Building ETL and ELT Pipelines

ETL and ELT in Python

Jake Roach

Data Engineer

Extract Data from a CSV File

import pandas as pd

# Read in the CSV file to a DataFrame
data_frame = pd.read_csv("raw_data.csv")

# Output the first few rows
data_frame.head()

   name         num_firms   total_income
0  Advertising     58         3892.41
1  Apparel         39         5422.69
...
49 Trucking        35         17324.36

read_csv()

Takes a file path, returns a DataFrame
delimiter, header, engine

.head()

Outputs the first n number of a DataFrame

Filtering a DataFrame

   name         num_firms   total_income
0  Advertising     58         3892.41
1  Apparel         39         5422.69
...
49 Trucking        35         17324.36


         name         num_firms
      1  Apparel         39
      37 Apparel         61

# First, by rows
data_frame.loc[data_frame["name"] == "Apparel", :]

# Then, by columns
data_frame.loc[:, ["name", "num_firms"]]

.loc

Filters a DataFrame
: means "all"

Write a DataFrame to a CSV File

# Write a DataFrame to a .csv file
data_frame.to_csv("cleaned_data.csv")

.to_csv()

Takes a path, creates DataFrame from file stored at that path
Can take other parameters to customize the output

Other options, like:

.to_json(), .to_excel(), .to_sql()

Running SQL Queries

data_warehouse.execute(  # Use Python clients or other tools to run SQL queries
    """
    CREATE TABLE total_sales AS 
        SELECT
            ds,
            SUM(sales)
        FROM raw_sales_data
        GROUP BY ds;
    """
)

Tools like .execute() to run SQL queries

Putting it all together!

# Define extract(), transform(), and load() functions
...

def transform(data_frame, value):
    return data_frame.loc[data_frame["name"] == value, ["name", "num_firms"]]

# First, extract data from a .csv
extracted_data = extract(file_name="raw_data.csv")

# Then, transform the `extracted_data`
transformed_data = transform(data_frame=extracted_data, value="Apparel")

# Finally, load the `transformed_data`
load(data_frame=transformed_data, file_name="cleaned_data.csv")

Let's practice!

ETL and ELT in Python