by Harish Garg | Released July 2017 | ISBN: 9781788397599

Explore popular datasets in R, while mastering advanced techniques used for themAbout This VideoThis course will help you explore techniques for expanding your knowledge with data sets in pandasData sets in Pandas can be utilized in different ways; you'll learn to do this using advanced techniquesKnow how you can select data, manipulate it, transform it, and reshape itIn DetailIn this course, you will learn how to start using pandas from end-to-end: from getting your data into pandas; using pandas to manipulate, transform, analyze, and visualize data; to ultimately taking your transformed data out of pandas into any number of formats.This course will get you (or anyone who has never used pandas) started on using it as a complete end-to-end data analysis workflow. You will start by setting up Python, pandas, and Jupyter notebooks. You will learn how to use Jupyter notebooks to run Python code. We will then show how to get data into pandas and do some exploratory analysis. You will learn how to manipulate and reshape data using pandas methods. You will also learn how to deal with missing data from your datasets, how to draw charts and plots using pandas and Matplotlib, and how to create some cool visualizations for your audience. Finally, you will wrap-up your newly gained pandas knowledge by learning how to get data out of pandas into some popular file formats. Show and hide more

1. Chapter 1 : Working with Different Kinds of Datasets
   • The Course Overview 00:04:36
   • Using Advanced Options While Reading Data from CSV Files 00:04:27
   • Reading Data from Excel Files 00:05:03
   • Reading Data from Some Other Popular Formats 00:05:02
2. Chapter 2 : Data Selection
   • Using Pandas Series Data Structure to Select a Subset of the Data 00:05:02
   • Selecting Multiple Rows and Columns from a Pandas DataFrame 00:04:57
   • Sorting a Pandas DataFrame or a Series 00:03:03
   • Filtering Rows of a Pandas DataFrame by Column Value 00:03:15
   • Applying Multiple Filter Criteria to a Pandas DataFrame 00:04:02
   • Using the "axis" Parameter in Pandas 00:03:10
   • Using String Methods in Pandas 00:02:38
   • Changing the Data Type of a Pandas Series 00:03:22
3. Chapter 3 : Manipulating, Transforming, and Reshaping Data
   • Modifying a Pandas DataFrame “inplace” 00:04:09
   • Using the "groupby" Method 00:04:13
   • Handling Missing Values in Pandas 00:04:58
   • Indexing in Pandas DataFrames 00:03:28
   • Renaming Columns in a Pandas DataFrame 00:03:39
   • Removing Columns from a Pandas DataFrame 00:03:36
   • Working with Dates and Times Data 00:04:33
   • Handling SettingWithCopyWarning 00:03:44
   • Applying a Function to a Pandas Series or DataFrame 00:03:20
   • Merging and Concatenating Multiple DataFrames into One 00:05:06
4. Chapter 4 : Visualizing Data Like a Pro
   • Controlling Plot Aesthetics 00:05:54
   • Choosing the Colors for the Plots 00:03:19
   • Plotting Categorical Data 00:02:49
   • Plotting with Data Aware Grids 00:03:43
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