Benchmark coverage reports
This is very much a work in progress, but I've hacked together a copy of vbench that can produce benchmark coverage reports using nosetests and coverage.

The following results are _very_ preliminary, as many benchmarks are failing in the current setup (especially PyTables) and I have to manually intervene to get this report at the moment. In any case, the results are:

```
Name                                Stmts   Miss Branch BrMiss  Cover   Missing
-------------------------------------------------------------------------------
pandas                                 29      0      0      0   100%   
pandas.compat                         380    252    130    121    27%   50-55, 61-90, 97, 100, 103, 129, 134-137, 143, 166-169, 178-188, 202-203, 212-214, 224, 240-241, 245-246, 269-277, 284-288, 295-298, 302-306, 310-314, 318-326, 334-350, 356, 360, 364, 368, 372-373, 377-378, 388-408, 419-425, 430-433, 450-456, 460, 467-470, 477-480, 483, 489, 493, 498, 506-507, 532, 535, 545-547, 560-562, 584-598, 603, 609-610, 634-641, 650-657, 666-674, 683-693, 696-697, 702-705, 725-728, 736-744, 747-750, 753-754
pandas.compat.chainmap                 21      8     10      6    55%   11-12, 16-20, 25
pandas.compat.chainmap_impl            75     34      8      6    52%   5-6, 16-24, 67, 73, 76, 79, 89, 93, 102-104, 109, 112, 115-118, 122-125, 129-132, 136
pandas.compat.pickle_compat            71     47     19     18    28%   15-48, 51-52, 76-97
pandas.compat.scipy                    59     52     26     26     8%   62-81, 88, 115-130, 149-151, 219-241
pandas.computation                      0      0      0      0   100%   
pandas.computation.align               96     27     45     20    67%   17-27, 48, 54, 80, 100, 107, 124-129, 133, 171-183
pandas.computation.api                  2      0      0      0   100%   
pandas.computation.common              14      5      2      2    56%   8-10, 18-20
pandas.computation.engines             63     10      4      2    82%   32-33, 94, 120-126, 144
pandas.computation.eval                62     12     28      9    77%   30, 39-40, 46, 63, 71-72, 90, 126, 136, 239-240
pandas.computation.expr               330    103    110     56    64%   54-55, 77, 103, 318, 333-350, 371, 386, 390, 396, 405-406, 409-411, 417, 423-424, 427-428, 434, 437-450, 454-464, 477-495, 498-514, 519-544, 606, 636, 645, 655
pandas.computation.expressions        101     58     48     39    35%   56, 63, 69-89, 94-118, 127-150, 177, 197, 207-208, 213-214, 220-222
pandas.computation.ops                231     74     48     30    63%   34, 46, 68, 119, 146, 150, 216-224, 231-239, 276-282, 311-314, 331, 355-374, 380-385, 390-396, 399-405, 429-432, 458-464, 468-469, 472
pandas.computation.pytables           350    272    153    153    16%   26-28, 34-38, 41, 45-54, 58, 64, 68, 76-80, 83, 87-128, 132-136, 141, 147, 152, 156-157, 163-206, 209, 215, 220-224, 228, 232-266, 269-272, 281, 287, 299, 303-327, 333-337, 344-358, 369-371, 376-380, 384, 387-389, 394-404, 408-430, 433, 436, 472-504, 509-556, 559-561, 566-577, 585-587, 592-596, 601-606
pandas.computation.scope              103     37     24      9    64%   32-38, 44-45, 72-74, 109-112, 130, 135-137, 172, 226-235, 244-254, 269-278, 288, 293
pandas.core                             0      0      0      0   100%   
pandas.core.algorithms                187     61     67     33    63%   46-47, 65-67, 75, 158, 194, 196, 203-205, 219-220, 228, 236-265, 324-327, 333, 339, 345-356, 363, 376-378, 396-404
pandas.core.api                        22      0      0      0   100%   
pandas.core.array                      17      0     10      0   100%   
pandas.core.base                      143     48     32     24    59%   33, 74, 78, 85, 93-96, 110-112, 118, 123, 127-129, 132-134, 139, 144, 147, 151, 155-156, 171-174, 178, 189-190, 199-201, 218-219, 224, 228-229, 238-239, 242-243, 248, 258, 264-265, 269-270
pandas.core.categorical               109     49     28     23    47%   19-24, 26-28, 81-82, 86, 88-89, 140, 143, 146-148, 154-156, 159-161, 163-168, 172-176, 179, 182-193, 196-203, 217-220, 228-232
pandas.core.common                   1400    518    744    428    56%   143, 162-171, 195-199, 212, 233-238, 243-244, 247-258, 288, 314-318, 367, 372-373, 401-402, 421, 423, 429-430, 439, 443-446, 456, 460-461, 589-592, 596, 599, 695-698, 702, 712, 720, 723, 725, 727, 729-734, 744-746, 749, 777-780, 789, 808-811, 817, 826-850, 861, 865, 884, 896-897, 907-908, 915-922, 934-936, 941, 947-964, 968, 982-984, 989-990, 992-993, 1003, 1011-1015, 1029-1032, 1041-1042, 1063, 1070-1074, 1085, 1092, 1094, 1096, 1098, 1107, 1120, 1132, 1140, 1145, 1149-1152, 1163-1166, 1168-1169, 1172-1173, 1176, 1178-1179, 1183-1184, 1201-1208, 1212-1215, 1224-1225, 1235, 1249, 1251-1252, 1261, 1265, 1267-1268, 1274-1277, 1279-1283, 1286-1290, 1302-1306, 1308, 1311, 1335-1336, 1340, 1360, 1363, 1387-1392, 1396-1399, 1405, 1416-1417, 1422-1428, 1438, 1444-1453, 1463-1467, 1478-1480, 1483, 1485-1492, 1494, 1498-1499, 1503, 1524, 1540-1541, 1551, 1576, 1589, 1596-1601, 1613, 1631, 1663, 1676, 1685-1686, 1691, 1694, 1698, 1711-1718, 1732, 1736-1738, 1760, 1763, 1776-1779, 1809-1813, 1816-1818, 1824, 1829-1830, 1832, 1839-1840, 1852-1854, 1883-1885, 1906-1909, 1924, 1932-1933, 1940-1941, 1977, 1981-1986, 1990, 1994-1995, 1998-1999, 2009, 2018, 2020, 2031-2036, 2039, 2054, 2085, 2101, 2119, 2121, 2146, 2159-2163, 2167, 2171-2176, 2207, 2210-2217, 2219-2238, 2242-2243, 2246, 2248, 2252, 2260, 2262, 2271-2274, 2284, 2287, 2290, 2293, 2296, 2309-2330, 2333-2336, 2362-2363, 2366-2367, 2384-2388, 2391-2407, 2410-2427, 2448, 2454-2459, 2463-2473, 2505-2510, 2513, 2522-2527, 2530, 2537-2543, 2581-2599, 2607-2621, 2657-2659, 2667, 2679, 2682, 2685, 2688-2692, 2700-2701, 2712, 2733-2736, 2749-2752, 2760
