import spacy
nlp = spacy.load('en_web_core_sm')
doc = nlp("I love spacy. Spacy is so cool.")
for token in doc:
print(token)
This prints every token in a new line. But if I want "cool" token separately, I should know the index of that token right!! What can I do about that?
Ex: If there are 1000 words in the doc, I want a particular word "simulate" as a separate token but I don't know the position of that token in that doc. Rather than looping around those 1000 words, Is there any way to directly obtain the index of "simulate"?
token.i has the token index of a token in the document. token.idx has the character index.
import spacy
nlp = spacy.blank("en")
doc = nlp("I like cheese")
assert doc[2].text == "cheese"
assert doc[2].i == 2
assert doc[2].idx == 7
python
I am using scrapy to scrape data from a website, where i want to scrape graphic cards title,price and whether they are in stock or not. The problem is my code is looping twice and instead of having 10 products I am getting 20.
import scrapy
class ThespiderSpider(scrapy.Spider):
name = 'Thespider'
start_urls = ['https://www.czone.com.pk/graphic-cards-pakistan-ppt.154.aspx?page=2']
def parse(self, response):
data = {}
cards = response.css('div.row')
for card in cards:
for c in card.css('div.product'):
data['Title'] = c.css('h4 a::text').getall()
data['Price'] = c.css('div.price span::text').getall()
data['Stock'] = c.css('div.product-stock span.product-data::text').getall()
yield data
You're doing a nested for loop when one isn't necessary.
Each card can be captured by the CSS selector response.css('div.product')
Code Example
def parse(self, response):
data = {}
cards = response.css('div.product')
for card in cards:
data['Title'] = card.css('h4 a::text').getall()
data['Price'] = card.css('div.price span::text').getall()
data['Stock'] = card.css('div.product-stock span.product-data::text').getall()
yield data
Additional Information
Use get() instead of getall(). The output you get is a list, you'll probably want a string which is what get() gives you.
If you're thinking about multiple pages, an items dictionary may be better than yielding a dictionary. Invariably there will be the thing you need to alter and an items dictionary gives you more flexibility to do this.
I am still learning about python and I face some trouble extracting data from a dict. I need to create a loop which check each values and extract the keys. So for this code I need to find the nice students. I am stuck at line 3 #blank.
How do i go about this?
Thanks in advance
class = {"James":"naughty", "Lisa":"nice", "Bryan":"nice"}
for student in class:
if #blank:
print("Hello, "+student+" students!")
else:
print("odd")
Uses dictionary methods "keys(), values(), items()":
def get_students_by_criteria(student_class, criteria):
students = []
for candidate, value in student_class.items():
if value == criteria:
students.append(candidate)
return students
my_class = {"James":"naughty", "Lisa":"nice", "Bryan":"nice"}
print(get_students_by_criteria(my_class, "nice"))
Warning to the word "class" it is a keyword reserved for python programming oriented object
I am trying to make an interactive dashboard with analysis, base on car side. I would like user to be able to pick car brand for example BMW, Audi etc. and base on this choise he will have only avaiablity to pick BMW/Audi etc. models. I have a problem after selecting each brand, I am not able to scrape the models that belongs to that brand. Page that I am scraping from:
main page --> https://www.otomoto.pl/osobowe/
sub car brand page example --> https://www.otomoto.pl/osobowe/audi/
I have tried to scrape every option, so later on I can maybe somehow clean the data to store only models
code:
otomoto_models - paste0("https://www.otomoto.pl/osobowe/"audi/")
models <- read_html(otomoto_models) %>%
html_nodes("option") %>%
html_text()
But it is just scraping the brands with other options avaiable on the page engine type etc. While after inspecting element I can clearly see models types.
