/usr/lib/python2.7/dist-packages/pandas/io/parsers.py is in python-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 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 | """
Module contains tools for processing files into DataFrames or other objects
"""
from __future__ import print_function
from pandas.compat import range, lrange, StringIO, lzip, zip, string_types, map
from pandas import compat
import re
import csv
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas.core.frame import DataFrame
import datetime
import pandas.core.common as com
from pandas.core.config import get_option
from pandas.io.date_converters import generic_parser
from pandas.io.common import get_filepath_or_buffer
from pandas.tseries import tools
from pandas.util.decorators import Appender
import pandas.lib as lib
import pandas.tslib as tslib
import pandas.parser as _parser
_parser_params = """Also supports optionally iterating or breaking of the file
into chunks.
Parameters
----------
filepath_or_buffer : string or file handle / StringIO. The string could be
a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a
host is expected. For instance, a local file could be
file ://localhost/path/to/table.csv
%s
lineterminator : string (length 1), default None
Character to break file into lines. Only valid with C parser
quotechar : string (length 1)
The character used to denote the start and end of a quoted item. Quoted
items can include the delimiter and it will be ignored.
quoting : int or csv.QUOTE_* instance, default None
Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
Default (None) results in QUOTE_MINIMAL behavior.
skipinitialspace : boolean, default False
Skip spaces after delimiter
escapechar : string
dtype : Type name or dict of column -> type
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
compression : {'gzip', 'bz2', None}, default None
For on-the-fly decompression of on-disk data
dialect : string or csv.Dialect instance, default None
If None defaults to Excel dialect. Ignored if sep longer than 1 char
See csv.Dialect documentation for more details
header : int row number(s) to use as the column names, and the start of the
data. Defaults to 0 if no ``names`` passed, otherwise ``None``. Explicitly
pass ``header=0`` to be able to replace existing names. The header can be
a list of integers that specify row locations for a multi-index on the
columns E.g. [0,1,3]. Intervening rows that are not specified will be
skipped. (E.g. 2 in this example are skipped)
skiprows : list-like or integer
Row numbers to skip (0-indexed) or number of rows to skip (int)
at the start of the file
index_col : int or sequence or False, default None
Column to use as the row labels of the DataFrame. If a sequence is given, a
MultiIndex is used. If you have a malformed file with delimiters at the end
of each line, you might consider index_col=False to force pandas to _not_
use the first column as the index (row names)
names : array-like
List of column names to use. If file contains no header row, then you
should explicitly pass header=None
prefix : string or None (default)
Prefix to add to column numbers when no header, e.g 'X' for X0, X1, ...
na_values : list-like or dict, default None
Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values
true_values : list
Values to consider as True
false_values : list
Values to consider as False
keep_default_na : bool, default True
If na_values are specified and keep_default_na is False the default NaN
values are overridden, otherwise they're appended to
parse_dates : boolean, list of ints or names, list of lists, or dict
If True -> try parsing the index.
If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.
If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.
{'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo'
A fast-path exists for iso8601-formatted dates.
keep_date_col : boolean, default False
If True and parse_dates specifies combining multiple columns then
keep the original columns.
date_parser : function
Function to use for converting a sequence of string columns to an
array of datetime instances. The default uses dateutil.parser.parser
to do the conversion.
dayfirst : boolean, default False
DD/MM format dates, international and European format
thousands : str, default None
Thousands separator
comment : str, default None
Indicates remainder of line should not be parsed
Does not support line commenting (will return empty line)
decimal : str, default '.'
Character to recognize as decimal point. E.g. use ',' for European data
nrows : int, default None
Number of rows of file to read. Useful for reading pieces of large files
iterator : boolean, default False
Return TextFileReader object
chunksize : int, default None
Return TextFileReader object for iteration
skipfooter : int, default 0
Number of line at bottom of file to skip
converters : dict. optional
Dict of functions for converting values in certain columns. Keys can either
be integers or column labels
verbose : boolean, default False
Indicate number of NA values placed in non-numeric columns
delimiter : string, default None
Alternative argument name for sep. Regular expressions are accepted.
encoding : string, default None
Encoding to use for UTF when reading/writing (ex. 'utf-8')
squeeze : boolean, default False
If the parsed data only contains one column then return a Series
na_filter: boolean, default True
Detect missing value markers (empty strings and the value of na_values). In
data without any NAs, passing na_filter=False can improve the performance
of reading a large file
usecols : array-like
Return a subset of the columns.
Results in much faster parsing time and lower memory usage.
mangle_dupe_cols: boolean, default True
Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'
tupleize_cols: boolean, default False
Leave a list of tuples on columns as is (default is to convert to
a Multi Index on the columns)
error_bad_lines: boolean, default True
Lines with too many fields (e.g. a csv line with too many commas) will by
default cause an exception to be raised, and no DataFrame will be returned.
If False, then these "bad lines" will dropped from the DataFrame that is
returned. (Only valid with C parser).
warn_bad_lines: boolean, default True
If error_bad_lines is False, and warn_bad_lines is True, a warning for each
"bad line" will be output. (Only valid with C parser).
infer_datetime_format : boolean, default False
If True and parse_dates is enabled for a column, attempt to infer
the datetime format to speed up the processing
Returns
-------
result : DataFrame or TextParser
"""
_csv_sep = """sep : string, default ','
Delimiter to use. If sep is None, will try to automatically determine
this. Regular expressions are accepted.
"""
_table_sep = """sep : string, default \\t (tab-stop)
Delimiter to use. Regular expressions are accepted."""
_read_csv_doc = """
Read CSV (comma-separated) file into DataFrame
%s
""" % (_parser_params % _csv_sep)
_read_table_doc = """
Read general delimited file into DataFrame
%s
""" % (_parser_params % _table_sep)
_fwf_widths = """\
colspecs : list of pairs (int, int) or 'infer'. optional
A list of pairs (tuples) giving the extents of the fixed-width
fields of each line as half-open intervals (i.e., [from, to[ ).
String value 'infer' can be used to instruct the parser to try
detecting the column specifications from the first 100 rows of
the data (default='infer').
widths : list of ints. optional
A list of field widths which can be used instead of 'colspecs' if
the intervals are contiguous.
"""
_read_fwf_doc = """
Read a table of fixed-width formatted lines into DataFrame
%s
Also, 'delimiter' is used to specify the filler character of the
fields if it is not spaces (e.g., '~').