pandas.core.config                    279    101    107     73    55%   80, 105-116, 118-125, 129-140, 145-157, 175-176, 182, 187, 189-195, 198, 221-222, 343, 350-356, 359, 362-363, 395, 408, 475, 497, 513-514, 572-580, 588-607, 613-637, 715, 738, 752
pandas.core.config_init               101     11     14      8    83%   198-207, 291-292, 324, 341
pandas.core.daterange                  21      8      4      2    60%   29, 37-48
pandas.core.datetools                  43     14      6      6    59%   14-15, 41-57
pandas.core.format                   1185    389    596    368    57%   73-78, 81-83, 86-106, 109, 114-123, 126-131, 138-148, 151-155, 158, 165-167, 170, 172, 175-177, 181-184, 187-189, 197, 205, 208, 210, 218, 233, 242, 301, 342, 348, 374-378, 430-432, 442-444, 448-452, 454, 464, 468-470, 481, 484-485, 488-493, 515, 532, 534, 539, 554, 598, 604, 608, 635-636, 645, 655, 687-689, 703, 719, 725, 728-733, 740, 749-786, 823-824, 837, 889-892, 901-908, 973, 994, 999-1000, 1015, 1032, 1042, 1049-1059, 1061, 1063-1065, 1067, 1077-1079, 1084, 1091, 1095-1100, 1106, 1109-1113, 1123, 1125-1143, 1148, 1162-1163, 1195, 1202-1204, 1206, 1212-1215, 1219, 1222-1223, 1235-1236, 1240-1262, 1319-1324, 1365, 1367-1368, 1370, 1380, 1384, 1391-1393, 1396, 1399-1400, 1407, 1422, 1424, 1430, 1433, 1439-1446, 1451, 1454, 1461-1463, 1468, 1474-1475, 1478, 1487-1492, 1500-1506, 1508-1512, 1518-1521, 1524-1531, 1535, 1555, 1561, 1574-1579, 1584-1593, 1598-1599, 1612, 1614, 1658, 1665-1667, 1672, 1682, 1684, 1710-1711, 1716, 1732, 1744-1745, 1748, 1782, 1785, 1791-1800, 1806, 1810, 1816, 1835-1839, 1843-1845, 1848-1862, 1879, 1891, 1915, 1922-1931, 1973-1974, 1978, 2008-2016, 2056-2057, 2080-2116, 2133-2134
pandas.core.frame                    1849    830    911    589    49%   203-212, 214, 222-226, 228, 235, 238, 252, 257-260, 273, 300-310, 330, 334, 346-349, 378, 392-393, 400-405, 410, 419, 429-431, 458, 479, 482-484, 504-505, 529, 539, 541, 566, 568-573, 575-584, 588, 590-592, 595, 620-632, 653, 657-658, 705, 738-741, 745-747, 750-777, 779-788, 793-794, 800, 802-804, 812, 825, 832-833, 835, 862-863, 868, 875, 882, 885-889, 914-930, 933-941, 947-948, 987-988, 1006-1007, 1023-1066, 1197-1215, 1248-1251, 1268, 1418-1470, 1474-1475, 1569, 1590, 1618-1619, 1623, 1631, 1642, 1664, 1667, 1681, 1705-1706, 1709-1711, 1715-1717, 1721, 1724-1731, 1735, 1807-1808, 1868, 1882, 1893-1894, 1898-1902, 1908-1915, 1920-1924, 1929, 1939-1942, 1945, 1949-1955, 2010, 2012, 2017, 2020, 2033, 2046-2048, 2054, 2084, 2092, 2216, 2224-2225, 2228, 2233-2234, 2236-2237, 2248, 2294, 2301, 2305-2308, 2315-2330, 2337-2345, 2352-2358, 2403-2405, 2413-2414, 2419, 2426-2428, 2494-2497, 2540, 2580, 2583, 2585, 2593, 2599, 2606-2609, 2618, 2659, 2664-2667, 2673, 2678-2679, 2696-2698, 2700, 2727-2729, 2744-2752, 2759-2766, 2774-2782, 2792-2798, 2802, 2806, 2816-2818, 2857-2864, 2868, 2873-2875, 2896-2901, 2904-2935, 2943-2946, 2956, 2960, 2990-2992, 3028-3041, 3043-3054, 3070, 3161, 3163-3164, 3239, 3299, 3304, 3307-3308, 3316, 3322, 3326-3333, 3338-3348, 3355, 3394-3404, 3410-3417, 3434, 3448-3449, 3455-3458, 3484, 3517-3529, 3531-3533, 3537, 3595-3597, 3604-3629, 3636-3637, 3669, 3675-3692, 3715-3730, 3749-3759, 3762-3780, 3798-3810, 3813-3815, 3821, 3827, 3858, 3864, 3875-3883, 3888-3897, 3903, 3928-3929, 3931-3933, 3959-3964, 3975, 3981-4021, 4025-4034, 4063-4067, 4094-4098, 4107, 4127-4129, 4147-4160, 4197-4206, 4228-4240, 4259-4267, 4270-4275, 4326-4341, 4344-4348, 4371, 4411-4412, 4414-4415, 4418-4430, 4458, 4461, 4510, 4519, 4522, 4526-4529, 4538-4551, 4559, 4564, 4574-4581, 4584-4585, 4587-4588, 4596, 4600, 4604-4605, 4618-4648, 4659, 4674-4695, 4704, 4743-4757, 4769, 4782, 4794-4795, 4797-4799, 4803, 4839-4844
pandas.core.generic                  1349    621    614    405    48%   45, 49-54, 57-64, 93, 95, 98, 127, 130-131, 144-145, 149, 216-217, 234-237, 258-260, 266-268, 280-283, 293-295, 306-308, 331, 346, 365, 370, 385-394, 437, 443, 454, 457-470, 482-486, 501-505, 539-574, 596-599, 622, 652, 691-694, 698, 701, 714, 738-749, 755-768, 925-928, 932-935, 959-960, 998-1001, 1004, 1035, 1048-1052, 1055-1058, 1068, 1078, 1083, 1092-1116, 1126-1129, 1134-1139, 1148-1149, 1169-1170, 1264-1283, 1301-1305, 1308, 1320, 1349-1359, 1407-1409, 1415-1416, 1420-1432, 1435, 1460-1461, 1475-1476, 1493-1502, 1569-1570, 1584, 1608, 1700, 1705-1711, 1731-1750, 1756-1759, 1765-1768, 1798, 1813-1822, 1855, 1865-1866, 1870-1871, 1885, 1915, 1925, 1938-1939, 1954-1955, 1978-1985, 1990, 2096, 2105, 2107-2114, 2126-2128, 2140, 2143, 2147-2148, 2154-2165, 2177, 2182, 2291-2292, 2300-2341, 2348, 2352-2371, 2375-2387, 2392-2400, 2406-2410, 2420, 2429, 2495-2497, 2503, 2506, 2510, 2516, 2531-2535, 2539, 2553, 2563, 2582-2591, 2605-2608, 2622-2625, 2699-2700, 2715-2719, 2737-2743, 2802-2817, 2836-2847, 2884, 2914, 2928-2929, 2941, 2954-2981, 2986, 3021-3027, 3030, 3039-3053, 3059-3073, 3077-3122, 3127-3129, 3174, 3181, 3208-3243, 3264-3292, 3308-3323, 3340-3355, 3371-3383, 3406-3414, 3420-3423, 3522-3535, 3544, 3546, 3548, 3565, 3569, 3578-3580, 3590-3608
