/usr/lib/python3/dist-packages/pandas/tseries/index.py is in python3-pandas 0.13.1-2ubuntu2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 | # pylint: disable=E1101
import operator
from datetime import time, datetime
from datetime import timedelta
import numpy as np
from pandas.core.common import (isnull, _NS_DTYPE, _INT64_DTYPE,
is_list_like,_values_from_object, _maybe_box,
notnull, ABCSeries)
from pandas.core.index import Index, Int64Index, _Identity
import pandas.compat as compat
from pandas.compat import u
from pandas.tseries.frequencies import (
infer_freq, to_offset, get_period_alias,
Resolution, get_reso_string)
from pandas.tseries.offsets import DateOffset, generate_range, Tick, CDay
from pandas.tseries.tools import parse_time_string, normalize_date
from pandas.util.decorators import cache_readonly
import pandas.core.common as com
import pandas.tseries.offsets as offsets
import pandas.tseries.tools as tools
from pandas.lib import Timestamp
import pandas.lib as lib
import pandas.tslib as tslib
import pandas.algos as _algos
import pandas.index as _index
def _utc():
import pytz
return pytz.utc
# -------- some conversion wrapper functions
def _field_accessor(name, field, docstring=None):
def f(self):
values = self.asi8
if self.tz is not None:
utc = _utc()
if self.tz is not utc:
values = self._local_timestamps()
return tslib.get_date_field(values, field)
f.__name__ = name
f.__doc__ = docstring
return property(f)
def _join_i8_wrapper(joinf, with_indexers=True):
@staticmethod
def wrapper(left, right):
if isinstance(left, (np.ndarray, ABCSeries)):
left = left.view('i8', type=np.ndarray)
if isinstance(right, (np.ndarray, ABCSeries)):
right = right.view('i8', type=np.ndarray)
results = joinf(left, right)
if with_indexers:
join_index, left_indexer, right_indexer = results
join_index = join_index.view('M8[ns]')
return join_index, left_indexer, right_indexer
return results
return wrapper
def _dt_index_cmp(opname):
"""
Wrap comparison operations to convert datetime-like to datetime64
"""
def wrapper(self, other):
func = getattr(super(DatetimeIndex, self), opname)
if isinstance(other, datetime):
other = _to_m8(other, tz=self.tz)
elif isinstance(other, list):
other = DatetimeIndex(other)
elif isinstance(other, compat.string_types):
other = _to_m8(other, tz=self.tz)
elif not isinstance(other, (np.ndarray, ABCSeries)):
other = _ensure_datetime64(other)
result = func(other)
return result.view(np.ndarray)
return wrapper
def _ensure_datetime64(other):
if isinstance(other, np.datetime64):
return other
raise TypeError('%s type object %s' % (type(other), str(other)))
_midnight = time(0, 0)
class DatetimeIndex(Int64Index):
"""
Immutable ndarray of datetime64 data, represented internally as int64, and
which can be boxed to Timestamp objects that are subclasses of datetime and
carry metadata such as frequency information.
Parameters
----------
data : array-like (1-dimensional), optional
Optional datetime-like data to construct index with
copy : bool
Make a copy of input ndarray
freq : string or pandas offset object, optional
One of pandas date offset strings or corresponding objects
start : starting value, datetime-like, optional
If data is None, start is used as the start point in generating regular
timestamp data.
periods : int, optional, > 0
Number of periods to generate, if generating index. Takes precedence
over end argument
end : end time, datetime-like, optional
If periods is none, generated index will extend to first conforming
time on or just past end argument
closed : string or None, default None
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None)
name : object
Name to be stored in the index
"""
_join_precedence = 10
_inner_indexer = _join_i8_wrapper(_algos.inner_join_indexer_int64)
_outer_indexer = _join_i8_wrapper(_algos.outer_join_indexer_int64)
_left_indexer = _join_i8_wrapper(_algos.left_join_indexer_int64)
_left_indexer_unique = _join_i8_wrapper(
_algos.left_join_indexer_unique_int64, with_indexers=False)
_arrmap = None
__eq__ = _dt_index_cmp('__eq__')
__ne__ = _dt_index_cmp('__ne__')
__lt__ = _dt_index_cmp('__lt__')
__gt__ = _dt_index_cmp('__gt__')
__le__ = _dt_index_cmp('__le__')
__ge__ = _dt_index_cmp('__ge__')
# structured array cache for datetime fields
_sarr_cache = None
_engine_type = _index.DatetimeEngine
offset = None
_comparables = ['name','freqstr','tz']
def __new__(cls, data=None,
freq=None, start=None, end=None, periods=None,
copy=False, name=None, tz=None,
verify_integrity=True, normalize=False,
closed=None, **kwds):
dayfirst = kwds.pop('dayfirst', None)
yearfirst = kwds.pop('yearfirst', None)
infer_dst = kwds.pop('infer_dst', False)
warn = False
if 'offset' in kwds and kwds['offset']:
freq = kwds['offset']
warn = True
freq_infer = False
if not isinstance(freq, DateOffset):
# if a passed freq is None, don't infer automatically
if freq != 'infer':
freq = to_offset(freq)
else:
freq_infer = True
freq = None
if warn:
import warnings
warnings.warn("parameter 'offset' is deprecated, "
"please use 'freq' instead",
FutureWarning)
offset = freq
if periods is not None:
if com.is_float(periods):
periods = int(periods)
elif not com.is_integer(periods):
raise ValueError('Periods must be a number, got %s' %
str(periods))
if data is None and offset is None:
raise ValueError("Must provide freq argument if no data is "
"supplied")
if data is None:
return cls._generate(start, end, periods, name, offset,
tz=tz, normalize=normalize, closed=closed,
infer_dst=infer_dst)
if not isinstance(data, (np.ndarray, ABCSeries)):
if np.isscalar(data):
raise ValueError('DatetimeIndex() must be called with a '
'collection of some kind, %s was passed'
% repr(data))
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
data = np.asarray(data, dtype='O')
# try a few ways to make it datetime64
if lib.is_string_array(data):
data = _str_to_dt_array(data, offset, dayfirst=dayfirst,
yearfirst=yearfirst)
else:
data = tools.to_datetime(data, errors='raise')
data.offset = offset
if isinstance(data, DatetimeIndex):
if name is not None:
data.name = name
if tz is not None:
return data.tz_localize(tz, infer_dst=infer_dst)
return data
