Count unique values using pandas groupby : <p>I have data of the following form:</p>

<pre><code>df = pd.DataFrame({
    'group': [1, 1, 2, 3, 3, 3, 4],
    'param': ['a', 'a', 'b', np.nan, 'a', 'a', np.nan]
})
print(df)

#    group param
# 0      1     a
# 1      1     a
# 2      2     b
# 3      3   NaN
# 4      3     a
# 5      3     a
# 6      4   NaN
</code></pre>

<p>Non-null values within groups are always the same. I want to count the non-null value for each group (where it exists) once, and then find the total counts for each value. </p>

<p>I'm currently doing this in the following (clunky and inefficient) way:</p>

<pre><code>param = []
for _, group in df[df.param.notnull()].groupby('group'):
    param.append(group.param.unique()[0])
print(pd.DataFrame({'param': param}).param.value_counts())

# a    2
# b    1
</code></pre>

<p>I'm sure there's a way to do this more cleanly and without using a loop, but I just can't seem to work it out. Any help would be much appreciated.</p>
