Pandas Basic tutorial 5 - Python Programming


Pandas Basic tutorial 5 - Python Programming harsha navalkar blog daily experience

Pandas Basic tutorial 5 - Python Programming
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If you have not seen the Pandas
 Basic tutorial 1 - Python Programming  CLICK HERE
 Basic tutorial 2 - Python Programming  CLICK HERE
Basic tutorial 3 - Python Programming  CLICK HERE
Basic tutorial 4 - Python Programming  CLICK HERE

PROGRAM 1

import pandas as pd
import numpy as np

s1 = pd.Series([1,2,3,4,5,4])
s2 = pd.Series(np.random.randn(10))

print (s1.pct_change())

d= pd.DataFrame(np.random.randn(5, 5),columns=['a', 'b', 'c', 'd', 'e'])

print('\n Percent change \n')
print (d.pct_change()) #percentage change
print('\n covariance 1 \n')
print (s1.cov(s2))    #covariance
print('\n covariance 2\n')
print (d['a'].cov(d['b']))
print (d.cov())
print('\n correlation\n')
print (d['a'].corr(d['b']))
print(d.corr())
print('\n Data Ranking \n')
d['d'] = d['b'] # so there's a tie
print (d.rank())


OUTPUT
runfile('C:/Users/dell/temp2.py', wdir='C:/Users/dell')
0         NaN
1    1.000000
2    0.500000
3    0.333333
4    0.250000
5   -0.200000
dtype: float64

 Percent change 

          a         b         c         d         e
0       NaN       NaN       NaN       NaN       NaN
1 -2.051009 -0.060892 -1.433328 -0.540440 -1.233141
2 -3.766884 -1.276649 -0.819115 -5.498268 -6.156215
3 -0.312564 -2.389023  0.354732 -0.625432 -0.337789
4 -2.214880 -2.328694 -1.138566 -0.603732 -0.876367

 covariance 1 

0.9431969011296396

 covariance 2

-0.08364434111414272
          a         b         c         d         e
a  0.523685 -0.083644 -0.159114 -0.530667 -0.413056
b -0.083644  3.155017  0.717062 -1.827976 -0.129688
c -0.159114  0.717062  1.767122 -0.847141  0.455338
d -0.530667 -1.827976 -0.847141  1.937669  0.398311
e -0.413056 -0.129688  0.455338  0.398311  0.426769

 correlation

-0.06507296323532948
          a         b         c         d         e
a  1.000000 -0.065073 -0.165402 -0.526801 -0.873731
b -0.065073  1.000000  0.303684 -0.739315 -0.111764
c -0.165402  0.303684  1.000000 -0.457807  0.524330
d -0.526801 -0.739315 -0.457807  1.000000  0.438012
e -0.873731 -0.111764  0.524330  0.438012  1.000000

 Data Ranking 

     a    b    c    d    e
0  3.0  1.0  1.0  1.0  2.0
1  2.0  2.0  5.0  2.0  5.0
2  5.0  4.0  3.0  4.0  1.0
3  4.0  3.0  4.0  3.0  3.0
 4  1.0  5.0  2.0  5.0  4.0


More tutorials to come soon!

Pandas Basic tutorial 5 - Python Programming