They are equivalent. If you'd rather specify q from [0, 1], use np.quantile. For [0, 100], use np.percentile.
Docs: https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html
Answer from Riebeckite on Stack OverflowKaggle
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np.percentile() vs. np.quantile()
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Numpy percentile and Pandas quantile not identical?
Too early to parse the syntax but off the top of my head both approaches probably use different interpolation methods. I know you can specify the interpolation method in pandas, maybe numpy has a similar argument. Edit: A quick glance at the docs suggest they both use linear interpolation by default, how different are your results? More on reddit.com
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January 31, 2019python 3.x - np.percentile not equal to quartiles - Stack Overflow
I'm trying to calculate the quartiles for an array of values in python using numpy. ... But this is incorrect, as the 1st and 3rd quartiles should be 2 and 8.5, respectively. ... I can't get my heads round what np.percentile is doing to give a different answer. More on stackoverflow.com
Inverse function of `quantile()`
The function `quantile()` returns the value in a vector that is on the designated percentile of the underlying estimate distribution of the values in… More on reddit.com
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June 5, 2024Why would np.percentile() return different values for q1 and q3 than a manual calculation? How accurate is it?
On Wikipedia it's mentioned "For discrete distributions, there is no universal agreement on selecting the quartile values." and gives 4 methods! BTW I wasn't aware of this so it's nice you asked about it :-) Also you have the interpolation argument of np.percentile which will influence the result. More on reddit.com
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March 29, 2021NumPy
numpy.org › doc › stable › reference › generated › numpy.percentile.html
numpy.percentile — NumPy v2.4 Manual
The behavior of numpy.percentile with percentage q is that of numpy.quantile with argument q/100.
NumPy
numpy.org › doc › 2.2 › reference › generated › numpy.percentile.html
numpy.percentile — NumPy v2.2 Manual
The behavior of numpy.percentile with percentage q is that of numpy.quantile with argument q/100.
RS Blog
reneshbedre.com › blog › quantile-vs-percentile-python.html
Quantile vs Percentile in Python
October 7, 2023 - The 25th percentile is equivalent to the 0.25 quantile. Similarly, the 75th percentile is equivalent to the 0.75 quantile. The 50th percentile is a value below which the 50% of the data falls. In python, percentiles can be calculated using the percentile() function from the NumPy
Reddit
reddit.com › r/learnpython › numpy percentile and pandas quantile not identical?
r/learnpython on Reddit: Numpy percentile and Pandas quantile not identical?
January 31, 2019 -
Hey, I read that numpy percentile method is faster than pandas quantile while being identical in output, but when I run it on a csv, I don't get an identical output. Are the two statements below not identical for cutting the bottom 10% out of a column?
This:
df = df[df["x"] > numpy.percentile(df["x"], 10)]
Produces a different result to this:
df["x"] = df["x"][df["x"] > df["x"].quantile(.10)]
df = df.dropna()
Programiz
programiz.com › python-programming › numpy › methods › quantile
NumPy quantile()
If you want to specify q from 0 to 100, use percentile() and if you want to specify q from 0.0 to 1.0, use quantile(). import numpy as np # create an array array1 = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])
SciPy
docs.scipy.org › doc › numpy-1.9.2 › reference › generated › numpy.percentile.html
numpy.percentile — NumPy v1.9 Manual
Given a vector V of length N, the q-th percentile of V is the q-th ranked value in a sorted copy of V. The values and distances of the two nearest neighbors as well as the interpolation parameter will determine the percentile if the normalized ranking does not match q exactly.
NumPy
numpy.org › doc › 2.1 › reference › generated › numpy.percentile.html
numpy.percentile — NumPy v2.1 Manual
The behavior of numpy.percentile with percentage q is that of numpy.quantile with argument q/100.
Medium
medium.com › @amit25173 › understanding-quartiles-in-numpy-step-by-step-80fb48b5587a
Understanding Quartiles in NumPy (Step-by-Step) | by Amit Yadav | Medium
February 8, 2025 - ... Quartiles help you detect outliers and summarize distributions without looking at every single value. If your data has weird spikes (outliers), Q1 and Q3 will tell you! Now, let’s get practical.
Codegive
codegive.com › blog › numpy_percentile_vs_quantile.php
Numpy percentile vs quantile
Recommendation: For most general data analysis tasks within a Python/NumPy environment, np.quantile() might feel slightly more "native" given the typical range of probabilities and proportions used in data science. However, if your audience or domain specifically uses "percentiles," then ...
Codepointtech
codepointtech.com › home › numpy percentile & quantile: master your data insights
NumPy Percentile & Quantile: Master Your Data Insights - codepointtech.com
January 18, 2026 - Essentially, percentiles are a specific type of quantile, where the data is divided into 100 parts. When you ask for the 0.25 quantile, you”re asking for the 25th percentile. NumPy makes calculating percentiles incredibly straightforward with its numpy.percentile() function.
NumPy
numpy.org › doc › 2.0 › reference › generated › numpy.percentile.html
numpy.percentile — NumPy v2.0 Manual
equivalent to percentile(..., 50) nanpercentile · quantile · equivalent to percentile, except q in the range [0, 1]. Notes ·
NumPy
numpy.org › devdocs › reference › generated › numpy.quantile.html
numpy.quantile — NumPy v2.5.dev0 Manual
If multiple probability levels are given, first axis of the result corresponds to the quantiles. The other axes are the axes that remain after the reduction of a. If the input contains integers or floats smaller than float64, the output data-type is float64. Otherwise, the output data-type is the same as that of the input. If out is specified, that array is returned instead. See also · mean · percentile ·
Machine Learning Plus
machinelearningplus.com › statistics › quantiles-and-percentiles
Quantiles and Percentiles - Understanding Quantiles and Percentiles, A Deep Dive with Python Examples - Machine Learning Plus
September 18, 2023 - Quantiles and percentiles offer valuable insights into the distribution and characteristics of datasets. They serve as pivotal instruments in diverse fields such as finance, e-commerce, and academic research. Equipped with Python and NumPy, extracting these metrics becomes a walk in the park, ...
NumPy
numpy.org › doc › 1.21 › reference › generated › numpy.percentile.html
numpy.percentile — NumPy v1.21 Manual
equivalent to percentile(..., 50) nanpercentile · quantile · equivalent to percentile, except with q in the range [0, 1]. Notes · Given a vector V of length N, the q-th percentile of V is the value q/100 of the way from the minimum to the maximum in a sorted copy of V.
Python Pool
pythonpool.com › home › blog › 6 ways to calculate percentile of numpy array
6 Ways to Calculate Percentile of Numpy Array - Python Pool
January 3, 2024 - Percentile – Percentile method in the numpy module through which we can calculate the nth percentile of the given data (array elements) along the specified axis. Numpy Quantile – Quantile method in the numpy module through which we can calculate ...
NumPy
numpy.org › devdocs › reference › generated › numpy.percentile.html
numpy.percentile — NumPy v2.5.dev0 Manual
The behavior of numpy.percentile with percentage q is that of numpy.quantile with argument q/100.