pandas.DataFrame.sum  
DataFrame.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs) [source]
 
Return the sum of the values for the requested axis    
Parameters:

 
axis : {index (0), columns (1)}   skipna : boolean, default True Exclude NA/null values when computing the result. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series numeric_only : boolean, default None Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. min_count : int, default 0 The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.  New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.    
Returns:

 
sum : Series or DataFrame (if level specified)      Examples By default, the sum of an empty or all-NA Series is 0. >>> pd.Series([]).sum()  # min_count=0 is the default
0.0
 This can be controlled with the min_count parameter. For example, if you’d like the sum of an empty series to be NaN, pass min_count=1. >>> pd.Series([]).sum(min_count=1)
nan
 Thanks to the skipna parameter, min_count handles all-NA and empty series identically. >>> pd.Series([np.nan]).sum()
0.0
 >>> pd.Series([np.nan]).sum(min_count=1)
nan
 


    
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