Series by Saritha Vijayachandran

Series (One Dimensional Data Structure)

saritha2014bvm — 2021-06-18 16:31:15 UTC

Creating Series

saritha2014bvm — 2021-06-18 16:31:29 UTC

Empty Series

saritha2014bvm — 2021-06-18 16:39:58 UTC

import pandas as pd

s1=pd.Series()

print(s1)

Using List

saritha2014bvm — 2021-06-18 16:50:19 UTC

import pandas as pd

l=[10,20,30,40] #Define list

s1=pd.Series(l)#Create Series

print(s1)

Using Dictionary

saritha2014bvm — 2021-06-18 16:54:18 UTC

import pandas as pd

d={'Jan':31,'Feb':28,'Mar':31,'Apr':30}

s1=pd.Series(d)

print(s1)

Using ndarray

saritha2014bvm — 2021-06-18 16:55:20 UTC

import pandas as pd

import numpy as np

l=[‘a’,’b’,’c’,’d’]

data = np.array(l)

s = pd.Series(data)

print(s)

Constant value

saritha2014bvm — 2021-06-18 17:35:22 UTC

import pandas as pd

import numpy as np

ind=['Aparna','Akash','Akshay','Sonia','Akshitha']

s=pd.Series('Welcome to 2020-21',index=ind)

print(s)

Mathematic Expression

saritha2014bvm — 2021-06-18 17:36:40 UTC

import pandas as pd

import numpy as np

d=np.array([1,2,3,4])

ind=['a','b','c','d']

s1=pd.Series(d*2,index=ind)

print(s1)

Using NaN values for missing values

saritha2014bvm — 2021-06-18 17:37:46 UTC

Attributes

saritha2014bvm — 2021-06-18 17:40:06 UTC

Display Values

saritha2014bvm — 2021-06-18 17:43:05 UTC

Mathematic Operations

saritha2014bvm — 2021-06-18 17:50:56 UTC

saritha2014bvm — 2021-06-18 17:55:46 UTC

import pandas as pd

import numpy as np

s=pd.Series([1,3,np.nan,5,7])

print(s)

saritha2014bvm — 2021-06-18 17:57:37 UTC

print(s1+s2)
In all the operations, if both the series are of the same index, based on the operator, operation will be performed.
If the index are different, NaN will be added.

Retrieving values based on condition

saritha2014bvm — 2021-06-18 17:58:06 UTC

Difference between s1<2 and s1[s1<2]

saritha2014bvm — 2021-06-18 17:59:30 UTC

s1<2
s1[s1<2]

Updating values

saritha2014bvm — 2021-06-18 18:00:49 UTC

saritha2014bvm — 2021-06-18 18:02:08 UTC

1. Using [ ] -
print(s1['d'])
2. Accessing multiple values
print(s1[['a','c','e']])
3. Slicing
print(s1[:3])
print(s1[-3:])
4. iloc
print(s1.iloc[1:4])
5. loc
print(s1.loc['a':'c'])
6. head()
7. tail()

saritha2014bvm — 2021-06-18 18:03:25 UTC

Series.index
Series.values
Series.dtype
Series.shape

Series.nbytes

Series.ndim

Series.size

Series.hasnans

Series.empty

saritha2014bvm — 2021-06-18 18:06:02 UTC

series[3]=12
series[[1,2]]=[90,89]
s1.index=['a','b','c']

Naming a Series

saritha2014bvm — 2021-06-18 18:09:23 UTC

saritha2014bvm — 2021-06-18 18:10:14 UTC

s1=pd.Series({'Jan':31,'Feb':28,'Mar':31,})

s1.name=‘Days’

s1.index.name=‘Month’

Deleting an element

saritha2014bvm — 2021-06-18 18:11:28 UTC

Drop() method

saritha2014bvm — 2021-06-18 18:12:11 UTC

Eg: S.drop(3)