Plotly 한번에 제대로

Plotly 한번에 제대로 배우기

인터랙티브 그래프 생성JSON 데이터 형식으로 저장벡터 이미지, 래스터 이미지로 Export 가능

홈페이지: https://plotly.com/python/

Plotly 특징

import numpy as np

import pandas as pd

from urllib.request import urlopen

import json

import plotly.io as pio

import plotly.express as px

import plotly.graph_objects as go

import plotly.figure_factory as ff

from plotly.subplots import make_subplots

from plotly.validators.scatter.marker import SymbolValidator

Plotly 차트

산점도(Scatter Plots)

iris = px.data.iris()

iris

https://plotly.com/python/

sepal_length sepal_width petal_length petal_width species species_id

0 5.1 3.5 1.4 0.2 setosa 1

1 4.9 3.0 1.4 0.2 setosa 1

2 4.7 3.2 1.3 0.2 setosa 1

3 4.6 3.1 1.5 0.2 setosa 1

4 5.0 3.6 1.4 0.2 setosa 1

... ... ... ... ... ... ...

145 6.7 3.0 5.2 2.3 virginica 3

146 6.3 2.5 5.0 1.9 virginica 3

147 6.5 3.0 5.2 2.0 virginica 3

148 6.2 3.4 5.4 2.3 virginica 3

149 5.9 3.0 5.1 1.8 virginica 3

150 rows × 6 columns

0 0.5 1

1

2

3

4

5

6

7

petal_widt

petal_leng

th

fig = px.scatter(iris, x='petal_width', y='petal_length')

fig.show()

fig = px.scatter(iris, x='petal_width', y='petal_length', color='species')

fig.show()

0 0.5 1 1.5

1

2

3

4

5

6

7

petal_width

petal_leng

th

fig = px.scatter(iris, x='petal_width', y='petal_length',

color='species', size='sepal_length',

hover_data=['sepal_width'])

fig.show()

0 0.5 1 1.5

1

2

3

4

5

6

7

petal_width

petal_leng

th

total_bill tip sex smoker day time size

0 16.99 1.01 Female No Sun Dinner 2

1 10.34 1.66 Male No Sun Dinner 3



4 24.59 3.61 Female No Sun Dinner 4

... ... ... ... ... ... ... ...

239 29.03 5.92 Male No Sat Dinner 3

240 27.18 2.00 Female Yes Sat Dinner 2

241 22.67 2.00 Male Yes Sat Dinner 2

242 17.82 1.75 Male No Sat Dinner 2

243 18.78 3.00 Female No Thur Dinner 2


tips = px.data.tips()

tips

fig = px.scatter(tips, x='total_bill', y='tip', color='sex', facet_row='day')

fig.show()

10 20 300

5

100

5

100

5

100

5

10

total_bill

tiptip

tiptip

0 20 40

2

4

6

8

10

0 20 40 0

total_bill total_bill

tip

day=Sun day=Sat

fig = px.scatter(tips, x='total_bill', y='tip', color='sex', facet_col='day')

fig.show()

t = np.linspace(0, 10, 100)

y1 = np.random.randn(100).cumsum()


fig = go.Figure()

fig.add_trace(go.Scatter(x=t, y=y1, name='red', mode='markers', marker_color='darkred'))

fig.add_trace(go.Scatter(x=t, y=y2, name='blue', mode='markers', marker_color='darkblue'))

fig.update_traces(mode='markers', marker_line_width=1, marker_size=10)

fig.show()

0 2 4

−15

−10

−5

0

fig = go.Figure(data=go.Scatter(x=[1, 2, 3, 4, 5],

y=[11, 12, 13, 14, 15],

mode='markers',

marker=dict(size=[20, 40, 60, 80, 100],

color=[1, 2, 3, 4, 5])))

fig.show()

1 1.5 2 2.5 3

11

12

13

14

15

16

country continent year lifeExp pop gdpPercap iso_alpha iso_num

0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4





... ... ... ... ... ... ... ... ...

1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE 716






gapminder = px.data.gapminder()

gapminder

gapminder_2007 = gapminder.query("year == 2007")

gapminder_2007

country continent year lifeExp pop gdpPercap iso_alpha iso_nu

11 Afghanistan Asia 2007 43.828 31889923 974.580338 AFG

23 Albania Europe 2007 76.423 3600523 5937.029526 ALB

35 Algeria Africa 2007 72.301 33333216 6223.367465 DZA 1

47 Angola Africa 2007 42.731 12420476 4797.231267 AGO 2

59 Argentina Americas 2007 75.320 40301927 12779.379640 ARG 3

... ... ... ... ... ... ... ... .

1655 Vietnam Asia 2007 74.249 85262356 2441.576404 VNM 70

1667 West Bankand Gaza Asia 2007 73.422 4018332 3025.349798 PSE 27

1679 Yemen,Rep. Asia 2007 62.698 22211743 2280.769906 YEM 88

1691 Zambia Africa 2007 42.384 11746035 1271.211593 ZMB 89


2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

gdpPercap

lifeE

xp

fig = px.scatter(gapminder_2007,

x='gdpPercap', y='lifeExp',

size='pop', color='continent',

hover_name='country', log_x=True, size_max=60)

fig.show()



color='continent', trendline='lowess')

fig.show()

/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWar

pandas.util.testing is deprecated. Use the functions in the public API at pandas.t

0 10k 20k

40

45

50

55

60

65

70

75

80

85

gdpPercap

lifeE

xp

fig = px.scatter(gapminder,


size='pop', color='continent',

facet_col='year', facet_col_wrap=4)

fig.show()

0 50k 100k20

40

60

80

0 50k 100k 0

20

40

60

80

20

40

60

80

gdpPercap gdpPercap

lifeExp

lifeExp

lifeExp

year=1992 year=1997

year=1972 year=1977

year=1952 year=1957

산점도 매트릭스(Scatter Matrix)

45678

234

246

012

setosa

versicolor

virginica

4 5 6 7 81

2

3

2 3 4 2 4 6

sepal_length sepal_width petal_length

sepal_leng

thsepal_width

petal_leng

thpetal_width

species

species_id

fig = px.scatter_matrix(iris)

fig.show()

fig = px.scatter_matrix(iris,

dimensions=['petal_width', 'petal_length',

'sepal_width', 'sepal_length'],

color='species')

fig.show()

0

1

2

2

4

6

2

3

4

0 1 245678

2 4 6 2

petal_width petal_length

petal_width

petal_leng

thsepal_width

sepal_leng

th

0

20

40

0

5

10

Sun

Sat

Thur

Fri

0 20 40

2

4

6

0 5 10 Sun

total_bill tip

total_bill

tipda

ysize

fig = px.scatter_matrix(tips,

dimensions=['total_bill', 'tip', 'day', 'size'],

color='sex')

fig.show()

라인 플롯(Line Plots)

0 1 2 3 4 5

−1

−0.5

0

0.5

1

time

cos(t)

t = np.linspace(0, np.pi**2, 100)

fig = px.line(x=t, y=np.cos(t), labels={'x':'time', 'y':'cos(t)'})

fig.show()

gapminder_asia = gapminder.query("continent == 'Asia'")

gapminder_asia

country continent year lifeExp pop gdpPercap iso_alpha iso_n






... ... ... ... ... ... ... ...





1960 1970 1980

30

40

50

60

70

80

year

lifeE

xp

fig = px.line(gapminder_asia, x='year', y='lifeExp', color='country')

fig.show()

x = np.array([1, 2, 3, 4, 5])

y = np.array([1, 3, 2, 3, 1])

fig = go.Figure()

fig.add_trace(go.Scatter(x=x, y=y, name='linear', line_shape='linear'))

fig.add_trace(go.Scatter(x=x, y=y+10, name='spline', hoverinfo='text+name', line_shape='spline'))

fig.add_trace(go.Scatter(x=x, y=y+20, name='vhv', line_shape='vhv'))

fig.add_trace(go.Scatter(x=x, y=y+30, name='hvh', line_shape='hvh'))

fig.add_trace(go.Scatter(x=x, y=y+40, name='vh', line_shape='vh'))

fig.add_trace(go.Scatter(x=x, y=y+50, name='hv', line_shape='hv'))

fig.update_traces(hoverinfo='text+name', mode='lines+markers')

fig.update_layout(legend=dict(y=0.5, traceorder='reversed', font_size=16))

fig.show()

1 1.5 2 2.5 3

0

10

20

30

40

50

N = 100

x = np.linspace(0, 1, N)

y0 = np.random.randn(N) + 5

y1 = np.random.randn(N)

y2 = np.random.randn(N) - 5

fig = go.Figure()

fig.add_trace(go.Scatter(x=x, y=y0, mode='markers', name='markers'))

fig.add_trace(go.Scatter(x=x, y=y1, mode='lines+markers', name='lines+markers'))

fig.add_trace(go.Scatter(x=x, y=y2, mode='lines', name='lines'))

fig.show()

0 0.2 0.4

−8

−6

−4

−2

0

2

4

6

8

시계열(Time Series)

https://raw.githubusercontent.com/plotly/datasets/master/�nance-charts-apple.csv

Date AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjust

0 2015-02-17 127.489998 128.880005 126.919998 127.830002 63152400 122.9052

1 2015-02-18 127.629997 128.779999 127.449997 128.720001 44891700 123.7609

2 2015-02-19 128.479996 129.029999 128.330002 128.449997 37362400 123.5013

3 2015-02-20 128.619995 129.500000 128.050003 129.500000 48948400 124.5109

4 2015-02-23 130.020004 133.000000 129.660004 133.000000 70974100 127.8760

... ... ... ... ... ... ...

