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Plotly Express set the width of a specific line on graph of multiple lines

I'm trying to make the 'Close' line wider than the others using plotly.express. I tried various options with a conditional statement, or adding a trace but nothing seems to work.
fig.for_each_trace(
lambda trace: trace.update(line=dict(color="Black", width=3)) if trace.name == "Close" else ()
This is the code:
import plotly.express as px
ticker = 'TSLA'
colours = ['steelblue', 'tan', 'black', 'red', 'green']
fig = px.line(df.tail(150), x=df.tail(150).index, y=['middle_band', 'SMA_200',
'upper_band', 'lower_band','Close'], title=f'{ticker}',
color_discrete_sequence=colours, width=800, height=600)
fig.update_traces(textfont_size=12, showlegend=True)
fig.update_layout(legend=dict(
yanchor="top",
y=0.9,
xanchor="left",
x=0.02
))
fig.update_xaxes(title_text='Price evolution over time', title_font = {"size": 16},)
fig.update_yaxes(title_text='Price', tickprefix="$")
fig.show()
which produces this:
As always, any help is welcome.

You can add this line just before fig.show() to increase the width of the 5th line - fig['data'][4]['line']['width']=5, changing the 4 to any number between 0-4 will increase the width / thickness by 5 (or any number you need). I have used random numbers and plotted your code. Same is below.
import plotly.express as px
df = pd.DataFrame(np.random.randint(0,1000,size=(5, 5)), columns=list('ABCDE'))
ticker = 'TSLA'
colours = ['steelblue', 'tan', 'black', 'red', 'green']
fig = px.line(df.tail(5), x=df.tail(5).index, y=['A', 'B',
'C', 'D','E'], title=f'{ticker}',
color_discrete_sequence=colours, width=800, height=600)
fig.update_traces(textfont_size=12, showlegend=True)
fig.update_layout(legend=dict(
yanchor="top",
y=0.9,
xanchor="left",
x=0.02
))
fig.update_xaxes(title_text='Price evolution over time', title_font = {"size": 16},)
fig.update_yaxes(title_text='Price', tickprefix="$")
#fig.update_traces(line=dict(color="Black", width=0.5))
fig['data'][4]['line']['width']=5
fig.show()
Plot

Plotly: How to add volume to a candlestick chart

code:
from plotly.offline import init_notebook_mode, iplot, iplot_mpl
def plot_train_test(train, test, date_split):
data = [Candlestick(x=train.index, open=train['open'], high=train['high'], low=train['low'], close=train['close'],name='train'),
Candlestick(x=test.index, open=test['open'], high=test['high'], low=test['low'], close=test['close'],name='test')
]
layout = {
'shapes': [
{'x0': date_split, 'x1': date_split, 'y0': 0, 'y1': 1, 'xref': 'x', 'yref': 'paper',
'line': {'color': 'rgb(0,0,0)', 'width': 1}}],
'annotations': [{'x': date_split, 'y': 1.0, 'xref': 'x', 'yref': 'paper', 'showarrow': False, 'xanchor': 'left','text': ' test data'},
{'x': date_split, 'y': 1.0, 'xref': 'x', 'yref': 'paper', 'showarrow': False, 'xanchor': 'right', 'text': 'train data '}] }
figure = Figure(data=data, layout=layout)
iplot(figure)
The above code is ok.But now I want to 'volume' in this candlestick chart
code:
from plotly.offline import init_notebook_mode, iplot, iplot_mpl
def plot_train_test(train, test, date_split):
data = [Candlestick(x=train.index, open=train['open'], high=train['high'], low=train['low'], close=train['close'],volume=train['volume'],name='train'),
Candlestick(x=test.index, open=test['open'], high=test['high'], low=test['low'],close=test['close'],volume=test['volume'],name='test')]
layout = {
'shapes': [
{'x0': date_split, 'x1': date_split, 'y0': 0, 'y1': 1, 'xref': 'x', 'yref': 'paper',
'line': {'color': 'rgb(0,0,0)', 'width': 1}}
],
'annotations': [
{'x': date_split, 'y': 1.0, 'xref': 'x', 'yref': 'paper', 'showarrow': False, 'xanchor': 'left',
'text': ' test data'},
{'x': date_split, 'y': 1.0, 'xref': 'x', 'yref': 'paper', 'showarrow': False, 'xanchor': 'right',
'text': 'train data '}
]
}
figure = Figure(data=data, layout=layout)
iplot(figure)
error:
ValueError: Invalid property specified for object of type
plotly.graph_objs.Candlestick: 'volume'

