Operations with petl: Basic Data Transformations

IN THIS COURSE

• 1.  Course Overview
  2m 35s
  In this video, you’ll learn more about the course and the instructor. In this course, you’ll learn about fundamental data transformations which can be performed using the PETL library. You’ll learn how to load data into a PETL table and filter columns. You’ll also learn how data from multiple tables can be combined using different concatenation operations. You’ll also learn to convert data in a PETL table into a form compatible with your requirements. FREE ACCESS
• 2.  Creating Tables in petl from Python Data Sources
  9m 27s
  In this video, you’ll learn more about petl's interoperability with some important Python libraries, notably NumPy and pandas. You’ll start by importing NumPy as np, petl as etl, and pandas as pd. You’ll discover NumPy is the go-to Python library for working with n-dimensional arrays. Onscreen, you’ll see this type of array. You’ll see the basic abstraction in NumPy is the ndarray and you’ll use the np.array function in order to construct an ndarray. FREE ACCESS
• 3.  Performing Basic Transformations on Data in petl
  9m 6s
  In this video, you’ll watch a demo. In this demo, you’ll continue to learn about Transform Utilities available in petl. You’ll see import statement onscreen, pandas as pd and petl as etl. Next, you’ll start by initializing a list of lists called employees_finance_info. You’ll see this list of lists is delimited by two opening and two closing square brackets. You’ll learn the first list enclosed within the outer list is the header row. FREE ACCESS
• 4.  Combining Data from Multiple Tables in petl
  8m 42s
  In this video, you’ll watch a demo. This demo will pick up where you left off in the last one. This time, you'll learn how to combine data from multiple tables in petl. You’ll look at how to use etl. cat to work with verticals. You’ll first look at the content of your list of lists. You’ll follow along with the directions onscreen to complete this demo. FREE ACCESS
• 5.  Inserting Rows and Columns in petl Data Tables
  10m 11s
  In this video, you’ll watch a demo. In this demo, you’ll perform various DDL operations from the world of SQL. Specifically, you’ll see how to add columns to an existing petl table. You’ll take a look at the data within the variable employees_analytics_info. This is a list of lists with four columns, 'name', 'designation', emp_id', and 'rating'. Next, you’ll invoke the etl.addfield function. You’ll also examine the petl table, looking at the columns. FREE ACCESS
• 6.  Implementing Replace and Type Change Operations
  12m 17s
  In this video, you’ll watch a demo. In this demo, you’ll look at the data contained in the petl tables. You’ll also experiment with various methods that allow you to change values contained within the petl table. You’ll need to import two statements. Once you have those, you’ll initialize the data. This is in the form of a list of lists. You’ll see the header has intentionally been omitted. FREE ACCESS
• 7.  Replacing Values in petl Data Tables
  9m 32s
  In this video, you’ll watch a demo. In this demo, you’ll see how etl.convert can be used with lambdas along with a special keyword argument called where. You’ll see the where argument has clear similarities to the where clause in SQL. It can be used with both select and update statements. You’ll first create a new column called room_type. Then, you’ll populate that column with a list. FREE ACCESS
• 8.  Querying Data in petl Data Tables
  11m 15s
  In this video, you’ll watch a demo. In this demo, you’ll use the APIs available in petl to effectively run select queries on petl tables. You’ll start by importing the required libraries. Next, you’ll read in our input data into a pandas DataFrame. You’ll make use of pd.read_csv, and specify the file you want to read in. This is a csv file. FREE ACCESS
• 9.  Filtering Data Based on Conditions in petl
  9m 10s
  In this video, you’ll watch a demo. In this demo, you’ll learn about selectcontains. Onscreen, you’ll see the etl.selectcontains function. That's the first input argument, the second input argument is a column called a 'neighborhood_group', and the third is a specific value called 'Manhattan'. The difference between selecteq and selectcontains, lies in the nature of the comparison: selectcontains is going to look for the substring Manhattan in the value of a 'neighborhood_group' for each row. FREE ACCESS
• 10.  Defining Filters for Specific Fields in petl
  10m 38s
  In this video, you’ll watch a demo. In this demo, you’ll continue to work with the data from the last demo. Onscreen you’ll see the simple fragments of code we performed to read the data into a Pandas DataFrame. Then, you’ll convert that Pandas DataFrame into a petl table. Next, you’ll complete other operations. FREE ACCESS
• 11.  Course Summary
  1m 35s
  In this video, you’ll summarize what you’ve learned in this course. You learned the transform step in the Extract Transform Load Pipeline. You also learned fundamental data transformations that can be performed using the PETL library. You also learned how data from multiple tables can be combined using different Concatenation operations to combine data both horizontally as well as vertically. You also learned to convert the data in a petl table into another form. FREE ACCESS