Simple way to measure cell execution time in ipython notebook

Simple way to measure cell execution time in ipython notebook

You can measure the execution time of a cell in an IPython Notebook using the built-in "magic" command %time or %timeit. These commands allow you to quickly check the execution time of a single code snippet or a code snippet repeated multiple times, respectively.

  1. %time command: Use %time followed by the code you want to measure. It will display the time taken to execute the code once.

    %time your_function_or_code_here()

  2. %timeit command: Use %timeit followed by the code you want to measure. It runs the code multiple times and provides a more accurate measurement of the average execution time.

    %timeit your_function_or_code_here()

Example:

import time def my_slow_function(): time.sleep(2) %time my_slow_function() # or %timeit my_slow_function()

Remember that %time and %timeit are IPython magic commands and should be used in a code cell within an IPython Notebook. These commands are useful for quick measurements, but for more complex profiling and detailed analysis, you might want to consider using the time module or other profiling tools.

Examples

  1. How to measure execution time of a cell in Jupyter Notebook with %%time magic

    • Description: This query shows how to use the %%time magic command to measure the execution time of a single code cell in a Jupyter Notebook.
    • Code:
      %%time # Code to measure total = sum(range(1_000_000))

  2. Using %%timeit to get average execution time for a cell in IPython Notebook

    • Description: Demonstrates how to use the %%timeit magic command to measure the average execution time with multiple iterations.
    • Code:
      %%timeit # Code to measure total = sum(range(1_000_000))

  3. Measuring cell execution time with timeit module in IPython Notebook

    • Description: Illustrates how to use the timeit module to measure the execution time of a specific code block in a cell.
    • Code:
      import timeit def my_code(): # Code to measure total = sum(range(1_000_000)) execution_time = timeit.timeit(my_code, number=100) # Measure execution time print("Average execution time over 100 iterations:", execution_time)

  4. Measuring the execution time for a cell in IPython Notebook with time module

    • Description: Demonstrates a simple way to measure execution time using the time module.
    • Code:
      import time start_time = time.time() # Record start time # Code to measure total = sum(range(1_000_000)) end_time = time.time() # Record end time execution_time = end_time - start_time # Calculate execution time print("Execution time:", execution_time, "seconds")

  5. Using cProfile to measure cell execution time in IPython Notebook

    • Description: Shows how to use cProfile to measure execution time with detailed profiling information.
    • Code:
      import cProfile # Code to profile def my_code(): total = sum(range(1_000_000)) cProfile.run('my_code()') # Profile the code

  6. Measuring cell execution time with a custom context manager

    • Description: This example demonstrates creating a custom context manager to measure execution time within a block of code.
    • Code:
      import time from contextlib import contextmanager @contextmanager def timer(): start_time = time.time() # Record start time yield end_time = time.time() # Record end time print("Execution time:", end_time - start_time, "seconds") with timer(): # Use the context manager # Code to measure total = sum(range(1_000_000))

  7. Combining %%timeit with numpy arrays to measure cell execution time

    • Description: Demonstrates using %%timeit with numpy arrays to measure execution time for numerical computations.
    • Code:
      import numpy as np %%timeit # Code to measure with numpy arr = np.arange(1_000_000) total = np.sum(arr)

  8. Measuring execution time in IPython Notebook with nested operations

    • Description: Illustrates how to measure execution time for nested code blocks to identify bottlenecks.
    • Code:
      import time # Outer operation start_time = time.time() for _ in range(100): # Inner operation inner_start_time = time.time() total = sum(range(1_000_000)) inner_end_time = time.time() print("Inner operation time:", inner_end_time - inner_start_time) end_time = time.time() print("Outer operation time:", end_time - start_time)

  9. Using datetime to measure cell execution time in IPython Notebook

    • Description: Shows how to use the datetime module to measure the execution time for a cell.
    • Code:
      from datetime import datetime start_time = datetime.now() # Record start time # Code to measure total = sum(range(1_000_000)) end_time = datetime.now() # Record end time execution_time = (end_time - start_time).total_seconds() # Calculate total execution time in seconds print("Execution time:", execution_time, "seconds")

  10. Measuring cell execution time and logging it to a file in IPython Notebook

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