I am trying to plot closing price with positive and negative sentiment. I was able to plot it as the picture below; however, the colors are not showing properly for the bar chart. Any ideas how to change them?
from plotly.subplots import make_subplots
import plotly.graph_objects as go
fig2 = make_subplots(specs=[[{"secondary_y": True}]])
fig2.add_trace(go.Scatter(x=data.index,y=data['close'],name='Price'),secondary_y=False)
fig2.add_trace(go.Bar(x=data.index,y=data['pos'],name='Positive'),secondary_y=True)
fig2.add_trace(go.Bar(x=data.index,y=data['neg'],name='Negative'),secondary_y=True)
fig2.show()
have implied you dataframe structure from your code and used plotly finance sample data set as starting point
two things to look at wrt to layout
make Close trace the primary trace at front
review bargroup parameter and reduce bargap to zero
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv(
"https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv"
)
# make plotly dataset compatible with OP implied structure
data = df.set_index(pd.date_range("1-Jan-2022", freq="5Min", periods=len(df))).rename(
columns={"AAPL.Close": "close", "dn": "neg", "up": "pos"}
)
fig2 = make_subplots(specs=[[{"secondary_y": True}]])
fig2.add_trace(
go.Scatter(x=data.index, y=data["close"], name="Price"), secondary_y=False
)
fig2.add_trace(go.Bar(x=data.index, y=data["pos"], name="Positive"), secondary_y=True)
fig2.add_trace(go.Bar(x=data.index, y=data["neg"], name="Negative"), secondary_y=True)
# a few changes to make layout work better
# 1. put close at front
# 2. reduce "whitespace" in bars
fig2.update_layout(
yaxis={"overlaying": "y2"}, yaxis2={"overlaying": None}, barmode="overlay", bargap=0
)
Related
I'm trying to add a point to the last observation on a time series chart with plotly. It is not very different from the example here https://stackoverflow.com/a/72539011/3021252 for instance. Except it is the last observation. Unfortunately following such pattern modifies the axis range.
Here is an example of an original chart
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()
But after adding a marker
import plotly.graph_objects as go
fig.add_trace(
go.Scatter(
x=[df["year"].values[-1]],
y=[df["lifeExp"].values[-1]],
mode='markers'
)
)
It looks like that
Has anyone have an idea how not to introduce this gap on the right?
I would like to add a second Y axis to my bar plot bellow, that is the number of citizens in integer:
this graph was made using plotly:
import plotly.express as px
fig = px.bar(df, x="country",y="pourcent_visit",color="city",barmode='group')
# fig.add_hline(y=10)
fig.show()
To my knowledge, there's no direct way to do this. But you can easily build a Plotly Express figure, grab the traces (and data structures) from there and combine them in a figure that allows multiple axes using fig = make_subplots(specs=[[{"secondary_y": True}]]). With no provided data sample, I'll use the built-in dataset px.data.tips() that I'm guessing to a large part resembles the structure of your real world dataset judging by the way you've applied the arguments in px.bar(). Details in the comments, but please don't hesitate to let me know if something is unclear.
Plot:
Complete code:
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# sample data
df = px.data.tips()
# figure setup with multiple axes
fig = make_subplots(specs=[[{"secondary_y": True}]])
# build plotly express plot
fig2 = px.bar(df, x="day", y="total_bill", color="smoker", barmode="group")
# add traces from plotly express figure to first figure
for t in fig2.select_traces():
fig.add_trace(t, secondary_y = False)
# handle data for secondary axis
df2 = df.groupby('day').agg('sum')#.reset_index()
df2 = df2.reindex(index = df['day'].unique()).reset_index()
#
fig.add_trace(go.Scatter(x = df2['day'], y = df2['size'], mode = 'lines'), secondary_y = True)
# fix layout
fig.update_layout(legend_title_text = 'smoker')
fig.show()
Is it possible to align two Y-axis by two different values? I would like to align my yaxis1 at zero with my yaxis2 at 1, like in the picture
picture
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
df = pd.DataFrame(dict(months=['jan','feb','mar','apr','may','jun'],
assets = [60,20,-25,-35,20,80],
liabilities = [70,75,80,90,70,50]))
# calculate ratio
df['ratio'] = df['assets'] / df['liabilities']
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(go.Bar(x=df['months'], y=df['assets'], marker_color='green'))
fig.add_trace(go.Bar(x=df['months'], y=df['liabilities'], marker_color='red'))
fig.add_trace(go.Scatter(x=df['months'], y=df['ratio'], marker_color='orange'), secondary_y=True)
fig.update_yaxes(showgrid=False, secondary_y=True)
fig.show()
To set the range of the 2nd y-axis, set the range in the layout. The manual adjustment of the second y-axis affected the scale of the first y-axis, which was corrected at the same time. If it is not the intended scale, you are responsible for correcting it.
