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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Fragrance analysis
Author
Step1:  Once we have the response, which is a huge chunk of minimized html tags, we need to navigate through the DOM structure to get exactly the information needed. The perfumes are thankfully listed in the tree with the ID #col1, so I can start there as the root and get all the perfume names by picking specific child DOM nodes.
Step2:  The extract() method returns a list, so it was easy to get the number of items by finding the length of the list.
Parsing the response
Once you have the response, you can parse the output to get all the bits of information needed. I'm interested in the name of the perfume, the gender it's made for, the image, and also the url to the product detail page. The function parse_perfume_data() takes the response and outputs the fields to a list of dictionaries.
Step3:  Now we call this function while passing in the response to get the data, which is a structured extraction from the response. I chose some easy to remember field names to use as dictionary keys.
Step4:  Here's how the data looks like after parsing.
Step5:  Data Analysis
Now we can do some quick stats, for example to see how many products per gender
Step6:  Values where, 
M = Male
F = Female
U = Unisex
T = Total
```
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Appendix
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Python Code:
import requests
from scrapy.http import TextResponse
url = "https://www.fragrantica.com/designers/Dolce%26Gabbana.html"
user_agent = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/58: .0.3029.110 Chrome/58.0.3029.110 Safari/537.36'}
r = requests.get(url, headers=user_agent)
response = TextResponse(r.url, body=r.text, encoding='utf-8')
Explanation: Fragrance analysis
Author: @katychuang
Description: I want to learn more about what's out there in the fragrance world so am starting this project to collect data. There's no existing API to a database of fragrance information so I'm scraping websites as a way to collect some data for analysis.
Scrapy example
Code in this notebook is an example of using scrapy to scrape data off one webpage of Fragrantica. The code below is for Python3, Scrapy (1.4.0).
Making requests
Using the requests library, which returns binary data and the scrapy TextResponse module to read the binary data.
Thanks to @jasonwirth's tip about using user agent strings, I was able to get around the 403 forbidden access error codes while scraping. There are many user agents available to use, the top ones are listed here, and it's conventionally good to rotate/randomize the use of the strings.
End of explanation
# Navigate the perfume list
c = response.xpath('//div[@id="col1"]/div[@class="perfumeslist"]/div/div/p/a//text()').extract()
print("There are {} perfumes from Dolce & Gabanna".format(len(c)))
print(c)
Explanation: Once we have the response, which is a huge chunk of minimized html tags, we need to navigate through the DOM structure to get exactly the information needed. The perfumes are thankfully listed in the tree with the ID #col1, so I can start there as the root and get all the perfume names by picking specific child DOM nodes.
End of explanation
def parse_perfume_data(response):
    my_list = []
    for row in response.xpath('//div[@id="col1"]/div[@class="perfumeslist"]'):
      perfume = {}
      perfume['name'] = row.xpath('div/div/p/a//text()').extract()[0]
      perfume['year'] = year(row.xpath('div/div/p/span[@class="mtext"]/span/strong/text()').extract())
      perfume['gender'] = row.xpath('div/@class').extract()[0].split(' ')[1][6:]
      perfume['img'] = row.xpath('div/div/p/a/img//@src').extract()[0]
      perfume['url'] = row.xpath('div/div/p/a/@href').extract()[0]
      my_list.append(perfume)
    return my_list
def year(y):
    if len(y) >= 1:
        return y[0]
    else:
        return ''
Explanation: The extract() method returns a list, so it was easy to get the number of items by finding the length of the list.
Parsing the response
Once you have the response, you can parse the output to get all the bits of information needed. I'm interested in the name of the perfume, the gender it's made for, the image, and also the url to the product detail page. The function parse_perfume_data() takes the response and outputs the fields to a list of dictionaries.
End of explanation
data = parse_perfume_data(response)
Explanation: Now we call this function while passing in the response to get the data, which is a structured extraction from the response. I chose some easy to remember field names to use as dictionary keys.
End of explanation
print(data[0])
print(data[1])
Explanation: Here's how the data looks like after parsing.
End of explanation
from collections import Counter
Counter(token['gender'] for token in data)
Counter(token['year'] for token in data)
years = list(sorted(set([p['year'] for p in data[1:]])))
yTotal = Counter(token['year'] for token in data)
print('Year', 'M', 'F', 'U', 'T')
for y in years:
    filtered = list(filter(lambda d: d['year'] == y, data))
    count = Counter(token['gender'] for token in filtered)
    print(y, count['male'], count['female'], count['unisex'], yTotal[y])
Explanation: Data Analysis
Now we can do some quick stats, for example to see how many products per gender
End of explanation
data
# display D&G Perfume Bottles
from IPython.display import Image, HTML, display
from glob import glob
def make_html(image):
     return '<img src="{}" style="display:inline;margin:1px"/>'.format(image)
item = ''.join( [make_html(x['img']) for x in data] )
display(HTML(item))
Explanation: Values where, 
M = Male
F = Female
U = Unisex
T = Total
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Appendix
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