Getting distinct values

Sometimes an analysis doesn't need every record, but rather unique values in one or more columns. Duplicate values can be removed after loading data into a dataframe, but it can also be done at import with SQL's DISTINCT keyword.

Since hpd311calls contains data about housing issues, we would expect most records to have a borough listed. Let's test this assumption by querying unique complaint_type/borough combinations.

pandas has been imported as pd, and the database engine has been created as engine.

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

Este ejercicio forma parte del curso

Streamlined Data Ingestion with pandas

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Instrucciones del ejercicio

Create a query that gets DISTINCT values for borough and complaint_type (in that order) from hpd311calls.
Use read_sql() to load the results of the query to a dataframe, issues_and_boros.
Print the dataframe to check if the assumption that all issues besides literature requests appear with boroughs listed.

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Prueba este ejercicio completando el código de muestra.

# Create query for unique combinations of borough and complaint_type
query = """
SELECT ____ ____, 
       ____
  ____ hpd311calls;
"""

# Load results of query to a dataframe
issues_and_boros = ____

# Check assumption about issues and boroughs
print(____)

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