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
df = pd.DataFrame(dict(months=['jan','feb','mar','apr','may','jun'],
assets = [60,20,-25,-35,20,80],
liabilities = [70,75,80,90,70,50]))
# calculate ratio
df['ratio'] = df['assets'] / df['liabilities']
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(go.Bar(x=df['months'], y=df['assets'], marker_color='green',name='assets'))
fig.add_trace(go.Bar(x=df['months'], y=df['liabilities'], marker_color='red',name='liabilities'))
fig.add_trace(go.Scatter(x=df['months'], y=df['ratio'], marker_color='orange',name='ratio'), secondary_y=True)
fig.update_yaxes(showgrid=False, secondary_y=True),
fig.update_layout(autosize=True, yaxis=dict(range=[-50,150]), yaxis2=dict(range=[0,4]))#height=600,
fig.show()
I'm making a line chart below. I want to make the lines colored by a variable Continent. I know it can be done easily using plotly.express
Does anyone know how I can do that with plotly.graph_objects? I tried to add color=gapminder['Continent'], but it did not work.
Thanks a lot for help in advance.
import plotly.express as px
gapminder = px.data.gapminder()
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=gapminder['year'], y=gapminder['lifeExp'],
mode='lines+markers'))
fig.show()
Using an approach like color=gapminder['Continent'] normally applies to scatterplots where you define categories to existing points using a third variable. You're trying to make a line plot here. This means that not only will you have a color per continent, but also a line per continent. If that is in fact what you're aiming to do, here's one approach:
Plot:
Code:
import plotly.graph_objects as go
import plotly.express as px
# get data
df_gapminder = px.data.gapminder()
# manage data
df_gapminder_continent = df_gapminder.groupby(['continent', 'year']).mean().reset_index()
df = df_gapminder_continent.pivot(index='year', columns='continent', values = 'lifeExp')
df.tail()
# plotly setup and traces
fig = go.Figure()
for col in df.columns:
fig.add_trace(go.Scatter(x=df.index, y=df[col].values,
name = col,
mode = 'lines'))
# format and show figure
fig.update_layout(height=800, width=1000)
fig.show()
I have a plotly-dash dashboard and I can't seem to rescale my secondary y-axis. Is there a way of doing this?
I've tried messing with the domain parameter and the range parameter in the go.Layout.
I need the volume bar chart to be scaled down and occupy maybe 10% of the height of the plot so it doesn't overlap with my candlesticks.
Thank you very much.
Any help is appreciated.
import pandas as pd
import pandas_datareader.data as web
import plotly.offline as pyo
import plotly.graph_objs as go
stock_ticker='AAPL'
start_date='2019-04-01'
end_date='2019-05-22'
data=[]
hist_stock_df = web.DataReader(stock_ticker,'iex',start_date, end_date)
data.append(go.Candlestick(x=hist_stock_df.index,
open=hist_stock_df['open'],
high=hist_stock_df['high'],
low=hist_stock_df['low'],
close=hist_stock_df['close'],
name='AAPL'))
data.append(go.Bar(x=hist_stock_df.index,
y=hist_stock_df['volume'].values,
yaxis='y2'))
#y0=1000000
layout=go.Layout(title= 'Candestick Chart of AAPL',
xaxis=dict(title='Date',rangeslider=dict(visible=False)),
yaxis=dict(title='Price'),
plot_bgcolor='#9b9b9b',
paper_bgcolor='#9b9b9b',
font=dict(color='#c4c4c4'),
yaxis2=dict(title='Volume',
overlaying='y',
side='right'))
#scaleanchor='y'))
#scaleratio=0.00000001,
#rangemode='tozero',
#constraintoward='bottom',
#domain=[0,0.1]))
fig = go.Figure(data=data, layout=layout)
pyo.iplot(fig)
I have tried messing with the commented parameters
UPDATE
With this combination of layout parameters I managed to rescale the bars, but now there are two x-axis, been trying to figure out how to bring the middle x-axis down.
layout=go.Layout(title= 'Candestick Chart of AAPL',
xaxis=dict(title='Date',rangeslider=dict(visible=False)),
yaxis=dict(title='Price'),
plot_bgcolor='#9b9b9b',
paper_bgcolor='#9b9b9b',
font=dict(color='#c4c4c4'),
yaxis2=dict(title='Volume',
overlaying='y',
side='right',
scaleanchor='y',
scaleratio=0.0000001))
Use secondary_y=True or secondary_y=False within fig.update_yaxes() to specify which axis to adjust.
Plot 1: Without manual adjustments
Plot 2: With manual adjustments
Code:
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import datetime
# data
np.random.seed(1234)
numdays=20
dates = pd.date_range('1/1/2020', periods=numdays)
A = (np.random.randint(low=-10, high=10, size=numdays).cumsum()+100).tolist()
B = (np.random.randint(low=0, high=100, size=numdays).tolist())
df = pd.DataFrame({'A': A,'B':B}, index=dates)
# plotly figure setup
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(go.Scatter(name='A', x=df.index, y=df['A'].values))
fig.add_trace(go.Bar(name='B', x=df.index, y=df['B'].values), secondary_y=True)
# plotly manual axis adjustments
fig.update_yaxes(range=[50,160], secondary_y=False)
fig.update_yaxes(range=[-10,200], secondary_y=True)
fig.show()