Develop Reference

matplotlib to plotly plot conversion

I wanted to create an interactive plot with matplotlib in google colab. It seems like a complex task so I want a little help to convert this piece of code which is in matplotlib to Plotly.
close = df['A']
fig = plt.figure(figsize = (15,5))
plt.plot(close, color='r', lw=2.)
plt.plot(close, '^', markersize=10, color='m', label = 'signal X', markevery = df_x)
plt.plot(close, 'v', markersize=10, color='k', label = 'signal Y', markevery = df_y)
plt.title('Turtle Agent: total gains %f, total investment %f%%'%(df_A, df_B))
plt.legend()
plt.show()

using sample data from plotly OHLC examples https://plotly.com/python/ohlc-charts/
create a line trace
add scatter traces based on filters of data frame with required formatting. This is done as a list comprehension, could be done as inline code
import pandas as pd
import numpy as np
import plotly.express as px
df = pd.read_csv(
"https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv"
)
df["Date"] = pd.to_datetime(df["Date"])
# make data set more useful for demonstrating this plot
df.loc[df.sample((len(df)//8)*7).index, "direction"] = np.nan
px.line(df, x="Date", y="AAPL.Close").update_traces(line_color="red").add_traces(
[
px.scatter(
df.loc[df["direction"].eq(filter)], x="Date", y="AAPL.Close"
)
.update_traces(marker=fmt)
.data[0]
for filter, fmt in zip(
["Increasing", "Decreasing"],
[
{"color": "black", "symbol": "triangle-up", "size": 10},
{"color": "blue", "symbol": "triangle-down", "size": 10},
],
)
]
)

Combined xaxis and header of table with plotly Python

I would like to do something quite similar to the picture with plotly on python. I tried to find a way with subplots and shared_axis but no way to find a correct way. Is it possible to share the x axis of a bar chart with the column titles of a table?
graph bar with shared xaxis

this can be simulated with two traces
first trace is a standard bar chart, with yaxis domain constrained to 80% of the figure
second trace is a bar showing values as text and a fixed height against a second yaxis. yaxis2 is constrained to 10% of the domain
import plotly.express as px
import pandas as pd
import numpy as np
df = pd.DataFrame({"year": range(2011, 2022)}).assign(
pct=lambda d: np.random.uniform(-0.08, 0.08, len(d))
)
px.bar(df, x="year", y="pct").add_traces(
px.bar(df, x="year", y=np.full(len(df), 1), text="pct")
.update_traces(
yaxis="y2",
marker={"line": {"color": "black", "width": 1.5}, "color": "#E5ECF6"},
texttemplate="%{text:,.2%}",
)
.data
).update_layout(
yaxis={"domain": [0.2, 1], "tickformat": ",.2%"},
yaxis2={"domain": [0, 0.1], "visible": False},
xaxis={"title": "", "dtick": 1},
)

Animated 3D Surface Plots with Plotly

For research data visualisation I'd like to make an animated 3D surface plot in Plotly. The goal is to see the evolution of temperature in a box in function of time. But I don't know how to animate it.
At this moment I only have my plot at a give time.
This is my code:
import plotly
import plotly.graph_objects as go
#import plotly.express as px
import pandas as pd
#import numpy as np
#read CSV
z_data = pd.read_csv('data1.csv')# Read data from a csv
fig = go.Figure(data=[go.Surface(z=z_data.values)])
#projection 2D
fig.update_traces(contours_z=dict(show=True, usecolormap=True,
highlightcolor="tomato", project_z=True),
colorscale='portland')
#fig
fig.update_layout(title='data HEATPILES', autosize=False, width=650, height=500, margin=dict(l=0, r=0, b=0, t=0))
#show
plotly.offline.plot(fig)
data1.csv is only this:
data1.csv
But I have more data of the point's position in function of time and I would want to make an animated plot, so we could clearly see the evolution on time.
Here is the result at a given time
Plot at a given time
I've seen on the plotly documentation that it's possible to make animation with px.scatter and px.line from here, and from there that we can do it with image, so I guess it would be possible with surface plot.
Here is some example of the animation: https://plotly.com/python/#animations
Here is some example of the 3D surface plot: https://plotly.com/python/3d-surface-plots
If you could help me do you I would much appreciate !
Thank you for your help,
Theophile

