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Data Analysis Crash Course for Beginners (Pandas + Python)
Data Analysis Crash Course for Beginners (Pandas + Python)

Data Analysis Crash Course for Beginners (Pandas + Python): Explore the world of data analysis basics with Pandas and Python for beginners

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Profile Icon Shubham Sarda
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$9.99 $49.99
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1 Ratings)
Video Dec 2021 1hr 1st Edition
Video
$9.99 $49.99
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Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Shubham Sarda
Arrow right icon
$9.99 $49.99
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1 Ratings)
Video Dec 2021 1hr 1st Edition
Video
$9.99 $49.99
Subscription
Free Trial
Renews at $19.99p/m
Video
$9.99 $49.99
Subscription
Free Trial
Renews at $19.99p/m

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Key benefits

  • Short, crisp, and clear course on data analysis using Pandas library in Python
  • Beginners-level course to give you hands-on knowledge to start your journey in data analysis
  • A good blend of theoretical and practical concepts along with resource files to follow along smoothly

Description

Pandas is an open-source library providing you with high-performance, easy-to-use data structures and data analysis tools for Python. Pandas provide a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modeling, and visualization. Fields with the widespread use of Pandas include data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering. This course covers fundamentals of data analysis with Python, predominantly using Pandas library. This course starts with covering the fundamentals of data analysis. You will be then working with Pandas, iPython, Jupyter Notebook. After that, you will explore important Jupyter Notebook commands. Post that, you will be working with CSV, Excel, TXT, JSON files, and API responses. Finally, you will be working with DataFrames (indexing, slicing, adding, and deleting). By the end of this course, you will have a good understanding of Pandas and will be ready to explore data analysis in-depth in the future. All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/pandas-resources

Who is this book for?

This course is ideal for Python programmers and developers, and students who are interested in learning Pandas for doing data analysis. This course is designed for beginners who want to start their journey in the field of data analytics. No prerequisites are required to opt for this course.

What you will learn

  • Learn fundamentals of data analysis
  • Understand important Jupyter Notebook commands
  • Work with DataFrames (indexing, slicing, adding and deleting)
  • Work with Pandas, iPython, Jupyter Notebook
  • Work with CSV, Excel, TXT, JSON Files, and API Responses
  • Add, delete, and update rows and columns

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 21, 2021
Length: 1hr
Edition : 1st
Language : English
ISBN-13 : 9781803242354
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Languages :
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Product Details

Publication date : Dec 21, 2021
Length: 1hr
Edition : 1st
Language : English
ISBN-13 : 9781803242354
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Total $ 129.97 169.97 40.00 saved
Data Analysis Crash Course for Beginners (Pandas + Python)
$9.99 $49.99
Machine Learning with PyTorch and Scikit-Learn
$54.99
Hands-On Data Analysis with Pandas
$64.99
Total $ 129.97 169.97 40.00 saved Stars icon
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Table of Contents

5 Chapters
Introduction to the Course Chevron down icon Chevron up icon
What is Pandas? Chevron down icon Chevron up icon
Jupyter Notebooks Chevron down icon Chevron up icon
Working on Data Chevron down icon Chevron up icon
Thank You for Being Here! Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(1 Ratings)
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1 star 0%
Jawed Mar 16, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
You need to add more content in data manipulation sections it is too basic. Also, I noted two errors: 1. when you were talking about df.ix method, you said depreciated/depreciation, correct word is deprecated. 2. df.T is not traverse, it is transpose.
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