if issubclass(data.dtype.type, compat.string_types):
data = _str_to_dt_array(data, offset, dayfirst=dayfirst,
yearfirst=yearfirst)
if issubclass(data.dtype.type, np.datetime64):
if isinstance(data, ABCSeries):
data = data.values
if isinstance(data, DatetimeIndex):
if tz is None:
tz = data.tz
subarr = data.values
if offset is None:
offset = data.offset
verify_integrity = False
else:
if data.dtype != _NS_DTYPE:
subarr = tslib.cast_to_nanoseconds(data)
else:
subarr = data
elif data.dtype == _INT64_DTYPE:
if isinstance(data, Int64Index):
raise TypeError('cannot convert Int64Index->DatetimeIndex')
if copy:
subarr = np.asarray(data, dtype=_NS_DTYPE)
else:
subarr = data.view(_NS_DTYPE)
else:
try:
subarr = tools.to_datetime(data, box=False)
except ValueError:
# tz aware
subarr = tools.to_datetime(data, box=False, utc=True)
if not np.issubdtype(subarr.dtype, np.datetime64):
raise ValueError('Unable to convert %s to datetime dtype'
% str(data))
if isinstance(subarr, DatetimeIndex):
if tz is None:
tz = subarr.tz
else:
if tz is not None:
tz = tools._maybe_get_tz(tz)
if (not isinstance(data, DatetimeIndex) or
getattr(data, 'tz', None) is None):
# Convert tz-naive to UTC
ints = subarr.view('i8')
subarr = tslib.tz_localize_to_utc(ints, tz,
infer_dst=infer_dst)
subarr = subarr.view(_NS_DTYPE)
subarr = subarr.view(cls)
subarr.name = name
subarr.offset = offset
subarr.tz = tz
if verify_integrity and len(subarr) > 0:
if offset is not None and not freq_infer:
inferred = subarr.inferred_freq
if inferred != offset.freqstr:
raise ValueError('Dates do not conform to passed '
'frequency')
if freq_infer:
inferred = subarr.inferred_freq
if inferred:
subarr.offset = to_offset(inferred)
return subarr
@classmethod
def _generate(cls, start, end, periods, name, offset,
tz=None, normalize=False, infer_dst=False, closed=None):
if com._count_not_none(start, end, periods) != 2:
raise ValueError('Must specify two of start, end, or periods')
_normalized = True
if start is not None:
start = Timestamp(start)
if end is not None:
end = Timestamp(end)
left_closed = False
right_closed = False
if start is None and end is None:
if closed is not None:
raise ValueError("Closed has to be None if not both of start"
"and end are defined")
if closed is None:
left_closed = True
right_closed = True
elif closed == "left":
left_closed = True
elif closed == "right":
right_closed = True
else:
raise ValueError("Closed has to be either 'left', 'right' or None")
try:
inferred_tz = tools._infer_tzinfo(start, end)
except:
raise ValueError('Start and end cannot both be tz-aware with '
'different timezones')
inferred_tz = tools._maybe_get_tz(inferred_tz)
tz = tools._maybe_get_tz(tz)
if tz is not None and inferred_tz is not None:
if not inferred_tz == tz:
raise AssertionError("Inferred time zone not equal to passed "
"time zone")
elif inferred_tz is not None:
tz = inferred_tz
if start is not None:
if normalize:
start = normalize_date(start)
_normalized = True
else:
_normalized = _normalized and start.time() == _midnight
if end is not None:
if normalize:
end = normalize_date(end)
_normalized = True
else:
_normalized = _normalized and end.time() == _midnight
if hasattr(offset, 'delta') and offset != offsets.Day():
if inferred_tz is None and tz is not None:
# naive dates
if start is not None and start.tz is None:
start = start.tz_localize(tz)
if end is not None and end.tz is None:
end = end.tz_localize(tz)
if start and end:
if start.tz is None and end.tz is not None:
start = start.tz_localize(end.tz)
if end.tz is None and start.tz is not None:
end = end.tz_localize(start.tz)
if _use_cached_range(offset, _normalized, start, end):
index = cls._cached_range(start, end, periods=periods,
offset=offset, name=name)
else:
index = _generate_regular_range(start, end, periods, offset)
else:
if inferred_tz is None and tz is not None:
# naive dates
if start is not None and start.tz is not None:
start = start.replace(tzinfo=None)
if end is not None and end.tz is not None:
end = end.replace(tzinfo=None)
if start and end:
if start.tz is None and end.tz is not None:
end = end.replace(tzinfo=None)
if end.tz is None and start.tz is not None:
start = start.replace(tzinfo=None)
if _use_cached_range(offset, _normalized, start, end):
index = cls._cached_range(start, end, periods=periods,
offset=offset, name=name)
else:
index = _generate_regular_range(start, end, periods, offset)
if tz is not None and getattr(index, 'tz', None) is None:
index = tslib.tz_localize_to_utc(com._ensure_int64(index), tz,
infer_dst=infer_dst)
index = index.view(_NS_DTYPE)
index = index.view(cls)
index.name = name
index.offset = offset
index.tz = tz
if not left_closed:
index = index[1:]
if not right_closed:
index = index[:-1]
return index
def _box_values(self, values):
return lib.map_infer(values, lib.Timestamp)
def _local_timestamps(self):
utc = _utc()
if self.is_monotonic:
return tslib.tz_convert(self.asi8, utc, self.tz)
else:
values = self.asi8
indexer = values.argsort()
result = tslib.tz_convert(values.take(indexer), utc, self.tz)
n = len(indexer)
reverse = np.empty(n, dtype=np.int_)
reverse.put(indexer, np.arange(n))
return result.take(reverse)
@classmethod
def _simple_new(cls, values, name, freq=None, tz=None):
if values.dtype != _NS_DTYPE:
values = com._ensure_int64(values).view(_NS_DTYPE)
result = values.view(cls)
result.name = name
result.offset = freq
result.tz = tools._maybe_get_tz(tz)
return result
@property
def tzinfo(self):
"""
Alias for tz attribute
"""
return self.tz
@classmethod
def _cached_range(cls, start=None, end=None, periods=None, offset=None,
name=None):
if start is None and end is None:
# I somewhat believe this should never be raised externally and therefore
# should be a `PandasError` but whatever...
raise TypeError('Must specify either start or end.')
if start is not None:
start = Timestamp(start)
if end is not None:
end = Timestamp(end)
if (start is None or end is None) and periods is None:
raise TypeError('Must either specify period or provide both start and end.')
if offset is None:
# This can't happen with external-facing code, therefore PandasError
raise TypeError('Must provide offset.')