pandas.core.groupby                  1507    454    616    356    62%   101-103, 105-107, 116-126, 130-140, 225, 253, 266, 271, 283-285, 288, 305-310, 314, 338, 364-368, 379, 467, 488, 499-500, 530-532, 579-584, 613, 638, 640-641, 645-647, 649, 651-653, 655, 670-672, 695, 719, 722, 742, 755, 759, 764, 768, 773, 802, 828, 932-935, 940-946, 959-965, 999, 1101-1105, 1120, 1160-1161, 1186-1196, 1199-1202, 1224, 1232-1234, 1239-1240, 1249-1254, 1265, 1285, 1296-1298, 1303-1305, 1320, 1324, 1327, 1330, 1385-1386, 1393-1395, 1432, 1456-1457, 1467, 1470, 1483, 1491-1492, 1496-1499, 1508-1509, 1511-1512, 1518, 1529, 1540, 1556-1558, 1566-1574, 1577-1578, 1604, 1629, 1636, 1654, 1721, 1731-1733, 1736-1741, 1744, 1746, 1748-1772, 1786, 1790, 1800, 1805-1808, 1811-1814, 1816-1817, 1820, 1840-1854, 1859-1861, 1865, 1888-1907, 1913-1927, 1934-1948, 1977, 1982-1983, 1986, 1990-1991, 2017, 2028-2035, 2044-2045, 2055-2058, 2061-2073, 2078-2099, 2102-2108, 2110, 2116-2120, 2122, 2136-2142, 2147, 2149, 2151-2163, 2166-2175, 2179, 2186, 2212-2213, 2225-2274, 2278-2288, 2297, 2337-2338, 2348, 2352, 2389, 2395-2397, 2399-2407, 2409-2411, 2435, 2446-2448, 2456, 2459, 2475-2476, 2486, 2503-2505, 2513, 2526, 2533, 2536, 2542, 2559, 2561-2564, 2568-2572, 2575, 2583, 2588, 2620-2621, 2628, 2632, 2649-2652, 2657-2658, 2660-2670, 2675-2689, 2728, 2745, 2770-2772, 2783, 2789-2791, 2795-2801, 2804, 2813, 2870-2871, 2884, 2898-2899, 2912-2913, 3031, 3035-3042
pandas.core.index                    1882    691    852    468    58%   53, 141, 159, 214, 219, 256, 258, 262, 302, 304-308, 317, 356, 389, 392-393, 400, 404, 415, 425, 444, 459, 472, 474, 476, 527-533, 543-545, 560, 565-569, 586, 594, 612-613, 631, 645, 648-650, 660, 681, 685-686, 702, 720, 738-742, 744, 746, 774, 794-795, 799, 802, 829-832, 837, 840, 851-856, 865-868, 882, 888, 903, 914-916, 921-923, 927, 938-939, 968, 978-980, 986-987, 1013, 1016, 1019-1020, 1023, 1067, 1070-1071, 1105, 1111-1113, 1119-1120, 1136, 1188, 1196, 1201, 1205, 1210, 1222, 1225, 1237, 1243, 1247, 1250, 1295, 1307, 1313, 1321-1324, 1353, 1365-1368, 1374, 1376-1378, 1381, 1384-1386, 1390-1391, 1401-1402, 1411-1412, 1415, 1417, 1419, 1425, 1429, 1438-1448, 1457-1461, 1465, 1468, 1474-1486, 1489-1491, 1501, 1518, 1526-1532, 1537, 1575-1577, 1579, 1585-1587, 1589-1593, 1598, 1601-1602, 1632, 1670-1671, 1675-1681, 1692-1693, 1697, 1701-1705, 1755, 1802, 1813, 1815, 1851-1853, 1894, 1902, 1915, 1918-1921, 1925-1928, 1935-1943, 1948-1956, 1959-1961, 1967-1973, 1977, 2011, 2013, 2015, 2017-2022, 2051, 2056, 2087, 2107-2113, 2117, 2132, 2139, 2160-2164, 2166-2167, 2200-2214, 2236, 2240, 2268-2271, 2273-2275, 2284, 2308, 2312, 2321, 2323-2324, 2326, 2332, 2337, 2342, 2351, 2361, 2370-2371, 2389, 2391-2393, 2411-2415, 2420-2426, 2428-2434, 2447-2455, 2472-2478, 2495-2500, 2510, 2518, 2533, 2568-2573, 2589, 2594-2597, 2604, 2682, 2686, 2692, 2747-2748, 2751, 2755-2756, 2760, 2765-2772, 2779, 2827, 2835-2836, 2839, 2855-2878, 2881-2888, 2927, 2943-2954, 2964-2973, 3009, 3057, 3060, 3066-3069, 3072-3075, 3096, 3099-3102, 3106, 3112-3118, 3122-3128, 3166-3180, 3183-3209, 3224-3234, 3252, 3259-3262, 3266-3279, 3285, 3289-3364, 3381-3396, 3402, 3425-3471, 3488-3500, 3519, 3539-3545, 3564-3567, 3582-3597, 3608-3634, 3641-3644, 3662-3683, 3694-3695, 3708-3712, 3715-3716, 3733-3735, 3738, 3752, 3755, 3761, 3766, 3773-3774, 3783, 3789, 3796, 3800-3803, 3816-3819, 3858-3861, 3872-3888, 3895-3900, 3904-3909, 3916
pandas.core.indexing                  941    388    539    329    52%   54-56, 89, 106, 110, 113, 131, 133-136, 140, 155, 157, 168, 171, 197-218, 257, 279, 298, 304-305, 311, 316, 321-323, 329, 339, 345, 348, 359-360, 364-365, 367-375, 385, 387, 397, 408, 419-424, 428-430, 442-443, 445-447, 451-452, 456, 460, 488, 496, 499-514, 530, 532-536, 541, 543-544, 547-554, 558, 563-566, 570-594, 597-612, 616, 618-620, 623-663, 666-667, 685-686, 688, 691-693, 702, 705, 710, 734-735, 739, 745-746, 753-768, 775, 784, 786, 800, 805, 814-815, 833-834, 847, 863, 868-870, 876-878, 882-884, 896, 899-901, 905, 916, 920, 928, 979-986, 1023, 1053-1054, 1057, 1060, 1067-1070, 1073-1075, 1087-1089, 1094-1095, 1106, 1109-1111, 1116-1123, 1134, 1137, 1162-1168, 1172-1183, 1195-1263, 1269-1270, 1272, 1277, 1280, 1282-1289, 1301-1309, 1316, 1323-1345, 1351, 1357, 1366-1367, 1379, 1388, 1392, 1398-1401, 1410, 1413-1422, 1425-1432, 1446, 1450-1456, 1471, 1473, 1475, 1477, 1481, 1484, 1514-1523, 1534-1539, 1559, 1578-1582, 1595, 1598, 1613, 1633, 1664, 1668, 1673-1687
