Q.Print the details of the car which gave the maximum mileage. (Dataset: the UCI 'auto-mpg' open dataset loaded into DataFrame autodf — 398 rows, nine attributes: mpg, cylinders, displacement, horsepower, weight, acceleration, model year, origin, car name.)

To find the car with the maximum mileage, we need to perform two main steps: first, identify the highest mileage value present in the dataset, and then, filter the DataFrame to retrieve all rows where the mileage matches this maximum value.

Here's how to achieve this using pandas:

import pandas as pd

# Assuming autodf is already loaded from the UCI 'auto-mpg' dataset.
# For demonstration, let's create a representative DataFrame structure.
# In a real scenario, autodf would be loaded from a CSV or similar source.
data = {
    'mpg': [18.0, 15.0, 18.0, 46.6, 17.0, 30.0, 28.0],
    'cylinders': [8, 8, 8, 4, 8, 4, 4],
    'displacement': [307.0, 350.0, 318.0, 86.0, 302.0, 90.0, 112.0],
    'horsepower': [130.0, 165.0, 150.0, 65.0, 140.0, 70.0, 88.0],
    'weight': [3504, 3693, 3436, 2110, 3449, 2000, 2600],
    'acceleration': [12.0, 11.5, 11.0, 17.9, 10.5, 18.0, 15.0],
    'model year': [70, 70, 70, 80, 70, 81, 82],
    'origin': [1, 1, 1, 3, 1, 2, 1],
    'car name': ['chevrolet chevelle malibu', 'buick skylark 320', 'plymouth satellite', 'mazda glc', 'ford torino', 'honda civic', 'ford escort 4w']
}
autodf = pd.DataFrame(data, index=[0, 1, 2, 322, 4, 5, 6]) # Using index 322 for mazda glc as per actual dataset

# Find the maximum mileage (mpg) value in the DataFrame
max_mileage_value = autodf['mpg'].max()

# Filter the DataFrame to select all rows where 'mpg' equals the maximum value
car_with_max_mileage = autodf[autodf['mpg'] == max_mileage_value]

# Print the details of the car(s)
print(car_with_max_mileage)

Explanation of Key Lines:

1. max_mileage_value = autodf['mpg'].max(): This line first selects the 'mpg' column from the autodf DataFrame. The .max() method is then applied to this Series to compute and return the highest mileage value present in the entire column. …

The general pattern: max-then-filter

Whenever you need "the record(s) that achieved an extreme value" rather than just the value itself, the pattern is always two steps:

1. Find the extreme value with .max() (or .min()).
2. Use that value as a boolean filter to pull out the matching row(s) — because a plain .max() only gives you the number, not the rest of that row's data.

# Step 1: find the maximum mileage value
max_mpg_value = autodf['mpg'].max()
print("Maximum mpg in the dataset:", max_mpg_value)

# Step 2: filter the DataFrame for the row(s) with that mpg
car_with_max_mileage = autodf[autodf['mpg'] == max_mpg_value]
print(car_with_max_mileage)

Output:

Maximum mpg in the dataset: 46.6
      mpg  cylinders  displacement  horsepower  weight  acceleration  model_year  origin   car_name
322  46.6          4          86.0        65.0    2110          17.9          80       3  mazda glc

Why these two lines do the job

• autodf['mpg'].max() selects the mpg column (a Series of 398 values) and returns its single largest number. It does not tell you which car achieved it — a Series has no notion of "the rest of the row."
• autodf[autodf['mpg'] == max_mpg_value] is boolean indexing: autodf['mpg'] == max_mpg_value produces a Series of True/False, one per row, True exactly where that row's mpg equals the maximum. Passing that boolean Series inside autodf[...] keeps only the matching row(s) — with every column intact, not just mpg. This is what actually answers "print the details of the car," since the question asks for the whole record, not just the number.

Reading the result

This particular car — a 1980-model, 4-cylinder Mazda GLC imported from Japan (origin = 3) — sits at the opposite extreme from the large 8-cylinder American cars that dominate head() (Exercise 3): far smaller displacement (86.0 vs 300+), far lower horsepower (65.0 vs 130–225), and less than two-thirds the weight (2110 lbs vs 3400–4400 lbs). This is exactly the trade-off the dataset is built to illustrate: mpg rises as engine size, horsepower and weight fall. …