[DOCKER] Easy to use Jupyter notebook (Python3.5)
Introduction
Jupyter notebook (Python) can be said to be the standard for scientific and technological calculations such as data analysis. Here's an easy way to get started with docker.
Install Docker Toolbox
Download the installer from the Docker Toolbox site (below) and run it. https://www.docker.com/products/docker-toolbox
Installation is not difficult, but if necessary, [Reference Link](#-% E5% 8F% 82% E8% 80% 83% E3% 83% AA% E3% 83% B3% E3% 82% AF )Please refer to.
For Linux, you can install docker as follows.
bash
wget -qO- https://get.docker.com/ | sh
Start Jupyter
When running on Linux
Please do as follows [^ 1].
[^ 1]: Both user ID and group ID are set to 1000. If it is different, please modify Dockerfile and "docker build" yourself.
bash
mkdir jupyter
docker run -it -d -p 8888:8888 -v $PWD/jupyter:/home/jupyter \
--name jupyter tsutomu7/alpine-python:jupyter
firefox localhost:8888
If you want to exit and delete the container, do the following:
bash
docker rm -f jupyter
When running on Windows or Mac
When you install Docker Toolbox, a tool called Kitematic is installed. Start Kitematic. At the first startup, the Docker Hub login screen will be displayed, but please skip it.
When Kitematic starts, enter "tsutomu7 / alpine-python" in the search box as shown below. The search results will appear below, so click "○○○" at the bottom right.
Click "SELECTED TAG" as shown below.
Click "jupyter".
Press "x" to go back.
Press "CREATE".
The download will start, and after a while, the container will start as shown below.
-** Click ** under "VOLUMES" and click "Enable" to leave the execution results described below on the host (Windows or Mac).
-** Click ** under "WEB PREVIEW" to open a browser and use Jupyter.
Try it
Graph
Type the following in a cell and hold down the Shift key and press the Enter key to execute.
jupyter
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'IPAexGothic'
plt.plot([2,1,3], label='sample')
plt.legend();
In this way, you can draw graphs with matplotlib and you can also handle Japanese.
optimisation
Enter the following in the cell and execute it. Solve combinatorial optimization problems Solve Sudoku / 4f919f453aae95b3834b) You can also.
jupyter
import pandas as pd, numpy as np
from more_itertools import grouper
from pulp import *
prob = """\
..6.....1
.7..6..5.
8..1.32..
..5.4.8..
.4.7.2.9.
..8.1.7..
..12.5..3
.6..7..8.
2.....4..
"""
r = range(9)
m = LpProblem() #Mathematical model
a = pd.DataFrame([(i, j, k, LpVariable('x%d%d%d'%(i,j,k), cat=LpBinary))
for i in r for j in r for k in r],
columns=['Vertical', 'side', 'number', 'x']) # (Formulation 1)
for i in r:
for j in r:
m += lpSum(a[(a.Vertical== i) & (a.side== j)].x) == 1 # (Formulation 2)
m += lpSum(a[(a.Vertical== i) & (a.number== j)].x) == 1 # (Formulation 3)
m += lpSum(a[(a.side== i) & (a.number== j)].x) == 1 # (Formulation 4)
for i in range(0, 9, 3):
for j in range(0, 9, 3):
for k in r:
m += lpSum(a[(a.Vertical>= i) & (a.Vertical< i+3) & # (Formulation 5)
(a.side>= j) & (a.side< j+3) & (a.number== k)].x) == 1
for i, s in enumerate(prob.split('\n')):
for j, c in enumerate(s):
if c.isdigit():
k = int(c)-1 # (Formulation 6)
m += lpSum(a[(a.Vertical== i) & (a.side== j) & (a.number== k)].x) == 1
m.solve() #Solved with solver
f = a.x.apply(lambda v: value(v) == 1) #Selected numbers
print(np.array(list(grouper(9, a.number[f] + 1))))
result
[[5 3 6 8 2 7 9 4 1]
[1 7 2 9 6 4 3 5 8]
[8 9 4 1 5 3 2 6 7]
[7 1 5 3 4 9 8 2 6]
[6 4 3 7 8 2 1 9 5]
[9 2 8 5 1 6 7 3 4]
[4 8 1 2 9 5 6 7 3]
[3 6 9 4 7 1 5 8 2]
[2 5 7 6 3 8 4 1 9]]
Comparison with Jupyter Project
Jupyter notebook also has an image of Jupyter Project (jupyter / notebook), The one introduced this time has the following merits.
