Pandas: Calculate Quantiles & Percentiles with quantile()

Compute values at specified quantiles or percentiles across Series or DataFrames using the Pandas quantile() method. Evaluate median ($q=0.5$), interquartile ranges, configure interpolation parameters, and compute multiple quantiles simultaneously.

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Introduction to quantile() 🔝

The quantile() method returns values at the given quantile $q$ (where $0.0 \le q \le 1.0$). Passing $q=0.5$ calculates the median (50th percentile), while $q=0.25$ and $q=0.75$ evaluate the 25th and 75th percentiles respectively.

Create numerical sample Series 🔝

We start with a sequential numerical Series to calculate percentiles.

import pandas as pd

s = pd.Series([10, 20, 30, 40, 50], name="SCORES")
print(s)
assert s.shape == (5,)

Expected output

0    10
1    20
2    30
3    40
4    50
Name: SCORES, dtype: int64

Single quantile calculation 🔝

Pass a single float value (such as 0.5 for 50th percentile) to quantile().

q50 = s.quantile(0.5)
print(f"Median (q=0.5): {q50}")
assert q50 == 30.0

Expected output

Median (q=0.5): 30.0

Computing multiple quantiles simultaneously 🔝

Pass a list of quantile fractions to quantile() to compute multiple percentiles in a single operation.

q_multi = s.quantile([0.25, 0.75])
print(q_multi)
assert q_multi.loc[0.75] == 40.0

Expected output

0.25    20.0
0.75    40.0
Name: SCORES, dtype: float64

Exercise: compute 90th percentile 🔝

Given a Series vals = pd.Series([100, 200, 300, 400, 500], name="VALS"), compute its 90th percentile ($q=0.90$) using quantile() and verify that it equals 460.0.

Exercise solution 🔝

Solution creating the Series and calculating quantile().

vals = pd.Series([100, 200, 300, 400, 500], name="VALS")
answer = vals.quantile(0.9)
print(answer)
assert answer == 460.0

Expected output

460.0

Practice in Google Colab 🔝

Interactive Colab Notebook

Practice quantile() examples interactively directly in your browser:

View on GitHub

Reference: Pandas quantile() documentation.

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

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