How to Use Quantile in pandas for Data Analysis

In pandas, use the quantile() method on a DataFrame or Series to find the value at a given quantile (between 0 and 1). For example, df['column'].quantile(0.5) returns the median value of that column.

Syntax

The quantile() method syntax is:

• Series.quantile(q=0.5, interpolation='linear')
• DataFrame.quantile(q=0.5, axis=0, numeric_only=None, interpolation='linear')

Where:

• q: float or list of floats between 0 and 1, representing the quantile(s) to compute.
• interpolation: method to use when the desired quantile lies between two data points (e.g., 'linear', 'lower', 'higher', 'midpoint', 'nearest').
• axis: axis to compute along (0 for columns, 1 for rows) in DataFrame.
• numeric_only: whether to include only numeric data.

python

series.quantile(q=0.5, interpolation='linear')
dataframe.quantile(q=0.5, axis=0, numeric_only=None, interpolation='linear')

Example

This example shows how to calculate the 25th, 50th (median), and 75th percentiles of a DataFrame column using quantile().

python

import pandas as pd

data = {'scores': [55, 70, 65, 80, 90, 85, 75]}
df = pd.DataFrame(data)

q25 = df['scores'].quantile(0.25)
q50 = df['scores'].quantile(0.5)  # median
q75 = df['scores'].quantile(0.75)

print(f"25th percentile: {q25}")
print(f"Median (50th percentile): {q50}")
print(f"75th percentile: {q75}")

Output

25th percentile: 67.5
Median (50th percentile): 75.0
75th percentile: 85.0

Common Pitfalls

Common mistakes when using quantile() include:

• Passing values for q outside the range 0 to 1, which causes errors.
• Not specifying numeric_only=True when the DataFrame has non-numeric columns, leading to unexpected results or warnings.
• Misunderstanding the interpolation parameter, which affects how quantiles are calculated when the exact quantile lies between data points.

python

import pandas as pd

data = {'A': [1, 2, 3], 'B': ['x', 'y', 'z']}
df = pd.DataFrame(data)

# Wrong: q value out of range
# df['A'].quantile(1.5)  # Raises ValueError

# Wrong: quantile on non-numeric column
# df['B'].quantile(0.5)  # Raises TypeError

# Right: numeric_only=True to ignore non-numeric columns
quantiles = df.quantile(q=[0.25, 0.5, 0.75], numeric_only=True)
print(quantiles)

Output

      A
0.25  1.5
0.50  2.0
0.75  2.5

Quick Reference

Parameter	Description	Default
q	Quantile value(s) between 0 and 1	0.5
interpolation	Method to interpolate between data points	'linear'
axis	Axis to compute along (DataFrame only)	0
numeric_only	Include only numeric data (DataFrame only)	None

Key Takeaways

Use quantile() on Series or DataFrame to find values at specific quantiles between 0 and 1.

The q parameter accepts a single float or list of floats representing quantiles.

Specify numeric_only=True in DataFrame to avoid errors with non-numeric data.

Choose the interpolation method to control how values between data points are calculated.

Quantiles help understand data distribution like median (0.5), quartiles (0.25, 0.75), and percentiles.