Task-4: Plot a scatter plot with the Model Year values in the x-axis and their value counts in the y-axis. Make sure the plot has proper labels and title. [Hint: You can use Series.index attribute to have model year as a data sequence]. Comment on your observations in a following markdown cell. 59]: #GIVE YOUR ANSWER FOR TASK-4 IN THIS CELL model_year_data-pd.DataFrame(model_year) plt.scatter(ev_pop[ "Model Year'], model_year_data[ 'Model Year'])
Task-4: Plot a scatter plot with the Model Year values in the x-axis and their value counts in the y-axis. Make sure the plot has proper labels and title. [Hint: You can use Series.index attribute to have model year as a data sequence]. Comment on your observations in a following markdown cell. 59]: #GIVE YOUR ANSWER FOR TASK-4 IN THIS CELL model_year_data-pd.DataFrame(model_year) plt.scatter(ev_pop[ "Model Year'], model_year_data[ 'Model Year'])
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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I'm stuck on converting the series into a data frame, so it can be used in a scatter plot. Task 4
![A Week 7 Assignment.ipynb
+ X O
Code
Python 3
Task-3: Extract the value counts for each 'Model Year' and save the pandas Series in the variable name 'model_year
[51]: #GIVE YOUR ANSWER FOR TASK-3 IN THIS CELL
model_year=ev_pop['Model Year'].value_counts()
model_year
[51]: 2018
13256
2019
10345
2020
9154
2017
8106
2016
6274
2015
5030
2013
4891
2014
3772
2012
1737
2021
946
2011
880
2010
31
2008
23
2000
1999
3
2002
2
1993
1
1997
1
1998
1
Name: Model Year, dtype: int64
At this point model_year is a pandas Series having the 'Model Year' values as its index and count of values as the Series.
If we explore the dataset we shall see the 'Base MSRP' column inexplacably contain a good number of values equal to 'zero' and in some case unusually high values (can be disregarded as outlier) which are
greater than 100,000.
Task-4: Plot a scatter plot with the Model Year values in the x-axis and their value counts in the y-axis. Make sure the plot has proper labels and title. [Hint: You can use Series.index attribute to have model year
as a data sequence]. Comment on your observations in a following markdown cell.
[59]: #GIVE YOUR ANSWER FOR TASK-4 IN THIS CELL
model_year_data=pd. DataFrame(model_year)
plt.scatter(ev_pop[ 'Model Year'],model_year_data['Model Year'])](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F5a479654-3586-458f-9b03-0cfcb5adbe3a%2F4a2e6e7c-3316-47c7-8816-ad8d2bea16e7%2Fvbvkbwk_processed.png&w=3840&q=75)
Transcribed Image Text:A Week 7 Assignment.ipynb
+ X O
Code
Python 3
Task-3: Extract the value counts for each 'Model Year' and save the pandas Series in the variable name 'model_year
[51]: #GIVE YOUR ANSWER FOR TASK-3 IN THIS CELL
model_year=ev_pop['Model Year'].value_counts()
model_year
[51]: 2018
13256
2019
10345
2020
9154
2017
8106
2016
6274
2015
5030
2013
4891
2014
3772
2012
1737
2021
946
2011
880
2010
31
2008
23
2000
1999
3
2002
2
1993
1
1997
1
1998
1
Name: Model Year, dtype: int64
At this point model_year is a pandas Series having the 'Model Year' values as its index and count of values as the Series.
If we explore the dataset we shall see the 'Base MSRP' column inexplacably contain a good number of values equal to 'zero' and in some case unusually high values (can be disregarded as outlier) which are
greater than 100,000.
Task-4: Plot a scatter plot with the Model Year values in the x-axis and their value counts in the y-axis. Make sure the plot has proper labels and title. [Hint: You can use Series.index attribute to have model year
as a data sequence]. Comment on your observations in a following markdown cell.
[59]: #GIVE YOUR ANSWER FOR TASK-4 IN THIS CELL
model_year_data=pd. DataFrame(model_year)
plt.scatter(ev_pop[ 'Model Year'],model_year_data['Model Year'])
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