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Week 2 Homework

In case you don't get one option exactly, select the closest one

For the homework, we'll be working with the green taxi dataset located here:

https://github.com/DataTalksClub/nyc-tlc-data/releases/tag/green/download

Assignment

The goal will be to construct an ETL pipeline that loads the data, performs some transformations, and writes the data to a database (and Google Cloud!).

  • Create a new pipeline, call it green_taxi_etl
  • Add a data loader block and use Pandas to read data for the final quarter of 2020 (months 10, 11, 12).
    • You can use the same datatypes and date parsing methods shown in the course.
    • BONUS: load the final three months using a for loop and pd.concat
  • Add a transformer block and perform the following:
    • Remove rows where the passenger count is equal to 0 or the trip distance is equal to zero.
    • Create a new column lpep_pickup_date by converting lpep_pickup_datetime to a date.
    • Rename columns in Camel Case to Snake Case, e.g. VendorID to vendor_id.
    • Add three assertions:
      • vendor_id is one of the existing values in the column (currently)
      • passenger_count is greater than 0
      • trip_distance is greater than 0
  • Using a Postgres data exporter (SQL or Python), write the dataset to a table called green_taxi in a schema mage. Replace the table if it already exists.
  • Write your data as Parquet files to a bucket in GCP, partioned by lpep_pickup_date. Use the pyarrow library!
  • Schedule your pipeline to run daily at 5AM UTC.

Questions

Question 1. Data Loading

Once the dataset is loaded, what's the shape of the data?

  • 266,855 rows x 20 columns
  • 544,898 rows x 18 columns
  • 544,898 rows x 20 columns
  • 133,744 rows x 20 columns

Question 2. Data Transformation

Upon filtering the dataset where the passenger count is greater than 0 and the trip distance is greater than zero, how many rows are left?

  • 544,897 rows
  • 266,855 rows
  • 139,370 rows
  • 266,856 rows

Question 3. Data Transformation

Which of the following creates a new column lpep_pickup_date by converting lpep_pickup_datetime to a date?

  • data = data['lpep_pickup_datetime'].date
  • data('lpep_pickup_date') = data['lpep_pickup_datetime'].date
  • data['lpep_pickup_date'] = data['lpep_pickup_datetime'].dt.date
  • data['lpep_pickup_date'] = data['lpep_pickup_datetime'].dt().date()

Question 4. Data Transformation

What are the existing values of VendorID in the dataset?

  • 1, 2, or 3
  • 1 or 2
  • 1, 2, 3, 4
  • 1

Question 5. Data Transformation

How many columns need to be renamed to snake case?

  • 3
  • 6
  • 2
  • 4

Question 6. Data Exporting

Once exported, how many partitions (folders) are present in Google Cloud?

  • 96
  • 56
  • 67
  • 108

Submitting the solutions

Solution

Will be added after the due date