Books

Mon 30 June 2025 
!pip install requests beautifulsoup4 pandas matplotlib seaborn wordcloud

Requirement already satisfied: requests in c:\users\hp\miniconda3\envs\py312\lib\site-packages (2.32.4)
Requirement already satisfied: beautifulsoup4 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (4.12.3)
Requirement already satisfied: pandas in c:\users\hp\miniconda3\envs\py312\lib\site-packages (2.3.0)
Requirement already satisfied: matplotlib in c:\users\hp\miniconda3\envs\py312\lib\site-packages (3.10.3)
Requirement already satisfied: seaborn in c:\users\hp\miniconda3\envs\py312\lib\site-packages (0.13.2)
Collecting wordcloud
  Downloading wordcloud-1.9.4-cp312-cp312-win_amd64.whl.metadata (3.5 kB)
Requirement already satisfied: charset_normalizer<4,>=2 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from requests) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from requests) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from requests) (2.5.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from requests) (2025.6.15)
Requirement already satisfied: soupsieve>1.2 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from beautifulsoup4) (2.5)
Requirement already satisfied: numpy>=1.26.0 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from pandas) (2.3.1)
Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from pandas) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from pandas) (2025.2)
Requirement already satisfied: tzdata>=2022.7 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from pandas) (2025.2)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from matplotlib) (1.3.2)
Requirement already satisfied: cycler>=0.10 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from matplotlib) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from matplotlib) (4.58.4)
Requirement already satisfied: kiwisolver>=1.3.1 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from matplotlib) (1.4.8)
Requirement already satisfied: packaging>=20.0 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from matplotlib) (24.2)
Requirement already satisfied: pillow>=8 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from matplotlib) (11.2.1)
Requirement already satisfied: pyparsing>=2.3.1 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from matplotlib) (3.2.3)
Requirement already satisfied: six>=1.5 in c:\users\hp\miniconda3\envs\py312\lib\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)
Downloading wordcloud-1.9.4-cp312-cp312-win_amd64.whl (301 kB)
Installing collected packages: wordcloud
Successfully installed wordcloud-1.9.4

import requests
from bs4 import BeautifulSoup
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud

C:\Users\HP\miniconda3\envs\py312\Lib\site-packages\requests\__init__.py:86: RequestsDependencyWarning: Unable to find acceptable character detection dependency (chardet or charset_normalizer).
  warnings.warn(

pip install chardet

Collecting chardet
  Downloading chardet-5.2.0-py3-none-any.whl.metadata (3.4 kB)
Downloading chardet-5.2.0-py3-none-any.whl (199 kB)
Installing collected packages: chardet
Successfully installed chardet-5.2.0
Note: you may need to restart the kernel to use updated packages.

import requests
from bs4 import BeautifulSoup
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud

base_url = "http://books.toscrape.com/catalogue/page-{}.html"

titles = []
prices = []
ratings = []

for page_num in range(1, 6):  # scraping 5 pages
    url = base_url.format(page_num)
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')

    books = soup.find_all('article', class_='product_pod')

    for book in books:
        title = book.h3.a['title']
        price = float(book.find('p', class_='price_color').text[1:])
        rating = book.p['class'][1]

        titles.append(title)
        prices.append(price)
        ratings.append(rating)

df = pd.DataFrame({
    "title": titles,
    "price": prices,
    "rating": ratings
})
df.head()
title price rating
0 A Light in the Attic 51.77 Three
1 Tipping the Velvet 53.74 One
2 Soumission 50.10 One
3 Sharp Objects 47.82 Four
4 Sapiens: A Brief History of Humankind 54.23 Five 
rating_map = {
    "One": 1,
    "Two": 2,
    "Three": 3,
    "Four": 4,
    "Five": 5
}
df["rating_num"] = df["rating"].map(rating_map)

df.sort_values(by="price", ascending=False).head(10)
title price rating rating_num
68 The Death of Humanity: and the Case for Life 58.11 Four 4
40 Slow States of Collapse: Poems 57.31 Three 3
15 Our Band Could Be Your Life: Scenes from the A... 57.25 Three 3
58 The Past Never Ends 56.50 Four 4
57 The Pioneer Woman Cooks: Dinnertime: Comfort C... 56.41 One 1
91 Masks and Shadows 56.40 Two 2
56 The Secret of Dreadwillow Carse 56.13 One 1
67 The Electric Pencil: Drawings from Inside Stat... 56.06 One 1
25 Birdsong: A Story in Pictures 54.64 Three 3
4 Sapiens: A Brief History of Humankind 54.23 Five 5 
plt.figure(figsize=(10, 5))
sns.histplot(df["price"], bins=15, kde=True, color="teal")
plt.title("Distribution of Book Prices")
plt.xlabel("Price (£)")
plt.ylabel("Number of Books")
plt.show()

png

plt.figure(figsize=(8, 5))
sns.boxplot(x="rating_num", y="price", data=df)
plt.title("Book Prices vs Ratings")
plt.xlabel("Rating (1 to 5)")
plt.ylabel("Price (£)")
plt.show()

png

high_rated = df[df["rating_num"] >= 4]
high_rated.head()
title price rating rating_num
3 Sharp Objects 47.82 Four 4
4 Sapiens: A Brief History of Humankind 54.23 Five 5
6 The Dirty Little Secrets of Getting Your Dream... 33.34 Four 4
8 The Boys in the Boat: Nine Americans and Their... 22.60 Four 4
11 Shakespeare's Sonnets 20.66 Four 4 
all_titles = " ".join(df["title"])
wordcloud = WordCloud(width=800, height=400, background_color="white").generate(all_titles)

plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.title("Word Cloud of Book Titles")
plt.show()

png

df.groupby("rating_num")["price"].mean().plot(kind='bar', color="orange")
plt.title("Average Price by Rating")
plt.ylabel("Average Price (£)")
plt.xlabel("Rating")
plt.show()

png

df["rating_num"].value_counts().sort_index().plot(kind='bar', color="purple")
plt.title("Book Count by Rating")
plt.ylabel("Number of Books")
plt.xlabel("Rating")
plt.show()

png

df.isnull().sum()

title         0
price         0
rating        0
rating_num    0
dtype: int64

df.to_csv("books.csv", index=False)

df.describe()
price rating_num
count 100.000000 100.000000
mean 34.560700 2.930000
std 14.638531 1.423149
min 10.160000 1.000000
25% 19.897500 2.000000
50% 34.775000 3.000000
75% 47.967500 4.000000
max 58.110000 5.000000 
df[df["rating_num"] == 5][["title", "price"]].head()
title price
4 Sapiens: A Brief History of Humankind 54.23
12 Set Me Free 17.46
13 Scott Pilgrim's Precious Little Life (Scott Pi... 52.29
14 Rip it Up and Start Again 35.02
23 Chase Me (Paris Nights #2) 25.27 
df["price_category"] = pd.cut(df["price"], bins=[0, 20, 40, 60], labels=["Low", "Medium", "High"])
df.head()
title price rating rating_num price_category
0 A Light in the Attic 51.77 Three 3 High
1 Tipping the Velvet 53.74 One 1 High
2 Soumission 50.10 One 1 High
3 Sharp Objects 47.82 Four 4 High
4 Sapiens: A Brief History of Humankind 54.23 Five 5 High 
df["price_category"].value_counts().plot(kind="pie", autopct="%1.1f%%", colors=["lightgreen", "gold", "salmon"])
plt.title("Price Category Distribution")
plt.ylabel("")
plt.show()

png

Score: 20

Category: basics