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TypeError: unhashable type: 'list' #96

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bjornjorgensen opened this issue Feb 10, 2021 · 15 comments
Closed

TypeError: unhashable type: 'list' #96

bjornjorgensen opened this issue Feb 10, 2021 · 15 comments
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bug Something isn't working

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@bjornjorgensen
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bjornjorgensen commented Feb 10, 2021

Hi just upgraded to 0.2.8 now and then I starting to get this error


TypeError                                 Traceback (most recent call last)
<ipython-input-4-7fe66a702bc1> in <module>
      1 from pandasgui import show
----> 2 show(df)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in show(settings, *args, **kwargs)
    362     kwargs = {**kwargs, **dataframes}
    363 
--> 364     pandas_gui = PandasGui(settings=settings, **kwargs)
    365     pandas_gui.caller_stack = inspect.currentframe().f_back
    366 

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in __init__(self, settings, **kwargs)
     74         # Adds DataFrames listed in kwargs to data store.
     75         for df_name, df in kwargs.items():
---> 76             self.store.add_dataframe(df, df_name)
     77 
     78         # Default to first item

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\store.py in add_dataframe(self, pgdf, name)
    413         pgdf.store = self
    414 
--> 415         pgdf.df = clean_dataframe(pgdf.df, name)
    416 
    417         # Add it to store and create widgets

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in clean_dataframe(df, name)
    264     converted_names = []
    265     dtypes_old = df.dtypes
--> 266     df = parse_dates(df)
    267     dtypes_new = df.dtypes
    268     for col_name in [df.columns[ix] for ix in range(len(dtypes_new)) if dtypes_old[ix] != dtypes_new[ix]]:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in parse_dates(df)
    229 def parse_dates(df: Union[pd.DataFrame, pd.Series]):
    230     if type(df) == pd.DataFrame:
--> 231         return df.apply(
    232             lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col,
    233             axis=0)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
   7763             kwds=kwds,
   7764         )
-> 7765         return op.get_result()
   7766 
   7767     def applymap(self, func, na_action: Optional[str] = None) -> DataFrame:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in get_result(self)
    183             return self.apply_raw()
    184 
--> 185         return self.apply_standard()
    186 
    187     def apply_empty_result(self):

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_standard(self)
    274 
    275     def apply_standard(self):
--> 276         results, res_index = self.apply_series_generator()
    277 
    278         # wrap results

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_series_generator(self)
    288             for i, v in enumerate(series_gen):
    289                 # ignore SettingWithCopy here in case the user mutates
--> 290                 results[i] = self.f(v)
    291                 if isinstance(results[i], ABCSeries):
    292                     # If we have a view on v, we need to make a copy because

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in <lambda>(col)
    230     if type(df) == pd.DataFrame:
    231         return df.apply(
--> 232             lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col,
    233             axis=0)
    234     elif type(df) == pd.Series:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
    799                 result = result.tz_localize(tz)
    800     elif isinstance(arg, ABCSeries):
--> 801         cache_array = _maybe_cache(arg, format, cache, convert_listlike)
    802         if not cache_array.empty:
    803             result = arg.map(cache_array)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in _maybe_cache(arg, format, cache, convert_listlike)
    171     if cache:
    172         # Perform a quicker unique check
--> 173         if not should_cache(arg):
    174             return cache_array
    175 

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in should_cache(arg, unique_share, check_count)
    135     assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
    136 
--> 137     unique_elements = set(islice(arg, check_count))
    138     if len(unique_elements) > check_count * unique_share:
    139         do_caching = False

TypeError: unhashable type: 'list'

Environment
OS: (eg. Windows 10)
Python: (eg. 3.9)

@bjornjorgensen bjornjorgensen added the bug Something isn't working label Feb 10, 2021
@bjornjorgensen
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bjornjorgensen commented Feb 10, 2021

I also get this error now.

