TST: code coverage enhancements

[jreback]

We now have codecov here running on every build. You can get the browser extension to look at coverage in github

• make sure extension is working
• doc on how to use this / look at coverage
• put additional excludes in .coveragerc to exclude things we don't care (Omit tests folders from coverage #12721)
• increase coverage!

[gliptak]

Where do you see this documented at? Thanks

[gliptak]

#12721

[jbrockmendel]

FYI, coverage report on cython code (as of e1dabf3):

Name                                      Stmts   Miss  Cover   Missing
-----------------------------------------------------------------------

pandas/_libs/algos.pxd                        5      1    80%   6
pandas/_libs/algos.pyx                      196     20    90%   60, 116, 224-233, 236-244, 246-249, 259-261
pandas/_libs/algos_common_helper.pxi       1884   1228    35%   25-49, 63-67, 70, 84-91, 97, 112-114, 123, 128-132, 138, 148-150, 159, 165, 167, 211-212, 226-230, 233, 241, 250, 261, 276-314, 323, 329, 331, 350, 375, 382-384, 407, 423-548, 557, 562-566, 574, 609-712, 721, 726-730, 738, 748-845, 859-863, 908-946, 955, 960-964, 972, 1007-1008, 1022-1026, 1072-1110, 1119, 1124-1128, 1136, 1146, 1164, 1171, 1178-1180, 1203, 1219-1641, 1656, 1658, 1704-1742, 1751, 1756-1760, 1768, 1803-1804, 1819, 1821, 1868-1906, 1915, 1920-1924, 1932, 1942, 1985-1987, 1999, 2015-2039, 2053-2057, 2060, 2067, 2071, 2102-2202, 2216-2220, 2223, 2235, 2241-2243, 2266-2340, 2358, 2362, 2365, 2372-2374, 2383-2385, 2397, 2413-2815, 2824-2830, 2835, 2844, 2858-2872, 2880-2895, 2898-2904, 2909, 2915-3100, 3112, 3120, 3125-3160, 3171, 3182, 3191-3200, 3209, 3216-3218, 3225-3227, 3236
pandas/_libs/algos_rank_helper.pxi          502    135    73%   16-17, 41, 67-68, 100, 102-103, 110, 115-116, 150, 170-173, 212, 215-216, 223, 234-235, 326-327, 426, 437-438, 469-471, 476, 495-510, 513, 518-617, 658, 676-677, 695-696, 703, 708-709, 755-757, 790-805
pandas/_libs/algos_take_helper.pxi         2056   1268    38%   14-17, 41-44, 68, 76-79, 99-108, 123-126, 155-164, 169-170, 179-182, 191, 207-210, 229, 237, 245-277, 301-304, 320-339, 383-386, 400-442, 451, 467-470, 485-537, 561-564, 588, 596-599, 619-628, 643-646, 675-684, 689-690, 699-702, 711, 727-730, 749, 757, 765-1379, 1423-1426, 1440-1577, 1601-1604, 1628, 1636-1639, 1659-1668, 1683-1686, 1715-1724, 1729-1730, 1739-1742, 1751, 1767-1770, 1789, 1797, 1805-2617, 2641-2644, 2668, 2676-2679, 2702-2703, 2723-2726, 2755-2764, 2769-2770, 2779-2782, 2791, 2807-2810, 2829, 2837, 2845-3137, 3161-3164, 3180-3199, 3243-3246, 3260-3302, 3311, 3327-3330, 3345-3397, 3421-3424, 3448, 3456-3459, 3482-3483, 3503-3506, 3535-3544, 3549-3550, 3559-3562, 3587-3590, 3609, 3617, 3625-3628, 3642-3643, 3647, 3654-3657, 3681-3684, 3700-3719, 3763-3766, 3780-3822, 3831, 3847-3850, 3865-3888, 3914-3917, 3941-3944, 3976-3979, 4002-4003, 4023-4026, 4055-4064, 4079-4082, 4091, 4107-4110, 4129, 4145-4437, 4461-4464, 4496-4499, 4543-4546, 4575-4584, 4599-4602, 4627-4630, 4649, 4665-4668, 4694-4697, 4721-4724, 4756-4759, 4803-4806, 4859-4862, 4887-4890, 4909, 4925-4955, 4969-4983, 4997
