Pandas Data Cleaning and Preprocessing: Exercises, Solutions with Explanation
This resource offers a total of 75 Pandas Data Cleaning and Preprocessing problems for practice. It includes 15 main exercises, each accompanied by solutions, detailed explanations, and four related problems.
More exercises focused on cleaning and preprocessing data, including dealing with outliers, duplicates, and data normalization.
[An Editor is available at the bottom of the page to write and execute the scripts.]
1. Handling Missing Data in Pandas
Write a Pandas program to fill missing values (NaN) in a DataFrame using fillna().
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2. Handling Duplicates in Pandas
Write a Pandas program to drop rows with missing data.
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3. Data Cleaning Techniques
Write a Pandas program to detect duplicates using duplicated() method.
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4. String Manipulation in Pandas
Write a Pandas program to remove duplicates rows from a DataFrame.
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5. Handling Outliers with Z-Score Method
Write a Pandas program to handle outliers in a DataFrame with Z-Score method.
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6. Normalizing Data with Min-Max Scaling
Write a Pandas program that normalizes data with Min-Max scaling.
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7. Binning Data into Categories
Write a Pandas program to bin data into categories.
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8. Converting Data Types and Column Operations
Write a Pandas program that handles text data with str.replace().
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9. Replacing Missing Data with Mean Value
Write a Pandas program to convert data types using astype().
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10. Removing Columns with Too Many Missing Values
Write a Pandas program to remove leading and trailing whitespace using str.strip().
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11. Reordering and Splitting Columns
Write a Pandas program to change column names to lowercase.
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12. Dropping Rows with Missing Data
Write a Pandas program to replacing missing data with mean value.
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13. Changing Column Names to Lowercase
Write a Pandas program to remove columns with too many missing values.
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14. Reordering Columns in a DataFrame
Write a Pandas program to reorder columns in a DataFrame.
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15. Splitting a Column into Multiple Columns
Write a Pandas program to split a column into multiple columns.
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