""" % (_parser_params % _fwf_widths)
def _read(filepath_or_buffer, kwds):
"Generic reader of line files."
encoding = kwds.get('encoding', None)
skipfooter = kwds.pop('skipfooter', None)
if skipfooter is not None:
kwds['skip_footer'] = skipfooter
filepath_or_buffer, _ = get_filepath_or_buffer(filepath_or_buffer,
encoding)
if kwds.get('date_parser', None) is not None:
if isinstance(kwds['parse_dates'], bool):
kwds['parse_dates'] = True
# Extract some of the arguments (pass chunksize on).
iterator = kwds.get('iterator', False)
nrows = kwds.pop('nrows', None)
chunksize = kwds.get('chunksize', None)
# Create the parser.
parser = TextFileReader(filepath_or_buffer, **kwds)
if nrows is not None:
return parser.read(nrows)
elif chunksize or iterator:
return parser
return parser.read()
_parser_defaults = {
'delimiter': None,
'doublequote': True,
'escapechar': None,
'quotechar': '"',
'quoting': csv.QUOTE_MINIMAL,
'skipinitialspace': False,
'lineterminator': None,
'header': 'infer',
'index_col': None,
'names': None,
'prefix': None,
'skiprows': None,
'na_values': None,
'true_values': None,
'false_values': None,
'skip_footer': 0,
'converters': None,
'keep_default_na': True,
'thousands': None,
'comment': None,
# 'engine': 'c',
'parse_dates': False,
'keep_date_col': False,
'dayfirst': False,
'date_parser': None,
'usecols': None,
# 'nrows': None,
# 'iterator': False,
'chunksize': None,
'verbose': False,
'encoding': None,
'squeeze': False,
'compression': None,
'mangle_dupe_cols': True,
'tupleize_cols': False,
'infer_datetime_format': False,
}
_c_parser_defaults = {
'delim_whitespace': False,
'as_recarray': False,
'na_filter': True,
'compact_ints': False,
'use_unsigned': False,
'low_memory': True,
'memory_map': False,
'buffer_lines': None,
'error_bad_lines': True,
'warn_bad_lines': True,
'dtype': None,
'decimal': b'.'
}
_fwf_defaults = {
'colspecs': 'infer',
'widths': None,
}
_c_unsupported = set(['skip_footer'])
_python_unsupported = set(_c_parser_defaults.keys())
def _make_parser_function(name, sep=','):
def parser_f(filepath_or_buffer,
sep=sep,
dialect=None,
compression=None,
doublequote=True,
escapechar=None,
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
skipinitialspace=False,
lineterminator=None,
header='infer',
index_col=None,
names=None,
prefix=None,
skiprows=None,
skipfooter=None,
skip_footer=0,
na_values=None,
na_fvalues=None,
true_values=None,
false_values=None,
delimiter=None,
converters=None,
dtype=None,
usecols=None,
engine='c',
delim_whitespace=False,
as_recarray=False,
na_filter=True,
compact_ints=False,
use_unsigned=False,
low_memory=_c_parser_defaults['low_memory'],
buffer_lines=None,
warn_bad_lines=True,
error_bad_lines=True,
keep_default_na=True,
thousands=None,
comment=None,
decimal=b'.',
parse_dates=False,
keep_date_col=False,
dayfirst=False,
date_parser=None,
memory_map=False,
nrows=None,
iterator=False,
chunksize=None,
verbose=False,
encoding=None,
squeeze=False,
mangle_dupe_cols=True,
tupleize_cols=False,
infer_datetime_format=False):
# Alias sep -> delimiter.
if delimiter is None:
delimiter = sep
kwds = dict(delimiter=delimiter,
engine=engine,
dialect=dialect,
compression=compression,
doublequote=doublequote,
escapechar=escapechar,
quotechar=quotechar,
quoting=quoting,
skipinitialspace=skipinitialspace,
lineterminator=lineterminator,
header=header,
index_col=index_col,
names=names,
prefix=prefix,
skiprows=skiprows,
na_values=na_values,
na_fvalues=na_fvalues,
true_values=true_values,
false_values=false_values,
keep_default_na=keep_default_na,
thousands=thousands,
comment=comment,
decimal=decimal,
parse_dates=parse_dates,
keep_date_col=keep_date_col,
dayfirst=dayfirst,
date_parser=date_parser,
nrows=nrows,
iterator=iterator,
chunksize=chunksize,
skipfooter=skipfooter or skip_footer,
converters=converters,
dtype=dtype,
usecols=usecols,
verbose=verbose,
encoding=encoding,
squeeze=squeeze,
memory_map=memory_map,
na_filter=na_filter,
compact_ints=compact_ints,
use_unsigned=use_unsigned,
delim_whitespace=delim_whitespace,
as_recarray=as_recarray,
warn_bad_lines=warn_bad_lines,
error_bad_lines=error_bad_lines,
low_memory=low_memory,
buffer_lines=buffer_lines,
mangle_dupe_cols=mangle_dupe_cols,
tupleize_cols=tupleize_cols,
infer_datetime_format=infer_datetime_format)
return _read(filepath_or_buffer, kwds)
parser_f.__name__ = name
return parser_f
read_csv = _make_parser_function('read_csv', sep=',')
read_csv = Appender(_read_csv_doc)(read_csv)
read_table = _make_parser_function('read_table', sep='\t')
read_table = Appender(_read_table_doc)(read_table)
@Appender(_read_fwf_doc)
def read_fwf(filepath_or_buffer, colspecs='infer', widths=None, **kwds):
# Check input arguments.
if colspecs is None and widths is None:
raise ValueError("Must specify either colspecs or widths")
elif colspecs not in (None, 'infer') and widths is not None:
raise ValueError("You must specify only one of 'widths' and "
"'colspecs'")