501 2017-02-10 132.460007 132.940002 132.050003 132.119995 20065500 132.1199

502 2017-02-13 133.080002 133.820007 132.750000 133.289993 23035400 133.2899

2017

aapl = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.c

aapl

fig = px.line(aapl, x='Date', y='AAPL.Close')

fig.show()

https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv

Apr 2015 Jul 2015 Oct 2015 Jan 2016

90

100

110

120

130

Date

AAPL

.Clo

se

fig = px.line(aapl, x='Date', y='AAPL.Close', range_x=['2016-02-01','2017-02-27'])

fig.show()

Mar 2016 May 2016 Jul 2016

90

100

110

120

130

Date

AAPL

.Clo

se

Apr 2015 Jul 2015 Oct 2015 Jan 2016

90

100

110

120

130

AAPL

.Clo

se

Date

fig = px.line(aapl, x='Date', y='AAPL.Close')

fig.update_xaxes(rangeslider_visible=True)

fig.show()

fig = go.Figure(data=[go.Candlestick(x=aapl['Date'],

open=aapl['AAPL.Open'],

high=aapl['AAPL.High'],

low=aapl['AAPL.Low'],

close=aapl['AAPL.Close'])])

fig.show()

Apr 2015 Jul 2015 Oct 2015 Jan 2016

90

100

110

120

130

Apr 2015 Jul 2015 Oct 2015 Jan 2016

90

100

110

120

130

fig = go.Figure(data=[go.Candlestick(x=aapl['Date'],

open=aapl['AAPL.Open'],

high=aapl['AAPL.High'],

low=aapl['AAPL.Low'],

close=aapl['AAPL.Close'],

increasing_line_color='red',

decreasing_line_color='blue')])

fig.show()

면적도(Area Plots)

1960 1970 19800

1B

2B

3B

4B

5B

6B

year

pop

fig = px.area(gapminder, x='year', y='pop',

color='continent', line_group='country')

fig.show()

fig = go.Figure()

fig.add_trace(go.Scatter(x=[1, 2, 3, 4, 5], y=[0, 2, 3, 5, 2], fill='tozeroy'))

fig.add_trace(go.Scatter(x=[1, 2, 3, 4, 5], y=[2, 3, 3, 2, 4], fill='tonexty'))

fig.show()

1 1.5 2 2.5 30

1

2

3

4

5

1 1.5 2 2.5 30

1

2

3

4

5

fig = go.Figure()

fig.add_trace(go.Scatter(x=[1, 2, 3, 4, 5], y=[0, 2, 3, 5, 2],

fill='tozeroy', mode='none'))


fill='tonexty', mode='none'))

fig.show()

fig = go.Figure()


1 1.5 2 2.5 3

0

1

2

3

4

5

fill='none', mode='lines', line_color='indigo'))


fill='tonexty', mode='lines', line_color='indigo'))

fig.show()

x = ['Winter', 'Spring', 'Summer', 'Fall']

fig = go.Figure()

fig.add_trace(go.Scatter(x=x, y=[30, 40, 50, 30],

hoverinfo='x+y',

mode='lines',

line=dict(width=0.5, color='skyblue'),

stackgroup='one'))


hoverinfo='x+y',

mode='lines',

line=dict(width=0.5, color='cyan'),

stackgroup='one'))


hoverinfo='x+y',

mode='lines',

line=dict(width=0.5, color='royalblue'),

stackgroup='one'))

fig.update_layout(yaxis_range=(0, 100))

fig.show()

Winter Spring0

20

40

60

80

100


hoverinfo='x+y',

mode='lines',

line=dict(width=0.5, color='purple'),

stackgroup='one'))

fig.update_layout(showlegend=True,

xaxis_type='category',

yaxis=dict(type='linear',

range=[1, 100],

ticksuffix='%'))

fig.show()

Winter Spring

20%

40%

60%

80%

100%

막대 차트(Bar Charts)

country continent year lifeExp pop gdpPercap iso_alpha iso_num

840 Korea,Rep. Asia 1952 47.453 20947571 1030.592226 KOR 410








gapminder_korea = gapminder.query("country == 'Korea, Rep.'")

gapminder_korea

fig = px.bar(gapminder_korea, x='year', y='pop')

fig.show()

1950 1960 1970 19800

10M

20M

30M

40M

50M

year

pop

1950 1960 1970 19800

10M

20M

30M

40M

50M

year

pop

fig = px.bar(gapminder_korea, x='year', y='pop',

hover_data=['lifeExp', 'gdpPercap'],

color='lifeExp', height=400)

fig.show()

fig = px.bar(tips, x='sex', y='total_bill', color='time')

fig.show()

Female0

500

1000

1500

2000

2500

3000

sex

total_bill

Female0

500

1000

1500

2000

sex

total_bill

fig = px.bar(tips, x='sex', y='total_bill',

color='smoker', barmode='group',

height=400)

fig.show()

Male Female0

200

400

600

800

Male Female Male

0

200

400

600

800

sex sex

total_bill

total_bill

day=Thur day=Fri

fig = px.bar(tips, x='sex', y='total_bill',

color='smoker', barmode='group',

facet_row='time', facet_col='day',

category_orders={'day': ['Thur', 'Fri', 'Sat', 'Sun'],

'time': ['Lunch', 'Dinner']})

fig.show()

fig = px.bar(tips, x='total_bill', y='day', orientation='h')

fig.show()

0 200 400 600 800

Sun

Sat

Thur

Fri

total_b

day

0 500 1000 1500

Female

Male

total_bill

sex

fig = px.bar(tips, x='total_bill', y='sex', color='day', orientation='h',

hover_data=['tip', 'size'], height=400)

fig.show()

colors = ['darkgray',] * 5

colors[1] = 'royalblue'

colors[3] = 'limegreen'

fig = go.Figure(data=[go.Bar(x=['A', 'B', 'C', 'D', 'E'],

y=[20, 14, 23, 25, 22],

marker_color=colors)])

fig.update_layout()

A B C0

5

10

15

20

25

fig = go.Figure(data=[go.Bar(x=[1, 3, 5.1, 6.9, 9.5],

y=[6, 8, 3, 5, 4],

width=[1, 2.1, 0.8, 2.6, 1.4])])

fig.show()

2 40

1

2

3

4

5

6

7

8

2018 2019

−30M

−20M

−10M

0

10M

20M

30M

40M

50M

years = ['2018', '2019', '2020']

fig = go.Figure()

fig.add_trace(go.Bar(x=years, y=[25000000, 20000000, 30000000],

base=[-25000000, -20000000, -30000000],

marker_color='crimson',

name='지출'))

fig.add_trace(go.Bar(x=years, y=[35000000, 40000000, 50000000],

base=0,

marker_color='limegreen',

name='수입'))

fig.show()

x = [1, 2, 3, 4]

y1 = np.random.randint(1, 10, 5)




fig = go.Figure()

fig.add_trace(go.Bar(x=x, y=y1))



( ( ))

0.5 1 1.5 2 2.50

5

10

15

20

25


fig.update_layout(barmode='relative')

fig.show()

mx = [['A', 'A', 'B', 'B'],

[5, 10, 15, 20]]

fig = go.Figure()

fig.add_trace(go.Bar(x=mx, y=y1))




fig.update_layout(barmode='relative')

fig.show()

5 10A

0

5

10

15

20

25

a = ['C', 'A', 'B', 'D']

fig = go.Figure()

fig.add_trace(go.Bar(x=a, y=y1))




fig.update_layout(barmode='stack',

xaxis={'categoryorder':'category ascending'})

fig.show()

A B0

5

10

15

20

25

D A0

5

10

15

20

25


xaxis={'categoryorder':'array',

'categoryarray':['D', 'A', 'C', 'B']})

fig.show()


xaxis={'categoryorder':'total descending'})

fig.show()

B C0

5

10

15

20

25

1 1.5 2 2.5 3

1.5

2

2.5

3

3.5

4

4.5

5

5.5


y=[3, 4, 2, 5, 2],

error_y=dict(type='data',

array=[1, 0.5, 0.6, 0.3, 0.5],

visible=True)))

fig.show()


y=[3, 4, 2, 5, 2],

error_y=dict(type='data',

1 1.5 2 2.5 3

1

2

3

4

5

_y ( yp ,

array=[1, 0.5, 0.6, 0.3, 0.5],

arrayminus=[0.3, 0.4, 1.2, 0.4, 1],

visible=True)))

fig.show()


y=[3, 4, 2, 5, 2],

error_y=dict(type='percent',

value=30,

visible=True)))

fig.show()

1 1.5 2 2.5 3

2

3

4

5

6

1 1.5 2 2.5 31.5

2

2.5

3

3.5

4

4.5

5

5.5


y=[3, 4, 2, 5, 2],

error_y=dict(type='percent',

symmetric=False,

value=10,

valueminus=20)))

fig.show()


y=[3, 4, 2, 5, 2],

1 2 3

2

2.5

3

3.5

4

4.5

5

error_x=dict(type='percent',

symmetric=False,

value=10,

valueminus=20)))

fig.show()

fig = px.bar(gapminder, x='continent', y='pop', color='continent',

animation_frame='year', animation_group='country',

range_y=[0, 4000000000])

fig.show()

Asia Europe Africa0

0.5B

1B

1.5B

2B

2.5B

3B

3.5B

4B

year=1952

1952 1957 1962 1967 1972 1977

continent

pop

▶ ◼

박스 플롯(Box Plots)

Sun Sat

10

20

30

40

50

day

total_bill

fig = px.box(tips, x='day', y='total_bill')

fig.show()

fig = px.box(tips, x='day', y='total_bill', points='all')

fig.show()

Sun Sat

10

20

30

40

50

day

total_bill

fig = px.box(tips, x='day', y='total_bill', color='sex')

fig.show()

Sun Sat

10

20

30

40

50

day

total_bill

Sun Sat

10

20

30

40

50

day

total_bill

fig = px.box(tips, x='day', y='total_bill', color='sex',

notched=True, hover_data=['time'])

fig.show()

x1 = np.random.randn(50)

x2 = np.random.randn(50) + 5

fig = go.Figure()

fig.add_trace(go.Box(x=x1))


fig.show()

−2 −1 0 1 2

trace 0

trace 1

−2 −1 0 1 2

trace 0

trace 1

fig = go.Figure()


fig.add_trace(go.Box(x=x2, boxpoints='all', jitter=0.3, pointpos=-1.8))

fig.show()

fig = go.Figure()

