Remove NaN/NULL columns in a Pandas dataframe?

Question

I have a dataFrame in pandas and several of the columns have all null values. Is there a built in function which will let me remove those columns?

Answer 1

Yes, dropna. See http://pandas.pydata.org/pandas-docs/stable/missing_data.html and the DataFrame.dropna docstring:

Definition: DataFrame.dropna(self, axis=0, how='any', thresh=None, subset=None) Docstring: Return object with labels on given axis omitted where alternately any or all of the data are missing  Parameters ---------- axis : {0, 1} how : {'any', 'all'}     any : if any NA values are present, drop that label     all : if all values are NA, drop that label thresh : int, default None     int value : require that many non-NA values subset : array-like     Labels along other axis to consider, e.g. if you are dropping rows     these would be a list of columns to include  Returns ------- dropped : DataFrame 

The specific command to run would be:

df=df.dropna(axis=1,how='all')