我需要以下方面的帮助：
我试图将csv文件导入我的Jupyter笔记本，但无济于事。
我使用的代码是：
dfa = pd.read_csv('文件名.csv'(
并给出了以下错误消息：
---------------------------------------------------------------------------
ParserError Traceback (most recent call last)
<ipython-input-3-164d461fc4d7> in <module>()
----> 1 dfa = pd.read_csv('Airpollution.csv')
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)
676 skip_blank_lines=skip_blank_lines)
677
--> 678 return _read(filepath_or_buffer, kwds)
679
680 parser_f.__name__ = name
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
444
445 try:
--> 446 data = parser.read(nrows)
447 finally:
448 parser.close()
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
1034 raise ValueError('skipfooter not supported for iteration')
1035
-> 1036 ret = self._engine.read(nrows)
1037
1038 # May alter columns / col_dict
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
1846 def read(self, nrows=None):
1847 try:
-> 1848 data = self._reader.read(nrows)
1849 except StopIteration:
1850 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()
ParserError: Error tokenizing data. C error: Expected 1 fields in line 4, saw 11
我已经检查了文件是否从同一文件夹打开，并且它们都存储在我的桌面中。
我安装了熊猫，matplotlib和seaborn。我已经尝试了所有方法(来自 Stackoverflow 的其他解决方案(，但无法弄清楚为什么我无法导入。请开导我。谢谢！
-
@jpp： 另一个 csv 文件能够工作 这很奇怪，因为我尝试使用另一个 csv 文件并且它起作用了。我无法加载这些文件。
我正在使用以下信息：
Subject: Environment
Topic : Air Quality and Climate
" Title : M890641 - Air Pollution Levels, Annual "
, , , , , , , , , ,
Variables , 2007 , 2008 , 2009 , 2010 , 2011 , 2012 , 2013 , 2014 , 2015 , 2016 ,
Sulphur Dioxide (Annual Mean) (Microgram Per Cubic Metre) , 12 , 11 , 9 , 11 , 10 , 13 , 14 , 12 , 12 , 13 ,
Sulphur Dioxide (Maximum 24-hour Mean) (Microgram Per Cubic Metre) , 84 , 80 , 93 , 104 , 80 , 98 , 75 , 83 , 75 , 61 ,
Nitrogen Dioxide (Annual Mean) (Microgram Per Cubic Metre) , 22 , 22 , 22 , 23 , 25 , 25 , 25 , 24 , 22 , 26 ,
Nitrogen Dioxide (Maximum 1-hour Mean) (Microgram Per Cubic Metre) , 177 , 126 , 147 , 153 , 189 , 154 , 132 , 121 , 99 , 123 ,
Particulate Matter (PM10) (Annual Mean) (Microgram Per Cubic Metre) , 27 , 25 , 29 , 26 , 27 , 29 , 31 , 30 , 37 , 26 ,
Particulate Matter (PM10) (99th Percentile 24-hour Mean) (Microgram Per Cubic Metre) , 53 , 49 , 59 , 76 , 55 , 57 , 215 , 75 , 186 , 61 ,
Particulate Matter (PM2.5) (Annual Mean) (Microgram Per Cubic Metre) , 19 , 16 , 19 , 17 , 17 , 19 , 20 , 18 , 24 , 15 ,
Particulate Matter (PM2.5) (99th Percentile 24-hour Mean) (Microgram Per Cubic Metre) , 37 , 32 , 44 , 56 , 41 , 42 , 176 , 51 , 145 , 40 ,
Carbon Monoxide (Maximum 8-hour Mean) (Milligram Per Cubic Metre) , 1.7 , 1.6 , 1.9 , 2.4 , 2 , 1.9 , 5.5 , 1.8 , 3.3 , 2.2 ,
Carbon Monoxide (Maximum 1-hour Mean) (Milligram Per Cubic Metre) , 2.5 , 2.3 , 3.9 , 2.8 , 2.6 , 2.4 , 7.5 , 2.7 , 3.5 , 2.7 ,
