Accessing Series Items in Pandas

Michael Zippo 18.07.2021

Let’s discuss the different ways to access elements of this Pandas Series.

First, create a Pandas Series.

       # import pandas module  
   import   pandas as pd 
   
  # create data frame  
   df   =   pd.read_csv (  " https://media.python.engineering/wp-content/uploads/nba.csv "  ) 
   
   ser   =   pd.Series (df [  ’Name’  ]) 
   ser.head (  10  ) 
   # or just df [& # 39; Name & # 39;]. head (10)

Output:

Example # 1: Get the first element of the series

# pandas module import

import pandas as pd

# create data frame

df = pd.read_csv ( " https://media. python.engineering/wp-content/uploads/nba.csv " )

df [ ’Name’ ]. head ( 10 )

# get first element

ser [ 0 ]

Output:

Example # 2: accessing multiple elements by providing element position

# pandas module import

import pandas as pd

# create data frame

df = pd.read_csv ( " https://media.python.engineering/wp-content/uploads/nba.csv " )

df [ ’ Name’ ]. Head ( 10 )

# get multiple items at a given index

ser [[ 0 , 3 , 6 , 9 ]]

Output:

Example # 3: Accessing the first 5 elements in a series

# pandas module import

import pandas as pd

# create data frame

df = pd.read_csv ( " https://media.python.engineering/wp-content/uploads/nba.csv " )

df [ ’Name’ ]. head ( 10 )

# get the first five names

ser [: 5 ]

Output:

Example # 4: Get the last 10 items in a series

       # pandas module import 
   import   pandas as pd 
   
  # create data frame  
   df   =   pd.read_csv (  " https://media.python.engineering/wp-content/uploads/nba.csv "  )  
   
   df [  ’Name ’ ]. head (  10  )  
   
  # get the last 10 names  
   ser [  -   10  :]

Output:

Example # 5: Accessing multiple items by providing an index label

       # import pandas module  
   import   pandas as pd 
   import   numpy as np 
   
   ser   =   pd.Series (np.arange (  3  ,   15  ), index   =   list   (  "abcdefghijkl"  ))  
   
   ser [[ ’ a’  ,   ’d’  ,   ’g’  ,  ’ l’  ]]