Python Code Samples for Data Scraping and Analysis Across Various Business Scenarios

1. News article scraping and analysis : Fetch a web page, parse HTML with BeautifulSoup, extract titles and contents, and print them.

import requests
from bs4 import BeautifulSoup
url = 'https://www.example.com/news'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
news_titles = soup.find_all('h2', class_='news-title')
news_contents = soup.find_all('div', class_='news-content')
for title, content in zip(news_titles, news_contents):
    print('Title:', title.text)
    print('Content:', content.text)
    print('---')

2. Social media data analysis : Use Tweepy to authenticate with the Twitter API, search tweets containing a keyword, and print each tweet.

import tweepy
consumer_key = 'your_consumer_key'
consumer_secret = 'your_consumer_secret'
access_token = 'your_access_token'
access_token_secret = 'your_access_token_secret'
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
tweets = api.search(q='python', count=10)
for tweet in tweets:
    print(tweet.text)
    print('---')

3. Stock data retrieval and analysis : Use yfinance to download historical price data for a ticker and display it.

import yfinance as yf
stock = yf.Ticker('AAPL')
history = stock.history(period='1y')
print(history)

4. E‑commerce data analysis : Load order data with pandas, count orders per user, and print the result.

import pandas as pd
data = pd.read_csv('ecommerce_orders.csv')
order_counts = data.groupby('user_id')['order_id'].count()
print(order_counts)

5. Website traffic analysis : Read traffic logs, plot daily visits using matplotlib.

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('website_traffic.csv')
plt.plot(data['date'], data['traffic'])
plt.xlabel('Date')
plt.ylabel('Traffic')
plt.show()

6. Natural language processing and text analysis : Tokenize a sentence, remove stopwords with NLTK, and display filtered tokens.

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
nltk.download('stopwords')
nltk.download('punkt')
text = 'This is a sample sentence for text analysis.'
tokens = word_tokenize(text.lower())
stop_words = set(stopwords.words('english'))
filtered_tokens = [token for token in tokens if token not in stop_words]
print(filtered_tokens)

7. Channel analysis and user behavior tracking : Load channel data, count users per channel, and plot a bar chart.

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('channel_data.csv')
user_counts = data['channel'].value_counts()
plt.bar(user_counts.index, user_counts.values)
plt.xlabel('Channel')
plt.ylabel('User Count')
plt.show()

8. Customer segmentation with K‑means : Read customer attributes, run K‑means clustering, and output cluster labels.

import pandas as pd
from sklearn.cluster import KMeans
data = pd.read_csv('customer_data.csv')
X = data[['age', 'income']]
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
labels = kmeans.labels_
print(labels)

9. Time‑series decomposition : Load a time‑series, decompose it into trend, seasonality, and residuals, and plot each component.

import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose
data = pd.read_csv('time_series_data.csv')
data['date'] = pd.to_datetime(data['date'])
data.set_index('date', inplace=True)
decomposition = seasonal_decompose(data['value'], model='additive')
plt.figure(figsize=(12, 8))
plt.subplot(411)
plt.plot(data['value'], label='Original')
plt.legend(loc='best')
plt.subplot(412)
plt.plot(decomposition.trend, label='Trend')
plt.legend(loc='best')
plt.subplot(413)
plt.plot(decomposition.seasonal, label='Seasonality')
plt.legend(loc='best')
plt.subplot(414)
plt.plot(decomposition.resid, label='Residuals')
plt.legend(loc='best')
plt.tight_layout()
plt.show()

10. Image data analysis : Load an image with OpenCV, convert to RGB, and display it using matplotlib.

import cv2
import matplotlib.pyplot as plt
image = cv2.imread('image.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)
plt.axis('off')
plt.show()

11. Text sentiment analysis : Use TextBlob to compute polarity and subjectivity of a sample sentence.

from textblob import TextBlob
text = 'I love this product! It works great.'
blob = TextBlob(text)
sentiment = blob.sentiment
print(sentiment.polarity)  # polarity
print(sentiment.subjectivity)  # subjectivity

12. Data visualization – bar chart : Load generic data and plot a bar chart of categories versus values.

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('data.csv')
plt.bar(data['category'], data['value'])
plt.xlabel('Category')
plt.ylabel('Value')
plt.show()

13. Geographic data visualization : Load world shapefile with GeoPandas and display a map.

import geopandas as gpd
import matplotlib.pyplot as plt
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world.plot()
plt.show()

14. Time‑series forecasting with ARIMA : Fit an ARIMA(1,1,1) model to data and plot forecasted values.

import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.arima.model import ARIMA
data = pd.read_csv('time_series_data.csv')
data['date'] = pd.to_datetime(data['date'])
data.set_index('date', inplace=True)
model = ARIMA(data['value'], order=(1, 1, 1))
model_fit = model.fit()
forecast = model_fit.predict(start=len(data), end=len(data)+10)
plt.plot(data['value'], label='Original')
plt.plot(forecast, label='Forecast')
plt.legend(loc='best')
plt.show()

15. Data aggregation and summarization : Group data by a categorical column and compute the mean value.

import pandas as pd
data = pd.read_csv('data.csv')
grouped_data = data.groupby('category')['value'].mean()
print(grouped_data)

16. Data cleaning and preprocessing : Remove missing rows and replace specific category values.

import pandas as pd
data = pd.read_csv('data.csv')
data.dropna(inplace=True)
data['category'].replace('Unknown', 'Other', inplace=True)
print(data)

17. Data merging and joining : Merge two datasets on a common key and display the result.

import pandas as pd
data1 = pd.read_csv('data1.csv')
data2 = pd.read_csv('data2.csv')
merged_data = pd.merge(data1, data2, on='key')
print(merged_data)

18. Pivot tables and cross‑tabulation : Create a pivot table and a cross‑tab from a dataset.

import pandas as pd
data = pd.read_csv('data.csv')
pivot_table = pd.pivot_table(data, values='value', index='category', columns='date', aggfunc='mean')
cross_table = pd.crosstab(data['category'], data['date'])
print(pivot_table)
print(cross_table)

19. Data sampling and splitting : Randomly sample rows and split the dataset into training and testing subsets.

import pandas as pd
data = pd.read_csv('data.csv')
sample = data.sample(n=100, random_state=42)
train_data = data[:800]
test_data = data[800:]
print(sample)
print(train_data)
print(test_data)

20. Data backup and restoration : Create a copy of the original DataFrame, perform processing, and restore from the backup when needed.

import pandas as pd
data = pd.read_csv('data.csv')
data_backup = data.copy()
# ... perform processing ...
# Restore original data
data = data_backup.copy()

These examples illustrate a wide range of Python techniques for web scraping, data cleaning, exploratory analysis, visualization, machine‑learning modeling, and time‑series forecasting that can be adapted to many practical business problems.