pandas Series

A pandas Series is a 1D labeled array. By default, elements in a Series are labeled with integers according to their position, like in a Python list.

However, you can specify custom labels instead. These labels need not be unique, but they must be of a hashable type, such as integers, floats, strings, or tuples.

The elements of a Series can be of any type (integers, strings, floats, Python objects, and so on), but a Series works best if all its elements are of the same type. Ultimately, a Series may become one column in a larger DataFrame, and it’s unlikely you’ll want to store different kinds of data in the same column. 

Creating a Series

There are several ways to create a Series. In most cases, you feed it some kind of 1D dataset. Here’s how you create a Series from a Python list:

import pandas as pd

data = ['Jeff Russell','Jane Boorman','Tom Heints']

emps_names = pd.Series(data)

print(emps_names)

You start by importing the pandas library and aliasing it as pd. Then you create a list of items to be used as the data for the Series. Finally, you create the Series, passing the list in to the Series constructor method.

This gives you a single list with numeric indices set by default, starting from 0:

0 Jeff Russell

1 Jane Boorman

2 Tom Heints

dtype: object

The dtype attribute indicates the type of the underlying data for the given Series. By default, pandas uses the data type object to store strings.

You can create a Series with user-defined indices as follows:

data = ['Jeff Russell','Jane Boorman','Tom Heints']

emps_names = pd.Series(data,index=[9001,9002,9003])

print(emps_names)

This time the data in the emps_names Series object appears as follows:

9001 Jeff Russell

9002 Jane Boorman

9003 Tom Heints

dtype: object

You start by importing the pandas library and aliasing it as pd. Then you create a list of items to be used as the data for the Series. Finally, you create the Series, passing the list in to the Series constructor method .

This gives you a single list with numeric indices set by default, starting from 0:

0 Jeff Russell

1 Jane Boorman

2 Tom Heints

dtype: object

The dtype attribute indicates the type of the underlying data for the given Series. By default, pandas uses the data type object to store strings.

You can create a Series with user-defined indices as follows:

data = ['Jeff Russell','Jane Boorman','Tom Heints']

emps_names = pd.Series(data,index=[9001,9002,9003])

print(emps_names)

This time the data in the emps_names Series object appears as follows:

9001 Jeff Russell

9002 Jane Boorman

9003 Tom Heints

dtype: object