pandas.core.internals                2291    735   1058    548    62%   61, 110-111, 129, 137, 141, 167-168, 190, 193-195, 209, 228-234, 329-332, 345-354, 368, 384, 397, 405, 408-409, 431, 455, 466-467, 492-495, 497-508, 523, 528, 532-534, 541, 627, 638, 647, 653, 670, 673, 697-698, 702, 706-707, 711, 715, 722, 736-737, 742, 745-771, 776-799, 804, 816, 829, 840-841, 857, 868-871, 895, 962, 997-999, 1019, 1025-1026, 1035-1036, 1042-1050, 1058, 1085-1086, 1099, 1103, 1115, 1133-1134, 1139-1141, 1149, 1202-1203, 1224-1225, 1240, 1242-1243, 1248, 1253, 1262-1263, 1267-1268, 1287, 1289-1290, 1293, 1295, 1305, 1308-1310, 1313-1321, 1325, 1329-1336, 1339, 1344-1349, 1351-1353, 1357, 1365, 1368, 1371-1372, 1377, 1381-1383, 1443, 1461, 1465, 1475, 1481-1486, 1489, 1495, 1504, 1507, 1514, 1518-1519, 1522, 1524, 1548-1554, 1556, 1561-1598, 1602, 1613, 1620-1623, 1628-1631, 1643-1644, 1647-1649, 1657-1661, 1665, 1669-1671, 1675-1679, 1693, 1708, 1716, 1730-1731, 1761-1762, 1765, 1776-1777, 1794, 1796, 1800, 1818, 1820, 1824, 1828, 1834, 1837, 1845, 1852, 1878-1880, 1884-1887, 1892-1895, 1897-1900, 1905, 1915-1921, 1942, 1953-1957, 1961, 1972-1973, 2025, 2084, 2090, 2093, 2096, 2118, 2152-2153, 2164-2171, 2215, 2252-2253, 2315, 2332-2334, 2337, 2341-2345, 2352-2353, 2359, 2367, 2407-2410, 2440, 2452, 2467-2470, 2472-2477, 2485, 2488, 2495-2497, 2501, 2505, 2525, 2528, 2558-2559, 2564-2565, 2590-2593, 2597-2599, 2606, 2618-2627, 2631-2632, 2648, 2675-2688, 2713, 2726, 2729, 2733, 2757, 2764, 2769, 2798-2801, 2809-2854, 2869-2884, 2907-2909, 2919-2920, 2926-2950, 2959-2963, 2968-2977, 2983-2990, 3008, 3018, 3038-3069, 3080, 3089-3097, 3125-3128, 3157, 3175-3192, 3198-3201, 3220-3231, 3241, 3272-3274, 3289, 3292, 3318-3334, 3363-3370, 3382-3383, 3411-3418, 3423-3427, 3440-3448, 3456, 3464, 3480-3490, 3495-3510, 3520-3526, 3531-3533, 3537-3550, 3553-3567, 3570-3571, 3574-3575, 3580-3591, 3595-3603, 3608, 3629, 3640, 3642, 3650-3667, 3675-3676, 3679, 3724-3731, 3735, 3745-3747, 3764, 3772, 3776, 3782, 3791-3792, 3796-3800, 3816-3819, 3828-3829, 3847-3848, 3868, 3871, 3874, 3880-3882, 3896-3898, 3924-3933, 4022-4030, 4036-4043, 4048, 4058-4059, 4077, 4082-4093, 4096, 4098, 4100, 4128, 4140-4142, 4145, 4156-4159, 4178-4179, 4187, 4189, 4194-4207
pandas.core.nanops                    421    174    175    116    51%   20, 66, 69-70, 74-76, 79-83, 88, 97-98, 102, 104, 116-122, 173, 187, 196-198, 206, 213, 219, 249, 262, 264, 266, 269, 277-283, 285-290, 302, 314-318, 328-349, 352, 357-382, 400-404, 409-417, 420-425, 430-438, 443-446, 450, 454-455, 460, 465-466, 469, 471-472, 479-482, 490-499, 517-518, 522, 528-531, 539-542, 546, 548-551, 554, 558-559, 562-571, 583-591, 597, 603, 605-611, 621-632, 648-649, 656-657
pandas.core.ops                       450    217    237    154    46%   116, 119-123, 125-126, 226, 268-269, 276-305, 315, 323-324, 329, 331-365, 378, 385-399, 430, 444-455, 463, 471, 516-525, 531-533, 541, 547-549, 564, 575, 589-609, 623-629, 634-647, 674-683, 731-750, 761, 763-770, 772-790, 805-825, 829-863, 876, 905-917, 921-926, 934-955, 959-966
pandas.core.panel                     647    375    239    188    36%   46-53, 95, 97-100, 142, 144, 150, 175, 177-178, 195-196, 202, 237-240, 259-261, 266-271, 277, 297, 303, 321, 325-336, 344, 347, 350-356, 364, 381-382, 427-436, 442, 466, 472, 493, 496-500, 502, 504-516, 519-520, 524-528, 532-535, 538-549, 551-557, 588-589, 620-640, 643-645, 647-648, 651-653, 656-670, 674-679, 682-684, 702, 720, 738-740, 743-748, 758-766, 771, 789-791, 811-813, 818, 821, 824-864, 894-901, 906-910, 914, 918-945, 950-974, 979-988, 991, 994, 997-1000, 1004-1014, 1018-1027, 1034-1051, 1055-1056, 1058-1060, 1073-1077, 1083, 1090, 1104-1110, 1126-1153, 1157, 1183-1205, 1229-1237, 1241-1251, 1270-1272, 1276, 1278, 1303, 1308, 1323, 1333, 1345-1347, 1353-1361, 1364, 1389-1400, 1405
pandas.core.panel4d                     6      0      0      0   100%   
pandas.core.panelnd                    55     23     18     13    51%   31-35, 53-54, 62-67, 69-71, 75-77, 83, 85, 89, 91-95
pandas.core.reshape                   473    182    166    101    56%   152-154, 158-168, 210-211, 254-263, 266, 271, 301-304, 307-308, 311, 334, 346-347, 377-387, 406-408, 434-450, 455-456, 470-478, 482, 485, 508-511, 540, 542-543, 546, 571, 575, 593, 675, 684, 694, 703, 755-778, 781-787, 847-872, 893-898, 948-982, 1006-1023, 1028-1053, 1060-1061
