Pythonビッグデータ分析学習.テストプログラム実行速度

Here, I introduce 2 magic functions which could only be operated in ipython console:
The first is %timeit

%timeit 100**3
Output[1]: 22.7 ns ± 0.897 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

The second is %lprun
If you desire to utilize '%lprun' magic function to time your codes, you need to install line_profiler in advance.
try:

conda install line_profiler

then, you should do 2 steps as following:

%load_ext line_profiler

%lprun -f function function(para)

Now, let's test:

def test(num):
    for i in range(num):
        print(num)
        print(str(num))
        print(num*2)
    return 0

%lprun -f test test(10)

Output [1]:
Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     1                                           def test(num):
     2        11         45.0      4.1      0.2      for i in range(num):
     3        10       7493.0    749.3     36.3          print(num)
     4        10       6816.0    681.6     33.1          print(str(num))
     5        10       6263.0    626.3     30.4          print(num*2)
     6         1          1.0      1.0      0.0      return 0