There is potentially a better way, more pythonic way to flatten MultiIndex columns. 1. Use map and join with string column headers: grouped.columns = grouped.columns.map(‘|’.join).str.strip(‘|’) print(grouped) Output: code colour size|sum size|average size|size size|idxmax \ 0 one black 862 53.875000 16 14 1 one white 554 46.166667 12 18 2 three black 842 49.529412 17 90 … Read more
g1 here is a DataFrame. It has a hierarchical index, though: In [19]: type(g1) Out[19]: pandas.core.frame.DataFrame In [20]: g1.index Out[20]: MultiIndex([(‘Alice’, ‘Seattle’), (‘Bob’, ‘Seattle’), (‘Mallory’, ‘Portland’), (‘Mallory’, ‘Seattle’)], dtype=object) Perhaps you want something like this? In [21]: g1.add_suffix(‘_Count’).reset_index() Out[21]: Name City City_Count Name_Count 0 Alice Seattle 1 1 1 Bob Seattle 2 2 2 Mallory … Read more
A pandas MultiIndex consists of a list of tuples. So the most natural approach would be to reshape your input dict so that its keys are tuples corresponding to the multi-index values you require. Then you can just construct your dataframe using pd.DataFrame.from_dict, using the option orient=”index”: user_dict = {12: {‘Category 1’: {‘att_1’: 1, ‘att_2’: … Read more
MultiIndex / Advanced Indexing Note This post will be structured in the following manner: The questions put forth in the OP will be addressed, one by one For each question, one or more methods applicable to solving this problem and getting the expected result will be demonstrated. Notes (much like this one) will be included … Read more