drc = _daterange_cache
if offset not in _daterange_cache:
xdr = generate_range(offset=offset, start=_CACHE_START,
end=_CACHE_END)
arr = tools.to_datetime(list(xdr), box=False)
cachedRange = arr.view(DatetimeIndex)
cachedRange.offset = offset
cachedRange.tz = None
cachedRange.name = None
drc[offset] = cachedRange
else:
cachedRange = drc[offset]
if start is None:
if not isinstance(end, Timestamp):
raise AssertionError('end must be an instance of Timestamp')
end = offset.rollback(end)
endLoc = cachedRange.get_loc(end) + 1
startLoc = endLoc - periods
elif end is None:
if not isinstance(start, Timestamp):
raise AssertionError('start must be an instance of Timestamp')
start = offset.rollforward(start)
startLoc = cachedRange.get_loc(start)
endLoc = startLoc + periods
else:
if not offset.onOffset(start):
start = offset.rollforward(start)
if not offset.onOffset(end):
end = offset.rollback(end)
startLoc = cachedRange.get_loc(start)
endLoc = cachedRange.get_loc(end) + 1
indexSlice = cachedRange[startLoc:endLoc]
indexSlice.name = name
indexSlice.offset = offset
return indexSlice
def _mpl_repr(self):
# how to represent ourselves to matplotlib
return tslib.ints_to_pydatetime(self.asi8, self.tz)
_na_value = tslib.NaT
"""The expected NA value to use with this index."""
@cache_readonly
def _is_dates_only(self):
from pandas.core.format import _is_dates_only
return _is_dates_only(self.values)
def __unicode__(self):
from pandas.core.format import _get_format_datetime64
formatter = _get_format_datetime64(is_dates_only=self._is_dates_only)
values = self.values
freq = None
if self.offset is not None:
freq = self.offset.freqstr
summary = str(self.__class__)
if len(self) == 1:
first = formatter(values[0], tz=self.tz)
summary += '\n[%s]' % first
elif len(self) == 2:
first = formatter(values[0], tz=self.tz)
last = formatter(values[-1], tz=self.tz)
summary += '\n[%s, %s]' % (first, last)
elif len(self) > 2:
first = formatter(values[0], tz=self.tz)
last = formatter(values[-1], tz=self.tz)
summary += '\n[%s, ..., %s]' % (first, last)
tagline = '\nLength: %d, Freq: %s, Timezone: %s'
summary += tagline % (len(self), freq, self.tz)
return summary
def __reduce__(self):
"""Necessary for making this object picklable"""
object_state = list(np.ndarray.__reduce__(self))
subclass_state = self.name, self.offset, self.tz
object_state[2] = (object_state[2], subclass_state)
return tuple(object_state)
def __setstate__(self, state):
"""Necessary for making this object picklable"""
if len(state) == 2:
nd_state, own_state = state
self.name = own_state[0]
self.offset = own_state[1]
self.tz = own_state[2]
np.ndarray.__setstate__(self, nd_state)
# provide numpy < 1.7 compat
if nd_state[2] == 'M8[us]':
new_state = np.ndarray.__reduce__(self.values.astype('M8[ns]'))
np.ndarray.__setstate__(self, new_state[2])
else: # pragma: no cover
np.ndarray.__setstate__(self, state)
def __add__(self, other):
if isinstance(other, Index):
return self.union(other)
elif isinstance(other, (DateOffset, timedelta)):
return self._add_delta(other)
elif isinstance(other, np.timedelta64):
return self._add_delta(other)
elif com.is_integer(other):
return self.shift(other)
else: # pragma: no cover
raise TypeError(other)
def __sub__(self, other):
if isinstance(other, Index):
return self.diff(other)
elif isinstance(other, (DateOffset, timedelta)):
return self._add_delta(-other)
elif isinstance(other, np.timedelta64):
return self._add_delta(-other)
elif com.is_integer(other):
return self.shift(-other)
else: # pragma: no cover
raise TypeError(other)
def _add_delta(self, delta):
if isinstance(delta, (Tick, timedelta)):
inc = offsets._delta_to_nanoseconds(delta)
new_values = (self.asi8 + inc).view(_NS_DTYPE)
tz = 'UTC' if self.tz is not None else None
result = DatetimeIndex(new_values, tz=tz, freq='infer')
utc = _utc()
if self.tz is not None and self.tz is not utc:
result = result.tz_convert(self.tz)
elif isinstance(delta, np.timedelta64):
new_values = self.to_series() + delta
result = DatetimeIndex(new_values, tz=self.tz, freq='infer')
else:
new_values = self.astype('O') + delta
result = DatetimeIndex(new_values, tz=self.tz, freq='infer')
return result
def __contains__(self, key):
try:
res = self.get_loc(key)
return np.isscalar(res) or type(res) == slice
except (KeyError, TypeError):
return False
def _format_with_header(self, header, **kwargs):
return header + self._format_native_types(**kwargs)
def _format_native_types(self, na_rep=u('NaT'),
date_format=None, **kwargs):
data = self._get_object_index()
from pandas.core.format import Datetime64Formatter
return Datetime64Formatter(values=data,
nat_rep=na_rep,
date_format=date_format,
justify='all').get_result()
def isin(self, values):
"""
Compute boolean array of whether each index value is found in the
passed set of values
Parameters
----------
values : set or sequence of values
Returns
-------
is_contained : ndarray (boolean dtype)
"""
if not isinstance(values, DatetimeIndex):
try:
values = DatetimeIndex(values)
except ValueError:
return self.asobject.isin(values)
value_set = set(values.asi8)
return lib.ismember(self.asi8, value_set)
def to_datetime(self, dayfirst=False):
return self.copy()
def groupby(self, f):
objs = self.asobject
return _algos.groupby_object(objs, f)
def summary(self, name=None):
if len(self) > 0:
index_summary = ', %s to %s' % (com.pprint_thing(self[0]),
com.pprint_thing(self[-1]))
else:
index_summary = ''
if name is None:
name = type(self).__name__
result = '%s: %s entries%s' % (com.pprint_thing(name),
len(self), index_summary)
if self.freq:
result += '\nFreq: %s' % self.freqstr
return result
def get_duplicates(self):
values = Index.get_duplicates(self)
return DatetimeIndex(values)
def astype(self, dtype):
dtype = np.dtype(dtype)
if dtype == np.object_:
return self.asobject
elif dtype == _INT64_DTYPE:
return self.asi8.copy()
else: # pragma: no cover
raise ValueError('Cannot cast DatetimeIndex to dtype %s' % dtype)
def _get_time_micros(self):
utc = _utc()
values = self.asi8
if self.tz is not None and self.tz is not utc:
values = self._local_timestamps()
return tslib.get_time_micros(values)
@property
def asobject(self):
"""
Convert to Index of datetime objects
"""
if isnull(self).any():
msg = 'DatetimeIndex with NaT cannot be converted to object'
raise ValueError(msg)
return self._get_object_index()
def tolist(self):
"""
See ndarray.tolist
"""
return list(self.asobject)
def _get_object_index(self):
boxfunc = lambda x: Timestamp(x, offset=self.offset, tz=self.tz)
boxed_values = lib.map_infer(self.asi8, boxfunc)
return Index(boxed_values, dtype=object)
def to_pydatetime(self):
"""
Return DatetimeIndex as object ndarray of datetime.datetime objects
Returns
-------
datetimes : ndarray
"""
return tslib.ints_to_pydatetime(self.asi8, tz=self.tz)
def to_period(self, freq=None):
"""
Cast to PeriodIndex at a particular frequency
"""
from pandas.tseries.period import PeriodIndex
if self.freq is None and freq is None:
msg = "You must pass a freq argument as current index has none."