−2 −1 0 1 2

trace 0

trace 1

fig go.Figure()

fig.add_trace(go.Box(x=x1, marker_color='olive', line_color='green'))

fig.add_trace(go.Box(x=x2, marker_color='skyblue', line_color='royalblue',

boxpoints='all', jitter=0.3, pointpos=-1.8))

fig.show()

바이올린 플롯(Violin Plot)

fig = px.violin(tips, y='tip')

fig.show()

0

2

4

6

8

10tip

0

2

4

6

8

10

tip

fig = px.violin(tips, y='tip', box=True, points='all')

fig.show()

fig = px.violin(tips, y='tip', x='sex',

color='day', box=True, points='all',

hover_data=tips.columns)

fig.show()

Female

0

2

4

6

8

10

sex

tip

히스토그램(Histogram)

fig = px.histogram(tips, x='total_bill')

fig.show()

10 200

5

10

15

20

25

30

total_b

coun

t

0 10 200

10

20

30

40

50

60

70

total_b

coun

t

fig = px.histogram(tips, x='total_bill', nbins=20)

fig.show()

fig = px.histogram(tips, x='total_bill', histnorm='probability density')

fig.show()

10 200

0.01

0.02

0.03

0.04

0.05

0.06

total_b

coun

t

10 209

1

2

3

4

5

6

789

10

2

3

Total B

coun

t

fig = px.histogram(tips, x='total_bill',

labels={'total_bill':'Total Bill'},

opacity=0.7, log_y=True,

color_discrete_sequence=['deepskyblue'])

fig.show()

fig = px.histogram(tips, x='total_bill', color='sex',

color_discrete_sequence=['crimson', 'deepskyblue'])

fig.show()

10 20 300

5

10

15

20

25

30

total_bill

coun

t

fig.show()

fig = px.histogram(tips, x='total_bill', y='tip', histfunc='avg')

fig.show()

10 200

2

4

6

8

10

total_b

avg

of t

ip

10 200

5

10

15

20

25

30

total_bill

coun

t

fig = px.histogram(tips, x='total_bill',

color='sex', marginal='rug',

hover_data=tips.columns)

fig.show()

fig = px.histogram(iris, x='petal_length', color='species', marginal='box')

fig.show()

1 2 3 40

10

20

30

40

petal_length

coun

t

40 50 600

10

20

30

40

lifeExp

coun

t

fig = px.histogram(gapminder_2007,

x='lifeExp', color='continent',

marginal='rug', hover_name='country')

fig.show()

n1 = np.random.randn(1000)

n2 = np random randn(1000) + 1

0 10 20 30 40

−3

−2

−1

0

1

2

n2 = np.random.randn(1000) + 1

fig = go.Figure(data=[go.Histogram(y=n1)])

fig.show()

fig = go.Figure()

fig.add_trace(go.Histogram(x=n1))


fig.update_layout(barmode='overlay')

fig.update_traces(opacity=0.7)

fig.show()

−3 −2 −1 00

10

20

30

40

50

60

70

80

−3 −2 −1 00

20

40

60

80

100

120

140

160

fig = go.Figure()



fig.update_layout(barmode='stack')

fig.show()

fig = go.Figure()

fig.add_trace(go.Histogram(x=n1, cumulative_enabled=True))

fig.add_trace(go.Histogram(x=n2, cumulative_enabled=True))

fig.update_layout(barmode='stack')

fig.show()

−3 −2 −1 00

500

1000

1500

2000

fig1 = px.bar(tips, x='day', y='tip', height=300)

fig1.show()

fig2 = px.histogram(tips, x='day', y='tip', histfunc='sum', height=300)

fig2.show()

Sun Sat0

100

200

day

tip

Sun Sat0

100

200

day

sum

of

tip

분포도(Distribution Plot)

0

0.1

0.2

0.3

0.4

−3 −2 −1 0

x = np.random.randn(1000)

hist_data = [x]

group_labels = ['distplot']

fig = ff.create_distplot(hist_data, group_labels)

fig.show()

x1 = np.random.randn(200) - 5

x2 = np random randn(200) - 2

0

0.2

0.4

0.6

−8 −6 −4 −2 0

x2 np.random.randn(200) 2



hist_data = [x1, x2, x3, x4]

group_labels = ['Graoup A', 'Group B', 'Group C', 'Group D']

fig = ff.create_distplot(hist_data, group_labels,

bin_size=[.1, .25, .5, 1])

fig.show()

fig = px.scatter(iris, x='petal_length', y='petal_width',

marginal_x='histogram', marginal_y='rug')

fig.show()

1 2 3 40

0.5

1

1.5

2

2.5

petal_length

petal_width

1 2 3 4 50

0.5

1

1.5

2

2.5

petal_length

petal_width

fig = px.scatter(iris, x='petal_length', y='petal_width', color='species',

marginal_x='box', marginal_y='violin')

fig.show()

히트맵(Heatmaps)

n np random randint(1 100 size (3 7))

0 1 2 3−0.5

0

0.5

1

1.5

2

2.5

n = np.random.randint(1, 100, size=(3, 7))

fig = go.Figure(data=go.Heatmap(z=n))

fig.show()

w = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']

t = ['Morning', 'Afternoon', 'Evening']

n = np.random.randint(1, 100, size=(3, 7))

fig = go.Figure(data=go.Heatmap(x=w, y=t, z=n))

fig.show()

Sun Mon Tue Wed

Morning

Afternoon

Evening

Sun Mon Tue Wed

Morning

Afternoon

Evening

78 91 56 49

33 51 77 9

80 85 34 25

fig = ff.create_annotated_heatmap(x=w, y=t, z=n)

fig.show()

fig = px.density_heatmap(iris, x='petal_length', y='petal_width')

fig.show()

0 1 2 3 4

0

0.5

1

1.5

2

2.5

petal_length

petal_width

fig = px.density_heatmap(iris, x='petal_length', y='petal_width',

nbinsx=20, nbinsy=20,

color_continuous_scale='viridis')

fig.show()

1 2 3 40

0.5

1

1.5

2

2.5

petal_length

petal_width

1 2 3 4 50

0.5

1

1.5

2

2.5

petal_length

petal_width



marginal_x='histogram', marginal_y='histogram')

fig.show()



facet_col='species')

fig.show()

2 4 60

0.5

1

1.5

2

2.5

2 4

petal_length petal_len

petal_width

species=setosa species=vers

0 1 2 3 4

0

0.5

1

1.5

2

2.5

petal_length

petal_width


z='sepal_length', histfunc='avg')

fig.show()


marginal_x='box', marginal_y='violin')

fig.show()

0 1 2 3 4 5

−0.5

0

0.5

1

1.5

2

2.5

3

petal_length

petal_width

파이 차트(Pie Charts)

fig = px.pie(gapminder_asia, values='pop', names='country')

fig.show()

327.6%

5.83%4.4%3.69%3.57%2.15%2.07%

113

India27.6%

Indonesia

5.83%

Japan

4.4%

Pakist

an3.

69%

Bang

lade

sh3.

57%

Viet

nam

2.15

%Ph

ilipp

ines

2.07

% Thailand1.77%

Iran1.66%

Korea, Rep.

1.44%

Myanm

ar

1.32%

Taiwan

0.664%0.651%

fig = px.pie(gapminder_asia, values='pop', names='country',

hover_data=['lifeExp'], labels={'lifeExp':'life expectancy'})

fig.update_traces(textposition='inside', textinfo='percent+label', textfont_size=14)

fig.show()

fig = px.pie(tips, values='tip', names='day',

color_discrete_sequence=px.colors.sequential.Viridis)

fig.show()

33.8%

23.5%

fig = px.pie(tips, values='tip', names='day',

color_discrete_sequence=px.colors.sequential.Viridis,

hole=.4)

fig.show()

33.8%

23.5%

247.39

171.83

fig.update_traces(hoverinfo='label+percent',

textinfo='value', textfont_size=20,

marker=dict(colors=colors, line=dict(color='black', width=2)))

fig.show()

Sunburst Chart

data = dict(

character=['Eve', 'Cain', 'Seth', 'Enos', 'Noam', 'Abel', 'Awan', 'Enoch', 'Azura'],

parent=['', 'Eve', 'Eve', 'Seth', 'Seth', 'Eve', 'Eve', 'Awan', 'Eve'],

value=[10, 14, 12, 10, 2, 6, 6, 4, 4])

fig = px.sunburst(data, names='character',

parents='parent',

values='value')

fig.show()

Eve

S

Cain

Awan

Abel AEnoch

폴라 차트(Polar Charts)

direction strength frequency

0 N 0-1 0.5

1 NNE 0-1 0.6

2 NE 0-1 0.5

3 ENE 0-1 0.4

4 E 0-1 0.4

... ... ... ...

123 WSW 6+ 0.1

124 W 6+ 0.9

125 WNW 6+ 2.2

126 NW 6+ 1.5

127 NNW 6+ 0.2


wind = px.data.wind()

wind

fig = px.scatter_polar(wind, r='frequency', theta='direction')

fig.show()

N

SSSW

SW

WSW

W

WNW

NW

NNW

0

fig = px.scatter_polar(wind, r='frequency', theta='direction',

color='strength', symbol='strength', size='frequency',

color_discrete_sequence=px.colors.sequential.Blues_r)

fig.show()

N

SSSW

SW

WSW

W

WNW

NW

NNW

0 0.5

N

SSSW

SW

WSW

W

WNW

NW

NNW

0 0.5

fig = px.line_polar(wind, r='frequency', theta='direction',

color='strength', line_close=True, template='plotly_dark',

color_discrete_sequence=px.colors.sequential.deep)

fig.show()

fig = px.bar_polar(wind, r='frequency', theta='direction',

color='strength', template='plotly_dark',

color_discrete_sequence=px.colors.sequential.deep)

fig.show()

N

SSSW

SW

WSW

W

WNW

NW

NNW

0 2 4

병렬 다이어그램(Parallel Diagram)

fig = px.parallel_categories(tips)

fig.show()

Female

sex

Male

No

smoker

Yes

Sun

day

Sat

Thur

Fri

Female

sex

Male

No

smoke

Yes

fig = px.parallel_categories(tips,

dimensions=['sex', 'smoker', 'day'],

color='size',

color_continuous_scale=px.colors.sequential.Viridis)

fig.show()

fig = px.parallel_coordinates(iris, color='species_id',

dimensions=['sepal_width', 'sepal_length',

'petal_width', 'petal_length'],

color_continuous_scale=px.colors.diverging.Armyrose,

color_continuous_midpoint=2)

fig.show()

2

2.5

3

3.5

4

sepal_width

4.4

2

4.5

5

5.5

6

6.5

7

7.5

sepal_length

7.9

4.3

덴드로그램(Dendrograms)

8 0 14 1 12 6 10 13 7 11 2 4 90

0.5

1

1.5

2

x = np.random.rand(15, 12)

fig = ff.create_dendrogram(x)

fig.update_layout(width=800, height=400)

fig.show()

fig = ff.create_dendrogram(x, color_threshold=1.5)


fig.show()

8 0 14 1 12 6 10 13 7 11 2 4 90

0.5

1

1.5

2

맵(Maps)

centroid_lat centroid_lon car_hours peak_hour

0 45.471549 -73.588684 1772.750000 2

1 45.543865 -73.562456 986.333333 23

2 45.487640 -73.642767 354.750000 20

3 45.522870 -73.595677 560.166667 23

4 45.453971 -73.738946 2836.666667 19

... ... ... ... ...