pandas.core.series                   1001    438    384    253    50%   71-73, 129, 133, 145, 172, 177, 181, 190, 194-195, 201, 203-205, 207, 215, 219, 225, 227, 244-245, 256, 260, 275-279, 294, 298, 302, 310, 319, 323, 328, 340, 343, 347, 356, 373, 394, 403, 406-422, 429, 433, 454, 458-459, 462-463, 466, 496-498, 500, 507-508, 512, 531, 533-536, 548-551, 555-567, 572-574, 579, 586-587, 592, 594, 596-605, 607-609, 613, 615, 624, 631, 633-636, 644, 647, 656, 658-668, 671-674, 676-678, 682-690, 694, 704-706, 713-714, 718-722, 761-768, 791-811, 822-831, 836, 843-846, 851-853, 862, 866-871, 905, 918, 920, 942, 946, 965-966, 979, 998, 1024-1030, 1045-1046, 1068-1088, 1135-1136, 1147, 1157, 1177, 1221, 1260-1261, 1281-1283, 1286-1289, 1292, 1310-1315, 1318, 1321-1327, 1329-1337, 1339-1353, 1375-1378, 1397-1400, 1427, 1442-1444, 1447-1457, 1462-1467, 1488-1491, 1519-1527, 1530, 1532, 1536-1537, 1542, 1563-1567, 1572, 1587-1592, 1603-1607, 1634, 1663-1665, 1693-1704, 1760-1763, 1776, 1784-1785, 1805-1810, 1826-1827, 1848-1850, 1922-1944, 1972, 1975, 1980, 1984, 1988-1989, 2003, 2017, 2025-2026, 2045, 2101, 2107-2118, 2137-2143, 2175-2182, 2215-2217, 2234, 2237, 2246-2254, 2260-2266, 2293, 2300-2301, 2304, 2309, 2313, 2340-2342, 2361-2367, 2384, 2393-2398, 2410-2412, 2424-2427, 2431-2435, 2451-2455, 2483, 2491-2499
pandas.core.sparse                      3      0      0      0   100%   
pandas.core.strings                   350     91    140     68    68%   14-17, 39, 42-68, 75, 80-85, 99, 104-105, 107-109, 112-113, 117, 178, 183, 277, 284, 303-309, 314, 359, 366, 377-382, 443, 488-499, 502-504, 570, 572, 620, 624, 776-777, 794-795, 844, 847-852, 922-923, 927-928, 947-948
pandas.info                             0      0      0      0   100%   
pandas.io                               0      0      0      0   100%   
pandas.io.api                          11      0      0      0   100%   
pandas.io.clipboard                     2      0      0      0   100%   
pandas.io.common                       67     20      8      4    68%   15, 19, 98, 118-119, 122-134, 151, 157-160
pandas.io.date_converters              38     29      6      6    20%   8-10, 14-17, 22-28, 33-40, 44-46, 50-62
pandas.io.excel                       354    265    165    158    18%   33, 40-42, 46-49, 100-107, 125-141, 143-147, 199-203, 216, 228, 232-241, 243, 252-308, 312, 316-317, 320, 323, 329, 331, 336-338, 341-345, 391-401, 411, 416, 432, 439, 444-458, 461-466, 472-479, 483, 486, 490, 499-507, 513, 517-569, 581-591, 602-608, 614, 619-655, 676-691, 702-712, 724-730, 736, 741-775, 789-817
pandas.io.gbq                         169    137     66     66    14%   22-30, 89, 111-125, 153-167, 211-344, 388-466, 509-530
pandas.io.html                        273    169     80     79    30%   45, 91-97, 112-124, 169-171, 197-199, 327-329, 333-337, 340-344, 347-351, 353, 370-373, 376, 379, 385, 398, 401, 404, 407, 410-415, 418-421, 424-425, 443-447, 472, 475, 478, 482, 485-489, 495-501, 519-555, 558, 561, 564, 567, 573, 579-582, 588-609, 637-658, 662, 666-684, 693-710, 828-838
pandas.io.json                        362    230    206    186    27%   41, 44, 84, 185-186, 189-192, 200, 225, 234-235, 238, 252-255, 263, 268-273, 277-282, 292-308, 311-343, 345-346, 353-359, 368-379, 382-386, 391-395, 407-409, 414, 427-435, 439-441, 444, 453-472, 482, 489-495, 501-519, 522-527, 531-553, 587-617, 669-756
pandas.io.packers                     264    169    144    119    29%   68, 98, 109-114, 138, 143, 151-152, 155-156, 168, 179-181, 197-198, 206, 216-221, 226-231, 239-258, 269, 280-281, 290, 302-317, 353, 367-434, 442-540, 604-605, 609-641
pandas.io.parsers                    1140    693    599    481    32%   438-439, 444, 446, 448-450, 452, 477-478, 480, 518-521, 525, 539, 542, 556, 576-577, 587, 600-607, 613, 615, 617, 625, 630, 690-699, 704, 717-726, 732-747, 752, 755, 762-777, 786, 793-804, 809, 819-839, 842-869, 872-892, 896-909, 916, 918, 920-935, 940, 966, 968, 1008-1010, 1024, 1029-1030, 1033, 1044, 1055-1060, 1063, 1068, 1073-1074, 1085-1087, 1089-1093, 1097-1106, 1116, 1133-1136, 1139-1146, 1149-1151, 1198-1199, 1203, 1212-1237, 1249-1262, 1264-1336, 1341-1364, 1367, 1370-1371, 1375-1428, 1431-1457, 1461-1464, 1468-1480, 1484-1486, 1490-1497, 1502-1522, 1524-1570, 1574-1600, 1606-1610, 1618-1635, 1641-1644, 1647-1664, 1672-1685, 1689-1692, 1702, 1707, 1724, 1729-1742, 1746, 1751, 1758, 1762-1764, 1779-1793, 1798-1812, 1815-1824, 1830-1839, 1843-1853, 1855-1876, 1880-1886, 1905, 1908-1909, 1916, 1946, 1948, 1954, 1960-1962, 1970-1971, 1992-1995, 2010, 2012-2027, 2033-2062, 2066-2076, 2081-2089, 2094-2114, 2118-2126, 2130-2137, 2143, 2161-2179, 2183-2189, 2193-2206, 2209-2218, 2232-2234, 2237
pandas.io.pickle                       18      2      2      1    85%   46, 52
pandas.io.pytables                   2209   1551    894    863    22%   54-55, 60, 63, 80-87, 89, 226-243, 278, 285-287, 320-343, 396-400, 402-404, 409-410, 414, 417, 420, 423, 427-432, 439-444, 447, 450-459, 461-471, 475, 483, 489-490, 503-563, 569-571, 578-580, 598-604, 618-621, 647-669, 684-685, 689-692, 713, 739-742, 747-757, 761-765, 771-798, 821-824, 847-874, 907-916, 942-943, 953-964, 966-987, 989-999, 1022, 1044, 1047, 1049, 1053, 1055-1057, 1083, 1085-1109, 1113, 1118-1121, 1124-1126, 1129, 1133-1138, 1144-1151, 1155-1156, 1159-1183, 1189-1219, 1222-1224, 1228, 1232-1237, 1240-1247, 1249-1250, 1252-1253, 1255-1259, 1270-1279, 1282-1284, 1307-1312, 1314-1320, 1323-1334, 1337-1338, 1341-1343, 1399, 1405, 1409-1410, 1413-1420, 1424, 1428, 1433-1436, 1439, 1444-1447, 1451, 1453-1459, 1461-1470, 1475, 1479, 1483-1484, 1488, 1492, 1497, 1502, 1505, 1511-1517, 1521-1524, 1530-1535, 1541, 1547-1549, 1557-1566, 1571-1581, 1588, 1593, 1597, 1606, 