Selecting columns with SQL

Datasets can contain columns that are not required for an analysis, like the weather table in data.db does. Some, such as elevation, are redundant, since all observations occurred at the same place, while others contain variables we are not interested in. After making a database engine, you'll write a query to SELECT only the date and temperature columns, and pass both to read_sql() to make a dataframe of high and low temperature readings.

pandas has been loaded as pd, and create_engine() has been imported from sqlalchemy.

Note: The SQL checker is quite picky about column positions and expects fields to be selected in the specified order.

This exercise is part of the course

Streamlined Data Ingestion with pandas

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Exercise instructions

Create a database engine for data.db.
Write a SQL query that SELECTs the date, tmax, and tmin columns from the weather table.
Make a dataframe by passing the query and engine to read_sql() and assign the resulting dataframe to temperatures.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create database engine for data.db
engine = ____

# Write query to get date, tmax, and tmin from weather
query = """
SELECT ____, 
       ____, 
       ____
  FROM ____;
"""

# Make a dataframe by passing query and engine to read_sql()
temperatures = ____

# View the resulting dataframe
print(temperatures)

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