Ozone (Maximum 8-hour Mean) (Microgram Per Cubic Metre) , 206 , 183 , 105 , 139 , 123 , 122 , 139 , 135 , 152 , 115 ,
SOURCE: NATIONAL ENVIRONMENT AGENCY
Generated by: SingStat Table Builder
Date generated: 05/09/2018
Contact: info@singstat.gov.sg
而这个：
Subject: Death and Life Expectancy
Topic : Death and Life Expectancy
" Title : M810131 - Deaths By Broad Groups Of Causes, Annual "
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,Number
Variables , 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 ,
Total Deaths By Causes ," 10,224 "," 10,717 "," 11,329 "," 11,522 "," 11,920 "," 11,674 "," 11,447 "," 11,648 "," 11,955 "," 12,065 "," 12,468 "," 12,505 "," 12,863 "," 12,896 "," 13,321 "," 13,162 "," 13,348 "," 12,821 "," 13,173 "," 13,690 "," 14,069 "," 13,891 "," 13,876 "," 14,337 "," 14,461 "," 14,946 "," 15,569 "," 15,590 "," 15,305 "," 15,657 "," 15,516 "," 15,693 "," 15,367 "," 15,820 "," 16,036 "," 15,860 "," 16,215 "," 16,393 "," 17,140 "," 17,222 "," 17,101 "," 17,610 "," 18,027 "," 18,481 "," 18,938 "," 19,393 "," 19,862 "," 20,017 "," 20,905 ",
Infective And Parasitic Diseases , 708 , 727 , 702 , 752 , 775 , 714 , 630 , 554 , 523 , 502 , 503 , 425 , 432 , 393 , 432 , 390 , 375 , 402 , 432 , 430 , 439 , 347 , 321 , 342 , 398 , 366 , 369 , 358 , 318 , 361 , 311 , 276 , 296 , 289 , 250 , 296 , 373 , 257 , 307 , 285 , 279 , 269 , 244 , 233 , 211 , 217 , 194 , 174 , 189 ,
Tuberculosis , 419 , 458 , 439 , 489 , 450 , 472 , 420 , 358 , 340 , 318 , 331 , 240 , 221 , 207 , 224 , 163 , 177 , 177 , 186 , 168 , 132 , 113 , 104 , 101 , 115 , 101 , 118 , 132 , 115 , 128 , 107 , 101 , 104 , 92 , 79 , 79 , 67 , 66 , 85 , 83 , 75 , 77 , 68 , 65 , 51 , 60 , 41 , 41 , 32 ,
Neoplasms ," 1,577 "," 1,633 "," 1,728 "," 1,821 "," 1,912 "," 2,002 "," 2,123 "," 2,278 "," 2,326 "," 2,415 "," 2,542 "," 2,623 "," 2,672 "," 2,729 "," 2,903 "," 2,817 "," 2,939 "," 2,921 "," 3,169 "," 3,233 "," 3,321 "," 3,314 "," 3,405 "," 3,497 "," 3,560 "," 3,785 "," 3,921 "," 4,034 "," 4,178 "," 4,091 "," 4,168 "," 4,278 "," 4,384 "," 4,465 "," 4,187 "," 4,353 "," 4,331 "," 4,722 "," 4,803 "," 5,081 "," 5,063 "," 5,078 "," 5,461 "," 5,651 "," 5,849 "," 5,790 "," 5,986 "," 5,993 "," 6,237 ",
Malignant Neoplasms ," 1,533 "," 1,596 "," 1,688 "," 1,773 "," 1,863 "," 1,955 "," 2,083 "," 2,245 "," 2,286 "," 2,386 "," 2,488 "," 2,561 "," 2,616 "," 2,668 "," 2,858 "," 2,776 "," 2,893 "," 2,887 "," 3,131 "," 3,194 "," 3,283 "," 3,269 "," 3,361 "," 3,456 "," 3,531 "," 3,756 "," 3,898 "," 3,985 "," 4,128 "," 4,050 "," 4,134 "," 4,238 "," 4,339 "," 4,425 "," 4,146 "," 4,303 "," 4,289 "," 4,677 "," 4,745 "," 5,038 "," 5,010 "," 5,025 "," 5,411 "," 5,565 "," 5,775 "," 5,701 "," 5,903 "," 5,925 "," 6,077 ",
