Pandas library is the de-facto standard tool for data scientists, nowadays. It is used widely by many data scientists around the globe. After being familiar with it I always use it for processing table-structured data whatever project I am working on. It works fast and reliable, supports CSV, Excel, JSON and so on.

However, as a person experienced in [SQL](https://hackernoon.com/tagged/sql), I had some difficulties and confusion with manipulating the tables (a.k.a. DataFrames) in the beginning. Eventually, I learned more APIs and ways of doing the things properly. I believe many people who do his/her first steps on [Pandas](https://hackernoon.com/tagged/pandas) may have the same experience. Some time ago I prepared the cheatsheet using SQL queries and their analogy in Pandas. And I am happy to share it with all of you.

In this post, I am sharing the queries using “SELECT”. I will post other queries and their analogies to Pandas soon later. I would be happy to receive your feedback and wish-list regarding the cheatsheet.

Here we go.

We have three simple tables:

*   users
*   courses
*   attendance

And the rest of the queries will be about data manipulations on them.

Lets start from basic SELECT command.

SELECT \* FROM users

SELECT \* FROM users LIMIT 0,10

SELECT \* FROM users WHERE email IS NULL

SELECT first\_name, last\_name FROM users

SELECT DISTINCT birth\_year FROM users

Basic math and arithmetic operations.

SELECT AVG(points) FROM users;  
SELECT SUM(points) FROM users;

Conditional operations and LIKE.

SELECT \* FROM users WHERE birth\_year BETWEEN 1998 AND 2018

SELECT \* FROM users WHERE first\_name LIKE 'Ch%'

SELECT \* FROM users WHERE first\_name LIKE '%es'

SELECT \* FROM users WHERE first\_name LIKE '%on%'

SELECT first\_name, last\_name FROM users WHERE first\_name LIKE '%on%'

I finalize this post with JOIN and ORDER BY related SQL queries. To make the code more simpler and easy to read I will assign the results into new variables and re-use them in next ones.

SELECT \* FROM attendance atn  
LEFT JOIN users usr ON atn.user\_id = usr.id

Now lets join the result above with course titles. The result it will be same as the result of following SQL command.

SELECT \* FROM attendance atn  
LEFT JOIN users usr ON atn.user\_id = usr.id  
LEFT JOIN courses co ON co.id = atn.course\_id

The query above will return the full table/dataframe with all columns included. To select necessary columns we can use the same method as we did before.

SELECT \* FROM users ORDER BY first\_name, last\_name

SELECT \* FROM users ORDER BY first\_name, last\_name DESC

SELECT first\_name, last\_name, birth\_year,  
points, course\_name, instructor FROM attendance atn  
LEFT JOIN users usr ON atn.user\_id = usr.id  
LEFT JOIN courses co ON co.id = atn.course\_id  
ORDER BY first\_name, last\_name

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Pandas cheatsheet for SQL people (part 1)

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