1611-1612, 1615, 1618, 1642-1648, 1650-1652, 1654-1656, 1679-1681, 1683-1684, 1688-1689, 1693-1694, 1696-1698, 1700-1706, 1713, 1719-1722, 1730, 1732, 1735-1738, 1745-1746, 1748-1754, 1756-1757, 1760-1767, 1773-1782, 1785, 1791-1815, 1818-1829, 1832, 1836-1841, 1844, 1848-1849, 1855-1857, 1859, 1865-1866, 1868-1869, 1873, 1878, 1882-1892, 1899-1903, 1907-1932, 1936-1943, 1947-1951, 1955-1957, 1961-1963, 1972, 1975, 1978, 1981, 1989, 2011-2013, 2022, 2025, 2027-2029, 2038, 2042-2043, 2045-2048, 2052-2053, 2057-2058, 2066, 2070, 2074, 2078, 2090, 2094, 2098, 2102, 2107, 2111, 2119-2121, 2125, 2131, 2133-2135, 2164, 2170, 2173-2178, 2181, 2185-2186, 2192, 2196, 2201-2202, 2205, 2209-2217, 2226-2230, 2232-2239, 2242-2246, 2257-2263, 2265-2267, 2270-2284, 2288-2290, 2293-2296, 2299-2300, 2303-2305, 2308-2310, 2315-2318, 2320-2327, 2330-2332, 2334-2336, 2338-2339, 2342-2343, 2349-2352, 2356-2357, 2360-2367, 2370-2373, 2378-2383, 2389-2391, 2396, 2399-2403, 2406-2410, 2412-2467, 2473-2476, 2482-2485, 2491-2495, 2504-2507, 2510-2511, 2513, 2516-2519, 2527-2530, 2538-2543, 2551-2558, 2565-2576, 2584-2593, 2597-2609, 2618-2645, 2648-2663, 2666-2681, 2695-2696, 2730-2737, 2741, 2745, 2749-2757, 2764-2767, 2771, 2774-2792, 2799, 2805-2808, 2810, 2816, 2821, 2825, 2830, 2834, 2838, 2842, 2847, 2851, 2856, 2873, 2877, 2881, 2885-2893, 2897-2912, 2918-2923, 2929-2941, 2948-2950, 2959, 2962-2963, 2993-3033, 3041-3056, 3060, 3067-3080, 3084-3093, 3117-3133, 3142-3150, 3153-3183, 3195-3203, 3205-3213, 3218-3227, 3239-3242, 3245-3253, 3258-3261, 3270-3275, 3281, 3287, 3294-3344, 3351-3368, 3376-3391, 3399-3411, 3414-3419, 3421-3424, 3472-3543, 3548, 3559, 3579-3612, 3618-3647, 3655-3656, 3660-3665, 3668-3670, 3674-3679, 3681, 3691-3696, 3699-3720, 3725-3739, 3743-3779, 3792, 3796-3798, 3802-3846, 3859, 3862, 3866-3870, 3875-3889, 3899-3904, 3916, 3920, 3924-3932, 3937-3950, 3966, 3969-3977, 3982-3990, 4001-4003, 4007, 4019-4036, 4041-4045, 4049-4114, 4123-4144, 4148-4157, 4163-4172, 4177-4194, 4198-4202, 4206-4212, 4218-4221, 4238-4281, 4285-4294, 4309-4314, 4320-4338, 4343-4350
pandas.io.sql                         403    319    136    136    16%   32-35, 40, 44-54, 62-69, 71-76, 101-103, 132, 137-138, 165-170, 224, 255-256, 279-280, 323-332, 340-361, 375-402, 405, 408, 411, 414, 417-420, 423-437, 441-467, 470-478, 481-495, 511-547, 550-569, 572-586, 595, 599, 610-616, 620, 623-624, 627-628, 632-646, 649-651, 655, 658-661, 664, 669-670, 674-677, 680-681, 735, 738, 742-756, 759-766, 772-791, 794-808, 814-818, 821-837, 840-849, 852-853, 857-870, 873-876, 892-894, 897-903, 906, 909-910, 927-930, 936-938, 944-945
pandas.io.stata                       570    479    294    285    12%   47-49, 100-127, 129-136, 150-166, 168-175, 198, 200-203, 211, 222-239, 261, 275, 292-295, 327-343, 346-378, 382, 385-389, 394-402, 405-418, 422-432, 436-461, 463-491, 497-545, 548, 552-555, 559-561, 563-565, 569-573, 575-580, 582, 585-589, 594, 631-632, 635-662, 666-683, 686-697, 718-731, 738-747, 751-773, 777, 783, 789-792, 796-799, 803-808, 815, 822, 831-833, 837-849, 868-881, 883-885, 903-906, 908-918, 961-973, 979-983, 988-1017, 1020-1030, 1033-1059, 1065-1131, 1134-1137, 1140-1159, 1163-1185, 1188-1194
pandas.sparse                           0      0      0      0   100%   
pandas.sparse.api                       5      0      0      0   100%   
pandas.sparse.array                   267    124    102     78    45%   29-40, 55-68, 71, 76, 82, 86, 94, 129, 131, 140-143, 169-171, 181, 188-189, 209, 218, 254, 264-270, 275-277, 283-287, 290-295, 299-309, 320-329, 332-345, 356-357, 359-368, 375-378, 385-388, 403-408, 412, 416-418, 429-433, 446-449, 461-469, 474-476, 484, 503, 506, 514, 521-522
pandas.sparse.frame                   407    255    222    187    30%   58, 60-61, 63-65, 67-75, 92, 98-99, 107, 121, 127, 134-142, 148, 169-174, 188, 190-203, 220-239, 249-250, 259-262, 270, 278-281, 285-288, 293-297, 303-307, 313-319, 321-327, 334, 340, 343-344, 372-373, 377-383, 385-388, 403-409, 415-417, 422-447, 449-451, 458-476, 481, 493-504, 507, 514-518, 526-552, 556, 561-572, 578-584, 587-601, 605-608, 611-615, 620-626, 629-631, 635-648, 656, 664, 679, 698-699, 702-703, 705-707, 713-716, 733, 748-759, 761-767, 775, 796-813, 816-820
pandas.sparse.list                     64     35     16     16    36%   22-25, 29, 36-45, 74-82, 86-88, 100-101, 112, 123-128, 132-142
pandas.sparse.panel                   246    171     84     82    23%   29, 32-38, 71-99, 103-104, 107-108, 115, 126, 136, 140, 145, 153, 157, 163, 166, 185-188, 194-197, 200, 203-204, 207-210, 214-215, 237-238, 242, 244-245, 249, 255-262, 273-282, 292, 299-333, 355-376, 385-386, 388-392, 395-411, 413-421, 424, 430-447, 468-469, 485-486, 495, 499, 508-531, 535-555
pandas.sparse.series                  284    150    104     77    41%   47-57, 77-82, 114-116, 122, 130-133, 136-137, 144, 147, 160, 162, 168, 176, 180, 183-186, 196, 218, 230, 238, 249, 258-259, 266-267, 276, 280-282, 288, 299-300, 304, 312-333, 337, 343, 347, 353-370, 374, 385, 406-410, 424-425, 449-460, 468-473, 481-486, 490-491, 498-500, 520-522, 538-543, 555-557, 567-570, 577, 579-586, 592-597, 600-622, 640-644
