2 Easy Ways To Create Pandas Series – The Ultimate Guide

Pandas series is the most important part of the data structure. Pandas series can be defined as a column in an excel sheet. We can create series by using SQL database, CSV files, and already stored data. There are many ways to create a series in Pandas but, we are going to practice in these two ways-

  • With ndarray or numpy array
  • With Python Dictionary

By the end of this pandas series tutorial, I am sure you can create and perform any task on series.

Pandas Series Tutorial

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1. What is pandas series?

A series in pandas can be thought to be the fundamental piece of data structure. It is basically nothing but a one-dimensional array-like structure, which can be used to handle and manipulate data. What makes it special is its index attribute, which has incredible functionality and is heavily mutable.

Parameters in pandas series:

  • data: This the value you want your series to possess.
  • index: This is the index related to the value you use for the series.
  • dtype: This specifies the type of values in the series.
  • copy: This copies the data which was input.

Do you know what makes python pandas unique?

Let’s start to code in pandas series-

To begin, we import the pandas library.

>>> import pandas as pd

2. How to create a pandas series?

In your second code box after importing the library, go ahead and enter the following code-

>>> dataflair_arr= pd.Series([2,3,-4,6])

This will create your series.

To access the series, code the below code-

>>> dataflair_arr


0   2
1   3
2   -4
3   6
dtype: int64

Congratulations! You have created your first own series in pandas.

Create Series in Pandas

2.1 Create a series with ndarray or numpy array

We can also create a series using a ndarray or numpy array:

First, we will import the numpy library:

>>> import numpy as np

This lets us refer to the library as np. After initializing, we create a numpy array and then turn it into a series.

>>> npa = np.array(['d','a','t','a'])
>>> dataflair_ar= pd.Series(npa)
>>> dataflair_ar

The first line creates the numpy array and the second line turns the array into pandas series.


0   d
1   a
2   t
3   a
dtype: object

Pandas Series with ndarray or numpy array

2.2 Create a series from a python dictionary

We can create a series from python dictionaries To do this, we first need to create a dictionary:

>>> dataflair_dict = { 'Delhi': 12.9, 'Mumbai': 8.4, 'Kolkata': 9.7 }

To turn this dictionary into a pandas series, all we have to do is:

>>> dataflair_arr3= pd.Series(dataflair_dict)
>>> dataflair_arr3


Delhi          12.9

Mumbai     8.4

Kolkata      9.7

dtype: float64

Create Pandas Series from a Python Dictionary

3. How to change the index of pandas series?

For indexing in pandas series first, we will create a list.

>>> num=[‘n1’,’n2’,’n3’,’n4’]

This is our list, and we want this to be the index to the values (we have provided). So, we write the following code and run it:

>>> dataflair_arr2= pd.Series([4,5,-2,2], index=num
>>> dataflair_arr2


Change the Index of Series

4. How to perform mathematical operations on a series?

If you want to check the value to a corresponding index, simply use the following command

>>> dataflair_arr2[‘n2’]

This will return the value 5.

We can use parameters to filter values in a series. For this, let’s take the following example:

>>> dataflair_arr2[dataflair_arr2>2]

What does this mean? This basically is telling the series that you want a list of all the values that are greater than 2.

Running the code given above, we get:

n1  4
n2   5
dtype: int64

Check out pandas basic functionality to enhance your skills

Because 4 and 5 are the only values in the pandas series, that is more than 2. If a certain index is present inside a series or not, then use the ‘in’ parameter from python’s native code.

>>> ‘n3’ in dataflair_arr2

This will return “True”.

Example of Mathematical operations on Pandas Series

>>> dataflair_arr2*5


n1    20
n2    25
n3   -10
n4    10
dtype: int64

Perform Mathematical Operations on Pandas Series

5. Demonstration of missing values

Let’s create a list of cities and implement it into a series as index:

>>> cities=['Delhi', 'Kolkata', 'Mumbai', 'Chennai']
>>> dataflair_arr4=pd.Series(dict,index=cities)
>>> dataflair_arr4

Did you notice something? Chennai is a new addition and there is no value pertaining to it in the original series. Here, the value for Chennai is represented as NaN.

Delhi         12.9
Kolkata      9.7
Mumbai     8.4
Chennai    NaN
dtype: float64

NaN is Pandas way to represent missing values.

Demonstration of Missing Values

6. How to add two series in pandas?

Yes, it’s possible to add two series in pandas.

>>> dataflair_arr4+dataflair_arr3


Chennai    NaN
Delhi          25.8
Kolkata     19.4
Mumbai    16.8
dtype: float64

Now, it’s time to learn how to sort in pandas series

Adding Pandas Series

7. How to access a range of elements in a pandas series?

Let’s say, we want to access the first 2 elements of arr4. All we have to do is use the range function in pandas, which we can use with the help of ‘:’

The code to access the first two elements will be:

>>> dataflair_arr4[:2]


Delhi      12.9
Kolkata    9.7
dtype: float64

The code for the last two is:

>>> dataflair_arr4[2:]


Mumbai     8.4
Chennai    NaN
dtype: float64

Therefore, the function basically works in the way series[x:y] where x is the number for the first row of the range and y is the last row of the range. Let’s try :

>>> dataflair_arr4[1:3]


Kolkata    9.7
Mumbai   8.4
dtype: float64

Access a Range of Elements in a Series


Now, you can create and perform any task on pandas series. It is very important to learn a series concept to become a master in pandas. With the help of pandas series, you can gain expertise in the other two data structures; dataframes, and panels.

The next step towards mastering pandas is dataframes

If you have any issues or questions, please drop a comment below.

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