PandasGUI WARNING — pandasgui.utility — In df, converted column names to string: 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, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2120, 2121, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2139, 2140, 2141, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2173, 2174, 2175, 2176, 2177, 2178, 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2237, 2238, 2239, 2240, 2241, 2242, 2243, 2244, 2245, 2246, 2247, 2248, 2249, 2250, 2251, 2252, 2253, 2254, 2255, 2256, 2257, 2258, 2259, 2260, 2261, 2262, 2263, 2264, 2265, 2266, 2267, 2268, 2269, 2270, 2271, 2272, 2273, 2274, 2275, 2276, 2277, 2278, 2279, 2280, 2281, 2282, 2283, 2284, 2285, 2286, 2287, 2288, 2289, 2290, 2291, 2292, 2293, 2294, 2295, 2296, 2297, 2298, 2299, 2300, 2301, 2302, 2303, 2304, 2305, 2306, 2307, 2308, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325, 2326, 2327, 2328, 2329, 2330, 2331, 2332, 2333, 2334, 2335, 2336, 2337, 2338, 2339, 2340, 2341, 2342, 2343, 2344, 2345, 2346, 2347, 2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357, 2358, 2359, 2360, 2361, 2362, 2363, 2364, 2365, 2366, 2367, 2368, 2369, 2370, 2371, 2372, 2373, 2374, 2375, 2376, 2377, 2378, 2379, 2380, 2381, 2382, 2383, 2384, 2385, 2386, 2387, 2388, 2389, 2390, 2391, 2392, 2393, 2394, 2395, 2396, 2397, 2398, 2399, 2400, 2401, 2402, 2403, 2404, 2405, 2406, 2407, 2408, 2409, 2410, 2411, 2412, 2413, 2414, 2415, 2416, 2417, 2418, 2419, 2420, 2421, 2422, 2423, 2424, 2425, 2426, 2427, 2428, 2429, 2430, 2431, 2432, 2433, 2434, 2435, 2436, 2437, 2438, 2439, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453, 2454, 2455, 2456, 2457, 2458, 2459, 2460, 2461, 2462, 2463, 2464, 2465, 2466, 2467, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478, 2479, 2480, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504, 2505, 2506, 2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2515, 2516, 2517, 2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539, 2540, 2541, 2542, 2543, 2544, 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2567, 2568, 2569, 2570, 2571, 2572, 2573, 2574, 2575, 2576, 2577, 2578, 2579, 2580, 2581, 2582, 2583, 2584, 2585, 2586, 2587, 2588, 2589, 2590, 2591, 2592, 2593, 2594, 2595, 2596, 2597, 2598, 2599, 2600, 2601, 2602, 2603, 2604, 2605, 2606, 2607, 2608, 2609, 2610, 2611, 2612, 2613, 2614, 2615, 2616, 2617, 2618, 2619, 2620, 2621, 2622, 2623, 2624, 2625, 2626, 2627, 2628, 2629, 2630, 2631, 2632, 2633, 2634, 2635, 2636, 2637, 2638, 2639, 2640, 2641, 2642, 2643, 2644, 2645, 2646, 2647, 2648, 2649, 2650, 2651, 2652, 2653, 2654, 2655, 2656, 2657, 2658, 2659, 2660, 2661, 2662, 2663, 2664, 2665, 2666, 2667, 2668, 2669, 2670, 2671, 2672, 2673, 2674, 2675, 2676, 2677, 2678, 2679, 2680, 2681, 2682, 2683, 2684, 2685, 2686, 2687, 2688, 2689, 2690, 2691, 2692, 2693, 2694, 2695, 2696, 2697, 2698, 2699, 2700, 2701, 2702, 2703, 2704, 2705, 2706, 2707, 2708, 2709, 2710, 2711, 2712, 2713, 2714, 2715, 2716, 2717, 2718, 2719, 2720, 2721, 2722, 2723, 2724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804, 2805, 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821, 2822, 2823, 2824, 2825, 2826, 2827, 2828, 2829, 2830, 2831, 2832, 2833, 2834, 2835, 2836, 2837, 2838, 2839, 2840, 2841, 2842, 2843, 2844, 2845, 2846, 2847, 2848, 2849, 2850, 2851, 2852, 2853, 2854, 2855, 2856, 2857, 2858, 2859, 2860, 2861, 2862, 2863, 2864, 2865, 2866, 2867, 2868, 2869, 2870, 2871, 2872, 2873, 2874, 2875, 2876, 2877, 2878, 2879, 2880, 2881, 2882, 2883, 2884, 2885, 2886, 2887, 2888, 2889, 2890, 2891, 2892, 2893, 2894, 2895, 2896, 2897, 2898, 2899, 2900, 2901, 2902, 2903, 2904, 2905, 2906, 2907, 2908, 2909, 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 2925, 2926, 2927, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2938, 2939, 2940, 2941, 2942, 2943, 2944, 2945, 2946, 2947, 2948, 2949, 2950, 2951, 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962, 2963, 2964, 2965, 2966, 2967, 2968, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2982, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3042, 3043, 3044, 3045, 3046, 3047, 3048, 3049, 3050, 3051, 3052, 3053, 3054, 3055, 3056, 3057, 3058, 3059, 3060, 3061, 3062, 3063, 3064, 3065, 3066, 3067, 3068, 3069, 3070, 3071, 3072, 3073, 3074, 3075, 3076, 3077, 3078, 3079, 3080, 3081, 3082, 3083, 3084, 3085, 3086, 3087, 3088, 3089, 3090, 3091, 3092, 3093, 3094, 3095, 3096, 3097, 3098, 3099, 3100, 3101, 3102, 3103, 3104, 3105, 3106, 3107, 3108, 3109, 3110, 3111, 3112, 3113, 3114, 3115, 3116, 3117, 3118, 3119, 3120, 3121, 3122, 3123, 3124, 3125, 3126, 3127, 3128, 3129, 3130, 3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139, 3140, 3141, 3142, 3143, 3144, 3145, 3146, 3147, 3148, 3149, 3150, 3151, 3152, 3153, 3154, 3155, 3156, 3157, 3158, 3159, 3160, 3161, 3162, 3163, 3164, 3165, 3166, 3167, 3168, 3169, 3170, 3171, 3172, 3173, 3174, 3175, 3176, 3177, 3178, 3179, 3180, 3181, 3182, 3183, 3184, 3185, 3186, 3187, 3188, 3189, 3190, 3191, 3192, 3193, 3194, 3195, 3196, 3197, 3198, 3199, 3200, 3201, 3202, 3203, 3204, 3205, 3206, 3207, 3208, 3209, 3210, 3211, 3212, 3213, 3214, 3215, 3216, 3217, 3218, 3219, 3220, 3221, 3222, 3223, 3224, 3225, 3226, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3235, 3236, 3237, 3238, 3239, 3240, 3241, 3242, 3243, 3244, 3245, 3246, 3247, 3248, 3249, 3250, 3251, 3252, 3253, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3272, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3296, 3297, 3298, 3299, 3300, 3301, 3302, 3303, 3304, 3305, 3306, 3307, 3308, 3309, 3310, 3311, 3312, 3313, 3314, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 3346, 3347, 3348, 3349, 3350, 3351, 3352, 3353, 3354, 3355, 3356, 3357, 3358, 3359, 3360, 3361, 3362, 3363, 3364, 3365, 3366, 3367, 3368, 3369, 3370, 3371, 3372, 3373, 3374, 3375, 3376, 3377, 3378, 3379, 3380, 3381, 3382, 3383, 3384, 3385, 3386, 3387, 