start of this run: %s \n" % (current_datetime) 
year = current_datetime.year
print "The year is now                 = %s \n" % (year), 
print "The month number is now         = %s \n" % (current_datetime.month), 
print "The day number is now           = %s \n" % (current_datetime.day), 
print "The hour (time) is now          = %s \n" % (current_datetime.hour),
print "The minutes (time) is now       = %s \n" % (current_datetime.minute),
print "The seconds (time) is now       = %s \n" % (current_datetime.second),
print "The  microseconds (time) is now = %s \n" % (current_datetime.microsecond)

just_date = datetime.now().date()
print "Just a date string = %s \n" % (just_date),
just_time = datetime.now().time()
print "Just a time string = %s \n" % (just_time),

utnow = datetime.utcnow()
print "The UT time now    = %s \n" % (utnow),
#--------------------------------------------------------------------------

# Here I show how to use a simple user-defined function  
s1 = anykey() 
print "I use my defined function anykey():  type,value = %s, %s\n" % ( type(s1), s1 )   

#--------------------------------------------------------------------------
# Just for completeness, I use the text load method of 2darray_ex2.py
# To find out more I could:
# % ipython 
# In [1]: import numpy as np
#    ...: help(np.loadtxt)         
# This gives tons of info and I first see:   function loadtxt in module numpy 

# I read the text lines directly into a numpy array
b = np.loadtxt('test.txt', dtype=float)
print "\nHere is my loaded (from test.txt) numpy array (named b):\n",
print b
print "Type of b         = %s \n" % ( type(b) ),
print "b.dtype = ", b.dtype
print "b.shape = ", b.shape
print "b.ndim = ", b.ndim
print "b.size = ", b.size
ss = anykey()
#--------------------------------------------------------------------------

# Now I begin doing thisng directly from 
# https://stackabuse.com/numpy-tutorial-a-simple-example-based-guide/#numpyoperations

# I make a list, and turn it into a numpy ndarray (n-diemnsional array) 
x = [2, 3, 4, 5, 6]
nums = np.array( x ) 
print "type(x)    = %s " % ( type(x) ) 
print "type(nums) = %s " % ( type(nums) ) 
print nums 

print "\nI use the arange method to generate an array:  A1 = np.arange(2, 11, 2) \n", 
A1 = np.arange(2, 11, 2)
print "A1 = ", A1 

print "\nI use the linspace method to generate an array:  A2 = np.linspace(1, 10, 20) \n", 
A2 = np.linspace(1, 10, 20)
print "A2 = ", A2

print "\nI use the random module to generate an array:  A3 = np.random.rand(2,6) \n", 
A3 = np.random.rand(2,6)
print "A3 = ", A3
print "A3.shape = ", A3.shape

print "\nTo get the same as abovem but using a gaussian with mean=0 sigma=1:  A3g = np.random.randn(2,6) \n", 
A3g = np.random.randn(2,6)
print "A3g = ", A3g

print "\nTo get 5 random integers between 50 and 100:  A4 = np.random.randint(50,100,5) \n", 
A4 = np.random.randint(50,100,5)
print "A4 = ", A4


print "\nI reshape A2 to be 4x5:  A2new = A2.reshape(4,5) \n", 
A2new = A2.reshape(4,5)
print "A2new = ", A2new
print "*** I have made 4 rows , each with 5 columns, from the 20 elemenst f A2\n" 

print "\nWhat is reshape?   I do:   print type(np.reshape), ans here is what I get:\n",
print type(np.reshape)

print "\nWhat are stats of my A3g array?   \n",
print "A3g.mean() gives: %f \n" % ( A3g.mean() ),
print "A3g.std() gives: %f \n" % ( A3g.std() ),
print "A3g.min() gives: %f \n" % ( A3g.min() ),
print "A3g.max() gives: %f \n" % ( A3g.max() ),
print "A3g.argmax() gives me the index of the max value: ", A3g.argmax(),

ss = anykey() 

print "\nNow I generate a 9-element array for slicing experiments:  num = np.arange(1,10)    \n",
num = np.arange(1,10)
print "Here is num: ", num 
print "Here is num[0]  : ", num[0] 
print "Here is num[1:4]: ", num[1:4] 
print "Here is num[-1] : ", num[-1] 


print "\nNow I make a 2d array for slicing play:   nums2d = np.array( [ [1,2,3],[4,5,6],[7,8,9] ] ) \n",
nums2d = np.array( [ [1,2,3],[4,5,6],[7,8,9] ] )
print "Here is nums2d:\n", 
print(nums2d) 

print "\nIwant to print row 2:    print( nums2d[1,:] ) \n",
print( nums2d[1,:] )
print "\nIwant to print the first 2 elements of row 3, i.e.  7 8 :    print( nums2d[2,0:2] )\n",
print( nums2d[2,0:2] ) 
print "\nIwant to print the column 3, i.e.   :    print( nums2d[:,2] )\n",
print( nums2d[:,2] )
ss = anykey 

#-----------------------------------------------------------------
# Now I do some math 
print "\nI make a new arrya:     A5 = np.arange(1, 16)\n"
A5 = np.arange(1, 16)
print(A5), 
A6 = A5 + 10.0 
print "\nA6 = A5 + 10.0, I print A6: \n",
print A6 

print "\nI take the log of the members:    Blog = np.log(A6) \n",
Blog = np.log(A6)
print(Blog) 

print "\nI take the exp of the members of Blog:    Bexp = np.log(Blog) \n",
Bexp = np.exp(Blog)
print(Bexp),
print "    # Notice I get A6 back again! \n" 


print "\nI take the sqrt of the members of A6:    Bsqrt = np.sqrt(A6) \n",
Bsqrt = np.sqrt(A6)
print(Bsqrt)

print "\nI take the sin of the members of A6:    Bsin = np.sin(A6) \n",
Bsin = np.sin(A6)
print(Bsin)
print "NOTE: elements are in units of radians!\n" 