# Compute 'colspecs' from 'widths', if specified.
if widths is not None:
colspecs, col = [], 0
for w in widths:
colspecs.append((col, col + w))
col += w
kwds['colspecs'] = colspecs
kwds['engine'] = 'python-fwf'
return _read(filepath_or_buffer, kwds)
# common NA values
# no longer excluding inf representations
# '1.#INF','-1.#INF', '1.#INF000000',
_NA_VALUES = set([
'-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A', 'N/A', 'NA', '#NA',
'NULL', 'NaN', '-NaN', 'nan', '-nan', ''
])
class TextFileReader(object):
"""
Passed dialect overrides any of the related parser options
"""
def __init__(self, f, engine='python', **kwds):
self.f = f
if kwds.get('dialect') is not None:
dialect = kwds['dialect']
kwds['delimiter'] = dialect.delimiter
kwds['doublequote'] = dialect.doublequote
kwds['escapechar'] = dialect.escapechar
kwds['skipinitialspace'] = dialect.skipinitialspace
kwds['quotechar'] = dialect.quotechar
kwds['quoting'] = dialect.quoting
if kwds.get('header', 'infer') == 'infer':
kwds['header'] = 0 if kwds.get('names') is None else None
self.orig_options = kwds
# miscellanea
self.engine = engine
self._engine = None
options = self._get_options_with_defaults(engine)
self.chunksize = options.pop('chunksize', None)
self.squeeze = options.pop('squeeze', False)
# might mutate self.engine
self.options, self.engine = self._clean_options(options, engine)
if 'has_index_names' in kwds:
self.options['has_index_names'] = kwds['has_index_names']
self._make_engine(self.engine)
def _get_options_with_defaults(self, engine):
kwds = self.orig_options
options = {}
for argname, default in compat.iteritems(_parser_defaults):
options[argname] = kwds.get(argname, default)
for argname, default in compat.iteritems(_c_parser_defaults):
if argname in kwds:
value = kwds[argname]
if engine != 'c' and value != default:
raise ValueError('The %r option is not supported with the'
' %r engine' % (argname, engine))
else:
value = default
options[argname] = value
if engine == 'python-fwf':
for argname, default in compat.iteritems(_fwf_defaults):
options[argname] = kwds.get(argname, default)
return options
def _clean_options(self, options, engine):
result = options.copy()
sep = options['delimiter']
delim_whitespace = options['delim_whitespace']
if sep is None and not delim_whitespace:
if engine == 'c':
print('Using Python parser to sniff delimiter')
engine = 'python'
elif sep is not None and len(sep) > 1:
# wait until regex engine integrated
if engine not in ('python', 'python-fwf'):
engine = 'python'
# C engine not supported yet
if engine == 'c':
if options['skip_footer'] > 0:
engine = 'python'
if engine == 'c':
for arg in _c_unsupported:
del result[arg]
if 'python' in engine:
for arg in _python_unsupported:
del result[arg]
index_col = options['index_col']
names = options['names']
converters = options['converters']
na_values = options['na_values']
skiprows = options['skiprows']
# really delete this one
keep_default_na = result.pop('keep_default_na')
if _is_index_col(index_col):
if not isinstance(index_col, (list, tuple, np.ndarray)):
index_col = [index_col]
result['index_col'] = index_col
names = list(names) if names is not None else names
# type conversion-related
if converters is not None:
if not isinstance(converters, dict):
raise TypeError('Type converters must be a dict or'
' subclass, input was '
'a {0!r}'.format(type(converters).__name__))
else:
converters = {}
# Converting values to NA
na_values, na_fvalues = _clean_na_values(na_values, keep_default_na)
if com.is_integer(skiprows):
skiprows = lrange(skiprows)
skiprows = set() if skiprows is None else set(skiprows)
# put stuff back
result['names'] = names
result['converters'] = converters
result['na_values'] = na_values
result['na_fvalues'] = na_fvalues
result['skiprows'] = skiprows
return result, engine
def __iter__(self):
try:
if self.chunksize:
while True:
yield self.read(self.chunksize)
else:
yield self.read()
except StopIteration:
pass
def _make_engine(self, engine='c'):
if engine == 'c':
self._engine = CParserWrapper(self.f, **self.options)
else:
if engine == 'python':
klass = PythonParser
elif engine == 'python-fwf':
klass = FixedWidthFieldParser
self._engine = klass(self.f, **self.options)
def _failover_to_python(self):
raise NotImplementedError
def read(self, nrows=None):
if nrows is not None:
if self.options.get('skip_footer'):
raise ValueError('skip_footer not supported for iteration')
ret = self._engine.read(nrows)
if self.options.get('as_recarray'):
return ret
# May alter columns / col_dict
index, columns, col_dict = self._create_index(ret)
df = DataFrame(col_dict, columns=columns, index=index)
if self.squeeze and len(df.columns) == 1:
return df[df.columns[0]]
return df
def _create_index(self, ret):
index, columns, col_dict = ret
return index, columns, col_dict
def get_chunk(self, size=None):
if size is None:
size = self.chunksize
return self.read(nrows=size)
def _is_index_col(col):
return col is not None and col is not False
class ParserBase(object):
def __init__(self, kwds):
self.names = kwds.get('names')
self.orig_names = None
self.prefix = kwds.pop('prefix', None)
self.index_col = kwds.get('index_col', None)
self.index_names = None
self.col_names = None
self.parse_dates = kwds.pop('parse_dates', False)
self.date_parser = kwds.pop('date_parser', None)
self.dayfirst = kwds.pop('dayfirst', False)
self.keep_date_col = kwds.pop('keep_date_col', False)
self.na_values = kwds.get('na_values')
self.na_fvalues = kwds.get('na_fvalues')
self.true_values = kwds.get('true_values')
self.false_values = kwds.get('false_values')
self.tupleize_cols = kwds.get('tupleize_cols', False)
self.infer_datetime_format = kwds.pop('infer_datetime_format', False)
self._date_conv = _make_date_converter(
date_parser=self.date_parser,
dayfirst=self.dayfirst,
infer_datetime_format=self.infer_datetime_format
)
# validate header options for mi
self.header = kwds.get('header')
if isinstance(self.header, (list, tuple, np.ndarray)):
if kwds.get('as_recarray'):
raise ValueError("cannot specify as_recarray when "
"specifying a multi-index header")
if kwds.get('usecols'):
raise ValueError("cannot specify usecols when "
"specifying a multi-index header")
if kwds.get('names'):
raise ValueError("cannot specify names when "
"specifying a multi-index header")
# validate index_col that only contains integers
if self.index_col is not None:
is_sequence = isinstance(self.index_col, (list, tuple,
np.ndarray))
if not (is_sequence and
all(map(com.is_integer, self.index_col)) or
com.is_integer(self.index_col)):
raise ValueError("index_col must only contain row numbers "
"when specifying a multi-index header")
self._name_processed = False
@property
def _has_complex_date_col(self):
return (isinstance(self.parse_dates, dict) or
(isinstance(self.parse_dates, list) and
len(self.parse_dates) > 0 and
isinstance(self.parse_dates[0], list)))
def _should_parse_dates(self, i):
if isinstance(self.parse_dates, bool):
return self.parse_dates
else:
name = self.index_names[i]
j = self.index_col[i]
if np.isscalar(self.parse_dates):
return (j == self.parse_dates) or (name == self.parse_dates)
else:
return (j in self.parse_dates) or (name in self.parse_dates)
def _extract_multi_indexer_columns(self, header, index_names, col_names,
passed_names=False):
""" extract and return the names, index_names, col_names
header is a list-of-lists returned from the parsers """
if len(header) < 2:
return header[0], index_names, col_names, passed_names
# the names are the tuples of the header that are not the index cols
# 0 is the name of the index, assuming index_col is a list of column
# numbers
ic = self.index_col
if ic is None:
ic = []
if not isinstance(ic, (list, tuple, np.ndarray)):
ic = [ic]
sic = set(ic)
# clean the index_names
index_names = header.pop(-1)
index_names, names, index_col = _clean_index_names(index_names,
self.index_col)
