下面是本系列分享的第二篇，关于线图的分享。
事实上计划中线图和点图应该在一篇中介绍完毕，因为线图内容也不少还有其他拓展，所以就单独介绍一下。
线图的接口与点图基本类似，同样的plotly.express快捷方式可以方便的绘制出线图，与graph_objects不同的是，plotly.express直接调用了line()方法，更加容易理解：
import plotly.express as px
df = px.data.gapminder().query("country=='Canada'")
fig = px.line(df, x="year", y="lifeExp", title='Life expectancy in Canada')
fig.show()
3-1.jpg
一个较为高阶的例子：
import plotly.express as px
df = px.data.gapminder().query("continent != 'Asia'") # remove Asia for visibility
fig = px.line(df, x="year", y="lifeExp", color="continent",
line_group="country", hover_name="country")
fig.show()
3-2.jpg
同样的，plotly.express不是本文的重点，撇开不谈。
3.1 基本的线图
默认情况下go.Scatter()默认即为线图，隐藏了mode="lines+markers"参数：
import plotly.graph_objects as go
import numpy as np
x = np.arange(10)
fig = go.Figure(data=go.Scatter(x=x, y=x**2))
fig.show()
3-3.jpg
下面这个例子更清晰的比较出了纯线图、线+点图和点图在mode参数传入后的效果区别：
import plotly.graph_objects as go
# Create random data with numpy
import numpy as np
np.random.seed(1)
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
fig = go.Figure()
fig.add_trace(go.Scatter(x=random_x, y=random_y0,
mode='lines',
name='lines'))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
mode='lines+markers',
name='lines+markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
mode='markers', name='markers'))
fig.show()
3-4.jpg
在掌握基础的线图绘制上，可以通过line传入字典型的参数，通过具体样式的color, width, dash输出不同样式的线型
import plotly.graph_objects as go
# Add data
month = ['January', 'February', 'March', 'April', 'May', 'June', 'July',
'August', 'September', 'October', 'November', 'December']
high_2000 = [32.5, 37.6, 49.9, 53.0, 69.1, 75.4, 76.5, 76.6, 70.7, 60.6, 45.1, 29.3]
low_2000 = [13.8, 22.3, 32.5, 37.2, 49.9, 56.1, 57.7, 58.3, 51.2, 42.8, 31.6, 15.9]
high_2007 = [36.5, 26.6, 43.6, 52.3, 71.5, 81.4, 80.5, 82.2, 76.0, 67.3, 46.1, 35.0]
low_2007 = [23.6, 14.0, 27.0, 36.8, 47.6, 57.7, 58.9, 61.2, 53.3, 48.5, 31.0, 23.6]
high_2014 = [28.8, 28.5, 37.0, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9]
low_2014 = [12.7, 14.3, 18.6, 35.5, 49.9, 58.0, 60.0, 58.6, 51.7, 45.2, 32.2, 29.1]
fig = go.Figure()
# Create and style traces
fig.add_trace(go.Scatter(x=month, y=high_2014, name='High 2014',
line=dict(color='firebrick', width=4)))
fig.add_trace(go.Scatter(x=month, y=low_2014, name = 'Low 2014',
line=dict(color='royalblue', width=4)))
fig.add_trace(go.Scatter(x=month, y=high_2007, name='High 2007',
line=dict(color='firebrick', width=4,
dash='dash') # dash options include 'dash', 'dot', and 'dashdot'
))
fig.add_trace(go.Scatter(x=month, y=low_2007, name='Low 2007',
line = dict(color='royalblue', width=4, dash='dash')))
fig.add_trace(go.Scatter(x=month, y=high_2000, name='High 2000',
line = dict(color='firebrick', width=4, dash='dot')))
fig.add_trace(go.Scatter(x=month, y=low_2000, name='Low 2000',
line=dict(color='royalblue', width=4, dash='dot')))
# Edit the layout
fig.update_layout(title='Average High and Low Temperatures in New York',
xaxis_title='Month',
yaxis_title='Temperature (degrees F)')
fig.show()
3-5.jpg
3.2 进阶线型效果
通过connectgaps对线型中数据的中断进行控制，默认情况下绘图中断，connectgaps=True后则会自动连接中断：
import plotly.graph_objects as go
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
fig = go.Figure()
fig.add_trace(go.Scatter(
x=x,
y=[10, 20, None, 15, 10, 5, 15, None, 20, 10, 10, 15, 25, 20, 10],
name = 'No Gaps', # Style name/legend entry with html tags
connectgaps=True # override default to connect the gaps
))
fig.add_trace(go.Scatter(
x=x,
y=[5, 15, None, 10, 5, 0, 10, None, 15, 5, 5, 10, 20, 15, 5],
name='Gaps',
# connectgaps=False # Default
))
fig.show()
3-6.jpg
通过底层的line_shape参数改变线段样式：
- linear 折线