244 45.547171 -73.556258 951.416667 3

245 45.546482 -73.574939 795.416667 2

246 45.495523 -73.627725 425.750000 8

247 45.521199 -73.581789 1044.833333 17

248 45.532564 -73.567535 694.916667 5


carshare = px.data.carshare()

carshare

fig = px.scatter_mapbox(carshare, lat='centroid_lat', lon='centroid_lon',

color='peak_hour', size='car_hours',

color_continuous_scale=px.colors.cyclical.Edge,

size_max=15, zoom=10,

mapbox_style="carto-positron")

fig.show()

https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-�ps.json

{'geometry': {'coordinates': [[[-86.496774, 32.344437], [-86.717897, 32.402814], [-86.814912, 32.340803], [-86.890581, 32.502974], [-86.917595, 32.664169], [-86.71339, 32.661732], [-86.714219, 32.705694], [-86.413116, 32.707386], [-86.411172, 32.409937], [-86.496774, 32.344437]]], 'type': 'Polygon'}, 'id': '01001', 'properties': {'CENSUSAREA': 594.436, 'COUNTY': '001',

with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json')

counties = json.load(response)

counties["features"][0]

https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json

'GEO_ID': '0500000US01001', 'LSAD': 'County', 'NAME': 'Autauga', 'STATE': '01'}, 'type': 'Feature'}

https://raw.githubusercontent.com/plotly/datasets/master/�ps-unemp-16.csv

fips unemp

0 01001 5.3

1 01003 5.4

2 01005 8.6

3 01007 6.6

4 01009 5.5

unemp = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",

dtype={"fips": str})

unemp.head()

fig = px.choropleth_mapbox(unemp, geojson=counties, locations='fips', color='unemp',

color_continuous_scale='blues',

range_color=(0, 12),

mapbox_style='carto-positron',

zoom=3, center={'lat': 37, 'lon': -95},

opacity=0.5,

labels={'unemp': 'unemployment rate'})

fig.update_layout(margin={'r':0, 't':0, 'l':0, 'b':0})

fig.show()

https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv

https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv

City State Population lat lon

1 Perris California 72326 33.782519 -117.228648

11 Buena Park California 82882 33.867514 -117.998118

19 Westminster California 91739 33.751342 -117.993992

26 Rancho Palos Verdes California 42448 33.744461 -118.387017

36 Buffalo New York 258959 42.886447 -78.878369

... ... ... ... ... ...

981 Stanton California 38623 33.802516 -117.993116

991 Santa Clara California 120245 37.354108 -121.955236

992 Oceanside California 172794 33.195870 -117.379483

993 Lake Forest California 79312 33.646966 -117.689218

999 Compton California 97877 33.895849 -118.220071


us_cities = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.

us_cities = us_cities.query("State in ['California', 'New York']")

us_cities

fig = px.line_mapbox(us_cities, lat='lat', lon='lon',

color='State', zoom=2, height=400)

fig.update_layout(mapbox_style='stamen-terrain',

mapbox_zoom=2, mapbox_center_lat=40,

margin={'r':0, 't':0, 'l':0, 'b':0})

fig.show()

https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv

fig = go.Figure(go.Scattergeo())

fig.update_geos(projection_type='orthographic')

fig.update_layout(height=300, margin={'r':0, 't':0, 'l':0, 'b':0})

fig.show()

fig = px.line_geo(gapminder_2007, locations='iso_alpha',

color='continent', projection='orthographic')

fig.show()

fig = go.Figure(go.Scattergeo())

fig.update_geos(projection_type='natural earth')

fig.update_layout(height=300, margin={'r':0, 't':0, 'l':0, 'b':0})

fig.show()

fig = px.scatter_geo(gapminder, locations='iso_alpha',

color='continent', hover_name='country', size='pop',

animation_frame='year',

projection='natural earth')

fig.show()

year=1952

1952 1957 1962 1967 1972 1977 198

▶ ◼

fig = px.choropleth(gapminder_2007, locations='iso_alpha',

color='lifeExp', hover_name='country',


fig.show()

fig = px.choropleth(gapminder, locations='iso_alpha',

color='lifeExp', hover_name='country',

color_continuous_scale=px.colors.sequential.Viridis,

animation_frame='year')

fig show()

year=1952

1952 1957 1962 1967 1972 1977

▶ ◼

fig.show()

https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv

gdp = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_cod

gdp

https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv

COUNTRY GDP (BILLIONS) CODE

0 Afghanistan 21.71 AFG

1 Albania 13.40 ALB

2 Algeria 227.80 DZA

3 American Samoa 0.75 ASM

4 Andorra 4.80 AND

... ... ... ...

217 Virgin Islands 5.08 VGB

218 West Bank 6.64 WBG

219 Yemen 45.45 YEM

220 Zambia 25.61 ZMB

221 Zimbabwe 13.74 ZWE


2014 Global GDP

fig = go.Figure(data=go.Choropleth(

locations = gdp['CODE'],

z = gdp['GDP (BILLIONS)'],

text = gdp['COUNTRY'],

colorscale = 'solar_r',

autocolorscale=False,

reversescale=True,

marker_line_color='darkgray',

marker_line_width=0.5,

colorbar_tickprefix = '$',

colorbar_title = 'GDP<br>Billions US $'

))

fig.update_layout(

title_text='2014 Global GDP',

geo = dict(showframe=False,

showcoastlines=False,

projection_type='equirectangular')

)

fig.show()

https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv

Date Latitude Longitude Magnitude

0 01/02/1965 19.2460 145.6160 6.0

1 01/04/1965 1.8630 127.3520 5.8

2 01/05/1965 -20.5790 -173.9720 6.2

3 01/08/1965 -59.0760 -23.5570 5.8

4 01/09/1965 11.9380 126.4270 5.8

... ... ... ... ...

23407 12/28/2016 38.3917 -118.8941 5.6

23408 12/28/2016 38.3777 -118.8957 5.5

23409 12/28/2016 36.9179 140.4262 5.9

23410 12/29/2016 -9.0283 118.6639 6.3

23411 12/30/2016 37.3973 141.4103 5.5


earthquakes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k

earthquakes

fig = px.density_mapbox(earthquakes, lat='Latitude', lon='Longitude',

z='Magnitude', radius=10,

center=dict(lat=0, lon=180), zoom=0,

mapbox_style='stamen-terrain')

fig.show()

https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv

3D 차트(3D Charts)

fig = px.line_3d(gapminder_asia, x='gdpPercap', y='pop', z='year', color='country')

fig.show()

fig = px.scatter_3d(iris, x='petal_length', y='petal_width', z='sepal_width',

color='sepal_length', symbol='species',

size='sepal_length', opacity=0.6)

fig.show()

fig = px.scatter_3d(gapminder, x='year', y='continent', z='pop',

size='gdpPercap', color='lifeExp',

hover_data=['country'])

fig.update_layout(scene_zaxis_type='log')

fig.show()

간트 차트(Gantt Charts): https://plotly.com/python/gantt/테이블(Tables): https://plotly.com/python/table/생키 다이어그램(Sankey Diagram): https://plotly.com/python/sankey-diagram/트리맵(Treemap): https://plotly.com/python/treemaps/트리플롯(Tree-plots): https://plotly.com/python/tree-plots/3차 플롯(Ternary Plots): https://plotly.com/python/ternary-plots/3차 오버레이(Ternary Overlay): https://plotly.com/python/ternary-scatter-contour/3차 등고선(Ternary Contours): https://plotly.com/python/ternary-contour/이미지쇼(Image Show): https://plotly.com/python/imshow/Quiver Plots: https://plotly.com/python/quiver-plots/스트림라인 플롯(Streamline Plots): https://plotly.com/python/streamline-plots/카펫 플롯(Carpet Plots): https://plotly.com/python/carpet-plot/카펫 등고선(Carpet Contour Plot): https://plotly.com/python/carpet-contour/카펫 산점도(Carpet Scatter Plot): https://plotly.com/python/carpet-scatter/네트워크 그래프(Network Graphs): https://plotly.com/python/network-graphs/깔대기 차트(Funnel Chart): https://plotly.com/python/funnel-charts/등고선 플롯(Contour Plot): https://plotly.com/python/contour-plots/2D 히스토그램 등고선(2D Histogram Contour): https://plotly.com/python/2d-histogram-contour/Trisurf Plots: https://plotly.com/python/trisurf/3D Mesh Plots: https://plotly.com/python/3d-mesh/3D Isosurface Plots: https://plotly.com/python/3d-isosurface-plots/3D Volume Plots: https://plotly.com/python/3d-volume-plots/3D Cone Plots: https://plotly.com/python/cone-plot/3D Streamtube Plots: https://plotly.com/python/streamtube-plot/3D Camera Controls: https://plotly.com/python/3d-camera-controls/