otomoto <- "https://www.otomoto.pl/osobowe/"
brands <- read_html(otomoto) %>%
html_nodes("option") %>%
html_text()
brands <- data.frame(brands)
for (i in 1:nrow(brands)){
no_marka_pojazdu <- i
if(brands[i,1] == "Marka pojazdu"){
break
}
}
no_marka_pojazdu <- no_marka_pojazdu + 1
for (i in 1:nrow(brands)){
zuk <- i
if(substr(brands[i,1],1,3) == "Żuk"){
break
}
}
Modele_pojazdow <- as.character(brands[no_marka_pojazdu:zuk,1])
Modele_pojazdow <- removeNumbers(Modele_pojazdow)
Modele_pojazdow <- substr(Modele_pojazdow,1,nchar(Modele_pojazdow)-2)
Modele_pojazdow <- data.frame(Modele_pojazdow)
Above code is only to pick supported car brands on the webpage and store them in the data frame. With that I am able to create html link and direct everything to one selected brand.
I would like to have similar object to "Modele_pojazdow" but with models limited on previous selected car brand.
Dropdown list with models appears as white box with text "Model pojazdu" next to the "Audi" box on the right side.
Some may frown on the solution language being Python, but the aim of this is was to give some pointers (high level process). I haven't written R in a long time so Python was quicker.
EDIT: R script now added
General outline:
The first dropdown options can be grabbed from the value attribute of each node returned by using a css selector of #param571 option. This uses an id selector (#) to target the parent dropdown select element, and then option type selector in descendant combination, to specify the option tag elements within. The html to apply this selector combination to can be retrieved by an xhr request to the url you initially provided. You want a nodeList returned to iterate over; akin to applying selector with js document.querySelectorAll.
The page uses ajax POST requests to update the second dropdown based on your first dropdown choice. Your first dropdown choice determines the value of a parameter search[filter_enum_make], which is used in the POST request to the server. The subsequent response contains a list of the available options (it includes some case alternatives which can be trimmed out).
I captured the POST request by using fiddler. This showed me the request headers and params in the request body. Screenshot sample shown at end.
The simplest way to extract the options from the response text, IMO, is to regex the appropriate string out (I wouldn't normally recommend regex for working with html but in this case it serves us nicely). If you don't want to use regex, you can grab the relevant info from the data-facets attribute of the element with id body-container. For the non-regex version you need to handle unquoted nulls, and retrieve the inner dictionary whose key is filter_enum_model. I show a function re-write, at the end, to handle this.
The retrieved string is a string representation of a dictionary. This needs converting to an actual dictionary object which you can then extract the option values from. Edit: As R doesn't have a dictionary object a similar structure needs to be found. I will look at this when converting.
I create a user defined function, getOptions(), to return the options for each make. Each car make value comes from the list of possible items in the first dropdown. I loop those possible values, use the function to return a list of options for that make, and add those lists as values to a dictionary, results ,whose keys are the make of car. Again, for R an object with similar functionality to a python dictionary needs to be found.
That dictionary of lists needs converting to a dataframe which includes a transpose operation to make a tidy output of headers, which are the car makes, and columns underneath each header, which contain the associated models.
The whole thing can be written to csv at the end.
So, hopefully that gives you an idea of one way to achieve what you want. Perhaps someone else can use this to help write you a solution.
