【精心解读】关于Jupyter Notebook的28个技巧

from IPython.core.interactiveshell import InteractiveShellInteractiveShell.ast_node_interactivity = "all"

from pydataset import dataquakes = data('quakes')quakes.head()quakes.tail()

c = get_config()# Run all nodes interactivelyc.InteractiveShell.ast_node_interactivity = "all"

?str.replace()

Docstring:S.replace(old, new[, count]) -> strReturn a copy of S with all occurrences of substringold replaced by new.  If the optional argument count isgiven, only the first count occurrences are replaced.Type:      method_descriptor

# This will list all magic commands%lsmagic

Available line magics:%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmodeAvailable cell magics:%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefileAutomagic is ON, % prefix IS NOT needed for line magics.

# Running %env without any arguments# lists all environment variables# The line below sets the environment# variable OMP_NUM_THREADS%env OMP_NUM_THREADS=4

env: OMP_NUM_THREADS=4

# this will execute and show the output from# all code cells of the specified notebook%run ./two-histograms.ipynb

# Before Running%load ./hello_world.py

# After Running# %load ./hello_world.pyif __name__ == "__main__":  print("Hello World!")

Hello World!

data = 'this is the string I want to pass to different notebook'%store datadel data # This has deleted the variable

Stored 'data' (str)

%store -r dataprint(data)

this is the string I want to pass to different notebook

one = "for the money"two = "for the show"three = "to get ready now go cat go" %who str

one   three   two

%%timeimport timefor _ in range(1000):    time.sleep(0.01)# sleep for 0.01 seconds

CPU times: user 21.5 ms, sys: 14.8 ms, total: 36.3 msWall time: 11.6 s

import numpy%timeit numpy.random.normal(size=100)

The slowest run took 7.29 times longer than the fastest. This could mean that an intermediate result is being cached.100000 loops, best of 3: 5.5 µs per loop

%%writefile pythoncode.pyimport numpydef append_if_not_exists(arr, x):    if x not in arr:        arr.append(x)def some_useless_slow_function():    arr = list()    for i in range(10000):        x = numpy.random.randint(0, 10000)        append_if_not_exists(arr, x)

Writing pythoncode.py%pycat pythoncode.py```pythonimport numpydef append_if_not_exists(arr, x):    if x not in arr:        arr.append(x)def some_useless_slow_function():    arr = list()    for i in range(10000):        x = numpy.random.randint(0, 10000)        append_if_not_exists(arr, x)### 13. IPython Magic - %prun: Show how much time your program spent in each function.Using `%prun statement_name` will give you an ordered table showing you the number of times each internal function was called within the statement, the time each call took as well as the cumulative time of all runs of the function.```python%prun some_useless_slow_function()26324 function calls in 0.556 secondsOrdered by: internal timencalls  tottime  percall  cumtime  percall filename:lineno(function) 10000    0.527    0.000    0.528    0.000 <ipython-input-46-b52343f1a2d5>:2(append_if_not_exists) 10000    0.022    0.000    0.022    0.000 {method 'randint' of 'mtrand.RandomState' objects}     1    0.006    0.006    0.556    0.556 <ipython-input-46-b52343f1a2d5>:6(some_useless_slow_function)  6320    0.001    0.000    0.001    0.000 {method 'append' of 'list' objects}     1    0.000    0.000    0.556    0.556 <string>:1(<module>)     1    0.000    0.000    0.556    0.556 {built-in method exec}     1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

%pdbdef pick_and_take():    picked = numpy.random.randint(0, 1000)    raise NotImplementedError()pick_and_take()Automatic pdb calling has been turned ON

---------------------------------------------------------------------------NotImplementedError                       Traceback (most recent call last)<ipython-input-24-0f6b26649b2e> in <module>()      5     raise NotImplementedError()      6 ----> 7 pick_and_take()<ipython-input-24-0f6b26649b2e> in pick_and_take()      3 def pick_and_take():      4     picked = numpy.random.randint(0, 1000)----> 5     raise NotImplementedError()      6       7 pick_and_take()NotImplementedError:

> <ipython-input-24-0f6b26649b2e>(5)pick_and_take()      3 def pick_and_take():      4     picked = numpy.random.randint(0, 1000)----> 5     raise NotImplementedError()      6       7 pick_and_take()

ipdb>

x = range(1000)y = [i ** 2 for i in x]plt.plot(x,y)plt.show();%config InlineBackend.figure_format = 'retina'plt.plot(x,y)plt.show();

%matplotlib inlinefrom matplotlib import pyplot as pltimport numpyx = numpy.linspace(0, 1, 1000)**1.5# Here you get the output of the functionplt.hist(x)(array([ 216.,  126.,  106.,   95.,   87.,   81.,   77.,   73.,   71.,   68.]), array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ]), <a list of 10 Patch objects>)# By adding a semicolon at the end, the output is suppressed.plt.hist(x);

!ls *.csvnba_2016.csv             titanic.csvpixar_movies.csv         whitehouse_employees.csv

!pip install numpy!pip list | grep pandasRequirement already satisfied (use --upgrade to upgrade): numpy in /Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packagespandas (0.18.1)

$$ P(A \mid B) = \frac{P(B \mid A) \, P(A)}{P(B)} $$

%%bashfor i in {1..5}do   echo "i is $i"donei is 1i is 2i is 3i is 4i is 5

conda install -c r r-essentials

install.packages(c('repr', 'IRdisplay', 'crayon', 'pbdZMQ', 'devtools'))devtools::install_github('IRkernel/IRkernel')IRkernel::installspec()  # to register the kernel in the current R installation

pip install rpy2%load_ext rpy2.ipython%R require(ggplot2)array([1], dtype=int32)
import pandas as pddf = pd.DataFrame({        'Letter': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'],        'X': [4, 3, 5, 2, 1, 7, 7, 5, 9],        'Y': [0, 4, 3, 6, 7, 10, 11, 9, 13],        'Z': [1, 2, 3, 1, 2, 3, 1, 2, 3]    })    %%R -i dfggplot(data = df) + geom_point(aes(x = X, y= Y, color = Letter, size = Z))

!pip install cython fortran-magic %load_ext Cython%%cythondef myltiply_by_2(float x):    return 2.0 * x    myltiply_by_2(23.)

%load_ext fortranmagic%%fortransubroutine compute_fortran(x, y, z)    real, intent(in) :: x(:), y(:)    real, intent(out) :: z(size(x, 1))    z = sin(x + y)end subroutine compute_fortrancompute_fortran([1, 2, 3], [4, 5, 6])

!pip install https://github.com/ipython-contrib/jupyter_contrib_nbextensions/tarball/master!pip install jupyter_nbextensions_configurator!jupyter contrib nbextension install --user!jupyter nbextensions_configurator enable --user

conda install -c damianavila82 rise
# Or alternatively pip:pip install RISE
# And then run the following code to install and enable the extension:
jupyter-nbextension install rise --py --sys-prefixjupyter-nbextension enable rise --py --sys-prefix

import osfrom IPython.display import display, Imagenames = [f for f in os.listdir('../images/ml_demonstrations/') if f.endswith('.png')]for name in names[:5]:    display(Image('../images/ml_demonstrations/' + name, width=100))

names = !ls ../images/ml_demonstrations/*.pngnames[:5]['../images/ml_demonstrations/colah_embeddings.png', '../images/ml_demonstrations/convnetjs.png', '../images/ml_demonstrations/decision_tree.png', '../images/ml_demonstrations/decision_tree_in_course.png', '../images/ml_demonstrations/dream_mnist.png']