基于python plotly交互式圖表大全

更新時間：2019年12月07日 11:16:29 作者：會spark的籃球少年

今天小編就為大家分享一篇基于python plotly交互式圖表大全，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧

plotly可以制作交互式圖表，直接上代碼：

import plotly.offline as py
from plotly.graph_objs import Scatter, Layout
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
import pandas as pd
import numpy as np

In [412]:

#讀取數(shù)據(jù)
df=pd.read_csv('seaborn.csv',sep=',',encoding='utf-8',index_col=0)
#展示數(shù)據(jù)
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折線圖，trace就代表折現(xiàn)的條數(shù)
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])

#填充區(qū)域
trace1=go.Scatter(x=[1,2,3,4,5],y=[2,1,3,5,2],fill="tonexty",fillcolor="#FF0")
py.iplot([trace1])

# 散點圖
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')

#氣泡圖
x=df['Attack']
y=df['Defense']
colors = np.random.rand(len(x))#set color equal to a variable
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 柱狀圖
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')

#組合bar  group
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='group')
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='grouped-bar')

#組合bar  gstack上下組合
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')

#餅圖
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
#雷達圖
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 箱子圖
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')

#箱子圖加平均線
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 條形圖 plotly條形圖
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')

#直方圖Histogram
data = [go.Histogram(x=df['Attack'])]
py.iplot(data, filename='basic histogram')

#distplot
import plotly.figure_factory as ff 
hist_data =[df['Defense']]
group_labels = ['distplot']
fig = ff.create_distplot(hist_data, group_labels)
# Add title
fig['layout'].update(title='Hist and Rug Plot',xaxis=dict(range=[0,200]))
py.iplot(fig, filename='Basic Distplot')

# Add histogram data
x1 = np.random.randn(200)-2 
x2 = np.random.randn(200) 
x3 = np.random.randn(200)+2 
x4 = np.random.randn(200)+4 
 
# Group data together
hist_data = [x1, x2, x3, x4]
group_labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4']
# Create distplot with custom bin_size
fig = ff.create_distplot(hist_data, group_labels,)
# Plot!
py.iplot(fig, filename='Distplot with Multiple Datasets')