# Trim leading/trailing whitespace from a string column
# df['text_column'] = df['text_column'].str.strip()
# Convert a string column to lowercase
# df['category_column'] = df['category_column'].str.lower()
Step 4: Content and Outlier Validation
Once the data is structurally sound, the focus shifts to validating the actual content of the data.
• Examine Categorical Data Consistency: Use
.value_counts() on categorical columns to spot inconsistencies, such as different spellings or capitalizations for the same category (e.g., "USA", "U.S.A.", "United States").print(df['category_column'].value_counts())
• Identify and Address Outliers: While not always an error, outliers can significantly skew results. Use statistical summaries or visualizations like box plots to find them. The decision to remove, cap, or keep an outlier depends entirely on the domain and analytical goals.
# A simple filter to remove entries based on a logical condition
# df = df[df['age_column'] <= 100]
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.info() or .isnull().sum() one last time to confirm that all cleaning operations were successful.df.info()
print("Final check for null values:\n", df.isnull().sum())
• Export the Cleaned DataFrame: Save the results to a new CSV file. Using
index=False prevents Pandas from writing the DataFrame index as a new column in the file.df.to_csv('cleaned_dataset.csv', index=False)By consistently applying this five-step methodology, you can replace guesswork with a dependable protocol, ensuring your data is always robust, reliable, and ready for insightful analysis.
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All Cheat Sheets Collection (3).pdf
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A comprehensive summary of the Seaborn Library.pdf
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2.4 MB
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Mastering pandas%22.pdf
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