pandas.stats                            0      0      0      0   100%   
pandas.stats.api                        3      0      0      0   100%   
pandas.stats.common                    24     18      8      6    25%   13-21, 25-30, 36-41
pandas.stats.fama_macbeth             110     79     16     16    25%   21-27, 35-45, 49, 53, 57, 61, 65, 68, 72, 76, 80, 84, 92-116, 119, 123-139, 147-158, 162, 165-188, 193, 197-199, 203, 211-226
pandas.stats.interface                 39     34     28     28     7%   86-135
pandas.stats.math                      48     38      8      8    18%   16-23, 28-31, 36-39, 43-44, 73-92, 119-130
pandas.stats.moments                  328    162    100     84    43%   182-194, 201-211, 218-219, 225-226, 230-253, 256-258, 281-283, 286, 289, 292, 329, 335, 341-353, 358, 363-365, 367-368, 374, 387-391, 397-399, 408-419, 431-437, 440, 447-449, 458-465, 468-470, 477, 480, 485, 489-490, 494-497, 510, 518, 534-537, 545, 616-619, 662-665, 728-747, 750-756, 761-768, 776-778, 787-791, 842, 875, 883-884, 893-894, 916-918, 954-955
pandas.stats.ols                      666    514    149    149    19%   50-74, 93-101, 108, 112, 116, 121, 126, 131, 136, 141, 149, 154, 159, 164, 169, 174-199, 207, 227-262, 267, 275, 280-283, 288, 293, 298, 303, 308, 313, 318, 323, 328, 333, 338, 343, 350-361, 365, 376-380, 383, 388-393, 396, 401, 433-457, 467-471, 475-508, 513-514, 519, 526-586, 589, 594, 630-635, 638-651, 665, 670, 675, 680, 685-689, 696, 700, 705, 712, 723-725, 730, 747-754, 765, 773, 778-780, 784, 788, 792, 795-824, 827-845, 848-872, 875-910, 915, 920, 925, 930, 935-976, 981-987, 991-1030, 1041, 1045-1050, 1055, 1057, 1062, 1067-1071, 1076, 1081-1121, 1126-1144, 1149-1170, 1175, 1180, 1184-1195, 1199, 1203, 1207-1216, 1219-1229, 1232, 1236-1237, 1241, 1245, 1258-1262, 1283-1306, 1309-1311, 1320-1340, 1351, 1355-1363
pandas.stats.plm                      388    291    108    108    20%   37-55, 75-114, 117, 123-161, 165, 172-199, 209-212, 222-248, 258-290, 293-296, 305, 310, 313, 319, 324, 329, 335-338, 342-349, 352-354, 359, 364-366, 370-371, 376, 384, 387-389, 393-403, 407, 418, 421-423, 427, 436, 440, 445-455, 460, 464, 466-468, 485-487, 510-526, 536, 541, 556-557, 562, 567-572, 577-615, 621-623, 627, 630-633, 637-639, 644-646, 649-655, 658-661, 668-674, 678-681, 684, 690, 735-747, 757, 762-791, 793-796, 804-814
pandas.tools                            0      0      0      0   100%   
pandas.tools.describe                   3      1      0      0    67%   17
pandas.tools.merge                    802    250    395    207    62%   111-149, 222-242, 269, 271, 317-320, 351-364, 380, 382-383, 386, 411, 414-415, 417-418, 422, 475, 486-503, 560, 575-576, 614, 617-618, 712, 714, 736-737, 775, 777-778, 829-844, 863, 872, 955, 968, 971, 974, 1023-1024, 1039, 1044-1046, 1064, 1066-1067, 1126, 1134-1135, 1179, 1184, 1194, 1200-1276, 1320-1322, 1329, 1334, 1338, 1341-1343, 1350, 1359-1361, 1370-1467
pandas.tools.pivot                    198    145     85     70    24%   70-78, 84-88, 93-101, 113-123, 129, 132, 135, 140, 143, 153-188, 193-205, 209-222, 229, 232, 236-252, 256-288, 293, 356-377, 381-394
pandas.tools.plotting                1342   1064    600    567    16%   25-26, 99-136, 164-165, 192-195, 241-282, 285-305, 307-310, 312-314, 319-325, 327-333, 340-341, 345-346, 349-355, 375-408, 410-438, 461-473, 475-504, 523-534, 537-565, 606-660, 662-665, 682-695, 712-734, 736-737, 835, 841, 843, 856-857, 867, 872-873, 879-888, 895-896, 900-903, 912-914, 920, 922-923, 926, 928-940, 944, 948-949, 964-975, 978, 985-986, 990, 999, 1002-1010, 1013, 1017-1034, 1038-1049, 1059-1075, 1077-1084, 1087-1088, 1093-1102, 1105-1116, 1120-1132, 1136-1141, 1144-1145, 1148-1159, 1162-1168, 1173, 1175, 1178-1181, 1187-1188, 1191-1207, 1211-1222, 1224-1231, 1234-1235, 1237-1245, 1249-1258, 1262-1264, 1267-1270, 1275-1282, 1285-1289, 1292-1310, 1313-1316, 1326, 1329-1338, 1341-1346, 1350, 1353-1360, 1363-1373, 1377, 1379-1405, 1408-1410, 1413-1421, 1424-1462, 1465-1479, 1482-1491, 1496-1518, 1534-1546, 1554-1563, 1566-1567, 1571-1580, 1583-1598, 1602-1608, 1611-1613, 1616-1628, 1637-1638, 1642-1643, 1648-1666, 1767-1776, 1779, 1788-1789, 1799-1801, 1804-1810, 1828-1832, 1879-1925, 1953-1991, 1998-2026, 2031-2036, 2060-2063, 2067-2085, 2120-2133, 2137-2178, 2182-2186, 2220-2236, 2240-2261, 2306-2330, 2336-2378, 2384-2402, 2406-2411, 2414-2417, 2421-2435, 2518-2594, 2598-2603, 2607-2612
pandas.tools.tile                     118    103     60     58    10%   68-101, 136-141, 147-196, 201-220, 224-250, 256-260
pandas.tools.util                      19      9      4      2    52%   6-8, 24-32
pandas.tseries                          0      0      0      0   100%   
pandas.tseries.api                      7      0      0      0   100%   
pandas.tseries.converter              646    541    231    230    12%   35-37, 41-48, 55-62, 67-71, 76, 86-98, 108-118, 120, 124-130, 147, 156-183, 186, 196-201, 210-213, 224-230, 233, 236, 238, 245-257, 260, 263, 271-272, 275, 279, 281-282, 286-341, 344, 350-373, 377-394, 407-420, 422, 436, 438, 449-454, 458-673, 675-681, 685-743, 745-751, 755-789, 791-797, 801-817, 821-827, 829-832, 858, 860-867, 872-879, 884-889, 891-898, 906-909, 911-913, 937-938, 940-947, 952-953, 956-962, 970-976, 979-983
