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DataWrangling.docx

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Running Head: DATA WRANGLING 1

DATA WRANGLING 2

Data Wrangling

Poojan Patel

National Louis University

Remove the Non-Null Count column

	Column	Non-Null Count	Dtype
0	id	5110 non-null	int64
1	gender	5110 non-null	object
2	age	5110 non-null	float64
3	hypertension	5110 non-null	int64
4	heart_disease	5110 non-null	int64
5	ever_maried	5110 non-null	object
6	work_type	5110 non-null	object
7	residence _type	5110 non-null	object
8	avg_glucose_level	5110 non-null	float64
9	bmi	5110 non-null	float64
10	smoking_status	5110 non-null	object
11	stroke	5110 non-null	int64

	Column	Dtype
0	id	int64
1	gender	object
2	age	float64
3	hypertension	int64
4	heart_disease	int64
5	ever_maried	object
6	work_type	object
7	residence _type	object
8	avg_glucose_level	float64
9	bmi	float64
10	smoking_status	object
11	stroke	int64

The rest of the database data is relevant hence no need to remove, there are no spelling mistake and remaining data is relevant and can be used for data analysis.

Reference

Rattenbury, T. (2019). Principles of data wrangling: Practical techniques for data preparation. O'Reilly Media.

Visochek, A. (2018). Practical data wrangling: Expert techniques for transforming your raw data into a valuable source for analytics. Packt Publishing.