pandas/_libs/groupby.pyx                    137     49    64%   60, 96-128, 155, 191-205, 208-224, 264
pandas/_libs/groupby_helper.pxi             830    314    62%   9-18, 31, 81, 94, 141, 151, 190, 245, 263, 266, 274, 291-414, 424, 437, 463-518, 531, 536, 539, 547, 568, 582, 593, 607, 614, 628, 639, 654, 661-754, 768, 779, 800, 814, 851, 864, 878, 914, 927, 941, 977, 999, 1013, 1035, 1048-1245, 1258, 1308, 1321, 1350, 1371, 1393, 1399, 1409, 1431, 1436, 1450, 1486, 1507, 1517, 1538, 1553, 1573-1575
pandas/_libs/hashing.pyx                    102     20    80%   71, 76, 100, 103-113, 123, 140-149
pandas/_libs/hashtable.pyx                   85     26    69%   63-67, 72, 75-76, 92, 101-111, 116, 119-120, 127-129, 136-143
pandas/_libs/hashtable_class_helper.pxi     904    334    63%   21, 30, 43-56, 68, 82, 86, 93, 98, 103-106, 110, 116-136, 140, 147-152, 157-160, 164, 170-186, 190, 197, 202, 207, 210, 214, 220, 224, 229, 239-309, 316, 319, 331, 334, 340, 355, 360, 363, 368, 373, 379, 387-414, 428, 447-455, 498-541, 568-569, 576, 581, 584, 589, 594, 600, 608-635, 649, 668-676, 694-695, 711, 719-759, 779-780, 787, 792, 795, 800, 805, 811, 819, 829-832, 846, 860, 879-887, 930, 973, 1003-1005, 1010, 1015-1077, 1112-1118, 1135, 1151, 1179, 1235-1239, 1243, 1248, 1251, 1259, 1265, 1275, 1283, 1289-1291, 1307, 1329, 1352-1354, 1389, 1394, 1399, 1402-1408, 1421-1422, 1424-1428, 1435-1448, 1464-1468, 1487, 1509, 1514-1520
pandas/_libs/hashtable_func_helper.pxi      355     41    88%   14, 41, 73, 84, 121, 172, 199, 231, 242, 279-330, 356, 384, 394, 428, 477, 504, 536, 547, 584, 643, 680, 717
pandas/_libs/index.pyx                      354     88    75%   36, 51, 57, 67-75, 81, 86, 104, 120, 126, 130, 132, 146, 158, 165, 194, 200-205, 211, 218, 226, 232, 250, 253-259, 264, 267, 283, 292, 296, 363, 369, 377, 380, 383-386, 403, 406-418, 421, 424, 440-444, 463-465, 470, 473, 477, 479, 484, 486, 494, 497, 514, 544, 550, 566-582, 588, 595, 599
pandas/_libs/index_class_helper.pxi         113     44    61%   14, 17, 21, 25, 27, 30, 63, 66, 70, 74, 76, 79, 81-82, 85-120, 123, 127, 131, 133, 139, 142, 177, 180, 184, 188
pandas/_libs/interval.pyx                   120     36    70%   21, 25, 29, 33, 37, 40-45, 88-91, 95, 97, 102, 105, 111, 132-136, 142-143, 147, 160, 167, 172, 179-184, 189-197
pandas/_libs/intervaltree.pxi              1541   1137    26%   11-46, 62, 78, 86, 93, 103, 117, 130, 153, 173-184, 190, 205-392, 408-409, 420-478, 497-902, 918-919, 948, 972, 988, 1022, 1028-1072, 1088-1089, 1118, 1142, 1158, 1183, 1198-1242, 1258-1259, 1288, 1312, 1328, 1368-1412, 1428-1429, 1458, 1482, 1498, 1523, 1532, 1538-1752, 1768-1769, 1780-1838, 1857-2262, 2278-2279, 2290-2348, 2367-2432, 2448-2449, 2460-2518, 2537-2602, 2618-2619, 2630-2688, 2707-2772, 2788-2789, 2800-2858, 2873-2924
pandas/_libs/join.pyx                       146     11    92%   79, 249-256, 259-261, 264-267
