基于python plotly交互式图表大全
plotly可以制作交互式图表，直接上代码：
importplotly.offlineaspy fromplotly.graph_objsimportScatter,Layout importplotly.graph_objsasgo py.init_notebook_mode(connected=True) importpandasaspd importnumpyasnp
In[412]:
#读取数据
df=pd.read_csv('seaborn.csv',sep=',',encoding='utf-8',index_col=0)
#展示数据
df.head()
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In[413]:
#plotly折线图，trace就代表折现的条数 trace1=go.Scatter(x=df['Attack'],y=df['Defense']) trace1=go.Scatter(x=[1,2,3,4,5],y=[2,1,3,5,2]) trace2=go.Scatter(x=[1,2,3,4,5],y=[2,1,4,6,7]) py.iplot([trace1,trace2])
#填充区域 trace1=go.Scatter(x=[1,2,3,4,5],y=[2,1,3,5,2],fill="tonexty",fillcolor="#FF0") py.iplot([trace1])
#散点图 trace1=go.Scatter(x=[1,2,3,4,5],y=[2,1,3,5,2],mode='markers') trace1=go.Scatter(x=df['Attack'],y=df['Defense'],mode='markers') py.iplot([trace1],filename='basic-scatter')
#气泡图
x=df['Attack']
y=df['Defense']
colors=np.random.rand(len(x))#setcolorequaltoavariable
sz=df['Defense']
fig=go.Figure()
fig.add_scatter(x=x,y=y,mode='markers',marker={'size':sz,'color':colors,'opacity':0.7,'colorscale':'Viridis','showscale':True})
py.iplot(fig)
#bar柱状图 df1=df[['Name','Defense']].sort_values(['Defense'],ascending=[0]) data=[go.Bar(x=df1['Name'],y=df1['Defense'])] py.iplot(data,filename='jupyter-basic_bar')
#组合bargroup trace1=go.Bar(x=['giraffes','orangutans','monkeys'],y=[20,14,23],name='SFZoo') trace2=go.Bar(x=['giraffes','orangutans','monkeys'],y=[12,18,29],name='LAZoo') data=[trace1,trace2] layout=go.Layout(barmode='group') fig=go.Figure(data=data,layout=layout) py.iplot(fig,filename='grouped-bar')
#组合bargstack上下组合 trace1=go.Bar(x=['giraffes','orangutans','monkeys'],y=[20,14,23],name='SFZoo') trace2=go.Bar(x=['giraffes','orangutans','monkeys'],y=[12,18,29],name='LAZoo',text=[12,18,29],textposition='auto') data=[trace1,trace2] layout=go.Layout(barmode='stack') fig=go.Figure(data=data,layout=layout) py.iplot(fig,filename='grouped-bar')
#饼图
fig={
"data":[
{
"values":df['Defense'][0:3],
"labels":df['Name'][0:3],
"domain":{"x":[0,1]},
"name":"GHGEmissions",
"hoverinfo":"label+percent+name",
"hole":.4,
"type":"pie"
}
],
"layout":{
"title":"GlobalEmissions1990-2011",
"annotations":[
{
"font":{"size":20},
"showarrow":False,
"text":"GHG",
"x":0.5,
"y":0.5
}
]
}
}
py.iplot(fig,filename='donut')
#LearnaboutAPIauthenticationhere:https://plot.ly/pandas/getting-started #Findyourapi_keyhere:https://plot.ly/settings/api #雷达图 data=[ go.Scatterpolar( r=[39,28,8,7,28,39], theta=['A','B','C','D','E','A'], fill='toself', name='GroupA' ), go.Scatterpolar( r=[1.5,10,39,31,15,1.5], theta=['A','B','C','D','E','A'], fill='toself', name='GroupB' ) ] layout=go.Layout( polar=dict( radialaxis=dict( visible=True, range=[0,50] ) ), showlegend=False ) fig=go.Figure(data=data,layout=layout) py.iplot(fig,filename="radar/multiple")
#box箱子图 df_box=df[['HP','Attack','Defense','Speed']] data=[] forcolindf_box.columns: data.append(go.Box(y=df_box[col],name=col,showlegend=True)) #data.append(go.Scatter(x=df_box.columns,y=df.mean(),mode='lines',name='mean')) py.iplot(data,filename='pandas-box-plot')
#箱子图加平均线 df_box=df[['HP','Attack','Defense','Speed']] data=[] forcolindf_box.columns: data.append(go.Box(y=df_box[col],name=col,showlegend=True)) data.append(go.Scatter(x=df_box.columns,y=df.mean(),mode='lines',name='mean')) py.iplot(data,filename='pandas-box-plot')
#BasicHorizontalBarChart条形图plotly条形图 df_hb=df[['Name','Attack','Defense','Speed']][0:5].sort_values(['Attack'],ascending=[1]) data=[ go.Bar( y=df_hb['Name'],#assignxasthedataframecolumn'x' x=df_hb['Attack'], orientation='h', text=df_hb['Attack'], textposition='auto' ) ] py.iplot(data,filename='pandas-horizontal-bar')
#直方图Histogram data=[go.Histogram(x=df['Attack'])] py.iplot(data,filename='basichistogram')
#distplot importplotly.figure_factoryasff hist_data=[df['Defense']] group_labels=['distplot'] fig=ff.create_distplot(hist_data,group_labels) #Addtitle fig['layout'].update(title='HistandRugPlot',xaxis=dict(range=[0,200])) py.iplot(fig,filename='BasicDistplot')
#Addhistogramdata x1=np.random.randn(200)-2 x2=np.random.randn(200) x3=np.random.randn(200)+2 x4=np.random.randn(200)+4 #Groupdatatogether hist_data=[x1,x2,x3,x4] group_labels=['Group1','Group2','Group3','Group4'] #Createdistplotwithcustombin_size fig=ff.create_distplot(hist_data,group_labels,) #Plot! py.iplot(fig,filename='DistplotwithMultipleDatasets')
好了，以上就是我研究的plotly，欢迎朋友们评论，补充，一起学习！
以上这篇基于pythonplotly交互式图表大全就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持毛票票。
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