Master Data Analysis with Pandas: Essential Concepts and Basics
Learn the essentials of data analysis using Pandas, a powerful Python package. Discover how to manipulate and analyze data sets, create DataFrames, and more. Join our course chat for assistance and deepen your understanding of data analysis techniques.
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Presentation Transcript
Lesson 12: Data Analysis Attendance: Link: In Gitter.im | Code: ???? Class Chat: https://gitter.im/IST256/Fudge Participation http://ist256.participoll.com/
Agenda What is Data Analysis? What is Pandas? How to perform data analysis with Pandas You ve Read: Readings online. Questions? Ask in Our Course Chat! https://gitter.im/IST256/Fudge
Connect Activity Question: The process of systematically applying techniques to evaluate data is known as ? A. Data Munging B. Data Analysis C. Data Science D. Data Bases 90% (44) 6% (3) 4% (2) A 49 B C D vote at ist256.participoll.com
Data Analysis: What is it? Apply logical techniques to Describe, condense, recap and evaluate Data and Illustrate Information Goals of Data Analysis: 1. Discover useful information 2. Provide insights 3. Suggest conclusions 4. Support Decision Making
What is pandas ? Pandas is Python package for data analysis. It Provides built-in data structures which simplify the manipulation and analysis of data sets. Pandas is easy to use and powerful, but with great power comes great responsibility We cannot teach you all things Pandas, we must focus on how it works, so you can figure out the rest on your own. http://pandas.pydata.org/pandas-docs/stable/
Pandas: Essential Concepts A Series is a named Python list (dict with list as value). { grades : [50,90,100,45] } A DataFrame is a dictionary of Series (dict of series): { { names : [ bob , ken , art , joe ]} { grades : [50,90,100,45] } }
Watch Me Code 1 Watch Me Code 1 Pandas Basics Series DataFrame Creating a DataFrame from a dict Select columns, Select rows with Boolean indexing
Check Yourself: Series or DataFrame? Match the code to the result. One result is a Series, the other a DataFrame 1.df[ Quarter ] 2.df[ [ Quarter ] ] A. Series B. Data Frame 1 A B vote at ist256.participoll.com
Check Yourself: Boolean Index Which rows are included in this Boolean index? df[ df[ Sold ] < 110 ] A. 0, 1, 2 B. 1, 2, 3 C. 0, 2 D. 0,1 42% (8) 42% (8) 11% (2) 5% (1) 19 A B C D vote at ist256.participoll.com
Watch Me Code 2 Data Analysis of Superhero Movies: read_csv file from web no column names head(), sample() value_counts dealing with nulls Feature engineering
End-To-End Example Data Analysis of iSchool Classes What percentage of the schedule are undergrad? How many undergrad classes on Friday? or 8AM? https://ischool.syr.edu/classes Read_html() append() Engineer Grad / Undergrad
Conclusion Activity "1 Important thing" Explain one important thing you learned today!
