e. Filter the vehicles that have mpg greater than 40 (inclusive) and acceleration greater than 11 (inclusive)f. Create a new dataset (variable) called ‘averages’ that groups by class and calculates means of msrp, acceleration and mpgg. Using ‘averages’ data, create the following 3 lineplots: mean msrp (y-axis) vs. class (x-axis), mean acceleration (y-axis) vs. class (x-axis) and mpg (y-axis) vs. class (x-axis) vehiclePrius (1st Gen)Tino2000 35354.97Prius (2nd Gen) 2000 26832.25Insight200018936.41Civic (1st Gen)200125833.38Insight2001 19036.71Insight200219137.01Alphard2003 38084.77Insight2003 19137.01Civic200314071.922004 36676.12004 19237.31200420355.64200430089.64200558521.14200526354.44200529186.21200519387.76200518236.33200519322.562005 16343.69200532647.26200634772.42006 29853.25200664547.56200636012.72006 29524.75EscapeInsightPriusSilverado 15 2WDLexus RX400hCivic (2nd Gen)HighlanderInsightCivicEscape 2WDAccordSilverado 15 2WDMercury MarinerCamryLexus GS450hEstimaAltimayear msrp1997 24509.74acceleration mpg class7.46 41.26 Compact8.254.1 Compact7.97 45.23 Compact9.52 53.0 Two Seater7.04 47.04 Compact9.52 53.0 Two Seater9.71 53.0 Two Seater8.33 40.46 Minivan9.52 53.0 Two Seater8.62 41.0 Compact10.32 31.99 SUV9.35 52.0 Two Seater9.9 46.0 Midsize9.0912.76 28.23 SUV7.63 39.99 Compact12.7629.4 SUV9.71 52.0 Two Seater8.2641.0 Compact9.52 29.0 SUV14.93 28.0 Midsize11.11 17.0 Pickup Truck8.98 32.93 SUV11.28 33.64 Midsize18.65 33.4 Midsize9.26 47.04 Minivan13.29 32.93 Midsize17.0 Pickup Truck

NOTE: Since programming langauge is not mentioned. Therefore answering question in Python programming langauge.The code imports the pandas and matplotlib libraries for data manipulation and visualization.A CSV file containing automotive data is read into a DataFrame using pandas.The DataFrame is filtered to include only rows where 'mpg' is at least 40 and 'acceleration' is at least 11, and the result is printed.The data is grouped by the 'class' column, and the mean values of 'msrp', 'acceleration', and 'mpg' for each class are calculated and printed.A line plot of the mean 'msrp' versus different car classes is created and displayed.Similar line plots are created and displayed for mean 'acceleration' and 'mpg' versus car classes.below is the code:1import pandas as pd 2import matplotlib.pyplot as plt 3df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/dataset.csv') 4 5# part e 6filtered_df = df[(df['mpg'] >= 40) & (df['acceleration'] >= 11)] 7print(filtered_df) 8 9# part f 10averages = df.groupby('class').mean()[['msrp', 'acceleration', 'mpg']] 11print(averages) 12 13# part g 14# Plot mean msrp vs class 15averages['msrp'].plot(kind='line') 16plt.xlabel('Class') 17plt.ylabel('Mean MSRP') 18plt.title('Mean MSRP vs Class') 19plt.show() 20 21# Plot mean acceleration vs class 22averages['acceleration'].plot(kind='line') 23plt.xlabel('Class') 24plt.ylabel('Mean Acceleration') 25plt.title('Mean Acceleration vs Class') 26plt.show() 27 28# Plot mpg vs class 29averages['mpg'].plot(kind='line') 30plt.xlabel('Class') 31plt.ylabel('MPG') 32plt.title('MPG vs Class') 33plt.show() 34