Optimize Pandas Performance: Exercises, Practice, Solutions

Pandas Performance Optimization [10 exercises with solution]

Exercises focusing on improving the performance of Pandas skills focused on performance optimization, including vectorization, efficient data manipulation, and memory usage.

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1. Write a Pandas program to create a large DataFrame and measure the time taken to sum a column using a for loop vs. using the sum method.
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2. Write a Pandas program to compare the performance of applying a custom function to a column using apply vs. using vectorized operations.
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3. Write a Pandas program that loads a large CSV file into a DataFrame and optimizes memory usage by specifying appropriate data types.
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4. Write a Pandas program that uses the "astype" method to convert the data types of a DataFrame and measures the reduction in memory usage.
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5. Write a Pandas program to filter rows of a DataFrame based on a condition using a for loop vs. using boolean indexing. Compare performance.
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6. Write a Pandas program that uses the groupby method to aggregate data and compares performance with manually iterating through the DataFrame.
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7. Write a Pandas program that performs a merge operation on two large DataFrames using the "merge" method. It compares the performance with a nested for loop.
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8. Write a Pandas program to create a DataFrame with categorical data and use the category data type to optimize memory usage. Measure the performance difference.
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9. Write a Pandas program that performs element-wise multiplication on a DataFrame using a for loop vs. using the * operator. Compare the performance.
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10. Write a Pandas program that uses the "eval" method to perform multiple arithmetic operations on DataFrame columns and compare performance with standard operations.
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