Step 1: Generating cars dataset

This block of Python code will generate the sample data for you. You will not be generating the data set using numpy module this week. Instead, the data set will be imported from a CSV file. To make the data unique to you, a random sample of size 30, without replacement, will be drawn from the data in the CSV file. The data set will be saved in a Python dataframe that will be used in later calculations.

Click the block of code below and hit theRunbutton above.

In[1]:

import pandas as pd from IPython.display import display, HTML # read data from mtcars.csv data set. cars_df_orig = pd.read_csv("https://s3-us-west-2.amazonaws.com/data-analytics.zybooks.com/mtcars.csv") # randomly pick 30 observations from the data set to make the data set unique to you. cars_df = cars_df_orig.sample(n=30, replace=False) # print only the first five observations in the dataset. print("Cars data frame (showing only the first five observations) ") display(HTML(cars_df.head().to_html()))

Cars data frame (showing only the first five observations)

	Unnamed: 0	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
3	Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
15	Lincoln Continental	10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4
20	Toyota Corona	21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1
17	Fiat 128	32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1
19	Toyota Corolla	33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1