multi-index

I am using to_csv to write a Multiindex DataFrame to csv files. The csv file has one column that contains the multiindexes in tuples, like: ('a', 'x') ('a', 'y') ('a', 'z') ('b', 'x') ('b', 'y') ('b', 'z') However, I want to be able to output the Multiindex to two columns instead of one column of tuples, such as: a, x , y , z b, x , y , z It looks like tupleize_cols can achieve this for columns, but there is no such option for the rows. Is there a way to achieve this? I think this will do it In [3]: df = DataFrame(dict(A = 'foo', B = 'bar', value = 1),index=range(5)).set_index(['A','B']) In [4

Suppose I have a DataFrame with MultiIndex columns. How can I collapse the levels to a concatenation of the values so that I only have one level? Setup np.random.seed([3, 14]) col = pd.MultiIndex.from_product([list('ABC'), list('DE'), list('FG')]) df = pd.DataFrame(np.random.rand(4, 12) * 10, columns=col).astype(int) print df A B C D E D E D E F G F G F G F G F G F G 0 2 1 1 7 5 9 9 2 7 4 0 3 1 3 7 1 1 5 3 1 4 3 5 6 0 2 2 6 9 9 9 5 7 0 1 2 7 5 3 2 2 8 0 3 9 4 7 0 8 2 5 I want the result to look like this: ADF ADG AEF AEG BDF BDG BEF BEG CDF CDG CEF CEG 0 2 1 1 7 5 9 9 2 7 4 0 3 1 3 7 1 1 5 3 1

Using the Pandas package in python, I would like to sum (marginalize) over one level in a series with a 3-level multiindex to produce a series with a 2 level multiindex. For example, if I have the following: ind = [tuple(x) for x in ['ABC', 'ABc', 'AbC', 'Abc', 'aBC', 'aBc', 'abC', 'abc']] mi = pd.MultiIndex.from_tuples(ind) data = pd.Series([264, 13, 29, 8, 152, 7, 15, 1], index=mi) A B C 264 c 13 b C 29 c 8 a B C 152 c 7 b C 15 c 1 I would like to sum over the variable C to produce the following output: A B 277 b 37 a B 159 b 16 What is the best way in Pandas to do this? If you know you

What is the best way to reshape the following dataframe in pandas? This DataFrame df has x,y values for each sample ( s1 and s2 in this case) and looks like this: In [23]: df = pandas.DataFrame({"s1_x": scipy.randn(10), "s1_y": scipy.randn(10), "s2_x": scipy.randn(10), "s2_y": scipy.randn(10)}) In [24]: df Out[24]: s1_x s1_y s2_x s2_y 0 0.913462 0.525590 -0.377640 0.700720 1 0.723288 -0.691715 0.127153 0.180836 2 0.181631 -1.090529 -1.392552 1.530669 3 0.997414 -1.486094 1.207012 0.376120 4 -0.319841 0.195289 -1.034683 0.286073 5 1.085154 -0.619635 0.396867 0.623482 6 1.867816 -0.928101 -0

I would like to run a pivot on a pandas DataFrame , with the index being two columns, not one. For example, one field for the year, one for the month, an 'item' field which shows 'item 1' and 'item 2' and a 'value' field with numerical values. I want the index to be year + month. The only way I managed to get this to work was to combine the two fields into one, then separate them again. is there a better way? Minimal code copied below. Thanks a lot! PS Yes, I am aware there are other questions with the keywords 'pivot' and 'multi-index', but I did not understand if/how they can help me with

After performing a groupby.sum() on a DataFrame I'm having some trouble trying to create my intended plot. How can I create a subplot ( kind='bar' ) for each Code , where the x-axis is the Month and the bars are ColA and ColB ? Reustonium I found the unstack(level) method to work perfectly, which has the added benefit of not needing a priori knowledge about how many Codes there are. df.unstack(level=0).plot(kind='bar', subplots=True) Using the following DataFrame ... # using pandas version 0.14.1 from pandas import DataFrame import pandas as pd import matplotlib.pyplot as plt data = {'ColB': {

I have my df with multi-index columns. All of my values are in float, and I want to merge values with in first level of multi-index. Please see below for detail. first bar baz foo second one two one two one A 0.895717 0.805244 1.206412 2.565646 1.431256 B 0.410835 0.813850 0.132003 0.827317 0.076467 C 1.413681 1.607920 1.024180 0.569605 0.875906 first bar baz foo A (0.895717+0.805244) (1.206412+2.565646) 1.431256 B (0.410835+0.813850) (0.132003+0.827317) 0.076467 C (1.413681+1.607920) (1.024180+0.569605) 0.875906 The values are actually added (I just didn't feel like doing all this :)). Bottom