raise ValueError(msg)
if freq is None:
freq = get_period_alias(self.freqstr)
return PeriodIndex(self.values, freq=freq, tz=self.tz)
def order(self, return_indexer=False, ascending=True):
"""
Return sorted copy of Index
"""
if return_indexer:
_as = self.argsort()
if not ascending:
_as = _as[::-1]
sorted_index = self.take(_as)
return sorted_index, _as
else:
sorted_values = np.sort(self.values)
if not ascending:
sorted_values = sorted_values[::-1]
return self._simple_new(sorted_values, self.name, None,
self.tz)
def snap(self, freq='S'):
"""
Snap time stamps to nearest occurring frequency
"""
# Superdumb, punting on any optimizing
freq = to_offset(freq)
snapped = np.empty(len(self), dtype=_NS_DTYPE)
for i, v in enumerate(self):
s = v
if not freq.onOffset(s):
t0 = freq.rollback(s)
t1 = freq.rollforward(s)
if abs(s - t0) < abs(t1 - s):
s = t0
else:
s = t1
snapped[i] = s
# we know it conforms; skip check
return DatetimeIndex(snapped, freq=freq, verify_integrity=False)
def shift(self, n, freq=None):
"""
Specialized shift which produces a DatetimeIndex
Parameters
----------
n : int
Periods to shift by
freq : DateOffset or timedelta-like, optional
Returns
-------
shifted : DatetimeIndex
"""
if freq is not None and freq != self.offset:
if isinstance(freq, compat.string_types):
freq = to_offset(freq)
result = Index.shift(self, n, freq)
result.tz = self.tz
return result
if n == 0:
# immutable so OK
return self
if self.offset is None:
raise ValueError("Cannot shift with no offset")
start = self[0] + n * self.offset
end = self[-1] + n * self.offset
return DatetimeIndex(start=start, end=end, freq=self.offset,
name=self.name, tz=self.tz)
def repeat(self, repeats, axis=None):
"""
Analogous to ndarray.repeat
"""
return DatetimeIndex(self.values.repeat(repeats),
name=self.name)
def take(self, indices, axis=0):
"""
Analogous to ndarray.take
"""
maybe_slice = lib.maybe_indices_to_slice(com._ensure_int64(indices))
if isinstance(maybe_slice, slice):
return self[maybe_slice]
indices = com._ensure_platform_int(indices)
taken = self.values.take(indices, axis=axis)
return self._simple_new(taken, self.name, None, self.tz)
def unique(self):
"""
Index.unique with handling for DatetimeIndex metadata
Returns
-------
result : DatetimeIndex
"""
result = Int64Index.unique(self)
return DatetimeIndex._simple_new(result, tz=self.tz,
name=self.name)
def union(self, other):
"""
Specialized union for DatetimeIndex objects. If combine
overlapping ranges with the same DateOffset, will be much
faster than Index.union
Parameters
----------
other : DatetimeIndex or array-like
Returns
-------
y : Index or DatetimeIndex
"""
if not isinstance(other, DatetimeIndex):
try:
other = DatetimeIndex(other)
except TypeError:
pass
this, other = self._maybe_utc_convert(other)
if this._can_fast_union(other):
return this._fast_union(other)
else:
result = Index.union(this, other)
if isinstance(result, DatetimeIndex):
result.tz = this.tz
if result.freq is None:
result.offset = to_offset(result.inferred_freq)
return result
def union_many(self, others):
"""
A bit of a hack to accelerate unioning a collection of indexes
"""
this = self
for other in others:
if not isinstance(this, DatetimeIndex):
this = Index.union(this, other)
continue
if not isinstance(other, DatetimeIndex):
try:
other = DatetimeIndex(other)
except TypeError:
pass
this, other = this._maybe_utc_convert(other)
if this._can_fast_union(other):
this = this._fast_union(other)
else:
tz = this.tz
this = Index.union(this, other)
if isinstance(this, DatetimeIndex):
this.tz = tz
if this.freq is None:
this.offset = to_offset(this.inferred_freq)
return this
def append(self, other):
"""
Append a collection of Index options together
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
appended : Index
"""
name = self.name
to_concat = [self]
if isinstance(other, (list, tuple)):
to_concat = to_concat + list(other)
else:
to_concat.append(other)
for obj in to_concat:
if isinstance(obj, Index) and obj.name != name:
name = None
break
to_concat = self._ensure_compat_concat(to_concat)
to_concat, factory = _process_concat_data(to_concat, name)
return factory(to_concat)
def join(self, other, how='left', level=None, return_indexers=False):
"""
See Index.join
"""
if (not isinstance(other, DatetimeIndex) and len(other) > 0 and
other.inferred_type not in ('floating', 'mixed-integer',
'mixed-integer-float', 'mixed')):
try:
other = DatetimeIndex(other)
except (TypeError, ValueError):
pass
this, other = self._maybe_utc_convert(other)
return Index.join(this, other, how=how, level=level,
return_indexers=return_indexers)
def _maybe_utc_convert(self, other):
this = self
if isinstance(other, DatetimeIndex):
if self.tz is not None:
if other.tz is None:
raise TypeError('Cannot join tz-naive with tz-aware '
'DatetimeIndex')
elif other.tz is not None:
raise TypeError('Cannot join tz-naive with tz-aware '
'DatetimeIndex')
if self.tz != other.tz:
this = self.tz_convert('UTC')
other = other.tz_convert('UTC')
return this, other
def _wrap_joined_index(self, joined, other):
name = self.name if self.name == other.name else None
if (isinstance(other, DatetimeIndex)
and self.offset == other.offset
and self._can_fast_union(other)):
joined = self._view_like(joined)
joined.name = name
return joined
else:
tz = getattr(other, 'tz', None)
return self._simple_new(joined, name, tz=tz)
def _can_fast_union(self, other):
if not isinstance(other, DatetimeIndex):
return False
