{"id":9963,"date":"2021-03-13T09:30:34","date_gmt":"2021-03-13T04:00:34","guid":{"rendered":"http:\/\/www.pythonpool.com\/?p=9963"},"modified":"2026-07-13T12:32:37","modified_gmt":"2026-07-13T07:02:37","slug":"numpy-percentile","status":"publish","type":"post","link":"https:\/\/www.pythonpool.com\/numpy-percentile\/","title":{"rendered":"NumPy percentile(): Quantiles, Axis, Methods, and NaN Values"},"content":{"rendered":"

Quick answer:<\/strong> np.percentile() reports a value at a requested percentage of a data distribution. The result depends on q, the reduced axis, the interpolation method, and the treatment of NaN values, so record those choices with the statistic instead of treating a percentile as a universal single answer.<\/p>\n

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A percentile is a distribution summary; q, axis, method, and NaN policy determine what the reported value means.<\/figcaption><\/figure>\n

numpy.percentile()<\/code> calculates percentile cut points from an array. A percentile answers the question: what value is at or below a given percentage of the data?<\/p>\n

The official NumPy documentation covers numpy.percentile()<\/a>, numpy.nanpercentile()<\/a>, and numpy.quantile()<\/a>.<\/p>\n

Percentiles are useful for summaries that should not depend only on the average. They are common in performance metrics, score reports, data quality checks, latency dashboards, and outlier review.<\/p>\n

The median is the 50th percentile. The 25th and 75th percentiles are often used to describe the middle spread of a dataset. Higher percentiles such as the 90th, 95th, and 99th help summarize tail behavior.<\/p>\n

The most important choices are the percentile number q<\/code>, the axis, and the calculation method. Pick those choices deliberately so the output matches the question being asked.<\/p>\n

Percentiles do not say how many data points exist above a threshold by themselves. They give a cut point based on rank. To count how many values exceed that cut point, calculate the percentile first and then compare the array with the returned value.<\/p>\n

For reports, label the percentile clearly. A 95th percentile latency, a 95th percentile score, and a 95th percentile file size can all have different business meanings even though the NumPy call looks similar.<\/p>\n

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