如何使用 pandas 模块导入具有标题到数据框的表 [英] How to import a table with headings to a data frame using pandas module
本文介绍了如何使用 pandas 模块导入具有标题到数据框的表的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在从互联网上的表格中获取信息，如下所示。我使用jupyter笔记本与python 2.7。我想在Python的熊猫模块中使用这些信息作为数据框架。但是当ı用表标题复制表格，然后使用read_clipboard命令时，我看到错误如下表所示。但没有表标题没有问题。如何从表头头链接到互联网上的数据。
import numpy as np/ pre>
import pandas as pd
从pandas import系列，DataFrame
from numpy.random import randn
df1 = pd.read_clipboard（）
df1
CParserError Traceback（最近的最后一次调用）
< ipython-输入4-151d7223d8dc>在< module>（）
----> 1 df1 = pd.read_clipboard（）
2 df1
C：\Anaconda3\envs\python2\lib\site-packages\pandas\io\clipboard .pyc in read_clipboard（** kwargs）
49 kwargs ['sep'] ='\s +'
50
---> 51 return read_table（StringIO（text），** kwargs）
52
53
C：\Anaconda3\envs\python2\lib\site-packages parser_f（filepath_or_buffer，sep，dialect，compression，doublequote，escapechar，quotechar，quoting，skipinitialspace，lineterminator，header，index_col，names，prefix，skiprows，skipfooter，skip_footer，na_values中的\pandas\io\parsers.pyc， true_values，false_values，delimiter，转换器，dtype，usecols，引擎，delim_whitespace，as_recarray，na_filter，compact_ints，use_unsigned，low_memory，buffer_lines，warn_bad_lines，error_bad_lines，keep_default_na，thousand，comment，decimal，parse_dates，keep_date_col，dayfirst，date_parser，memory_map， float_precision，nrows，iterator，chunksize，verbose，encoding，squeeze，mangle_dupe_cols，tupleize_cols，infer_datetime_format，skip_blank_lines）
496 skip_blank_lines = skip_blank_lines
497
- > 498 return _read（filepath_or_buffer，kwds）
499
500 parser_f .__ name__ = name
C：\Anaconda3\envs\python2\lib\site-packages \pandas\io\parsers.pyc在_read（filepath_or_buffer，kwds）
283 return parser
284
- > 285 return parser.read（）
286
287 _parser_defaults = {
C：\Anaconda3\envs\python2\lib\site- packages\pandas读取（self，nrows）中的\io\parsers.pyc
745 raise ValueError（'skip_footer不支持迭代'）
746
- > 747 ret = self._engine.read（nrows）
748
749如果self.options.get（'as_recarray'）：
C：\Anaconda3\envs\\读取（self，nrows）
1195 def read（self，nrows = None）：
1196 try：\\ bin /
- > 1197 data = self._reader.read（nrows）
1198除了StopIteration：
1199 if self._first_chunk：
pandas\parser.pyx在pandas.parser.TextReader中。 （pandas\parser.c：7988）（）
pandas\parser.pyx在pandas.parser.TextReader._read_low_memory（pandas\parser.c：8244）（）$ b $ pandas.parser中的pandas.parser.TextReader._read_rows（pandas\parser.c：8970）（）
pandas\parser.pyx中的b
pandas\parser.pyx。在pandas.parser.raise_parser_error（pandas\parser.c：22649）（）$ b $中的TextReader._tokenize_rows（pandas\parser.c：8838）（）
pandas\parser.pyx b
CParserError：令牌化数据错误。 C错误：期望在第14行中的1个字段，看到2
解决方案您可以在页面上使用一个csv，其中所有数据
read_csv可以轻松解析：import pandas as pd
df = pd.read_csv（http://real-chart.finance.yahoo.com/table.csv?s=AAPL&d = 1& e = 16& f = 2016& g = d& a = 11& b = 12& c = 1980& ignore = .csv）
如果您想要某些时间段，您只需要更改网址中的参数即
s = AAPL& d = 1& e = 16& f = 2016& ;如果我们将1980年更改为2015年，则g = d& a = 11& b = 12& c = 1980code> df = pd.read_csv（http://real-chart.finance.yahoo.com/table.csv?s=AAPL&d=1&e=16&f=2016&g=d& a = 11& b = 12& c = 2015& ignore = .csv，parse_dates = 0）
print（df）
我们得到：
日期打开高低关闭数量\
0 2016-02-12 94.190002 94.500000 93.010002 93.989998 40121700
1 2016-02-11 93.790001 94.720001 92.589996 93.699997 49686200
2 2016-02-10 95.919998 96.349998 94.099998 94.269997 42245000
3 2016-02-09 94.290001 95.940002 93.930000 94.989998 44331200
4 2016-02-08 93.129997 95.699997 93.040001 95.010002 54021400
