I want to use plotly to show 2 sinuse waves
I want to use slider to show the progress from time=0 to current slider step.
I tried to write the following code:
import numpy as np
import pandas as pd
if __name__ == "__main__":
time = np.arange(0, 10, 0.1)
val1 = np.sin(time)
val2 = np.sin(time) * np.sin(time)
df = pd.DataFrame(val1, columns=['val-1'])
df['val-2'] = val2
fig = px.scatter(df, animation_frame=df.index)
fig.update_layout(xaxis_range=[-100, 100])
fig.update_layout(yaxis_range=[-1.1, 1.1])
fig.show()
but I can see the current value of the 2 sinuse waves (and not the whole waves from step=0 to current step)
How can I change my code and see the whole sinuse waves from step=0 to current step ?
I don't think it is possible to animate a line chart in Express, so I would have to use a graph object. There is an example in the reference, which I will adapt to your assignment. As for the graph structure, create the initial graph data and the respective frames in the animation, add them to the layout by creating steps and sliders.
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
if __name__ == "__main__":
time = np.arange(0, 10, 0.1)
val1 = np.sin(time)
val2 = np.sin(time) * np.sin(time)
df = pd.DataFrame(val1, columns=['val-1'])
df['val-2'] = val2
data = [go.Scatter(mode='lines', x=df.index, y=df['val-1'], name='val-1'),
go.Scatter(mode='lines', x=df.index, y=df['val-2'], name='val-2')]
steps = []
for i in df.index:
step = dict(method="animate", args=[[i], {"title": f'step:{i}'}], label=f'{i}')
steps.append(step)
sliders = [dict(active=0, currentvalue={"prefix": "Step: "}, steps=steps)]
layout = go.Layout(dict(xaxis=dict(range=[-100,100]),
yaxis=dict(range=[-1.1,1.1]),
updatemenus=[dict(type='buttons',
buttons=[dict(label='Start', method='animate', args=[None]),
dict(label='Pause',
method='animate',
args=[[None], dict(frame=dict(
duration=0,
redraw=False),
mode="immediate",
formcurrent=True,
transition=dict(duration=0, easing="linear")
)])],
direction="left",
pad=dict(r=10, t=40),
showactive=False,
x=0.00,
xanchor="right",
y=0,
yanchor="top")],
sliders=sliders
))
frames = []
for i in df.index:
frame = go.Frame(data=[go.Scatter(x=df.index[0:i], y=df.loc[0:i,'val-1']),
go.Scatter(x=df.index[0:i], y=df.loc[0:i,'val-2'])],
layout=go.Layout(title_text=f'Step:{i}'),
name=i)
frames.append(frame)
fig = go.Figure(data=data, layout=layout, frames=frames)
fig.show()
When I specify a range of y, the graph is not displayed correctly in that range.
I want to display the y-axis in the range of 10f~5u on the log scale, but it doesn't display properly. How can I solve this problem?
# imports
import pandas as pd
import plotly.graph_objs as go
import numpy as np
# data
np.random.seed(4)
x = np.linspace(0, 1, 50)
y = np.cumsum(np.random.randn(50))
# plotly line chart
fig = go.Figure(data=go.Scatter(x=x, y=y, mode='lines'), layout_yaxis_range=[10 ** -15, 5 * 10 ** -6])
fig.update_layout(
xaxis_type="linear",
yaxis_type="log",
)
fig.show()
In the documentation for log plots, it says that
Setting the range of a logarithmic axis with plotly.graph_objects is very different than setting the range of linear axes: the range is set using the exponent rather than the actual value:
So for your example, you can remove the 10 ** when setting the range, and your range can look like [-15, 1.000011]
The output with this change produces a graph that looks like this:
For reference, the complete code looks like:
# imports
import pandas as pd
import plotly.graph_objs as go
import numpy as np
# data
np.random.seed(4)
x = np.linspace(0, 1, 50)
y = np.cumsum(np.random.randn(50))
# plotly line chart
fig = go.Figure(data=go.Scatter(x=x, y=y, mode='lines'), layout_yaxis_range=[-15, 1.000011])
fig.update_layout(
xaxis_type="linear",
yaxis_type="log",
)
fig.show()
this is my first foray into Plotly. I love the ease of use compared to matplotlib and bokeh. However I'm stuck on some basic questions on how to beautify my plot. First, this is the code below (its fully functional, just copy and paste!):
import plotly.express as px