pandas.tseries.frequencies            521    116    261    117    70%   49, 53-55, 57, 61-62, 64, 68-69, 71, 90, 96, 98-99, 116, 249-250, 273-275, 319, 358-359, 365, 388-390, 393-395, 402-403, 604, 622-623, 625-628, 652, 661, 723, 738, 745-749, 752-764, 766, 768-769, 772, 780, 795, 801, 811-812, 817, 823-825, 831-832, 843-845, 852-853, 862-874, 881, 903, 911, 914, 918, 920, 922, 924, 928-932, 960-967, 969, 971, 973, 975, 979, 989-993, 1002-1004
pandas.tseries.index                 1085    364    506    272    60%   45, 64, 163-164, 177, 186, 188, 192, 201-208, 213, 218, 220, 224-226, 246, 253, 266, 269, 273, 297, 311, 325, 333-334, 337, 341, 349, 365-366, 387, 397, 400, 428, 436-439, 445, 447, 467, 475, 481, 485, 503-509, 511-517, 543-544, 547-559, 568-573, 586-587, 592, 604-607, 612-619, 631-637, 644-645, 648, 673-674, 676-679, 682, 689-696, 699-702, 705-706, 712, 720, 723, 763-764, 771, 786, 794, 796-798, 801, 810, 826-831, 833-836, 839-842, 858-860, 862-868, 871, 925, 948-949, 952-953, 993, 1010, 1021-1022, 1025, 1029-1030, 1034-1042, 1058, 1064, 1072-1076, 1080, 1083, 1089, 1097-1100, 1131-1139, 1152, 1159-1169, 1173, 1176-1182, 1184, 1187, 1191-1203, 1210, 1214-1236, 1240, 1253, 1260-1273, 1280, 1303-1305, 1308-1309, 1311, 1318-1320, 1343-1355, 1358-1364, 1371, 1374, 1377, 1381, 1393, 1405, 1416-1418, 1432-1433, 1463, 1470, 1497, 1501-1502, 1525, 1532-1533, 1537, 1548-1553, 1556, 1562, 1582-1594, 1604-1605, 1608-1609, 1611-1612, 1622-1630, 1649, 1673-1687, 1708-1737, 1746-1750, 1756-1760, 1776-1779, 1929-1933, 1944, 1950-1957, 1971, 1984-1985, 2000, 2009-2010, 2015, 2018, 2022, 2031-2035
pandas.tseries.offsets               1028    310    394    248    61%   100, 107, 112-115, 121, 175, 184-186, 189, 206, 214, 243, 274-275, 278, 290, 351-357, 359, 361-362, 364-379, 389, 401, 413, 417-421, 500, 508, 520, 548, 582, 633, 635, 672, 687-689, 691, 693-703, 712-715, 719-723, 774, 777-778, 794-795, 803, 806, 809, 814, 825, 829, 866, 870, 873, 881, 884-886, 905, 931, 934, 936, 969, 1024, 1099, 1103, 1122-1125, 1192, 1211, 1217, 1224-1226, 1231-1234, 1242-1243, 1248, 1271, 1274, 1279-1280, 1289-1290, 1292-1295, 1348, 1352-1357, 1361-1362, 1365, 1368, 1373-1380, 1383-1431, 1434-1437, 1440-1442, 1445-1455, 1458-1459, 1463-1464, 1467, 1470-1473, 1476, 1482-1493, 1501, 1555-1560, 1566, 1569-1605, 1608-1615, 1618-1627, 1630-1642, 1646-1647, 1652, 1664, 1680-1688, 1692-1694, 1699, 1715, 1727, 1731-1734, 1743-1759, 1768, 1861-1863, 1872-1876, 1882, 1893, 1933
pandas.tseries.period                 669    378    252    211    36%   30-31, 38-39, 83, 85, 89-90, 101, 110-119, 135-138, 142, 145, 148, 151-152, 157-163, 169-172, 204, 213, 218, 238-245, 266, 428-429, 437-451, 453, 457, 464-467, 471-475, 484-495, 497-499, 579, 585, 599, 602-603, 608, 615-666, 668-670, 673-677, 681, 693-700, 703, 712, 715-720, 723-724, 735-740, 746-749, 753, 764, 775, 794-798, 800, 803-804, 806, 810, 816-819, 825, 842-849, 852, 868-871, 874, 877, 883, 890-922, 932-942, 944-945, 951-973, 976, 979-999, 1002-1003, 1009-1015, 1017, 1020-1023, 1028-1029, 1031, 1034, 1036-1037, 1046-1050, 1057, 1062, 1066-1067, 1070-1071, 1096, 1098-1101, 1103, 1106-1107, 1110-1112, 1118-1123, 1137-1150, 1153-1162, 1166-1169, 1174-1175, 1180, 1198, 1203-1204, 1224-1249, 1252-1260, 1264, 1266-1275, 1280-1287, 1291-1298, 1309, 1314
pandas.tseries.resample               231     73    101     69    57%   49-52, 86, 91-94, 112, 123, 135, 151-152, 161, 163, 165, 168, 180, 182-187, 192-196, 198, 205-206, 228-231, 234, 240-243, 249, 252-255, 263-264, 282-289, 296, 299, 309, 318-319, 322, 325, 329, 348-356, 362, 381-399
pandas.tseries.timedeltas             118    102     62     62     9%   32-62, 75-82, 88-142, 153-202
pandas.tseries.tools                  321    140    152     93    51%   37, 40-41, 47, 54-55, 64-65, 93-99, 115, 128, 130-135, 142-161, 167, 169-172, 176, 182, 242-248, 264-265, 275-276, 280-292, 303, 306-307, 335-336, 338-339, 344-361, 397, 416, 421-441, 447-452, 457, 472, 504, 507, 511, 514-527, 529, 531, 533, 542, 548-563, 571, 578-585
pandas.util                             0      0      0      0   100%   
pandas.util.decorators                 63     26     12      9    53%   11-13, 60, 70-72, 111, 117-124, 162-183
pandas.util.print_versions             71     62     45     45     8%   12-53, 57-130, 135-147
pandas.util.terminal                   70     63     16     16     8%   28-42, 46-67, 74-88, 92-117
pandas.version                          2      0      0      0   100%   
```

The basic idea is:
- use vbench to gather Benchmarks
- make a mapping between a Benchmark and a nosetests test case
- output all the new test files
- run `nosetests --with-coverage --cover-branches` on them

I'll be submitting PRs to vbench and pandas when this is more ready, but hopefully this can be a useful starting point for improving vbench coverage.
