Inspecting Data: head, tail, info, describe

Inspecting data means using Pandas' built-in summary methods — head , tail , info , and describe — to get a fast, reliable overview of a dataset's contents, structure, and statistics before you analyse it.

Learn Inspecting Data: head, tail, info, describe in our free Pandas course — a beginner-friendly interactive lesson with worked examples, a practice…

Part of the free Pandas course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

Never trust data you haven't looked at. These methods are the first thing every analyst runs on a fresh dataset.

What You'll Learn in This Lesson

1 Previewing Rows: head() and tail()

Real datasets can have thousands of rows. df.head() shows the first 5 and df.tail() shows the last 5 — a quick peek without flooding your screen:

2 Structure: info(), shape, columns, dtypes

df.info() prints a compact summary of the DataFrame's structure : how many rows, each column's name, its non-null count (great for spotting missing data), and its dtype.

3 Statistics: describe(), nunique(), value_counts()

df.describe() computes summary statistics for every numeric column — count, mean, standard deviation, min, the three quartiles, and max:

nunique() tells you how many distinct values a column has, while value_counts() tallies how many times each value appears — ideal for category columns.

📋 Quick Reference

Method

Tells You

df.head() / df.tail()

First / last rows

df.shape

(rows, columns)

df.info()

Types, non-null counts, memory

df.describe()

Numeric summary statistics

df.nunique()

Distinct values per column

df["col"].value_counts()

Tally of each value

🎯 Mini Challenge: Quick Audit

❓ Frequently Asked Questions

Lesson 6 complete — you can audit any dataset!

You can preview rows with head and tail, check structure and missing data with info, summarise numbers with describe, and tally categories with nunique and value_counts.

🚀 Up next: Selecting Columns and Rows — pick out exactly the data you need from any DataFrame.

Practice quiz

How many rows does df.head() show by default?

  • 10
  • 5
  • 3
  • All of them

Answer: 5. df.head() returns the first 5 rows by default.

What does df.tail(2) return?

  • The first 2 rows
  • Every other row
  • The last 2 rows
  • 2 random rows

Answer: The last 2 rows. tail(n) shows the last n rows; tail(2) shows the final two.

Which method summarises structure: column names, non-null counts, and dtypes?

  • df.describe()
  • df.head()
  • df.value_counts()
  • df.info()

Answer: df.info(). df.info() reports structure, non-null counts, dtypes, and memory usage.

Which method gives count, mean, std, min, quartiles, and max for numeric columns?

  • df.describe()
  • df.info()
  • df.shape
  • df.head()

Answer: df.describe(). df.describe() computes summary statistics for numeric columns.

What does df.shape return?

  • The column names
  • A tuple of (rows, columns)
  • The dtypes
  • The memory usage

Answer: A tuple of (rows, columns). df.shape is a (rows, columns) tuple.

What does df['col'].value_counts() do?

  • Returns the column mean
  • Sorts the column
  • Tallies how many times each distinct value appears
  • Counts the columns

Answer: Tallies how many times each distinct value appears. value_counts() counts occurrences of each value, sorted high to low.

What does df.nunique() report?

  • The total row count
  • The mean of each column
  • The sum per column
  • The number of distinct values per column

Answer: The number of distinct values per column. nunique() returns how many distinct values each column has.

What does df.info() return as its value?

  • None (it prints the summary)
  • A DataFrame
  • A dictionary
  • A list of dtypes

Answer: None (it prints the summary). info() prints its summary and returns None, so do not assign its result.

For scores [88, 72, 95, 60], what mean does describe() report?

  • 78.75
  • 80.0
  • 72.0
  • 95.0

Answer: 78.75. (88+72+95+60)/4 = 78.75.

What is the recommended first pair of methods to run on a new dataset?

  • df.melt() and df.pivot()
  • df.groupby() and df.agg()
  • df.shape and df.head()
  • df.drop() and df.fillna()

Answer: df.shape and df.head(). Start with df.shape and df.head() to see the size and a sample.

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