================ by Jawad Haider

Chpt 2 - Data Manipulation with Pandas

03 -Operations on Data in Pandas

---

import numpy as np
import pandas as pd

rng=np.random.RandomState(42)
ser=pd.Series(rng.randint(0,10,4))

ser

0    6
1    3
2    7
3    4
dtype: int64

df=pd.DataFrame(rng.randint(0,10,(3,4)),columns=['A',"B",'C','D'])
df

	A	B	C	D
0	6	9	2	6
1	7	4	3	7
2	7	2	5	4

np.exp(ser)

0     403.428793
1      20.085537
2    1096.633158
3      54.598150
dtype: float64

np.sin(df*np.pi/4)

	A	B	C	D
0	-1.000000	7.071068e-01	1.000000	-1.000000e+00
1	-0.707107	1.224647e-16	0.707107	-7.071068e-01
2	-0.707107	1.000000e+00	-0.707107	1.224647e-16

area = pd.Series({'Alaska': 1723337, 'Texas': 695662,
'California': 423967}, name='area')
population = pd.Series({'California': 38332521, 'Texas': 26448193,
'New York': 19651127}, name='population')

area

Alaska        1723337
Texas          695662
California     423967
Name: area, dtype: int64

population

California    38332521
Texas         26448193
New York      19651127
Name: population, dtype: int64

population/area

Alaska              NaN
California    90.413926
New York            NaN
Texas         38.018740
dtype: float64

area.index | population.index

/tmp/ipykernel_15930/3572280633.py:1: FutureWarning: Index.__or__ operating as a set operation is deprecated, in the future this will be a logical operation matching Series.__or__.  Use index.union(other) instead.
  area.index | population.index

Index(['Alaska', 'California', 'New York', 'Texas'], dtype='object')