How Faster Numpy Array Compare To Python List

Numpy array operation is more faster than python list, it also cost less memory than python list. With Numpy array, you do not need to create a loop to operate all array element, you can operate all array elements just use one line code. This example will create a Numpy array and a python list which has 1000000 elements, and then calculate each element’s quadratic value, and print out both Numpy and python list excution times. You can see which one is more faster.

1. Compare Numpy And Python List Operation Example.

  1. First you should startup jupter notebook web server in a terminal.
    ~$ jupyter notebook
  2. Then login to the jupyter notebook website, and create a new jupyter notebook file ( .ipynb).
  3. Input below ipython source code in on line cell and run it. Please note the ipython %time command, it can print out the times that the code execution cost.
    import numpy as np
    # create a Numpy array.
    my_arr = np.arange(1000000)
    # create a python list.
    my_list = list(range(1000000))
    # print out execution time for 
    %time for _ in range(10): my_arr2 = my_arr ** 2
    %time for _ in range(10): my_list2 = [x ** 2 for x in my_list]
  4. When you run above line cell code, you will get below output. We can see that Numpy array runs very fast than python list.
    CPU times: user 18.3 ms, sys: 0 ns, total: 18.3 ms
    Wall time: 19.7 ms
    CPU times: user 2.12 s, sys: 107 ms, total: 2.23 s
    Wall time: 2.24 s
  5. If you print out the Numpy array and python list values, you can get below result, Numpy array data can be printed out, but python list can not because of python list use more memory so the jupyter notebook server will crash if it print out python list elements.
    [     0      1      2 ... 999997 999998 999999]
    [           0            1            4 ... 999994000009 999996000004 999998000001]
    IOPub data rate exceeded.
    The notebook server will temporarily stop sending output
    to the client in order to avoid crashing it.
    To change this limit, set the config variable
    Current values:
    NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)
    NotebookApp.rate_limit_window=3.0 (secs)

    numpy array runs faster than python list example


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