Pandas Introduction

pandas lib in python

Pandas is the most comprehensive Python package available to Data Scientists and Analysts today. While machine learning and visualizing data may get all the attention, Python Pandas is the foundation of many projects.

What is Pandas?

Python Pandas library is used to work with data sets.

Data can be analyzed, cleaned, explored, and manipulated with it.

Wes McKinney created Pandas in 2008 to reference both “Panel Data” and “Python Data Analysis“.

Why Use Pandas?

Our data can be analyzed using Pandas and statistics can be applied to make conclusions.

Using Pandas, you can make disorganized data sets comprehensible and relevant by cleaning them.

Data science relies heavily on relevant data.


Applied Data Science is the study of analyzing, storing, and using data for the purpose of obtaining information.

Pandas can do what?

You can get detailed information about data using Pandas. For example:

  1. Can two or more columns be correlated?
  2. What is the average value?
  3. What is the maximum value?
  4. What is the minimum value?
  5. How does the data distribution look in any column?
  6. You can clean data by removing missing values and filtering rows or columns.
  7. Matplotlib can be used to visualize the data. Graph bars, lines, histograms, bubbles, and more.
  8. Put the transformed, cleaned data back in a CSV or database.

Pandas are also capable of deleting rows with irrelevant values, such as NULLs or empty rows. The process of cleaning data is called data cleansing.

To understand your dataset, you need to understand the nature of it, and pandas is the ideal for this.

Where is the Pandas Codebase?

When Wes McKinney worked at AQR Capital Management in 2008, he initially developed Pandas. He convinced AQR to open source Pandas. A second AQR employee, Chang She, contributed significantly to the library in 2012. Pandas have been updated many times over the years.

You can download Panda’s latest version from official’s.

This GitHub repository contains Pandas source code.

Install Pandas

Installing Pandas is extremely easy if Python and PIP are already installed. Python installation instructions can be found in the respective posts for Windows, Mac, and Linux

You can install it by running the following command:

pip install pandas

The library must be imported after pandas has been installed on the system. Please see the following.

Import Pandas

Import Pandas into your applications by adding the import command:

import pandas


import pandas as pd

Pandas has now been imported and is ready to be used.

Panda’s as pd – Alias

An alias for Panda’s is pd. The alias does not require importing the library, it just simplifies the code when a method or property is called.

When importing, use the as prefix to create an alias:



import pandas as pd firstdataset = { 'firms': ["Tesla", "Google", "Facebook", "Microsoft", "Blackrock"], 'passings': [3, 7, 2] } mrxvar = pd.DataFrame(firstdataset) print(mrxvar)

Check Version

Panda stores version strings under the version attribute.



import pandas as pd print(pd.__version__)

There are two types of data structures, Series and DataFrames, for manipulating data.

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