Python demonstration of this below:
import requests
from bs4 import BeautifulSoup as bs
import re
import ast
import pandas as pd
headers = {
'User-Agent' : 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36'
}
def getOptions(make): #function to return options based on make
data = {
'search[filter_enum_make]': make,
'search[dist]' : '5',
'search[category_id]' : '29'
}
r = requests.post('https://www.otomoto.pl/ajax/search/list/', data = data, headers = headers)
try:
# verify the regex here: https://regex101.com/r/emvqXs/1
data = re.search(r'"filter_enum_model":(.*),"new_used"', r.text ,flags=re.DOTALL).group(1) #regex to extract the string containing the models associated with the car make filter
aDict = ast.literal_eval(data) #convert string representation of dictionary to python dictionary
d = len({k.lower(): v for k, v in aDict.items()}.keys()) #find length of unique keys when accounting for case
dirtyList = list(aDict)[:d] #trim to unique values
cleanedList = [item for item in dirtyList if item != 'other' ] #remove 'other' as doesn't appear in dropdown
except:
cleanedList = [] # sometimes there are no associated values in 2nd dropdown
return cleanedList
r = requests.get('https://www.otomoto.pl/osobowe/')
soup = bs(r.content, 'lxml')
values = [item['value'] for item in soup.select('#param571 option') if item['value'] != '']
results = {}
# build a dictionary of lists to hold options for each make
for value in values:
results[value] = getOptions(value) #function call to return options based on make
# turn into a dataframe and transpose so each column header is the make and the options are listed below
df = pd.DataFrame.from_dict(results,orient='index').transpose()
#write to csv
df.to_csv(r'C:\Users\User\Desktop\Data.csv', sep=',', encoding='utf-8-sig',index = False )
Sample of csv output:
Example as sample json for alfa-romeo:
Example of regex match for alfa-romeo:
{"145":1,"146":1,"147":218,"155":1,"156":118,"159":559,"164":2,"166":39,"33":1,"Alfasud":2,"Brera":34,"Crosswagon":2,"GT":89,"GTV":7,"Giulia":251,"Giulietta":378,"Mito":224,"Spider":24,"Sportwagon":2,"Stelvio":242,"alfasud":2,"brera":34,"crosswagon":2,"giulia":251,"giulietta":378,"gt":89,"gtv":7,"mito":224,"spider":24,"sportwagon":2,"stelvio":242}
Example of the filter option list returned from function call with make parameter value alfa-romeo:
['145', '146', '147', '155', '156', '159', '164', '166', '33', 'Alfasud', 'Brera', 'Crosswagon', 'GT', 'GTV', 'Giulia', 'Giulietta', 'Mito', 'Spider', 'Sportwagon', 'Stelvio']
Sample of fiddler request:
Sample of ajax response html containing options:
<section id="body-container" class="om-offers-list"
data-facets='{"offer_seek":{"offer":2198},"private_business":{"business":1326,"private":872,"all":2198},"categories":{"29":2198,"161":953,"163":953},"categoriesParent":[],"filter_enum_model":{"145":1,"146":1,"147":219,"155":1,"156":116,"159":561,"164":2,"166":37,"33":1,"Alfasud":2,"Brera":34,"Crosswagon":2,"GT":88,"GTV":7,"Giulia":251,"Giulietta":380,"Mito":226,"Spider":25,"Sportwagon":2,"Stelvio":242,"alfasud":2,"brera":34,"crosswagon":2,"giulia":251,"giulietta":380,"gt":88,"gtv":7,"mito":226,"spider":25,"sportwagon":2,"stelvio":242},"new_used":{"new":371,"used":1827,"all":2198},"sellout":null}'
data-showfacets=""
data-pagetitle="Alfa Romeo samochody osobowe - otomoto.pl"
data-ajaxurl="https://www.otomoto.pl/osobowe/alfa-romeo/?search%5Bbrand_program_id%5D%5B0%5D=&search%5Bcountry%5D="
data-searchid=""
data-keys=''
data-vars=""
Alternative version of function without regex:
from bs4 import BeautifulSoup as bs
def getOptions(make): #function to return options based on make
data = {
'search[filter_enum_make]': make,
'search[dist]' : '5',
'search[category_id]' : '29'
}
r = requests.post('https://www.otomoto.pl/ajax/search/list/', data = data, headers = headers)
soup = bs(r.content, 'lxml')
data = soup.select_one('#body-container')['data-facets'].replace('null','"null"')
aDict = ast.literal_eval(data)['filter_enum_model'] #convert string representation of dictionary to python dictionary
d = len({k.lower(): v for k, v in aDict.items()}.keys()) #find length of unique keys when accounting for case
dirtyList = list(aDict)[:d] #trim to unique values
cleanedList = [item for item in dirtyList if item != 'other' ] #remove 'other' as doesn't appear in dropdown
return cleanedList
print(getOptions('alfa-romeo'))
R conversion and improved python:
Whilst converting to R I found a better way of extracting the parameters from a js file on the server. If you open dev tools you can see the file listed in the sources tab.
