Remove NaN/NULL columns in a Pandas dataframe?

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?

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')

Here is a simple function which you can use directly by passing dataframe and threshold

df
'''
     pets   location     owner     id
0     cat  San_Diego     Champ  123.0
1     dog        NaN       Ron    NaN
2     cat        NaN     Brick    NaN
3  monkey        NaN     Champ    NaN
4  monkey        NaN  Veronica    NaN
5     dog        NaN      John    NaN
'''

---

def rmissingvaluecol(dff,threshold):
    l = []
    l = list(dff.drop(dff.loc[:,list((100*(dff.isnull().sum()/len(dff.index))>=threshold))].columns, 1).columns.values)
    print("# Columns having more than %s percent missing values:"%threshold,(dff.shape[1] - len(l)))
    print("Columns:\n",list(set(list((dff.columns.values))) - set(l)))
    return l


rmissingvaluecol(df,1) #Here threshold is 1% which means we are going to drop columns having more than 1% of missing values

#output
'''
# Columns having more than 1 percent missing values: 2
Columns:
 ['id', 'location']
'''

Now create new dataframe excluding these columns

l = rmissingvaluecol(df,1)
df1 = df[l]

PS: You can change threshold as per your requirement

Bonus step

You can find the percentage of missing values for each column (optional)

def missing(dff):
    print (round((dff.isnull().sum() * 100/ len(dff)),2).sort_values(ascending=False))

missing(df)

#output
'''
id          83.33
location    83.33
owner        0.00
pets         0.00
dtype: float64
'''

Another solution would be to create a boolean dataframe with True values at not-null positions and then take the columns having at least one True value. This removes columns with all NaN values.

df = df.loc[:,df.notna().any(axis=0)]

If you want to remove columns having at least one missing (NaN) value;

df = df.loc[:,df.notna().all(axis=0)]

This approach is particularly useful in removing columns containing empty strings, zeros or basically any given value. For example;

df = df.loc[:,(df!='').all(axis=0)]

removes columns having at least one empty string.

Function for removing all null columns from the data frame:

def Remove_Null_Columns(df):
    dff = pd.DataFrame()
    for cl in fbinst:
        if df[cl].isnull().sum() == len(df[cl]):
            pass
        else:
            dff[cl] = df[cl]
    return dff 

This function will remove all Null columns from the df.