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I have a set of data from which I want to plot the number of keys per unique id count (x=unique_id_count, y=key_count), and I'm trying to learn how to take advantage of pandas . In this case: unique_ids 1 = key count 2 unique_ids 2 = key count 1 from pandas import * key_items = ("a", "a", "a", "a", "a", "b", "b", "b", "b", "b", "c", "c", "c") id_data = ("X", "X", "X", "X", "X", "X", "X", "Y", "Y", "Y", "X", "X", "X") df = DataFrame({'keys': key_items, 'ids': id_data}) I've managed to mangle the data into what I want by pulling out the data from the dataframe and restructuring it, and rebuilding a new dataframe. In this case it's probably better to do it all in python without pandas... unique_values = defaultdict(list) for items in df.itertuples(index=False): key = items[1] v = items[0] unique_values[key].append(v) unique_values_count = {} for k, values in unique_values.iteritems(): unique_values_count[k] = [len(set(values))] # reformat for plotting key_col = ("a", "b", "c") id_col = [unique_values_count[k][0] for k in key_col] df2 = DataFrame({"keys":key_col, "unique_id_count": id_col}) df2.groupby("unique_id_count").size().plot(kind="bar") Is there a better way to do this more directly using the initial dataframe?

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