[데이터 분석] python을 활용한 데이터 분석 (2) - ipyhon

ipython에 대하여 살펴 보기

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mac의 경우, 작업을 할 폴더로 이동 후 아래와 같이 실행해주면된다.

jupyter notebook

In [1]:

import numpy as np
from numpy.random import randn

In [2]:

data = {i:randn() for i in range(7)}
data

Out[2]:

{0: 1.402129791256454,
 1: -0.5401556293704064,
 2: -0.29486216075501115,
 3: 1.1642281840557518,
 4: -2.094833336203848,
 5: -1.9576522024426697,
 6: 0.44024554175567876}

 

random number를 100 by 100으로 생성¶

In [3]:

a2 = np.random.randn(100, 100)
a2

Out[3]:

array([[-1.13945322, -1.66715472, -1.62137561, ..., -0.34817203,
         2.38118554, -0.06834492],
       [-0.00701701,  2.0812329 , -0.38181581, ...,  1.00713729,
        -0.24599059,  0.91234044],
       [ 0.69678786,  0.13881484,  2.83276204, ..., -0.59557937,
        -0.96265296,  0.53557611],
       ...,
       [-0.33311091,  1.24032408, -0.64654917, ...,  1.02471965,
         1.26338342,  0.90633647],
       [ 0.58624713, -0.12180394, -0.63936549, ...,  0.07708997,
         0.40539882, -0.1600808 ],
       [-0.58096273, -1.10159639, -0.82030345, ...,  0.55856334,
         1.34404538, -0.43031426]])

 

행 과 행을 곱한것¶

1. %timeit을 이용하면 반복 실행 했을때, 평균 시간
2. %time은 한번 실행했을때 시간

In [4]:

%timeit np.dot(a2, a2)

 

76.7 µs ± 937 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In [5]:

%time np.dot(a2, a2)

 

CPU times: user 358 µs, sys: 164 µs, total: 522 µs
Wall time: 276 µs

Out[5]:

array([[ 10.67019333, -11.62486936, -20.73570744, ..., -16.54157559,
         10.7856975 ,  15.35963037],
       [  1.16104496,   7.08105523, -19.77829259, ...,  -7.55541681,
          7.4976283 ,   3.97853515],
       [ -7.19040278,   2.00419161,   9.13214687, ..., -15.27729955,
          8.61928142, -10.26241573],
       ...,
       [ -1.62143925,  -0.09000002,   3.71179739, ...,   5.56667868,
        -13.68002075,  10.15632574],
       [  9.83111316,   6.17116871, -21.20346129, ..., -13.37869532,
         15.12944082,  11.89424262],
       [ -8.17721586,  -0.61544843,  -6.99874515, ...,   7.41555788,
        -13.11156169,   7.56629781]])

 

_만 치면, 바로 위에 했던 것을 한번더 반복함¶

In [6]:

_

Out[6]:

array([[ 10.67019333, -11.62486936, -20.73570744, ..., -16.54157559,
         10.7856975 ,  15.35963037],
       [  1.16104496,   7.08105523, -19.77829259, ...,  -7.55541681,
          7.4976283 ,   3.97853515],
       [ -7.19040278,   2.00419161,   9.13214687, ..., -15.27729955,
          8.61928142, -10.26241573],
       ...,
       [ -1.62143925,  -0.09000002,   3.71179739, ...,   5.56667868,
        -13.68002075,  10.15632574],
       [  9.83111316,   6.17116871, -21.20346129, ..., -13.37869532,
         15.12944082,  11.89424262],
       [ -8.17721586,  -0.61544843,  -6.99874515, ...,   7.41555788,
        -13.11156169,   7.56629781]])

In [7]:

foo = 'automatic'
foo

Out[7]:

'automatic'

 

!를 이용하면, 기본 시스템 명령어도 사용 가능하다¶

In [8]:

!ls

 

Untitled.ipynb

In [1]:

!cd

In [3]:

from IPython.core.display import display, HTML
display(HTML("<style>.container {width:90% !important; }</style>"))

 

 

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