Strategies for Multiple Transformations

Exploratory Data Analysis in SQL

Christina Maimone

Data Scientist

Multiple transformations

SELECT * FROM naics;

   id   |                  category                   | businesses 
--------+---------------------------------------------+------------
 111110 | Agriculture: Soybean Farming                |       4788
 111130 | Agriculture | Dry Pea and Bean Farming      |       3606
 111140 | Agriculture: Wheat Farming                  |       6393
 111150 | Agriculture - Corn Farming                  |      26469
 111160 | Agriculture: Rice Farming                   |        949
 111199 | Agriculture - All Other Grain Farming       |      15035
 111211 | Agriculture | Potato Farming                |        617
 611110 | Education - Elementary and Secondary        |     187859
 611210 | Education | Junior Colleges                 |       3961
 611310 | Education: Colleges and Universities        |      29148

CASE WHEN

-- Case for each of :, -, and |

SELECT CASE WHEN category LIKE '%: %' THEN split_part(category, ': ', 1)

            WHEN category LIKE '% - %' THEN split_part(category, ' - ', 1)

            ELSE split_part(category, ' | ', 1)

        END AS major_category,  -- alias the result

       sum(businesses)          -- also select number of businesses

  FROM naics

 GROUP BY major_category;       -- Group by categories created above

 major_category |  sum   
----------------+--------
 Education      | 220968
 Agriculture    |  57857

Recoding table

Original values: fruit table

 customer | fav_fruit 
----------+-----------
      349 | apple
      874 | Apple
      703 | apple
      667 | bannana
      622 | banana
      387 | BANANA
      300 | APPLES
      313 |  apple
      499 |  banana
      418 | apple
      841 | BANANA
      300 | APPLE
      754 | apple

Standardized values: recode table

 original | standardized  
----------+--------------
 APPLES   | apple
 apple    | apple  
 Apple    | apple
 bannana  | banana
 apple    | apple
  banana  | banana
 banana   | banana
 APPLE    | apple
  apple   | apple
 BANANA   | banana

Step 1: CREATE TEMP TABLE

CREATE TEMP TABLE recode AS

  SELECT DISTINCT fav_fruit AS original,  -- original, messy values

         fav_fruit AS standardized        -- new standardized values

    FROM fruit;

Initial table

SELECT * 
  FROM recode;

 original | standardized 
----------+--------------
 APPLES   | APPLES
 apple    | apple  
 Apple    | Apple
 bannana  | bannana
 apple    | apple
  banana  |  banana
 banana   | banana
 APPLE    | APPLE
  apple   |  apple
 BANANA   | BANANA
(10 rows)

Step 2: UPDATE values

UPDATE table_name
   SET column_name = new_value
 WHERE condition;

Step 2: UPDATE values

-- All rows: lower case, remove white space on ends

UPDATE recode 
   SET standardized=trim(lower(original));

-- Specific rows: correct a misspelling

UPDATE recode
   SET standardized='banana'
 WHERE standardized LIKE '%nn%';

-- All rows: remove any s

UPDATE recode
   SET standardized=rtrim(standardized, 's');

Resulting recode table

SELECT * 
  FROM recode;

 original | standardized 
----------+--------------
 APPLES   | apple
 apple    | apple
 Apple    | apple
 apple    | apple
  banana  | banana
 banana   | banana
 APPLE    | apple
  apple   | apple
 BANANA   | banana
 bannana  | banana
(10 rows)

Step 3: JOIN original and recode tables

Original only

SELECT fav_fruit, count(*) 
  FROM fruit 
 GROUP BY fav_fruit;

 fav_fruit | count 
-----------+-------
 APPLES    |     1
 apple     |     1
 apple     |     3
  banana   |     1
 BANANA    |     2
  apple    |     1
 APPLE     |     1
 bannana   |     1
 banana    |     1
 Apple     |     1
(10 rows)

With recoded values

SELECT standardized, 
       count(*) 
  FROM fruit
       LEFT JOIN recode
       ON fav_fruit=original
 GROUP BY standardized;

 standardized | count 
--------------+-------
 apple        |     8
 banana       |     5
(2 rows)

Recap

CREATE TEMP TABLE with original values
UPDATE to create standardized values
JOIN original data to standardized data

Clean up the Evanston 311 data!

Exploratory Data Analysis in SQL