" Endocrine, Nutritional And Metabolic Diseases ", 331 , 250 , 308 , 271 , 342 , 377 , 375 , 408 , 429 , 403 , 403 , 359 , 404 , 397 , 423 , 512 , 492 , 508 , 521 , 525 , 461 , 388 , 359 , 269 , 309 , 374 , 327 , 403 , 366 , 401 , 444 , 458 , 629 , 530 , 473 , 545 , 593 , 620 , 722 , 551 , 378 , 272 , 356 , 279 , 253 , 296 , 270 , 363 , 340 ,
Diabetes , 184 , 134 , 212 , 207 , 247 , 257 , 259 , 334 , 377 , 334 , 347 , 319 , 368 , 361 , 373 , 469 , 464 , 479 , 492 , 501 , 419 , 332 , 320 , 238 , 264 , 334 , 271 , 320 , 282 , 308 , 350 , 355 , 512 , 425 , 373 , 474 , 510 , 536 , 609 , 463 , 290 , 182 , 299 , 268 , 247 , 277 , 250 , 343 , 321 ,
Diseases Of The Blood And Blood-forming Organs , 71 , 51 , 60 , 50 , 61 , 60 , 52 , 32 , 50 , 45 , 41 , 31 , 42 , 33 , 33 , 28 , 29 , 30 , 35 , 35 , 48 , 50 , 40 , 33 , 34 , 24 , 37 , 37 , 44 , 35 , 50 , 54 , 52 , 44 , 39 , 33 , 40 , 36 , 31 , 46 , 30 , 41 , 41 , 20 , 14 , 23 , 10 , 14 , 17 ,
Diseases Of The Nervous System And Sense Organs , 221 , 173 , 166 , 171 , 169 , 149 , 133 , 129 , 110 , 114 , 122 , 131 , 114 , 121 , 92 , 97 , 87 , 87 , 102 , 133 , 111 , 143 , 117 , 127 , 93 , 71 , 89 , 89 , 95 , 110 , 105 , 107 , 122 , 94 , 67 , 81 , 68 , 62 , 64 , 75 , 68 , 92 , 117 , 166 , 137 , 144 , 210 , 226 , 185 ,
Diseases Of The Circulatory System ," 2,733 "," 2,899 "," 3,120 "," 2,999 "," 3,169 "," 3,295 "," 3,369 "," 3,798 "," 3,889 "," 3,983 "," 4,233 "," 4,305 "," 4,413 "," 4,430 "," 4,436 "," 4,637 "," 4,651 "," 4,482 "," 4,675 "," 4,847 "," 5,082 "," 5,152 "," 5,070 "," 5,270 "," 5,315 "," 5,460 "," 5,560 "," 5,896 "," 5,680 "," 5,711 "," 5,810 "," 5,749 "," 5,588 "," 5,401 "," 5,727 "," 5,423 "," 5,397 "," 5,441 "," 5,835 "," 5,794 "," 5,611 "," 5,807 "," 5,720 "," 5,747 "," 5,765 "," 5,987 "," 6,101 "," 6,107 "," 6,541 ",
Heart And Hypertensive Diseases ," 1,761 "," 1,780 "," 1,925 "," 1,819 "," 1,967 "," 2,014 "," 2,000 "," 2,283 "," 2,426 "," 2,518 "," 2,752 "," 2,777 "," 2,892 "," 2,866 "," 2,911 "," 3,156 "," 3,129 "," 3,028 "," 3,251 "," 3,318 "," 3,416 "," 3,385 "," 3,234 "," 3,457 "," 3,552 "," 3,653 "," 3,742 "," 3,984 "," 3,943 "," 3,950 "," 4,061 "," 3,976 "," 4,075 "," 3,856 "," 4,067 "," 3,714 "," 3,656 "," 3,833 "," 4,197 "," 4,201 "," 4,081 "," 4,161 "," 3,920 "," 3,848 "," 3,914 "," 4,165 "," 4,534 "," 4,576 "," 4,970 ",
Cerebrovascular Disease , 863 ," 1,038 "," 1,103 "," 1,080 "," 1,131 "," 1,213 "," 1,244 "," 1,427 "," 1,360 "," 1,382 "," 1,409 "," 1,447 "," 1,438 "," 1,469 "," 1,454 "," 1,413 "," 1,418 "," 1,355 "," 1,343 "," 1,414 "," 1,551 "," 1,666 "," 1,700 "," 1,697 "," 1,652 "," 1,692 "," 1,701 "," 1,805 "," 1,645 "," 1,633 "," 1,633 "," 1,625 "," 1,409 "," 1,393 "," 1,556 "," 1,562 "," 1,616 "," 1,462 "," 1,490 "," 1,435 "," 1,375 "," 1,472 "," 1,628 "," 1,714 "," 1,680 "," 1,620 "," 1,357 "," 1,317 "," 1,310 ",