If you looking add smaller subplot of volume just below OHLC chart, you can use:
rows and cols to specify the grid for subplots.
shared_xaxes=True for same zoom and filtering
row_width=[0.2, 0.7] to change height ratio of charts. ie. smaller volume chart than OHLC
Plot:
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# data
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
# Create subplots and mention plot grid size
fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
vertical_spacing=0.03, subplot_titles=('OHLC', 'Volume'),
row_width=[0.2, 0.7])
# Plot OHLC on 1st row
fig.add_trace(go.Candlestick(x=df["Date"], open=df["AAPL.Open"], high=df["AAPL.High"],
low=df["AAPL.Low"], close=df["AAPL.Close"], name="OHLC"),
row=1, col=1
)
# Bar trace for volumes on 2nd row without legend
fig.add_trace(go.Bar(x=df['Date'], y=df['AAPL.Volume'], showlegend=False), row=2, col=1)
# Do not show OHLC's rangeslider plot
fig.update(layout_xaxis_rangeslider_visible=False)
fig.show()

You haven't provided a complete code snippet with a data sample, so I'm going to have to suggest a solution that builds on an example here.
In any case, you're getting that error message simply because go.Candlestick does not have a Volume attribute. And it might not seem so at first, but you can easily set up go.Candlestick as an individual trace, and then include an individual go.Bar() trace for Volumes using:
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_traces(go.Candlestick(...), secondary_y=True)
fig.add_traces(go.Bar(...), secondary_y=False)
Plot:
Complete code:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
# data
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
# include candlestick with rangeselector
fig.add_trace(go.Candlestick(x=df['Date'],
open=df['AAPL.Open'], high=df['AAPL.High'],
low=df['AAPL.Low'], close=df['AAPL.Close']),
secondary_y=True)
# include a go.Bar trace for volumes
fig.add_trace(go.Bar(x=df['Date'], y=df['AAPL.Volume']),
secondary_y=False)
fig.layout.yaxis2.showgrid=False
fig.show()

Here is my improvement implementation based on the previous answer by Vestland, with some labelling and colouring improvements.
import plotly.graph_objects as go
from plotly.subplots import make_subplots
candlesticks = go.Candlestick(
x=candles.index,
open=candles['open'],
high=candles['high'],
low=candles['low'],
close=candles['close'],
showlegend=False
)
volume_bars = go.Bar(
x=candles.index,
y=candles['volume'],
showlegend=False,
marker={
"color": "rgba(128,128,128,0.5)",
}
)
fig = go.Figure(candlesticks)
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(candlesticks, secondary_y=True)
fig.add_trace(volume_bars, secondary_y=False)
fig.update_layout(title="ETH/USDC pool after Uniswap v3 deployment", height=800)
fig.update_yaxes(title="Price $", secondary_y=True, showgrid=True)
fig.update_yaxes(title="Volume $", secondary_y=False, showgrid=False)
fig.show()
You can find the full source code in this open-source notebook.

If you want to add different colors for buy/sell isay 'green'/'red', you can use some libs (e.g. mplfinance) which do these automatically however the plots are non-interactive. To get interactive plot with plotly with separate colors for buy/sell colors, one needs to add trace for each data point. Here is code:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
# Create subplots and mention plot grid size
title=df.symbol.unique()[0]
fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
vertical_spacing=0.02,
row_width=[0.25, 0.75])
# Plot OHLC on 1st row
fig.add_trace(go.Candlestick(x=df.index,
open=df['open'], high=df['high'],
low=df['low'], close=df['close'],showlegend=False),row=1, col=1,)
# Bar trace for volumes on 2nd row without legend
# fig.add_trace(go.Bar(x=df.index, y=df['volume'], showlegend=False), row=2, col=1)
df['color']=''
df['color']=['red' if (x>y) else t for x,y,t in zip(df['open'],df['close'],df['color'])]
df['color']=['green' if (x<y) else t for x,y,t in zip(df['open'],df['close'],df['color'])]
colors=df.color.tolist()
df['prev_color']=[colors[0]]+colors[:(len(colors)-1)]
df.loc[((df.open==df.close) & (df.color=='')),'color']=[z for x,y,z,t in zip(df['open'],df['close'],df['prev_color'],df['color']) if (x==y and t=='')]
colors=df.color.tolist()
df['prev_color']=[colors[0]]+colors[:(len(colors)-1)]
df.loc[((df.open==df.close) & (df.color=='')),'color']=[z for x,y,z,t in zip(df['open'],df['close'],df['prev_color'],df['color']) if (x==y and t=='')]
markers=['green','red']
for t in markers:
df_tmp=df.loc[~(df.color==t)] ## somehow the color it takes is opposite so take negation to
fig.add_trace(go.Bar(x=df_tmp.index, y=df_tmp['volume'], showlegend=False), row=2, col=1)
# Do not show OHLC's rangeslider plot
fig.update(layout_xaxis_rangeslider_visible=False)
fig.layout.yaxis2.showgrid=False
fig.update_layout(title_text=title,title_x=0.45)
fig.show()

