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I'm trying to plot a vertical line that's dynamically positioned so that when filtering happens, the line will move accordingly. For example, with the below code, I can plot a stationary vertical line at 25K which works with the full dataset as the median, but when the data is filtered to "Americas" only since the x-axis range is now 45K, the line is not at the median position anymore.
So how can I plot a vertical line that's positioned at the x-axis range's median position? Thanks
import pandas as pd
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
df = pd.read_csv('https://raw.githubusercontent.com/yankev/test/master/life-expectancy-per-GDP-2007.csv')
americas = df[(df.continent=='Americas')]
europe = df[(df.continent=='Europe')]
trace_comp0 = go.Scatter(
x=americas.gdp_percap,
y=americas.life_exp,
mode='markers',
marker=dict(size=12,
line=dict(width=1),
color="navy"
),
name='Americas',
text=americas.country,
)
trace_comp1 = go.Scatter(
x=europe.gdp_percap,
y=europe.life_exp,
mode='markers',
marker=dict(size=12,
line=dict(width=1),
color="red"
),
name='Europe',
text=europe.country,
)
data_comp = [trace_comp0, trace_comp1]
layout_comp = go.Layout(
title='Life Expectancy v. Per Capita GDP, 2007',
hovermode='closest',
xaxis=dict(
title='GDP per capita (2000 dollars)',
ticklen=5,
zeroline=False,
gridwidth=2,
range=[0, 50_000],
),
yaxis=dict(
title='Life Expectancy (years)',
ticklen=5,
gridwidth=2,
range=[0, 90],
),
shapes=[
{
'type': 'line',
'x0': 25000,
'y0': 0,
'x1': 25000,
'y1': 85,
'line': {
'color': 'black',
'width': 1
}
}
]
)
fig_comp = go.Figure(data=data_comp, layout=layout_comp)
iplot(fig_comp)
629
Setting the Range of Axes Manually The visible x and y axis range can be configured manually by setting the range axis property to a list of two values, the lower and upper boundary. Here's an example of manually specifying the x and y axis range for a faceted scatter plot created with Plotly Express.
To create multiple line charts on the same plot with plotly graph objects, all you have to do is add another trace to the plot. If you look closely, you can see that I have also added the name parameter to add the labels for the legend and also explicitly added the mode='lines' to tell plotly that i want a line chart.
Scatter() function to make a scatter plot. The go. Figure() function takes in data as input where we set the mode as 'lines' using mode='lines'. We have used the magic underscore notation i.e layout_yaxis_range=[-8,8] to set the y-axis range from -8 to 8.
Scatter plots with Plotly Express Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. With px. scatter , each data point is represented as a marker point, whose location is given by the x and y columns.
With the help of the @rpanai's answer and using plotly update buttons, the following solution is developed. Check this.
import pandas as pd
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
df = pd.read_csv('https://raw.githubusercontent.com/yankev/test/master/life-expectancy-per-GDP-2007.csv')
americas = df[(df.continent=='Americas')]
europe = df[(df.continent=='Europe')]
# med_eur = europe["gdp_percap"].median()
# med_ame = americas["gdp_percap"].median()
# med_total=pd.DataFrame(list(europe["gdp_percap"])+list(americas["gdp_percap"])).median()[0]
med_eur = europe["gdp_percap"].max()/2
med_ame = americas["gdp_percap"].max()/2
med_total=25000
trace_median0 = go.Scatter(x=[med_total, med_total],
y=[0,85],
mode="lines",
legendgroup="a",
showlegend=False,
marker=dict(size=12,
line=dict(width=0.8),
color="green"
),
name="Median Total"
)
trace_comp1 = go.Scatter(
x=americas.gdp_percap,
y=americas.life_exp,
mode='markers',
marker=dict(size=12,
line=dict(width=1),
color="navy"
),
name='Americas',
text=americas.country
)
trace_median1 = go.Scatter(x=[med_ame, med_ame],
y=[0,90],
mode="lines",
legendgroup="a",
showlegend=False,
marker=dict(size=12,
line=dict(width=0.8),
color="navy"
),
name="Median Americas",
visible=False
)
trace_comp2 = go.Scatter(
x=europe.gdp_percap,
y=europe.life_exp,
mode='markers',
marker=dict(size=12,
line=dict(width=1),
color="red"
),
name='Europe',
text=europe.country,
)
trace_median2 = go.Scatter(x=[med_eur, med_eur],
y=[0,90],
mode="lines",
legendgroup="b",
showlegend=False,
marker=dict(size=12,
line=dict(width=0.8),
color="red"
),
name="Median Europe",
visible=False
)
data_comp = [trace_comp1,trace_median1]+[trace_comp2,trace_median2]+[trace_median0]
layout_comp = go.Layout(
title='Life Expectancy v. Per Capita GDP, 2007',
hovermode='closest',
xaxis=dict(
title='GDP per capita (2000 dollars)',
ticklen=5,
zeroline=False,
gridwidth=2,
range=[0, 50_000],
),
yaxis=dict(
title='Life Expectancy (years)',
ticklen=5,
gridwidth=2,
range=[0, 90],
),
showlegend=False
)
updatemenus = list([
dict(type="buttons",
active=-1,
buttons=list([
dict(label = 'Total Dataset ',
method = 'update',
args = [{'visible': [True,False,True,False,True]},
{'title': 'Life Expectancy v. Per Capita GDP, 2007'}]),
dict(label = 'Americas',
method = 'update',
args = [{'visible': [True,True, False, False,False]},
{'title': 'Americas'}]),
dict(label = 'Europe',
method = 'update',
args = [{'visible': [False, False,True,True,False]},
{'title': 'Europe'}])
]),
)
])
annotations = list([
dict(text='Trace type:', x=0, y=1.085, yref='paper', align='left', showarrow=False)
])
layout_comp['updatemenus'] = updatemenus
layout_comp['annotations'] = annotations
fig_comp = go.Figure(data=data_comp, layout=layout_comp)
iplot(fig_comp)
174
You need to add so called callbacks to your program, so that the whole figure is updated when the database changes. Then you include a mean() to the definition of your x1 and x0 shape definitions. However this requires you to use dash.
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