hello im trying to make a heatmap with btc asks and bids, this graph will have a line representing the close price. im using :
plotly.expres as px
plotly.graph_objects as go
the code is :
go.Figure(go.Heatmap(x=df3["timestamp"], y=df2["asks"], z=df["baseVolume"])
px.line(df3, x="timestamp", y="close") ## not working
fig.show()
graph output
Since no data is presented, I have used the heatmap in the official reference as an example for my code. Since the heatmap seems to deal with categorical variables, we need to add two axes for the y-axis, so we add a subplot. If we set the line chart to the added second axis, the heatmap and the line chart will be superimposed.
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import datetime
import numpy as np
np.random.seed(1)
programmers = ['Alex','Nicole','Sara','Etienne','Chelsea','Jody','Marianne']
base = datetime.datetime.today()
dates = base - np.arange(180) * datetime.timedelta(days=1)
z = np.random.poisson(size=(len(programmers), len(dates)))
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(go.Heatmap(
z=z,
x=dates,
y=programmers,
colorscale='Viridis'))
fig.add_trace(go.Scatter(
mode='lines',
x=dates,
y=np.linspace(0,5, len(dates)).cumsum(),
line=dict(color='red',width=2)), secondary_y=True,)
fig.update_layout(
title='GitHub commits per day',
xaxis_nticks=36)
fig.show()
Related
I have a simple candle stick chart made with plotly.
import yfinance as yf
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
djia = yf.Ticker("DJIA")
df = djia.history(start="2022-01-01", end="2022-12-31", interval="1d")
fig = go.Figure()
fig = make_subplots(rows=1, cols=1)
# candlestick
fig.append_trace(
go.Candlestick(
x=df.index,
open=df["Open"],
high=df["High"],
low=df["Low"],
close=df["Close"]
), row=1, col=1
)
fig.update_xaxes(rangebreaks=[dict(bounds=["sat", "mon"])])
fig.show()
I don't know what it is called but I would like to add an indicator on x-axis to tell if certain condition is true during that the time frame of each candle. Here is a reference picture that explains what I'm looking for. The yellow and purple dots that are marked with the red square.
The idea would be to have an extra column in the dataframe with True/False values for each candle and that would be used to decide the color of the indicator dots. How could I achieve this?
A scatter plot is drawn on the current candlestick graph at the price 18 location. The color is determined by comparing the opening and closing prices. The easiest way to do this is to add a column of colors to the original data frame and set it to the color of the marker.
import yfinance as yf
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
djia = yf.Ticker("DJIA")
df = djia.history(start="2022-01-01", end="2022-12-31", interval="1d")
# color column add
df['colors'] = df[['Open', 'Close']].apply(lambda x: 'green' if x['Open'] <= x['Close'] else 'red', axis=1)
fig = go.Figure()
fig = make_subplots(rows=1, cols=1)
# candlestick
fig.append_trace(
go.Candlestick(
x=df.index,
open=df["Open"],
high=df["High"],
low=df["Low"],
close=df["Close"]
), row=1, col=1
)
fig.add_trace(go.Scatter(mode='markers', x=df.index, y=[18]*len(df), marker=dict(size=5, color=df['colors'])),row=1, col=1)
fig.update_xaxes(rangebreaks=[dict(bounds=["sat", "mon"])])
fig.update_layout(height=500)
fig.show()
I would like to create a subplot with 2 plot generated with the function plotly.express.line, is it possible? Given the 2 plot:
fig1 =px.line(df, x=df.index, y='average')
fig1.show()
fig2 = px.line(df, x=df.index, y='Volume')
fig2.show()
I would like to generate an unique plot formed by 2 subplot (in the example fig1 and fig2)
Yes, you can build subplots using plotly express. Either
1. directly through the arguments facet_row and facet_colums (in which case we often talk about facet plots, but they're the same thing), or
2. indirectly through "stealing" elements from figures built with plotly express and using them in a standard make_subplots() setup with fig.add_traces()
Method 1: Facet and Trellis Plots in Python
Although plotly.express supports data of both wide and long format, I often prefer building facet plots from the latter. If you have a dataset such as this:
Date variable value
0 2019-11-04 average 4
1 2019-11-04 average 2
.
.
