基于python plotly交互式图表大全

Python  /  管理员 发布于 7年前   277

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。