기타 차트

Plotly 스타일

https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv

데이터 레이블(Data Label)

data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv')

fig = go Figure(data=go Scatter(x=data['Postal']

https://plotly.com/python/gantt/

https://plotly.com/python/table/

https://plotly.com/python/sankey-diagram/

https://plotly.com/python/treemaps/

https://plotly.com/python/tree-plots/

https://plotly.com/python/ternary-plots/

https://plotly.com/python/ternary-scatter-contour/

https://plotly.com/python/ternary-contour/

https://plotly.com/python/imshow/

https://plotly.com/python/quiver-plots/

https://plotly.com/python/streamline-plots/

https://plotly.com/python/carpet-plot/

https://plotly.com/python/carpet-contour/

https://plotly.com/python/carpet-scatter/

https://plotly.com/python/network-graphs/

https://plotly.com/python/funnel-charts/

https://plotly.com/python/contour-plots/

https://plotly.com/python/2d-histogram-contour/

https://plotly.com/python/trisurf/

https://plotly.com/python/3d-mesh/

https://plotly.com/python/3d-isosurface-plots/

https://plotly.com/python/3d-volume-plots/

https://plotly.com/python/cone-plot/

https://plotly.com/python/streamtube-plot/

https://plotly.com/python/3d-camera-controls/

https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv

AL

AK

AZ

AR

CA

CO

CT

DE

DC

FL GA

HI

ID IL IN IA KS

KY

LA ME

MD

MA

MI

MN

MS

MO

MT

NE

0

5M

10M

15M

20M

25M

30M

35M

40M

Population of USA States

fig go.Figure(data go.Scatter(x data[ Postal ],

y=data['Population'],

mode='markers',

marker_color=data['Population'],

text=data['State']))

fig.update_layout(title='Population of USA States')

fig.show()

색상 차원(Color Dimension)

fig = go.Figure(data=go.Scatter(

y = np.random.randn(400).cumsum(),

mode='markers',

marker=dict(

size=12,

color=np.random.randn(400).cumsum(),

colorscale='YlOrRd',

showscale=True

)

))

fig.show()

0 50 100 150 200

−10

−5

0

5

10

템플릿(Templates)

Templates configuration----------------------- Default template: 'plotly' Available templates: ['ggplot2', 'seaborn', 'simple_white', 'plotly', 'plotly_white', 'plotly_dark', 'presentation', 'xgridoff', 'ygridoff', 'gridon', 'none']

pio.templates

gapminder_2002 = gapminder.query("year == 2002")

for template in ['ggplot2', 'seaborn', 'simple_white', 'plotly', 'plotly_white', 'plotly_dark', 'pr


x='gdpPercap', y='lifeExp', size='pop', color='continent',

log_x=True, size_max=60,

template=template, title="Template: '%s'" % template)

fig.show()

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'gg

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'sea

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'simple_white'

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'plotly'

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'plotly_white'

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'plotly_dark'

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5 6 7

40

50

60

70

80

Template: 'pres

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'xg

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'yg

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'gr

gdpPercap

lifeE

xp

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'n

gdpPercap

lifeE

xp

pio.templates.default = "plotly_white"


x='gdpPercap', y='lifeExp', size='pop', color='continent',

log_x=True, size_max=60,

title="Template: '%s'" % template)

fig.show()

2 3 4 5 6 7 8 91000

2 3 4 5

40

50

60

70

80

Template: 'none'

gdpPercap

lifeE

xp

구성(Con�guration)

1 1.5 2 2.5 30

5

10

15

20

25

30

Click to enter Plot title

Click to enter X a

Clic

k to

ent

er Y

axi

s tit

le

fig = go.Figure()

config = dict({

'scrollZoom': True,

'displayModeBar': True,

'editable': True

})

fig.add_trace(go.Scatter(x = [1, 2, 3, 4, 5],

y = [1, 3, 7, 15, 31]))

fig.show(config=config)

높이, 너비 및 여백 조정

fi tt (ti 't t l bill' 'ti ' f t l ' '

https://plotly.com/

0 10 20 30 40 50

2

4

6

8

10

0 10 20 30

total_bill total_bill

tip

sex=Female sex=Male

fig = px.scatter(tips, x='total_bill', y='tip', facet_col='sex',

width=800, height=400)

fig.update_layout(margin=dict(l=10, r=20, t=30, b=40),

paper_bgcolor='skyblue')

fig.show()

fig = go.Figure()

fig.add_trace(go.Scatter(x=np.arange(1, 10),

y=np.arange(10, 20)))

fig.update_layout(autosize=False, width=400, height=400,

margin=dict(l=10, r=20, t=30, b=40, pad=4),

paper_bgcolor='skyblue')

fig.show()

2 4 6 8

10

11

12

13

14

15

16

17

18

A B C D

long long long long

long long long

short

long long

Y ax

is T

itle

fig = go.Figure()

fig.add_trace(go.Bar(x=['A', 'B', 'C', 'D'], y=[2, 3, 1, 4]))

fig.update_layout(autosize=False, width=400, height=400,

yaxis=dict(

title_text='Y axis Title',

ticktext=['long long long long',

'long long long',

'short',

'long long'],

tickvals=[1, 2, 3, 4],

tickmode='array',

titlefont=dict(size=40)

))

fig.update_yaxes(automargin=True)

fig.show()

눈금(Tick) 형식 지정

fig = go.Figure(go.Scatter(x = np.arange(1, 10),

y = np.random.randn(10).cumsum()))

fig.update_layout(xaxis = dict(tickmode = 'linear',

tick0 = 0.8, dtick = 0.4))

fig.show()

0.8 1.2 1.6 2 2.4 2.8 3.2 3.6 4 4.4 4.8

0.5

1

1.5

2

2.5

3

3.5

4

4.5

fig.update_layout(xaxis = dict(

tickmode = 'array',

tickvals = [1, 3, 5, 7, 9],

ticktext = ['One', 'Three', 'Five', 'Seven', 'Nine']))

fig.show()

One Three Five

0.5

1

1.5

2

2.5

3

3.5

4

4.5

One Three Five

50%

100%

150%

200%

250%

300%

350%

400%

450%

fig.update_layout(yaxis_tickformat = '%')

fig.show()

fig = go.Figure(go.Scatter(x = np.arange(1, 10),

y = np.random.randint(10000, 100000, size=10)))

fig.update_layout(yaxis=dict(showexponent='all', exponentformat='e'))

fig.show()

1 2 3 4 5

2e+4

3e+4

4e+4

5e+4

6e+4

7e+4

8e+4

9e+4

2015-Apr-01(Wed) 2015-Jul-01(Wed) 2015-Oct-01(Thu) 2016-Jan-01(Fri) 201

90

100

110

120

130

fig = go.Figure(go.Scatter(x=aapl['Date'], y=aapl['AAPL.Close']))

fig.update_layout(xaxis_tickformat='%Y-%b-%d(%a)')

fig.show()

fig = go.Figure(go.Scatter(x=aapl['Date'], y=aapl['AAPL.Close']))

fig.update_layout(

xaxis_tickformatstops=[

dict(dtickrange=[None 1000] value="%H:%M:%S %L")

Apr 2015 Jul 2015 Oct 2015 Jan 2016

90

100

110

120

130

dict(dtickrange [None, 1000], value %H:%M:%S.%L ),

dict(dtickrange=[1000, 60000], value="%H:%M:%S"),

dict(dtickrange=[60000, 3600000], value="%H:%M"),

dict(dtickrange=[3600000, 86400000], value="%H:%M"),

dict(dtickrange=[86400000, 604800000], value="%b-%e"),

dict(dtickrange=[604800000, 'M1'], value="%Y-%b"),

dict(dtickrange=["M12", None], value="%Y")

])

fig.show()

fig = go.Figure(go.Bar(x=['A', 'B', 'C'], y=[1, 2, 3]))

fig.update_xaxes(showgrid=True,

ticks='outside',

tickson='boundaries',

ticklen=20)

fig.show()

A B0

0.5

1

1.5

2

2.5

3

글꼴, 제목, 범례 항목 및 축 제목 설정

1 2 3 40

0.5

1

1.5

2

2.5

petal_length

petal_width

fig = px.scatter(iris, x='petal_length', y='petal_width', color='species')

fig.show()

1 2 3 40

0.5

1

1.5

2

2.5

Petal Length (cm)

Peta

l Wid

th (

cm)

fig = px.scatter(iris,

x='petal_length', y='petal_width', color='species',

labels={'petal_length':'Petal Length (cm)',

'petal_width':'Petal Width (cm)',

'species': "Species"})

fig.show()

fig = go.Figure(go.Scatter(y=[3, 1, 4, 2, 5],

x=['Mon', 'Tue', 'Wed', 'Thu', 'Fri']))

fig.update_layout(title={'text': 'Title',

'y':0.8, 'x':0.5,

'xanchor':'center', 'yanchor':'top'})

fig.show()

Mon Tue Wed

1

1.5

2

2.5

3

3.5

4

4.5

5Title

축(Axes)

8 91

2 3 4 5 6 7

1

2

5

10

2

5

100

2

5

1000

2

x

y

x = np.arange(50)

fig = px.scatter(x=x, y=x**2, log_x=True, log_y=True)

fig.show()

1 2 30

10

20

30

40

50

x

y

fig = px.bar(x=[1, 2, 3, 4, 5], y=[10, 40, 30, 20, 50])

fig.update_layout(xaxis_type='category')

fig.show()

fig = go.Figure()

fig.add_trace(go.Box(x=np.random.randint(1, 10, 4),

y=[['A', 'A', 'A', 'A'],

['a1', 'a1', 'a1', 'a1']],

name='a1',

orientation='h'))


y=[['A', 'A', 'A', 'A'],

['a2', 'a2', 'a2', 'a2']],

name='a2',

orientation='h'))


y=[['B', 'B', 'B', 'B'],

['b1', 'b1', 'b1', 'b1']],

name='b1',

orientation='h'))


y=[['B', 'B', 'B', 'B'],

['b2', 'b2', 'b2', 'b2']],

name='b2',

orientation='h'))

fig.update_layout()

2 3 4 5

a1

a2

b1

b2

A

B

fig.show()

fig = px.scatter(iris, x='petal_width', y='petal_length', facet_col='species')

fig.update_xaxes(ticks='inside')

fig.update_yaxes(ticks='outside', col=1)

fig.show()

