python plotly Tutorial

1, plotly Introduction

lotly of the publication quality graphics library Python interactive charts become online. How to make the line, scatter, area, bar, line errors, box plots, a histogram, FIG hot, sub-picture, multi-axis, and an example of the polar plot of FIG bubble.

Recommend the best use jupyter notebook, using pycharm if not very convenient.

2, installation

pip install plotly

2, using

1) Use Online

Find the username and api key in setting in

 

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##在线使用
import plotly.plotly as py
from plotly import tools from plotly.graph_objs import * tools.set_credentials_file(username='yours', api_key='yours') trace0 = Scatter( x=[1, 2, 3, 4], y=[10, 15, 13, 17], mode='markers' ) trace1 = Scatter( x=[1, 2, 3, 4], y=[16, 5, 11, 9] ) data = Data([trace0, trace1]) py.iplot(data) 

Scatter

 

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2）offline

import plotly.offline as of
import plotly.graph_objs as go of.offline.init_notebook_mode(connected=True) trace0 = go.Scatter( x=[1, 2, 3, 4], y=[10, 15, 13, 17], mode='markers' ) trace1 = go.Scatter( x=[1, 2, 3, 4], y=[16, 5, 11, 9] ) data = go.Data([trace0, trace1]) of.plot(data) 

3, other figures

Let's draw a few other types of diagrams

Histogram

import plotly.figure_factory as ff
import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv") data = [Bar(x=df.School, y=df.Gap)] py.iplot(data) 

 

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3D map

import numpy as np

s = np.linspace(0, 2 * np.pi, 240) t = np.linspace(0, np.pi, 240) tGrid, sGrid = np.meshgrid(s, t) r = 2 + np.sin(7 * sGrid + 5 * tGrid) # r = 2 + sin(7s+5t) x = r * np.cos(sGrid) * np.sin(tGrid) # x = r*cos(s)*sin(t) y = r * np.sin(sGrid) * np.sin(tGrid) # y = r*sin(s)*sin(t) z = r * np.cos(tGrid) # z = r*cos(t) surface = Surface(x=x, y=y, z=z) data = Data([surface]) layout = Layout( title='Parametric Plot', scene=Scene( xaxis=XAxis( gridcolor='rgb(255, 255, 255)', zerolinecolor='rgb(255, 255, 255)', showbackground=True, backgroundcolor='rgb(230, 230,230)' ), yaxis=YAxis( gridcolor='rgb(255, 255, 255)', zerolinecolor='rgb(255, 255, 255)', showbackground=True, backgroundcolor='rgb(230, 230,230)' ), zaxis=ZAxis( gridcolor='rgb(255, 255, 255)', zerolinecolor='rgb(255, 255, 255)', showbackground=True, backgroundcolor='rgb(230, 230,230)' ) ) ) fig = Figure(data=data, layout=layout) py.iplot(fig,) 

 

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line chart

import numpy as np

N = 100
random_x = np.linspace(0, 1, N) random_y0 = np.random.randn(N)+5 random_y1 = np.random.randn(N) random_y2 = np.random.randn(N)-5 # Create traces trace0 = go.Scatter( x = random_x, y = random_y0, mode = 'markers', name = 'markers' ) trace1 = go.Scatter( x = random_x, y = random_y1, mode = 'lines+markers', name = 'lines+markers' ) trace2 = go.Scatter( x = random_x, y = random_y2, mode = 'lines', name = 'lines' ) data = [trace0, trace1, trace2] py.iplot(data) 

 

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Figure stacking

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='stack' ) fig = go.Figure(data=data, layout=layout) py.iplot(fig) 

 

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pie

labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
values = [4500,2500,1053,500]
colors = ['#FEBFB3', '#E1396C', '#96D38C', '#D0F9B1'] trace = go.Pie(labels=labels, values=values, hoverinfo='label+percent', textinfo='value', textfont=dict(size=20), marker=dict(colors=colors, line=dict(color='#000000', width=2))) py.iplot([trace]) 

 

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I do not know what's chart

title = 'Main Source for News'

labels = ['Television', 'Newspaper', 'Internet', 'Radio'] colors = ['rgba(67,67,67,1)', 'rgba(115,115,115,1)', 'rgba(49,130,189, 1)', 'rgba(189,189,189,1)'] mode_size = [8, 8, 12, 8] line_size = [2, 2, 4, 2] x_data = [ [2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013], [2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013], [2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013], [2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013], ] y_data = [ [74, 82, 80, 74, 73, 72, 74, 70, 70, 66, 66, 69], [45, 42, 50, 46, 36, 36, 34, 35, 32, 31, 31, 28], [13, 14, 20, 24, 20, 24, 24, 40, 35, 41, 43, 50], [18, 21, 18, 21, 16, 14, 13, 18, 17, 16, 19, 23], ] traces = [] for i in range(0, 4): traces.append(go.Scatter( x=x_data[i], y=y_data[i], mode='lines', line=dict(color=colors[i], width=line_size[i]), connectgaps=True, )) traces.append(go.Scatter( x=[x_data[i][0], x_data[i][11]], y=[y_data[i][0], y_data[i][11]], mode='markers', marker=dict(color=colors[i], size=mode_size[i]) )) layout = go.Layout( xaxis=dict( showline=True, showgrid=False, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, autotick=False, ticks='outside', tickcolor='rgb(204, 204, 204)', tickwidth=2, ticklen=5, tickfont=dict( family='Arial', size=12, color='rgb(82, 82, 82)', ), ), yaxis=dict( showgrid=False, zeroline=False, showline=False, showticklabels=False, ), autosize=False, margin=dict( autoexpand=False, l=100, r=20, t=110, ), showlegend=False, ) annotations = [] # Adding labels for y_trace, label, color in zip(y_data, labels, colors): # labeling the left_side of the plot annotations.append(dict(xref='paper', x=0.05, y=y_trace[0], xanchor='right', yanchor='middle', text=label + ' {}%'.format(y_trace[0]), font=dict(family='Arial', size=16, color=colors,), showarrow=False)) # labeling the right_side of the plot annotations.append(dict(xref='paper', x=0.95, y=y_trace[11], xanchor='left', yanchor='middle', text='{}%'.format(y_trace[11]), font=dict(family='Arial', size=16, color=colors,), showarrow=False)) # Title annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05, xanchor='left', yanchor='bottom', text='Main Source for News', font=dict(family='Arial', size=30, color='rgb(37,37,37)'), showarrow=False)) # Source annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.1, xanchor='center', yanchor='top', text='Source: PewResearch Center & ' + 'Storytelling with data', font=dict(family='Arial', size=12, color='rgb(150,150,150)'), showarrow=False)) layout['annotations'] = annotations fig = go.Figure(data=traces, layout=layout) py.iplot(fig) 

 

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4, various concrete syntax

pdf

 

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5, summary

Figure painting is really good-looking, but also across the map will be automatically uploaded to the cloud.

 

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Author: Five longevity
link: https: //www.jianshu.com/p/57bad75139ca
Source: Jane book
Jane book copyright reserved by the authors, are reproduced in any form, please contact the author to obtain authorization and indicate the source.