R (To be improved):
library(httr)
library(jsonlite)
url <- 'https://www.otomoto.pl/ajax/jsdata/params/'
r <- GET(url)
contents <- content(r, "text")
data <- strsplit(contents, "var searchConditions = ")[[1]][2]
data <- strsplit(as.character(data), ";var searchCondition")[[1]][1]
source <- fromJSON(data)$values$'573'$'571'
makes <- names(source)
for(make in makes){
print(make)
print(source[make][[1]]$value)
#break
}
Python:
import requests
import json
import pandas as pd
r = requests.get('https://www.otomoto.pl/ajax/jsdata/params/')
data = r.text.split('var searchConditions = ')[1]
data = data.split(';var searchCondition')[0]
items = json.loads(data)
source = items['values']['573']['571']
makes = [item for item in source]
results = {}
for make in makes:
df = pd.DataFrame(source[make]) ## build a dictionary of lists to hold options for each make
results[make] = list(df['value'])
dfFinal = pd.DataFrame.from_dict(results,orient='index').transpose() # turn into a dataframe and transpose so each column header is the make and the options are listed below
mask = dfFinal.applymap(lambda x: x is None) #tidy up None values to empty strings https://stackoverflow.com/a/31295814/6241235
cols = dfFinal.columns[(mask).any()]
for col in dfFinal[cols]:
dfFinal.loc[mask[col], col] = ''
print(dfFinal)
Right now my flashcard game is using a prepvocab() method where I
define the terms and translations for a week's worth of terms as a dictionary
add a description of that week's terms
lump them into a list of dictionaries, where a user selects their "weeks" to study
Every time I add a new week's worth of terms and translations, I'm stuck adding another element to the list of available dictionaries. I can definitely see this as not being a Good Thing.
class Vocab(object):
def __init__(self):
vocab = {}
self.new_vocab = vocab
self.prepvocab()
def prepvocab(self):
week01 = {"term":"translation"} #and many more...
week01d = "Simple Latvian words"
week02 = {"term":"translation"}
week02d = "Simple Latvian colors"
week03 = {"I need to add this":"to self.selvocab below"}
week03d = "Body parts"
self.selvocab = [week01, week02] #, week03, weekn]
self.descs = [week01d, week02d] #, week03, weekn]
Vocab.selvocab(self)
def selvocab(self):
"""I like this because as long as I maintain self.selvocab,
the for loop cycles through the options just fine"""
for x in range(self.selvocab):
YN = input("Would you like to add week " \
+ repr(x + 1) + " vocab? (y or n) \n" \
"Description: " + self.descs[x] + " ").lower()
if YN in "yes":
self.new_vocab.update(self.selvocab[x])
self.makevocab()
I can definitely see that this is going to be a pain with 20+ yes no questions. I'm reading up on curses at the moment, and was thinking of printing all the descriptions at once, and letting the user pick all that they'd like to study for the round.
How do I keep this part of my code better maintained? Anybody got a radical overhaul that isn't so....procedural?
You should store your term:translation pairs and descriptions in a text file in some manner. Your program should then parse the text file and discover all available lessons. This will allow you to extend the set of lessons available without having to edit any code.
As for your selection of lessons, write a print_lesson_choices function that displays the available lessons and descriptions to the user, and then ask for their input in selecting them. Instead of asking a question of them for every lesson, why not make your prompt something like:
self.selected_weeks = []
def selvocab(self):
self.print_lesson_choices()
selection = input("Select a lesson number or leave blank if done selecting: ")
if selection == "": #Done selecting
self.makevocab()
elif selection in self.available_lessons:
if selection not in self.selected_weeks:
self.selected_weeks.append(selection)
print "Added lesson %s"%selection
self.selvocab() #Display the list of options so the user can select again
else:
print "Bad selection, try again."
self.selvocab()
Pickling objects into a database means it'll take some effort to create an interface to modify the weekly lessons from the front end, but is well worth the time.