3388, 3389, 3390, 3391, 3392, 3393, 3394, 3395, 3396, 3397, 3398, 3399, 3400, 3401, 3402, 3403, 3404, 3405, 3406, 3407, 3408, 3409, 3410, 3411, 3412, 3413, 3414, 3415, 3416, 3417, 3418, 3419, 3420, 3421, 3422, 3423, 3424, 3425, 3426, 3427, 3428, 3429, 3430, 3431, 3432, 3433, 3434, 3435, 3436, 3437, 3438, 3439, 3440, 3441, 3442, 3443, 3444, 3445, 3446, 3447, 3448, 3449, 3450, 3451, 3452, 3453, 3454, 3455, 3456, 3457, 3458, 3459, 3460, 3461, 3462, 3463, 3464, 3465, 3466, 3467, 3468, 3469, 3470, 3471, 3472, 3473, 3474, 3475, 3476, 3477, 3478, 3479, 3480, 3481, 3482, 3483, 3484, 3485, 3486, 3487, 3488, 3489, 3490, 3491, 3492, 3493, 3494, 3495, 3496, 3497, 3498, 3499, 3500, 3501, 3502, 3503, 3504, 3505, 3506, 3507, 3508, 3509, 3510, 3511, 3512, 3513, 3514, 3515, 3516, 3517, 3518, 3519, 3520, 3521, 3522, 3523, 3524, 3525, 3526, 3527, 3528, 3529, 3530, 3531, 3532, 3533, 3534, 3535, 3536, 3537, 3538, 3539, 3540, 3541, 3542, 3543, 3544, 3545, 3546, 3547, 3548, 3549, 3550, 3551, 3552, 3553, 3554, 3555, 3556, 3557, 3558, 3559, 3560, 3561, 3562, 3563, 3564, 3565, 3566, 3567, 3568, 3569, 3570, 3571, 3572, 3573, 3574, 3575, 3576, 3577, 3578, 3579, 3580, 3581, 3582, 3583, 3584, 3585, 3586, 3587, 3588, 3589, 3590, 3591, 3592, 3593, 3594, 3595, 3596, 3597, 3598, 3599, 3600, 3601, 3602, 3603, 3604, 3605, 3606, 3607, 3608, 3609, 3610, 3611, 3612, 3613, 3614, 3615, 3616, 3617, 3618, 3619, 3620, 3621, 3622, 3623, 3624, 3625, 3626, 3627, 3628, 3629, 3630, 3631, 3632, 3633, 3634, 3635, 3636, 3637, 3638, 3639, 3640, 3641, 3642, 3643, 3644, 3645, 3646, 3647, 3648, 3649, 3650, 3651, 3652, 3653, 3654, 3655, 3656, 3657, 3658, 3659, 3660, 3661, 3662, 3663, 3664, 3665, 3666, 3667, 3668, 3669, 3670, 3671, 3672, 3673, 3674, 3675, 3676, 3677, 3678, 3679, 3680, 3681, 3682, 3683, 3684, 3685, 3686, 3687, 3688, 3689, 3690, 3691, 3692, 3693, 3694, 3695, 3696, 3697, 3698, 3699, 3700, 3701, 3702, 3703, 3704, 3705, 3706, 3707, 3708, 3709, 3710, 3711, 3712, 3713, 3714, 3715, 3716, 3717, 3718, 3719, 3720, 3721, 3722, 3723, 3724, 3725, 3726, 3727, 3728, 3729, 3730, 3731, 3732, 3733, 3734, 3735, 3736, 3737, 3738, 3739, 3740, 3741, 3742, 3743, 3744, 3745, 3746, 3747, 3748, 3749, 3750, 3751, 3752, 3753, 3754, 3755, 3756, 3757, 3758, 3759, 3760, 3761, 3762, 3763, 3764, 3765, 3766, 3767, 3768, 3769, 3770, 3771, 3772, 3773, 3774, 3775, 3776, 3777, 3778, 3779, 3780, 3781, 3782, 3783, 3784, 3785, 3786, 3787, 3788, 3789, 3790, 3791, 3792, 3793, 3794, 3795, 3796, 3797, 3798, 3799, 3800, 3801, 3802, 3803, 3804, 3805, 3806, 3807, 3808, 3809, 3810, 3811, 3812, 3813, 3814, 3815, 3816, 3817, 3818, 3819, 3820, 3821, 3822, 3823, 3824, 3825, 3826, 3827, 3828, 3829, 3830, 3831, 3832, 3833, 3834, 3835, 3836, 3837, 3838, 3839, 3840, 3841, 3842, 3843, 3844, 3845, 3846, 3847, 3848, 3849, 3850, 3851, 3852, 3853, 3854, 3855, 3856, 3857, 3858, 3859, 3860, 3861, 3862, 3863, 3864, 3865, 3866, 3867, 3868, 3869, 3870, 3871, 3872, 3873, 3874, 3875, 3876, 3877, 3878, 3879, 3880, 3881, 3882, 3883, 3884, 3885, 3886, 3887, 3888, 3889, 3890, 3891, 3892, 3893, 3894, 3895, 3896, 3897, 3898, 3899, 3900, 3901, 3902, 3903, 3904, 3905, 3906, 3907, 3908, 3909, 3910, 3911, 3912, 3913, 3914, 3915, 3916, 3917, 3918, 3919, 3920, 3921, 3922, 3923, 3924, 3925, 3926, 3927, 3928, 3929, 3930, 3931, 3932, 3933, 3934, 3935, 3936, 3937, 3938, 3939, 3940, 3941, 3942, 3943, 3944, 3945, 3946, 3947, 3948, 3949, 3950, 3951, 3952, 3953, 3954, 3955, 3956, 3957, 3958, 3959, 3960, 3961, 3962, 3963, 3964, 3965, 3966, 3967, 3968, 3969, 3970, 3971, 3972, 3973, 3974, 3975, 3976, 3977, 3978, 3979, 3980, 3981, 3982, 3983, 3984, 3985, 3986, 3987, 3988, 3989, 3990, 3991, 3992, 3993, 3994, 3995, 3996, 3997, 3998, 3999, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 4036, 4037, 4038, 4039, 4040, 4041, 4042, 4043, 4044, 4045, 4046, 4047, 4048, 4049, 4050, 4051, 4052, 4053, 4054, 4055, 4056, 4057, 4058, 4059, 4060, 4061, 4062, 4063, 4064, 4065, 4066, 4067, 4068, 4069, 4070, 4071, 4072, 4073, 4074, 4075, 4076, 4077, 4078, 4079, 4080, 4081, 4082, 4083, 4084, 4085, 4086, 4087, 4088, 4089, 4090, 4091, 4092, 4093, 4094, 4095, 4096, 4097, 4098, 4099, 4100, 4101, 4102, 4103, 4104, 4105, 4106, 4107, 4108, 4109, 4110, 4111, 4112, 4113, 4114, 4115, 4116, 4117, 4118, 4119, 4120, 4121, 4122, 4123, 4124, 4125, 4126, 4127, 4128, 4129, 4130, 4131, 4132, 4133, 4134, 4135, 4136, 4137, 4138, 4139, 4140, 4141, 4142, 4143, 4144, 4145, 4146, 4147, 4148, 4149, 4150, 4151, 4152, 4153, 4154, 4155, 4156, 4157, 4158, 4159, 4160, 4161, 4162, 4163, 4164, 4165, 4166, 4167, 4168, 4169, 4170, 4171, 4172, 4173, 4174, 4175, 4176, 4177, 4178, 4179, 4180, 4181, 4182, 4183, 4184, 4185, 4186, 4187, 4188, 4189, 4190, 4191, 4192, 4193, 4194, 4195, 4196, 4197, 4198, 4199, 4200, 4201, 4202, 4203, 4204, 4205, 4206, 4207, 4208, 4209, 4210, 4211, 4212, 4213, 4214, 4215, 4216, 4217, 4218, 4219, 4220, 4221, 4222, 4223, 4224, 4225, 4226, 4227, 4228, 4229, 4230, 4231, 4232, 4233, 4234, 4235, 4236, 4237, 4238, 4239, 4240, 4241, 4242, 4243, 4244, 4245, 4246, 4247, 4248, 4249, 4250, 4251, 4252, 4253, 4254, 4255, 4256, 4257, 4258, 4259, 4260, 4261, 4262, 4263, 4264, 4265, 4266, 4267, 4268, 4269, 4270, 4271, 4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281, 4282, 4283, 4284, 4285, 4286, 4287, 4288, 4289, 4290, 4291, 4292, 4293, 4294, 4295, 4296, 4297, 4298, 4299, 4300, 4301, 4302, 4303, 4304, 4305, 4306, 4307, 4308, 4309, 4310, 4311, 4312, 4313, 4314, 4315, 4316, 4317, 4318, 4319, 4320, 4321, 4322, 4323, 4324, 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---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-62e126021871> in <module>
----> 1 show(df)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in show(settings, *args, **kwargs)
    362     kwargs = {**kwargs, **dataframes}
    363 
--> 364     pandas_gui = PandasGui(settings=settings, **kwargs)
    365     pandas_gui.caller_stack = inspect.currentframe().f_back
    366 

