Data Visualization Recipes in Python [Video]

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Data Visualization Recipes in Python [Video]

Theodore Petrou
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Utilize Python’s most efficient libraries—pandas, matplotlib, and Seaborn—for data visualization and time series analysis
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Video Details

ISBN 139781789340495
Course Length1 hour and 43 minutes

Video Description

Visualization 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.

Style and Approach

The author relies on his vast experience teaching Python in a professional setting to deliver very detailed explanations for each line of code in all of the recipes. All code and dataset explanations reside in Jupyter Notebooks, an excellent interface for exploring data.

Table of Contents

Visualization with Matplotlib and Pandas
The Course Overview
Getting Started with Matplotlib
Visualizing Data with Matplotlib
Plotting Basics with Pandas
Visualizing the Flights Dataset
Visualization with Seaborn and Pandas
Stacking Area Charts to Discover Emerging Trends
Understanding the Differences Between Seaborn and Pandas
Doing Multivariate Analysis with Seaborn Grids
Uncovering Simpson's Paradox in the Diamonds Dataset with Seaborn
Time Series Analysis
Understanding the Difference Between Python and Pandas Date Tools
Slicing Time Series Intelligently
Using Methods That Only Work with a DatetimeIndex
Counting the Number of Weekly Crimes
Measuring Crime by Weekday and Year
Grouping and Aggregating with Time Series Analysis
Aggregating Weekly Crime and Traffic Accidents Separately
Grouping with Anonymous Functions with a DatetimeIndex
Grouping By a Timestamp and Another Column
Finding the Last Time Crime Was 20% Lower with merge_asof

What You Will Learn

  • Create beautiful and insightful visualizations through pandas' direct hooks to Matplotlib and Seaborn
  • Utilize pandas' unparalleled time series functionality
  • Split data into independent groups before applying aggregations and transformations to each group
  • Prepare real-world messy datasets for machine learning
  • Combine and merge data from different sources through pandas' SQL-like operations

Authors

Table of Contents

Visualization with Matplotlib and Pandas
The Course Overview
Getting Started with Matplotlib
Visualizing Data with Matplotlib
Plotting Basics with Pandas
Visualizing the Flights Dataset
Visualization with Seaborn and Pandas
Stacking Area Charts to Discover Emerging Trends
Understanding the Differences Between Seaborn and Pandas
Doing Multivariate Analysis with Seaborn Grids
Uncovering Simpson's Paradox in the Diamonds Dataset with Seaborn
Time Series Analysis
Understanding the Difference Between Python and Pandas Date Tools
Slicing Time Series Intelligently
Using Methods That Only Work with a DatetimeIndex
Counting the Number of Weekly Crimes
Measuring Crime by Weekday and Year
Grouping and Aggregating with Time Series Analysis
Aggregating Weekly Crime and Traffic Accidents Separately
Grouping with Anonymous Functions with a DatetimeIndex
Grouping By a Timestamp and Another Column
Finding the Last Time Crime Was 20% Lower with merge_asof

Video Details

ISBN 139781789340495
Course Length1 hour and 43 minutes
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