Advanced Data Processing Techniques for Web Scraped Data in Python

Introduction

Web scraping is an essential technique for gathering data from the internet. However, the data obtained from web scraping often needs further processing to extract valuable insights. In this article, we will explore advanced data processing techniques to clean and analyze web scraped data in Python.

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Outline

1. Data Aggregation
2. Text Processing
3. Date and Time Processing
4. Geolocation Data Processing
5. Handling Nested Data
6. Data Transformation
7. Feature Engineering

1. Data Aggregation

Data aggregation is a technique used to group and summarize data. Using the groupby() and aggregation functions in Pandas, we can analyze web scraped data more effectively.

import pandas as pd

# Sample DataFrame
data = {
    'category': ['A', 'A', 'B', 'B', 'A', 'B'],
    'value': [10, 20, 30, 40, 50, 60]
}
df = pd.DataFrame(data)
# Group by category and calculate the mean value
agg_df =…