| Feature | What was introduced | Jupyter Project |
|---|---|---|
| new | Python 3.5.1 | Python 3.4.3 |
| Small size | 658.5 MB | 863.1 MB |
| Many installed packages | 69 | 38 |
tsutomu7 / alpine-python: installed package of jupyter
| package | ver | package | ver | package | ver | package | ver |
|---|---|---|---|---|---|---|---|
| blist | 1.3.6 | bokeh | 0.11.1 | chest | 0.2.3 | cloudpickle | 0.1.1 |
| conda | 4.0.5 | conda-env | 2.4.5 | cycler | 0.10.0 | dask | 0.8.2 |
| decorator | 4.0.9 | entrypoints | 0.2 | flask | 0.10.1 | fontconfig | 2.11.1 |
| freetype | 2.5.5 | heapdict | 1.0.0 | ipykernel | 4.3.1 | ipython | 4.1.2 |
| ipython-genutils | 0.1.0 | ipython_genutils | 0.1.0 | ipywidgets | 4.1.1 | itsdangerous | 0.24 |
| jinja2 | 2.8 | jsonschema | 2.4.0 | jupyter | 1.0.0 | jupyter-client | 4.2.2 |
| jupyter-console | 4.1.1 | jupyter-core | 4.1.0 | jupyter_client | 4.2.2 | jupyter_console | 4.1.1 |
| jupyter_core | 4.1.0 | libgfortran | 3.0 | libpng | 1.6.17 | libsodium | 1.0.3 |
| libxml2 | 2.9.2 | locket | 0.2.0 | markdown | 2.6.6 | markupsafe | 0.23 |
| matplotlib | 1.5.1 | mistune | 0.7.2 | more-itertools | 2.2 | mpmath | 0.19 |
| nbconvert | 4.2.0 | nbformat | 4.0.1 | ncurses | 5.9 | networkx | 1.11 |
| nomkl | 1.0 | notebook | 4.1.0 | numpy | 1.11.0 | openblas | 0.2.14 |
| openssl | 1.0.2g | pandas | 0.18.0 | partd | 0.3.2 | path.py | 8.2 |
| patsy | 0.4.1 | pexpect | 4.0.1 | pickleshare | 0.5 | pip | 8.1.1 |
| psutil | 4.1.0 | ptyprocess | 0.5 | pulp | 1.6.1 | pycosat | 0.6.1 |
| pycrypto | 2.6.1 | pygments | 2.1.3 | pyjade | 4.0.0 | pyparsing | 2.0.3 |
| pyqt | 4.11.4 | python | 3.5.1 | python-dateutil | 2.5.2 | pytz | 2016.3 |
| pyyaml | 3.11 | pyzmq | 15.2.0 | qt | 4.8.7 | qtconsole | 4.2.1 |
| readline | 6.2 | requests | 2.9.1 | scikit-learn | 0.17.1 | scipy | 0.17.0 |
| seaborn | 0.7.0 | setuptools | 20.3 | simplegeneric | 0.8.1 | sip | 4.16.9 |
| six | 1.10.0 | sqlite | 3.9.2 | statsmodels | 0.6.1 | sympy | 1.0 |
| terminado | 0.5 | tk | 8.5.18 | toolz | 0.7.4 | tornado | 4.3 |
| traitlets | 4.2.1 | werkzeug | 0.11.5 | wheel | 0.29.0 | xz | 5.0.5 |
| yaml | 0.1.6 | zeromq | 4.1.3 | zlib | 1.2.8 |
installed packages of jupyter / notebook
| package | ver | package | ver | package | ver | package | ver |
|---|---|---|---|---|---|---|---|
| backports-abc | (0.4) | cffi | (1.5.2) | cryptography | (1.2.2) | decorator | (4.0.9) |
| idna | (2.0) | ipykernel | (4.2.2) | ipython | (4.1.1) | ipython-genutils | (0.1.0) |
| Jinja2 | (2.8) | jsonschema | (2.5.1) | jupyter-client | (4.1.1) | jupyter-core | (4.0.6) |
| MarkupSafe | (0.23) | mistune | (0.7.1) | nbconvert | (4.1.0) | nbformat | (4.0.1) |
| ndg-httpsclient | (0.4.0) | nose | (1.3.7) | notebook | (5.0.0.dev0) | path.py | (8.1.2) |
| pexpect | (4.0.1) | pickleshare | (0.6) | pip | (8.0.2) | ptyprocess | (0.5.1) |
| pyasn1 | (0.1.9) | pycparser | (2.14) | Pygments | (2.1.1) | pyOpenSSL | (0.15.1) |
| pyzmq | (15.2.0) | requests | (2.9.1) | setuptools | (20.1.1) | simplegeneric | (0.8.1) |
| six | (1.10.0) | terminado | (0.6) | tornado | (4.3) | traitlets | (4.1.0) |
| wheel | (0.29.0) | widgetsnbextension | (0.0.2.dev0) |
Reference link
-Install Docker Toolbox: Windows -Powerful notepad for modern engineers Jupyter notebook recommendation --ubuntu-based jupyter: tsutomu7 / jupyter (827MB)
that's all
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