Here is the full code for you:
import pandas as pd
import plotly.graph_objects as go
z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv').values
print(z_data)
z_data2 = z_data * 1.1
z_data3 = z_data * 1.2
z_data4 = z_data * 0.5
z_data_list = []
z_data_list.append(z_data)
z_data_list.append(z_data2)
z_data_list.append(z_data3)
z_data_list.append(z_data4)
z_data_list.append(z_data)
z_data_list.append(z_data2)
z_data_list.append(z_data3)
z_data_list.append(z_data4)
fig = go.Figure(
data=[go.Surface(z=z_data_list[0])],
layout=go.Layout(updatemenus=[dict(type="buttons", buttons=[dict(label="Play", method="animate", args=[None])])]),
frames=[go.Frame(data=[go.Surface(z=k)], name=str(i)) for i, k in enumerate(z_data_list)]
)
fig.update_traces(contours_z=dict(show=True, usecolormap=True, highlightcolor="tomato", project_z=True), colorscale='portland')
fig.update_layout(title='data HEATPILES', autosize=False, width=650, height=500, margin=dict(l=0, r=0, b=0, t=0))
def frame_args(duration):
return {
"frame": {"duration": duration},
"mode": "immediate",
"fromcurrent": True,
"transition": {"duration": duration, "easing": "linear"},
}
sliders = [
{
"pad": {"b": 10, "t": 60},
"len": 0.9,
"x": 0.1,
"y": 0,
"steps": [
{
"args": [[f.name], frame_args(0)],
"label": str(k),
"method": "animate",
}
for k, f in enumerate(fig.frames)
],
}
]
fig.update_layout(sliders=sliders)
import plotly.io as pio
ii = 1
pio.write_html(fig, file="Live3D_"+str(ii)+".html", auto_open=True)
# plotly.offline.plot(fig)

After a good research I built this code to plot a proper smooth 3D surface plot. Simply put the data_frame into this function. You'll get a proper smoothen surface plot. Incase you face any error, just choose only those features from data_frame which are numerical.
'data_frame = data_frame.select_dtypes(include='number')'
from scipy import interpolate
from mpl_toolkits.mplot3d import axes3d, Axes3D
def surface(data_frame, title=None, title_x=0.5, title_y=0.9):
X, Y = np.mgrid[-10:10:complex(0,data_frame.shape[0]),
-10:10:complex(0,data_frame.shape[1])]
Z = data_frame.values
xnew, ynew = np.mgrid[-1:1:80j, -1:1:80j]
tck = interpolate.bisplrep(X, Y, Z, s=0)
znew = interpolate.bisplev(xnew[:,0], ynew[0,:], tck)
fig = go.Figure(data=[go.Surface(z=znew)])
fig.update_layout(template='plotly_dark',
width=800,
height=800,
title = title,
title_x = title_x,
title_y = title_y
)
return fig

Overlapping colorscales in plotly

I have plotted a figure with 2 subplots, each with different scales. Everything plots correctly, except the colorscales are both plotted on the right and completely overlap - they are are not readable. I cannot find out how to position/reposition the individual subplot scales. I have included my code below. Thanks.
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
df = pd.read_csv(entry)
custColorscale = [[0, 'green'], [0.5, 'red'], [1, 'rgb(50, 50, 50)']]
fig = make_subplots(
rows=1, cols=2, subplot_titles=('one', 'two'))
fig.add_trace(
go.Scatter(x=df['tO'],
y=df['t1'],
mode='markers',
marker=dict(colorscale=custColorscale,
cmin=0, cmax=2,
size=6, color=df['Var1'],
showscale=True),
text=df['Var2']),
1, 1)
fig.add_trace(
go.Scatter(x=df['tO'],
y=df['t1'],
mode='markers',
marker=dict(
size=6, color=df['Var2'],
showscale=True),
text=df['Var2']),
1, 2)
fig.update_layout(height=700, width=1900,
title='Raw data')
fig.update_layout(coloraxis=dict(
colorscale='Bluered_r'))
fig.write_html(fig, file='raw plots.html', auto_open=True)

