Pandas Data Validation: Exercises and Solutions for Data Integrity
This resource offers a total of 75 Pandas Data Validation problems for practice. It includes 15 main exercises, each accompanied by solutions, detailed explanations, and four related problems.
Exercises on validating data integrity, including checking for constraints, validating data types, and ensuring data consistency.
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1. Checking Missing Values in a DataFrame
Write a Pandas program to check for missing values in a DataFrame.
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2. Ensuring Consistent Data Types of All Columns
Write a Pandas program to check and ensure that the data types of all columns are consistent.
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3. Validating the Data Type of a Specific Column
Write a Pandas program to validate the data type of a specific column in a DataFrame.
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4. Checking Duplicate Rows in a DataFrame
Write a Pandas program to check duplicate rows in a DataFrame.
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5. Removing Duplicate Rows from a DataFrame
Write a Pandas program to remove duplicate rows from a DataFrame.
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6. Validating Data Based on Custom Conditions
Write a Pandas program to validate data based on custom conditions.
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7. Checking That a Specific Column Contains Only Unique Values
Write a Pandas program to check that a specific column contains only unique values.
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8. Detecting Outliers in a DataFrame
Write a Pandas program to detect outliers in a DataFrame.
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9. Validating Date Formats in a DataFrame
Write a Pandas program to validate date formats in a DataFrame.
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10. Ensuring No Missing Values in a Critical Column
Write a Pandas program that ensures no missing values in a critical column.
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11. Validating String Length in a DataFrame Column
Write a Pandas program that validates string length in a DataFrame column.
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12. Verifying Numeric Range in a DataFrame Column
Write a Pandas program to verify numeric range in a DataFrame column.
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13. Validating the Format of Email Addresses
Write a Pandas program that validates the format of email addresses.
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14. Checking Consistent Date Order in a Column
Write a Pandas program to check consistent date order in a column.
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15. Ensuring No Negative Values in a DataFrame Column
Write a Pandas program that ensures no negative values in a DataFrame column.
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