Python: lxml xpath to extract content -


below code able extract pe reuters link below. however, method not robust webpage stock has 2 lines lesser , result shift of data. how can encounter problem. point straight part of pe extract data not know how it. link 1: http://www.reuters.com/finance/stocks/financialhighlights?symbol=myeg.kl link 2: http://www.reuters.com/finance/stocks/financialhighlights?symbol=annj.kl 

from lxml import html import lxml  page2 = requests.get('http://www.reuters.com/finance/stocks/financialhighlights?symbol=myeg.kl') treea = html.fromstring(page2.content) tree4 = treea.xpath('//td[@class]/text()') pe= tree4[37]

this part wish code can extract part changes of webpage not affected.

 <tr class="stripe">                 <td>p/e ratio (ttm)</td>                 <td class="data">36.79</td>                 <td class="data">25.99</td>                 <td class="data">21.70</td>             </tr>

use text find first td extract sibling td's:

 treea.xpath('//td[contains(.,"p/e ratio")]/following-sibling::td/text()')

that work regardless:

in [8]: page2 = requests.get('http://www.reuters.com/finance/stocks/financialhighlights?symbol=myeg.kl')  in [9]: treea = html.fromstring(page2.content)     in [10]: tree4 = treea.xpath('//td[contains(.,"p/e ratio")]/following-sibling::td/text()')  in [11]: print(tree4) ['36.79', '25.99', '21.41']  in [12]: page2 = requests.get('http://www.reuters.com/finance/stocks/financialhighlights?symbol=annj.kl') in [13]: treea = html.fromstring(page2.content)  in [14]: tree4 = treea.xpath('//td[contains(.,"p/e ratio")]/following-sibling::td/text()')  in [15]: print(tree4) ['--', '25.49', '17.30']

-

Comments

Post a Comment

Popular posts from this blog

serialization - Convert Any type in scala to Array[Byte] and back -

i have following question: i have variable value in program declared value. i want convert value byte array.. how can serialize byte array , back? found examples related other types such double or int, not any. this should need. it's pretty similar how 1 in java. import java.io.{bytearrayinputstream, bytearrayoutputstream, objectinputstream, objectoutputstream} object serialization extends app { def serialise(value: any): array[byte] = { val stream: bytearrayoutputstream = new bytearrayoutputstream() val oos = new objectoutputstream(stream) oos.writeobject(value) oos.close stream.tobytearray } def deserialise(bytes: array[byte]): = { val ois = new objectinputstream(new bytearrayinputstream(bytes)) val value = ois.readobject ois.close value } println(deserialise(serialise("my test"))) println(deserialise(serialise(list(1)))) println(deserialise(serialise(map(1 -> 2)))) println(deserialise(seri
Read more

matplotlib support failed in PyCharm on OSX -

any idea how fix ? how can debug might problem ? not seem : https://youtrack.jetbrains.com/issue/py-15520 i in console: python 2.6.9 (unknown, jul 14 2015, 19:46:31) [gcc 4.2.1 compatible apple llvm 6.0 (clang-600.0.39)] on darwin >>> import matplotlib.pyplot plt matplotlib support failed the backend : >>> import matplotlib >>> print(matplotlib.rcparams['backend']) macosx
Read more

python - Matplotlib: TypeError: 'AxesSubplot' object is not callable -

i new programming please pardon me if rookie mistake. i writing python script have defined functions create subplots of distributions. i have slider widget end user change value of variables(mu , sigma) , see effect on distribution. here's code import numpy np print np.__version__ import scipy print scipy.__version__ scipy.stats import norm, lognorm, uniform import matplotlib print matplotlib.__version__ import matplotlib.pyplot plt matplotlib.widgets import slider, button, radiobuttons, checkbuttons #####calculating mean , standard deviation##### global mu_a1 mu_a1 = 1 global mu_b1 mu_b1 = 10 global mu_c1 mu_c1 = -13 global sigma_a1 sigma_a1 = 0.14 global sigma_b1 sigma_b1 = 1.16 global sigma_c1 sigma_c1 = 2.87 mu_x01 = -11 sigma_x01 = 1.9 #####_____##### #####generating random data##### a1 = 0.75*mu_a1 + (1.25 - 0.75)*sigma_a1*np.random.sample(10000) b1 = 8*mu_b1 + (12 - 8)*sigma_b1*np.random.sample(10000) c1 = -12*mu_c1 + 2*sigma_c1*np.random.sample(10000)
Read more