 

Here is a run:  
% python np_big_examples.py 

Output from my test code:   /home/sco/NumPy/np_big_examples.py

To run this code:     % python /home/sco/NumPy/np_big_examples.py 



date and time at start of this run: 2020-08-23 16:33:20.187495 

The year is now                 = 2020 
The month number is now         = 8 
The day number is now           = 23 
The hour (time) is now          = 16 
The minutes (time) is now       = 33 
The seconds (time) is now       = 20 
The  microseconds (time) is now = 187495 

Just a date string = 2020-08-23 
Just a time string = 16:33:20.187527 
The UT time now    = 2020-08-23 21:33:20.187531 

Enter any key to continue = Boot 
I use my defined function anykey():  type,value = , Boot


Here is my loaded (from test.txt) numpy array (named b):
[[1.1 1.2 1.3 1.4 1.5]
 [2.1 2.2 2.3 2.4 2.5]
 [3.1 3.2 3.3 3.4 3.5]]
Type of b         =  
b.dtype =  float64
b.shape =  (3, 5)
b.ndim =  2
b.size =  15

Enter any key to continue = My answer 
type(x)    =  
type(nums) =  
[2 3 4 5 6]

I use the arange method to generate an array:  A1 = np.arange(2, 11, 2) 
A1 =  [ 2  4  6  8 10]

I use the linspace method to generate an array:  A2 = np.linspace(1, 10, 20) 
A2 =  [ 1.          1.47368421  1.94736842  2.42105263  2.89473684  3.36842105
  3.84210526  4.31578947  4.78947368  5.26315789  5.73684211  6.21052632
  6.68421053  7.15789474  7.63157895  8.10526316  8.57894737  9.05263158
  9.52631579 10.        ]

I use the random module to generate an array:  A3 = np.random.rand(2,6) 
A3 =  [[0.3978989  0.12105978 0.75914616 0.937583   0.51945677 0.90087656]
 [0.59219778 0.81088236 0.01729787 0.74102429 0.53944398 0.17641226]]
A3.shape =  (2, 6)

To get the same as abovem but using a gaussian with mean=0 sigma=1:  A3g = np.random.randn(2,6) 
A3g =  [[ 1.63442436  1.1188107   0.7282905  -0.52592227 -0.52455239  0.49032327]
 [ 0.51917032 -1.04750827  0.57425342 -1.26995444 -1.25693003  1.38053183]]

To get 5 random integers between 50 and 100:  A4 = np.random.randint(50,100,5) 
A4 =  [63 57 98 85 78]

I reshape A2 to be 4x5:  A2new = A2.reshape(4,5) 
A2new =  [[ 1.          1.47368421  1.94736842  2.42105263  2.89473684]
 [ 3.36842105  3.84210526  4.31578947  4.78947368  5.26315789]
 [ 5.73684211  6.21052632  6.68421053  7.15789474  7.63157895]
 [ 8.10526316  8.57894737  9.05263158  9.52631579 10.        ]]
*** I have made 4 rows , each with 5 columns, from the 20 elemenst f A2


What is reshape?   I do:   print type(np.reshape), ans here is what I get:


What are stats of my A3g array?   
A3g.mean() gives: 0.151745 
A3g.std() gives: 0.990136 
A3g.min() gives: -1.269954 
A3g.max() gives: 1.634424 
A3g.argmax() gives me the index of the max value:  0 
Enter any key to continue = hh 

Now I generate a 9-element array for slicing experiments:  num = np.arange(1,10)    
Here is num:  [1 2 3 4 5 6 7 8 9]
Here is num[0]  :  1
Here is num[1:4]:  [2 3 4]
Here is num[-1] :  9

Now I make a 2d array for slicing play:   nums2d = np.array( [ [1,2,3],[4,5,6],[7,8,9] ] ) 
Here is nums2d:
[[1 2 3]
 [4 5 6]
 [7 8 9]]

Iwant to print row 2:    print( nums2d[1,:] ) 
[4 5 6]

Iwant to print the first 2 elements of row 3, i.e.  7 8 :    print( nums2d[2,0:2] )
[7 8]

Iwant to print the column 3, i.e.   :    print( nums2d[:,2] )
[3 6 9]

I make a new arrya:     A5 = np.arange(1, 16)

[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15] 
A6 = A5 + 10.0, I print A6: 
[11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25.]

I take the log of the members:    Blog = np.log(A6) 
[2.39789527 2.48490665 2.56494936 2.63905733 2.7080502  2.77258872
 2.83321334 2.89037176 2.94443898 2.99573227 3.04452244 3.09104245
 3.13549422 3.17805383 3.21887582]

I take the exp of the members of Blog:    Bexp = np.log(Blog) 
[11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25.]     # Notice I get A6 back again! 


I take the sqrt of the members of A6:    Bsqrt = np.sqrt(A6) 
[3.31662479 3.46410162 3.60555128 3.74165739 3.87298335 4.
 4.12310563 4.24264069 4.35889894 4.47213595 4.58257569 4.69041576
 4.79583152 4.89897949 5.        ]

I take the sin of the members of A6:    Bsin = np.sin(A6) 
[-0.99999021 -0.53657292  0.42016704  0.99060736  0.65028784 -0.28790332
 -0.96139749 -0.75098725  0.14987721  0.91294525  0.83665564 -0.00885131
 -0.8462204  -0.90557836 -0.13235175]
NOTE: elements are in units of radians!