Optimize Pandas Performance: Exercises, Practice, Solutions
This resource offers a total of 100 Pandas Performance Optimization problems for practice. It includes 20 main exercises, each accompanied by solutions, detailed explanations, and four related problems.
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. Large DataFrame Sum Performance
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. Custom Function: Apply vs. Vectorized Operations
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. Optimize Memory Usage When Loading CSV
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. Data Type Conversion with astype
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. Row Filtering: For Loop vs. Boolean Indexing
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. GroupBy Aggregation vs. Manual Iteration
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. Merge Operation: merge() vs. Nested For Loop
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. Optimize Memory with Categorical Data
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. Element-wise Multiplication: For Loop vs. * Operator
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. Arithmetic Operations with eval() vs. Standard Operations
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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11. Concatenation: concat() vs. For Loop
Write a Pandas program to measure the time taken to concatenate multiple DataFrames using the "concat" method vs. using a "for" loop.
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12. Query Method vs. Boolean Indexing
Write a Pandas program that uses the query method to filter rows of a DataFrame based on a condition. Compare the performance with boolean indexing.
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13. Resample Method vs. Manual Resampling
Write a Pandas program to create a time series DataFrame and use the resample method to downsample the data. Measure the performance improvement over manual resampling.
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14. Cumulative Sum: cumsum() vs. For Loop
Write a Pandas program to compare the performance of calculating the cumulative sum of a column using the "cumsum" method vs. using a "for" loop.
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15. String Operations: str Accessor vs. apply() with Custom Function
Write a Pandas program to optimize the performance of string operations on a DataFrame column by using the str accessor vs. applying a custom function with apply.
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16. Pivot Table Reshaping: pivot_table() vs. Manual Reshaping
Write a Pandas program that uses the pivot_table method to reshape a DataFrame and compares the performance with manual reshaping using for loops.
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17. Sorting Performance: sort_values() vs. Custom apply()
Write a Pandas program to measure the time taken to sort a large DataFrame using the sort_values method vs. using a custom sorting function with apply.
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18. Rolling Window Calculation: rolling() vs. Manual Calculation
Write a Pandas program to perform a rolling window calculation on a time series DataFrame using the rolling method. Compare the performance with manual calculation.
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19. Multiple Aggregations with agg() vs. Individual Aggregations
Write a Python program that uses the agg method to apply multiple aggregation functions to a DataFrame and compares the performance with applying each function individually.
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20. Optimized Excel File Reading
Write a Pandas program to optimize the performance of reading a large Excel file into a DataFrame by specifying data types and using the "usecols" parameter.
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