What is Series in Pandas – Pandas Tutorial Part 3

Imagine a Data Table like Excel sheet or SQL Table, In that table there are rows, columns and an index.

Series is just like a column which is 1 Dimensional. Additionally a series has an index also.

It is a one-dimensional array holding data of any type.

Properties of a Pandas Series

  • Pandas Series is Indexed
  • Pandas Series is Mutable
  • Pandas Series is Ordered

Pandas Series can be created using a list or any similar object.


import pandas as pd
ml = [2,8, 3]
s= pd.Series(ml)

In the above given example, ml is a list, s is the series that we are creating. This code will produce following output

0    2
1    8
2    3
dtype: int64

In this output the first column is called Index and the second column has our Values

In total it is a Single Dimensional Data.

If you want you can change the Index as per your liking

import pandas as pd
a = [3, 7, 9]
s= pd.Series(a, index = [\”x\”, \”y\”, \”z\”])


x    1
y    7
z    2
dtype: int64

Here you can see the index has been changed.

\”s\” is the main object here which is of Pandas Series type.

How to access values in a Series using Index

In the above given example we can access values by writing following method

print(s[‘x’])      # when the index is non numerical

print(s[2])   # when the index is integer

How to Create Pandas Series from a Python Dictionary:

import pandas as pd
marks= {“Mohan”: 420, “Sohan”: 380, “John”: 390}
s = pd.Series(marks)
# Here s is a series
Output will be
Mohan    420
Sohan    380
John     390
dtype: int64
So now if we have to get marks of Sohan , we can use

We will see more use of Series in other chapters as well.

Previous Chapter

Leave a Comment