# extract the columns
field_count = len(header[0])
def extract(r):
return tuple([r[i] for i in range(field_count) if i not in sic])
columns = lzip(*[extract(r) for r in header])
names = ic + columns
# if we find 'Unnamed' all of a single level, then our header was too
# long
for n in range(len(columns[0])):
if all(['Unnamed' in c[n] for c in columns]):
raise _parser.CParserError(
"Passed header=[%s] are too many rows for this "
"multi_index of columns"
% ','.join([str(x) for x in self.header])
)
# clean the column names (if we have an index_col)
if len(ic):
col_names = [r[0] if len(r[0]) and 'Unnamed' not in r[0] else None
for r in header]
else:
col_names = [None] * len(header)
passed_names = True
return names, index_names, col_names, passed_names
def _maybe_make_multi_index_columns(self, columns, col_names=None):
# possibly create a column mi here
if (not self.tupleize_cols and len(columns) and
not isinstance(columns, MultiIndex) and
all([isinstance(c, tuple) for c in columns])):
columns = MultiIndex.from_tuples(columns, names=col_names)
return columns
def _make_index(self, data, alldata, columns, indexnamerow=False):
if not _is_index_col(self.index_col) or not self.index_col:
index = None
elif not self._has_complex_date_col:
index = self._get_simple_index(alldata, columns)
index = self._agg_index(index)
elif self._has_complex_date_col:
if not self._name_processed:
(self.index_names, _,
self.index_col) = _clean_index_names(list(columns),
self.index_col)
self._name_processed = True
index = self._get_complex_date_index(data, columns)
index = self._agg_index(index, try_parse_dates=False)
# add names for the index
if indexnamerow:
coffset = len(indexnamerow) - len(columns)
index = index.set_names(indexnamerow[:coffset])
# maybe create a mi on the columns
columns = self._maybe_make_multi_index_columns(columns, self.col_names)
return index, columns
_implicit_index = False
def _get_simple_index(self, data, columns):
def ix(col):
if not isinstance(col, compat.string_types):
return col
raise ValueError('Index %s invalid' % col)
index = None
to_remove = []
index = []
for idx in self.index_col:
i = ix(idx)
to_remove.append(i)
index.append(data[i])
# remove index items from content and columns, don't pop in
# loop
for i in reversed(sorted(to_remove)):
data.pop(i)
if not self._implicit_index:
columns.pop(i)
return index
def _get_complex_date_index(self, data, col_names):
def _get_name(icol):
if isinstance(icol, compat.string_types):
return icol
if col_names is None:
raise ValueError(('Must supply column order to use %s as '
'index') % str(icol))
for i, c in enumerate(col_names):
if i == icol:
return c
index = None
to_remove = []
index = []
for idx in self.index_col:
name = _get_name(idx)
to_remove.append(name)
index.append(data[name])
# remove index items from content and columns, don't pop in
# loop
for c in reversed(sorted(to_remove)):
data.pop(c)
col_names.remove(c)
return index
def _agg_index(self, index, try_parse_dates=True):
arrays = []
for i, arr in enumerate(index):
if (try_parse_dates and self._should_parse_dates(i)):
arr = self._date_conv(arr)
col_na_values = self.na_values
col_na_fvalues = self.na_fvalues
if isinstance(self.na_values, dict):
col_name = self.index_names[i]
if col_name is not None:
col_na_values, col_na_fvalues = _get_na_values(
col_name, self.na_values, self.na_fvalues)
arr, _ = self._convert_types(arr, col_na_values | col_na_fvalues)
arrays.append(arr)
index = MultiIndex.from_arrays(arrays, names=self.index_names)
return index
def _convert_to_ndarrays(self, dct, na_values, na_fvalues, verbose=False,
converters=None):
result = {}
for c, values in compat.iteritems(dct):
conv_f = None if converters is None else converters.get(c, None)
col_na_values, col_na_fvalues = _get_na_values(c, na_values,
na_fvalues)
coerce_type = True
if conv_f is not None:
values = lib.map_infer(values, conv_f)
coerce_type = False
cvals, na_count = self._convert_types(
values, set(col_na_values) | col_na_fvalues, coerce_type)
result[c] = cvals
if verbose and na_count:
print('Filled %d NA values in column %s' % (na_count, str(c)))
return result
def _convert_types(self, values, na_values, try_num_bool=True):
na_count = 0
if issubclass(values.dtype.type, (np.number, np.bool_)):
mask = lib.ismember(values, na_values)
na_count = mask.sum()
if na_count > 0:
if com.is_integer_dtype(values):
values = values.astype(np.float64)
np.putmask(values, mask, np.nan)
return values, na_count
if try_num_bool:
try:
result = lib.maybe_convert_numeric(values, na_values, False)
except Exception:
result = values
if values.dtype == np.object_:
na_count = lib.sanitize_objects(result, na_values, False)
else:
result = values
if values.dtype == np.object_:
na_count = lib.sanitize_objects(values, na_values, False)
if result.dtype == np.object_ and try_num_bool:
result = lib.maybe_convert_bool(values,
true_values=self.true_values,
false_values=self.false_values)
return result, na_count
def _do_date_conversions(self, names, data):
# returns data, columns
if self.parse_dates is not None:
data, names = _process_date_conversion(
data, self._date_conv, self.parse_dates, self.index_col,
self.index_names, names, keep_date_col=self.keep_date_col)
return names, data
class CParserWrapper(ParserBase):
"""
"""
def __init__(self, src, **kwds):
self.kwds = kwds
kwds = kwds.copy()
self.as_recarray = kwds.get('as_recarray', False)
ParserBase.__init__(self, kwds)
if 'utf-16' in (kwds.get('encoding') or ''):
if isinstance(src, compat.string_types):
src = open(src, 'rb')
src = com.UTF8Recoder(src, kwds['encoding'])
kwds['encoding'] = 'utf-8'
# #2442
kwds['allow_leading_cols'] = self.index_col is not False
self._reader = _parser.TextReader(src, **kwds)
# XXX
self.usecols = self._reader.usecols
passed_names = self.names is None
if self._reader.header is None:
self.names = None
else:
if len(self._reader.header) > 1:
# we have a multi index in the columns
self.names, self.index_names, self.col_names, passed_names = (
self._extract_multi_indexer_columns(
self._reader.header, self.index_names, self.col_names,
passed_names
)
)
else:
self.names = list(self._reader.header[0])
if self.names is None:
if self.prefix:
self.names = ['%s%d' % (self.prefix, i)
for i in range(self._reader.table_width)]
else:
self.names = lrange(self._reader.table_width)
# If the names were inferred (not passed by user) and usedcols is
# defined, then ensure names refers to the used columns, not the
# document's columns.
if self.usecols and passed_names:
col_indices = []
for u in self.usecols:
if isinstance(u, string_types):
col_indices.append(self.names.index(u))
else:
col_indices.append(u)
self.names = [n for i, n in enumerate(self.names)
if i in col_indices]
if len(self.names) < len(self.usecols):
raise ValueError("Usecols do not match names.")
self._set_noconvert_columns()
self.orig_names = self.names
if not self._has_complex_date_col:
if (self._reader.leading_cols == 0 and
_is_index_col(self.index_col)):
self._name_processed = True
(index_names, self.names,
self.index_col) = _clean_index_names(self.names,
self.index_col)
if self.index_names is None:
self.index_names = index_names
if self._reader.header is None and not passed_names:
self.index_names = [None] * len(self.index_names)
self._implicit_index = self._reader.leading_cols > 0
def _set_noconvert_columns(self):
names = self.names
def _set(x):
if com.is_integer(x):
self._reader.set_noconvert(x)
else:
self._reader.set_noconvert(names.index(x))
if isinstance(self.parse_dates, list):
for val in self.parse_dates:
if isinstance(val, list):
for k in val:
_set(k)
else:
_set(val)
elif isinstance(self.parse_dates, dict):
for val in self.parse_dates.values():
if isinstance(val, list):
for k in val:
_set(k)
else:
_set(val)
def set_error_bad_lines(self, status):
self._reader.set_error_bad_lines(int(status))
def read(self, nrows=None):
if self.as_recarray:
# what to do if there are leading columns?
return self._reader.read(nrows)
try:
data = self._reader.read(nrows)
except StopIteration:
if nrows is None:
return None, self.names, {}
else:
raise
names = self.names
if self._reader.leading_cols:
if self._has_complex_date_col:
raise NotImplementedError('file structure not yet supported')
# implicit index, no index names
arrays = []
for i in range(self._reader.leading_cols):
if self.index_col is None:
values = data.pop(i)
else:
values = data.pop(self.index_col[i])
values = self._maybe_parse_dates(values, i,
try_parse_dates=True)
arrays.append(values)
index = MultiIndex.from_arrays(arrays)
if self.usecols is not None:
names = self._filter_usecols(names)
# rename dict keys
data = sorted(data.items())
data = dict((k, v) for k, (i, v) in zip(names, data))
names, data = self._do_date_conversions(names, data)
else:
# rename dict keys
data = sorted(data.items())
# ugh, mutation
names = list(self.orig_names)
if self.usecols is not None:
names = self._filter_usecols(names)
# columns as list
alldata = [x[1] for x in data]
data = dict((k, v) for k, (i, v) in zip(names, data))
names, data = self._do_date_conversions(names, data)
index, names = self._make_index(data, alldata, names)
# maybe create a mi on the columns
names = self._maybe_make_multi_index_columns(names, self.col_names)
return index, names, data
def _filter_usecols(self, names):
# hackish
if self.usecols is not None and len(names) != len(self.usecols):
names = [name for i, name in enumerate(names)
if i in self.usecols or name in self.usecols]
return names
def _get_index_names(self):
names = list(self._reader.header[0])
idx_names = None
if self._reader.leading_cols == 0 and self.index_col is not None:
(idx_names, names,
self.index_col) = _clean_index_names(names, self.index_col)
return names, idx_names
def _maybe_parse_dates(self, values, index, try_parse_dates=True):
if try_parse_dates and self._should_parse_dates(index):
values = self._date_conv(values)
return values
def TextParser(*args, **kwds):
"""
Converts lists of lists/tuples into DataFrames with proper type inference
and optional (e.g. string to datetime) conversion. Also enables iterating
lazily over chunks of large files
Parameters
----------
data : file-like object or list
delimiter : separator character to use
dialect : str or csv.Dialect instance, default None
Ignored if delimiter is longer than 1 character
names : sequence, default
header : int, default 0
Row to use to parse column labels. Defaults to the first row. Prior
rows will be discarded
index_col : int or list, default None
Column or columns to use as the (possibly hierarchical) index
has_index_names: boolean, default False
True if the cols defined in index_col have an index name and are
not in the header
na_values : iterable, default None
Custom NA values
keep_default_na : bool, default True
thousands : str, default None
Thousands separator
comment : str, default None
Comment out remainder of line
parse_dates : boolean, default False
keep_date_col : boolean, default False
date_parser : function, default None
skiprows : list of integers
Row numbers to skip
skip_footer : int
Number of line at bottom of file to skip
encoding : string, default None
Encoding to use for UTF when reading/writing (ex. 'utf-8')
squeeze : boolean, default False
returns Series if only one column
infer_datetime_format: boolean, default False
If True and `parse_dates` is True for a column, try to infer the
datetime format based on the first datetime string. If the format
can be inferred, there often will be a large parsing speed-up.
"""
kwds['engine'] = 'python'
return TextFileReader(*args, **kwds)
def count_empty_vals(vals):
return sum([1 for v in vals if v == '' or v is None])
def _wrap_compressed(f, compression, encoding=None):
"""wraps compressed fileobject in a decompressing fileobject
NOTE: For all files in Python 3.2 and for bzip'd files under all Python
versions, this means reading in the entire file and then re-wrapping it in
StringIO.
"""
compression = compression.lower()
encoding = encoding or get_option('display.encoding')
if compression == 'gzip':
import gzip
f = gzip.GzipFile(fileobj=f)
if compat.PY3_2:
# 3.2's gzip doesn't support read1
f = StringIO(f.read().decode(encoding))
elif compat.PY3:
from io import TextIOWrapper
f = TextIOWrapper(f)
return f
elif compression == 'bz2':
import bz2
# bz2 module can't take file objects, so have to run through decompress
# manually
data = bz2.decompress(f.read())
if compat.PY3:
data = data.decode(encoding)
f = StringIO(data)
return f
else:
raise ValueError('do not recognize compression method %s'
% compression)
class PythonParser(ParserBase):
def __init__(self, f, **kwds):
"""
Workhorse function for processing nested list into DataFrame
Should be replaced by np.genfromtxt eventually?
"""
ParserBase.__init__(self, kwds)
self.data = None
self.buf = []
self.pos = 0
self.encoding = kwds['encoding']
self.compression = kwds['compression']
self.skiprows = kwds['skiprows']
self.skip_footer = kwds['skip_footer']
self.delimiter = kwds['delimiter']
self.quotechar = kwds['quotechar']
self.escapechar = kwds['escapechar']
self.doublequote = kwds['doublequote']
self.skipinitialspace = kwds['skipinitialspace']
self.lineterminator = kwds['lineterminator']
self.quoting = kwds['quoting']
self.mangle_dupe_cols = kwds.get('mangle_dupe_cols', True)
self.usecols = kwds['usecols']
self.names_passed = kwds['names'] or None
self.has_index_names = False
if 'has_index_names' in kwds:
self.has_index_names = kwds['has_index_names']
self.verbose = kwds['verbose']
self.converters = kwds['converters']
self.thousands = kwds['thousands']
self.comment = kwds['comment']
self._comment_lines = []
if isinstance(f, compat.string_types):
f = com._get_handle(f, 'r', encoding=self.encoding,
compression=self.compression)
elif self.compression:
f = _wrap_compressed(f, self.compression, self.encoding)
# in Python 3, convert BytesIO or fileobjects passed with an encoding
elif compat.PY3 and isinstance(f, compat.BytesIO):
from io import TextIOWrapper
f = TextIOWrapper(f, encoding=self.encoding)
# Set self.data to something that can read lines.
if hasattr(f, 'readline'):
self._make_reader(f)
else:
self.data = f
# Get columns in two steps: infer from data, then
# infer column indices from self.usecols if is is specified.
self._col_indices = None
self.columns, self.num_original_columns = self._infer_columns()