from plotly.subplots import make_subplots
import plotly as py
import pandas as pd
from plotly import tools
d = {'Mkt_cd': ['Mkt1','Mkt2','Mkt3','Mkt4','Mkt5','Mkt1','Mkt2','Mkt3','Mkt4','Mkt5'],
'Category': ['Apple','Orange','Grape','Mango','Orange','Mango','Apple','Grape','Apple','Orange'],
'CategoryKey': ['Mkt1Apple','Mkt2Orange','Mkt3Grape','Mkt4Mango','Mkt5Orange','Mkt1Mango','Mkt2Apple','Mkt3Grape','Mkt4Apple','Mkt5Orange'],
'Current': [15,9,20,10,20,8,10,21,18,14],
'Goal': [50,35,21,44,20,24,14,29,28,19]
}
dataset = pd.DataFrame(d)
grouped = dataset.groupby('Category', as_index=False).sum()
data = grouped.to_dict(orient='list')
v_cat = grouped['Category'].tolist()
v_current = grouped['Current']
v_goal = grouped['Goal']
fig1 = px.bar(dataset, x = v_current, y = v_cat, orientation = 'h',
color_discrete_sequence = ["#ff0000"],height=10)
fig2 = px.bar(dataset, x = v_goal, y = v_cat, orientation = 'h',height=15)
trace1 = fig1['data'][0]
trace2 = fig2['data'][0]
fig = make_subplots(rows = 1, cols = 1, shared_xaxes=True, shared_yaxes=True)
fig.add_trace(trace2, 1, 1)
fig.add_trace(trace1, 1, 1)
fig.update_layout(barmode = 'overlay')
fig.show()
Here is the Output:
Question1: how do I make the width of v_current (shown in red bar) smaller? As in, it should be smaller in height since this is a horizontal bar. I added the height as 10 for trace1 and 15 for trace2, but they are still showing at the same heights.
Question2: Is there a way to make the v_goal (shown in blue bar) only show it's right edge, instead of a filled out bar? Something like this:
If you noticed, I also added a line under each of the category. Is there a quick way to add this as well? Not a deal breaker, just a bonus. Other things I'm trying to do is add animation, etc but that's for some other time!
Thanks in advance for answering!
Running plotly.express wil return a plotly.graph_objs._figure.Figure object. The same goes for plotly.graph_objects running go.Figure() together with, for example, go.Bar(). So after building a figure using plotly express, you can add lines or traces through references directly to the figure, like:
fig['data'][0].width = 0.4
Which is exactly what you need to set the width of your bars. And you can easily use this in combination with plotly express:
Code 1
fig = px.bar(grouped, y='Category', x = ['Current'],
orientation = 'h', barmode='overlay', opacity = 1,
color_discrete_sequence = px.colors.qualitative.Plotly[1:])
fig['data'][0].width = 0.4
Plot 1
In order to get the bars or shapes to indicate the goal levels, you can use the approach described by DerekO, or you can use:
for i, g in enumerate(grouped.Goal):
fig.add_shape(type="rect",
x0=g+1, y0=grouped.Category[i], x1=g, y1=grouped.Category[i],
line=dict(color='#636EFA', width = 28))
Complete code:
import plotly.express as px
from plotly.subplots import make_subplots
import plotly as py
import pandas as pd
from plotly import tools
d = {'Mkt_cd': ['Mkt1','Mkt2','Mkt3','Mkt4','Mkt5','Mkt1','Mkt2','Mkt3','Mkt4','Mkt5'],
'Category': ['Apple','Orange','Grape','Mango','Orange','Mango','Apple','Grape','Apple','Orange'],
'CategoryKey': ['Mkt1Apple','Mkt2Orange','Mkt3Grape','Mkt4Mango','Mkt5Orange','Mkt1Mango','Mkt2Apple','Mkt3Grape','Mkt4Apple','Mkt5Orange'],
'Current': [15,9,20,10,20,8,10,21,18,14],
'Goal': [50,35,21,44,20,24,14,29,28,19]
}
dataset = pd.DataFrame(d)
grouped = dataset.groupby('Category', as_index=False).sum()
fig = px.bar(grouped, y='Category', x = ['Current'],
orientation = 'h', barmode='overlay', opacity = 1,
color_discrete_sequence = px.colors.qualitative.Plotly[1:])
fig['data'][0].width = 0.4
fig['data'][0].marker.line.width = 0
for i, g in enumerate(grouped.Goal):
fig.add_shape(type="rect",
x0=g+1, y0=grouped.Category[i], x1=g, y1=grouped.Category[i],
line=dict(color='#636EFA', width = 28))
f = fig.full_figure_for_development(warn=False)
fig.show()
You can use Plotly Express and then directly access the figure object as #vestland described, but personally I prefer to use graph_objects to make all of the changes in one place.
