DROP TABLE IF EXISTS sales_table; CREATE TEMPORARY TABLE sales_table ( key varchar(6), customerID varchar

In the second example, I calculate the average price only with products that are sold in the US, which is usually what we want. Note, you don’t need to include ELSE when using CASE WHEN as it defaults to NULL.

You can breakdown a complex query and create multiple temporary tables. Then you can run “sanity check” queries against those tables to make sure they contain correct entries. I highly recommend this approach when designing a new non-trivial query or report.

You can breakdown a complex query and create multiple temporary tables. Then you can run “sanity check” queries against those tables to make sure they contain correct entries. I highly recommend this approach when designing a new non-trivial query or report.

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way to learn how to stop doing this is twofold. You have to tell yourself no when you get the urge to self-sacrifice and replace it with a selfish action. If you feel the need to spend five hundred bucks buying your partner something you know they’ll love but don’t need, try instead spending just a fraction of that money (or time, energy, anything else) on yourself.

This is the basis for all of SymPy and makes scientific computing with the Python programming language a lot easier. The use of this function can actually allow Python to explore one of the benefits to the Julia language, syntactical expressions. If you’d like to learn more about how the Julia language uses expressions, you can check out a full tutorial on it here:

Many awesome implementations of Pythonic code and math use SymPy for mathematical calculations on the back-end. Here are some notable examples pulled straight from their homepage:

But let’s be clear: The desire to spend money on things at the intersection of affordability, convenience, and speed all have very human and environmental costs. The whole idea of consumerism is inherently racist and predatory.

SELECT * FROM ( SELECT t1.*, t2.price, ROW_NUMBER() OVER(PARTITION by some_id ORDER BY timestamp_field) as row_count FROM table1 AS t1 JOIN table2 AS t2 ON COALESCE(t1.string_field, ') = COALESCE(t2.string_field, ') ) WHERE row_count = 1

SELECT customerID, productID, SUM(price) AS sales, SUM(SUM(price)) OVER (PARTITION BY customerID) AS sales_all FROM sales_table GROUP BY customerID, productID ORDER BY customerID, productID

I wrote a query similar at its core to the query in the image above. When I was performing data validation, many records were missing. How is this possible? It is such a simple JOIN!

Happy Black Friday — a holiday centered around our love for consumerism, in which we get sucked into sales (usually at big-box stores) and start racking up gifts for the holidays. Black Friday — called so because it’s the day retailers traditionally get out of yearlong deficits—has become an odd consumer-driven holiday, especially now amid the coronavirus… when Cyber Monday’s digital shopping landscape has far more appeal.

It turned out that many entries in the table 1 and table 2 had string_field column with NULL values. I thought that JOIN would keep records with NULL values because NULL is equal to NULL, isn’t it?

The Blaze Ecosystem is a set of Pythonic libraries that make it far easier to query and process data in the Python programming language. The Blaze ecosystem actually consists of several different packages:

Easily the coolest thing that SymPy has to offer is the ability to lambdify any expression or function inside of the language. I have talked before about why I think Lambda is one of the greatest tools available for Python developers. If you would like to read an article all about that topic, you can check it out here:

In the first example, I use 0 for all products that are not from the US, which lowers the overall average price. If there would be many non-US products, the average could get close to 0.