Optimize Pandas Performance: Exercises, Practice, Solutions

Pandas Performance Optimization [20 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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11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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