Cocoon Cleaning Summary 🤗

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📃 1. Table Summary

The table contains patient data. It includes demographic information like birth date, SSN, name, address, and ethnicity. The table also has financial data such as healthcare expenses, coverage, and income.

📊 2. Column Rename

😎 13 columns have been renamed

	Column	Summary	New Column Name	Renamed?
0	Id	Unique identifier for the patient	PATIENT_ID	✔️ Yes
1	BIRTHDATE	Patient's date of birth	BIRTHDATE	❌ No
2	DEATHDATE	Patient's date of death (if applicable)	DEATHDATE	❌ No
3	SSN	Patient's Social Security Number	SSN	❌ No
4	DRIVERS	Patient's driver's license number	DRIVERS_LICENSE	✔️ Yes
5	PASSPORT	Patient's passport number	PASSPORT_NUMBER	✔️ Yes
6	PREFIX	Patient's name prefix (e.g., Mr., Mrs.)	NAME_PREFIX	✔️ Yes
7	FIRST_	Patient's first name	FIRST_NAME	✔️ Yes
8	LAST_	Patient's last name	LAST_NAME	✔️ Yes
9	SUFFIX	Patient's name suffix (if applicable)	NAME_SUFFIX	✔️ Yes
10	MAIDEN	Patient's maiden name (if applicable)	MAIDEN_NAME	✔️ Yes
11	MARITAL	Patient's marital status	MARITAL_STATUS	✔️ Yes
12	RACE	Patient's race	RACE	❌ No
13	ETHNICITY	Patient's ethnicity	ETHNICITY	❌ No
14	GENDER	Patient's gender	GENDER	❌ No
15	BIRTHPLACE	Patient's place of birth	BIRTHPLACE	❌ No
16	ADDRESS	Patient's home address	ADDRESS	❌ No
17	CITY	City where the patient lives	CITY	❌ No
18	STATE	State where the patient lives	STATE	❌ No
19	COUNTY	County where the patient lives	COUNTY	❌ No
20	FIPS	FIPS code for the patient's location	FIPS_CODE	✔️ Yes
21	ZIP	ZIP code where the patient lives	ZIP_CODE	✔️ Yes
22	LAT	Latitude of the patient's address	LATITUDE	✔️ Yes
23	LON	Longitude of the patient's address	LONGITUDE	✔️ Yes
24	HEALTHCARE_EXPENSES	Patient's healthcare expenses	HEALTHCARE_EXPENSES	❌ No
25	HEALTHCARE_COVERAGE	Patient's healthcare coverage amount	HEALTHCARE_COVERAGE	❌ No
26	INCOME	Patient's income	INCOME	❌ No

🚧 3. Erroneous Values

🔍 3 columns have erroneous values

1. FIRST_NAME: All values are unusual because they combine a first name with a number.
   ✔️ The column has been cleaned:
   

   	Original Value	Cleaned Value
   0	Hayden835	Hayden
   1	Adelia946	Adelia
   2	Andera917	Andera
   3	Antony83	Antony
   4	Burton124	Burton
   5	Carmelita854	Carmelita
   6	Carrol931	Carrol
   7	Cedrick207	Cedrick
   8	Cheyenne169	Cheyenne
   9	Christal240	Christal
   10	Cindy893	Cindy
   11	Coleman27	Coleman
   12	Corey514	Corey
   13	Damon455	Damon
   14	Daniela614	Daniela
   15	Dione665	Dione
   16	Dominick530	Dominick
   17	Elden718	Elden
   18	Ethel888	Ethel
   19	Fletcher87	Fletcher
   20	Garry927	Garry
   21	Gayle448	Gayle
   22	Grant908	Grant
   23	Guadalupe206	Guadalupe
   24	Herb645	Herb
   25	Herschel574	Herschel
   26	Hildred696	Hildred
   27	Huey641	Huey
   28	Hunter736	Hunter
   29	Irving123	Irving
   30	Janeth814	Janeth
   31	Jenae263	Jenae
   32	Jimmie93	Jimmie
   33	Kaycee352	Kaycee
   34	Kirk871	Kirk
   35	Lacey714	Lacey
   36	Lavette209	Lavette
   37	Leandro563	Leandro
   38	Lessie363	Lessie
   39	Lillia547	Lillia
   40	Linn541	Linn
   41	Lonny638	Lonny
   42	Luke971	Luke
   43	Manuel446	Manuel
   44	Martín25	Martín
   45	Mel236	Mel
   46	Mirta419	Mirta
   47	Moises22	Moises
   48	Raymon366	Raymon
   49	Shaquana156	Shaquana
   50	Shayla126	Shayla
   51	Shiela18	Shiela
   52	Steve819	Steve
   53	Stewart672	Stewart
   54	Tyrell880	Tyrell
   55	Vanesa40	Vanesa
   56	Whitney250	Whitney
   57	Yetta429	Yetta
   58	Zoila41	Zoila

   
   
2. LAST_NAME: All values are unusual because last names do not typically include numbers.
   ✔️ The column has been cleaned:
   