I'll also point out that since you are stacking bars in one chart, you don't need subplots. You can create a graph_object with fig = go.Figure() and add traces to get stacked bars, similar to what you already did.
For question 1, if you are using go.Bar(), you can pass a width parameter. However, this is in units of the position axis, and since your y-axis is categorical, width=1 will fill the entire category, so I have chosen width=0.25 for the red bar, and width=0.3 (slightly larger) for the blue bar since that seems like it was your intention.
For question 2, the only thing that comes to mind is a hack. Split the bars into two sections (one with height = original height - 1), and set its opacity to 0 so that it is transparent. Then place down bars of height 1 on top of the transparent bars.
If you don't want the traces to show up in the legend, you can set this individually for each bar by passing showlegend=False to fig.add_trace, or hide the legend entirely by passing showlegend=False to the fig.update_layout method.
import plotly.express as px
import plotly.graph_objects as go
# from plotly.subplots import make_subplots
import plotly as py
import pandas as pd
from plotly import tools
d = {'Mkt_cd': ['Mkt1','Mkt2','Mkt3','Mkt4','Mkt5','Mkt1','Mkt2','Mkt3','Mkt4','Mkt5'],
'Category': ['Apple','Orange','Grape','Mango','Orange','Mango','Apple','Grape','Apple','Orange'],
'CategoryKey': ['Mkt1Apple','Mkt2Orange','Mkt3Grape','Mkt4Mango','Mkt5Orange','Mkt1Mango','Mkt2Apple','Mkt3Grape','Mkt4Apple','Mkt5Orange'],
'Current': [15,9,20,10,20,8,10,21,18,14],
'Goal': [50,35,21,44,20,24,14,29,28,19]
}
dataset = pd.DataFrame(d)
grouped = dataset.groupby('Category', as_index=False).sum()
data = grouped.to_dict(orient='list')
v_cat = grouped['Category'].tolist()
v_current = grouped['Current']
v_goal = grouped['Goal']
fig = go.Figure()
## you have a categorical plot and the units for width are in position axis units
## therefore width = 1 will take up the entire allotted space
## a width value of less than 1 will be the fraction of the allotted space
fig.add_trace(go.Bar(
x=v_current,
y=v_cat,
marker_color="#ff0000",
orientation='h',
width=0.25
))
## you can show the right edge of the bar by splitting it into two bars
## with the majority of the bar being transparent (opacity set to 0)
fig.add_trace(go.Bar(
x=v_goal-1,
y=v_cat,
marker_color="#ffffff",
opacity=0,
orientation='h',
width=0.30,
))
fig.add_trace(go.Bar(
x=[1]*len(v_cat),
y=v_cat,
marker_color="#1f77b4",
orientation='h',
width=0.30,
))
fig.update_layout(barmode='relative')
fig.show()
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.
I use plotly package to show dynamic finance chart at python. However I didn't manage to put my all key points lines on one chart with for loop. Here is my code:
fig.update_layout(
for i in range(0,len(data)):
shapes=[
go.layout.Shape(
type="rect",
x0=data['Date'][i],
y0=data['Max_alt'][i],
x1='2019-12-31',
y1=data['Max_ust'][i],
fillcolor="LightSkyBlue",
opacity=0.5,
layer="below",
line_width=0)])
fig.show()
I have a data like below one. It is time series based EURUSD parity financial dataset. I calculated two constraits for both Local Min and Max. I wanted to draw rectangule shape to based on for each Min_alt / Min_ust and Max_alt / Max_range. I can draw for just one date like below image however I didn't manage to show all ranges in same plotly graph.
