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Data Cleaning and Preprocessing After Scraping

Once data is scraped, it may contain inconsistencies like missing values, irregular date formats, or extraneous whitespace and symbols. Effective data cleaning and preprocessing ensures you have a reliable dataset for analysis. This step is especially important when dealing with real-world data from diverse sources.

Key Topics

Cleaning Raw Data and Handling Missing Values

Scraped data can have incomplete or incorrect fields. You might see empty cells, None, or placeholder text. Decide how to address these cases: fill them with mean or median values (for numerical data), leave them blank, or discard the row if it's critical information.

import pandas as pd

# Example dataset with missing values
data = {
    "Name": ["Alice", "Bob", None],
    "Age": [30, None, 25],
    "City": ["New York", "Los Angeles", "Chicago"]
}

df = pd.DataFrame(data)

print("Original Data:\n", df)

# Drop rows where all fields are missing
df.dropna(how='all', inplace=True)

# Fill missing numeric fields with a default (e.g., 0)
df['Age'] = df['Age'].fillna(0)

print("\nCleaned Data:\n", df)

Explanation: In this example, a small dataset is created with missing entries. We drop rows that have all fields missing, then fill Age with 0 if it's absent. In practice, you might use more nuanced strategies based on the nature of your data.

Normalizing Data Formats (e.g., Date, Currency)

Dates, currencies, and numeric values often appear in inconsistent formats on the web. Converting them to a standard format makes them easier to sort, filter, and analyze. Pandas offers built-in functionality for parsing dates and performing numeric transformations.

import pandas as pd

date_series = pd.Series([
    "01-02-2023",
    "2023/03/10",
    "Mar 15, 2023"
])

# Convert all to a standard datetime
parsed_dates = pd.to_datetime(date_series, errors='coerce')
print(parsed_dates)

Explanation: pd.to_datetime() intelligently handles diverse date representations. For currency or numeric fields, you might strip currency symbols ($, £) and convert the remaining string to a float.

Using Pandas for Data Analysis Post-Scraping

Pandas is a powerful library for tabular data manipulation. After cleansing and normalizing your scraped data, you can quickly generate summaries, pivot tables, or visualizations. This helps derive insights more efficiently.

# Example: Basic analysis on a cleaned DataFrame
print("Statistical summary of numerical columns:\n", df.describe())
print("\nValue counts in 'City' column:\n", df['City'].value_counts())

Explanation: df.describe() provides statistics like mean, min, and max for numerical columns, while value_counts() shows the frequency of each category in a column.

Key Takeaways

  • Data Integrity: Clean and address missing or incorrect fields before deeper analysis.
  • Consistency: Convert dates, currencies, and numeric values into standard formats.
  • Pandas Toolkit: Powerful methods in Pandas can streamline cleaning, transformation, and analysis.
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