New Year Offer - Flat 50% Off On All Courses


What is Python Pandas? Everything you need to know about Pandas

Last updated on Dec. 24, 2020, 10:49 a.m. 755 Views


Niharika |

Niharika is an Experienced Technical Writer. She has written lots of articles and blogs on latest technologies including Data Science, Machine Learning and IoT. She has keen interest in learning and writing for emerging technologies.

What is Python Pandas? Everything you need to know about Pandas

Last updated on Dec. 24, 2020, 10:49 a.m. 755 Views


What is Python Pandas? Everything you need to know about Pandas

Python Pandas are a popular package of Python and are primarily used for data manipulation, cleaning of data, and analyzing it. Pandas can be used for various purposes like to handle various data types and datasets that include unlabelled data, time-series, and ordered data. Pandas are home to your data and numerous operations can be performed on data with this tool.

The formats of data files can be easily formatted or changed. One can easily merge two data sets, visualize the data, and make calculations. The richness of the features and functionalities of Pandas makes it the first choice for every Data Science professional. That is why every professional wants to learn Data Science. Without understanding Pandas, you cannot understand and learn it perfectly.

Role of Pandas in Data Science

Pandas library is an essential part of every Data Science professional. Python Pandas are built on another popular library of Python that is known as NumPy. Pandas and NumPy share a lot of features and structures, so one who is familiar with NumPy can easily learn Pandas as well.

Most of the time Data Science professionals use Pandas to feed data in SciPy for statistical analysis. Data scientists also learn Scikit-learn and Matplotlib functions for machine learning and plotting functions. 

Learning Prerequisites

Before going further in detail for learning Pandas and their working and operations, we must know who can or who cannot use Pandas. One should know and learn the fundamentals of Python.-

Learning the basics or fundamentals of Python is of utmost importance. You can learn Python code working, moreover by understanding the underlying code you can use it wherever it is required by reading this Pandas tutorial. Moreover, it is important to learn NumPy to practice Pandas. NumPy can help you in understanding Pandas easily.

Online Pandas documentation are really helpful, you can make yourself familiar with the concepts and learn Pandas practically. Let us take a look at Pandas and their required functions. We can use Pandas for various types of data handling like:

  • Ordered and Unordered time series data
  • Unlabeled Data
  • Tabular data with heterogeneously-typed columns
  • Matrix data with rows and column labels
  • Any other form of data

Pandas Installation

You can install Pandas in Python and for that, you can use command-line instruction. Here you can type the following command to initiate Pandas installation:

pip install pandas

Moreover, if you have installed anaconda in your system, then just type “conda install pandas” to begin the installation. After completion of the installation process, you can go to your IDE and import pandas libraries.

Now, as you have installed Pandas let us check some of its basic operations that can be helpful for the Data Scientists.

Popular Operations of Pandas

You can perform lots of operations by using Pandas data frames, missing data, group, or series. Some of the most-used data manipulation operations of Pandas are listed below:

  1. Joining and Merging
  2. Slicing
  3. Data Munging
  4. Index Changing
  5. Concatenation
  6. Column Header Change

Now let us look at all of the operations one-by-one.

1. How to Create Pandas DataFrames

To perform any operation one needs to create data frames in Pandas and this is the first step for every operation. You can create your data frames from scratch and convert any existing NumPy array or lists to Data Frames as well. If you creating frame in NumPy then you can follow the following code to convert the NumPy array to Data Frame:

>>>import numpy as np,pandas as pd



 You can copy the NumPy arrays to Pandas in this way.

  2. Joining and Merging

You can make a single data frame by merging two data frames, moreover you can also decide the column that you want to make common. Let us understand the concept with the help of an example, in which we suppose that we have three data frames with one common column, let us merge these frames to get the desired result:

>>>import pandas as pd

>>>df1= pd.DataFrame ({ “HP1”:[80,90],”It_Rate”:[2,1],”IND_GDP”:[50,45]}, index=[2001, 2002])

>>>df2=pd.DataFrame({“HP1”:[180,90],”It_Rate”:[12,1],”IND_GDP”:[50,45]},index=[2004, 2002])



Here, the two data frames have merged and one data frame is formed with the matching data. Here the user can also specify a column that you want to make common. Like in the above example if we want to make It_Rate column common and want to keep every other column separate, then we can implement the concept in the following way:

>>>import pandas as pd

>>>df1= pd.DataFrame ({ “HP1”:[80,90],”It_Rate”:[2,1],”IND_GDP”:[50,45]}, index=[2001, 2002])

>>>df2= PD.DataFrame ({ “HP1”:[80,90],”It_Rate”:[2,1],”IND_GDP”:[50,45]}, index=[2001, 2002])

>>>merged=pd.merge(df1,df2,on=” It_Rate”)


Well, this was about the merging operation of Pandas, let us look now at the joining operation of Pandas. This is again a convenient operation method of Panda that can join two differently indexed columns of data frames and result in a single data frame. Here, the joining operation is only performed on the index instead of on other columns. Let us look at this practically.