offset = self.offset
if offset is None or offset != other.offset:
return False
if not self.is_monotonic or not other.is_monotonic:
return False
if len(self) == 0 or len(other) == 0:
return True
# to make our life easier, "sort" the two ranges
if self[0] <= other[0]:
left, right = self, other
else:
left, right = other, self
right_start = right[0]
left_end = left[-1]
# Only need to "adjoin", not overlap
return (right_start == left_end + offset) or right_start in left
def _fast_union(self, other):
if len(other) == 0:
return self.view(type(self))
if len(self) == 0:
return other.view(type(self))
# to make our life easier, "sort" the two ranges
if self[0] <= other[0]:
left, right = self, other
else:
left, right = other, self
left_start, left_end = left[0], left[-1]
right_end = right[-1]
if not self.offset._should_cache():
# concatenate dates
if left_end < right_end:
loc = right.searchsorted(left_end, side='right')
right_chunk = right.values[loc:]
dates = com._concat_compat((left.values, right_chunk))
return self._view_like(dates)
else:
return left
else:
return type(self)(start=left_start,
end=max(left_end, right_end),
freq=left.offset)
def __array_finalize__(self, obj):
if self.ndim == 0: # pragma: no cover
return self.item()
self.offset = getattr(obj, 'offset', None)
self.tz = getattr(obj, 'tz', None)
self.name = getattr(obj, 'name', None)
self._reset_identity()
def intersection(self, other):
"""
Specialized intersection for DatetimeIndex objects. May be much faster
than Index.intersection
Parameters
----------
other : DatetimeIndex or array-like
Returns
-------
y : Index or DatetimeIndex
"""
if not isinstance(other, DatetimeIndex):
try:
other = DatetimeIndex(other)
except (TypeError, ValueError):
pass
result = Index.intersection(self, other)
if isinstance(result, DatetimeIndex):
if result.freq is None:
result.offset = to_offset(result.inferred_freq)
return result
elif (other.offset is None or self.offset is None or
other.offset != self.offset or
not other.offset.isAnchored() or
(not self.is_monotonic or not other.is_monotonic)):
result = Index.intersection(self, other)
if isinstance(result, DatetimeIndex):
if result.freq is None:
result.offset = to_offset(result.inferred_freq)
return result
if len(self) == 0:
return self
if len(other) == 0:
return other
# to make our life easier, "sort" the two ranges
if self[0] <= other[0]:
left, right = self, other
else:
left, right = other, self
end = min(left[-1], right[-1])
start = right[0]
if end < start:
return type(self)(data=[])
else:
lslice = slice(*left.slice_locs(start, end))
left_chunk = left.values[lslice]
return self._view_like(left_chunk)
def _partial_date_slice(self, reso, parsed, use_lhs=True, use_rhs=True):
is_monotonic = self.is_monotonic
if reso == 'year':
t1 = Timestamp(datetime(parsed.year, 1, 1), tz=self.tz)
t2 = Timestamp(datetime(parsed.year, 12, 31, 23, 59, 59, 999999), tz=self.tz)
elif reso == 'month':
d = tslib.monthrange(parsed.year, parsed.month)[1]
t1 = Timestamp(datetime(parsed.year, parsed.month, 1), tz=self.tz)
t2 = Timestamp(datetime(parsed.year, parsed.month, d, 23, 59, 59, 999999), tz=self.tz)
elif reso == 'quarter':
qe = (((parsed.month - 1) + 2) % 12) + 1 # two months ahead
d = tslib.monthrange(parsed.year, qe)[1] # at end of month
t1 = Timestamp(datetime(parsed.year, parsed.month, 1), tz=self.tz)
t2 = Timestamp(datetime(parsed.year, qe, d, 23, 59, 59, 999999), tz=self.tz)
elif (reso == 'day' and (self._resolution < Resolution.RESO_DAY or not is_monotonic)):
st = datetime(parsed.year, parsed.month, parsed.day)
t1 = Timestamp(st, tz=self.tz)
t2 = st + offsets.Day()
t2 = Timestamp(Timestamp(t2, tz=self.tz).value - 1)
elif (reso == 'hour' and (
self._resolution < Resolution.RESO_HR or not is_monotonic)):
st = datetime(parsed.year, parsed.month, parsed.day,
hour=parsed.hour)
t1 = Timestamp(st, tz=self.tz)
t2 = Timestamp(Timestamp(st + offsets.Hour(),
tz=self.tz).value - 1)
elif (reso == 'minute' and (
self._resolution < Resolution.RESO_MIN or not is_monotonic)):
st = datetime(parsed.year, parsed.month, parsed.day,
hour=parsed.hour, minute=parsed.minute)
t1 = Timestamp(st, tz=self.tz)
t2 = Timestamp(Timestamp(st + offsets.Minute(),
tz=self.tz).value - 1)
elif (reso == 'second' and (
self._resolution == Resolution.RESO_SEC or not is_monotonic)):
st = datetime(parsed.year, parsed.month, parsed.day,
hour=parsed.hour, minute=parsed.minute, second=parsed.second)
t1 = Timestamp(st, tz=self.tz)
t2 = Timestamp(Timestamp(st + offsets.Second(),
tz=self.tz).value - 1)
else:
raise KeyError
stamps = self.asi8
if is_monotonic:
# we are out of range
if len(stamps) and (
(use_lhs and t1.value < stamps[0] and t2.value < stamps[0]) or (
(use_rhs and t1.value > stamps[-1] and t2.value > stamps[-1]))):
raise KeyError
# a monotonic (sorted) series can be sliced
left = stamps.searchsorted(t1.value, side='left') if use_lhs else None
right = stamps.searchsorted(t2.value, side='right') if use_rhs else None
return slice(left, right)
lhs_mask = (stamps >= t1.value) if use_lhs else True
rhs_mask = (stamps <= t2.value) if use_rhs else True
# try to find a the dates
return (lhs_mask & rhs_mask).nonzero()[0]
def _possibly_promote(self, other):
if other.inferred_type == 'date':
other = DatetimeIndex(other)
return self, other
def get_value(self, series, key):
"""
Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing
"""
timestamp = None
#if isinstance(key, Timestamp):
# timestamp = key
#el
