1、plotly介绍

lotly的Python图形库使互动的出版质量图表成为在线。 如何制作线图，散点图，面积图，条形图，误差线，箱形图，直方图，热图，子图，多轴，极坐标图和气泡图的示例。

推荐最好使用jupyter notebook，使用pycharm的话不是很方便。

2、安装

pip install plotly

2、使用

1)在线使用

在setting里找到用户名和api key

image.png

##在线使用

import plotly.plotly as py

from plotly import tools

from plotly.graph_objs import *

tools.set_credentials_file(username='yours', api_key='yours')

trace0 = Scatter(

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

y=[10, 15, 13, 17],

mode='markers'

)

trace1 = Scatter(

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

y=[16, 5, 11, 9]

)

data = Data([trace0, trace1])

py.iplot(data)

散点图

散点图.png

2)offline

import plotly.offline as of

import plotly.graph_objs as go

of.offline.init_notebook_mode(connected=True)

trace0 = go.Scatter(

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

y=[10, 15, 13, 17],

mode='markers'

)

trace1 = go.Scatter(

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

y=[16, 5, 11, 9]

)

data = go.Data([trace0, trace1])

of.plot(data)

3、其他图

下面我们画几个其他类型的图

柱状图

import plotly.figure_factory as ff

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

data = [Bar(x=df.School,

y=df.Gap)]

py.iplot(data)

image.png

3D图

import numpy as np

s = np.linspace(0, 2 * np.pi, 240)

t = np.linspace(0, np.pi, 240)

tGrid, sGrid = np.meshgrid(s, t)

r = 2 + np.sin(7 * sGrid + 5 * tGrid) # r = 2 + sin(7s+5t)

x = r * np.cos(sGrid) * np.sin(tGrid) # x = r*cos(s)*sin(t)

y = r * np.sin(sGrid) * np.sin(tGrid) # y = r*sin(s)*sin(t)

z = r * np.cos(tGrid) # z = r*cos(t)

surface = Surface(x=x, y=y, z=z)

data = Data([surface])

layout = Layout(

title='Parametric Plot',

scene=Scene(

xaxis=XAxis(

gridcolor='rgb(255, 255, 255)',

zerolinecolor='rgb(255, 255, 255)',

showbackground=True,

backgroundcolor='rgb(230, 230,230)'

),

yaxis=YAxis(

gridcolor='rgb(255, 255, 255)',

zerolinecolor='rgb(255, 255, 255)',

showbackground=True,

backgroundcolor='rgb(230, 230,230)'

),

zaxis=ZAxis(

gridcolor='rgb(255, 255, 255)',

zerolinecolor='rgb(255, 255, 255)',

showbackground=True,

backgroundcolor='rgb(230, 230,230)'

)

)

)

fig = Figure(data=data, layout=layout)

py.iplot(fig,)

image.png

折线图

import numpy as np

N = 100

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

random_y0 = np.random.randn(N)+5

random_y1 = np.random.randn(N)

random_y2 = np.random.randn(N)-5

# Create traces

trace0 = go.Scatter(

x = random_x,

y = random_y0,

mode = 'markers',

name = 'markers'

)

trace1 = go.Scatter(

x = random_x,

y = random_y1,

mode = 'lines+markers',

name = 'lines+markers'

)

trace2 = go.Scatter(

x = random_x,

y = random_y2,

mode = 'lines',

name = 'lines'

)

data = [trace0, trace1, trace2]

py.iplot(data)

image.png

堆叠图

trace1 = go.Bar(

x=['giraffes', 'orangutans', 'monkeys'],

y=[20, 14, 23],

name='SF Zoo'

)

trace2 = go.Bar(

x=['giraffes', 'orangutans', 'monkeys'],

y=[12, 18, 29],

name='LA Zoo'

)

data = [trace1, trace2]

layout = go.Layout(

barmode='stack'

)

fig = go.Figure(data=data, layout=layout)

py.iplot(fig)

image.png

pie

labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']

values = [4500,2500,1053,500]

colors = ['#FEBFB3', '#E1396C', '#96D38C', '#D0F9B1']

trace = go.Pie(labels=labels, values=values,

hoverinfo='label+percent', textinfo='value',

textfont=dict(size=20),

marker=dict(colors=colors,

line=dict(color='#000000', width=2)))

py.iplot([trace])

image.png

不知道叫什么图

title = 'Main Source for News'

labels = ['Television', 'Newspaper', 'Internet', 'Radio']

colors = ['rgba(67,67,67,1)', 'rgba(115,115,115,1)', 'rgba(49,130,189, 1)', 'rgba(189,189,189,1)']

mode_size = [8, 8, 12, 8]

line_size = [2, 2, 4, 2]

x_data = [

[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013],

[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013],

[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013],

[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013],

]

y_data = [

[74, 82, 80, 74, 73, 72, 74, 70, 70, 66, 66, 69],

[45, 42, 50, 46, 36, 36, 34, 35, 32, 31, 31, 28],

[13, 14, 20, 24, 20, 24, 24, 40, 35, 41, 43, 50],

[18, 21, 18, 21, 16, 14, 13, 18, 17, 16, 19, 23],

]

traces = []

for i in range(0, 4):

traces.append(go.Scatter(

x=x_data[i],

y=y_data[i],

mode='lines',

line=dict(color=colors[i], width=line_size[i]),

connectgaps=True,

))

traces.append(go.Scatter(

x=[x_data[i][0], x_data[i][11]],

y=[y_data[i][0], y_data[i][11]],

mode='markers',

marker=dict(color=colors[i], size=mode_size[i])

))

layout = go.Layout(

xaxis=dict(

showline=True,

showgrid=False,

showticklabels=True,

linecolor='rgb(204, 204, 204)',

linewidth=2,

autotick=False,

ticks='outside',

tickcolor='rgb(204, 204, 204)',

tickwidth=2,

ticklen=5,

tickfont=dict(

family='Arial',

size=12,

color='rgb(82, 82, 82)',

),

),

yaxis=dict(

showgrid=False,

zeroline=False,

showline=False,

showticklabels=False,

),

autosize=False,

margin=dict(

autoexpand=False,

l=100,

r=20,

t=110,

),

showlegend=False,

)

annotations = []

# Adding labels

for y_trace, label, color in zip(y_data, labels, colors):

# labeling the left_side of the plot

annotations.append(dict(xref='paper', x=0.05, y=y_trace[0],

xanchor='right', yanchor='middle',

text=label + ' {}%'.format(y_trace[0]),

font=dict(family='Arial',

size=16,

color=colors,),

showarrow=False))

# labeling the right_side of the plot

annotations.append(dict(xref='paper', x=0.95, y=y_trace[11],

xanchor='left', yanchor='middle',

text='{}%'.format(y_trace[11]),

font=dict(family='Arial',

size=16,

color=colors,),

showarrow=False))

# Title

annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,

xanchor='left', yanchor='bottom',

text='Main Source for News',

font=dict(family='Arial',

size=30,

color='rgb(37,37,37)'),

showarrow=False))

# Source

annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.1,

xanchor='center', yanchor='top',

text='Source: PewResearch Center & ' +

'Storytelling with data',

font=dict(family='Arial',

size=12,

color='rgb(150,150,150)'),

showarrow=False))

layout['annotations'] = annotations

fig = go.Figure(data=traces, layout=layout)

py.iplot(fig)

image.png

4、各种具体语法

image.png

5、总结

画的图真是好看，而且划过的图会自动上传到云端。

image.png