# Pandas Series Example | What Is Series In Python Pandas

Pandas Series is a one-dimensional data structure designed for the particular use case. The Series is the one-dimensional labeled array capable of holding any data type. Series is the one-dimensional labeled array capable of carrying data of any data type like integer, string, float, python objects, etc.

**Pandas Series Example**

Content Overview

The axis labels are collectively called index. In layman’s terms, Pandas Series is nothing but the column in an excel sheet.

You can control the index(label) of elements. A series label can be thought of as similar to the python dictionary.

Series also supports vector operations. Any operation to perform on the series, get’s performed on every single element.

**How to Create a Series in Pandas?**

The Pandas Series can be created out of the Python list or NumPy array.

Unlike Python lists, the Series will always contain data of the same type. This makes NumPy array the better candidate for creating a pandas series.

Let’s create a series using the NumPy library.

# app.py import pandas as pd import numpy as np data = np.array(['a','b','c','d']) seri = pd.Series(data) print(seri)

In the above example, we have imported two libraries which are Pandas and Numpy.

Then we have used the NumPy to construct the data and passed that to the series function of pandas and created a series.

Run the above file and see the output.

**Syntax**

A pandas Series can be created using the following constructor.

pandas.Series( data, index, dtype, copy)

The **data** parameter takes various forms like ndarray, list, constants.

The **index** parameter values must be unique and hashable, the same length as data.

The **dtype **parameter is for the data type. If None, the data type will be inferred.

The **copy **parameter is to copy the data. The default parameter is False.

**Create an Empty Series**

A primary series, which can be created is an Empty Series. See the following code.

# app.py import pandas as pd seri = pd.Series() print(seri)

See the below output.

**Create a Series from ndarray**

If data is a ndarray, then the index passed must be of the same length. If no index is passed, then by default index will be **range(n)** where **n** is array length, i.e., [0,1,2,3…. **range(len(array))-1].**

# app.py import pandas as pd import numpy as np data = np.array(['A','B','C','D']) seri = pd.Series(data) print(seri)

In this example, we have imported the NumPy library and created a data array and pass that data to the series function to create a Pandas Series.

If we did not pass any index, by default, it would be assigned the indexes ranging from 0 to **len(data)-1**, i.e., 0 to 3.

**Provide the Indexes With Data in Series**

Let’s take an example where we pass the data as well as indexes and see the output.

# app.py import pandas as pd import numpy as np data = np.array(['A','B','C','D','E']) seri = pd.Series(data, index=[18, 19, 20, 21, 22]) print(seri)

See the output below.

**Create a Series from the dictionary**

A **dictionary** can be passed as input, and if there is no index is specified, then the dictionary keys are taken in the sorted order to construct an index.

If an** index** is passed, the values in data corresponding to the labels in the index will be pulled out.

# app.py import pandas as pd import numpy as np data = {'name' : 'krunal', 'website' : 'appdividend.com', 'role' : 'author'} siri = pd.Series(data) print(siri)

We have taken the Python Dictionary as data. See the below output.

**Create a Series from Scalar**

If data is the scalar value, then an index must be provided.

The value will be repeated until the length of the** index. **See the following example.

# app.py import pandas as pd import numpy as np siri = pd.Series(21, index=[0, 1, 2, 3, 4]) print(siri)

See the below output.

**Accessing Data from Series**

We can access the items through its index.

Data in the series can be accessed similarly to that in an **ndarray.**

See the below example.

# app.py import pandas as pd import numpy as np data = np.array(['A','B','C','D','E']) seri = pd.Series(data, index=[18, 19, 20, 21, 22]) print(seri[20])

In the above example, we have already provided the indexes which start from **18 **to **22. **We get the output **C **because the index maps to that element. Now, see the below output.

Finally, Python Pandas Series Tutorial | Data Structure Example In Pandas is over.