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Is it possible to calculate the median of a list without explicitly removing the NaN's, but rather, ignoring them?
I want median([1,2,3,NaN,NaN,NaN,NaN,NaN,NaN]) to be 2, not NaN.
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To remove nan values from list in python using the math. isnan() function, we will first create an empty list named newList . After that, we will traverse each element of the list using a for loop and check if it is a nan value or not using the math. isnan() function.
To remove NaN from a list using Python, the easiest way is to use the isnan() function from the Python math module and list comprehension. You can also use the Python filter() function. The Python numpy module also provides an isnan() function that we can use to check if a value is NaN.
dropna() is used to drop rows with NaN / None values from DataFrame. numpy. nan is Not a Number (NaN), which is of Python build-in numeric type float (floating point).
We can replace NaN values with 0 to get rid of NaN values. This is done by using fillna() function. This function will check the NaN values in the dataframe columns and fill the given value.
numpy 1.9.0 has the function nanmedian:
nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False)
Compute the median along the specified axis, while ignoring NaNs.
Returns the median of the array elements.
.. versionadded:: 1.9.0
E.g.
>>> from numpy import nanmedian, NaN
>>> nanmedian([1,2,3,NaN,NaN,NaN,NaN,NaN,NaN])
2.0
If you can't use version 1.9.0 of numpy, something like @Parker's answer will work; e.g.
>>> import numpy as np
>>> x = np.array([1,2,3,NaN,NaN,NaN,NaN,NaN,NaN])
>>> np.median(x[~np.isnan(x)])
2.0
or
>>> np.median(x[np.isfinite(x)])
2.0
(When applied to a boolean array, ~ is the unary operator notation for not.)
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