5 2016-02-05 96.519997 96.919998 93.690002 94.019997 46418100
6 2016 -02-04 95.860001 97.330002 95.190002 96.599998 46471700
7 2016-02-03 95.000000 96.839996 94.080002 96.349998 45964300
8 2016-02-02 95.419998 96.040001 94.279999 94.480003 37357200
9 2016-02-01 96.470001 96.709999 95.400002 96.430000 40943500
10 2016-01-29 94.790001 97.339996 94.349998 97.339996 64416500
11 2016-01-28 93.790001 94.519997 92.389999 94.089996 556 78800
12 2016-01-27 96.040001 96.629997 93.339996 93.419998 133369700
13 2016-01-26 99.930000 100.879997 98.070000 99.989998 75077000
14 2016-01-25 101.519997 101.529999 99.209999 99.440002 51794500
15 2016-01-22 98.629997 101.459999 98.370003 101.419998 65800500
16 2016-01-21 97.059998 97.879997 94.940002 96.300003 52161500
17 2016-01-20 95.099998 98.190002 93.419998 96.790001 72334400
18 2016-01- 19 98.410004 98.650002 95.500000 96.660004 53087700
19 2016-01-15 96.199997 97.709999 95.360001 97.129997 79833900
20 2016-01-14 97.959999 100.480003 95.739998 99.519997 63170100
21 2016-01-13 100.320000 101.190002 97.300003 97.389999 62439600
22 2016-01-12 100.550003 100.690002 98.839996 99.959999 49154200
23 2016-01-11 98.970001 99.059998 97.339996 98.529999 49739400
24 2016-01-08 98.550003 99.110001 96.760002 96.959999 70798000
25 2016-01-07 98.680000 100.129997 96.430000 96.449997 81094400
26 2016-01-06 100.559998 102.370003 99.870003 100.699997 68457400
27 2016-01-05 105.750000 105.849998 102.410004 102.709999 55791000
28 2016-01-104 102.610001 105.370003 102.000000 105.349998 67649400
29 2015-12-31 107.010002 107.029999 104.820000 105.260002 40912300
30 2015-12-30 108.580002 108.699997 107.180000 107.320000 25213800
31 2015-12-29 106.959999 109.430000 106.860001 108.739998 30931200
32 2015-12-28 107.589996 107.690002 106.180000 106.820000 26704200
33 2015-12-24 109.000000 109.000000 107.949997 108.029999 13596700
34 2015-12-23 107.269997 108.849998 107.199997 108.610001 32657400
35 2015-12-22 107.400002 107.720001 106.449997 107.230003 32789400
36 2015-12-21 10 7.279999 107.370003 105.570000 107.330002 47590600
37 2015-12-18 108.910004 109.519997 105.809998 106.029999 96453300
38 2014-12-17 112.019997 112.250000 108.980003 108.980003 44772800
39 2015-12-16 111.070000 111.989998 108.800003 111.339996 56238500
40 2015-12-15 111.940002 112.800003 110.349998 110.489998 52978100
41 2015-12-14 112.180000 112.680000 109.790001 112.480003 64318700
Adj关闭
0 93.989998
1 93.699997
2 94.269997
3 94.989998
4 95.010002
5 94.019997
6 96.599998
7 95.830001
8 93.970098
9 95.909571
10 96.814656
11 93.582196
12 92.915814
13 99.450356
14 98.903329
15 100.872638
16 95.780276
17 96.267629
18 96.138333
19 96.605790
20 98.982891
21 96.864389
22 99.420519
23 97.998236
24 96.436710
25 95.929460
26 100.156523
27 102.155677
28 104.781429
29 104.691918
30 106.740798
31 108.153132
32 106.243496
33 107.446965
34 108.023837
35 106.651287
36 106.750746
37 105.457759
38 108.391842
39 110.739099
40 109.893688
41 111.872953
I'm trying to get information from a table in the internet as shown below. I'm using jupyter notebook with python 2.7. I want to use this information in Python's panda modüle as data frame. But when ı copy the table with table headings and then use the read_clipboard command, I see the error as shown below the table link. But without table headings there is no problem. How can ı get the data from internet with table headindgs.
