{"id":3619,"date":"2020-07-17T22:15:34","date_gmt":"2020-07-17T16:45:34","guid":{"rendered":"http:\/\/www.pythonpool.com\/?p=3619"},"modified":"2026-07-13T12:27:25","modified_gmt":"2026-07-13T06:57:25","slug":"ln-in-python","status":"publish","type":"post","link":"https:\/\/www.pythonpool.com\/ln-in-python\/","title":{"rendered":"ln in Python: math.log(), np.log(), Domain, and Precision"},"content":{"rendered":"

Quick answer:<\/strong> Use math.log(x) for the natural logarithm of a positive scalar, numpy.log(array) for element-wise array values, and math.log1p(x) when calculating log(1 + x) near zero. Validate the domain before calling the function.<\/p>\n

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Use math.log for scalars, np.log for arrays, and make the positive-domain policy explicit.<\/figcaption><\/figure>\n

ln in Python<\/code> means the natural logarithm, or log base e<\/code>. Python does not have a separate ln()<\/code> function in the standard library. The standard scalar function is math.log(x)<\/code>, which returns the natural log when you pass one argument.<\/p>\n

Use natural logs when formulas involve continuous growth, decay, entropy, log likelihoods, or values measured on an exponential scale. The inverse operation is math.exp(x)<\/code>, which raises e<\/code> to a power. That pairing is the most important idea: math.log()<\/code> moves from a positive value to its natural-log scale, and math.exp()<\/code> moves back.<\/p>\n

The official Python math.log documentation<\/a> covers the scalar function. For array-based numeric work, the NumPy log documentation<\/a> explains numpy.log()<\/code>.<\/p>\n

Natural logs only accept positive real inputs in the standard math<\/code> module. If the input is zero or negative, Python raises ValueError<\/code>. Validate data before logging it, especially when values come from files, forms, sensors, or calculations that can produce zero.<\/p>\n

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