基于python plotly交互式图表大全

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

#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]:

#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 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  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")

#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")