Python Libraries

Contents

• NumPy
• Pundas
• Xarray
• SciPy
• Matplotlib

Reference

Use ” https://colab.research.google.com ” for Editing.

Numpy

a-range, re-shape

import numpy as np

a = np.arange(30)
print(a)
b = a.reshape(2,5,3)
print(b)

////output
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
 24 25 26 27 28 29]
[[[ 0  1  2]
  [ 3  4  5]
  [ 6  7  8]
  [ 9 10 11]
  [12 13 14]]

 [[15 16 17]
  [18 19 20]
  [21 22 23]
  [24 25 26]
  [27 28 29]]]

array

import numpy as np
x = np.array([[0,1,2],[3,4,5],[6,7,8],[9,10,11]])
print("Input array is:")
print(x)
print("\n")
rows = np.array([[0,0],[3,3]])
colomns = np.array([[0,2],[0,2]])
print('Rows \n{}'.format(rows))
print('Colomns \n{}\n'.format(colomns))
y = x[rows,colomns]
print('Output array is:\n{}'.format(y))
z = x[1:4,1:3]
print('\nUsing slice operation\n{}'.format(z))
m = x[1:4,[0,1]] 
print('\nUsing advanced indexing\n{}\n'.format(m))
print('\nBoolean indexing\n{}'.format(x[x > 5]))

////output
Input array is:
[[ 0  1  2]
 [ 3  4  5]
 [ 6  7  8]
 [ 9 10 11]]


Rows 
[[0 0]
 [3 3]]
Colomns 
[[0 2]
 [0 2]]

Output array is:
[[ 0  2]
 [ 9 11]]

Using slice operation
[[ 4  5]
 [ 7  8]
 [10 11]]

Using advanced indexing
[[ 3  4]
 [ 6  7]
 [ 9 10]]


Boolean indexing
[ 6  7  8  9 10 11]

array addition

import numpy as np 
a = np.array([[0.0,0.0,0.0],[10.0,10.0,10.0],[20.0,20.0,20.0],[30.0,30.0,30.0]]) 
b = np.array([1.0,2.0,3.0])
print('First array:\n{}\n'.format(a))
   
print('Second array:\n{}\n'.format(b)) 
  
print('First Array + Second Array\n{}\n'.format(a+b)) 

///output
First array:
[[ 0.  0.  0.]
 [10. 10. 10.]
 [20. 20. 20.]
 [30. 30. 30.]]

Second array:
[1. 2. 3.]

First Array + Second Array
[[ 1.  2.  3.]
 [11. 12. 13.]
 [21. 22. 23.]
 [31. 32. 33.]]

Array Transpose

import numpy as np 
a = np.arange(0,60,5) 
a = a.reshape(3,4) 
   
print( 'Original array is:')
print( a )
print ('\n'  )
   
print ('Transpose of the original array is:' )
b = a.T 
print (b )
print ('\n'  )
   
print( 'Modified array is:' )
for x in np.nditer(b): 
   print (x)

/////Output
Original array is:
[[ 0  5 10 15]
 [20 25 30 35]
 [40 45 50 55]]


Transpose of the original array is:
[[ 0 20 40]
 [ 5 25 45]
 [10 30 50]
 [15 35 55]]


Modified array is:
0
5
10
15
20
25
30
35
40
45
50
55

Pandas

Series

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve the first three element
print(s[:3])
print(s['e'])

////Output
a    1
b    2
c    3
dtype: int64
5

DataFrame

import pandas as pd
data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}
df = pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])
print(df)

////Output
        Name  Age
rank1    Tom   28
rank2   Jack   34
rank3  Steve   29
rank4  Ricky   42