Cocoon Cleaning Summary 🤗

---

📃 1. Table Summary

The table contains data about used cars for sale. It includes details about each car's make, model, specs, mileage, price, license plate, and the dealership selling it.

📊 2. Column Rename

😎 20 columns have been renamed

	Column	Summary	New Column Name	Renamed?
0	id_ucd	Unique identifier for the record	record_id	✔️ Yes
1	id_merk	Car make or brand	make	✔️ Yes
2	type	Specific variant or submodel	variant	✔️ Yes
3	model	Car model	model	❌ No
4	color	Color of the car	color	❌ No
5	year_	Year the car was manufactured	year_	✔️ Yes
6	id_transmission	Type of transmission	transmission_type	✔️ Yes
7	id_fuel_type	Type of fuel	fuel_type	✔️ Yes
8	door	Number of doors	num_doors	✔️ Yes
9	cylinder_size	Engine cylinder size in cc	engine_size_cc	✔️ Yes
10	cylinder_total	Total number of engine cylinders	num_cylinders	✔️ Yes
11	turbo	Indicates if the car has a turbocharger	has_turbo	✔️ Yes
12	mileage	Mileage of the car	mileage	❌ No
13	license_plate	License plate number	license_plate	❌ No
14	price_cash	Cash price of the car	cash_price	✔️ Yes
15	price_credit	Credit price of the car	credit_price	✔️ Yes
16	nilai_jual_pkb	Sales value for tax purposes	tax_sale_value	✔️ Yes
17	pkb_pokok	Base motor vehicle tax amount	base_vehicle_tax	✔️ Yes
18	stnk_date	Expiry date of vehicle registration certificate	registration_expiry	✔️ Yes
19	levy_date	Date of the levy	levy_date	❌ No
20	swdkllj	Traffic accident fund fee	accident_fund_fee	✔️ Yes
21	total_levy	Total levy amount	total_levy	❌ No
22	showroom_name	Name of the showroom	showroom_name	❌ No
23	showroom_location	Address of the showroom	showroom_address	✔️ Yes
24	showroom_regency	Regency where the showroom is located	showroom_regency	❌ No
25	photo_1	URL or filename of the first photo	photo1_url	✔️ Yes
26	photo_2	URL or filename of the second photo	photo2_url	✔️ Yes
27	photo_3	URL or filename of the third photo	photo3_url	✔️ Yes
28	link	Link to the listing (empty in data)	listing_url	✔️ Yes
29	created_at	Timestamp when the record was created	created_at	❌ No
30	updated_at	Timestamp when the record was last updated	updated_at	❌ No

🚧 3. Erroneous Values

🔍 1 columns have erroneous values

1. model: 'MINIBUS' and 'Minibus', 'SEDAN' and 'Sedan' are inconsistently capitalized representations of the same concepts.
   ✔️ The column has been cleaned:
   

   	Original Value	Cleaned Value
   0	Minibus	MINIBUS
   1	Sedan	SEDAN

   
   

🕵️‍♂️ 4. Disguised Missing Values

🔍 1 columns have disguised missing values
✔️ 1 columns have been cleaned

	Column	Disguised Missing Values	Impute to NULL?	Values to NULL (Sep By ,)
0	license_plate	[10000]	True	10000

