Q.Display the first 10 rows of the DataFrame autodf. (Dataset: the UCI 'auto-mpg' open dataset — 398 rows, nine attributes: mpg, cylinders, displacement, horsepower, weight, acceleration, model year, origin, car name.)

Shape: Code task — write a pandas statement to display the first 10 rows of a DataFrame.

The head() method returns the first $n$ rows of a DataFrame; by default it shows 5, but you can pass any integer to specify how many you want. Here we need 10 rows from autodf.

import pandas as pd

# Assuming autodf is already loaded with the auto-mpg dataset
autodf.head(10)

Key point: head(n) is the standard way to inspect the top portion of a DataFrame. Without an argument, head() defaults to 5 rows; passing 10 explicitly requests the first 10.

Expected output (a table with 10 rows and 9 columns):

The actual values will match the UCI auto-mpg dataset; the table above illustrates the structure — 10 rows indexed 0–9, with all nine attributes displayed.

Why look at the first rows at all?

Before you compute a single statistic, you inspect the data: are the column names what you expect, do the values look like the right kind of number, is anything obviously broken (wrong units, text where you expected numbers, all-zero columns)? head(n) is the fastest way to do that — it prints the top n rows without scanning or summarizing the whole DataFrame, so it's cheap even on a dataset far bigger than 398 rows.

import pandas as pd

# Assuming autodf is already loaded
autodf.head(10)

Output:

Reading what the output tells you

Even before running any statistics, this preview already tells a story:

• model_year is 70 for all ten rows — the dataset is loaded in chronological order, so its earliest records are all from 1970 (stored as the two-digit code 70, not 1970).
• cylinders is 8 and origin is 1 for every one of these rows — every early car is a large-engine, American-made (origin == 1) model. This is a real, visible pattern in the printed rows, not a coincidence of a random sample: if you only ever looked at head(), you would wrongly conclude the whole dataset is 8-cylinder American cars. Fuel-efficient 4-cylinder and non-US cars (origin 2 = Europe, 3 = Japan) appear later, as model years progress — which is exactly why head() is a sanity check, not a substitute for looking at the full distribution (e.g. autodf['cylinders'].value_counts() or autodf['origin'].value_counts()).
• mpg in this early slice ranges from 14.0 to 18.0 — comparatively low, consistent with the large-displacement engines shown in the same rows. That relationship (bigger engine → lower mpg) is one of the first things this dataset is normally used to illustrate.

head() vs. related methods

head()/tail() are deterministic and index-order-based, which is why they're the right tool for "does this look like it loaded correctly?" — sample() is the better tool once you want an unbiased look at the dataset as a whole, precisely because (as above) the first 10 rows alone are not representative of the full 398.