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in __init__(self, settings, **kwargs)
     74         # Adds DataFrames listed in kwargs to data store.
     75         for df_name, df in kwargs.items():
---> 76             self.store.add_dataframe(df, df_name)
     77 
     78         # Default to first item

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\store.py in add_dataframe(self, pgdf, name)
    419         if pgdf.dataframe_explorer is None:
    420             from pandasgui.widgets.dataframe_explorer import DataFrameExplorer
--> 421             pgdf.dataframe_explorer = DataFrameExplorer(pgdf)
    422         dfe = pgdf.dataframe_explorer
    423         self.gui.stacked_widget.addWidget(dfe)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\widgets\dataframe_explorer.py in __init__(self, pgdf)
     34 
     35         # Filters tab
---> 36         self.filter_viewer = FilterViewer(pgdf)
     37         self.filters_dock = self.add_view(self.filter_viewer, "Filters")
     38 

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\widgets\filter_viewer.py in __init__(self, pgdf)
     67         valid_values = [f"`{col}`" for col in columns]
     68         categoricals = columns[self.pgdf.df_unfiltered.dtypes == "category"]
---> 69         low_cardinality = columns[self.pgdf.df_unfiltered.nunique() < CATEGORICAL_THRESHOLD]
     70 
     71         # make unique the column names

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\frame.py in nunique(self, axis, dropna)
   8923         dtype: int64
   8924         """
-> 8925         return self.apply(Series.nunique, axis=axis, dropna=dropna)
   8926 
   8927     def idxmin(self, axis=0, skipna=True) -> Series:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
   7763             kwds=kwds,
   7764         )
-> 7765         return op.get_result()
   7766 
   7767     def applymap(self, func, na_action: Optional[str] = None) -> DataFrame:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in get_result(self)
    183             return self.apply_raw()
    184 
--> 185         return self.apply_standard()
    186 
    187     def apply_empty_result(self):