pandas/_libs/join_func_helper.pxi          3690   3105    16%   15-1267, 1326-1332, 1345-1346, 1360, 1369-1372, 1384-1386, 1391-1397, 1439-1801, 1814-1815, 1829, 1838-1841, 1853-1855, 1860-1979, 1992-1993, 2007, 2016-2019, 2031-2033, 2038-2157, 2170-2171, 2185, 2194-2197, 2209-2211, 2216-2335, 2348-2349, 2363, 2372-2375, 2387-2389, 2394-2513, 2526-2527, 2541, 2550-2553, 2565-2567, 2572-2691, 2704-2705, 2719, 2728-2731, 2743-2745, 2750-2869, 2882-2883, 2897, 2906-2909, 2921-2923, 2928-3047, 3060-3061, 3075, 3084-3087, 3099-3101, 3106-3225, 3238-3239, 3253, 3262-3265, 3277-3279, 3284-3403, 3416-3417, 3431, 3440-3443, 3455-3457, 3462-5364, 5375-5376, 5388, 5396-5398, 5407-5409, 5414-5509, 5520-5521, 5533, 5541-5543, 5552-5554, 5559-5654, 5665-5666, 5678, 5686-5688, 5697-5699, 5704-5799, 5810-5811, 5823, 5831-5833, 5842-5844, 5849-5944, 5955-5956, 5968, 5976-5978, 5987-5989, 5994-6089, 6100-6101, 6113, 6121-6123, 6132-6134, 6139-6234, 6245-6246, 6258, 6266-6268, 6277-6279, 6284-6379, 6429-6433, 6484-6488, 6520-6524, 6535-6536, 6548, 6556-6558, 6567-6569, 6574-6669, 6680-6681, 6693, 6701-6703, 6712-6714, 6719-6807
pandas/_libs/join_helper.pxi               1596    601    62%   18-168, 188, 198, 203-213, 227, 240, 245-255, 262, 295, 362, 396-640, 664-665, 672-677, 684, 689-690, 722-728, 739-744, 751, 759-763, 774, 806-807, 820, 855-861, 903-909, 924, 954, 996, 1018, 1051, 1118, 1152-1396, 1420-1421, 1428-1433, 1440, 1445-1446, 1478-1484, 1495-1500, 1507, 1515-1519, 1530, 1562-1563, 1576, 1680, 1774, 1908, 1926-1928, 1940-1941, 1954, 1989-2000, 2037-2051, 2058, 2088, 2130, 2152, 2167, 2169, 2173-2174, 2185, 2191-2193, 2214-2217, 2219-2222, 2226-2232, 2252, 2258-2260
pandas/_libs/lib.pyx                        976    224    77%   32, 93-111, 134, 149, 153, 157, 159, 164, 171, 178, 302, 321-336, 360, 387, 427, 490, 561-579, 672, 687-688, 717, 746-747, 762, 792-808, 819-820, 833, 863, 876, 942, 956-970, 1048, 1056, 1066-1074, 1135, 1139, 1142, 1180-1194, 1202-1216, 1244-1250, 1255-1260, 1295-1304, 1319-1354, 1396, 1456-1499, 1501-1514, 1520, 1531, 1551, 1609, 1634, 1659-1660, 1667, 1682, 1705-1712, 1716, 1723, 1733, 1736, 1741, 1748, 1753, 1763, 1767, 1779, 1782, 1789, 1826, 1833, 1836-1839
pandas/_libs/parsers.pyx                   1265    211    83%   100-101, 305-313, 316-334, 336-379, 404, 430, 443, 466-467, 505, 577-580, 590, 596, 607-610, 634, 643, 681-690, 695, 701-703, 716, 725, 732-735, 785, 787-788, 886-889, 912, 934, 950, 963, 977, 1015-1018, 1021, 1026, 1029-1032, 1142, 1150, 1211, 1265-1266, 1287, 1295, 1298, 1302, 1312, 1333, 1340, 1344, 1347, 1374, 1381, 1383, 1395-1406, 1430, 1440, 1493, 1546, 1579-1581, 1588, 1605, 1664, 1668, 1676, 1693, 1712, 1752, 1822, 1847, 1857, 1886-1891, 1895, 1923, 1962-2026, 2053, 2097, 2117, 2122, 2139, 2145, 2165, 2187, 2198, 2202, 2276, 2298-2308, 2342, 2369
