Well, it may seem a little strange that something called “not a number” is considered a number, but NaN is still a numeric type, despite that fact 🙂 NaN just means the specific value cannot be represented within the limitations of the numeric type (although that could be said for all numbers that have to … Read more
You could use the fillna method on the DataFrame and specify the method as ffill (forward fill): >>> df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]]) >>> df.fillna(method=’ffill’) 0 1 2 0 1 2 3 1 4 2 3 2 4 2 9 This method… propagate[s] last valid observation forward to next … Read more
Cast from string using float(): >>> float(‘NaN’) nan >>> float(‘Inf’) inf >>> -float(‘Inf’) -inf >>> float(‘Inf’) == float(‘Inf’) True >>> float(‘Inf’) == 1 False
pandas >= 1.1 From pandas 1.1 you have better control over this behavior, NA values are now allowed in the grouper using dropna=False: pd.__version__ # ‘1.1.0.dev0+2004.g8d10bfb6f’ # Example from the docs df a b c 0 1 2.0 3 1 1 NaN 4 2 2 1.0 3 3 1 2.0 2 # without NA (the … Read more
UPDATE: using Pandas 0.22.0 Newer Pandas versions have new methods ‘DataFrame.isna()’ and ‘DataFrame.notna()’ In [71]: df Out[71]: a b c 0 NaN 7.0 0 1 0.0 NaN 4 2 2.0 NaN 4 3 1.0 7.0 0 4 1.0 3.0 9 5 7.0 4.0 9 6 2.0 6.0 9 7 9.0 6.0 4 8 3.0 0.0 … Read more
You can simply use DataFrame.fillna to fill the nan‘s directly: In [27]: df Out[27]: A B C 0 -0.166919 0.979728 -0.632955 1 -0.297953 -0.912674 -1.365463 2 -0.120211 -0.540679 -0.680481 3 NaN -2.027325 1.533582 4 NaN NaN 0.461821 5 -0.788073 NaN NaN 6 -0.916080 -0.612343 NaN 7 -0.887858 1.033826 NaN 8 1.948430 1.025011 -2.982224 9 0.019698 … Read more
Use the static Double.isNaN(double) method, or your Double‘s .isNaN() method. // 1. static method if (Double.isNaN(doubleValue)) { … } // 2. object’s method if (doubleObject.isNaN()) { … } Simply doing: if (var == Double.NaN) { … } is not sufficient due to how the IEEE standard for NaN and floating point numbers is defined.
[Updated to adapt to modern pandas, which has isnull as a method of DataFrames..] You can use isnull and any to build a boolean Series and use that to index into your frame: >>> df = pd.DataFrame([range(3), [0, np.NaN, 0], [0, 0, np.NaN], range(3), range(3)]) >>> df.isnull() 0 1 2 0 False False False 1 … Read more
In version 0.24.+ pandas has gained the ability to hold integer dtypes with missing values. Nullable Integer Data Type. Pandas can represent integer data with possibly missing values using arrays.IntegerArray. This is an extension types implemented within pandas. It is not the default dtype for integers, and will not be inferred; you must explicitly pass … Read more
To remove NaN values from a NumPy array x: x = x[~numpy.isnan(x)] Explanation The inner function numpy.isnan returns a boolean/logical array which has the value True everywhere that x is not-a-number. Since we want the opposite, we use the logical-not operator ~ to get an array with Trues everywhere that x is a valid number. … Read more