plotly可以制作交互式图表，直接上代码：

import plotly.offline as pyfrom plotly.graph_objs import Scatter, Layoutimport plotly.graph_objs as gopy.init_notebook_mode(connected=True)import pandas as pdimport numpy as np

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()Out[412]: Name Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed Stage Legendary # 1 Bulbasaur Grass Poison 318 45 49 49 65 65 45 1 False 2 Ivysaur Grass Poison 405 60 62 63 80 80 60 2 False 3 Venusaur Grass Poison 525 80 82 83 100 100 80 3 False 4 Charmander Fire NaN 309 39 52 43 60 50 65 1 False 5 Charmeleon Fire NaN 405 58 64 58 80 65 80 2 False

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))#set color equal to a variablesz =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bar grouptrace1 = 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='group')fig = go.Figure(data=data, layout=layout)py.iplot(fig, filename='grouped-bar')#组合bar gstack上下组合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',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": "GHG Emissions", "hoverinfo":"label+percent+name", "hole": .4, "type": "pie" } ], "layout": { "title":"Global Emissions 1990-2011", "annotations": [ { "font": {"size": 20}, "showarrow": False, "text": "GHG", "x": 0.5, "y": 0.5 } ] } }py.iplot(fig, filename='donut')# Learn about API authentication here: https://plot.ly/pandas/getting-started# Find your api_key here: 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 = 'Group A' ), go.Scatterpolar( r = [1.5, 10, 39, 31, 15, 1.5], theta = ['A','B','C', 'D', 'E', 'A'], fill = 'toself', name = 'Group B' )] 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 = []for col in df_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 = []for col in df_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')#Basic Horizontal Bar Chart 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'], # assign x as the dataframe column 'x' x=df_hb['Attack'], orientation='h', text=df_hb['Attack'], textposition = 'auto' )]py.iplot(data, filename='pandas-horizontal-bar')#直方图Histogramdata = [go.Histogram(x=df['Attack'])]py.iplot(data, filename='basic histogram')#distplotimport plotly.figure_factory as ff hist_data =[df['Defense']]group_labels = ['distplot']fig = ff.create_distplot(hist_data, group_labels)# Add titlefig['layout'].update(title='Hist and Rug Plot',xaxis=dict(range=[0,200]))py.iplot(fig, filename='Basic Distplot')# Add histogram datax1 = np.random.randn(200)-2 x2 = np.random.randn(200) x3 = np.random.randn(200)+2 x4 = np.random.randn(200)+4 # Group data togetherhist_data = [x1, x2, x3, x4]group_labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4']# Create distplot with custom bin_sizefig = ff.create_distplot(hist_data, group_labels,)# Plot!py.iplot(fig, filename='Distplot with Multiple Datasets')

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python plotly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。