if isinstance(key, datetime):
# needed to localize naive datetimes
timestamp = Timestamp(key, tz=self.tz)
if timestamp:
return self.get_value_maybe_box(series, timestamp)
try:
return _maybe_box(self, Index.get_value(self, series, key), series, key)
except KeyError:
try:
loc = self._get_string_slice(key)
return series[loc]
except (TypeError, ValueError, KeyError):
pass
if isinstance(key, time):
locs = self.indexer_at_time(key)
return series.take(locs)
try:
return self.get_value_maybe_box(series, key)
except (TypeError, ValueError, KeyError):
raise KeyError(key)
def get_value_maybe_box(self, series, key):
# needed to localize naive datetimes
if self.tz is not None:
key = Timestamp(key, tz=self.tz)
elif not isinstance(key, Timestamp):
key = Timestamp(key)
values = self._engine.get_value(_values_from_object(series), key)
return _maybe_box(self, values, series, key)
def get_loc(self, key):
"""
Get integer location for requested label
Returns
-------
loc : int
"""
if isinstance(key, datetime):
# needed to localize naive datetimes
stamp = Timestamp(key, tz=self.tz)
return self._engine.get_loc(stamp)
try:
return Index.get_loc(self, key)
except (KeyError, ValueError):
try:
return self._get_string_slice(key)
except (TypeError, KeyError, ValueError):
pass
if isinstance(key, time):
return self.indexer_at_time(key)
try:
stamp = Timestamp(key, tz=self.tz)
return self._engine.get_loc(stamp)
except (KeyError, ValueError):
raise KeyError(key)
def _get_string_slice(self, key, use_lhs=True, use_rhs=True):
freq = getattr(self, 'freqstr',
getattr(self, 'inferred_freq', None))
_, parsed, reso = parse_time_string(key, freq)
loc = self._partial_date_slice(reso, parsed, use_lhs=use_lhs,
use_rhs=use_rhs)
return loc
def slice_indexer(self, start=None, end=None, step=None):
"""
Index.slice_indexer, customized to handle time slicing
"""
if isinstance(start, time) and isinstance(end, time):
if step is not None and step != 1:
raise ValueError('Must have step size of 1 with time slices')
return self.indexer_between_time(start, end)
if isinstance(start, time) or isinstance(end, time):
raise KeyError('Cannot mix time and non-time slice keys')
if isinstance(start, float) or isinstance(end, float):
raise TypeError('Cannot index datetime64 with float keys')
return Index.slice_indexer(self, start, end, step)
def slice_locs(self, start=None, end=None):
"""
Index.slice_locs, customized to handle partial ISO-8601 string slicing
"""
if isinstance(start, compat.string_types) or isinstance(end, compat.string_types):
if self.is_monotonic:
try:
if start:
start_loc = self._get_string_slice(start).start
else:
start_loc = 0
if end:
end_loc = self._get_string_slice(end).stop
else:
end_loc = len(self)
return start_loc, end_loc
except KeyError:
pass
else:
# can't use a slice indexer because we are not sorted!
# so create an indexer directly
try:
if start:
start_loc = self._get_string_slice(start,
use_rhs=False)
else:
start_loc = np.arange(len(self))
if end:
end_loc = self._get_string_slice(end, use_lhs=False)
else:
end_loc = np.arange(len(self))
return start_loc, end_loc
except KeyError:
pass
if isinstance(start, time) or isinstance(end, time):
raise KeyError('Cannot use slice_locs with time slice keys')
return Index.slice_locs(self, start, end)
def __getitem__(self, key):
"""Override numpy.ndarray's __getitem__ method to work as desired"""
arr_idx = self.view(np.ndarray)
if np.isscalar(key):
val = arr_idx[key]
return Timestamp(val, offset=self.offset, tz=self.tz)
else:
if com._is_bool_indexer(key):
key = np.asarray(key)
key = lib.maybe_booleans_to_slice(key.view(np.uint8))
new_offset = None
if isinstance(key, slice):
if self.offset is not None and key.step is not None:
new_offset = key.step * self.offset
else:
new_offset = self.offset
result = arr_idx[key]
if result.ndim > 1:
return result
return self._simple_new(result, self.name, new_offset, self.tz)
_getitem_slice = __getitem__
# Try to run function on index first, and then on elements of index
# Especially important for group-by functionality
def map(self, f):
try:
result = f(self)
if not isinstance(result, np.ndarray):
raise TypeError
return result
except Exception:
return _algos.arrmap_object(self.asobject, f)
# alias to offset
@property
def freq(self):
return self.offset
@cache_readonly
def inferred_freq(self):
try:
return infer_freq(self)
except ValueError:
return None
@property
def freqstr(self):
return self.offset.freqstr
year = _field_accessor('year', 'Y')
month = _field_accessor('month', 'M', "The month as January=1, December=12")
day = _field_accessor('day', 'D')
hour = _field_accessor('hour', 'h')
minute = _field_accessor('minute', 'm')
second = _field_accessor('second', 's')
microsecond = _field_accessor('microsecond', 'us')
nanosecond = _field_accessor('nanosecond', 'ns')
weekofyear = _field_accessor('weekofyear', 'woy')
week = weekofyear
dayofweek = _field_accessor('dayofweek', 'dow',
"The day of the week with Monday=0, Sunday=6")
weekday = dayofweek
dayofyear = _field_accessor('dayofyear', 'doy')
quarter = _field_accessor('quarter', 'q')
@property
def time(self):
"""
Returns numpy array of datetime.time. The time part of the Timestamps.
"""
# can't call self.map() which tries to treat func as ufunc
# and causes recursion warnings on python 2.6
return _algos.arrmap_object(self.asobject, lambda x: x.time())
@property
def date(self):
"""
Returns numpy array of datetime.date. The date part of the Timestamps.