# Now self.columns has the set of columns that we will process.
# The original set is stored in self.original_columns.
if len(self.columns) > 1:
# we are processing a multi index column
self.columns, self.index_names, self.col_names, _ = (
self._extract_multi_indexer_columns(
self.columns, self.index_names, self.col_names
)
)
# Update list of original names to include all indices.
self.num_original_columns = len(self.columns)
else:
self.columns = self.columns[0]
# get popped off for index
self.orig_names = list(self.columns)
# needs to be cleaned/refactored
# multiple date column thing turning into a real spaghetti factory
if not self._has_complex_date_col:
(index_names,
self.orig_names, columns_) = self._get_index_name(self.columns)
self._name_processed = True
if self.index_names is None:
self.index_names = index_names
self._first_chunk = True
if self.parse_dates:
self._no_thousands_columns = self._set_no_thousands_columns()
else:
self._no_thousands_columns = None
def _set_no_thousands_columns(self):
# Create a set of column ids that are not to be stripped of thousands
# operators.
noconvert_columns = set()
def _set(x):
if com.is_integer(x):
noconvert_columns.add(x)
else:
noconvert_columns.add(self.columns.index(x))
if isinstance(self.parse_dates, list):
for val in self.parse_dates:
if isinstance(val, list):
for k in val:
_set(k)
else:
_set(val)
elif isinstance(self.parse_dates, dict):
for val in self.parse_dates.values():
if isinstance(val, list):
for k in val:
_set(k)
else:
_set(val)
return noconvert_columns
def _make_reader(self, f):
sep = self.delimiter
if sep is None or len(sep) == 1:
if self.lineterminator:
raise ValueError('Custom line terminators not supported in '
'python parser (yet)')
class MyDialect(csv.Dialect):
delimiter = self.delimiter
quotechar = self.quotechar
escapechar = self.escapechar
doublequote = self.doublequote
skipinitialspace = self.skipinitialspace
quoting = self.quoting
lineterminator = '\n'
dia = MyDialect
sniff_sep = True
if sep is not None:
sniff_sep = False
dia.delimiter = sep
# attempt to sniff the delimiter
if sniff_sep:
line = f.readline()
while self.pos in self.skiprows:
self.pos += 1
line = f.readline()
line = self._check_comments([line])[0]
self.pos += 1
sniffed = csv.Sniffer().sniff(line)
dia.delimiter = sniffed.delimiter
if self.encoding is not None:
self.buf.extend(list(
com.UnicodeReader(StringIO(line),
dialect=dia,
encoding=self.encoding)))
else:
self.buf.extend(list(csv.reader(StringIO(line),
dialect=dia)))
if self.encoding is not None:
reader = com.UnicodeReader(f, dialect=dia,
encoding=self.encoding,
strict=True)
else:
reader = csv.reader(f, dialect=dia,
strict=True)
else:
def _read():
line = next(f)
pat = re.compile(sep)
yield pat.split(line.strip())
for line in f:
yield pat.split(line.strip())
reader = _read()
self.data = reader
def read(self, rows=None):
try:
content = self._get_lines(rows)
except StopIteration:
if self._first_chunk:
content = []
else:
raise
# done with first read, next time raise StopIteration
self._first_chunk = False
columns = list(self.orig_names)
if not len(content): # pragma: no cover
# DataFrame with the right metadata, even though it's length 0
return _get_empty_meta(self.orig_names,
self.index_col,
self.index_names)
# handle new style for names in index
count_empty_content_vals = count_empty_vals(content[0])
indexnamerow = None
if self.has_index_names and count_empty_content_vals == len(columns):
indexnamerow = content[0]
content = content[1:]
alldata = self._rows_to_cols(content)
data = self._exclude_implicit_index(alldata)
columns, data = self._do_date_conversions(self.columns, data)
data = self._convert_data(data)
index, columns = self._make_index(data, alldata, columns, indexnamerow)
return index, columns, data
def _exclude_implicit_index(self, alldata):
if self._implicit_index:
excl_indices = self.index_col
data = {}
offset = 0
for i, col in enumerate(self.orig_names):
while i + offset in excl_indices:
offset += 1
data[col] = alldata[i + offset]
else:
data = dict((k, v) for k, v in zip(self.orig_names, alldata))
return data
# legacy
def get_chunk(self, size=None):
if size is None:
size = self.chunksize
return self.read(nrows=size)
def _convert_data(self, data):
# apply converters
clean_conv = {}
for col, f in compat.iteritems(self.converters):
if isinstance(col, int) and col not in self.orig_names:
col = self.orig_names[col]
clean_conv[col] = f
return self._convert_to_ndarrays(data, self.na_values, self.na_fvalues,
self.verbose, clean_conv)
def _infer_columns(self):
names = self.names
num_original_columns = 0
clear_buffer = True
if self.header is not None:
header = self.header
# we have a mi columns, so read and extra line
if isinstance(header, (list, tuple, np.ndarray)):
have_mi_columns = True
header = list(header) + [header[-1] + 1]
else:
have_mi_columns = False
header = [header]
columns = []
for level, hr in enumerate(header):
line = self._buffered_line()
while self.pos <= hr:
line = self._next_line()
unnamed_count = 0
this_columns = []
for i, c in enumerate(line):
if c == '':
if have_mi_columns:
this_columns.append('Unnamed: %d_level_%d'
% (i, level))
else:
this_columns.append('Unnamed: %d' % i)
unnamed_count += 1
else:
this_columns.append(c)
if not have_mi_columns and self.mangle_dupe_cols:
counts = {}
for i, col in enumerate(this_columns):
cur_count = counts.get(col, 0)
if cur_count > 0:
this_columns[i] = '%s.%d' % (col, cur_count)
counts[col] = cur_count + 1
elif have_mi_columns:
# if we have grabbed an extra line, but its not in our
# format so save in the buffer, and create an blank extra
# line for the rest of the parsing code
if hr == header[-1]:
lc = len(this_columns)
ic = (len(self.index_col)
if self.index_col is not None else 0)
if lc != unnamed_count and lc-ic > unnamed_count:
clear_buffer = False
this_columns = [None] * lc
self.buf = [self.buf[-1]]
columns.append(this_columns)
if len(columns) == 1:
num_original_columns = len(this_columns)
if clear_buffer:
self._clear_buffer()
if names is not None:
if ((self.usecols is not None
and len(names) != len(self.usecols))
or (self.usecols is None
and len(names) != len(columns[0]))):
raise ValueError('Number of passed names did not match '
'number of header fields in the file')
if len(columns) > 1:
raise TypeError('Cannot pass names with multi-index '
'columns')
if self.usecols is not None:
# Set _use_cols. We don't store columns because they are
# overwritten.
self._handle_usecols(columns, names)
else:
self._col_indices = None
num_original_columns = len(names)
columns = [names]
else:
columns = self._handle_usecols(columns, columns[0])
else:
# header is None
line = self._buffered_line()
ncols = len(line)
num_original_columns = ncols
if not names:
if self.prefix:
columns = [['%s%d' % (self.prefix,i) for i in range(ncols)]]
else:
columns = [lrange(ncols)]
columns = self._handle_usecols(columns, columns[0])
else:
if self.usecols is None or len(names) == num_original_columns:
columns = self._handle_usecols([names], names)
num_original_columns = len(names)
else:
if self.usecols and len(names) != len(self.usecols):
raise ValueError(
'Number of passed names did not match number of '
'header fields in the file'
)
# Ignore output but set used columns.
self._handle_usecols([names], names)
columns = [names]
num_original_columns = ncols
return columns, num_original_columns
def _handle_usecols(self, columns, usecols_key):
"""
Sets self._col_indices
usecols_key is used if there are string usecols.
"""
if self.usecols is not None:
if any([isinstance(u, string_types) for u in self.usecols]):
if len(columns) > 1:
raise ValueError("If using multiple headers, usecols must "
"be integers.")
col_indices = []
for u in self.usecols:
if isinstance(u, string_types):
col_indices.append(usecols_key.index(u))
else:
col_indices.append(u)
else:
col_indices = self.usecols
columns = [[n for i, n in enumerate(column) if i in col_indices]
for column in columns]
self._col_indices = col_indices
return columns
def _buffered_line(self):
"""
Return a line from buffer, filling buffer if required.