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_standard(self)
    274 
    275     def apply_standard(self):
--> 276         results, res_index = self.apply_series_generator()
    277 
    278         # wrap results

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_series_generator(self)
    288             for i, v in enumerate(series_gen):
    289                 # ignore SettingWithCopy here in case the user mutates
--> 290                 results[i] = self.f(v)
    291                 if isinstance(results[i], ABCSeries):
    292                     # If we have a view on v, we need to make a copy because

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in f(x)
    108 
    109             def f(x):
--> 110                 return func(x, *args, **kwds)
    111 
    112         else:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\base.py in nunique(self, dropna)
   1084         """
   1085         obj = remove_na_arraylike(self) if dropna else self
-> 1086         return len(obj.unique())
   1087 
   1088     @property

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\series.py in unique(self)
   1868         Categories (3, object): ['a' < 'b' < 'c']
   1869         """
-> 1870         result = super().unique()
   1871         return result
   1872 

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\base.py in unique(self)
   1045                     result = np.asarray(result)
   1046         else:
-> 1047             result = unique1d(values)
   1048 
   1049         return result

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\algorithms.py in unique(values)
    405 
    406     table = htable(len(values))
--> 407     uniques = table.unique(values)
    408     uniques = _reconstruct_data(uniques, original.dtype, original)
    409     return uniques

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.unique()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable._unique()

TypeError: unhashable type: 'dict'

@adamerose
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@bjornjorgensen

TypeError: unhashable type: 'list'

This was due to a new feature using df.unique() on DataFrames containing unhashable data like lists, I've replaced it with an alternative that works with unhashable data by converting to strings. Can you try out the dev branch and let me know if it's fixed for you? pip install git+https://github.com/adamerose/pandasgui.git

I also get this error now.

PandasGUI WARNING — pandasgui.utility — In df, converted column names to string: 0, 1, 2, 3, 4, 5....

This isn't an error, it's a warning that PandasGUI automatically converts DataFrame column names to strings. You just got a really long warning message because it seems like your data has over 10k columns all with numeric column values.

For example

from pandasgui import show
import pandas as pd

df = pd.DataFrame({1: ["some_value1", "some_value2"], 2: ["some_value1", "some_value2"]})
gui = show(df)
df2 = gui['df']

print(df.columns.values, df2.columns.values)
# [1 2] ['1' '2']

@bjornjorgensen
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I did pip install git+https://github.com/adamerose/pandasgui.git
But I get errors


TypeError Traceback (most recent call last)
in
----> 1 show(df)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in show(settings, *args, **kwargs)
362 kwargs = {**kwargs, **dataframes}
363
--> 364 pandas_gui = PandasGui(settings=settings, **kwargs)
365 pandas_gui.caller_stack = inspect.currentframe().f_back
366

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in init(self, settings, **kwargs)
74 # Adds DataFrames listed in kwargs to data store.
75 for df_name, df in kwargs.items():
---> 76 self.store.add_dataframe(df, df_name)
77
78 # Default to first item

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\store.py in add_dataframe(self, pgdf, name)
413 pgdf.store = self
414
--> 415 pgdf.df = clean_dataframe(pgdf.df, name)
416
417 # Add it to store and create widgets

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in clean_dataframe(df, name)
272 converted_names = []
273 dtypes_old = df.dtypes
--> 274 df = parse_dates(df)
275 dtypes_new = df.dtypes
276 for col_name in [df.columns[ix] for ix in range(len(dtypes_new)) if dtypes_old[ix] != dtypes_new[ix]]:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in parse_dates(df)
237 def parse_dates(df: Union[pd.DataFrame, pd.Series]):
238 if type(df) == pd.DataFrame:
--> 239 return df.apply(
240 lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col,
241 axis=0)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
7763 kwds=kwds,
7764 )
-> 7765 return op.get_result()
7766
7767 def applymap(self, func, na_action: Optional[str] = None) -> DataFrame:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in get_result(self)
183 return self.apply_raw()
184
--> 185 return self.apply_standard()
186
187 def apply_empty_result(self):

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_standard(self)
274
275 def apply_standard(self):
--> 276 results, res_index = self.apply_series_generator()
277
278 # wrap results

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_series_generator(self)
288 for i, v in enumerate(series_gen):
289 # ignore SettingWithCopy here in case the user mutates
--> 290 results[i] = self.f(v)
291 if isinstance(results[i], ABCSeries):
292 # If we have a view on v, we need to make a copy because

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in (col)
238 if type(df) == pd.DataFrame:
239 return df.apply(
--> 240 lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col,
241 axis=0)
242 elif type(df) == pd.Series:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
799 result = result.tz_localize(tz)
800 elif isinstance(arg, ABCSeries):
--> 801 cache_array = _maybe_cache(arg, format, cache, convert_listlike)
802 if not cache_array.empty:
803 result = arg.map(cache_array)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in _maybe_cache(arg, format, cache, convert_listlike)
171 if cache:
172 # Perform a quicker unique check
--> 173 if not should_cache(arg):
174 return cache_array
175

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in should_cache(arg, unique_share, check_count)
135 assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
136
--> 137 unique_elements = set(islice(arg, check_count))
138 if len(unique_elements) > check_count * unique_share:
139 do_caching = False

TypeError: unhashable type: 'list'

@adamerose
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Can you share a sample dataset that causes this error?

