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:
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]:
#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 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")
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云海天教程。