You are right, such errors mostly caused by NaN representing empty cells. It is common to filter out such data, before applying your further operations, using this idiom on your dataframe df: Alternatively, it may be more handy to use fillna() method to impute (to replace) null values with something default. E.g. all null or NaN‘s can be replaced with the average … Read more
If you’re using numpy for your arrays, you can also use Equivalently [Thanks to chbrown for the added shorthand] Explanation The inner function, numpy.isnan returns a boolean/logical array which has the value True everywhere that x is not-a-number. As we want the opposite, we use the logical-not operator, ~ to get an array with Trues … Read more
You can simply use DataFrame.fillna to fill the nan‘s directly: The docstring of fillna says that value should be a scalar or a dict, however, it seems to work with a Series as well. If you want to pass a dict, you could use df.mean().to_dict().
This should work: In the above case where_are_NaNs is:
I believe DataFrame.fillna() will do this for you. Link to Docs for a dataframe and for a Series. Example: To fill the NaNs in only one column, select just that column. in this case I’m using inplace=True to actually change the contents of df. EDIT: To avoid a SettingWithCopyWarning, use the built in column-specific functionality:
Try this code: For checking whether any value is NaN, instead of just numbers, see here: How do you test for NaN in Javascript?