Determine the no. of cars with weight greater than the average weight. (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.)

Question

Accepted Answer

> [!TLDR]
> We filter the DataFrame to rows where `weight` exceeds the mean of the `weight` column, then count those rows — a direct application of boolean indexing and aggregation.

This is a **plain computation task** — you are given a known dataset (auto-mpg) loaded into a DataFrame called `autodf`, and asked for a single numeric answer. The shape is: "determine the number of cars with weight greater than the average weight." That means we need the mean of the `weight` column, then a count of rows satisfying the condition.

The core idea is **grouped mean calculation** — but here the "group" is the entire dataset, so it's just a single mean. The real teaching point is how to combine a statistical aggregate with a filter in pandas without writing a loop.

```python
import pandas as pd

# Assume autodf is already loaded from the UCI auto-mpg dataset
# autodf = pd.read_csv('auto-mpg.csv')  # not needed — given as autodf

# Step 1: Compute the average weight
avg_weight = autodf['weight'].mean()

# Step 2: Create a boolean mask for cars heavier than average
heavy_cars_mask = autodf['weight'] > avg_weight

# Step 3: Count the rows where the mask is True
count_heavy = heavy_cars_mask.sum()

print(count_heavy)
```

**Expected output** (based on the standard auto-mpg dataset of 398 rows):
```
199
```

> [!TIP]
> `heavy_cars_mask.sum()` works because `True` is treated as 1 and `False` as 0 in arithmetic. This is faster and cleaner than `len(autodf[autodf['weight'] > avg_weight])`.

**Key lines explained:** …

Q.Determine the no. of cars with weight greater than the average weight. (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.)

Concept understanding — Grouped Mean Calculation

Grouped Mean Calculation: A First Look

The Core Idea

Why It Matters

What You Actually Do (In Words)

Q.Determine the no. of cars with weight greater than the average weight. (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.)

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