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Introduction to Pandas for Developers

Video Description

In this Introduction to Pandas for Developers training course, expert author Matt Harrison will teach you how to load data in Pandas data structures. This course is designed for users that are already familiar with Python.

You will start by learning about Python lists, Pandas series, and boolean arrays, then jump into learning about dataframes. From there, Matt will teach you about relational algebra, tweaking dataframes, and joins. This video tutorial also covers filtering dataframes, grouping, serialization, plotting, and time series. Finally, you will learn about machine learning and using Pandas with scikit-learn.

Once you have completed this computer based training course, you will have learned about basic workflows and gotchas of crawling, munging, and plotting data.

Table of Contents

  1. Introduction
    1. What Is Pandas? 00:02:27
    2. About The Author 00:00:43
    3. Installation 00:03:52
    4. Pandas Vs. Other Platforms 00:03:30
    5. How To Access Your Working Files 00:01:15
  2. Basic Series
    1. Python Lists 00:02:30
    2. Numpy 1D Arrays 00:04:40
    3. Pandas Series 00:02:48
    4. Boolean Arrays 00:03:35
    5. Series Index 00:03:23
  3. More Series
    1. Data Types 00:03:24
    2. Iteration 00:03:30
    3. Broadcasting Operations 00:06:01
    4. Crud Operations Reading 00:07:01
    5. Crud Operations Update 00:05:25
    6. Crud Operations Deletion 00:02:23
    7. Summary Statistics 00:04:42
    8. Dealing With Duplicates 00:06:23
    9. Dealing With Nan 00:05:59
    10. Plotting 00:02:40
    11. Serialization 00:04:47
  4. Dataframe Basics
    1. Columnar Data 00:03:54
    2. Similarities To Python Dictionaries 00:05:00
    3. Creation From Lists And Dicts 00:04:00
    4. Reading CSV Files 00:01:49
  5. Relational Algebra
    1. Selection 00:08:16
    2. Projection 00:03:30
    3. Cartesian Product 00:06:55
    4. Union 00:03:53
    5. Difference 00:02:57
  6. More Dataframes
    1. Exploring Data 00:03:40
    2. Axes Of Dataframes 00:02:02
    3. Index And Columns 00:03:03
    4. Summary Statistics 00:03:48
    5. Histograms 00:05:09
    6. Transposing Data 00:01:39
  7. Tweaking Dataframes
    1. Adding Rows 00:06:33
    2. Adding Columns 00:02:58
    3. Applying Functions To Columns 00:04:12
    4. Removing Columns 00:02:35
    5. Sorting Data 00:05:23
    6. Iteration Over Data 00:05:33
    7. Setting Data 00:05:34
  8. Joins
    1. Concat With Rows 00:03:08
    2. Concat With Cols 00:01:44
    3. Inner Join 00:02:23
    4. Outer Join 00:03:29
    5. Left Join 00:02:47
    6. Right Join 00:01:24
    7. Join On Index 00:01:49
  9. Filtering Dataframes
    1. Filtering Columns 00:02:47
    2. Boolean Arrays 00:01:04
    3. Filtering Rows 00:01:04
    4. Using Functions To Filter 00:04:11
    5. Boolean Operations For Filtering 00:02:38
    6. Dealing With Nan 00:03:44
  10. Grouping
    1. Basic Grouping 00:03:16
    2. More Grouping 00:03:58
    3. Pivoting 00:04:04
    4. Stacking/Unstacking 00:02:50
  11. Serialization
    1. CSV Reading And Writing 00:07:19
    2. JSON 00:05:26
    3. Pickle 00:01:33
    4. Numpy 00:02:33
    5. Excel 00:03:32
  12. Plotting
    1. Basic Plotting 00:05:20
    2. Histograms 00:06:02
    3. Bar Plots 00:02:44
    4. Line Plots 00:03:09
    5. Faceting 00:05:23
  13. Time Series
    1. Date Manipulation 00:05:07
    2. Window Functions 00:05:30
    3. Plotting 00:01:38
  14. Machine Learning
    1. Using Pandas With Scikit-Learn 00:05:39
    2. Sample Regression 00:04:37
  15. Sample Application
    1. Crawling Data 00:08:53
    2. Crawling Data - Part 2 00:05:33
    3. Munging Data 00:07:51
    4. Munging Data - Part 2 00:06:53
    5. Plotting Data 00:09:09
    6. Creating An Infographic 00:05:36
    7. Creating An Infographic - Part 2 00:06:02
  16. Conclusion
    1. Conclusion 00:00:33