multi-index

pandas allows for cool slicing on time indexes. For example, I can slice a dataframe df for the months from Janurary 2012 to March 2012 by doing: df['2012-01':'2012-03'] However, I have a dataframe df with a multiindex where the time index is the second level. It looks like: A B C D E a 2001-01-31 0.864841 0.789273 0.370031 0.448256 0.178515 2001-02-28 0.991861 0.079215 0.900788 0.666178 0.693887 2001-03-31 0.016674 0.855109 0.984115 0.436574 0.480339 2001-04-30 0.120924 0.046013 0.659807 0.210534 0.694029 2001-05-31 0.788149 0.296244 0.478201 0.845042 0.437814 b 2001-01-31 0.497646 0.349958 0

I often have MultiIndex indices and I'd like to iterate over groups where higher level indices are equal. It basically looks like from random import choice import pandas as pd N = 100 df = pd.DataFrame([choice([1, 2, 3]) for _ in range(N)], columns=["A"], index=pd.MultiIndex.from_tuples([(choice("ab"), choice("cd"), choice("de")) for _ in range(N)])) for idx in zip(df.index.get_level_values(0), df.index.get_level_values(1)): df_select = df.ix[idx] Is there a way to do the for loop iteration more neatly? Use groupby . The index of the df_select view includes the first two level values, but

I have a Pandas multiindex dataframe and I need to assign values to one of the columns from a series. The series shares its index with the first level of the index of the dataframe. import pandas as pd import numpy as np idx0 = np.array(['bar', 'bar', 'bar', 'baz', 'foo', 'foo']) idx1 = np.array(['one', 'two', 'three', 'one', 'one', 'two']) df = pd.DataFrame(index = [idx0, idx1], columns = ['A', 'B']) s = pd.Series([True, False, True],index = np.unique(idx0)) print df print s out: A B bar one NaN NaN two NaN NaN three NaN NaN baz one NaN NaN foo one NaN NaN two NaN NaN bar True baz False foo

I'm a newbie to both Python and Pandas. I am trying to construct a dataframe, and then later populate it with values. I have constructed my dataframe from pandas import * ageMin = 21 ageMax = 31 ageStep = 2 bins_sumins = [0, 10000, 20000] bins_age = list(range(ageMin, ageMax, ageStep)) indeks_sex = ['M', 'F'] indeks_age = ['[{0}-{1})'.format(bins_age[i-1], bins_age[i]) for i in range(1, len(bins_age))] indeks_sumins = ['[{0}-{1})'.format(bins_sumins[i-1], bins_sumins[i]) for i in range(1, len(bins_sumins))] indeks = MultiIndex.from_product([indeks_age, indeks_sex, indeks_sumins], names=['Age',

I've been reading through the documentation and many explanations and examples use levels as something taken for granted. Imho the docs lack a bit on a fundamental explanation of the data structure and definitions. What are levels in a data frame? What are levels in a MultiIndex index? Andrzej Gis I stumbled across this question while analyzing the answer to my own question , but I didn't find the John's answer satisfying enough. After a few experiments though I think I understood the levels and decided to share: Short answer: Levels are parts of the index or column. Long answer: I think this

I have a Pandas data frame which is MultiIndexed. The second level contains a year ([2014,2015]) and the third contains the month number ([1, 2, .., 12]). I would like to merge these two into a single level like - [1/2014, 2/2014 ..., 6/2015]. How could this be done? I'm new to Pandas. Searched a lot but could not find any similar question/solution. Edit: I found a way to avoid having to do this altogether with the answer to this question . I should have been creating my data frame that way. This seems to be the way to go for indexing by DateTime. Consider the pd.MultiIndex and pd.DataFrame ,

I need to work with large dimension data frame with multi index, so i tried to create a data frame to learn how to store it in an hdf5 file. The data frame is like this: (with the multi index in the first 2 columns) Symbol Date 0 C 2014-07-21 4792 B 2014-07-21 4492 A 2014-07-21 5681 B 2014-07-21 8310 A 2014-07-21 1197 C 2014-07-21 4722 2014-07-21 7695 2014-07-21 1774 I'm using the pandas.to_hdf but it creates a "Fixed format store", when I try to select the datas in a group: store.select('table','Symbol == "A"') it returns some errors and the main problem is this TypeError: cannot pass a where

Is there anyway to use groupby on the columns in a Multiindex. I know you can on the rows and there is good documentation in that regard. However I cannot seem to groupby on columns. The only solution I have is transposing the dataframe. #generate data (copied from pandas example) 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.DataFrame(np.random.randn(3, 8), index=['A', 'B', 'C'], columns=index) Now I will try to