How to do data analysis (like counts, ucounts, frequency) with pandas?

Question

I have DataFrame like below:

df = pd.DataFrame([
    ("i", 1, 'GlIrbixGsmCL'),
    ("i", 1, 'GlIrbixGsmCL'),
    ("i", 1, '3IMR1UteQA'),
    ("c", 1, 'GlIrbixGsmCL'),
    ("i", 2, 'GlIrbixGsmCL'),
], columns=['type', 'cid', 'userid'])

Expected output like:

For more details:

i_counts, c_counts      => df.groupby(["cid","type"]).size()
i_ucounts, c_ucounts    => df.groupby(["cid","type"])["userid"].nunique()
i_frequency,u_frequency => df.groupby(["cid","type"])["userid"].value_counts()

Looks it's a little complex for me, how to do with pandas to get the expected result?

The related screenshots:

Answer 1

This is how I would approach this:

aggfuncs= {
    'counts': ('userid', 'count'), 
    'ucounts': ('userid', 'nunique'),
    'frequency': ('userid', lambda S: S.value_counts().to_dict()),
}

output = df.groupby(['cid', 'type']).agg(**aggfuncs).unstack()
output.columns = output.columns.map(lambda tup: '_'.join(tup[::-1]))

output:

     c_counts  i_counts  c_ucounts  i_ucounts          c_frequency                           i_frequency
cid
1         1.0       3.0        1.0        2.0  {'GlIrbixGsmCL': 1}  {'GlIrbixGsmCL': 2, '3IMR1UteQA': 1}
2         NaN       1.0        NaN        1.0                  NaN                   {'GlIrbixGsmCL': 1}

I think that is the core of what you want. You will need some cosmetic amendments to get the output exactly as in your example (e.g. fillna etc.).