- spline 曲线
- vhv/hvh/vh/hv 不同样式的折线效果
import plotly.graph_objects as go
import numpy as np
x = np.array([1, 2, 3, 4, 5])
y = np.array([1, 3, 2, 3, 1])
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, name="linear",
line_shape='linear'))
fig.add_trace(go.Scatter(x=x, y=y + 5, name="spline",
text=["tweak line smoothness
with 'smoothing' in line object"],
hoverinfo='text+name',
line_shape='spline'))
fig.add_trace(go.Scatter(x=x, y=y + 10, name="vhv",
line_shape='vhv'))
fig.add_trace(go.Scatter(x=x, y=y + 15, name="hvh",
line_shape='hvh'))
fig.add_trace(go.Scatter(x=x, y=y + 20, name="vh",
line_shape='vh'))
fig.add_trace(go.Scatter(x=x, y=y + 25, name="hv",
line_shape='hv'))
fig.update_traces(hoverinfo='text+name', mode='lines+markers')
fig.update_layout(legend=dict(y=0.5, traceorder='reversed', font_size=16))
fig.show()
3-7.jpg
下面这个例子是非常值得推荐学习的例子，通过对线型的调整，输出了杂志级的商业数据图
事实上我自己就是运用这个参数略微变化并封装函数用作我日常的分析。
针对这个例子，值得注意的细节有：
- np.vstack()/np.hstack() 按照垂直(行)/水平(列)方向生成新的数组
- 链接断点参数 connectgaps=True
- 在首、尾部使用点的绘制方式，高端大气上档次
- 通过对几个线段的分别绘制，提前准备好label和color列表
- 针对xaxis和yaxis分别设置样式
import plotly.graph_objects as go
import numpy as np
title = 'Main Source for News'
labels = ['Television', 'Newspaper', 'Internet', 'Radio']
colors = ['rgb(67,67,67)', 'rgb(115,115,115)', 'rgb(49,130,189)', 'rgb(189,189,189)']
mode_size = [8, 8, 12, 8]
line_size = [2, 2, 4, 2]
x_data = np.vstack((np.arange(2001, 2014),)*4)
y_data = np.array([
[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],
])
fig = go.Figure()
for i in range(0, 4):
fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines',
name=labels[i],
line=dict(color=colors[i], width=line_size[i]),
connectgaps=True,
))
# endpoints
fig.add_trace(go.Scatter(
x=[x_data[i][0], x_data[i][-1]],
y=[y_data[i][0], y_data[i][-1]],
mode='markers',
marker=dict(color=colors[i], size=mode_size[i])
))
fig.update_layout(
xaxis=dict(
showline=True,
showgrid=False,
showticklabels=True,
linecolor='rgb(204, 204, 204)',
linewidth=2,
ticks='outside',
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,
plot_bgcolor='white'
)
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),
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),
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))
fig.update_layout(annotations=annotations)
fig.show()
3-8.jpg
线图的填充效果
这种效果主要靠两次绘画线段，除了正常线段绘制，底色线段采用fill='toself'参数的方式，同时使用fillcolor、line_color进行颜色配置，最终用showlegend=False将底色的线段图例隐藏掉。
import plotly.graph_objects as go
import numpy as np
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
x_rev = x[::-1]
# Line 1
y1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y1_upper = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
y1_lower = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
y1_lower = y1_lower[::-1]
# Line 2
y2 = [5, 2.5, 5, 7.5, 5, 2.5, 7.5, 4.5, 5.5, 5]
y2_upper = [5.5, 3, 5.5, 8, 6, 3, 8, 5, 6, 5.5]
y2_lower = [4.5, 2, 4.4, 7, 4, 2, 7, 4, 5, 4.75]
y2_lower = y2_lower[::-1]
# Line 3
y3 = [10, 8, 6, 4, 2, 0, 2, 4, 2, 0]
y3_upper = [11, 9, 7, 5, 3, 1, 3, 5, 3, 1]
y3_lower = [9, 7, 5, 3, 1, -.5, 1, 3, 1, -1]
y3_lower = y3_lower[::-1]
fig = go.Figure()
fig.add_trace(go.Scatter(
x=x+x_rev,
y=y1_upper+y1_lower,
fill='toself',
fillcolor='rgba(0,100,80,0.2)',
line_color='rgba(255,255,255,0)',
showlegend=False,
name='Fair',
))
fig.add_trace(go.Scatter(
x=x+x_rev,
y=y2_upper+y2_lower,
fill='toself',
fillcolor='rgba(0,176,246,0.2)',
line_color='rgba(255,255,255,0)',
name='Premium',
showlegend=False,
))
fig.add_trace(go.Scatter(
x=x+x_rev,
y=y3_upper+y3_lower,
fill='toself',
fillcolor='rgba(231,107,243,0.2)',
line_color='rgba(255,255,255,0)',