My two cents on Plotting Volume in a different subplot with colors, it is just making #user6397960 response shorter without hacks to get the right color, just use marker_color. Think about it, what makes a candle green? The fact of having Close price above the Open price, and what about red candle? well, having a close price below the open price, so with this basics:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Create a Figure with 2 subplots, one will contain the candles
# the other will contain the Volume bars
figure = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.7, 0.3])
# Plot the candles in the first subplot
figure.add_trace(go.Candlestick(x=df.index, open=df.open, high=df.high, low=df.low, close=df.close, name='price',
increasing_line_color='#26a69a', decreasing_line_color='#ef5350'),
row=1, col=1)
# From our Dataframe take only the rows where the Close > Open
# save it in different Dataframe, these should be green
green_volume_df = df[df['close'] > df['open']]
# Same for Close < Open, these are red candles/bars
red_volume_df = df[df['close'] < df['open']]
# Plot the red bars and green bars in the second subplot
figure.add_trace(go.Bar(x=red_volume_df.index, y=red_volume_df.volume, showlegend=False, marker_color='#ef5350'), row=2,
col=1)
figure.add_trace(go.Bar(x=green_volume_df.index, y=green_volume_df.volume, showlegend=False, marker_color='#26a69a'),
row=2, col=1)
# Hide the Range Slider
figure.update(layout_xaxis_rangeslider_visible=False)
figure.update_layout(title=f'BTC/USDT', yaxis_title=f'Price')
figure.update_yaxes(title_text=f'Volume', row=2, col=1)
figure.update_xaxes(title_text='Date', row=2)
References
https://plotly.com/python/subplots/
https://plotly.com/python/candlestick-charts/

Plotly: How to change default date on x-axis and remove year from axis?

I'm working on creating a line graph via plotly. I am running into two problems. Plotly is defaulting to show every 7th value on the x axis and it is showing a year value for my first x axis value (as seen in the screenshot). Here is the code running. If either could be fixed, that would be great!
from connect_to_db import get_df
import plotly.express as px
import os
df = get_df()
fig = px.bar(df, x='Date', y='Total', color='CUSTOMER_TYPE',
color_discrete_sequence=["#E15759","#8CD17D"], width=800, height=400)
fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)', 'paper_bgcolor': 'rgba(0, 0, 0, 0)', })
fig.update_layout(
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=.4))
fig.update_layout({
'legend_title_text': ''
},
xaxis_title="",
yaxis_title="Tested Count"
)
fig.update_xaxes(showline=True, linewidth=1, linecolor='#FBFBFB', mirror=False)
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor="#FBFBFB",showline=True, linewidth=2, linecolor='#FBFBFB', mirror=False)
fig.show()
Thanks all!

In order to make the year disappear you'll just have to make another specification for xaxis_tickformat, like xaxis_tickformat = '%b' to get:
Or you can go for xaxis_tickformat = '%B' and get:
In both cases the year is removed, as per your request.
Complete code:
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')
fig = go.Figure(go.Scatter(
x = df['Date'],
y = df['AAPL.High'],
))
fig.update_layout(
title = 'Time Series with Custom Date-Time Format',
#xaxis_tickformat = '%d %B (%a)<br>%Y'
xaxis_tickformat = '%d %B <br>%Y'
#xaxis_tickformat = '%B'
)
fig.show()

Plotly subplot does not like looped bar plots?

I'm trying to create a simple pdf of a subplot consisting of one bar chart and one data table:
The Bar and table function works but when i try and create a subplot plotly gives the following error:
ValueError:
Invalid element(s) received for the 'data' property of
Invalid elements include
I wrote the following: Does it not like that i looped the bar traces?
I first create functions to build the bar chart and data table:
import plotly
import plotly.graph_objects as go
import plotly.figure_factory as ff
import os
import numpy as np
from plotly.subplots import make_subplots
import pandas as pd
def build_barchart(df,columns):
x = df.City.unique() # City column should always be the x axis
fig = go.Figure()
fig.update_layout(barmode='stack')
for column in columns:
Y=df[column].tolist()
fig.add_trace(go.Bar(name=column,x=x,y=Y))
return fig
def build_table(df):
table = ff.create_table(df)
return table
def create_pdf(df,columns):
fig = make_subplots(rows=1, cols=2)
fig.add_trace(
build_barchart(df,columns),
row=1, col=1
)
fig.add_trace(
build_table(df),
row=1, col=2
)
if not os.path.exists("images"):
os.mkdir("images")
fig.write_image("images/fig1.pdf")
return
After creating the build functions i try and use them...
df = pd.read_csv('DATA.csv', delimiter=';')
columns=['%Something','%Dogs','%Cats','%Cars']
table = build_table(df)
bars = build_barchart(df,columns)
fig = make_subplots(rows=1, cols=2)
fig.add_trace(
bars,
row=1, col=1
)
fig.add_trace(
table,
row=1, col=2
)
fig.show()
test data
City;%Something;%Dogs;%Cats;%Cars;Nr Alarms;Nr Block
USA;60;10;5;25;1;1
CANADA;20;10;5;65;2;2
MEXICO;90;5;5;0;3;3
SWEDEN;10;10;5;75;4;4

