How to scrape quiz question from website?-CodePudding

Scraping course content from a website but can't get the exactly result, too many noisy code. (used F12 for chorme devtools,confused..) how to get it done simply?

My code:

import requests,bs4
res = requests.get('https://brilliant.org/practice/computational-models-of-the-neuron/?p=2')
#check work or not
res.raise_for_status()  #raise_for_status()
res.text

bs = bs4.BeautifulSoup(res.text)
bs.select('.course-quiz-content ') # or bs.select('p ')  both didn't work well

add:I just want get the text,the result like:

[<div >
 <div >
 <div >
 <div >
 <p><span >
 <img alt="" src="https://ds055uzetaobb.cloudfront.net/brioche/uploads/QjYrKg7An9-group-17.svg?height=200" srcset="https://ds055uzetaobb.cloudfront.net/brioche/uploads/QjYrKg7An9-group-17.svg?height=200 1x,https://ds055uzetaobb.cloudfront.net/brioche/uploads/QjYrKg7An9-group-17.svg?height=400 2x,https://ds055uzetaobb.cloudfront.net/brioche/uploads/QjYrKg7An9-group-17.svg?height=600 3x" style="max-height:200px;max-width:100%;"/>
 </span></p>
 <p>A neuron has many inputs but only one output, so it must "integrate" its inputs into one output (a single number). Recall that the inputs to a neuron are generally outputs from other neurons. What is the most natural way to represent the set of these inputs to a single neuron in an ANN?</p>
 </div>...

Expected result:

A neuron has many inputs but only one output, so it must "integrate" its inputs into one output (a single number). Recall that the inputs to a neuron are generally outputs from other neurons. What is the most natural way to represent the set of these inputs to a single neuron in an ANN?

CodePudding user response：

Getting the text for each item in your resultset simply call get_text(strip=True) while iterating.

Following list comprehension will give you a list of texts:

[t.get_text(strip=True) for t in bs.select('.course-quiz-content')]

Example

import requests,bs4
res = requests.get('https://brilliant.org/practice/computational-models-of-the-neuron/?p=2')
bs = bs4.BeautifulSoup(res.text)
data = [t.get_text(strip=True) for t in bs.select('.course-quiz-content')]
print(data)

Output:

['A neuron has many inputs but only one output, so it must "integrate" its inputs into one output (a single number). Recall that the inputs to a neuron are generally outputs from other neurons. What is the most natural way to represent the set of these inputs to a single neuron in an ANN?',
 'In our computational model of a neuron, the inputs defined by the vectorx⃗\\vec{x}xare “integrated” by taking thebiasbbbplus the dot product of theinputsx⃗\\vec{x}xandweightsw⃗:\\vec{w}:w:w⃗⋅x⃗ b.\\vec{w} \\cdot \\vec{x}   b.w⋅x b.The dot product represents a "weighted sum" because it multiplies each input by a weight.A biological interpretation is that the inputs definingx⃗\\vec{x}xare the outputs of other neurons, the weights definingw⃗\\vec{w}ware the strengths of the connections to those neurons, and the biasbbbimpacts the threshold the computing neuron must surpass in order to fire.',
 'Given the inputs, weights, and bias shown above, what is the integration of these inputs given by the weighted sumw⃗⋅x⃗ b?\\vec{w} \\cdot \\vec{x}   b?w⋅x b?Note:If you are unfamiliar with dot products, our wiki on thedot product in Cartesian coordinatesmight be helpful.',
 'An activation function,H(v),H(v),H(v),is used to transform the integration (weighted sum) into a single output which determines whether or not the neuron would fire. For example, we might haveH(v)H(v)H(v)as the Heaviside step function, that is,H(v)={1ifv≥00ifv<0.H(v) = \\begin{cases}\n1 & \\mbox{if } v \\ge 0 \\\\\n0 & \\mbox{if } v \\lt 0. \\\\\n\\end{cases}H(v)={10\u200bifv≥0ifv<0.\u200bConsideringH(w⃗⋅x⃗ b),H(\\vec{w} \\cdot \\vec{x}   b),H(w⋅x b),how doesincreasingthe biasbbbaffect the likelihood of the neuron firing (all else equal), assuming that a111corresponds to firing?',
 'WhenH(v)H(v)H(v)is the Heaviside step function, the neuron modeled byH(w⃗⋅x⃗ b)H(\\vec{w} \\cdot \\vec{x}   b)H(w⋅x b)fires whenw⃗⋅x⃗ b≥0.\\vec{w} \\cdot \\vec{x}   b\\ge 0.w⋅x b≥0.The hypersurfacew⃗⋅x⃗ b=0\\vec{w} \\cdot \\vec{x}   b =  0w⋅x b=0is called thedecision boundary, since it divides the input vector space into two parts based on whether the input would cause the neuron to fire. This model is known as a linear classifier because this boundary is based on a linear combination of the inputs.',
 'The model above shows a decision boundary for predicting college admission based on the inputx⃗=(SAT\xa0scoreGPA)\\vec{x} = \\begin{pmatrix}\\text{SAT score} \\\\ \\text{GPA} \\end{pmatrix}x=(SAT\xa0scoreGPA\u200b)and the activation functionH(w⃗⋅x⃗ b)H(\\vec{w} \\cdot \\vec{x}   b)H(w⋅x b), whereH(v)H(v)H(v)is the Heaviside step function.  Which of the following is a possible value for the weight vector,w⃗?\\vec{w}?w?',
 "So far, we’ve considered an activation functionH(v)H(v)H(v)with binary outputs, as inspired by a physical neuron. However, in ANNs, we don’t need to restrict ourselves to a binary function. Functions like the ones below avoid counterintuitive jumps and can model continuous values (e.g. a probability):The power of ANNs is illustrated by theuniversal approximation theorem, which states that ANNs using activation functions like these can modelanycontinuous function, given some general requirements about the size and layout of the ANN.We can't prove the universal approximation theorem here, but its implications are still important. No matter how complicated a situation is, a sufficiently large ANN with the appropriate parameters can model it.",
 "Consider the activation functionH(v)=11 e−vH(v) = \\dfrac{1}{1 e^{-v}}H(v)=1 e−v1\u200b, whereeeestands in for Euler's Number,2.71828…2.71828\\ldots2.71828…H(v)H(v)H(v)is known as the sigmoid function. In our image above, we multiply our inputs by their corresponding weights and add a bias of222to getvvv. Then the value invvvis fed into the activation function to get the output of the neuron.Given the inputs, weights, and bias shown in the image above (which are the same as in an earlier question), what is the approximate output (to the nearest thousandth) from this neuron after the integrated value of the inputs is evaluated by the activation function?",
 'We’ve now built up a basic computational model of neurons. While one neuron might not seem powerful, connecting many together in a clever manner can yield a highly effective learning model. This turns out to be true for ANNs, as evidenced by the universal approximation theorem.The remainder of this course focuses on the methods used to construct and train ANNs, highlighting the intuition behind the models and their applications.Let’s dive in!']