@bjornjorgensen
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bjornjorgensen commented Feb 11, 2021

`import pandas as pd
import json
import re
from pandasgui import show
from arango import ArangoClient

client = ArangoClient(hosts='http://bjhjemme.duckdns.org:8529')

db = client.db('test_2015-2020', username='test', password='test')

aql = db.aql

cursor = db.aql.execute('FOR c IN FORM_3 RETURN c', batch_size=1)
result = [c for c in cursor]

df = pd.json_normalize(result, max_level=None)

df = df.drop(columns=['_key', '_id', '_rev'])
df = df.dropna(axis=1, how='all')
show(df)`

@adamerose
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Looks like this is a problem with Pandas, I made an issue here pandas-dev/pandas#39756

But in the meantime I've added a workaround to PandasGUI, can you try again after pip install --upgrade git+https://github.com/adamerose/pandasgui.git

@bjornjorgensen
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Hi, yours patch dont work.

when I downgrade pandas to 1.0.5 I get this

pip install pandas==1.0.5


TypeError Traceback (most recent call last)
in
----> 1 show(df)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in show(settings, *args, **kwargs)
362 kwargs = {**kwargs, **dataframes}
363
--> 364 pandas_gui = PandasGui(settings=settings, **kwargs)
365 pandas_gui.caller_stack = inspect.currentframe().f_back
366

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in init(self, settings, **kwargs)
74 # Adds DataFrames listed in kwargs to data store.
75 for df_name, df in kwargs.items():
---> 76 self.store.add_dataframe(df, df_name)
77
78 # Default to first item

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\store.py in add_dataframe(self, pgdf, name)
413 pgdf.store = self
414
--> 415 pgdf.df = clean_dataframe(pgdf.df, name)
416
417 # Add it to store and create widgets

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in clean_dataframe(df, name)
272 converted_names = []
273 dtypes_old = df.dtypes
--> 274 df = parse_dates(df)
275 dtypes_new = df.dtypes
276 for col_name in [df.columns[ix] for ix in range(len(dtypes_new)) if dtypes_old[ix] != dtypes_new[ix]]:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in parse_dates(df)
237 def parse_dates(df: Union[pd.DataFrame, pd.Series]):
238 if type(df) == pd.DataFrame:
--> 239 return df.apply(
240 lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col,
241 axis=0)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
6876 kwds=kwds,
6877 )
-> 6878 return op.get_result()
6879
6880 def applymap(self, func) -> "DataFrame":

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in get_result(self)
184 return self.apply_raw()
185
--> 186 return self.apply_standard()
187
188 def apply_empty_result(self):

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_standard(self)
311
312 # compute the result using the series generator
--> 313 results, res_index = self.apply_series_generator()
314
315 # wrap results

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_series_generator(self)
339 else:
340 for i, v in enumerate(series_gen):
--> 341 results[i] = self.f(v)
342 keys.append(v.name)
343

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in (col)
238 if type(df) == pd.DataFrame:
239 return df.apply(
--> 240 lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col,
241 axis=0)
242 elif type(df) == pd.Series:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
722 result = result.tz_localize(tz)
723 elif isinstance(arg, ABCSeries):
--> 724 cache_array = _maybe_cache(arg, format, cache, convert_listlike)
725 if not cache_array.empty:
726 result = arg.map(cache_array)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in _maybe_cache(arg, format, cache, convert_listlike)
145 if cache:
146 # Perform a quicker unique check
--> 147 if not should_cache(arg):
148 return cache_array
149

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in should_cache(arg, unique_share, check_count)
114 assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
115
--> 116 unique_elements = set(islice(arg, check_count))
117 if len(unique_elements) > check_count * unique_share:
118 do_caching = False

TypeError: unhashable type: 'list'

Then I upgrade everything and pip install --upgrade git+https://github.com/adamerose/pandasgui.git

I get this


TypeError Traceback (most recent call last)
in
----> 1 show(df)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in show(settings, *args, **kwargs)
362 kwargs = {**kwargs, **dataframes}
363
--> 364 pandas_gui = PandasGui(settings=settings, **kwargs)
365 pandas_gui.caller_stack = inspect.currentframe().f_back
366

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\gui.py in init(self, settings, **kwargs)
74 # Adds DataFrames listed in kwargs to data store.
75 for df_name, df in kwargs.items():
---> 76 self.store.add_dataframe(df, df_name)
77
78 # Default to first item

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\store.py in add_dataframe(self, pgdf, name)
413 pgdf.store = self
414
--> 415 pgdf.df = clean_dataframe(pgdf.df, name)
416
417 # Add it to store and create widgets

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in clean_dataframe(df, name)
272 converted_names = []
273 dtypes_old = df.dtypes
--> 274 df = parse_dates(df)
275 dtypes_new = df.dtypes
276 for col_name in [df.columns[ix] for ix in range(len(dtypes_new)) if dtypes_old[ix] != dtypes_new[ix]]:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in parse_dates(df)
237 def parse_dates(df: Union[pd.DataFrame, pd.Series]):
238 if type(df) == pd.DataFrame:
--> 239 return df.apply(
240 lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col,
241 axis=0)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
7763 kwds=kwds,
7764 )
-> 7765 return op.get_result()
7766
7767 def applymap(self, func, na_action: Optional[str] = None) -> DataFrame:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in get_result(self)
183 return self.apply_raw()
184
--> 185 return self.apply_standard()
186
187 def apply_empty_result(self):

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_standard(self)
274
275 def apply_standard(self):
--> 276 results, res_index = self.apply_series_generator()
277
278 # wrap results

