Python for Data Analysis (9)

魔法命令

a=1;b=100
a*b

%timeit a*b

The slowest run took 15.46 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 201 ns per loop

import numpy as np
a=np.random.randn(100,100)
%timeit np.dot(a,a)
%time np.dot(a,a)

The slowest run took 1636.68 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 46.3 µs per loop
CPU times: user 93 µs, sys: 1e+03 ns, total: 94 µs
Wall time: 73 µs





array([[  1.6460291 ,   8.04479056,   3.55858006, ...,  -5.11887218,
         -2.34480825,   0.29470307],
       [ -2.31945251, -19.91884282, -16.80738536, ...,  -5.11245243,
          5.26080793,   2.11886289],
       [  3.14523094,  10.21521818,   9.28415132, ...,  -8.38065148,
         17.53078937,  -7.68667463],
       ..., 
       [ 13.92807526,  13.70927476, -14.50414208, ...,   0.71719941,
          6.33660503,  -6.9980424 ],
       [  9.13700782,   1.43313504,  -1.26994739, ...,   8.07787293,
         14.41700316,   5.32727609],
       [  1.38417926,  17.93342167,   2.68956568, ..., -11.20007195,
        -14.31456352,   4.74833645]])

插入圖片

from IPython.display import Image
Image(filename="pic/magic1.png")

Image(filename="pic/magic2.png")

插入Latex

from IPython.display import Latex

Latex("$Z=\sqrt{x^2+y^2}$")

Z=x2+y2‾‾‾‾‾‾‾√

%hist #查看歷史輸入

%logstart #日誌記錄開始,有一個系列：%logoff,logon,logstate,logstop

Activating auto-logging. Current session state plus future input saved.
Filename       : #日誌記錄開始,有一個系列：%logoff,logon,logstate,logstop
Mode           : backup
Output logging : False
Raw input log  : False
Timestamping   : False
State          : active

%logoff #日誌記錄結束

Switching logging OFF

Image('pic/magic3.png') #其中帶！的命令表示是其後面的內容需要在系統shell中執行,這個很厲害，打通了Ipython和shell的溝通

Image('pic/magic4.png')

#%alias ,可以爲shell命令自定義簡稱
%alias ll ls -l

ll

total 162160
-rw-r--r--  1 momo  staff       636  9 19 21:25 #日誌記錄開始,有一個系列：%logoff,logon,logstate,logstop
-rw-r--r--  1 momo  staff      1414  9  9 16:02 Python for Data Analysis (1).ipynb
-rw-r--r--  1 momo  staff     13654  9 11 11:39 Python for Data Analysis (2).ipynb
-rw-r--r--  1 momo  staff      5288  9 11 23:38 Python for Data Analysis (3).ipynb
-rw-r--r--  1 momo  staff      7618  9 12 10:09 Python for Data Analysis (4).ipynb
-rw-r--r--  1 momo  staff      5462  9 12 22:38 Python for Data Analysis (5).ipynb
-rw-r--r--  1 momo  staff      3284  9 13 23:49 Python for Data Analysis (6).ipynb
-rw-r--r--  1 momo  staff      4815  9 16 09:36 Python for Data Analysis (7).ipynb
-rw-r--r--  1 momo  staff      6022  9 19 19:36 Python for Data Analysis (8).ipynb
-rw-r--r--  1 momo  staff    714346  9 19 21:29 Untitled.ipynb
-rw-r--r--  1 momo  staff      1020  9 19 21:15 ipython_log.py
drwxr-xr-x  6 momo  staff       204  9 19 21:17 [34mpic[m[m
-rwxr-xr-x@ 1 momo  staff  82233792  9 19  2015 [31m利用Python進行數據分析.pdf[m[m

#還可以一次性定義多條爲一個命令！！！太牛了
#只需用分號將多個命令隔開
%alias test_alias (cd pic;ls)
%test_alias

magic1.png magic2.png magic3.png magic4.png