showlegend=False,
name='Ideal',
))
fig.add_trace(go.Scatter(
x=x, y=y1,
line_color='rgb(0,100,80)',
name='Fair',
))
fig.add_trace(go.Scatter(
x=x, y=y2,
line_color='rgb(0,176,246)',
name='Premium',
))
fig.add_trace(go.Scatter(
x=x, y=y3,
line_color='rgb(231,107,243)',
name='Ideal',
))
fig.update_traces(mode='lines')
fig.show()
3-9.jpg
3.3 ，面积图
由线图衍生出的图形样式：面积图
核心参数是fill
- toself 对自身的填充
- tozeroy 对坐标轴y轴方向的填充
- tonexty 对上个图形y轴方向的填充
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[0, 2, 3, 5], fill='tozeroy')) # fill down to xaxis
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 5, 1, 7], fill='tonexty')) # fill to trace0 y
fig.show()
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隐藏生成线
通过用mode="none"替代掉mode="markers+lines"的默认值
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[0, 2, 3, 5], fill='tozeroy',
mode='none' # override default markers+lines
))
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 5, 1, 7], fill='tonexty',
mode= 'none'))
fig.show()
3-10.jpg
两线的内部填充效果
主要是通过第二条线型中的fill="tonexty"实现，特别注意第一条线型的fill=None
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 4, 8, 3],
fill=None,
mode='lines',
line_color='indigo',
))
fig.add_trace(go.Scatter(
x=[1, 2, 3, 4],
y=[1, 6, 2, 6],
fill='tonexty', # fill area between trace0 and trace1
mode='lines', line_color='indigo'))
fig.show()
3-11.jpg
3.4 堆积面积图
stackgroup是堆积图中的关键参数，参数用于堆积同一组中不同记录的y值。同一组中的记录将在下次同组出现的时候作为基础值叠加到新的y值上。
import plotly.graph_objects as go
x=['Winter', 'Spring', 'Summer', 'Fall']
fig = go.Figure()
fig.add_trace(go.Scatter(
x=x, y=[40, 60, 40, 10],
hoverinfo='x+y',
mode='lines',
line=dict(width=0.5, color='rgb(131, 90, 241)'),
stackgroup='one' # define stack group
))
fig.add_trace(go.Scatter(
x=x, y=[20, 10, 10, 60],
hoverinfo='x+y',
mode='lines',
line=dict(width=0.5, color='rgb(111, 231, 219)'),
stackgroup='one'
))
fig.add_trace(go.Scatter(
x=x, y=[40, 30, 50, 30],
hoverinfo='x+y',
mode='lines',
line=dict(width=0.5, color='rgb(184, 247, 212)'),
stackgroup='one'
))
fig.update_layout(yaxis_range=(0, 100))
fig.show()
3-12.jpg
含标准值的面积堆积图
通过groupnorm的参数设置，制定了stackgroup对y值数据的叠加方式。
import plotly.graph_objects as go
x=['Winter', 'Spring', 'Summer', 'Fall']
fig = go.Figure()
fig.add_trace(go.Scatter(
x=x, y=[40, 20, 30, 40],
mode='lines',
line=dict(width=0.5, color='rgb(184, 247, 212)'),
stackgroup='one',
groupnorm='percent' # sets the normalization for the sum of the stackgroup
))
fig.add_trace(go.Scatter(
x=x, y=[50, 70, 40, 60],
mode='lines',
line=dict(width=0.5, color='rgb(111, 231, 219)'),
stackgroup='one'
))
fig.add_trace(go.Scatter(
x=x, y=[70, 80, 60, 70],
mode='lines',
line=dict(width=0.5, color='rgb(127, 166, 238)'),
stackgroup='one'
))
fig.add_trace(go.Scatter(
x=x, y=[100, 100, 100, 100],
mode='lines',
line=dict(width=0.5, color='rgb(131, 90, 241)'),
stackgroup='one'
))
fig.update_layout(
showlegend=True,
xaxis_type='category',
yaxis=dict(
type='linear',
range=[1, 100],
ticksuffix='%'))
fig.show()
3-13.jpg
选择悬停点
- hoveron = 'points+fills'/'points' 在何处激活悬停显示信息
- text="Points only" 具体信息文本设置
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=[0,0.5,1,1.5,2], y=[0,1,2,1,0],
fill='toself', fillcolor='darkviolet',
hoveron = 'points+fills', # select where hover is active
line_color='darkviolet',
text="Points + Fills",
hoverinfo = 'text+x+y'))
fig.add_trace(go.Scatter(x=[3,3.5,4,4.5,5], y=[0,1,2,1,0],
fill='toself', fillcolor = 'violet',
hoveron='points',
line_color='violet',
text="Points only",
hoverinfo='text+x+y'))
fig.update_layout(
title = "hover on points or fill",
xaxis_range = [0,5.2],
yaxis_range = [0,3]
)
fig.show()
3-14.jpg