8 2019-12-30 volume 5
9 2019-12-30 volume 2
then you can build your subplots through:
fig = px.line(df, x='Date', y = 'value', facet_row = 'variable')
Plot 1:
By default, px.line() will apply the same color to both lines, but you can easily handle that through:
fig.update_traces(line_color)
This complete snippet shows you how:
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
df = pd.DataFrame({'Date': ['2019-11-04', '2019-11-04', '2019-11-18', '2019-11-18', '2019-12-16', '2019-12-16', '2019-12-30', '2019-12-30'],
'variable':['average', 'volume', 'average', 'volume', 'average','volume','average','volume'],
'value': [4,2,6,5,6,7,5,2]})
fig = px.line(df, x='Date', y = 'value', facet_row = 'variable')
fig.update_traces(line_color = 'red', row = 2)
fig.show()
Method 2: make_subplots
Since plotly express can do some pretty amazing stuff with fairly complicated datasets, I see no reason why you should not stumple upon cases where you would like to use elements of a plotly express figure as a source for a subplot. And that is very possible.
Below is an example where I've built to plotly express figures using px.line on the px.data.stocks() dataset. Then I go on to extract some elements of interest using add_trace and go.Scatter in a For Loop to build a subplot setup. You could certainly argue that you could just as easily do this directly on the data source. But then again, as initially stated, plotly express can be an excellent data handler in itself.
Plot 2: Subplots using plotly express figures as source:
Complete code:
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
from plotly.subplots import make_subplots
df = px.data.stocks().set_index('date')
fig1 = px.line(df[['GOOG', 'AAPL']])
fig2 = px.line(df[['AMZN', 'MSFT']])
fig = make_subplots(rows=2, cols=1)
for d in fig1.data:
fig.add_trace((go.Scatter(x=d['x'], y=d['y'], name = d['name'])), row=1, col=1)
for d in fig2.data:
fig.add_trace((go.Scatter(x=d['x'], y=d['y'], name = d['name'])), row=2, col=1)
fig.show()
There is no need to use graph_objects module if you have just already generated px figures for making subplots. Here is the full code.
import plotly.express as px
import pandas as pd
from plotly.subplots import make_subplots
df = px.data.stocks().set_index('date')
fig1 = px.line(df[['GOOG', 'AAPL']])
fig2 = px.line(df[['AMZN', 'MSFT']])
fig = make_subplots(rows=2, cols=1)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.show()
If there are more than two variables in each plot, one can use for loop also to add the traces using fig.add_trace method.
From the documentation, Plotly express does not support arbitrary subplot capabilities. You can instead use graph objects and traces (note that go.Scatter is equivalent):
import pandas as pd
from plotly.subplots import make_subplots
import plotly.graph_objects as go
## create some random data
df = pd.DataFrame(
data={'average':[1,2,3], 'Volume':[7,3,6]},
index=['a','b','c']
)
fig = make_subplots(rows=1, cols=2)
fig.add_trace(
go.Scatter(x=df.index, y=df.average, name='average'),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=df.index, y=df.Volume, name='Volume'),
row=1, col=2
)
fig.show()
How to make x-axis of the following histogram, logarithmic?
The following code:
data_list = [1,1,5,5,5,100,100]
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Histogram(x=data_list, nbinsx=100))
import plotly.offline as py
py.init_notebook_mode(connected=False)
py.offline.plot(fig, filename = 'test.html')
Produces typical histogram.
How can I change the x-axis logarithmic?
I also tried:
data_list = [1,1,5,5,5,100,100]
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Histogram(x=data_list, nbinsx=100))
fig.update_layout(xaxis_type="log")
fig.update_xaxes(tick0=0, dtick=1, range=[0,2.5])
import plotly.offline as py
py.init_notebook_mode(connected=False)
py.offline.plot(fig, filename = 'test.html')
but this will result in non-logarithmic bins, and essentially, the bin at 100 disappears!
As said above in the comments, logarithmic axes are not possible with plotly. You could try to create bins using numpy and create a bar plot with those.
I am trying to plot a cumulative histogram using Plotly in python, but make it look like "steps", i.e. bars with no color and only the top line is displayed. Something like this:
Basically, I'm trying to reproduce the behavior of the following matplotlib code:
import matplotlib.pyplot as plt
plt.hist(x, cumulative=True, histtype='step')
So far, the best I've been able to do is:
import plotly.graph_objs as go
from plotly.offline import iplot
h = go.Histogram(x=x,
cumulative=dict(enabled=True),
marker=dict(color="rgba(0,0,0,0)",
line=dict(color="red", width=1)))
iplot([h])
Which results in something like:
So what's the trick?
If you're willing to handle the binning and accumulation before you plot the data, you can use a go.Scatter object with the shape property of the line set to 'hvh'.