Here is the sample data set.
Here is the solution for added lines:
import datetime
colors = ["LightSkyBlue", "RoyalBlue", "forestgreen", "lightseagreen"]
ply_shapes = {}
for i in range(0, len(data1)):
ply_shapes['shape_' + str(i)]=go.layout.Shape(type="rect",
x0=data1['Date'][i].strftime('%Y-%m-%d'),
y0=data1['Max_alt'][i],
x1='2019-12-31',
y1=data1['Max_ust'][i],
fillcolor="LightSkyBlue",
opacity=0.5,
layer="below"
)
lst_shapes=list(ply_shapes.values())
fig1.update_layout(shapes=lst_shapes)
fig1.show()
However I have still problems to add traces to those lines. I mean text attribute.
Here is my code:
add_trace = {}
for i in range(0, len(data1)):
add_trace['scatter_' + str(i)] = go.Scatter(
x=['2019-12-31'],
y=[data1['Max_ust'][i]],
text=[str(data['Max_Label'][i])],
mode="text")
lst_trace = list(add_trace.values())
fig2=go.Figure(lst_trace)
fig2.show()
The answer:
For full control of each and every shape you insert, you could follow this logic:
fig = go.Figure()
#[...] data, traces and such
ply_shapes = {}
for i in range(1, len(df)):
ply_shapes['shape_' + str(i)]=go.layout.Shape()
lst_shapes=list(ply_shapes.values())
fig.update_layout(shapes=lst_shapes)
fig.show()
The details:
I'm not 100% sure what you're aimin to do here, but the following suggestion will answer your question quite literally regarding:
How to add more than one shape with loop in plotly?
Then you'll have to figure out the details regarding:
manage to put my all key points lines on one chart
Plot:
The plot itself is most likely not what you're looking for, but since you for some reason are adding a plot by the length of your data for i in range(0,len(data), I've made this:
Code:
This snippet will show how to handle all desired traces and shapes with for loops:
# Imports
import pandas as pd
#import matplotlib.pyplot as plt
import numpy as np
import plotly.graph_objects as go
#from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
# data, random sample to illustrate stocks
np.random.seed(12345)
rows = 20
x = pd.Series(np.random.randn(rows),index=pd.date_range('1/1/2020', periods=rows)).cumsum()
y = pd.Series(x-np.random.randn(rows)*5,index=pd.date_range('1/1/2020', periods=rows))
df = pd.concat([y,x], axis = 1)
df.columns = ['StockA', 'StockB']
# lines
df['keyPoints1']=np.random.randint(-5,5,len(df))
df['keyPoints2']=df['keyPoints1']*-1
# plotly traces
fig = go.Figure()
stocks = ['StockA', 'StockB']
df[stocks].tail()
traces = {}
for i in range(0, len(stocks)):
traces['trace_' + str(i)]=go.Scatter(x=df.index,
y=df[stocks[i]].values,
name=stocks[i])
data=list(traces.values())
fig=go.Figure(data)
# shapes update
colors = ["LightSkyBlue", "RoyalBlue", "forestgreen", "lightseagreen"]
ply_shapes = {}
for i in range(1, len(df)):
ply_shapes['shape_' + str(i)]=go.layout.Shape(type="line",
x0=df.index[i-1],
y0=df['keyPoints1'].iloc[i-1],
x1=df.index[i],
y1=df['keyPoints2'].iloc[i-1],
line=dict(
color=np.random.choice(colors,1)[0],
width=30),
opacity=0.5,
layer="below"
)
lst_shapes=list(ply_shapes.values())
fig.update_layout(shapes=lst_shapes)
fig.show()
Also you can use fig.add_{shape}:
fig = go.Figure()
fig.add_trace(
go.Scatter( ...)
for i in range( 1, len( vrect)):
fig.add_vrect(
x0=vrect.start.iloc[ i-1],
x1=vrect.finish.iloc[ i-1],
fillcolor=vrect.color.iloc[ i-1]],
opacity=0.25,
line_width=0)
fig.show()