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\apply.py in apply_series_generator(self)
288 for i, v in enumerate(series_gen):
289 # ignore SettingWithCopy here in case the user mutates
--> 290 results[i] = self.f(v)
291 if isinstance(results[i], ABCSeries):
292 # If we have a view on v, we need to make a copy because

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\utility.py in (col)
238 if type(df) == pd.DataFrame:
239 return df.apply(
--> 240 lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col,
241 axis=0)
242 elif type(df) == pd.Series:

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
799 result = result.tz_localize(tz)
800 elif isinstance(arg, ABCSeries):
--> 801 cache_array = _maybe_cache(arg, format, cache, convert_listlike)
802 if not cache_array.empty:
803 result = arg.map(cache_array)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in _maybe_cache(arg, format, cache, convert_listlike)
171 if cache:
172 # Perform a quicker unique check
--> 173 if not should_cache(arg):
174 return cache_array
175

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\tools\datetimes.py in should_cache(arg, unique_share, check_count)
135 assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
136
--> 137 unique_elements = set(islice(arg, check_count))
138 if len(unique_elements) > check_count * unique_share:
139 do_caching = False

TypeError: unhashable type: 'list'

But when I downgrade pandasgui to pip install pandasgui==0.2.7
It works.

@adamerose
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I don't think you were on the latest version, because your stack trace still shows

--> 231         return df.apply(
    232             lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col,
    233             axis=0)

which was removed from the code.

I released the fix to PyPi, please try 0.2.9

@bjornjorgensen
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Ok, 0.2.9 works now

PandasGUI WARNING — pandasgui.utility — In df, converted columns to datetime: DOFFIN_ESENDERS.DOFFIN_APPENDIX.DOFFIN_FORM_TYPE.NATIONAL.REFERENCE_SECTION.REFERENCE_DOFFIN.DATE

<pandasgui.gui.PandasGui at 0x1b12641cd30>

but pandasgui show :)

@bjornjorgensen
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Author

Hi again I am trying this on mulitindexing https://github.com/ZaxR/pandas_multiindex_tutorial/blob/master/Pandas%20MultiIndex%20Tutorial.ipynb

In [29]:

multi_col_lvl_df.query("Date == '2018-07-10' and Category == 'Wine'")

that works in atom and hydrogen pluging.

but if I do show(multi_col_lvl_df)
and in the filters I type "Date == '2018-07-10' and Category == 'Wine'"
then I get

<pandasgui.gui.PandasGui at 0x1b3eea8f790>
PandasGUI ERROR — pandasgui.store — cannot do slice indexing on DatetimeIndex with these indexers [Date == '2018-07-10' and Category == 'Wine'] of type str
Traceback (most recent call last):
File "pandas_libs\tslibs\conversion.pyx", line 639, in pandas._libs.tslibs.conversion._convert_str_to_tsobject
File "pandas_libs\tslibs\parsing.pyx", line 243, in pandas._libs.tslibs.parsing.parse_datetime_string
File "C:\Users\ujorbjo00\AppData\Roaming\Python\Python39\site-packages\dateutil\parser_parser.py", line 1374, in parse
return DEFAULTPARSER.parse(timestr, **kwargs)
File "C:\Users\ujorbjo00\AppData\Roaming\Python\Python39\site-packages\dateutil\parser_parser.py", line 649, in parse
raise ParserError("Unknown string format: %s", timestr)
dateutil.parser._parser.ParserError: Unknown string format: Date == '2018-07-10' and Category == 'Wine'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\datetimes.py", line 664, in get_loc
key = self._maybe_cast_for_get_loc(key)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\datetimes.py", line 692, in _maybe_cast_for_get_loc
key = Timestamp(key)
File "pandas_libs\tslibs\timestamps.pyx", line 1095, in pandas._libs.tslibs.timestamps.Timestamp.new
File "pandas_libs\tslibs\conversion.pyx", line 398, in pandas._libs.tslibs.conversion.convert_to_tsobject
File "pandas_libs\tslibs\conversion.pyx", line 642, in pandas._libs.tslibs.conversion._convert_str_to_tsobject
ValueError: could not convert string to Timestamp

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\multi.py", line 3117, in _get_level_indexer
start = level_index.get_loc(key.start)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\datetimes.py", line 666, in get_loc
raise KeyError(key) from err
KeyError: "Date == '2018-07-10' and Category == 'Wine'"

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "pandas_libs\tslibs\parsing.pyx", line 322, in pandas._libs.tslibs.parsing.parse_datetime_string_with_reso
File "pandas_libs\tslibs\parsing.pyx", line 503, in pandas._libs.tslibs.parsing.dateutil_parse
ValueError: Unknown datetime string format, unable to parse: Date == '2018-07-10' and Category == 'Wine'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\datetimes.py", line 722, in _maybe_cast_slice_bound
parsed, reso = parsing.parse_time_string(label, freq)
File "pandas_libs\tslibs\parsing.pyx", line 281, in pandas._libs.tslibs.parsing.parse_time_string
File "pandas_libs\tslibs\parsing.pyx", line 327, in pandas._libs.tslibs.parsing.parse_datetime_string_with_reso
pandas._libs.tslibs.parsing.DateParseError: Unknown datetime string format, unable to parse: Date == '2018-07-10' and Category == 'Wine'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandasgui\store.py", line 378, in apply_filters
df = df.query(filt.expr)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\frame.py", line 3472, in query
result = self.loc[res]
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexing.py", line 895, in getitem
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexing.py", line 1117, in _getitem_axis
locs = labels.get_locs(key)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\multi.py", line 3296, in get_locs
self._get_level_indexer(k, level=i, indexer=indexer)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\multi.py", line 3131, in _get_level_indexer
start = stop = level_index.slice_indexer(
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\datetimes.py", line 784, in slice_indexer
return Index.slice_indexer(self, start, end, step, kind=kind)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\base.py", line 5277, in slice_indexer
start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\base.py", line 5476, in slice_locs
start_slice = self.get_slice_bound(start, "left", kind)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\base.py", line 5386, in get_slice_bound
label = self._maybe_cast_slice_bound(label, side, kind)
File "C:\Users\ujorbjo00\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexes\datetimes.py", line 724, in _maybe_cast_slice_bound
raise self._invalid_indexer("slice", label) from err
TypeError: cannot do slice indexing on DatetimeIndex with these indexers [Date == '2018-07-10' and Category == 'Wine'] of type str

