multi-index

There are already a couple of questions on SO relating to this, most notably this one , however none of the answers seem to work for me and quite a few links to docs (especially on lexsorting) are broken, so I'll ask another one. I'm trying do to something (seemingly) very simple. Consider the following MultiIndexed Dataframe: import pandas as pd; import random arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] tuples = list(zip(*arrays)) index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second']) df = pd

I have a time series dataframe and I would like to reindex it by Trials and Measurements. Simplified, I have this: value Trial 1 0 13 1 3 2 4 2 3 NaN 4 12 3 5 34 Which I want to turn into this: value Trial 1 0 13 1 3 2 4 2 0 NaN 1 12 3 0 34 How can I best do this? Dan Allan Just yesterday, the illustrious Andy Hayden added this feature to version 0.13 of pandas, which will be released any day now. See here for usage example he added to the docs. If you are comfortable installing the development version of pandas from source, you can use it now. df['Measurements'] = df.reset_index().groupby(

This question already has an answer here: Pandas - How to flatten a hierarchical index in columns 16 answers Here's some data from another question: date type value 1/1/2016 a 1 1/1/2016 b 2 1/1/2016 a 1 1/1/2016 b 4 1/2/2016 a 1 1/2/2016 b 1 Run this line of code: x = df.groupby(['date', 'type']).value.agg(['sum', 'max']).unstack() x should look like this: sum max type a b a b date 1/1/2016 2 6 1 4 1/2/2016 1 1 1 1 I want to combine the columns on the upper and lower level to get this: sum_a sum_b max_a max_b date 1/1/2016 2 6 1 4 1/2/2016 1 1 1 1 Is there an easy way to do this? greg_data

Questions are at the end, in bold . But first, let's set up some data: import numpy as np import pandas as pd from itertools import product np.random.seed(1) team_names = ['Yankees', 'Mets', 'Dodgers'] jersey_numbers = [35, 71, 84] game_numbers = [1, 2] observer_names = ['Bill', 'John', 'Ralph'] observation_types = ['Speed', 'Strength'] row_indices = list(product(team_names, jersey_numbers, game_numbers, observer_names, observation_types)) observation_values = np.random.randn(len(row_indices)) tns, jns, gns, ons, ots = zip(*row_indices) data = pd.DataFrame({'team': tns, 'jersey': jns, 'game':

I need to reshape a dataframe that looks like df1 and turn it into df2. There are 2 considerations for this procedure: I need to be able to set the number of rows to be sliced as a parameter (length). I need to split date and time from the index, and use date in the reshape as the column names and keep time as the index. Current df1 2007-08-07 18:00:00 1 2007-08-08 00:00:00 2 2007-08-08 06:00:00 3 2007-08-08 12:00:00 4 2007-08-08 18:00:00 5 2007-11-02 18:00:00 6 2007-11-03 00:00:00 7 2007-11-03 06:00:00 8 2007-11-03 12:00:00 9 2007-11-03 18:00:00 10 Desired Output df2 - With the parameter

I created a multiIndex DataFrame by: df.set_index(['Field1', 'Field2'], inplace=True) If this is not a multiIndex DataFrame please tell me how to make one. I want to: Group by the same columns that are in the index Aggregate a count of each group Then return the whole thing as a Series with Field1 and Field2 as the index How do I go about doing this? ADDITIONAL INFO I have a multiIndex dataFrame that looks like this: Continent Sector Count Asia 1 4 2 1 Australia 1 1 Europe 1 1 2 3 3 2 North America 1 1 5 1 South America 5 1 How can I return this as a Series with the index of [Continent, Sector

I have a multi-indexed dataframe and I'm looking to backfill missing values within a group. The dataframe I have currently looks like this: df = pd.DataFrame({ 'group': ['group_a'] * 7 + ['group_b'] * 3 + ['group_c'] * 2, 'Date': ["2013-06-11", "2013-07-02", "2013-07-09", "2013-07-30", "2013-08-06", "2013-09-03", "2013-10-01", "2013-07-09", "2013-08-06", "2013-09-03", "2013-07-09", "2013-09-03"], 'Value': [np.nan, np.nan, np.nan, 9, 4, 40, 18, np.nan, np.nan, 5, np.nan, 2]}) df.Date = df['Date'].apply(lambda x: pd.to_datetime(x).date()) df = df.set_index(['group', 'Date']) I'm trying to get a