Storing tabular data as pandas dataframe: Data preprocessing is one of the steps in machine learning. The pandas library in python is suitable to deal with tabular data. Create a variable ‘emissions’ and assign to it the following data (Table 1) as padas DataFrame. Create an excel file ‘emissions_from_pandas.xlsx’ from the ‘emissions’ variable using python. Table 1. Particulate matter (PM) emissions (in g/gal) for 15 vehicles driven at low altitude and another 15 vehicles driven at high altitude. Low Altitude High Altitude 1.50 7.59 1.48 2.06 2.98 8.86 1.40 8.67 3.12 5.61 0.25 6.28 6.73 4.04 5.30 4.40 9.30 9.52 6.96 1.50 7.21 6.07 0.87 17.11 1.06 3.57 7.39 2.68 1.37 6.46   iloc[] method: (b) Using the .iloc[] method, we can access any part of the dataframe. Run the following commands and show the outputs: emissions.head() emissions.iloc[0,0] emissions.iloc[1,1] emissions.iloc[0:2,0:2] emissions.iloc[2:4,:]

Storing tabular data as pandas dataframe:

(a) Data preprocessing is one of the steps in machine learning. The pandas library in python is suitable to deal with tabular data. Create a variable ‘emissions’ and assign to it the following data (Table 1) as padas DataFrame. Create an excel file ‘emissions_from_pandas.xlsx’ from the ‘emissions’ variable using python.

Table 1. Particulate matter (PM) emissions (in g/gal) for 15 vehicles driven at low altitude and another 15 vehicles driven at high altitude.

iloc[] method:

(b) Using the .iloc[] method, we can access any part of the dataframe. Run the following commands and show the outputs:

emissions.head()

emissions.iloc[0,0]

emissions.iloc[1,1]

emissions.iloc[0:2,0:2]

emissions.iloc[2:4,:]