Diseases Of The Respiratory System ," 1,235 "," 1,473 "," 1,502 "," 1,653 "," 1,663 "," 1,631 "," 1,632 "," 1,651 "," 1,902 "," 1,724 "," 2,024 "," 1,965 "," 2,196 "," 2,257 "," 2,429 "," 2,096 "," 2,241 "," 1,974 "," 1,942 "," 2,110 "," 2,167 "," 2,112 "," 2,289 "," 2,522 "," 2,588 "," 2,564 "," 2,912 "," 2,534 "," 2,385 "," 2,579 "," 2,357 "," 2,505 "," 2,239 "," 2,763 "," 2,992 "," 2,851 "," 3,124 "," 2,913 "," 2,948 "," 2,989 "," 3,188 "," 3,434 "," 3,493 "," 3,708 "," 4,061 "," 4,232 "," 4,417 "," 4,440 "," 4,757 ",
Pneumonia , 655 , 843 , 875 , 951 , 950 , 969 , 948 ," 1,010 "," 1,215 ", 942 ," 1,124 "," 1,129 "," 1,284 "," 1,375 "," 1,513 "," 1,204 "," 1,287 "," 1,082 ", 998 ," 1,039 "," 1,130 "," 1,191 "," 1,285 "," 1,420 "," 1,596 "," 1,670 "," 2,028 "," 1,693 "," 1,553 "," 1,780 "," 1,641 "," 1,794 "," 1,540 "," 2,079 "," 2,340 "," 2,232 "," 2,437 "," 2,244 "," 2,375 "," 2,387 "," 2,614 "," 2,766 "," 2,879 "," 3,096 "," 3,512 "," 3,680 "," 3,859 "," 3,855 "," 4,212 ",
Diseases Of The Digestive System , 402 , 454 , 463 , 463 , 453 , 451 , 423 , 384 , 382 , 359 , 382 , 368 , 385 , 400 , 403 , 369 , 394 , 326 , 329 , 380 , 363 , 374 , 406 , 353 , 361 , 394 , 409 , 416 , 357 , 418 , 412 , 326 , 307 , 339 , 383 , 356 , 385 , 384 , 392 , 377 , 351 , 436 , 426 , 414 , 418 , 482 , 477 , 467 , 485 ,
Diseases Of The Genito-urinary System , 234 , 239 , 252 , 279 , 275 , 320 , 311 , 281 , 324 , 381 , 349 , 366 , 366 , 319 , 375 , 405 , 319 , 343 , 393 , 380 , 370 , 346 , 369 , 362 , 371 , 444 , 483 , 444 , 399 , 494 , 470 , 486 , 487 , 594 , 587 , 641 , 634 , 637 , 739 , 753 , 861 , 893 , 918 , 934 , 967 , 951 , 928 , 913 , 925 ,
Congenital Anomalies , 181 , 150 , 186 , 172 , 189 , 177 , 146 , 156 , 141 , 185 , 184 , 185 , 178 , 182 , 155 , 172 , 189 , 202 , 171 , 201 , 170 , 189 , 164 , 163 , 160 , 148 , 157 , 130 , 108 , 112 , 95 , 85 , 79 , 69 , 59 , 49 , 67 , 70 , 55 , 60 , 60 , 60 , 53 , 54 , 47 , 50 , 62 , 72 , 49 ,
Congenital Anomalies Of Heart , 84 , 76 , 102 , 93 , 94 , 101 , 76 , 70 , 70 , 98 , 105 , 111 , 109 , 101 , 86 , 91 , 84 , 101 , 87 , 98 , 75 , 84 , 82 , 92 , 94 , 90 , 89 , 74 , 68 , 57 , 48 , 48 , 33 , 40 , 32 , 28 , 38 , 42 , 40 , 32 , 36 , 35 , 21 , 25 , 21 , 26 , 32 , 38 , 22 ,
Certain Causes Of Perinatal Mortality , 460 , 463 , 455 , 502 , 477 , 322 , 254 , 221 , 247 , 239 , 261 , 227 , 208 , 215 , 149 , 151 , 147 , 128 , 128 , 127 , 135 , 123 , 89 , 82 , 76 , 68 , 51 , 64 , 61 , 62 , 52 , 48 , 24 , 52 , 41 , 22 , 39 , 43 , 32 , 39 , 49 , 34 , 49 , 44 , 43 , 42 , 30 , 36 , 39 ,
" Accidents, Poisonings And Violence ", 811 , 836 , 968 , 982 , 995 , 894 , 887 , 890 , 914 ," 1,057 ", 876 , 899 , 938 , 966 ," 1,085 "," 1,095 "," 1,082 "," 1,025 ", 931 , 958 ," 1,042 "," 1,008 "," 1,074 "," 1,127 "," 1,066 "," 1,122 "," 1,113 "," 1,040 "," 1,187 "," 1,110 "," 1,066 "," 1,133 "," 1,036 "," 1,053 "," 1,062 "," 1,028 "," 1,017 "," 1,027 "," 1,036 "," 1,006 ", 978 , 973 , 989 ," 1,030 ", 933 , 909 , 895 , 890 , 840 ,
