In this article, I am going to talk about Pandas, Python’s one of the most important libraries. 
 
If you want to analyze huge sets of data, manipulate spreadsheets and CSV’s with just a few lines of code, then Pandas is the library that you are looking for.

What is Pandas

Pandas is a high-level, fast, powerful, flexible, and easy to use open-source library used for data manipulation and analysis written for the Python programming language developed by Wes McKinney.  
 
The word pandas is an acronym which is derived from “Python and data analysis” and “panel data“. In particular, it offers data structures and operations for manipulating numerical tables and time series.
 
Unlike NumPy library that provides objects for multi-dimensional arrays, Pandas provides an in-memory 2d table object called Dataframe.
 
It can be installed in a simple way as follows :
pip install pandas 
It can be imported in a simple way as follows :
import pandas as pd
#  pd label can be used to access the pandas library. 

Why Use Pandas

 

  • Great Handling of Data : 
The Pandas library provides us Series and DataFrames that helps us to represent data efficently. So We can manage and explore data fast and efficently.
 
 
  • Cleaning up Data :

Data cleaning is very important and the Pandas library makes it very easy for us. 

 

  • Handling Missing Data : 
 
Data can be quite confusing to read and one of the many problems associated with data is the occurrence of missing data or value.  Pandas features have you covered on this end because handling missing values is integrated within the library.
 
 
  • Input and Output Tools : 
 
Pandas provide a wide variety of built-in tools for reading and writing data.  When analyzing, you need to read and write data into web services, databases. Too much code would have been required to perform these operations. However, this has been made extremely simple with the help of Pandas’ built-in tools.
 
 
  • Multiple File Formats Supported : 
 
Pandas can support a wide variety of file formats. (including JSON, CSV, Excel and HDF5, etc. )
 
 
 
  • Optimized Performance
 
The critical code for Pandas is written in C or Cython, which makes it extremely responsive and fast.
 
 
 
  • Perform Mathematical operations on the data :
 
The apply functionality in Pandas allows you to perform a mathematical operation on data. This helps a lot because sometimes the dataset you have may not be in the correct order. This will be corrected simply by using a mathematical operation in the data set.
 
 
 
 
 
So how do you learn Pandas ? As with everything else in life, the best way to learn something is to learn by doing it.
 
Below I have listed the best courses and books for you to learn Pandas . These courses will enable you to learn Pandas as efficiently as possible.
 
 

Best Pandas Courses

 

Codeacademy is one of the best platforms among e-learning platforms. The course I recommend here is Codeacademy – Learn Data Analysis with Pandas.

 

In this course : 

  • In the first part, you are going to use Pandas to create and manipulate tables so that you can process your data faster and get your insights sooner.
  • In the second part, you are going to learn the basics of aggregate functions in Pandas, which let us calculate quantities that describe groups of data.
  • In the third part, you are going to learn how to combine information from multiple DataFrames.

 

Also, in this course, you are going to create 3 projects for your portfolio.

 

Udacity is an e-learning platform that offers high quality content, especially in the field of artificial intelligence and data science.

The course I recommend here is the udacity intro to data analysis course.

In this course :

  • In the first part, you are going to learn Data analysis Process : 
 
            – Learn about the data analysis process, pose a question
            – Wrangle your data, draw conclusions and/or make predictions
            – Complete an analysis of Udacity student data using pure Python, with few additional libraries.
 
  • In the second part you are going to learn NumPy and Pandas for 1D Data :
            – Learnto use NumPy and Pandas to make the data analysis process easier.
            – Features that apply to one-dimensional data.
            – Learn to use NumPy arrays, Pandas Series, and vectorized operations.
 
 
  • In the third part you are going to learn NumPy and Pandas for 2D Data : 
 
            – Continue learning about NumPy and Pandas, this time focusing on two-dimensional data.
            – Learn to use two-dimensional NumPy arrays and Pandas DataFrames.
            – Group your data and to combine data from multiple files.
 
 
  • In the fourth part yo ure going to learn Investigate a Dataset : 
 
            – Use NumPy and Pandas to go through the data analysis process on one of a list of recommended datasets.

Udemy - Data Analysis with Pandas and Python

 

In this course you are going to learn : 
 
  • Perform a multitude of data operations in Python’s popular “pandas” library including grouping, pivoting, joining and more!
  • Learn hundreds of methods and attributes across numerous pandas objects
  • Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
  • Resolve common issues in broken or incomplete data sets

Best Pandas Books

Official Description 
 
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
 
  • Use the IPython shell and Jupyter notebook for exploratory computing
  • Learn basic and advanced features in NumPy (Numerical Python)
  • Get started with data analysis tools in the pandas library
  • Use flexible tools to load, clean, transform, merge, and reshape data
  • Create informative visualizations with matplotlib
  • Apply the pandas groupby facility to slice, dice, and summarize datasets
  • Analyze and manipulate regular and irregular time series data
  • Learn how to solve real-world data analysis problems with thorough, detailed examples.
Official Description 
 
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
 
With this handbook, you’ll learn how to use:

  • IPython and Jupyter: provide computational environments for data scientists using Python
  • NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
  • Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
  • Matplotlib: includes capabilities for a flexible range of data visualizations in Python
  • Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Official Description 
 
This learner’s guide will help you understand how to use the features of pandas for interactive data manipulation and analysis.
This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas’ Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance.
With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis.
 

What You Will Learn

  • Install pandas on Windows, Mac, and Linux using the Anaconda Python distribution
  • Learn how pandas builds on NumPy to implement flexible indexed data
  • Adopt pandas’ Series and DataFrame objects to represent one- and two-dimensional data constructs
  • Index, slice, and transform data to derive meaning from information
  • Load data from files, databases, and web services
  • Manipulate dates, times, and time series data
  • Group, aggregate, and summarize data
  • Visualize techniques for pandas and statistical data
 
 

About the Author

Michael Heydt is an independent consultant, educator, and trainer with nearly 30 years of professional software development experience, during which time, he focused on Agile software design and implementation using advanced technologies in multiple verticals, including media, finance, energy, and healthcare. Since 2005, he has specialized in building energy and financial trading systems for major investment banks on Wall Street and for several global energy-trading companies, utilizing .NET, C#, WPF, TPL, DataFlow, Python, R, Mono, iOS, and Android. His current interests include creating seamless applications using desktop, mobile, and wearable technologies, which utilize high-concurrency, high-availability, and real-time data analytics; augmented and virtual reality; cloud services; messaging; computer vision; natural user interfaces; and software-defined networks. He is the author of numerous technology articles, papers, and books. He is a frequent speaker at .NET user groups and various mobile and cloud conferences, and he regularly delivers webinars and conducts training courses on emerging and advanced technologies.

 

Table of Content

  1. A Tour of pandas
  2. Installing pandas
  3. Numpy for pandas
  4. The pandas Series Object
  5. The pandas Dataframe Object
  6. Accessing Data
  7. Tidying up Your Data
  8. Combining and Reshaping Data
  9. Grouping and Aggregating Data
  10. Time-series Data
  11. Visualization
  12. Applications to Finance

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