How to Use describe() in pandas for Data Summary

Use the describe() method in pandas on a DataFrame or Series to get summary statistics like count, mean, min, max, and quartiles. It helps quickly understand the distribution and key metrics of your data columns.

Syntax

The describe() method is called on a pandas DataFrame or Series. It returns summary statistics for numeric columns by default.

• DataFrame.describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)
• percentiles: List of percentiles to include (default includes 25%, 50%, 75%).
• include: Data types to include (e.g., 'all', 'object', 'number').
• exclude: Data types to exclude.

python

df.describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)

Example

This example shows how to use describe() on a DataFrame with numeric and categorical data. It returns count, mean, std, min, quartiles, and max for numeric columns.

python

import pandas as pd

data = {
    'age': [25, 30, 22, 40, 28],
    'salary': [50000, 60000, 45000, 80000, 52000],
    'department': ['HR', 'IT', 'IT', 'Finance', 'HR']
}
df = pd.DataFrame(data)

summary = df.describe()
print(summary)

Output

             age        salary
count   5.000000      5.000000
mean   29.000000  57400.000000
std     7.071068  13416.407864
min    22.000000  45000.000000
25%    25.000000  50000.000000
50%    28.000000  52000.000000
75%    30.000000  60000.000000
max    40.000000  80000.000000

Common Pitfalls

• By default, describe() only summarizes numeric columns. Non-numeric columns are ignored unless you specify include='all'.
• Using include='all' may show different statistics for categorical data like count, unique, top, and freq.
• Passing invalid data types to include or exclude raises errors.

python

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie'], 'age': [25, 30, 35]}
df = pd.DataFrame(data)

# Wrong: Only numeric columns summarized
print(df.describe())

# Right: Include all columns
print(df.describe(include='all'))

Output

             age
count   3.000000
mean   30.000000
std     5.000000
min    25.000000
25%    27.500000
50%    30.000000
75%    32.500000
max    35.000000

         name        age
count       3   3.000000
unique      3        NaN
top     Alice        NaN
freq        1        NaN
mean      NaN  30.000000
std       NaN   5.000000
min       NaN  25.000000
25%       NaN  27.500000
50%       NaN  30.000000
75%       NaN  32.500000
max       NaN  35.000000

Quick Reference

Parameter	Description	Default
percentiles	List of percentiles to include in output	[0.25, 0.5, 0.75]
include	Data types to include (e.g., 'all', 'number', 'object')	None (numeric only)
exclude	Data types to exclude	None
datetime_is_numeric	Treat datetime columns as numeric	False

Key Takeaways

Use describe() to quickly get summary statistics of numeric data in pandas.

Add include='all' to summarize all columns including categorical data.

The output includes count, mean, std, min, quartiles, and max for numeric columns.

Be careful with include and exclude parameters to avoid errors.

It helps understand data distribution and detect anomalies fast.