Programming with Data: Python and Pandas LiveLessons

Video description

5 Hours of Video Instruction

Learn how to use Pandas and Python to load and transform tabular data and perform your own analyses.

Overview

In Programming with Data: Python and Pandas LiveLessons, data scientist Daniel Gerlanc prepares learners who have no experience working with tabular data to perform their own analyses. The video course focuses on both the distinguishing features of Pandas and the commonalities Pandas shares with other data analysis environments.

In this LiveLesson, Dan starts by introducing univariate and multivariate data structures in Pandas and describes how to understand them both in the context of the Pandas framework and in relation to other libraries and environments for tabular data like R and relational databases. Next, Dan covers reading and writing to external file formats, split-apply-combine computations, introductory and advanced time series, and merging and reshaping datasets. After watching this video, Python programmers will gain a deep understanding of the Pandas framework through exposures to all of its APIs and feature sets.

Skill Level

  • Beginner
  • Intermediate

Learn How To
  • Avoid common pitfalls and “gotchas” in Pandas by understanding the conceptual underpinnings common to most data manipulation libraries and environments
  • Create univariate (Series) and multivariate (DataFrame) data structures in Pandas
  • Read from and write to external data sources like text and binary files and databases
  • Use the Split-Apply-Combine technique to calculate grouped summary statistics like mean, median, and standard deviation on your data
  • Handle time series data; apply lead, lag, and rolling computations to them; and interpolate missing data
  • Merge and reshape datasets
  • Understand how data alignment is a central concept of Pandas

Who Should Take This Course
  • People with a solid understanding of Python programming who want to learn how to load and transform tabular data using Pandas and understand general principles and requirements common to tabular data manipulation frameworks

Course Requirements
  • Intermediate-level programming ability in Python. You should know the difference between a dict, list, and tuple. Familiarity with control-flow (if/else/for/while) and error handling (try/catch) are required.
  • No statistics background is required.

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video training at  http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Table of contents

  1. Introduction
    1. Introduction 00:05:20
  2. Lesson 1: Series
    1. Learning objectives 00:01:02
    2. 1.1 Install Python and Pandas 00:04:33
    3. 1.2 Learn two ways to conceptualize a Series 00:04:44
    4. 1.3 Create and examine a Series 00:06:37
    5. 1.4 Select from a Series 00:04:05
    6. 1.5 Write vectorized queries against a Series 00:03:09
    7. 1.6 Handle missing data in Pandas 00:05:25
  3. Lesson 2: DataFrames
    1. Learning objectives 00:00:39
    2. 2.1 Learn different conceptualizations of a DataFrame 00:03:47
    3. 2.2 Create a DataFrame 00:03:22
    4. 2.3 Select only columns or rows from a DataFrame 00:04:11
    5. 2.4 Select both rows and columns of a DataFrame 00:05:39
    6. 2.5 Modify a DataFrame in place 00:07:43
    7. 2.6 Align and add a column to a DataFrame 00:03:19
  4. Lesson 3: Reading and Writing External Data
    1. Learning objectives 00:00:55
    2. 3.1 Read data from text files, e.g. CSV 00:05:18
    3. 3.2 Read data from binary files 00:05:03
    4. 3.3 Read data from a database 00:04:00
    5. 3.4 Write data to CSV and other text files 00:03:16
    6. 3.5 Write data to parquet format 00:06:19
    7. 3.6 Write data to a database 00:02:58
  5. Lesson 4: Split-Apply-Combine
    1. Learning objectives 00:00:32
    2. 4.1 Understand the theory of split-apply-combine 00:02:31
    3. 4.2 Split data by groups 00:04:23
    4. 4.3 Apply and reduce by group 00:07:56
  6. Lesson 5: Time Series
    1. Learning objectives 00:00:49
    2. 5.1 Create a time series 00:06:33
    3. 5.2 Select from a time series 00:02:44
    4. 5.3 Perform lead and lag operations 00:05:49
    5. 5.4 Resample a time series 00:02:32
    6. 5.5 Fill and interpolate missing data 00:03:09
    7. 5.6 Align time series 00:03:49
    8. 5.7 Apply rolling calculations 00:03:29
  7. Lesson 6: Merging and Joining
    1. Learning objectives 00:00:38
    2. 6.1 Learn different types of joins 00:02:24
    3. 6.2 Use merge for general purpose joins 00:07:35
    4. 6.3 Understand append and concat 00:13:26
    5. 6.4 Perform advanced merges 00:06:16
  8. Lesson 7: Reshape and Pivot
    1. Learning objectives 00:00:44
    2. 7.1 Understand the concept of reshaping 00:05:44
    3. 7.2 Perform wide to long and long to wide reshaping 00:07:27
    4. 7.3 Learn convenience methods for reshaping 00:06:46
    5. 7.4 Create pivot tables 00:10:56
  9. Lesson 8: Alignment as a Central Concept of Pandas
    1. Learning objectives 00:00:28
    2. 8.1 Create a standalone index 00:06:52
    3. 8.2 Create a MultiIndex 00:13:34
    4. 8.3 Use align and reindex 00:06:51
  10. Lesson 9: Advanced Time Series
    1. Learning objectives 00:00:24
    2. 9.1 Create a custom calendar 00:06:06
    3. 9.2 Understand time zone considerations 00:08:46
  11. Summary
    1. Programming with Data: Python and Pandas LiveLessons (Video Training): Summary 00:01:17

Product information

  • Title: Programming with Data: Python and Pandas LiveLessons
  • Author(s): Daniel Gerlanc
  • Release date: February 2020
  • Publisher(s): Addison-Wesley Professional
  • ISBN: 0136623759