How to use df.head(), df.info(), and df.describe() to explore any dataset

Learn how to quickly explore any dataset in Python using df.head(), df.info(), and df.describe() for a fast overview of data structure, types, and summary statistics.


When you first load a dataset in Python with pandas, it’s crucial to understand its structure and contents. Three core commands give you a rapid overview:

1. df.head()

  • Shows the first 5 rows by default (you can pass a number to view more).

  • Helps check if the data loaded correctly and inspect sample values.

import pandas as pd

df = pd.read_csv("your_dataset.csv")
print(df.head())
print(df.head(10))  # first 10 rows

2. df.info()

  • Displays a concise summary of the DataFrame.

  • Key details: number of rows, columns, column names, non-null counts, and data types.

df.info()

Output includes:

  • Total rows and columns

  • Column names

  • Data type of each column (int64, float64, object, etc.)

  • Number of non-null entries (useful for spotting missing data)

3. df.describe()

  • Provides summary statistics for numerical columns.

  • Includes count, mean, std (standard deviation), min, max, and quartiles (25%, 50%, 75%).

df.describe()

Optional: Include include='all' to get statistics for all columns, including categorical ones:

df.describe(include='all')



Key Takeaways:

  • df.head() → preview sample rows

  • df.info() → understand structure, types, missing values

  • df.describe() → get numerical summaries quickly

These three commands give a fast, reliable first look at any dataset before deeper analysis.



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