Jupyter Notebook Tutorial : Part 2

Abhishek Ghosh

By Abhishek Ghosh September 17, 2017 2:55 pm Updated on September 17, 2017

Jupyter Notebook Tutorial : Part 2

In Part 2 of our Jupyter Notebook Tutorial series, we will learn installing packages in Jupyter, importing data, plotting inline etc works. Our Part 1 of Jupyter Notebook Tutorial was on how to install on localhost or server – on Mac OS X or GNU/Linux. For quick recapitulation to unused with Python, normally on terminal, if you run :

python

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python

then it will start Python interactive command shell like this :

Python 2.7.13 (default, Jun 24 2017, 02:53:00)
[GCC 4.2.1 Compatible Apple LLVM 8.1.0 (clang-802.0.42)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
&gt;&gt;&gt;

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Python 2.7.13 (default, Jun 24 2017, 02:53:00)

[GCC 4.2.1 Compatible Apple LLVM 8.1.0 (clang-802.0.42)] on darwin

Type "help", "copyright", "credits" or "license" for more information.

>>>

To obtain current directory, we first will run :

import os

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import os

then :

os.getcwd()

1	os.getcwd()

To exit that shell, we will type :

exit()

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exit()

If you type :

ipython

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ipython

you’ll get enhanced interactive Python shell.

---

Jupyter Notebook Tutorial : Part 2

Now open Jupyter Notebook on browser like we talked in Part 1 of this series. On the right hand side there will be a “New” dropdown menu. If you click it, that first option will be “Python3” (which you’ll normally click to start works), other options includes a terminal. You’ll see the input field :

In [ ]

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In [ ]

Copy paste this :

print("The answer is", 2*2)

1	print("The answer is", 2*2)

then click the RUN button, which should return “The answer is 4”. If you want a LaTeX output, run this :

from IPython.display import display, Math, Latex
display(Math(r'sqrt{a^2 + b^2}'))

1 2	from IPython.display import display, Math, Latex display(Math(r'sqrt{a^2 + b^2}'))

For the next steps of guides, you need to install :

Pandas
NumPy
SciPy
Plotly
matplotlib

via pi. For Mac OS X appendicectomy sudo -H before the commands and run :

pip install pandas
pip install numpy
pip install scipy
pip install plotly
pip install matplotlib

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pip install pandas

pip install numpy

pip install scipy

pip install plotly

pip install matplotlib

pip install numpy will show that previous command already installed it. Now again freshly launch Jupyter notebook with the command :

jupyter notebook

1	jupyter notebook

In that Jupiter notebook’s input, enter this and run, which is example of using Plotly :

import plotly.plotly as py
from plotly.graph_objs import *

import pandas as pd

mapbox_access_token = 'pk.eyJ1IjoiY2hlbHNlYXBsb3RseSIsImEiOiJjaXFqeXVzdDkwMHFrZnRtOGtlMGtwcGs4In0.SLidkdBMEap9POJGIe1eGw'

json_data = pd.read_json('https://raw.githubusercontent.com/ottlngr/2016-15/ottlngr/ottlngr/sites.json')
rating_one_site_lat = []
rating_one_site_lon = []
locations_name = []

for index in range(len(json_data)):
    if json_data['locations'][index] != []:
        if json_data['site'][index]['rating'] == 1:
            rating_one_site_lat.append(json_data['locations'][index][0]['latitude'])
            rating_one_site_lon.append(json_data['locations'][index][0]['longitude'])
            locations_name.append(json_data['site'][index]['name'])

data = Data([
    Scattermapbox(
        lat=rating_one_site_lat,
        lon=rating_one_site_lon,
        mode='markers',
        marker=Marker(
            size=18,
            color='rgb(155, 240, 225)',
            opacity=0.7
        ),
        text=locations_name,
        hoverinfo='text'
    ),
    Scattermapbox(
        lat=rating_one_site_lat,
        lon=rating_one_site_lon,
        mode='markers',
        marker=Marker(
            size=8,
            color='rgb(205, 245, 100)'
        ),
        hoverinfo='skip'
    )]
)
        
layout = Layout(
    title='Nuclear Waste Sites on Campus',
    autosize=True,
    hovermode='closest',
    showlegend=False,
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,
        center=dict(
            lat=38,
            lon=-94
        ),
        pitch=0,
        zoom=3,
        style='dark'
    ),
)

fig = dict(data=data, layout=layout)
py.iplot(fig, filename='jupyter/Nuclear Waste Sites on American Campuses')

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import plotly.plotly as py

from plotly.graph_objs import *

import pandas as pd

mapbox_access_token = 'pk.eyJ1IjoiY2hlbHNlYXBsb3RseSIsImEiOiJjaXFqeXVzdDkwMHFrZnRtOGtlMGtwcGs4In0.SLidkdBMEap9POJGIe1eGw'

json_data = pd.read_json('https://raw.githubusercontent.com/ottlngr/2016-15/ottlngr/ottlngr/sites.json')

rating_one_site_lat = []

rating_one_site_lon = []

locations_name = []

for index in range(len(json_data)):

if json_data['locations'][index] != []:

if json_data['site'][index]['rating'] == 1:

rating_one_site_lat.append(json_data['locations'][index][0]['latitude'])

rating_one_site_lon.append(json_data['locations'][index][0]['longitude'])

locations_name.append(json_data['site'][index]['name'])

data = Data([

Scattermapbox(

lat=rating_one_site_lat,

lon=rating_one_site_lon,

mode='markers',

marker=Marker(

size=18,

color='rgb(155, 240, 225)',

opacity=0.7

),

text=locations_name,

hoverinfo='text'

),

Scattermapbox(

lat=rating_one_site_lat,

lon=rating_one_site_lon,

mode='markers',

marker=Marker(

size=8,

color='rgb(205, 245, 100)'

),

hoverinfo='skip'

)]

)

layout = Layout(

title='Nuclear Waste Sites on Campus',

autosize=True,

hovermode='closest',

showlegend=False,

mapbox=dict(

accesstoken=mapbox_access_token,

bearing=0,

center=dict(

lat=38,

lon=-94

),

pitch=0,

zoom=3,

style='dark'

),

)

fig = dict(data=data, layout=layout)

py.iplot(fig, filename='jupyter/Nuclear Waste Sites on American Campuses')

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