Pandas

Pandas, or the Python Data Analysis Library, was created by Wes McKinney in 2008. It’s primary use to manipulate data in DataFrames or 2-dimensional labeled data structure with columns of potentially different types. The insertion, manipulation, and transformation of DataFrames are of significant use to Analysts using Python. Featuring many of the aspects that Excel and other data analysis tools possess, but able to process much larger datasets, Pandas use has grown significantly and is one of the most used libraries for Analysts, Scientists, and Data Engineers.

Pandas has core features which include the following:

  • DataFrames & Series objects
  • Reading & Writing Data
  • Aggregating & Grouping Data
  • Pivoting Tables
  • Time Series Analysis
  • Visualizations in Pandas
  • Merging & Joining data

For more on Pandas see our extensive post on its history, usage, and support within the analytics community.

Adding rows to a Pandas Dataframe

While studying Data Science, we often come across DataFrames ready to be used. Normally, those DataFrames already contains all of…

6 days ago

How to Install & Import Pandas in Python

Pandas is one of the most powerful libraries for data analysis and is the most popular Python library, with growing…

2 weeks ago

A Holistic Guide to Groupby Statements in Pandas

The Importance of Groupby Functions In Data Analysis Whether working in SQL, R, Python, or other data manipulation languages, the…

4 weeks ago

Pandas-Profiling, explore your data faster in Python

All datasets have one obvious thing in common, information, but this information is easy and fast to extract? Normally, no.…

1 month ago

Create a DataFrame or Series from a List or Dictionary

Use Pandas Series or DataFrames to make your data life easier In this article, we will take you through one…

2 months ago

Pandas-Log and Its Debugging Capabilities

Introduction: Working with data has a strong connection with programming. Therefore, a Data Scientist who knows the best practices of…

2 months ago

Extract Google Analytics Data from BigQuery with Python

Why Query BigQuery? BigQuery is a seriously powerful data warehousing technology by Google that has direct integration into Google Analytics…

2 months ago

Linear Regression in Python Using Statsmodels

What is Regression? In the simplest terms, regression is the method of finding relationships between different phenomena. It is a…

3 months ago

Using Pandas to explore data in Excel files

Introduction: When it comes to Data Science, we need to talk about data, and data comes in a lot of…

3 months ago

Pandas: An Open Source Library for Python

A Brief Introduction Pandas is an Open Source library built on top of NumPy. It allows for fast analysis and…

5 months ago