Description
1. (100 points) Some NumPy Practice
a. In IDLE or the Python development environment of your choice, create a Python code filehw6.1.py. At the top of the file, add comments giving the name of the file and your homework group members as authors. Next, import the numpy module with the conventional abbreviation, np. Create a 1-dimensional ndarray object named a1 containing the sequence of values 6, 7, 8, 9, and 10; then, display a1 using the print() function.
b. The observations.csv file contains a record of information for each patient at a doctor’s office during a day. Each record gives the age in years, height in inches, and weight in pounds of the patient. Read from the observations.csv file into a 2-dimensional ndarray named aobs. Display aobs, the shape of aobs, and the number of dimensions of aobs.
c. Display just the first 8 patient records of aobs. (Hint: display a slice of rows.)
d. Display just the last 4 patient records of aobs.
e. Display the middle 10 patient records of aobs.
f. Display just the ages of all the patients. (Hint: display all rows, and one column.)
g. Display the heights and weights of all the patients. (Hint: display all rows, and a slice of columns.)
h. Display the heights and weights of the middle 10 patients. (Hint: display a slice of columns from a slice of rows.)
i. A Boolean index is more flexible in selecting rows/columns from a 2-dimensional ndarray. For example, this statement prints the heights of all the patients using a slice for rows and a Boolean index for columns:
print(aobs[:, [False, True, False]])
Display the ages and weights of the first 10 patients.
j. Create a Boolean index for the rows of aobs, where the Boolean value is True if the patient’s height is >= 70 inches (5 ft. 10 in.), and False otherwise. Display this Boolean index. (Hint: see slide 13 of the Week 6 Part 2 Lecture materials.)
k. Using the Boolean index from (j), select just the rows from aobs, where the patient’s height is >= 70 inches (5 ft. 10 in.). Display the selected rows. (Hint: see slide 10 of the Week 6 Part 2 Lecture materials.)
l. NumPy provides many statistical functions that work on ndarrays: mean(), min(), max(), std() (standard deviation), corrcoef() (correlation coefficients), etc. For example, given:
a2 = np.array([1, 2, 3, 4, 6])
then a2.mean() is 3.2. Define a2 as shown above, and display the mean, min, max, and
standard deviation for a2.
m. The correlation coefficient describes how correlated (or not) two data sets are. A given data set is perfectly correlated with itself, that is, has correlation 1.0. The ndarraysa1 (from part (a)) and a2 (above) are highly but not perfectly correlated:
print(np.corrcoef(a1, a2))
displays: [[1. 0.98639392]
[0.98639392 1.]]
n. Display the mean, min, max, and standard deviation of the ages of all patients in aobs.
o. Display the mean, min, max, and standard deviation of the heights of all patients in aobs.
p. Display the mean, min, max, and standard deviation of the weights of all patients in aobs.
q. What is the correlation between age and height for all patients in aobs? (Is this surprising? Make a comment in your code.)
r. What is the correlation between age and weight for all patients in aobs? (Is this surprising? Make a comment in your code.)
s. What is the correlation between height and weight for all patients in aobs? (Is this surprising? Make a comment in your code.)