0 1 2

1

2

3

4

5

6

7

0 1

petal_width petal_w

petal_length

species=setosa species=ver

0 1 2

2

4

6

0 1

petal_width petal_w

petal_length


fig.update_yaxes(nticks=5)

fig.show()

fig.update_yaxes(tick0=0.5, dtick=0.25)

fig.show()

0 1 20.751

1.251.51.752

2.252.52.753

3.253.53.754

4.254.54.755

5.255.55.756

6.256.56.757

7.25

0 1

petal_width petal_w

petal_length


0 1 2

1.2

3.5

4.3

0 1

petal_width petal_w

petal_length


fig.update_yaxes(tickvals=[1.2, 3.5, 4.3, 7.5])

fig.show()

fig.update_xaxes(ticks='inside', tickwidth=4, tickcolor='orange', ticklen=5)

fig.update_yaxes(ticks='outside', tickwidth=2, tickcolor='purple', ticklen=10, col=1)

fig.show()

0 1 2

1.2

3.5

4.3

0 1

petal_width petal_w

petal_length


fig.show()

fig.update_xaxes(showticklabels=False)

fig.update_yaxes(showticklabels=True)

fig.show()

1.2

3.5

4.3

1.2

3.5

4.3

petal_width petal_w

petal_length


Female

Male

0

50

100

150

200

250

300

sex

sum

of

tip

smoker=No

fig = px.histogram(tips, x='sex', y='tip', histfunc='sum', facet_col='smoker')

fig.update_xaxes(tickangle=45, tickfont=dict(family='Arial', color='orange', size=14))

fig.show()

fig = px.line(y=np.random.randn(20))

fig.update_layout(xaxis_showgrid=False, yaxis_showgrid=False)

fig.show()

0 2 4 6 8

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5y

0 2 4 6 8

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

y

fig.update_layout(

xaxis=dict(showgrid=False, zeroline=False),

yaxis=dict(showgrid=False, zeroline=False),

)

fig.show()

0 1 2

1

2

3

4

5

6

7

0 1

petal_width petal_w

petal_length



fig.update_xaxes(showgrid=False, zeroline=False)

fig.update_yaxes(showgrid=False, zeroline=False)

fig.show()

fig = px.histogram(tips, x='sex', y='tip', histfunc='sum', facet_col='smoker')

fig.update_xaxes(showgrid=False, linewidth=3, linecolor='red')

fig.update_yaxes(showgrid=False, linewidth=3, linecolor='red')

fig.show()

Female Male0

50

100

150

200

250

300

sex

sum

of

tip

smoker=No

Female Male0

50

100

150

200

250

300

sex

sum

of

tip

smoker=No

fig.update_xaxes(showgrid=False, linewidth=3, linecolor='red', mirror=True)

fig.update_yaxes(showgrid=False, linewidth=3, linecolor='red', mirror=True)

fig.show()


fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='orange')

fig.update_yaxes(showgrid=True, gridwidth=2, gridcolor='red')

fig.show()

0 1 2

1

2

3

4

5

6

7

0 1

petal_width petal_widt

petal_length

species=setosa species=versic

fig = px.line(y=np.random.randn(10))

fig.update_xaxes(zeroline=True, zerolinewidth=2, zerolinecolor='orange')

fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='orange')

fig.show()

0 1 2 3 4

−1

−0.5

0

0.5

1

1.5y

0 1 2 3 4

−1

−0.5

0

0.5

1

1.5

Time

Value

fig.update_xaxes(title_text = 'Time')

fig.update_yaxes(title_text = 'Value')

fig.show()

fig = go.Figure(go.Scatter(

mode='lines+markers',

y=[22, 24, 21, 22, 23],

x=['Mon', 'Tue', 'Wed', 'Thu', 'Fri']

))

fig.update_layout(

xaxis=dict(

tickangle=60,

title_text='Week',

title_font={'size':30},

title_standoff=20),

yaxis=dict(

title_text='Temperature',

title_font={'size':40},

title_standoff=40)

)

Mon

Tue

W

21

21.5

22

22.5

23

23.5

24

We

Temperature

)

fig.show()


fig.update_xaxes(title_font=dict(size=18, family='Courier', color='orange'))

fig.update_yaxes(title_font=dict(size=18, family='Courier', color='orange'))

fig.show()

0 1 2

1

2

3

4

5

6

7

0 1

petal_width petal_wid

petal_length


0 1 2 3 4 50

2

4

6

8

10

0 1 2 3

petal_width petal_wid

petal_length


fig.update_xaxes(range=[0, 5])

fig.update_yaxes(range=[0, 10])

fig.show()

fig.update_xaxes(fixedrange=True)

fig.show()

0 1 2 3 4 50

2

4

6

8

10

0 1 2

petal_width petal_w

petal_length


fig = go.Figure()

fig.add_trace(go.Scatter(x=[1, 1, 1, 0, 0, 1, 1, 2, 2, 3, 3, 2, 2, 1],

y=[0, 1, 1, 1, 2, 2, 3, 3, 2, 2, 1, 1, 0, 0]))

fig.update_layout(width=600, height=500,

yaxis=dict(scaleanchor='x', scaleratio=1))

fig.show()

0 1 2 3

0

0.5

1

1.5

2

2.5

3

−1 0 1 2 3 4

0

0.5

1

1.5

2

2.5

3


xaxis=dict(range=[-1, 4],

constrain='domain'),


fig.show()


xaxis=dict(range=[-0.5, 3.5],

constrain='domain'),


fig.show()

0 1 2 3

0

0.5

1

1.5

2

2.5

3

0 1 2

7

6

5

4

3

2

1

0 1

petal_width petal_w

petal_length



fig.update_yaxes(autorange='reversed')

fig.show()


fig.show()

0 1 2

7

6

5

4

3

2

1

0 1

petal_width petal_w

petal_length


x = np.linspace(1, 300, 40)

fig = px.scatter(x=x, y=x**2, log_x=True, log_y=True, range_x=[0.5, 350])

fig.show()

5 6 7 8 91

2 3 4 5 6 7 8 910

5

1

2

5

10

2

5

100

2

5

1000

2

5

10k

2

5

100k

x

y

5 6 7 8 91

2 3 4 5 6 7 8 910

5

1

2

5

10

2

5

100

2

5

1000

2

5

10k

2

5

100k

x

y

fig.update_xaxes(type='log', range=[np.log10(0.5), np.log10(350)])

fig.update_yaxes(type='log')

fig.show()


fig.update_xaxes(rangemode='nonnegative')

fig.update_yaxes(rangemode='tozero')

fig.show()

0 1 20

1

2

3

4

5

6

7

0 1

petal_width petal_widt

petal_length


범례(Legends)

0 10 20 30 40 50

2

4

6

8

10

0 10

total_bill

tip

time=Dinner

fig = px.scatter(tips, x='total_bill', y='tip',

color='sex', symbol='smoker', facet_col='time',

labels={'sex': 'Gender', 'smoker':'Smokes'})

fig.show()

Female0

20

40

60

80

100

120

140

160

sex

coun

t of

tot

al_b

ill

fig = px.histogram(tips, x='sex', y='total_bill', color='time')

fig.update_layout(showlegend=False)

fig.show()

fig = px.scatter(gapminder_2007, x='gdpPercap', y='lifeExp',

color='continent', size='pop',

size_max=45, log_x=True)

fig.update_layout(legend=dict(yanchor='top', y=0.96,

xanchor='left', x=0.02))

fig.show()

2 3 4 5 6 7 8 91000

2 3

40

45

50

55

60

65

70

75

80

85continent=Asiacontinent=Europecontinent=Africacontinent=Americascontinent=Oceania

gdpPer

lifeExp

2 3 4 5 6 7 8 91000

2 3

40

45

50

55

60

65

70

75

80

85

continent=Asia continent=

gdpPer

lifeExp

fig.update_layout(legend=dict(orientation='h',

yanchor='bottom', y=1,

xanchor='right', x=1))

fig = go.Figure()


y=np.random.randn(10)+1,

#showlegend=False,

name="Positive"))


y=np.random.randn(10)-1,

1 2 3 4 5

−2

−1

0

1

2

#visible='legendonly',

name="Negative"))

fig.show()

fig = go.Figure()


y=np.random.randn(10)+1,

mode='markers',

marker={'size':10}))


y=np.random.randn(10)-1,

mode='markers',

marker={'size':30}))

fig.update_layout(legend={'itemsizing': 'constant'})

fig.show()

1 2 3 4 5

−2

−1

0

1

2

x = np.arange(10)

y1 = np.random.randn(10)




fig = go.Figure()

fig.add_trace(go.Scatter(x=x, y=y1,

legendgroup='Group A',

name='A - markers',

mode='markers',

marker=dict(color='darkred', size=10)))


legendgroup='Group A',

name='A - lines',

mode='lines',

marker=dict(color='darkred')))


legendgroup='Group B',

name='B -markers',

mode='markers',

marker=dict(color='darkblue', size=10)))


legendgroup='Group B',

name='B - lines',

mode='lines',

marker=dict(color='darkblue')))

fig.update_layout()

fig.show()

0 2 4−3

−2

−1

0

1

2

3

4

다중 축(Multiple Axes)

x = np.arange(1, 10, 5)



fig = make_subplots(rows=2, cols=2,

specs=[[{'secondary_y':True}, {'secondary_y':True}],

[{'secondary_y':True}, {'secondary_y':True}]])

fig.add_trace(go.Scatter(x=x, y=y1, name='yaxis1 data'),

row=1, col=1, secondary_y=False)


row=1, col=1, secondary_y=True)












row=2 col=2 secondary y=True)

2 4 6

0.8

1

1.2

−3.5

−3

−2.5

2 4 6

0.8

1

1.2

−3.5

−3

−2.5

row 2, col 2, secondary_y True)

fig.show()

서브플롯(Subplots)

x1 = np.arange(0, 11)








fig = make_subplots(rows=1, cols=2)

fig.add_trace(go.Scatter(x=x1, y=y1), row=1, col=1)


fig.update_layout(height=600, width=800)

fig.show()

