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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)
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'])
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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