pandas/_libs/period.pyx                     646    144    78%   180, 190, 198, 234, 240, 254, 261, 283, 289, 291, 293, 298, 301, 303, 305, 307, 309, 311-314, 320-324, 329, 339, 357, 361-363, 365, 367, 369-370, 381, 388-390, 401, 415, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436, 438, 487, 493, 505, 513, 526, 536, 542-554, 561, 566-573, 577, 589, 600-601, 610-611, 628, 633-639, 645-646, 668-669, 674, 705, 724, 727, 748-750, 765-767, 790-792, 823, 827, 833, 866, 871, 876, 881, 886, 891, 896, 901, 905, 910, 914, 919, 924, 929, 934-938, 946, 950, 953, 958, 970-974, 978, 1141-1142, 1201, 1206-1208, 1217
pandas/_libs/properties.pyx                  40     17    58%   14-16, 22, 27, 45-64, 67, 71
pandas/_libs/reshape.pyx                      5      0   100%
pandas/_libs/reshape_helper.pxi             131     66    50%   14-254, 302, 350, 398, 446, 494
pandas/_libs/sparse.pyx                     409     75    82%   30-60, 67, 71, 76, 113, 125, 128, 131, 135, 170, 186, 210, 240, 261, 271-279, 330-338, 356, 360, 368, 371, 386, 391, 402-404, 416, 419, 436, 519, 538, 564, 594-637, 642, 658-661, 681, 729, 741, 806-818
pandas/_libs/sparse_op_helper.pxi          3017    708    77%   11, 16, 22, 25, 30-33, 38, 44, 49, 55, 58, 64, 77, 178, 236, 248-258, 359, 417, 429-439, 540, 598, 610-620, 721, 779, 791-801, 902, 960, 972-982, 1083, 1141, 1153-1525, 1626, 1684, 1696-1706, 1751-1759, 1807, 1865, 1877-1887, 1988, 2046, 2058-2068, 2169, 2227, 2239-2249, 2350, 2408, 2420-2430, 2475-2483, 2531, 2589, 2601-2611, 2712, 2770, 2782-2792, 2893, 2951, 2963-2973, 3018-3026, 3074, 3102-3104, 3132, 3144-3154, 3199-3207, 3255, 3283-3285, 3313, 3325-3335, 3380-3388, 3436, 3464-3466, 3494, 3506-3516, 3561-3569, 3617, 3645-3647, 3675, 3687-3697, 3742-3750, 3798, 3826-3828, 3856, 3868-3878, 3923-3931, 3979, 4007-4009, 4037, 4049-4059, 4104-4112, 4160, 4188-4190, 4218, 4230-4240, 4285-4293, 4341, 4369-4371, 4399, 4411-4421, 4466-4474, 4522, 4550-4552, 4580, 4592-4602, 4647-4655, 4703, 4731-4733, 4761, 4773-4783, 4828-4836, 4884, 4912-4914, 4942, 4954-4964, 5009-5017, 5065, 5093-5095, 5123, 5135-5326, 5359-5367, 5427, 5461-5463, 5485, 5497-5688, 5721-5729, 5789, 5823-5825, 5847, 5859-5864
pandas/_libs/src/inference.pyx              879    203    77%   1-18, 22, 26, 30, 34, 38, 42, 48-115, 138, 171, 178, 192, 196, 200, 204, 215, 337, 350, 382, 391-392, 399, 436-437, 457, 524, 534, 542, 550, 562, 574, 584, 587, 590, 593, 604-608, 614, 619, 626-630, 643, 654, 657, 660-667, 670, 673, 676, 684, 687, 691, 703, 706, 710, 721, 724, 728, 739, 742, 746, 753, 756, 760, 772, 775, 779, 791-816, 823-833, 840, 846, 849, 853, 864, 868, 877, 887, 907, 910, 914, 925, 929, 940, 944, 956, 960, 967, 971, 978, 981, 985, 992, 996, 1010-1018, 1116, 1118-1119, 1132, 1160, 1168, 1178-1185, 1231-1232, 1234-1235, 1246-1248, 1350-1353, 1357-1358, 1360-1361, 1365, 1367-1368, 1370-1375, 1380, 1384-1385, 1408-1409, 1457, 1487, 1500, 1539, 1581, 1598, 1629, 1651, 1685, 1691, 1694-1698, 1710, 1713-1719
pandas/_libs/src/reduce.pyx                 356     51    86%   2-8, 29-30, 54, 74, 76-77, 81, 123, 173-176, 200, 204-205, 209, 211, 214, 218, 301-304, 313, 324, 328-329, 335, 338, 342, 418, 426, 440, 444, 458, 461, 468, 471, 478-482, 541-549, 567, 570, 588, 601