0 5

0

2

4

6

8

10 15

10

20

30

40

50

60

70

80

90

100






fig.show()

0 2 4 6 80

5

10 12 14 16 18

50

100

20 22 24 26 28200

400

600

800

0 2 4 6 80

550

100

20 22 24 26 28200

400

600

800

2000

4000

6000

fig = make_subplots(rows=2, cols=2, start_cell='bottom-left')





fig.show()


subplot_titles=('Plot A', 'Plot B', 'Plot C', 'Plot D'))


fi dd t ( S tt ( 2 2) 1 l 2)

0 2 4 6 80

5

1

20 22 24 26 28200

400

600

800

20

40

60

Plot A

Plot C




fig.show()


fig.add_trace(go.Scatter(x=x1, y=y1,

mode='markers+text',

text=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],

textposition='bottom center'),

row=1, col=1)

fig.add_trace(go.Scatter(x=x2, y=y2,

mode='markers+text',

text=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],

textposition='middle right'),

row=1, col=2)


fig.show()

A

B

C

D

E

F

G

H

I J

0 5

0

2

4

6

8

A

B

C

D

E

F

G

H

10 15

10

20

30

40

50

60

70

80

90

100

fig = make_subplots(rows=1, cols=2, column_width=[0.7, 0.3])



fig.show()

20 22 24 26 2200

300

400

500

600

700

800

900fig = make_subplots(rows=2, cols=2,

subplot_titles=('Plot A', 'Plot B', 'Plot C', 'Plot D'))





fig.update_xaxes(title_text='xaxes 1 title', row=1, col=1)

fig.update_xaxes(title_text='xaxes 2 title', range=[10, 50], row=1, col=2)

fig.update_xaxes(title_text='xaxes 3 title', showgrid=False, row=2, col=1)

fig.update_xaxes(title_text='xaxes 4 title', type='log', row=2, col=2)

fig.update_yaxes(title_text='yaxes 1 title', row=1, col=1)

fig.update_yaxes(title_text='yaxes 2 title', range=[10, 50], row=1, col=2)

fig.update_yaxes(title_text='yaxes 3 title', showgrid=False, row=2, col=1)

fig.update_yaxes(title_text='yaxes 4 title', row=2, col=2)

fig.update_layout(height=700)

fig.show()

0 2 4 6 8

0

2

4

6

8

1010

20

30

40

50

20 22 24 26 28200

400

600

800

2000

4000

6000

xaxes 1 title

xaxes 3 title

yaxe

s 1

title

yaxe

s 2

title

yaxe

s 3

title

yaxe

s 4

title

Plot A

Plot C

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trace 0trace 1trace 2trace 3


shared_xaxes=True,

vertical_spacing=0.02)






fig.show()

fig = make_subplots(rows=1, cols=2, shared_yaxes=True)

fig.add_trace(go.Bar(x=x1, y=y1,

marker=dict(color=y1, coloraxis='coloraxis')), 1, 1)

fig.add_trace(go.Bar(x=x2, y=y2,

marker=dict(color=y2, coloraxis='coloraxis')), 1, 2)

fig.update_layout(coloraxis=dict(colorscale='YlGn'), showlegend=False)

0 2 4 6 80

20

40

60

80

100

fig.show()

fig = make_subplots(rows=2, cols=2, specs=[[{}, {}],[{'colspan':2}, None]],

subplot_titles=('Subplot A', 'Subplot B', 'Subplot C'))





fig.show()

0 2 4 6 80

5

20 22 24200

400

600

800

Subplot A

Subplo


specs=[[{'type':'xy'}, {'type':'polar'}],

[{'type':'domain'}, {'type':'scene'}]])

fig.add_trace(go.Bar(y=y1), row=1, col=1)

fig.add_trace(go.Barpolar(theta=[0, 60, 120], r=y2), row=1, col=2)

fig.add_trace(go.Pie(values=y3), row=2, col=1)

fig.add_trace(go.Scatter3d(x=x4, y=y4, z=[0.5, 1, 2], mode='lines'), row=2, col=2)

fig.update_layout(height=700, showlegend=False)

fig.show()

0 2 4 6 80

2

4

6

8

14.4%13%

12.5%

11.4%

10.7%10.2%

9.94%

8.72%

5.12%3.92%

다중 차트 유형(Multiple Chart Types)

0 2 4

0

20

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60

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100

fig = go.Figure()

fig.add_trace(go.Scatter(x=x1, y=y1))

fig.add_trace(go.Bar(x=x1, y=y2))

fig.show()

인수(Arguments)

fig = px.scatter(iris, x=iris.petal_length, y=iris.petal_width,

color=iris.species, size=iris.petal_length)

fig.show()

1 2 3 4

0

0.5

1

1.5

2

2.5

petal_length

petal_width

fig = px.scatter(iris, x='petal_length', y='petal_width',

color='species', size='petal_length')

fig.show()

1 2 3 4

0

0.5

1

1.5

2

2.5

petal_length

petal_width

1 2 3 4

0

0.5

1

1.5

2

2.5

petal_length

petal_width

fig = px.scatter(iris, x=iris.petal_length, y=iris.petal_width,

color=iris.species, size=iris.petal_length,

hover_data=[iris.index])

fig.show()

gdp = gapminder['pop'] * gapminder['gdpPercap']

fig = px.bar(gapminder, x='year', y=gdp, color='continent', labels={'y':'gdp'},


fig.show()

1950 1960 1970 19800

10T

20T

30T

40T

50T

60T

year

gdp

1950 1960 1970 19800

10T

20T

30T

40T

50T

60T

year

log

gdp

gdp = gapminder['pop'] * gapminder['gdpPercap']

fig = px.bar(gapminder, x='year', y=gdp, color='continent', labels={'y':'log gdp'},


fig.show()

마커 스타일(Styling Markers)

0 0.5 1 1

1

2

3

4

5

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7

petal_width

petal_leng

th

fig = px.scatter(iris, x='petal_width', y='petal_length', color='species')

fig.update_traces(marker=dict(size=12, line=dict(width=2, color='navy')),

selector=dict(mode='markers'))

fig.show()

x1 = np.random.uniform(low=1, high=5, size=(200,))

y1 = np.random.uniform(low=1, high=5, size=(200,))

x2 = np.random.uniform(low=5, high=10, size=(200,))

y2 = np.random.uniform(low=1, high=5, size=(200,))

fig = go.Figure()

fig.add_trace(go.Scatter(mode='markers',

x=x1, y=y1,

opacity=0.5,

marker=dict(color='red',

size=10,

line=dict(color='darkred', width=2)),

name='Opacity 0.5'))

fig.add_trace(go.Scatter(mode='markers',

x=x2, y=y2,

marker=dict(color='limegreen',

size=10,

line=dict(color='green', width=2)),

name='Opacity 1.0'))

fig.show()

2 4 6

1

1.5

2

2.5

3

3.5

4

4.5

5

raw_symbols = SymbolValidator().values

namestems = []

namevariants = []

symbols = []

for i in range(0, len(raw_symbols), 2):

name = raw_symbols[i+1]

symbols.append(raw_symbols[i])

namestems.append(name.replace("-open", "").replace("-dot", ""))

namevariants.append(name[len(namestems[-1]):])

fig = go.Figure(go.Scatter(mode='markers', x=namevariants, y=namestems, marker_symbol=symbols,

marker_line_color='darkblue', marker_color='skyblue',

marker_line_width=2, marker_size=15,

hovertemplate="%{y}%{x}(num: %{marker.symbol})"))

fig.update_layout(xaxis_range=[-1, 4], yaxis_range=[len(set(namestems)), -1],

margin=dict(b=0, r=0), xaxis_side='top', height=1200, width=400)

fig.show()

-open -dot -open-dot

y-down

y-up

hash

asterisk

x-thin

cross-thin

diamond-x

diamond-cross

square-x

square-cross

circle-x

circle-cross

bowtie

hourglass

diamond-wide

diamond-tall

star-diamond

star-square

star-triangle-down

star-triangle-up

hexagram

star

octagon

hexagon2

hexagon

pentagon

triangle-nw

triangle-sw

triangle-se

triangle-ne

triangle-right

triangle-left

triangle-down

triangle-up

x

cross

diamond

square

circle

line-nw

line-ne

line-ns

line-ew

y-right

y-left

y

색상 척도 및 색상 막대

10 20

2

4

6

8

10

total_bill

tip

fig = px.scatter(tips, x='total_bill', y='tip', color='size')

fig.show()

fig = px.scatter(tips, x='total_bill', y='tip', color=tips['size'].astype(str))

fig.show()

10 20

2

4

6

8

10

total_bill

tip

10 20

2

4

6

8

10

total_b

tip

fig = px.scatter(tips, x='total_bill', y='tip', color=tips['size'].astype(float))

fig.show()


color='petal_length',


fig.show()

0 0.5 1

1

2

3

4

5

6

7

petal_width

petal_leng

th



color_continuous_scale='Inferno')

fig.show()

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2

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5

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7

petal_width

petal_leng

th

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petal_width

petal_leng

th



color_continuous_scale='Inferno_r')

fig.show()



color_continuous_scale=['red', 'green', 'blue'])

fig.show()

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1

2

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5

6

7

petal_width

petal_leng

th

0 0.5 1

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3

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petal_width

petal_leng

th



color_continuous_scale=[(0, 'red'), (0.3, 'green'), (1, 'blue')])

fig.show()

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1

2

3

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6

7

petal_width

petal_leng

th



range_color=[2, 6])

fig.show()

fig = px.scatter(gapminder_2007, y='lifeExp', x='pop',

color=np.log10(gapminder_2007['pop']),

hover_name='country', log_x=True)

fig.update_layout(coloraxis_colorbar=dict(

title='Population',

tickvals=[6, 7, 8, 9],

ticktext=['1M', '10M', '100M', '1B'],

))

fig.show()

2 5 1M 2 5 10M 2

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85

pop

lifeE

xp

fig = go.Figure()

values = list(range(40))

fig.add_trace(go.Scatter(x=values, y=values,

marker=dict(size=14,

cmax=30, cmin=0,

color=values,

colorbar=dict(title='Colorbar'),

colorscale='Inferno'),

mode='markers'))

fig.show()

