Pandas Series

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.

Create series

Introduction

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

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:

>>> 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.

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

This ouputs the following:

>>> 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:

>>> 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:

>>> 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.

>>> 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:

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

>>> s[3:5]
3    4
4    5
dtype: int64
>>>