怎么用Python matplotlib plotly绘制图表

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一、整理数据

以300部电影作为数据源

import pandas as pd cnboo=pd.read_excel("cnboNPPD1.xls")

cnboo

import seaborn as snsimport numpy as np import matplotlib as mplfrom matplotlib import pyplot as plt import pandas as pd from datetime import datetime,timedelta%matplotlib inlineplt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号from datetime import datetime

! pip install plotly # 安装import matplotlib.pyplot as pltimport plotlyfrom plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot

x=cnboo['BO'].tolist()y=cnboo['PERSONS'].tolist()dict01={"x":x,"y":y}dict01

二、折线图

# 折线图iplot([dict01])

三、散点图

import plotly.graph_objs as goiplot([go.Scatter(x=x,y=y,mode='markers')])

# 随机生成的点图import numpy as npiplot([go.Scatter(x=np.random.randn(100),y=np.random.randn(100),mode='markers')])

go

trace=go.Scatter(x=cnboo['PRICE'],y=y,mode='markers',)data=[trace]iplot(data)

trace=go.Scatter(x=cnboo['PRICE'],y=y,mode='markers',marker=dict(color='red',size=9,opacity=0.4))data=[trace]iplot(data)

四、饼图

colors=['#dc2624','#2b4750','#45a0a2','#e87a59','#7dcaa9','#649E7D','#dc8018',       '#C89F91','#6c6d6c','#4f6268','#c7cccf']

filmtype=cnboo['TYPE']filmbo=cnboo['PRICE']trace=go.Pie(labels=filmtype,values=filmbo,            hoverinfo='label+percent',textinfo='value',textfont=dict(size=10),             marker=dict(colors=colors,line=dict(color='#000000',width=3)))data=[trace]

iplot(data)

filmtype=cnboo['TYPE']filmbo=cnboo['PRICE']trace=go.Pie(labels=filmtype,values=filmbo,            hoverinfo='label+percent',textinfo='value',textfont=dict(size=12),             marker=dict(colors=colors))data=[trace]iplot(data)

五、柱形图

# plotly bartrace1=go.Bar(x=cnboo['TYPE'],y=cnboo['PRICE'],name="类型与票价")trace2=go.Bar(x=cnboo['TYPE'],y=y,name="类型与人数")layout=go.Layout(title="中国电影类型与票价，人数的关系",xaxis=dict(title='电影类型'))data=[trace1,trace2]fig=go.Figure(data,layout=layout)iplot(fig)

六、点图（设置多个go对象）

trace1=go.Scatter(x=cnboo['TYPE'],y=cnboo['PRICE'],name="类型与票价",mode="markers",                  marker=dict(color="red",size=8))trace2=go.Scatter(x=cnboo['TYPE'],y=cnboo['PERSONS'],name="类型与人数",mode="markers",                  marker=dict(color="blue",size=5))data=[trace1,trace2]iplot(data)

trace1=go.Scatter(x=cnboo['TYPE'],y=cnboo['PRICE'],name="类型与票价",mode="markers",                  marker=dict(color="red",size=8))trace2=go.Scatter(x=cnboo['TYPE'],y=cnboo['PERSONS'],name="类型与人数",mode="markers",                  marker=dict(color="blue",size=5))layout=go.Layout(title="中国电影类型与票价，人数的关系",plot_bgcolor="#FFFFFF")data=[trace1,trace2]fig=go.Figure(data,layout=layout)iplot(fig)

七、2D密度图

import plotly.figure_factory as ff

fig=ff.create_2d_density(x,y,colorscale=colors,hist_color='#dc2624',point_size=5)iplot(fig,filename='评分与人次')

colorscale=['rgb(20, 38, 220)', 'rgb(255, 255, 255)'] # 最后一个颜色都是调用背景

fig=ff.create_2d_density(x,y,colorscale=colorscale,hist_color='#dc2624',point_size=5)iplot(fig,filename='评分与人次')

八、简单3D图

layout=go.Layout(title="中国电影票房与人次，票价的关系",barmode="group")

trace01=go.Scatter3d(    x=cnboo['BO'],    y=cnboo['PRICE'],    z=cnboo['PERSONS'],    mode='markers',    marker=dict(size=12,color=colors,colorscale='Viridis',               opacity=0.5,showscale=True)  #opacity是透明度)

data=[trace01]fig=go.Figure(data=data,layout=layout)iplot(fig,filename='3d')

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