How to Efficiently Query Total and Conditional Counts in SQL using Google BigQuery

Learn how to combine total counts and conditional counts in a single SQL query with Google BigQuery for better insights on your dataset.
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Understanding SQL Queries for Count Instances

When working with large datasets, you often face the challenge of extracting specific insights without having to run multiple queries. In this guide, we will delve into a common scenario faced by data analysts: how to count total instances in a column as well as conditional counts for specific instances within the same SQL query. This is particularly useful in data handling tools like Google BigQuery.

The Problem

Suppose you are dealing with a public dataset where you want to analyze loan purposes. You wish to determine not only the total number of loans processed but also how many of those loans fall under specific purposes, such as loan purpose 1 and 2. The initial intuition may lead you to use multiple queries, which can be cumbersome and inefficient.

For example, the initial query attempted by a user was structured as follows:

[[See Video to Reveal this Text or Code Snippet]]

While this query aimed to segregate counts based on the loan purpose, it didn’t function as intended due to incorrect usage of conditions in the count function.

The Solution

Let's refine that query! The key lies in using CASE statements to achieve the desired conditions within the COUNT function. Here’s how you can do this efficiently:

[[See Video to Reveal this Text or Code Snippet]]

Breaking Down the Solution

Count Total Instances:

count(legalName) as UnitCountLegalName: This counts all instances of loan entries associated with the given legal name (here, "Quicken Loans").

Sum Loan Amounts:

sum(loanamount) as USDLoanVolume: This line calculates the total loan amount by summing up the loanamount column.

Count Conditional Instances:

For counting specific loan purposes:

count(case when loanPurpose < 2 then 1 else null end) as Purch: This counts entries where loanPurpose is less than 2.

count(case when loanpurpose = 2 then 1 else null end) as Reno: This counts entries where loanPurpose equals 2.

Benefits of This Approach

Efficiency: Reduces the number of queries needed to obtain multiple counts.

Cleaner Code: Makes the SQL more readable and easier to manage.

Direct Insights: Provides both total and conditional counts in one query, streamlining the analysis process.

Conclusion

Combining total and conditional counts in a single SQL query can greatly enhance your data analysis capabilities. By using CASE statements with COUNT, we can efficiently get the insights we require without running multiple queries. This structured approach not only saves time but also helps in maintaining a cleaner codebase. Happy querying!