"""
return _algos.arrmap_object(self.asobject, lambda x: x.date())
def normalize(self):
"""
Return DatetimeIndex with times to midnight. Length is unaltered
Returns
-------
normalized : DatetimeIndex
"""
new_values = tslib.date_normalize(self.asi8, self.tz)
return DatetimeIndex(new_values, freq='infer', name=self.name,
tz=self.tz)
def __iter__(self):
return iter(self._get_object_index())
def searchsorted(self, key, side='left'):
if isinstance(key, np.ndarray):
key = np.array(key, dtype=_NS_DTYPE, copy=False)
else:
key = _to_m8(key, tz=self.tz)
return self.values.searchsorted(key, side=side)
def is_type_compatible(self, typ):
return typ == self.inferred_type or typ == 'datetime'
def argmin(self):
# hack to workaround argmin failure
try:
return self.values.argmin()
except Exception: # pragma: no cover
return self.asi8.argmin()
@property
def inferred_type(self):
# b/c datetime is represented as microseconds since the epoch, make
# sure we can't have ambiguous indexing
return 'datetime64'
@property
def dtype(self):
return _NS_DTYPE
@property
def is_all_dates(self):
return True
@cache_readonly
def is_normalized(self):
"""
Returns True if all of the dates are at midnight ("no time")
"""
return tslib.dates_normalized(self.asi8, self.tz)
@cache_readonly
def resolution(self):
"""
Returns day, hour, minute, second, or microsecond
"""
reso = self._resolution
return get_reso_string(reso)
@cache_readonly
def _resolution(self):
return tslib.resolution(self.asi8, self.tz)
def equals(self, other):
"""
Determines if two Index objects contain the same elements.
"""
if self.is_(other):
return True
if (not hasattr(other, 'inferred_type') or
other.inferred_type != 'datetime64'):
if self.offset is not None:
return False
try:
other = DatetimeIndex(other)
except:
return False
if self.tz is not None:
if other.tz is None:
return False
same_zone = tslib.get_timezone(
self.tz) == tslib.get_timezone(other.tz)
else:
if other.tz is not None:
return False
same_zone = True
return same_zone and np.array_equal(self.asi8, other.asi8)
def insert(self, loc, item):
"""
Make new Index inserting new item at location
Parameters
----------
loc : int
item : object
if not either a Python datetime or a numpy integer-like, returned
Index dtype will be object rather than datetime.
Returns
-------
new_index : Index
"""
if isinstance(item, datetime):
item = _to_m8(item, tz=self.tz)
try:
new_index = np.concatenate((self[:loc].asi8,
[item.view(np.int64)],
self[loc:].asi8))
return DatetimeIndex(new_index, freq='infer')
except (AttributeError, TypeError):
# fall back to object index
if isinstance(item,compat.string_types):
return self.asobject.insert(loc, item)
raise TypeError("cannot insert DatetimeIndex with incompatible label")
def delete(self, loc):
"""
Make new DatetimeIndex with passed location deleted
Returns
-------
new_index : DatetimeIndex
"""
arr = np.delete(self.values, loc)
return DatetimeIndex(arr, tz=self.tz)
def _view_like(self, ndarray):
result = ndarray.view(type(self))
result.offset = self.offset
result.tz = self.tz
result.name = self.name
return result
def tz_convert(self, tz):
"""
Convert DatetimeIndex from one time zone to another (using pytz)
Returns
-------
normalized : DatetimeIndex
"""
tz = tools._maybe_get_tz(tz)
if self.tz is None:
# tz naive, use tz_localize
raise TypeError('Cannot convert tz-naive timestamps, use '
'tz_localize to localize')
# No conversion since timestamps are all UTC to begin with
return self._simple_new(self.values, self.name, self.offset, tz)
def tz_localize(self, tz, infer_dst=False):
"""
Localize tz-naive DatetimeIndex to given time zone (using pytz)
Parameters
----------
tz : string or pytz.timezone
Time zone for time. Corresponding timestamps would be converted to
time zone of the TimeSeries
infer_dst : boolean, default False
Attempt to infer fall dst-transition hours based on order
Returns
-------
localized : DatetimeIndex
"""
if self.tz is not None:
raise TypeError("Already tz-aware, use tz_convert to convert.")
tz = tools._maybe_get_tz(tz)
# Convert to UTC
new_dates = tslib.tz_localize_to_utc(self.asi8, tz, infer_dst=infer_dst)
new_dates = new_dates.view(_NS_DTYPE)
return self._simple_new(new_dates, self.name, self.offset, tz)
def indexer_at_time(self, time, asof=False):
"""
Select values at particular time of day (e.g. 9:30AM)
Parameters
----------
time : datetime.time or string
tz : string or pytz.timezone
Time zone for time. Corresponding timestamps would be converted to
time zone of the TimeSeries
Returns
-------
values_at_time : TimeSeries
"""
from dateutil.parser import parse
if asof:
raise NotImplementedError
if isinstance(time, compat.string_types):
time = parse(time).time()
if time.tzinfo:
# TODO
raise NotImplementedError
time_micros = self._get_time_micros()
micros = _time_to_micros(time)
return (micros == time_micros).nonzero()[0]
def indexer_between_time(self, start_time, end_time, include_start=True,
include_end=True):
"""
Select values between particular times of day (e.g., 9:00-9:30AM)
Parameters
----------
start_time : datetime.time or string
end_time : datetime.time or string
include_start : boolean, default True
include_end : boolean, default True
tz : string or pytz.timezone, default None
Returns
-------
values_between_time : TimeSeries
"""
from dateutil.parser import parse
if isinstance(start_time, compat.string_types):
start_time = parse(start_time).time()
if isinstance(end_time, compat.string_types):
end_time = parse(end_time).time()
if start_time.tzinfo or end_time.tzinfo:
raise NotImplementedError
time_micros = self._get_time_micros()
start_micros = _time_to_micros(start_time)
end_micros = _time_to_micros(end_time)
if include_start and include_end:
lop = rop = operator.le
elif include_start:
lop = operator.le
rop = operator.lt
elif include_end:
lop = operator.lt
rop = operator.le
else:
lop = rop = operator.lt
if start_time <= end_time:
join_op = operator.and_
else:
join_op = operator.or_
mask = join_op(lop(start_micros, time_micros),
rop(time_micros, end_micros))
return mask.nonzero()[0]
def min(self, axis=None):
"""
Overridden ndarray.min to return a Timestamp
"""
if self.is_monotonic:
return self[0]
else:
min_stamp = self.asi8.min()
return Timestamp(min_stamp, tz=self.tz)
def max(self, axis=None):
"""
Overridden ndarray.max to return a Timestamp
"""
if self.is_monotonic:
return self[-1]
else:
max_stamp = self.asi8.max()
return Timestamp(max_stamp, tz=self.tz)
def _generate_regular_range(start, end, periods, offset):
if isinstance(offset, Tick):
stride = offset.nanos
if periods is None:
b = Timestamp(start).value
e = Timestamp(end).value
e += stride - e % stride
# end.tz == start.tz by this point due to _generate implementation
tz = start.tz
elif start is not None:
b = Timestamp(start).value
e = b + periods * stride
tz = start.tz
elif end is not None:
e = Timestamp(end).value + stride
b = e - periods * stride
tz = end.tz
else:
raise NotImplementedError
data = np.arange(b, e, stride, dtype=np.int64)
data = DatetimeIndex._simple_new(data, None, tz=tz)
else:
if isinstance(start, Timestamp):
start = start.to_pydatetime()
if isinstance(end, Timestamp):
end = end.to_pydatetime()
xdr = generate_range(start=start, end=end,
periods=periods, offset=offset)
dates = list(xdr)
# utc = len(dates) > 0 and dates[0].tzinfo is not None
data = tools.to_datetime(dates)
return data
def date_range(start=None, end=None, periods=None, freq='D', tz=None,
normalize=False, name=None, closed=None):
"""
Return a fixed frequency datetime index, with day (calendar) as the default
frequency
Parameters
----------
start : string or datetime-like, default None
Left bound for generating dates
end : string or datetime-like, default None
Right bound for generating dates
periods : integer or None, default None
If None, must specify start and end
freq : string or DateOffset, default 'D' (calendar daily)
Frequency strings can have multiples, e.g. '5H'
tz : string or None
Time zone name for returning localized DatetimeIndex, for example
Asia/Hong_Kong
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
name : str, default None
Name of the resulting index
closed : string or None, default None
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None)
Notes
-----
2 of start, end, or periods must be specified
Returns
-------
rng : DatetimeIndex
"""
return DatetimeIndex(start=start, end=end, periods=periods,
freq=freq, tz=tz, normalize=normalize, name=name,
closed=closed)
def bdate_range(start=None, end=None, periods=None, freq='B', tz=None,
normalize=True, name=None, closed=None):
"""
Return a fixed frequency datetime index, with business day as the default
frequency
Parameters
----------
start : string or datetime-like, default None
Left bound for generating dates
end : string or datetime-like, default None
Right bound for generating dates
periods : integer or None, default None
If None, must specify start and end
freq : string or DateOffset, default 'B' (business daily)
Frequency strings can have multiples, e.g. '5H'
tz : string or None
Time zone name for returning localized DatetimeIndex, for example
Asia/Beijing
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
name : str, default None
Name for the resulting index
closed : string or None, default None
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None)
Notes
-----
2 of start, end, or periods must be specified
Returns
-------
rng : DatetimeIndex
"""
return DatetimeIndex(start=start, end=end, periods=periods,
freq=freq, tz=tz, normalize=normalize, name=name,
closed=closed)
def cdate_range(start=None, end=None, periods=None, freq='C', tz=None,
normalize=True, name=None, closed=None, **kwargs):
"""
**EXPERIMENTAL** Return a fixed frequency datetime index, with
CustomBusinessDay as the default frequency
.. warning:: EXPERIMENTAL
The CustomBusinessDay class is not officially supported and the API is
likely to change in future versions. Use this at your own risk.
Parameters
----------
start : string or datetime-like, default None
Left bound for generating dates
end : string or datetime-like, default None
Right bound for generating dates
periods : integer or None, default None
If None, must specify start and end
freq : string or DateOffset, default 'C' (CustomBusinessDay)
Frequency strings can have multiples, e.g. '5H'
tz : string or None
Time zone name for returning localized DatetimeIndex, for example
Asia/Beijing
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
name : str, default None
Name for the resulting index
weekmask : str, Default 'Mon Tue Wed Thu Fri'
weekmask of valid business days, passed to ``numpy.busdaycalendar``
holidays : list
list/array of dates to exclude from the set of valid business days,
passed to ``numpy.busdaycalendar``
closed : string or None, default None
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None)
Notes
-----
2 of start, end, or periods must be specified
Returns
-------
rng : DatetimeIndex
"""
if freq=='C':
holidays = kwargs.pop('holidays', [])
weekmask = kwargs.pop('weekmask', 'Mon Tue Wed Thu Fri')
freq = CDay(holidays=holidays, weekmask=weekmask)
return DatetimeIndex(start=start, end=end, periods=periods, freq=freq,
tz=tz, normalize=normalize, name=name,
closed=closed, **kwargs)
def _to_m8(key, tz=None):
'''
Timestamp-like => dt64
'''
if not isinstance(key, Timestamp):
# this also converts strings
key = Timestamp(key, tz=tz)
return np.int64(tslib.pydt_to_i8(key)).view(_NS_DTYPE)
def _str_to_dt_array(arr, offset=None, dayfirst=None, yearfirst=None):
def parser(x):
result = parse_time_string(x, offset, dayfirst=dayfirst,
yearfirst=yearfirst)
return result[0]
arr = np.asarray(arr, dtype=object)
data = _algos.arrmap_object(arr, parser)
return tools.to_datetime(data)
_CACHE_START = Timestamp(datetime(1950, 1, 1))
_CACHE_END = Timestamp(datetime(2030, 1, 1))
_daterange_cache = {}
def _naive_in_cache_range(start, end):
if start is None or end is None:
return False
else:
if start.tzinfo is not None or end.tzinfo is not None:
return False
return _in_range(start, end, _CACHE_START, _CACHE_END)
def _in_range(start, end, rng_start, rng_end):
return start > rng_start and end < rng_end
def _use_cached_range(offset, _normalized, start, end):
return (offset._should_cache() and
not (offset._normalize_cache and not _normalized) and
_naive_in_cache_range(start, end))
def _time_to_micros(time):
seconds = time.hour * 60 * 60 + 60 * time.minute + time.second
return 1000000 * seconds + time.microsecond
def _process_concat_data(to_concat, name):
klass = Index
kwargs = {}
concat = np.concatenate
all_dti = True
need_utc_convert = False
has_naive = False
tz = None
for x in to_concat:
if not isinstance(x, DatetimeIndex):
all_dti = False
else:
if tz is None:
tz = x.tz
if x.tz is None:
has_naive = True
if x.tz != tz:
need_utc_convert = True
tz = 'UTC'
if all_dti:
need_obj_convert = False
if has_naive and tz is not None:
need_obj_convert = True
if need_obj_convert:
to_concat = [x.asobject.values for x in to_concat]
else:
if need_utc_convert:
to_concat = [x.tz_convert('UTC').values for x in to_concat]
else:
to_concat = [x.values for x in to_concat]
# well, technically not a "class" anymore...oh well
klass = DatetimeIndex._simple_new
kwargs = {'tz': tz}
concat = com._concat_compat
else:
for i, x in enumerate(to_concat):
if isinstance(x, DatetimeIndex):
to_concat[i] = x.asobject.values
elif isinstance(x, Index):
to_concat[i] = x.values
factory_func = lambda x: klass(concat(x), name=name, **kwargs)
return to_concat, factory_func
|