>>>import pandas as pd

>>>df1=pd.DataFrame({"It_rate":[2,1,2,3],"IND_GDP":[50,45,45,67]},index=[2001,2002,2003,   2004])

>>>df2=pd.DataFrame({"Low_Tier_HPI":[80,90,70,60],"Unemployment":[1,3,5,6]},index = [2001,2003,2004,2008])



python training in vijayawada by codegnan

3. Concatenation

To join two data frames together, you can use this operation that is known as dataframes. You can use any dimension of your choice on which you want to perform concatenation. The command that is used to perform concatenation is :pd.concat, here we have to pass the data frames that we want to concatenate together. Here is the example of the same:

>>>import pandas as pd

>>>df1=pd.DataFrame({"HPI":[80,85,88,85],"It_rate":[2,1,2,3],"US_GDP_Thousands":[50,45,45,67]},index = [2001,2002,2003,2004])

>>>df2=pd.DataFrame({"HPI":[80,85,88,85],"It_rate":[2,1,2],"US_GDP_Thousands":[50,45,45,67]},index = [2005,2006,2007,2008])

>>>Concat = pd.concat([df1,df2])


As a result of concatenation, you will get “NaN (Not a Number)” at many places, this will happen when the data frame will not have any value especially for those indexes. When you concatenate or join any two data frames try to make sure to line up all information correctly.

 4. Change in Index

In the data frames of Pandas, we can change the index values as well. Here we will see this with an example, how can you change the index value of a data frame.

>>>import pandas as PD

>>>df1= PD.DataFrame ({ “HP1”:[80,90],”It_Rate”:[2,1],”IND_GDP”:[50,45]}, index=[2001, 2002])

>>>df1.set_index(“HP1”, inplace=True)


As you can see that we can change the index to any column_name like we have created a new index with the name of HP1.

 5. Change Column Header

We can anytime change the headers of columns through Pandas. Let us check it with an example. We will change the header of any column. Let us implement the same concept of changing the header name:

>>>import pandas as pd

>>>df1= PD.DataFrame ({ “HP1”:[80,90],”It_Rate”:[2,1],”IND_GDP”:[50,45]}, index=[2001, 2002])



Here through this code, we have changed the name of the IND_GDP column to New_GDP.

 6. Data Munging

You can convert data easily from one format to another as if we have any file in a .csv file, you can easily convert this file to .html format. You can convert the data files from one to another format as shown in the following example:

>>>import pandas as PD



In this way, we can change the file type and rename it as well. The file path can be copied directly from the browser display or address bar.

7. Viewing Some Rows and Columns 

If you want to see some of the rows or columns of your data frame. You can easily do this by using the .head() function.


By this function, you will get the first five lines or rows of the data frame. You can also pass the number of rows that you want to display, for example, if you want to display the first twenty rows of the data frame, then you can write and follow this command as:


You can also view the last five rows of the data frame. This can be done by you just by using the. tail() function. This function is similar to the .head() function, you can also pass any argument as well to this function. This will return the required number of rows from the data frame. The syntax of the code is:


This code will return 20 rows from the last.

8. To Fetch the Information

One of the most used functions by the Data Scientists is .info(). This function is used to display data frame information and gives you a deeper understanding of the data frame. Pandas users can use the function in the following way:

You can get a lot of useful information about a dataset or data frame through this command. Mostly used information is number of rows, non-null values, type of data present in the column, and many more


The Python Pandas tutorial that we have discussed is a brief of the full library. We hope you will find it useful and informative. Python Pandas tutorial is a vast topic and cannot be covered in a single blog. Experienced professionals or trainers can guide the aspirants and make them proficient in technology. By joining any online or offline course you can also learn the same. So, join the course of your choice and make yourself a proficient Python developer.


Trending Certifications at Codegnan

Python for Data Science Certification



MTA Certification - Python for Data Science

555 Learners

200 Hours

Next Batch:
4th Jan | Weekday (Online) | 07:00PM - 09:00PM

Key Skills:
Mathematical Thinking, Critical Thinking

Machine Learning with Python Certification



Codegnan Certification for Machine Learning

431 Learners

50 Hours

Next Batch:
Nov 23rd | Weekday (Online) | 10:00AM - 12:00PM

Key Skills:
Mathematical thinking, Python Programming Skills

Web Development with Python - Django Certification



Codegnan Certification for Django

380 Learners

40 Hours

Next Batch:
Dec 1st | Weekday (Online) | 6:00PM - 7:00PM

Key Skills:
Python Programming Skills

ReactJS Certification



Codegnan Certification for ReactJs

98 Learners

60 Hours

Next Batch:
Nov 30th | Weekday (Online) | 2:00PM - 4:00PM

Key Skills:
HTML, CSS, Java Script

Browse Categories

Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1 Browse Btn1
Become a Certified Professional  


Video Reviews