   	Original Value	Cleaned Value
   0	Casper496	Casper
   1	Hamill307	Hamill
   2	MacGyver246	MacGyver
   3	Nolan344	Nolan
   4	O'Conner199	O'Conner
   5	Schimmel440	Schimmel
   6	Schumm995	Schumm
   7	Anderson154	Anderson
   8	Armstrong51	Armstrong
   9	Bailey598	Bailey
   10	Becker968	Becker
   11	Bermúdez789	Bermúdez
   12	Brakus656	Brakus
   13	Collier206	Collier
   14	Collins926	Collins
   15	Corwin846	Corwin
   16	Cruickshank494	Cruickshank
   17	Doyle959	Doyle
   18	Ernser583	Ernser
   19	Feest103	Feest
   20	Gislason620	Gislason
   21	Graham902	Graham
   22	Hagenes547	Hagenes
   23	Hahn503	Hahn
   24	Hane680	Hane
   25	Hayes766	Hayes
   26	Heathcote539	Heathcote
   27	Hoppe518	Hoppe
   28	Jenkins714	Jenkins
   29	Johnson679	Johnson
   30	Keebler762	Keebler
   31	Klein929	Klein
   32	Koss676	Koss
   33	Kreiger457	Kreiger
   34	Kshlerin58	Kshlerin
   35	Langworth352	Langworth
   36	Lemke654	Lemke
   37	Lueilwitz711	Lueilwitz
   38	Marks830	Marks
   39	McGlynn426	McGlynn
   40	Mills423	Mills
   41	Nikolaus26	Nikolaus
   42	Orn563	Orn
   43	Quitzon246	Quitzon
   44	Rath779	Rath
   45	Rico947	Rico
   46	Roldán470	Roldán
   47	Rutherford999	Rutherford
   48	Stehr398	Stehr
   49	Trantow673	Trantow
   50	Wilkinson796	Wilkinson
   51	Willms744	Willms
   52	Zboncak558	Zboncak

   
   
3. MAIDEN_NAME: All values are unusual because last names don't contain appended numbers.
   ✔️ The column has been cleaned:
   

   	Original Value	Cleaned Value
   0	Barela183	Barela
   1	Beier427	Beier
   2	Deckow585	Deckow
   3	DuBuque211	DuBuque
   4	Gorczany269	Gorczany
   5	Hegmann834	Hegmann
   6	Hermann103	Hermann
   7	Kohler843	Kohler
   8	Lowe577	Lowe
   9	Predovic534	Predovic
   10	Shanahan202	Shanahan

   
   

🔧 4. Data Type

✔️ 3 columns have been casted

	Column Name	Clause	Reasoning
0	BIRTHDATE	CAST("BIRTHDATE" AS DATE)	To transform the 'BIRTHDATE' column from 'VARCHAR' to 'DATE' data type in Snowflake, we can simply use the CAST function. The 'BIRTHDATE' values are already in the 'YYYY-MM-DD' format, which is a valid date format in Snowflake. Therefore, we don't need any additional formatting or conversion functions like TO_DATE. We can directly cast the 'BIRTHDATE' column to the 'DATE' data type using CAST(BIRTHDATE AS DATE).\n
1	DEATHDATE	CAST("DEATHDATE" AS DATE)	To transform the data type of the 'DEATHDATE' column from 'VARCHAR' to 'DATE', we can simply use the CAST function in Snowflake. The 'DEATHDATE' column is already in a format that Snowflake recognizes as a valid DATE format ('YYYY-MM-DD'), so no additional formatting is needed. We just need to cast the 'DEATHDATE' column as DATE.\n
2	ZIP_CODE	CAST("ZIP_CODE" AS VARCHAR)	To transform the data type of the 'ZIP_CODE' column from 'INT' to 'VARCHAR' in Snowflake, we need to use the CAST function. The CAST function allows us to convert a value from one data type to another. In this case, we will cast the 'ZIP_CODE' column from 'INT' to 'VARCHAR'. The syntax for the CAST function in Snowflake is CAST(expression AS target_data_type).\n

❓ 5. Missing Values

🔍 8 columns have missing values
✔️ 5 of them are acceptable

	Column	NULL (%)	Is NULL Acceptable?	Explanation
0	DEATHDATE	83.33	True	Not applicable for patients still alive.
1	DRIVERS_LICENSE	10.00	False
2	FIPS_CODE	23.33	False
3	MAIDEN_NAME	81.67	True	Not applicable for unmarried women or men.
4	MARITAL_STATUS	33.33	True	Not applicable for children/teens.
5	NAME_PREFIX	16.67	True	Not applicable if person doesn't have a prefix.
6	NAME_SUFFIX	93.33	True	Not applicable if person doesn't have a suffix.
7	PASSPORT_NUMBER	21.67	False

🧩 These missing values are handled as follows:

	Column	NULL (%)	Strategy
0	DRIVERS_LICENSE	10.00	🔄 Unchanged
1	FIPS_CODE	23.33	🔄 Unchanged
2	PASSPORT_NUMBER	21.67	🔄 Unchanged