0 5 10 15 20

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5

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15

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40

텍스트 및 주석(Text and Annotation)

gapminder_asia_2007 = gapminder.query("year == 2007 and continent == 'Asia'")

gapminder_asia_2007

country continent year lifeExp pop gdpPercap iso_alpha is


95 Bahrain Asia 2007 75.635 708573 29796.048340 BHR

107 Bangladesh Asia 2007 64.062 150448339 1391.253792 BGD

227 Cambodia Asia 2007 59.723 14131858 1713.778686 KHM

299 China Asia 2007 72.961 1318683096 4959.114854 CHN

671 Hong Kong,China Asia 2007 82.208 6980412 39724.978670 HKG

707 India Asia 2007 64.698 1110396331 2452.210407 IND

719 Indonesia Asia 2007 70.650 223547000 3540.651564 IDN

731 Iran Asia 2007 70.964 69453570 11605.714490 IRN

743 Iraq Asia 2007 59.545 27499638 4471.061906 IRQ

767 Israel Asia 2007 80.745 6426679 25523.277100 ISR

803 Japan Asia 2007 82.603 127467972 31656.068060 JPN

815 Jordan Asia 2007 72.535 6053193 4519.461171 JOR

839 Korea,Dem. Rep. Asia 2007 67.297 23301725 1593.065480 KOR

851 Korea,Rep. Asia 2007 78.623 49044790 23348.139730 KOR

863 Kuwait Asia 2007 77.588 2505559 47306.989780 KWT

875 Lebanon Asia 2007 71.993 3921278 10461.058680 LBN

947 Malaysia Asia 2007 74.241 24821286 12451.655800 MYS

1007 Mongolia Asia 2007 66.803 2874127 3095.772271 MNG

1055 Myanmar Asia 2007 62.069 47761980 944.000000 MMR

1079 Nepal Asia 2007 63.785 28901790 1091.359778 NPL

1163 Oman Asia 2007 75.640 3204897 22316.192870 OMN

1175 Pakistan Asia 2007 65.483 169270617 2605.947580 PAK

1223 Philippines Asia 2007 71.688 91077287 3190.481016 PHL

1319 SaudiArabia Asia 2007 72.777 27601038 21654.831940 SAU

1367 Singapore Asia 2007 79.972 4553009 47143.179640 SGP

1439 Sri Lanka Asia 2007 72.396 20378239 3970.095407 LKA

1499 Syria Asia 2007 74.143 19314747 4184.548089 SYR

1511 Taiwan Asia 2007 78.400 23174294 28718.276840 TWN

fig = px.scatter(gapminder_asia_2007, x='gdpPercap', y='lifeExp',

text='country', log_x=True, size_max=60)

fig.update_traces(textposition='top center')

fig.update_layout(height=800)

fig.show()

Afghanistan

Bangladesh

Cambodia

China

India

Indonesia

Iraq

Jordan

Korea, Dem. Rep. Mongolia

Myanmar

Nepal

Pakistan

PhilippinesSri Lanka

Syria

Thaila

VietnamWest Bank and Gaza

Yemen, Rep.

8 91000

2 3 4 5 6 7

45

50

55

60

65

70

75

80

85

gdpPercap

lifeE

xp

32M710k

150M

14M

1.3G

7.0M

1.1G

220M

69M27M 6.4M

130M

6.1M 23M 49M2.5M 3.9M 25

Afghanistan

BahrainBangladesh

Cambodia

ChinaHong Kong, China

IndiaIndonesia

IranIraq

IsraelJapan

JordanKorea, Dem. Rep.

Korea, Rep.

KuwaitLebano

M0

0.2B

0.4B

0.6B

0.8B

1B

1.2B

country

pop

fig = px.bar(gapminder_asia_2007, y='pop', x='country', text='pop')

fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')

fig.update_layout()

fig.show()

fig = go.Figure()

fig.add_trace(go.Scatter(x=[0, 1, 2, 3, 4, 5, 6, 7, 8],

y=[0, 1, 4, 2, 3, 3, 2, 5, 6]))

fig.add_trace(go.Scatter(x=[0, 1, 2, 3, 4, 5, 6, 7, 8],

y=[0, 2, 3, 1, 4, 2, 3, 4, 5]))

fig.add_annotation(x=2, y=4, text='Annotation 1')

fig.add_annotation(x=4, y=4, text='Annotation 1')

fig.update_annotations(dict(xref='x', yref='y',

showarrow=True, arrowhead=7, ax=20, ay=-40))


fig.show()

0 1 2 3 4

0

1

2

3

4

5

6

Annotation 1 Anno

fig.add_annotation(x=5, y=3, xref='x', yref='y',

text='(5, 3)', showarrow=True,

font=dict(family='Courier New, monospace',

size=16,

color='blue'),

align='center',

arrowhead=2,

arrowsize=1,

arrowwidth=2,

arrowcolor='orange',

ax=20, ay=-40,

bordercolor='red',

borderwidth=2,

borderpad=4,

bgcolor='yellow',

opacity=0.8)


fig.show()

0 1 2 3 4

0

1

2

3

4

5

6

Annotation 1 Anno

1950 1960 1970 1980

30

40

50

60

70

80

year

lifeE

xp


fig.update_traces(mode='markers+lines')

fig.show()

fig.update_traces(mode='markers+lines', hovertemplate=None)

fig.update_layout(hovermode='x')

fig.show()

1950 1960 1970 1980

30

40

50

60

70

80

year

lifeE

xp


fig.update_layout(hoverlabel=dict(bgcolor='white',

font_size=14,

font_family="Courier New"))

fig.show()

1950 1960 1970 1980

30

40

50

60

70

80

year

lifeE

xp

3 4 5 6 7 8 91000

2 3 4 5 6 7

30

40

50

60

70

80

gdpPercap

lifeE

xp

fig = px.scatter(gapminder_asia, x='gdpPercap', y='lifeExp', log_x=True,

hover_name='country', hover_data=['continent', 'pop'])

fig.show()

fig = px.scatter(gapminder_2007, x='gdpPercap', y='lifeExp', log_x=True,

color='continent')

fig.update_traces(hovertemplate='GDP: %{x} <br>Life Expectancy: %{y}')

fig.show()

3 4 5 6 7 8 91000

2 3 4 5 6

40

45

50

55

60

65

70

75

80

85

gdpPercap

lifeE

xp



fig.update_xaxes(showspikes=True)

fig.update_yaxes(showspikes=True)

fig.show()

1950 1960 1970 1980

30

40

50

60

70

80

year

lifeE

xp

1950 1960 1970 1980

30

40

50

60

70

80

year

lifeE

xp



fig.update_xaxes(showspikes=True, spikecolor='green', spikesnap='cursor', spikemode='across')

fig.update_yaxes(showspikes=True, spikecolor='orange', spikethickness=2)

fig.update_layout(spikedistance=1000, hoverdistance=100)

fig.show()

모양(Shape)

fig = go.Figure()

fig.add_trace(go.Scatter(

x=[2, 3.5, 6], y=[1, 1.5, 1],

text=['Vertical Line',

'Horizontal Dashed Line',

'Diagonal Dotted Line'],

mode='text',

))

fig.update_xaxes(range=[0, 7])

fig.update_yaxes(range=[0, 2.5])

fig.add_shape(dict(type='line', x0=1, y0=0, x1=1, y1=2,

line=dict(color='royalblue', width=3)))

fig.add_shape(type='line', x0=2, y0=2, x1=5, y1=2,

line=dict(color='lightseagreen' width=4 dash='dashdot'))

Vertical Line

Horizontal Da

0 1 2 30

0.5

1

1.5

2

2.5

line=dict(color= lightseagreen , width=4, dash= dashdot ))

fig.add_shape(type='line', x0=4, y0=0, x1=6, y1=2,

line=dict(color='mediumpurple', width=4, dash='dot'))

fig.update_shapes(dict(xref='x', yref='y'))

fig.show()

fig = go.Figure()

fig.add_trace(go.Scatter(x=[1.5, 4.5], y=[0.75, 0.75],

text=['Unfilled Rectangle', 'Filled Rectangle'],

mode='text'))

fig.update_xaxes(range=[0, 7], showgrid=False)


fig.add_shape(type='rect', x0=1, y0=1, x1=2, y1=3,

line=dict(color='royalblue'))

fig.add_shape(type='rect', x0=3, y0=1, x1=6, y1=2,

line=dict(color='royalblue', width=2),

fillcolor='lightskyblue')

fig.update_shapes(dict(xref='x', yref='y'))

fig.show()

Unfilled Rectangle

0 1 2 30

0.5

1

1.5

2

2.5

3

3.5

fig = go.Figure()

fig.add_trace(go.Scatter(x=[1.5, 3], y=[2.5, 2.5],

text=['Rectangle reference to the plot',

'Rectangle reference to the axes'],

mode='text'))

fig.update_xaxes(range=[0, 4], showgrid=False)


fig.add_shape(type='rect', xref='x', yref='y',

x0=2.5, y0=0, x1=3.5, y1=2,


fillcolor='lightskyblue')

fig.add_shape(type='rect', xref='paper', yref='paper',

x0=0.25, y0=0, x1=0.5, y1=0.5,


fillcolor='darkblue')

fig.show()

Rectangle reference to the plot

0 0.5 1 1.5 20

0.5

1

1.5

2

2.5

3

3.5

4

fig = go.Figure()

fig.add_trace(go.Scatter(x=[1.5, 3.5], y=[0.75, 2.5],

text=['Unfilled Circle',

'Filled Circle'],

mode='text'))

fig.update_xaxes(range=[0, 4.5], zeroline=False)


fig.add_shape(type='circle', xref='x', yref='y',

x0=1, y0=1, x1=3, y1=3,

line_color='lightskyblue')

fig.add_shape(type='circle', xref='x', yref='y',

x0=3, y0=3, x1=4, y1=4,

line=dict(color='royalblue'),

fillcolor='darkblue')


fig.show()

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