Jupyter notebooks

This is a page to demonstrate the look and feel of Jupyter Notebook elements.

Hiding elements

Hiding inputs

# Hide input
square = np.random.randn(100, 100)
wide = np.random.randn(100, 1000)

fig, ax = plt.subplots()
ax.imshow(square)

fig, ax = plt.subplots()
ax.imshow(wide)

<matplotlib.image.AxesImage at 0x7f7209b5bbb0>

Hiding outputs

# Hide output
square = np.random.randn(100, 100)
wide = np.random.randn(100, 1000)

fig, ax = plt.subplots()
ax.imshow(square)

fig, ax = plt.subplots()
ax.imshow(wide)

<matplotlib.image.AxesImage at 0x7f7209b41460>

Hiding markdown

备注

This is a hidden markdown cell

It should be hidden!

And here’s a toggleable note

With a body!

Hiding both inputs and outputs

square = np.random.randn(100, 100)
wide = np.random.randn(100, 1000)

fig, ax = plt.subplots()
ax.imshow(square)

fig, ax = plt.subplots()
ax.imshow(wide)

<matplotlib.image.AxesImage at 0x7f720899bb20>

Hiding the whole cell

square = np.random.randn(100, 100)
wide = np.random.randn(100, 1000)

fig, ax = plt.subplots()
ax.imshow(square)

fig, ax = plt.subplots()
ax.imshow(wide)

<matplotlib.image.AxesImage at 0x7f720899b070>

Enriched outputs

Math

# You can also include enriched outputs like Math
from IPython.display import Math
Math("\sum_{i=0}^n i^2 = \frac{(n^2+n)(2n+1)}{6}")

\[\displaystyle \sum_{i=0}^n i^2 = rac{(n^2+n)(2n+1)}{6}\]

Pandas DataFrames

import pandas as pd
df = pd.DataFrame([['hi', 'there'], ['this', 'is'], ['a', 'DataFrame']], columns=['Word A', 'Word B'])
df

	Word A	Word B
0	hi	there
1	this	is
2	a	DataFrame

Styled DataFrames (see the Pandas Styling docs).

import pandas as pd

np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
               axis=1)
df.iloc[3, 3] = np.nan
df.iloc[0, 2] = np.nan

def color_negative_red(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, black otherwise.
    """
    color = 'red' if val < 0 else 'black'
    return 'color: %s' % color

def highlight_max(s):
    '''
    highlight the maximum in a Series yellow.
    '''
    is_max = s == s.max()
    return ['background-color: yellow' if v else '' for v in is_max]

df.style.\
    applymap(color_negative_red).\
    apply(highlight_max).\
    set_table_attributes('style="font-size: 10px"')

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

Interactive outputs

Folium

import folium

m = folium.Map(
    location=[45.372, -121.6972],
    zoom_start=12,
    tiles='Stamen Terrain'
)

folium.Marker(
    location=[45.3288, -121.6625],
    popup='Mt. Hood Meadows',
    icon=folium.Icon(icon='cloud')
).add_to(m)

folium.Marker(
    location=[45.3311, -121.7113],
    popup='Timberline Lodge',
    icon=folium.Icon(color='green')
).add_to(m)

folium.Marker(
    location=[45.3300, -121.6823],
    popup='Some Other Location',
    icon=folium.Icon(color='red', icon='info-sign')
).add_to(m)


m

Make this Notebook Trusted to load map: File -> Trust Notebook

Stdout

# The ! causes this to run as a shell command
!jupyter -h

usage: jupyter [-h] [--version] [--config-dir] [--data-dir] [--runtime-dir]
               [--paths] [--json] [--debug]
               [subcommand]

Jupyter: Interactive Computing

positional arguments:
  subcommand     the subcommand to launch

optional arguments:
  -h, --help     show this help message and exit
  --version      show the versions of core jupyter packages and exit
  --config-dir   show Jupyter config dir
  --data-dir     show Jupyter data dir
  --runtime-dir  show Jupyter runtime dir
  --paths        show all Jupyter paths. Add --json for machine-readable
                 format.
  --json         output paths as machine-readable json
  --debug        output debug information about paths

Available subcommands: bundlerextension dejavu execute kernel kernelspec
migrate nbconvert nbextension notebook run server serverextension troubleshoot
trust

Formatting code cells

Scrolling cell outputs

for ii in range(40):
    print(f"this is output line {ii}")

this is output line 0
this is output line 1
this is output line 2
this is output line 3
this is output line 4
this is output line 5
this is output line 6
this is output line 7
this is output line 8
this is output line 9
this is output line 10
this is output line 11
this is output line 12
this is output line 13
this is output line 14
this is output line 15
this is output line 16
this is output line 17
this is output line 18
this is output line 19
this is output line 20
this is output line 21
this is output line 22
this is output line 23
this is output line 24
this is output line 25
this is output line 26
this is output line 27
this is output line 28
this is output line 29
this is output line 30
this is output line 31
this is output line 32
this is output line 33
this is output line 34
this is output line 35
this is output line 36
this is output line 37
this is output line 38
this is output line 39

Scrolling cell inputs

b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
b = "This line has no meaning"
print(b)

This line has no meaning