df.describe() generates summary statistics for a DataFrame's numeric columns by default — count, mean, standard deviation, min, max, and quartiles — in one call.

Syntax

df.describe(include=None, percentiles=None)

Deep Dive Course

By default, describe() only summarizes numeric columns, silently skipping text/object columns entirely, since statistics like mean and standard deviation don't apply to them. Passing include='object', or include='all', expands the summary to include non-numeric columns too, which get a different set of statistics instead — count, number of unique values, the most frequent value, and its frequency — since numeric summary stats don't make sense for them.

1Understanding df.describe()

💡

Pass include='all' to df.describe() to get a summary covering every column, both numeric and non-numeric, instead of describe()'s default of silently including only the numeric ones.

editor.html

import pandas as pd

df = pd.DataFrame({"age": [25, 30, 35, 40, 45]})
print(df.describe())

localhost:3000

2Practical Example

Here is a real-world application of df.describe() showing how it is used in production Pandas code.

editor.html

import pandas as pd

df = pd.DataFrame({"name": ["Alice", "Bob", "Alice"], "age": [30, 25, 35]})
print(df.describe(include="all"))

localhost:3000

3Best Practices

Follow these guidelines when working with df.describe():

1. Use describe() as a fast first look at a numeric dataset's overall distribution, before diving into more detailed analysis or visualization

2. Pass include='object' or include='all' explicitly when text/categorical columns also need summarizing, since the default silently excludes them

3. Compare describe()'s min/max/quartiles against expected real-world ranges to catch obviously invalid data, like a negative age, early

⚠️

Tip: Pass include='all' to df.describe() to get a summary covering every column, both numeric and non-numeric, instead of describe()'s default of silently including only the numeric ones.

editor.html

import pandas as pd

df = pd.DataFrame({"age": [25, 30, 35, 40, 45]})
print(df.describe())

localhost:3000

Examples

Example 01Basic Usage

import pandas as pd

df = pd.DataFrame({"age": [25, 30, 35, 40, 45]})
print(df.describe())

Example 02Advanced Example

import pandas as pd

df = pd.DataFrame({"name": ["Alice", "Bob", "Alice"], "age": [30, 25, 35]})
print(df.describe(include="all"))

Best Practices

Use describe() as a fast first look at a numeric dataset's overall distribution, before diving into more detailed analysis or visualization
Pass include='object' or include='all' explicitly when text/categorical columns also need summarizing, since the default silently excludes them
Compare describe()'s min/max/quartiles against expected real-world ranges to catch obviously invalid data, like a negative age, early

Interview Question

Why does df.describe() silently ignore text columns by default, and what statistics does it show for them instead if you explicitly include them?▼

Hint: Think about which statistics are actually meaningful for non-numeric data.

Statistics like mean, standard deviation, and quartiles are only mathematically meaningful for numeric data — there's no sensible average of a column of names, for instance — so describe()'s default behavior focuses only on the numeric columns where those calculations make sense, silently skipping the rest. Passing include='object' or include='all' brings text/categorical columns into the summary too, but with a different, appropriate set of statistics: the count of non-null values, the number of unique values, the most frequently occurring value, and how many times that top value appears.

Exercises

MediumPractice using df.describe() in a real scenario.

View Solution

import pandas as pd

df = pd.DataFrame({"age": [25, 30, 35, 40, 45]})
print(df.describe())

Frequently Asked Questions

Why does df.describe() silently ignore text columns by default, and what statistics does it show for them instead if you explicitly include them?▼

Related Functions

df-infodf-dtypesdf-mean