Cocoon Cleaning Summary 🤗

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📃 1. Table Summary

The table is about SAT exam results from schools in 2012. It lists the school DBN, name, number of test takers, and average scores in critical reading, math, and writing.

📊 2. Column Rename

😎 5 columns have been renamed

	Column	Summary	New Column Name	Renamed?
0	DBN	School identifier code	school_id	✔️ Yes
1	SCHOOL_NAME	Full name of the school	SCHOOL_NAME	❌ No
2	Num_of_SAT_Test_Takers	Number of students who took the SAT	num_sat_takers	✔️ Yes
3	SAT_Critical_Reading_Avg__Score	Average SAT score in critical reading section	sat_critical_reading_avg	✔️ Yes
4	SAT_Math_Avg__Score	Average SAT score in math section	sat_math_avg	✔️ Yes
5	SAT_Writing_Avg__Score	Average SAT score in writing section	sat_writing_avg	✔️ Yes

🚧 3. Erroneous Values

🔍 4 columns have erroneous values

1. num_sat_takers: 's' value is unusual because it is not a number like the other values.
   ❌ The column has not been cleaned
   
   
2. sat_critical_reading_avg: 's' is unusual because it is not a valid numeric SAT score.
   ❌ The column has not been cleaned
   
   
3. sat_math_avg: 's' value is unusual because it is not a valid numeric SAT score.
   ❌ The column has not been cleaned
   
   
4. sat_writing_avg: 's' is unusual because it is not a valid numeric SAT score.
   ❌ The column has not been cleaned
   
   

🕵️‍♂️ 4. Disguised Missing Values

🔍 4 columns have disguised missing values
✔️ 4 columns have been cleaned

	Column	Disguised Missing Values	Impute to NULL?	Values to NULL (Sep By ,)
0	num_sat_takers	[s]	True	s
1	sat_critical_reading_avg	[s]	True	s
2	sat_math_avg	[s]	True	s
3	sat_writing_avg	[s]	True	s

🔧 5. Data Type

✔️ 4 columns have been casted

	Column Name	Clause	Reasoning
0	num_sat_takers	CAST("num_sat_takers" AS INT)	To transform the data type of the 'num_sat_takers' column from 'VARCHAR' to 'INT', we simply need to use the CAST function in Snowflake. Since the distinct values in the column are already in a valid integer format, we don't need any additional string manipulation or regular expressions to extract the numeric part. We can directly cast the column to the 'INT' data type.\n
1	sat_critical_reading_avg	CAST("sat_critical_reading_avg" AS INT)	To transform the data type of the 'sat_critical_reading_avg' column from VARCHAR to INT, we can use a simple CAST clause in Snowflake. Since the distinct values in the column are all valid integers ('367', '370', '384', '398', '363'), we don't need any additional transformations or regex parsing. We can directly cast the column to the INT data type.\n
2	sat_math_avg	CAST("sat_math_avg" AS INT)	To transform the data type of the 'sat_math_avg' column from 'VARCHAR' to 'INT', we can use a simple CAST clause in Snowflake. Since the distinct values in the column are all valid integers ('385', '364', '371', '381', '391'), we don't need any additional parsing or extraction. We can directly cast the column to the 'INT' data type.\n
3	sat_writing_avg	CAST("sat_writing_avg" AS INT)	To transform the 'sat_writing_avg' column from VARCHAR to INT, we can simply use the CAST function in Snowflake. The existing regex pattern '\b([2-5]\d{2}|6[0-3]\d)\b' ensures that the values are valid integers between 200 and 639, so no additional regex extraction is needed. We can directly cast the column to INT.\n

❓ 6. Missing Values

🔍 4 columns have missing values
✔️ 1 of them are acceptable

	Column	NULL (%)	Is NULL Acceptable?	Explanation
0	num_sat_takers	11.92	True	No students took SAT at that school.
1	sat_critical_reading_avg	11.92	False
2	sat_math_avg	11.92	False
3	sat_writing_avg	11.92	False

🧩 These missing values are handled as follows:

	Column	NULL (%)	Strategy
0	sat_critical_reading_avg	11.92	🔄 Unchanged
1	sat_math_avg	11.92	🔄 Unchanged
2	sat_writing_avg	11.92	🔄 Unchanged