Looking through the Plotly documentation you find this which provide some hints as to how to solve the problem. Scroll to the 'marker' attributes and you will find that it has sub-attribute called 'colorbar'. The colorbar in turn has multiple options that could help set the plot the way you want. Particularly you find the 'x', 'y' and 'len' attributes of the colorbar very useful. You can use them to position the scales.
This question is also related to this but for a contour plot - you are making a scatterplot which is why the scatterplot reference would be what one should search.
A minimal working example (MWE) is shown below but with a toy dataset.
## make necessary imports
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import pandas as pd
## make a fake dataset with pandas
d = {'t0': [i for i in np.arange(0.,10.,1.)], 't1': [i for i in
np.arange(10.,20.,1.)],'Var1': [i for i in np.arange(20.,30.,1.)],'Var2':
[i for i in np.arange(30.,40.,1.)] }
df = pd.DataFrame(data=d) #the dataset is made to mock the example code you provided
And for your plot you have the following :
# make subplots
custColorscale = [[0, 'green'], [0.5, 'red'], [1, 'rgb(50, 50, 50)']]
fig = make_subplots(
rows=1, cols=2, subplot_titles=('one', 'two'),horizontal_spacing = 0.4)
# plot 1
fig.add_trace(
go.Scatter(x=df['t0'],
y=df['t1'],
mode='markers',
marker=dict(colorscale=custColorscale,
cmin=0, cmax=2,
size=6, color=df['Var1'],
showscale=True,colorbar=dict(len=1.05, x=0.35
,y=0.49)), text=df['Var2']), 1, 1)
## plot 2
fig.add_trace(
go.Scatter(x=df['t0'],
y=df['t1'],
mode='markers',
marker=dict(
size=6, color=df['Var2'],
showscale=True,colorbar=dict(len=1.05, x=1.2 , y=0.49)),
text=df['Var2']),
1, 2 )
# show plots
fig.update_layout(height=500, width=700,
title='Raw data')
fig.update_layout(coloraxis=dict(
colorscale='Bluered_r'))
fig.show()
The only additions were:
The colorbar attribute of the marker.
The horizontal spacing to allow space for the first scale.
Feel free to play with these attributes.
I hope this helps!
Best regards.

Is there a better way to use Jupyter IntSlider with Python Plotly?

In the following code block I use a Jupyter IntSlider to adjust the number of dots visualized in a Plotly express scatter 3d plot. The example already fits my use case, but I noticed that Plotly has built-in slider functionalities that could improve the performance.
As a Plotly beginner I find it quite hard to map the slider example from Plotly to my use case.
Any suggestions?
import numpy as np
import plotly.express as px
import pandas as pd
from ipywidgets import interact, widgets
NUM_DOTS = 100
NUM_DIMS = 3
random_data = pd.DataFrame(np.random.random((NUM_DOTS,NUM_DIMS) ), columns=['x_1','x_2','x_3'])
def update_plotly(x):
fig = px.scatter_3d(random_data[:x], x='x_1', y='x_2', z='x_3')
fig.show()
interact(update_plotly, x=widgets.IntSlider(min=1, max=NUM_DOTS, step=1, value=NUM_DOTS))

Actually it's not that hard to build the slider, just follow the path of the example shown by plotly:
import plotly.graph_objects as go
import numpy as np
NUM_DOTS = 100
NUM_DIMS = 3
# Create figure
fig = go.Figure()
# Add traces, one for each slider step
for step in np.arange(1, NUM_DOTS, 1):
#Random data
random_data = pd.DataFrame(np.random.random((step, NUM_DIMS)), columns=['x_1','x_2','x_3'])
fig.add_trace(
go.Scatter3d(
visible=False,
line=dict(color="#00CED1", width=6),
name="𝜈 = " + str(step),
z=random_data['x_3'],
x=random_data['x_1'],
y=random_data['x_2']))
# Make 10th trace visible
fig.data[10].visible = True
# Create and add slider
steps = []
for i in range(len(fig.data)):
step = dict(
method="restyle",
args=["visible", [False] * len(fig.data)],
)
step["args"][1][i] = True # Toggle i'th trace to "visible"
steps.append(step)
sliders = [dict(
active=10,
currentvalue={"prefix": "Frequency: "},
pad={"t": 50},
steps=steps
)]
fig.update_layout(
sliders=sliders
)
fig.show()
resulting:
or with more points:
As you correctly figured out, it is way more performant than the widget slider, because with this method, you just toggle the trace visibility in the 3D Scatter chart.