a) Make an array X that has the same data as the above table (shape of X: (4, 6)). Check whether the shape of X is (4, 6) by using assert () function. (Note: assert (cond) gives an error if cond is not true.) b) Then, extract 4 parameters (size, # of bedrooms, floors, age of house) from X and put them into a new matrix P. Check if the shape of P is (4, 4) by using assert () function. c) Find the mean value of each parameter using the numpy mean function.

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Exercises 2
We have some data about House prices and parameters that affect the price.
Bias
1
1
1
1
Size
2104
1416
1544
852
Number of
bedrooms
5
3
Number of
floors
1
2
2
1
Age of
house
45
40
30
36
Price(1000000won)
460
232
315
178
a) Make an array X that has the same data as the above table (shape of X: (4, 6)). Check whether the shape of
X is (4, 6) by using assert () function. (Note: assert (cond) gives an error if cond is not True.)
b) Then, extract 4 parameters (size, # of bedrooms, floors, age of house) from X and put them into a new
matrix P. Check if the shape of P is (4, 4) by using assert () function.
c) Find the mean value of each parameter using the numpy mean function.
Transcribed Image Text:Exercises 2 We have some data about House prices and parameters that affect the price. Bias 1 1 1 1 Size 2104 1416 1544 852 Number of bedrooms 5 3 Number of floors 1 2 2 1 Age of house 45 40 30 36 Price(1000000won) 460 232 315 178 a) Make an array X that has the same data as the above table (shape of X: (4, 6)). Check whether the shape of X is (4, 6) by using assert () function. (Note: assert (cond) gives an error if cond is not True.) b) Then, extract 4 parameters (size, # of bedrooms, floors, age of house) from X and put them into a new matrix P. Check if the shape of P is (4, 4) by using assert () function. c) Find the mean value of each parameter using the numpy mean function.
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