Impara Data Consistency Techniques | Ensuring Data Consistency and Correctness

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Data consistency is a key aspect of data cleaning, directly affecting the reliability and accuracy of your analysis. Common consistency issues include inconsistent categories, such as variations in spelling or capitalization within a column that should contain uniform values; mixed data types, where a single column contains both strings and numbers, making calculations or grouping unreliable; and formatting errors, such as inconsistent date formats or misplaced whitespace. These problems can lead to misleading results or errors in downstream analysis if not properly addressed.


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import pandas as pd

data = {
    "City": ["New York", "new york", "Los Angeles", "los angeles", "Chicago", "CHICAGO"],
    "Population": [8000000, "8000000", 4000000, "4000000", 2700000, "2,700,000"]
}

df = pd.DataFrame(data)
print(df)

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Sezione 3. Capitolo 1

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Sezione 3. Capitolo 1