Pandas series is a one-dimensional data structure. It can hold data of many types including objects, floats, strings and integers. You can create a series by calling pandas.Series().

An list, numpy array, dict can be turned into a pandas series. You should use the simplest data structure that meets your needs. In this article we’ll discuss the series data structure.

Related course: Data Analysis with Python Pandas

Create series

Introduction

Pandas comes with many data structures for processing data. One of them is a series.
The syntax for a series is:

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import pandas as pd
s = pd.Series()
print(s)

This creates an empty series.

Create series from list

To turn a list into a series, all you have to do is:

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>>> import pandas as pd
>>> items = [1,2,3,4]
>>> s = pd.Series(items)

The contents of s is:

0 1
1 2
2 3
3 4
dtype: int64

By default is assigns an index. First it shows the index, then the element value.

Create series from ndarray

You can create a series from a numpy ndarray.

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>>> import pandas as pd
>>> import numpy as np
>>> data = np.array(['x','y','z'])
>>> s = pd.Series(data)

This ouputs the following:

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>>> s
0    x
1    y
2    z
dtype: object
>>>

Create a series from a dict

If you have a dictionary, you can turn it into a series:

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>>> import pandas as pd
>>> import numpy as np
>>> data = { 'uk':'united kingdom','fr':'france' }
>>> s = pd.Series(data)

The contents of the series is as follows:

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>>> s
uk    united kingdom
fr            france
dtype: object
>>>

As index it used the dictionary keys.

Pandas series

Pandas series get index

You can access series data like you would with a list or ndarray.

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>>> import pandas as pd
>>> import numpy as np
>>> data = np.array(['x','y','z'])
>>> s = pd.Series(data)
>>> s[0]
'x'
>>> s[1]
'y'
>>>

You slice a series, like you would with a list:

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>>> data = np.array([1,2,3,4,5,6])
>>> s = pd.Series(data)
>>> s[:3]
0    1
1    2
2    3
dtype: int64
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4
5
>>> s[3:5]
3    4
4    5
dtype: int64
>>>

Related course: Data Analysis with Python Pandas