import numpy as np import pandas as pd from pandas import Series, DataFrame from numpy.random import randn df1 = pd.read_clipboard() df1The table which I want to import as a data frame.
CParserError Traceback (most recent call last) <ipython-input-4-151d7223d8dc> in <module>() ----> 1 df1 = pd.read_clipboard() 2 df1 C:\Anaconda3\envs\python2\lib\site-packages\pandas\io\clipboard.pyc in read_clipboard(**kwargs) 49 kwargs['sep'] = '\s+' 50 ---> 51 return read_table(StringIO(text), **kwargs) 52 53 C:\Anaconda3\envs\python2\lib\site-packages\pandas\io\parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines) 496 skip_blank_lines=skip_blank_lines) 497 --> 498 return _read(filepath_or_buffer, kwds) 499 500 parser_f.__name__ = name C:\Anaconda3\envs\python2\lib\site-packages\pandas\io\parsers.pyc in _read(filepath_or_buffer, kwds) 283 return parser 284 --> 285 return parser.read() 286 287 _parser_defaults = { C:\Anaconda3\envs\python2\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows) 745 raise ValueError('skip_footer not supported for iteration') 746 --> 747 ret = self._engine.read(nrows) 748 749 if self.options.get('as_recarray'): C:\Anaconda3\envs\python2\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows) 1195 def read(self, nrows=None): 1196 try: -> 1197 data = self._reader.read(nrows) 1198 except StopIteration: 1199 if self._first_chunk: pandas\parser.pyx in pandas.parser.TextReader.read (pandas\parser.c:7988)() pandas\parser.pyx in pandas.parser.TextReader._read_low_memory (pandas\parser.c:8244)() pandas\parser.pyx in pandas.parser.TextReader._read_rows (pandas\parser.c:8970)() pandas\parser.pyx in pandas.parser.TextReader._tokenize_rows (pandas\parser.c:8838)() pandas\parser.pyx in pandas.parser.raise_parser_error (pandas\parser.c:22649)() CParserError: Error tokenizing data. C error: Expected 1 fields in line 14, saw 2
解决方案There is a csv you can use on the page with all the data which
read_csvcan parse easily:import pandas as pd df = pd.read_csv("http://real-chart.finance.yahoo.com/table.csv?s=AAPL&d=1&e=16&f=2016&g=d&a=11&b=12&c=1980&ignore=.csv")If you want certain time periods you just have to change the params in the url i.e
s=AAPL&d=1&e=16&f=2016&g=d&a=11&b=12&c=1980, if we change 1980 to 2015:df = pd.read_csv("http://real-chart.finance.yahoo.com/table.csv?s=AAPL&d=1&e=16&f=2016&g=d&a=11&b=12&c=2015&ignore=.csv",parse_dates=0) print(df)We get:
Date Open High Low Close Volume \ 0 2016-02-12 94.190002 94.500000 93.010002 93.989998 40121700 1 2016-02-11 93.790001 94.720001 92.589996 93.699997 49686200 2 2016-02-10 95.919998 96.349998 94.099998 94.269997 42245000 3 2016-02-09 94.290001 95.940002 93.930000 94.989998 44331200 4 2016-02-08 93.129997 95.699997 93.040001 95.010002 54021400 5 2016-02-05 96.519997 96.919998 93.690002 94.019997 46418100 6 2016-02-04 95.860001 97.330002 95.190002 96.599998 46471700 7 2016-02-03 95.000000 96.839996 94.080002 