Suicides , 188 , 185 , 230 , 235 , 240 , 229 , 252 , 257 , 224 , 266 , 249 , 271 , 191 , 239 , 267 , 211 , 327 , 329 , 302 , 367 , 395 , 354 , 319 , 298 , 296 , 347 , 401 , 271 , 346 , 371 , 309 , 348 , 357 , 361 , 346 , 381 , 405 , 419 , 374 , 364 , 401 , 353 , 361 , 467 , 422 , 415 , 409 , 429 , 361 ,
Transport Accidents , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , na , 199 , 232 , 226 , 201 , 208 , 207 , 192 , 176 , 183 , 168 , 164 , 141 ,
Other Diseases And Causes ," 1,260 "," 1,369 "," 1,419 "," 1,407 "," 1,440 "," 1,282 "," 1,112 ", 866 , 718 , 658 , 548 , 621 , 515 , 454 , 406 , 393 , 403 , 393 , 345 , 331 , 360 , 345 , 173 , 190 , 130 , 126 , 141 , 145 , 127 , 173 , 176 , 188 , 124 , 127 , 167 , 182 , 147 , 181 , 176 , 166 , 185 , 221 , 160 , 201 , 240 , 270 , 282 , 322 , 301 ,
"Deaths prior to 1979 are classified according to the eighth (1965) revision of the International Classification of Diseases. Deaths from 1979 to 2011 are classified according to the ninth (1975) revision. From 2012, deaths are classified according to the tenth revision."
SOURCE: REGISTRY OF BIRTHS AND DEATHS
Generated by: SingStat Table Builder
Date generated: 05/09/2018
Contact: info@singstat.gov.sg
我不太确定它是否与文件或Mac中的设置有关..谢谢！
您应该考虑使用可用于pd.read_csv的参数。例如，您可以指定分隔符和跳过行。您的末尾有一个空列，底部有一个垃圾，但这可以在读取文件后处理。
例如：
df = pd.read_csv('file.csv', sep=' *, *', skiprows=4, engine='python')
.dropna(subset=['2007'])
.iloc[:, :-1]
print(df)
Variables 2007 2008 2009
0 Sulphur Dioxide (Annual Mean) (Microgram Per C... 12.0 11.0 9.0
1 Sulphur Dioxide (Maximum 24-hour Mean) (Microg... 84.0 80.0 93.0
2 Nitrogen Dioxide (Annual Mean) (Microgram Per ... 22.0 22.0 22.0
3 Nitrogen Dioxide (Maximum 1-hour Mean) (Microg... 177.0 126.0 147.0
4 Particulate Matter (PM10) (Annual Mean) (Micro... 27.0 25.0 29.0
5 Particulate Matter (PM10) (99th Percentile 24-... 53.0 49.0 59.0
6 Particulate Matter (PM2.5) (Annual Mean) (Micr... 19.0 16.0 19.0
7 Particulate Matter (PM2.5) (99th Percentile 24... 37.0 32.0 44.0
8 Carbon Monoxide (Maximum 8-hour Mean) (Milligr... 1.7 1.6 1.9
9 Carbon Monoxide (Maximum 1-hour Mean) (Milligr... 2.5 2.3 3.9
10 Ozone (Maximum 8-hour Mean) (Microgram Per Cub... 206.0 183.0 105.0
2010 2011 2012 2013 2014 2015 2016
0 11.0 10.0 13.0 14.0 12.0 12.0 13.0
1 104.0 80.0 98.0 75.0 83.0 75.0 61.0
2 23.0 25.0 25.0 25.0 24.0 22.0 26.0
3 153.0 189.0 154.0 132.0 121.0 99.0 123.0
4 26.0 27.0 29.0 31.0 30.0 37.0 26.0
5 76.0 55.0 57.0 215.0 75.0 186.0 61.0
6 17.0 17.0 19.0 20.0 18.0 24.0 15.0
7 56.0 41.0 42.0 176.0 51.0 145.0 40.0
8 2.4 2.0 1.9 5.5 1.8 3.3 2.2
9 2.8 2.6 2.4 7.5 2.7 3.5 2.7
10 139.0 123.0 122.0 139.0 135.0 152.0 115.0
您可以跳过坏行(字段数不匹配(：
dfa = pd.read_csv('Filename.csv',error_bad_lines=False)