pandas/_libs/src/skiplist.pyx                75     18    76%   14, 18-33, 39-50, 55-61, 75, 114, 132
pandas/_libs/testing.pyx                    125     17    86%   28, 31, 34, 37, 40, 46, 62-65, 124-126, 130, 159, 181-183, 194
pandas/_libs/tslib.pyx                     2168    278    87%   98, 113, 135-136, 157, 215, 343, 481-483, 646-647, 730, 746-748, 891, 894, 899, 965, 992, 997, 1014, 1018, 1023-1025, 1037, 1083, 1117-1120, 1125, 1141-1142, 1157-1159, 1172, 1178, 1204-1226, 1235, 1245, 1259, 1263, 1276, 1294-1295, 1298, 1339, 1348, 1356, 1376, 1380, 1386, 1397-1409, 1416, 1430, 1436-1438, 1440, 1446-1450, 1457-1461, 1464, 1476, 1484, 1488-1491, 1494, 1497-1503, 1506-1517, 1522, 1592, 1631, 1669-1670, 1751-1753, 1758, 1779-1780, 1795-1796, 1816-1819, 1839, 1886, 1976, 1997, 2094, 2096-2115, 2147, 2152-2155, 2192-2196, 2254-2255, 2304-2308, 2330, 2346, 2349, 2365-2366, 2383-2398, 2404, 2421-2429, 2443, 2487, 2509, 2516, 2599, 2632, 2634, 2640, 2681, 2691, 2869-2870, 2878, 2911, 2981, 2985, 3014-3026, 3047-3051, 3062, 3088, 3111-3112, 3120, 3128, 3130, 3150, 3152, 3167, 3213, 3224, 3241, 3282-3292, 3306, 3311, 3325, 3341, 3346, 3359, 3365, 3391, 3404, 3413-3417, 3424, 3535-3538, 3554-3555, 3561-3563, 3606, 3614, 3667-3668, 3678, 3689-3690, 3697-3698, 3712, 3719-3720, 3727-3728, 3735, 3787, 3794, 3797, 3814-3817, 3822, 3856-3857, 3874-3875, 3899-3900, 3917
pandas/_libs/tslibs/__init__.py               0      0   100%
pandas/_libs/tslibs/fields.pyx              410     26    94%   45-50, 99-100, 107, 112, 164-165, 193-194, 231-232, 261-262, 300-301, 330-331, 365, 571-574, 578
pandas/_libs/tslibs/frequencies.pyx          58      3    95%   18, 76, 196
pandas/_libs/tslibs/parsing.pyx             362     55    85%   33-34, 69, 94, 104, 151, 179, 183, 201, 257, 308, 346, 349-362, 364, 372, 385, 398, 417, 420-422, 429, 440-441, 448, 459, 465, 484, 510, 544, 591, 600-610, 624, 628-631, 668, 681
pandas/_libs/tslibs/strptime.pyx            332     99    70%   18-25, 55, 63, 103-104, 114-123, 162-163, 178-180, 185-191, 214-222, 224, 226-229, 232, 235, 238, 247-254, 256, 258, 265-269, 275-277, 285-300, 323-327, 342-351, 423, 427-432, 479-483, 500, 514, 520, 539, 589, 630, 651
pandas/_libs/tslibs/timedeltas.pyx          138     16    88%   59, 88, 103, 154-155, 183-185, 190-191, 204, 222-223, 247, 272, 285
pandas/_libs/tslibs/timezones.pyx           145     24    83%   19, 41, 45, 49, 54, 58, 76, 96, 109, 113, 126, 146, 162, 169, 177-178, 184, 199, 217, 239, 267-269, 295
pandas/_libs/window.pyx                     778     54    93%   31-32, 77, 79, 86, 119, 150-152, 188-190, 233, 251, 297, 305, 420, 431, 440, 522, 541, 553, 638, 658, 672, 784, 805, 818, 906, 931, 945, 1053, 1069, 1087, 1103, 1113, 1120, 1125, 1130, 1135, 1141, 1151, 1193, 1292, 1311, 1412, 1429, 1476, 1498, 1522, 1563, 1632-1633, 1692-1694

[mroeschke]

It appears the issue has been taken as far as it can go. If we have more specific coverage issues we can create a new issue. Closing