96.349998 45964300 8 2016-02-02 95.419998 96.040001 94.279999 94.480003 37357200 9 2016-02-01 96.470001 96.709999 95.400002 96.430000 40943500 10 2016-01-29 94.790001 97.339996 94.349998 97.339996 64416500 11 2016-01-28 93.790001 94.519997 92.389999 94.089996 55678800 12 2016-01-27 96.040001 96.629997 93.339996 93.419998 133369700 13 2016-01-26 99.930000 100.879997 98.070000 99.989998 75077000 14 2016-01-25 101.519997 101.529999 99.209999 99.440002 51794500 15 2016-01-22 98.629997 101.459999 98.370003 101.419998 65800500 16 2016-01-21 97.059998 97.879997 94.940002 96.300003 52161500 17 2016-01-20 95.099998 98.190002 93.419998 96.790001 72334400 18 2016-01-19 98.410004 98.650002 95.500000 96.660004 53087700 19 2016-01-15 96.199997 97.709999 95.360001 97.129997 79833900 20 2016-01-14 97.959999 100.480003 95.739998 99.519997 63170100 21 2016-01-13 100.320000 101.190002 97.300003 97.389999 62439600 22 2016-01-12 100.550003 100.690002 98.839996 99.959999 49154200 23 2016-01-11 98.970001 99.059998 97.339996 98.529999 49739400 24 2016-01-08 98.550003 99.110001 96.760002 96.959999 70798000 25 2016-01-07 98.680000 100.129997 96.430000 96.449997 81094400 26 2016-01-06 100.559998 102.370003 99.870003 100.699997 68457400 27 2016-01-05 105.750000 105.849998 102.410004 102.709999 55791000 28 2016-01-04 102.610001 105.370003 102.000000 105.349998 67649400 29 2015-12-31 107.010002 107.029999 104.820000 105.260002 40912300 30 2015-12-30 108.580002 108.699997 107.180000 107.320000 25213800 31 2015-12-29 106.959999 109.430000 106.860001 108.739998 30931200 32 2015-12-28 107.589996 107.690002 106.180000 106.820000 26704200 33 2015-12-24 109.000000 109.000000 107.949997 108.029999 13596700 34 2015-12-23 107.269997 108.849998 107.199997 108.610001 32657400 35 2015-12-22 107.400002 107.720001 106.449997 107.230003 32789400 36 2015-12-21 107.279999 107.370003 105.570000 107.330002 47590600 37 2015-12-18 108.910004 109.519997 105.809998 106.029999 96453300 38 2015-12-17 112.019997 112.250000 108.980003 108.980003 44772800 39 2015-12-16 111.070000 111.989998 108.800003 111.339996 56238500 40 2015-12-15 111.940002 112.800003 110.349998 110.489998 52978100 41 2015-12-14 112.180000 112.680000 109.790001 112.480003 64318700 Adj Close 0 93.989998 1 93.699997 2 94.269997 3 94.989998 4 95.010002 5 94.019997 6 96.599998 7 95.830001 8 93.970098 9 95.909571 10 96.814656 11 93.582196 12 92.915814 13 99.450356 14 98.903329 15 100.872638 16 95.780276 17 96.267629 18 96.138333 19 96.605790 20 98.982891 21 96.864389 22 99.420519 23 97.998236 24 96.436710 25 95.929460 26 100.156523 27 102.155677 28 104.781429 29 104.691918 30 106.740798 31 108.153132 32 106.243496 33 107.446965 34 108.023837 35 106.651287 36 106.750746 37 105.457759 38 108.391842 39 110.739099 40 109.893688 41 111.872953
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![我想要的表导入为数据框](\"http://i.stack.imgur.com/0pSRu.jpg\"){kind=link}