Plot:
Code: Setup for a Jupyter Notebook
#imports
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import numpy as np
import pandas as pd
# qtconsole for debugging
#%qtconsole -- style vim
# Notebook settings
init_notebook_mode(connected=True)
# Some sample data
x = np.random.normal(50, 5, 500)
binned = np.histogram(x, bins=25, density=True)
plot_y = np.cumsum(binned[0])
# Line
trace1 = go.Scatter(
x=binned[1],
y=plot_y,
mode='lines',
name="X",
hoverinfo='all',
line=dict(color = 'rgb(1255, 0, 0)', shape='hvh'
)
)
data = [trace1]
# Layout
layout = dict(title = 'Binned data from normal distribution',
legend=dict(
y=0.5,
traceorder='reversed',
font=dict(
size=16
)
)
)
# Make figure
fig = dict(data=data, layout=layout)
# Plot
iplot(fig, filename='line-shapes')
I hope this is something you can use!
Don't hesitate to let me know if not.
Some details:
The data sample is made using np.random.normal(). x is a sampled normal distribution with mean = 50, sigma = 5 and 500 observations. x is then put in 50 bins using np.histogram() which returns two arrays. These are used as data source for the plot.
Possible alternative approaches:
I also tried using your snippet with some random sample data and include shape='hvh' in your line=dict(color="red", width=1). That did not seem to work though. I also considered modifying the layout of your go.Histogram() so that only the top line of the bars were plotted, but I don't think it's possible.
The accepted solution works but may be limiting as the bins are all of equal width. One approach is to use matplotlib to calculate stats, then plot with plotly:
# sample data
# I am not using a normal distribution on purpose so that the effect of varying bin widths is apparent.
x = np.random.rand(100)
# use matplotlib to get "n" and "bins"
# n_bins will affect the resolution of the cumilative histogram but not dictate the bin widths.
n_bins = 100
n, bins, patches = plt.hist(x, n_bins, density=True, histtype='step', cumulative=-1)
# use plotly (v3) to plot
data = []
trace = go.Scatter(
x=bins,
y=n,
mode='lines',
name= "test",
line=dict(
shape='hvh'
)
)
data.append(trace)
fig = go.Figure(data=data)
iplot(fig)
The result should look something like this:
How do I remove the numbers and ticks from the internal y-axis in the Plot.ly heatmap subplot graph below? Both plots share the same y-axis range so there is no reason to show both.
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
import pandas as pd
import numpy as np
dfl = []
dfl.append(pd.DataFrame(np.random.random((100,100,))))
dfl.append(pd.DataFrame(np.random.random((100,100,))))
fig = tools.make_subplots(rows=1, cols=len(dfl) ,print_grid=False);
for index, a in enumerate(dfl):
sn = str(index)
data = go.Heatmap(
z=a.values.tolist(),
colorscale='Viridis',
colorbar=dict(title='units'),
)
fig.append_trace(data, 1, index+1)
fig['layout']['xaxis'+str(index+1)].update(title='xaxis '+str(index))
fig['layout']['yaxis1'].update(title='y-axis')
fig['layout'].update(height=600, width=800, title='heatmap subplots')
py.iplot(fig)
Simply pass the setting 'shared_yaxes=True' to the tools.make_subplots function call, that is:
fig = tools.make_subplots(rows=1, cols=len(dfl) ,print_grid=False, shared_yaxes=True)
Updated answer for newer versions of Plotly. Based upon the v4 migration guide.
Differences include
calling the make_subplots function from the plotly.subplots library, instead of plotly.tools, and
using fig.show inplace of py.iplot to show the figure in Jupyter.
The code below was tested in Plotly version 5.1.0 using Jupyter Notebook.
import plotly
import plotly.graph_objs as go
import pandas as pd
import numpy as np
dfl = []
dfl.append(pd.DataFrame(np.random.random((100,100,))))
dfl.append(pd.DataFrame(np.random.random((100,100,))))
fig = plotly.subplots.make_subplots(rows=1, cols=len(dfl) ,print_grid=False, shared_yaxes=True);
for index, a in enumerate(dfl):
sn = str(index)
data = go.Heatmap(
z=a.values.tolist(),
colorscale='Viridis',
colorbar=dict(title='units'),
)
fig.append_trace(data, 1, index+1)
fig['layout']['xaxis'+str(index+1)].update(title='xaxis '+str(index))
fig['layout']['yaxis1'].update(title='y-axis')
fig['layout'].update(height=600, width=800, title='heatmap subplots')
fig.show()
The output: