by Theodore Petrou | Released April 2018 | ISBN: 9781789340495

Utilize Python's most efficient libraries—pandas, matplotlib, and Seaborn—for data visualization and time series analysisAbout This VideoProduce aesthetically pleasing visualizations on real-world datasetsLeverage fast, robust data structures in pandas to gain useful insights from your dataPractical, easy to implement recipes for quick solutions to common problems in data analysisExplore how to analyze data using Python with different data visualizing techniquesIn DetailVisualization is a critical component in exploratory data analysis, as well as presentations and applications. If you are struggling in your day-to-day data analysis tasks, then this is the right course for you. This fast-pace guide follows a recipe-based approach, each video focusing on a commonly-faced issue.This course covers advanced and powerful time series capabilities so you can dissect by any possible dimension of time. It introduces the Matplotlib library, which is responsible for all of the plotting in pandas, at the same time focusing on the pandas plot method and the Seaborn library, which is capable of producing aesthetically pleasing visualizations not directly available in pandas. This course guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Show and hide more

1. Chapter 1 : Visualization with Matplotlib and Pandas
   • The Course Overview 00:03:18
   • Getting Started with Matplotlib 00:12:56
   • Visualizing Data with Matplotlib 00:07:31
   • Plotting Basics with Pandas 00:04:53
   • Visualizing the Flights Dataset 00:11:35
2. Chapter 2 : Visualization with Seaborn and Pandas
   • Stacking Area Charts to Discover Emerging Trends 00:04:23
   • Understanding the Differences Between Seaborn and Pandas 00:04:55
   • Doing Multivariate Analysis with Seaborn Grids 00:04:37
   • Uncovering Simpson's Paradox in the Diamonds Dataset with Seaborn 00:05:17
3. Chapter 3 : Time Series Analysis
   • Understanding the Difference Between Python and Pandas Date Tools 00:10:15
   • Slicing Time Series Intelligently 00:03:30
   • Using Methods That Only Work with a DatetimeIndex 00:04:50
   • Counting the Number of Weekly Crimes 00:04:24
   • Measuring Crime by Weekday and Year 00:07:59
4. Chapter 4 : Grouping and Aggregating with Time Series Analysis
   • Aggregating Weekly Crime and Traffic Accidents Separately 00:03:10
   • Grouping with Anonymous Functions with a DatetimeIndex 00:02:29
   • Grouping By a Timestamp and Another Column 00:04:13
   • Finding the Last Time Crime Was 20% Lower with merge_asof 00:02:55
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