How to check for NaN in golang
Use math.IsNaN(…) for that: playground
nan
Using NaN in C++?
After looking into this some more, it looks like signaling_NaN is useless as provided. If floating point exceptions are enabled, then calling it counts as processing a signaling NaN, so it immediately raises an exception. If floating point exceptions are disabled, then processing a signaling NaN automatically demotes it to a quiet NaN, so signaling_NaN … Read more
Differences between null and NaN in spark? How to deal with it?
null values represents “no value” or “nothing”, it’s not even an empty string or zero. It can be used to represent that nothing useful exists. NaN stands for “Not a Number”, it’s usually the result of a mathematical operation that doesn’t make sense, e.g. 0.0/0.0. One possible way to handle null values is to remove … Read more
Pandas Changing the format of NaN values when saving to CSV
Pandas to the rescue, use na_rep to fix your own representation for NaNs. df.to_csv(‘file.csv’, na_rep=’NULL’) file.csv ,index,x,y,z 0,0,1.0,NULL,2 1,1,NULL,3.0,4
How to check if value is nan in unittest?
I came up with assertTrue(math.isnan(nan_value))
How to check if particular value (in cell) is NaN in pandas DataFrame?
Try pd.isna(): In [7]: pd.isna(df.iloc[1,0]) Out[7]: True AKA pd.isnull
How can I plot NaN values as a special color with imshow?
Hrm, it appears I can use a masked array to do this: masked_array = np.ma.array (a, mask=np.isnan(a)) cmap = matplotlib.cm.jet cmap.set_bad(‘white’,1.) ax.imshow(masked_array, interpolation=’nearest’, cmap=cmap) This should suffice, though I’m still open to suggestions. :]
Replace NaN in one column with value from corresponding row of second column
Assuming your DataFrame is in df: df.Temp_Rating.fillna(df.Farheit, inplace=True) del df[‘Farheit’] df.columns=”File heat Observations”.split() First replace any NaN values with the corresponding value of df.Farheit. Delete the ‘Farheit’ column. Then rename the columns. Here’s the resulting DataFrame: File heat Observations 0 1 YesQ 75 1 1 NoR 115 2 1 YesA 63 3 1 NoT 41 … Read more
How to replace NaN values in a dataframe column
I believe DataFrame.fillna() will do this for you. Link to Docs for a dataframe and for a Series. Example: In [7]: df Out[7]: 0 1 0 NaN NaN 1 -0.494375 0.570994 2 NaN NaN 3 1.876360 -0.229738 4 NaN NaN In [8]: df.fillna(0) Out[8]: 0 1 0 0.000000 0.000000 1 -0.494375 0.570994 2 0.000000 0.000000 … Read more
How to check for NaN values
Use math.isnan: >>> import math >>> x = float(‘nan’) >>> math.isnan(x) True