Cómo importar una tabla con encabezados a un dataframe utilizando el módulo pandas

Estoy tratando de obtener información de una mesa en internet como se muestra a continuación. Estoy usando el cuaderno jupyter con python 2.7. Quiero usar esta información en el modu panda de Python como dataframe. Pero cuando copie la tabla con encabezados de tabla y luego use el comando read_clipboard, veo el error como se muestra debajo del enlace de la tabla. Pero sin encabezados de tabla no hay problema. ¿Cómo puedo obtener los datos de internet con headindgs tabla.

import numpy as np import pandas as pd from pandas import Series, DataFrame from numpy.random import randn df1 = pd.read_clipboard() df1 

La tabla que quiero importar como un dataframe.

 CParserError Traceback (most recent call last)  in () ----> 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 

Hay un csv que puede usar en la página con todos los datos que read_csv puede analizar fácilmente:

 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") 

Si desea ciertos períodos de tiempo, solo tiene que cambiar los parámetros en la URL, es decir, s=AAPL&d=1&e=16&f=2016&g=d&a=11&b=12&c=1980 , si cambiamos de 1980 a 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) 

Obtenemos:

  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 

Considere utilizar un raspador web html como el módulo lxml de python’s, el método html() para raspar los datos de la tabla html y luego migrar a un dataframe de pandas. Si bien existen funciones de automatización como pandas.read_html () , este enfoque proporciona más control sobre los matices en el contenido html como el intervalo de columnas del 4 de febrero . A continuación se usa una expresión xpath en la posición

en la tabla usando corchetes, [] :

 import requests import pandas as pd from lxml import etree # READ IN AND PARSE WEB DATA url = "https://finance.yahoo.com/q/hp?s=AAPL+Historical+Prices" rq = requests.get(url) htmlpage = etree.HTML(rq.content) # INITIALIZE LISTS dates = [] openstock = [] highstock = [] lowstock = [] closestock = [] volume = [] adjclose = [] # ITERATE THROUGH SEVEN COLUMNS OF TABLE for i in range(1,8): htmltable = htmlpage.xpath("//tr[td/@class='yfnc_tabledata1']/td[{}]".format(i)) # APPEND COLUMN DATA TO CORRESPONDING LIST for row in htmltable: if i == 1: dates.append(row.text) if i == 2: openstock.append(row.text) if i == 3: highstock.append(row.text) if i == 4: lowstock.append(row.text) if i == 5: closestock.append(row.text) if i == 6: volume.append(row.text) if i == 7: adjclose.append(row.text) # CLEAN UP COLSPAN VALUE (AT FEB. 4) dates = [d for d in dates if len(d.strip()) > 3] del dates[7] del openstock[7] # MIGRATE LISTS TO DATA FRAME df = pd.DataFrame({'Dates':dates, 'Open':openstock, 'High':highstock, 'Low':lowstock, 'Close':closestock, 'Volume':volume, 'AdjClose':adjclose}) # AdjClose Close Dates High Low Open Volume #0 93.99 93.99 Feb 12, 2016 94.50 93.01 94.19 40,121,700 #1 93.70 93.70 Feb 11, 2016 94.72 92.59 93.79 49,686,200 #2 94.27 94.27 Feb 10, 2016 96.35 94.10 95.92 42,245,000 #3 94.99 94.99 Feb 9, 2016 95.94 93.93 94.29 44,331,200 #4 95.01 95.01 Feb 8, 2016 95.70 93.04 93.13 54,021,400 #5 94.02 94.02 Feb 5, 2016 96.92 93.69 96.52 46,418,100 #... #61 111.73 112.34 Nov 13, 2015 115.57 112.27 115.20 45,812,400 #62 115.10 115.72 Nov 12, 2015 116.82 115.65 116.26 32,525,600 #63 115.48 116.11 Nov 11, 2015 117.42 115.21 116.37 45,218,000 #64 116.14 116.77 Nov 10, 2015 118.07 116.06 116.90 59,127,900 #65 119.92 120.57 Nov 9, 2015 121.81 120.05 120.96 33,871,400