🔧 5. Data Type

✔️ 9 columns have been casted

	Column Name	Clause	Reasoning
0	created_at	TO_TIMESTAMP("created_at", 'MM/DD/YYYY HH24:MI')	To transform the 'created_at' column from VARCHAR to TIMESTAMP data type in Snowflake, we can directly cast it using the CAST function and specifying the target data type as TIMESTAMP.\nThe existing format of the 'created_at' values ('M/D/YYYY HH24:MI') is a valid input format for automatic casting to TIMESTAMP in Snowflake. Therefore, no additional formatting or conversion is needed before casting.\n
1	has_turbo	CAST("has_turbo" AS BOOLEAN)	To transform the data type of the 'has_turbo' column from 'INT' to 'BOOLEAN' in Snowflake, we can use the CAST function. Since the column only contains the distinct values 0 and 1, we can directly cast it to a BOOLEAN type. In Snowflake, the BOOLEAN data type represents true and false values, where 0 is interpreted as false and 1 as true.\n
2	levy_date	CAST("levy_date" AS DATE)	To transform the 'levy_date' column from VARCHAR to DATE, we can use a simple CAST clause. The existing format of the dates ('1/11/2024', '1/12/2024', etc.) matches the default DATE format in Snowflake, which is 'MM/DD/YYYY'. Therefore, we don't need to specify a format string in the CAST function. A simple CAST(levy_date AS DATE) will suffice to convert the VARCHAR values to DATE values.\n
3	listing_url	CAST("listing_url" AS VARCHAR)	To transform the data type of the 'listing_url' column from 'FLOAT' to 'VARCHAR' in Snowflake, we can use the CAST function. The CAST function allows us to convert a value from one data type to another. In this case, we simply specify the column name 'listing_url' and use the AS keyword to define the target data type 'VARCHAR'. No additional transformations or considerations are needed since we are directly casting the float values to varchar.\n
4	photo2_url	CAST("photo2_url" AS VARCHAR)	The photo2_url column has no distinct values, indicating it is an empty column. However, we can still transform the data type from FLOAT to VARCHAR using a simple CAST function in Snowflake. This will convert any existing FLOAT values to VARCHAR, and future inserted values will be stored as VARCHAR.\n
5	photo3_url	CAST("photo3_url" AS VARCHAR)	To transform the data type of the 'photo3_url' column from FLOAT to VARCHAR in Snowflake, we can use a simple CAST function in the SELECT clause. Since the column has no distinct values, it suggests the column is currently empty or contains all NULL values. Casting it to VARCHAR will allow the column to store string URLs in the future.\n
6	registration_expiry	CAST("registration_expiry" AS DATE)	To transform the 'registration_expiry' column from VARCHAR to DATE, we can simply use the CAST function in Snowflake. The existing format of the dates ('1/11/2024', '6/13/2027', etc.) matches the default DATE format in Snowflake ('MM/DD/YYYY'), so we don't need to specify a format string. The CAST function will automatically parse the string values into DATE values.\n
7	transmission_type	CAST("transmission_type" AS VARCHAR)	To transform the data type of the 'transmission_type' column from INT to VARCHAR in Snowflake, we simply 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 'transmission_type' column to the VARCHAR data type.\n
8	updated_at	TO_TIMESTAMP("updated_at", 'MM/DD/YYYY HH24:MI')	To transform the 'updated_at' column from VARCHAR to TIMESTAMP, we can directly cast the string to a TIMESTAMP data type. Snowflake will automatically parse the string using the default format 'MM/DD/YYYY HH24:MI'. Since the provided format matches this default format, no additional formatting is needed in the CAST function.\n

❓ 6. Missing Values

🔍 13 columns have missing values
✔️ 3 of them are acceptable

	Column	NULL (%)	Is NULL Acceptable?	Explanation
0	accident_fund_fee	5.71	False
1	base_vehicle_tax	5.71	False
2	created_at	3.81	False
3	levy_date	4.29	False
4	license_plate	0.48	False
5	listing_url	100.00	True	Some listings may not have a URL.
6	photo1_url	10.00	False
7	photo2_url	100.00	True	Listings may not have a second photo.
8	photo3_url	100.00	True	Listings may not have a third photo.
9	registration_expiry	4.29	False
10	tax_sale_value	4.29	False
11	total_levy	5.71	False
12	updated_at	3.81	False

🧩 These missing values are handled as follows:

	Column	NULL (%)	Strategy
0	accident_fund_fee	5.71	🔄 Unchanged
1	base_vehicle_tax	5.71	🔄 Unchanged
2	created_at	3.81	🔄 Unchanged
3	levy_date	4.29	🔄 Unchanged
4	license_plate	0.48	🔄 Unchanged
5	photo1_url	10.00	🔄 Unchanged
6	registration_expiry	4.29	🔄 Unchanged
7	tax_sale_value	4.29	🔄 Unchanged
8	total_levy	5.71	🔄 Unchanged
9	updated_at	3.81	🔄 Unchanged