by Daniel Y. Chen | Released February 2017 | ISBN: 0134692276

3+ Hours of Video InstructionPandas Data Analysis with Python Fundamentals LiveLessons provides analysts and aspiring data scientists with a practical introduction to Python and pandas, the analytics stack that enables you to move from spreadsheet programs such as Excel into automation of your data analysis workflows.In this video training, Daniel starts by introducing Python and pandas and why they are great tools for data analysis. He then covers installing and starting Python. The video then moves into the basics of working with data sets in Python and with pandas, followed by plotting and visualization, data assembly and manipulations, missing data, and tidy data. After watching this video, analysts and those new to data science will understand why Python and pandas are so popular with data scientists and should be able to begin to create automated data workflows.About the Instructor Daniel Chen, trainer and data scientist, is a graduate student in the interdisciplinary Ph.D. program in genetics, bioinformatics & computational biology (GBCB) at Virginia Polytechnic Institute and State University (Virginia Tech). He is involved with Software Carpentry and Data Carpentry as an instructor and lesson maintainer. He completed his master's degree in public health at Columbia University Mailman School of Public Health in epidemiology with a certificate in advanced epidemiology and is currently extending his master's thesis work on attitude diffusion in social networks in the Social and Decision Analytics Laboratory under the Biocomplexity Institute of Virginia Tech. Skill Level Beginner to Intermediate What You Will Learn Installing and starting PythonLoading data sets into pandas and beginning to assess and analyze themUsing pandas data structures and importing/exporting dataCombining multiple data setsDealing with missing dataTidying and reshaping data Who Should Take This Course Analysts and aspiring data scientists looking to move beyond spreadsheets into automated data workflows. Course Requirements Basic understanding of programming and developmentSome familiarity with basic data analysis Table of Contents Lesson 1: Installing and Running Python Lesson 1 explains why the Python and pandas combination is great for data analysis. It also shows you how to install Python and the analytics stack and how to run Python. Lesson 2: Pandas Basics Lesson 2 covers some of the initial steps to take after you are given a dataset to analyze. You load data into pandas and then look at different subsets of the data. Finally, you learn how to perform your first simple set of analyses. Lesson 3: Pandas Data Structures Lesson 3 dives a little further into how pandas works. You learn how to create the pandas series and dataframe data structures. Next, you learn how you can use the pandas series object and pandas dataframe object. Last, how you import and export various types of data are covered. Lesson 4: Introduction to Plotting Lesson 4 emphasizes why visualization is important. You learn how to create a basic set of plots within matplotlib, Seaborn, and pandas. Lesson 5: Data Assembly Now that you know how to load and look at your data, the next step is assembling the data you need for analysis. Lesson 5 begins with concatenating data, that is, how to append data along the rows or columns. The lessons end with how to merge multiple data sets together. Lesson 6: Missing Data By now you have seen a few datasets with missing data. In Lesson 6 we begin to discuss what missing data is and how we get missing data. Your start learning how to work with missing data, including ways to find, count, and clean missing data. These are all important considerations when missing data is used in calculations. Lesson 7: Tidy Data Lesson 7 is concerned with tidy data. Tidy data describes the shape of your data that makes it easier to manipulate and analyze. The lesson covers Hadley Wickham's tidy data paper that describes the ways data can be dirty. Finally, it covers the various ways you can reshape data. About LiveLessons Video Training The LiveLessons Video Training series publishes hundreds of hands-on, expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. This professional and personal technology video series features world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, IBM Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include: IT Certification, Programming, Web Development, Mobile Development, Home and Office Technologies, Business and Management, and more. View all LiveLessons on InformIT at: http://www.informit.com/livelessons. Show and hide more

1. Introduction
   • Pandas Data Analysis with Python Fundamentals: Introduction 00:02:10
2. Lesson 1: Installing and Running Python
   • Learning objectives 00:00:22
   • 1.1 Understand why Python/Pandas for data analytics 00:00:57
   • 1.2 Install Python 00:03:23
   • 1.3 Run Python 00:06:24
3. Lesson 2: Pandas Basics
   • Learning objectives 00:00:26
   • 2.1 Load your first data set 00:06:29
   • 2.2 Look at your data 00:16:21
   • 2.3 Analyze your first data set 00:06:38
4. Lesson 3: Pandas Data Structures
   • Learning objectives 00:00:32
   • 3.1 Create data structures 00:04:24
   • 3.2 Use Pandas Series 00:09:03
   • 3.3 Use Pandas DataFrame 00:08:10
   • 3.4 Import and Export data 00:08:10
5. Lesson 4: Introduction to Plotting
   • Learning objectives 00:00:23
   • 4.1 Understand why data visualization is important 00:08:02
   • 4.2 Create basic plots in matplotlib 00:10:48
   • 4.3 Create basic plots in seaborn 00:27:42
   • 4.4 Use plotting in Pandas 00:04:37
6. Lesson 5: Data Assembly
   • Learning objectives 00:00:26
   • 5.1 Concatenate data (stitch data) 00:12:45
   • 5.2 Merge data (denormalization) 00:10:12
7. Lesson 6: Missing Data
   • Learning objectives 00:00:28
   • 6.1 Understand the concept of a NaN value 00:08:44
   • 6.2 Work with missing data 00:10:18
8. Lesson 7: Tidy Data
   • Learning objectives 00:00:45
   • 7.1 Understand the concept of tidy data 00:01:48
   • 7.2 Melt your data when columns contain values not variables (melt) 00:05:28
   • 7.3 Melt and parse when columns contain multiple variables 00:08:53
   • 7.4 Pivot data when variables are in both rows and columns 00:04:09
   • 7.5 Normalize data by separating multiple observational units in a table 00:04:59
   • 7.6 Denormalize and assemble data when observational units are across multiple tables 00:04:55
9. Summary
   • Pandas Data Analysis with Python Fundamentals: Summary 00:00:46
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