"""
if len(self.buf) > 0:
return self.buf[0]
else:
return self._next_line()
def _next_line(self):
if isinstance(self.data, list):
while self.pos in self.skiprows:
self.pos += 1
try:
line = self.data[self.pos]
except IndexError:
raise StopIteration
else:
while self.pos in self.skiprows:
next(self.data)
self.pos += 1
line = next(self.data)
line = self._check_comments([line])[0]
self.pos += 1
self.buf.append(line)
return line
def _check_comments(self, lines):
if self.comment is None:
return lines
ret = []
for l in lines:
rl = []
for x in l:
if (not isinstance(x, compat.string_types) or
self.comment not in x):
rl.append(x)
else:
x = x[:x.find(self.comment)]
if len(x) > 0:
rl.append(x)
break
ret.append(rl)
return ret
def _check_thousands(self, lines):
if self.thousands is None:
return lines
nonnum = re.compile('[^-^0-9^%s^.]+' % self.thousands)
ret = []
for l in lines:
rl = []
for i, x in enumerate(l):
if (not isinstance(x, compat.string_types) or
self.thousands not in x or
(self._no_thousands_columns
and i in self._no_thousands_columns)
or nonnum.search(x.strip())):
rl.append(x)
else:
rl.append(x.replace(self.thousands, ''))
ret.append(rl)
return ret
def _clear_buffer(self):
self.buf = []
_implicit_index = False
def _get_index_name(self, columns):
"""
Try several cases to get lines:
0) There are headers on row 0 and row 1 and their
total summed lengths equals the length of the next line.
Treat row 0 as columns and row 1 as indices
1) Look for implicit index: there are more columns
on row 1 than row 0. If this is true, assume that row
1 lists index columns and row 0 lists normal columns.
2) Get index from the columns if it was listed.
"""
orig_names = list(columns)
columns = list(columns)
try:
line = self._next_line()
except StopIteration:
line = None
try:
next_line = self._next_line()
except StopIteration:
next_line = None
# implicitly index_col=0 b/c 1 fewer column names
implicit_first_cols = 0
if line is not None:
# leave it 0, #2442
# Case 1
if self.index_col is not False:
implicit_first_cols = len(line) - self.num_original_columns
# Case 0
if next_line is not None:
if len(next_line) == len(line) + self.num_original_columns:
# column and index names on diff rows
self.index_col = lrange(len(line))
self.buf = self.buf[1:]
for c in reversed(line):
columns.insert(0, c)
# Update list of original names to include all indices.
self.num_original_columns = len(next_line)
return line, columns, orig_names
if implicit_first_cols > 0:
# Case 1
self._implicit_index = True
if self.index_col is None:
self.index_col = lrange(implicit_first_cols)
index_name = None
else:
# Case 2
(index_name, columns,
self.index_col) = _clean_index_names(columns, self.index_col)
return index_name, orig_names, columns
def _rows_to_cols(self, content):
zipped_content = list(lib.to_object_array(content).T)
col_len = self.num_original_columns
zip_len = len(zipped_content)
if self._implicit_index:
col_len += len(self.index_col)
if self.skip_footer < 0:
raise ValueError('skip footer cannot be negative')
# Loop through rows to verify lengths are correct.
if col_len != zip_len and self.index_col is not False:
i = 0
for (i, l) in enumerate(content):
if len(l) != col_len:
break
footers = 0
if self.skip_footer:
footers = self.skip_footer
row_num = self.pos - (len(content) - i + footers)
msg = ('Expected %d fields in line %d, saw %d' %
(col_len, row_num + 1, zip_len))
raise ValueError(msg)
if self.usecols:
if self._implicit_index:
zipped_content = [
a for i, a in enumerate(zipped_content)
if (i < len(self.index_col)
or i - len(self.index_col) in self._col_indices)
]
else:
zipped_content = [a for i, a in enumerate(zipped_content)
if i in self._col_indices]
return zipped_content
def _get_lines(self, rows=None):
source = self.data
lines = self.buf
new_rows = None
# already fetched some number
if rows is not None:
# we already have the lines in the buffer
if len(self.buf) >= rows:
new_rows, self.buf = self.buf[:rows], self.buf[rows:]
# need some lines
else:
rows -= len(self.buf)
if new_rows is None:
if isinstance(source, list):
if self.pos > len(source):
raise StopIteration
if rows is None:
new_rows = source[self.pos:]
new_pos = len(source)
else:
new_rows = source[self.pos:self.pos + rows]
new_pos = self.pos + rows
# Check for stop rows. n.b.: self.skiprows is a set.
if self.skiprows:
new_rows = [row for i, row in enumerate(new_rows)
if i + self.pos not in self.skiprows]
lines.extend(new_rows)
self.pos = new_pos
else:
new_rows = []
try:
if rows is not None:
for _ in range(rows):
new_rows.append(next(source))
lines.extend(new_rows)
else:
rows = 0
while True:
try:
new_rows.append(next(source))
rows += 1
except csv.Error as inst:
if 'newline inside string' in str(inst):
row_num = str(self.pos + rows)
msg = ('EOF inside string starting with '
'line ' + row_num)
raise Exception(msg)
raise
except StopIteration:
if self.skiprows:
new_rows = [row for i, row in enumerate(new_rows)
if self.pos + i not in self.skiprows]
lines.extend(new_rows)
if len(lines) == 0:
raise
self.pos += len(new_rows)
self.buf = []
else:
lines = new_rows
if self.skip_footer:
lines = lines[:-self.skip_footer]
lines = self._check_comments(lines)
return self._check_thousands(lines)
def _make_date_converter(date_parser=None, dayfirst=False,
infer_datetime_format=False):
def converter(*date_cols):
if date_parser is None:
strs = _concat_date_cols(date_cols)
try:
return tools.to_datetime(
com._ensure_object(strs),
utc=None,
box=False,
dayfirst=dayfirst,
infer_datetime_format=infer_datetime_format
)
except:
return lib.try_parse_dates(strs, dayfirst=dayfirst)
else:
try:
result = date_parser(*date_cols)
if isinstance(result, datetime.datetime):
raise Exception('scalar parser')
return result
except Exception:
try:
return lib.try_parse_dates(_concat_date_cols(date_cols),
parser=date_parser,
dayfirst=dayfirst)
except Exception:
return generic_parser(date_parser, *date_cols)
return converter
def _process_date_conversion(data_dict, converter, parse_spec,
index_col, index_names, columns,
keep_date_col=False):
def _isindex(colspec):
return ((isinstance(index_col, list) and
colspec in index_col)
or (isinstance(index_names, list) and
colspec in index_names))
new_cols = []
new_data = {}
orig_names = columns
columns = list(columns)
date_cols = set()
if parse_spec is None or isinstance(parse_spec, bool):
return data_dict, columns
if isinstance(parse_spec, list):
# list of column lists
for colspec in parse_spec:
if np.isscalar(colspec):
if isinstance(colspec, int) and colspec not in data_dict:
colspec = orig_names[colspec]
if _isindex(colspec):
continue
data_dict[colspec] = converter(data_dict[colspec])
else:
new_name, col, old_names = _try_convert_dates(
converter, colspec, data_dict, orig_names)
if new_name in data_dict:
raise ValueError('New date column already in dict %s' %
new_name)
new_data[new_name] = col
new_cols.append(new_name)
date_cols.update(old_names)
elif isinstance(parse_spec, dict):
# dict of new name to column list
for new_name, colspec in compat.iteritems(parse_spec):
if new_name in data_dict:
raise ValueError('Date column %s already in dict' %
new_name)
_, col, old_names = _try_convert_dates(converter, colspec,
data_dict, orig_names)
new_data[new_name] = col
new_cols.append(new_name)
date_cols.update(old_names)
data_dict.update(new_data)
new_cols.extend(columns)
if not keep_date_col:
for c in list(date_cols):
data_dict.pop(c)
new_cols.remove(c)
return data_dict, new_cols
def _try_convert_dates(parser, colspec, data_dict, columns):
colset = set(columns)
colnames = []
for c in colspec:
if c in colset:
colnames.append(c)
elif isinstance(c, int) and c not in columns:
colnames.append(str(columns[c]))
else:
colnames.append(c)
new_name = '_'.join([str(x) for x in colnames])
to_parse = [data_dict[c] for c in colnames if c in data_dict]
new_col = parser(*to_parse)
return new_name, new_col, colnames
def _clean_na_values(na_values, keep_default_na=True):
if na_values is None:
if keep_default_na:
na_values = _NA_VALUES
else:
na_values = []
na_fvalues = set()
elif isinstance(na_values, dict):
if keep_default_na:
for k, v in compat.iteritems(na_values):
v = set(list(v)) | _NA_VALUES
na_values[k] = v
na_fvalues = dict([
(k, _floatify_na_values(v)) for k, v in na_values.items()
])
else:
if not com.is_list_like(na_values):
na_values = [na_values]
na_values = _stringify_na_values(na_values)
if keep_default_na:
na_values = na_values | _NA_VALUES
na_fvalues = _floatify_na_values(na_values)
return na_values, na_fvalues
def _clean_index_names(columns, index_col):
if not _is_index_col(index_col):
return None, columns, index_col
columns = list(columns)
cp_cols = list(columns)
index_names = []
# don't mutate
index_col = list(index_col)
for i, c in enumerate(index_col):
if isinstance(c, compat.string_types):
index_names.append(c)
for j, name in enumerate(cp_cols):
if name == c:
index_col[i] = j
columns.remove(name)
break
else:
name = cp_cols[c]
columns.remove(name)
index_names.append(name)
# hack
if isinstance(index_names[0], compat.string_types)\
and 'Unnamed' in index_names[0]:
index_names[0] = None
return index_names, columns, index_col
def _get_empty_meta(columns, index_col, index_names):
columns = list(columns)
if index_col is not None:
index = MultiIndex.from_arrays([[]] * len(index_col),
names=index_names)
for n in index_col:
columns.pop(n)
else:
index = Index([])
return index, columns, {}
def _floatify_na_values(na_values):
# create float versions of the na_values
result = set()
for v in na_values:
try:
v = float(v)
if not np.isnan(v):
result.add(v)
except:
pass
return result
def _stringify_na_values(na_values):
""" return a stringified and numeric for these values """
result = []
for x in na_values:
result.append(str(x))
result.append(x)
try:
v = float(x)
# we are like 999 here
if v == int(v):
v = int(v)
result.append("%s.0" % v)
result.append(str(v))
result.append(v)
except:
pass
try:
result.append(int(x))
except:
pass
return set(result)
def _get_na_values(col, na_values, na_fvalues):
if isinstance(na_values, dict):
if col in na_values:
values = na_values[col]
fvalues = na_fvalues[col]
return na_values[col], na_fvalues[col]
else:
return _NA_VALUES, set()
else:
return na_values, na_fvalues
def _get_col_names(colspec, columns):
colset = set(columns)
colnames = []
for c in colspec:
if c in colset:
colnames.append(c)
elif isinstance(c, int):
colnames.append(columns[c])
return colnames
def _concat_date_cols(date_cols):
if len(date_cols) == 1:
if compat.PY3:
return np.array([compat.text_type(x) for x in date_cols[0]],
dtype=object)
else:
return np.array([
str(x) if not isinstance(x, compat.string_types) else x
for x in date_cols[0]
], dtype=object)
rs = np.array([' '.join([compat.text_type(y) for y in x])
for x in zip(*date_cols)], dtype=object)
return rs
class FixedWidthReader(object):
"""
A reader of fixed-width lines.
"""
def __init__(self, f, colspecs, delimiter, comment):
self.f = f
self.buffer = None
self.delimiter = '\r\n' + delimiter if delimiter else '\n\r\t '
self.comment = comment
if colspecs == 'infer':
self.colspecs = self.detect_colspecs()
else:
self.colspecs = colspecs
if not isinstance(self.colspecs, (tuple, list)):
raise TypeError("column specifications must be a list or tuple, "
"input was a %r" % type(colspecs).__name__)
for colspec in self.colspecs:
if not (isinstance(colspec, (tuple, list)) and
len(colspec) == 2 and
isinstance(colspec[0], (int, np.integer)) and
isinstance(colspec[1], (int, np.integer))):
raise TypeError('Each column specification must be '
'2 element tuple or list of integers')
def get_rows(self, n):
rows = []
for i, row in enumerate(self.f, 1):
rows.append(row)
if i >= n:
break
self.buffer = iter(rows)
return rows
def detect_colspecs(self, n=100):
# Regex escape the delimiters
delimiters = ''.join([r'\%s' % x for x in self.delimiter])
pattern = re.compile('([^%s]+)' % delimiters)
rows = self.get_rows(n)
max_len = max(map(len, rows))
mask = np.zeros(max_len + 1, dtype=int)
if self.comment is not None:
rows = [row.partition(self.comment)[0] for row in rows]
for row in rows:
for m in pattern.finditer(row):
mask[m.start():m.end()] = 1
shifted = np.roll(mask, 1)
shifted[0] = 0
edges = np.where((mask ^ shifted) == 1)[0]
return list(zip(edges[::2], edges[1::2]))
def next(self):
if self.buffer is not None:
try:
line = next(self.buffer)
except StopIteration:
self.buffer = None
line = next(self.f)
else:
line = next(self.f)
# Note: 'colspecs' is a sequence of half-open intervals.
return [line[fromm:to].strip(self.delimiter)
for (fromm, to) in self.colspecs]
# Iterator protocol in Python 3 uses __next__()
__next__ = next
class FixedWidthFieldParser(PythonParser):
"""
Specialization that Converts fixed-width fields into DataFrames.
See PythonParser for details.
"""
def __init__(self, f, **kwds):
# Support iterators, convert to a list.
self.colspecs = kwds.pop('colspecs')
PythonParser.__init__(self, f, **kwds)
def _make_reader(self, f):
self.data = FixedWidthReader(f, self.colspecs, self.delimiter,
self.comment)
|