from IPython.core.interactiveshell import InteractiveShell

InteractiveShell.ast_node_interactivity = "all"　

from pydataset import data

quakes = data('quakes')

quakes.head()

quakes.tail()

c = get_config()

#Run all nodes interactively

c.InteractiveShell.ast_node_interactivity = "all"

In [3]:

?str.replace()

Docstring:

S.replace(old, new[, count]) -> str

Return a copy of S with all occurrences of substring

old replaced by new.  If the optional argument count is

given, only the first count occurrences are replaced.

Type:      method_descriptor

In [53]:

# This will list all magic commands

%lsmagic

Out[53]:

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  %xmode

Available 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  %%writefile

In [55]:

# 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

In [56]:

# this will execute and show the output from

# all code cells of the specified notebook

%run ./two-histograms.ipynb

In [ ]:

# Before Running

%load ./hello_world.py

In [61]:

# After Running

# %load ./hello_world.py

if __name__ == "__main__":

        print("Hello World!")

Hello World!

In [62]:

data = 'this is the string I want to pass to different notebook'

%store data

del data # This has deleted the variable 

Stored 'data' (str)

Now, in a new notebook…

In [1]:

%store -r data

print(data)

this is the string I want to pass to different notebook

In [1]:

one = "for the money"

two = "for the show"

three = "to get ready now go cat go"

%who str

one three two

In [4]:

%%time

import time

for _ 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 ms

Wall time: 11.6 s

In [3]:

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.py

import numpy

def 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

In [8]:

%pycat pythoncode.py

import numpy

def 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)

 %prun some_useless_slow_function()

          26324 function calls in 0.556 seconds

    Ordered by: internal time

    ncalls  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}

In [ ]:

%pdb

def 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()

In [4]:

%matplotlib inline

from matplotlib import pyplot as plt

import numpy

x = numpy.linspace(0, 1, 1000)**1.5

In [5]:

# Here you get the output of the function

plt.hist(x)

Out[5]:

(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>)

In [6]:

# By adding a semicolon at the end, the output is suppressed.

plt.hist(x);

In [7]:

!ls *.csv

nba_2016.csv             titanic.csv

pixar_movies.csv         whitehouse_employees.csv

Or to check and manage packages.

In [8]:

!pip install numpy

!pip list | grep pandas

Requirement already satisfied (use --upgrade to upgrade): numpy in /Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages

pandas (0.18.1)

In []:

%%bash

for i in {..}

do

   echo "i is $i"

done

i is

i is

i is

i is

i is 

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

In [1]:

%load_ext rpy2.ipython

In [2]:

%R require(ggplot2)

Out[2]:

array([1], dtype=int32)

In [3]:

import pandas as pd

df = 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]

    })

In [4]:

%%R -i df

ggplot(data = df) + geom_point(aes(x = X, y= Y, color = Letter, size = Z))

!pip install cython fortran-magic

In [ ]:

%load_ext Cython

In [ ]:

%%cython

def myltiply_by_2(float x):

    return 2.0 * x

In [ ]:

myltiply_by_2(23.)

Personally I prefer to use fortran, which I found very convenient for writing number-crunching functions. More details of usage can be found here.

In [ ]:

%load_ext fortranmagic

In [ ]:

%%fortran

subroutine compute_fortran(x, y, z)

    real, intent(in) :: x(:), y(:)

    real, intent(out) :: z(size(x, 1))

    z = sin(x + y)

end subroutine compute_fortran

In [ ]:

compute_fortran([1, 2, 3], [4, 5, 6])

conda install -c damianavila82 rise

In []:

names = !ls ../images/ml_demonstrations/*.png

names[:5]

Out[10]:

['../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']