But if I type Date == '2018-07-10' and Category == 'Wine'
then it works.

Is it a bug or?

@adamerose
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@bjornjorgensen Can you give a minimal example with a URL link to the CSV?

@rjsdotorg
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I have a related issue where non-dates are often converted to datetime.

I changed utility.py line 242 to

return pd.to_datetime(s, format="%b %d %Y")
and now I only see
PandasGUI WARNING — pandasgui.utility — In metadata_df, converted columns to datetime: RecordingDate
which is desired.

It does convert everything else to a string (like float columns), but that is not a big issue for me, visually.
Here are the first two lines and header which are a result of df.to_csv('metadata_df.csv') which I do just before show(metadata_df)
metadata_df.txt

@adamerose
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@rjsdotorg

I have a related issue where non-dates are often converted to datetime.

The date conversion seems to work fine on my end, it detected dates in RecordingDate, RecordingStartTime and RecordingStopTime. Can you try again with the latest version 0.2.11 on PyPi?

PandasGUI just tries calling pd.to_datetime on each column

# Automatically try to parse dates for all columns
def parse_dates(df: Union[pd.DataFrame, pd.Series]):
    def parse_dates_series(s: pd.Series):
        try:
            return pd.to_datetime(s)
        except:
            return s

    if type(df) == pd.DataFrame:
        return df.apply(lambda col: parse_dates_series(col) if col.dtype == object else col)
    elif type(df) == pd.Series:
        return parse_dates_series(df)

image

It does convert everything else to a string (like float columns), but that is not a big issue for me, visually.

That shouldn't happen either and doesn't happen for me on your txt file, I only get 7 string columns.

@rjsdotorg
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Firstly, when I upgraded to 0.2.11 it seems to need a new module
>pip install pandasgui==0.2.11
I got ModuleNotFoundError: No module named 'qtstylish'

>python
Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] on win32
>>> import pandas
>>> pandas.__version__
'1.1.4'
>>> import pandasgui
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Python38\lib\site-packages\pandasgui\__init__.py", line 15, in <module>
    from pandasgui.gui import show
  File "C:\Python38\lib\site-packages\pandasgui\gui.py", line 20, in <module>
    import qtstylish
ModuleNotFoundError: No module named 'qtstylish'
>>> import pandasgui
>>> pandasgui.__version__
'0.2.11'

but it did install fine: pip install qtstylish, so now I have

>python
Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] on win32
>>> import pandas
>>> pandas.__version__
'1.1.4'
>>> import pandasgui
>>> pandasgui.__version__
'0.2.11'

I did see the simple call in utility.py parse_dates_series(), so it appears to me now it is a pandas "thing", but maybe the question was why is pandasgui trying to recast columns at all?

So, it is likely that reading from CSV is not a proper test here.
The data was strings read from report forms, and the df types as created are

...
Side                    object
Location                object
NIHSScore              float64
TICIScore               object
IV_tPA_timing           object
EVT_timing              object
RecordingDate           object
RecordingStartTime      object
RecordingStopTime       object
Gender                  object
Age                     object
SystolicBP              object
DiastolicBP             object

my calling code saves that df to csv, then opens pandasgui on the dataframe for a look

        metadata_df.to_csv('metadata_df.csv')
        try:
            show(metadata_df)
        except ValueError:
            pass

When I call show() I get:
Clipboard01

I might need to generate a pickle of metadata_df for you to test, if you'd like; if I "filter" the df through CSV, I get a result very similar to your screen cap, but with floats instead of ints
Clipboard01

@adamerose
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adamerose commented Jun 11, 2021

At this point your bug doesn't seem to be related to this issue, can you make a new issue for us to continue this discussion? A pickle would be good, but I think the easiest way to debug would be to try calling my cleaning function directly so we can rule out any graphic bugs. For example

from pandasgui.utility import clean_dataframe
from pandasgui.datasets import trump_tweets
df = clean_dataframe(trump_tweets)

Once you make a new issue I will try to replicate your bug with your version of pandas

why is pandasgui trying to recast columns at all

The goal of this project is to make viewing and analyzing data in Pandas as quick and intuitive as possible, and I found having to specify a list of column names in my dataset that are dates very tedious. Other EDA and viz programs like JMP, Tableau and PowerBI all recognize columns containing strings that represent dates and that's what I wanted for PandasGUI too.

Aside from automatically parsing dates, it also removes non-string and duplicate column names, this all happens in pandasgui.utility.clean_dataframe. The latter two are mandatory (a lot of my logic assumes no duplicate columns) but I could add an option to the user preferences to disable automatic date parsing.

I got ModuleNotFoundError: No module named 'qtstylish'

Yep that's expected, I pulled my CSS into a separate package for easier maintenance.

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