ENH: Better string representation for MultiIndex

[jtratner]

Fixes #3347.

All interested parties are invited to submit test cases. cc @y-p @hayd

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Examples:

In [1]: import pandas as pd In [2]: pd.MultiIndex.from_arrays([[1, 1, 1, 1], [1, 3, 5, 7], [9, 9, 1, 1]]) Out[2]: MultiIndex(levels=[[1], [1, 3, 5, 7], [1, 9]] labels=[[0, 0, 0, 0], [0, 1, 2, 3], [1, 1, 0, 0]]) In [3]: mi = _ In [4]: mi.names = list('abc') In [5]: print mi a b c 1 1 9 3 9 5 1 7 1 

And with too many rows:

In [10]: pd.set_option('display.max_rows', 15) In [11]: lst1 = [1] * 3 + [2] * 5 + [3] * 2 In [12]: lst1 Out[12]: [1, 1, 1, 2, 2, 2, 2, 2, 3, 3] In [13]: lst2 = ['a'] * 6 + ['b'] * 3 + ['c'] * 1 In [14]: lst2 Out[14]: ['a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'c'] In [15]: mi = pd.MultiIndex.from_arrays([lst1 * 10, lst2 * 10, range(100)]); mi Out[15]: MultiIndex(levels=[[1, 2, 3], [u'a', u'b', u'c'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]] labels=[[0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2], [0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]) In [16]: mi = _ In [17]: print mi 1 a 0 1 2 2 a 3 4 5 ... 2 a 93 94 95 b 96 97 3 b 98 c 99 

Not sure how to handle too wide -- wrap? format() doesn't try to sparsify after a column with values everywhere.

In [19]: mi = pd.MultiIndex.from_arrays([lst1 * 10, lst2 * 10, range(100)] * 10) In [20]: print mi 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 a 0 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 1 a 1 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 1 a 2 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 2 a 3 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 2 a 4 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 2 a 5 ... 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 2 a 93 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 2 a 94 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 2 a 95 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 2 b 96 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 2 b 97 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 3 b 98 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 3 c 99 

In [9]: labels = [('foo', '2012-07-26T00:00:00', 'b5c2700'), ...: ('foo', '2012-08-06T00:00:00', '900b2ca'), ...: ('foo', '2012-08-15T00:00:00', '07f1ce0'), ...: ('foo', '2012-09-25T00:00:00', '5c93e83'), ...: ('foo', '2012-09-25T00:00:00', '9345bba')] In [10]: print pd.MultiIndex.from_tuples(labels) foo 2012-07-26T00:00:00 b5c2700 2012-08-06T00:00:00 900b2ca 2012-08-15T00:00:00 07f1ce0 2012-09-25T00:00:00 5c93e83 9345bba 

[cpcloud]

+1 ... the current MultiIndex repr always makes me feel a bit strange

[jreback]

looks ok