This tutorial explains several examples of how to use these functions in practice. Level(s) to set (None for all levels). But Pandas also supports a MultiIndex , in which the index for a row is some composite key of several columns. DataFrame.set_index (self, keys, drop=True, append=False, inplace=False, verify_integrity=False) Parameters: keys - label or array-like or list of labels/arrays drop - (default True) Delete columns to be used as the new index. df.columns = ['A','B','C'] In [3]: df Out[3]: A B C 0 0.785806 -0.679039 0.513451 1 -0.337862 -0.350690 … I might get the reason, I found some code in /pandas/io/formats/excel.py as below. fill_value int, str or dict. to_flat_index Convert a MultiIndex to an Index of Tuples containing the level values. Parameters level int, str, or list of these, default -1 (last level) Level(s) of index to unstack, can pass level name. Example 1: Group by Two Columns and Find Average. I would like to subselect all the A (or B) columns of this DataFrame. It works like a primary key in a database table. sortlevel ([level, ascending, sort_remaining]) While thegroupby() function in Pandas would work, this case is also an example of where a MultiIndex could come in handy. Parameters levels sequence or list of sequence. It has MultiIndex columns with names=[‘Name’, ‘Col’] and hierarchical levels. Check out the columns and see if any matches these criteria. However I don't know how to fix this issue # MultiIndex columns require an extra row # with index names (blank if None) for # unambiguous round-trip, unless not merging, # in which case the names all go on one row Issue #11328 Notice that the date column contains unique dates so it makes sense to label each row by the date column. New level(s) to apply. pandas.MultiIndex ¶ class pandas. level int, level name, or sequence of int/level names (default None). That is,you can make the date column the index of the DataFrame using the .set_index() method (n.b. Often you may want to group and aggregate by multiple columns of a pandas DataFrame. Pandas set_index() method provides the functionality to set the DataFrame index using existing columns. Replace NaN with this value if the unstack produces missing values. A MultiIndex, also known as a multi-level index or hierarchical index, allows you to have multiple columns acting as a row identifier, while having each index column related to another through a parent/child relationship. is_lexsorted Return True if the codes are lexicographically sorted. inplace=True means you're actually altering the DataFrame df inplace): I am trying to create a multi-index pandas dataframe, that should look something like this: It includes 2 groups of data items. MultiIndex can also be used to create DataFrames with multilevel columns. pandas.MultiIndex.set_levels¶ MultiIndex.set_levels (levels, level = None, inplace = None, verify_integrity = True) [source] ¶ Set new levels on MultiIndex. Fortunately this is easy to do using the pandas .groupby() and .agg() functions. Product info – In my code I have an outer loop that accumulates data in lists such as Product_Id[], Date[], Title[], Brand[] etc, as the loop cycles through each product_id. The Name label goes from 0 to n, and for each label, there are two A and B columns. Create a DataFrame with the levels of the MultiIndex as columns. pandas documentation: MultiIndex Columns. Example. It may not drop or duplicate levels. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. pandas.DataFrame.unstack ... (the analogue of stack when the columns are not a MultiIndex). 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