Please don't take it as an answer. Given that your code has some pitfalls I'm adding a polished version. In particular in case you have duplicated countries x = df.City.astype('str').unique() is not going to work well with Y and you should arrange/check your data before to plot.
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
df = pd.DataFrame({
'City': {0: 'USA', 1: 'CANADA', 2: 'MEXICO', 3: 'SWEDEN'},
'%Something': {0: 60, 1: 20, 2: 90, 3: 10},
'%Dogs': {0: 10, 1: 10, 2: 5, 3: 10},
'%Cats': {0: 5, 1: 5, 2: 5, 3: 5},
'%Cars': {0: 25, 1: 65, 2: 0, 3: 75},
'Nr Alarms': {0: 1, 1: 2, 2: 3, 3: 4},
'Nr Block': {0: 1, 1: 2, 2: 3, 3: 4}})
def build_subplot(df, columns=None):
if columns is None:
columns = df.columns
# only columns starting with %
columns = [col for col in columns if col.startswith("%")]
fig = make_subplots(rows=1, cols=2,
specs=[
[{"type": "bar"},{"type": "table"}]
]
)
fig.update_layout(barmode='stack')
for column in columns:
fig.append_trace(
go.Bar(x=df["City"],
y=df[column],
name=column),
row=1,col=1)
fig.add_trace(
go.Table(
header=dict(
values=df.columns,
font=dict(size=10),
align="left"
),
cells=dict(
values=df.T.values.tolist(),
align = "left")
),
row=1, col=2
)
return fig
build_subplot(df, ['%Something', '%Dogs', 'Nr Block'])
Addendum: I think it will look better if you can have your subplot with 1 column and 2 rows as some details from the table could be hard to read.

Figured out what i did wrong!
Instead of using plotly.figure_factory's create_table() i used plotly.graph_objects Table()
I also had to define the type of figure used in the creation of the subplot.
The final solution for creating the subplot looks like this:
def build_subplot(df,columns):
x = df.City.astype('str').unique()
fig = make_subplots(rows=1, cols=2,
specs=[
[{"type": "bar"},{"type": "table"}]
]
)
fig.update_layout(barmode='stack')
for column in columns:
Y=df[column].tolist()
fig.append_trace(go.Bar(name=column,x=x,y=Y),row=1,col=1)
fig.add_trace(
go.Table(
header=dict(
values=df.columns,
font=dict(size=10),
align="left"
),
cells=dict(
values=[df[k].tolist() for k in df.columns],
align = "left")
),
row=1, col=2
)
return fig
I hope this helps someone else :)

How do I annotate each subplot separately in Plotly (Python)

I am trying to associate a separate annotation object with each subplot in Plotly (Python), how can this be done?
What I tried
I am setting up the plot like this:
from plotly import tools
fig = tools.make_subplots(rows=2, cols=1)
fig.append_trace(traces[0], 1, 1)
fig.append_trace(traces[1], 2, 1)
where each trace is formed like this:
import plotly.graph_objs as go
traces[0] = go.Scatter(
x=[1,2,3,4],
y=[4,4,2,1],
mode='markers'
)
I know I can access the xaxis of each subplot separately via:
fig['layout']['xaxis1'].update(title='hello1')
fig['layout']['xaxis2'].update(title='hello2')
But how can I access the annotation of each subplot? I tried "annotations1" and "annotation1", with no luck. I also tried to access the layout of subplot 1 via "layout1" as in:
fig['layout1'][...].update(...)
This did not work either.

1) You could assign annotation to specific subplot through setting xref and yref with subplot axis id, such as x1 and y1 represents x axis and y axis of subplot1, as seen from example below and more on link
fig['layout'].update(
annotations=[
dict(
x=2, y=2, # annotation point
xref='x1',
yref='y1',
text='dict Text',
showarrow=True,
arrowhead=7,
ax=10,
ay=70
),
dict(
...
# if have multiple annotations
)
])
2) After you assigned it, you could get access to annotations through
fig['layout']['annotations']
which will return a list of dictionary items:
[{'xref': 'x2', 'arrowhead': 7, 'yref': 'y2', 'text': 'dict Text', 'ay': 40, 'ax': 10, 'y': -1.9491807521563174, 'x': 0.77334098360655923, 'showarrow': True}, {'xref': 'x2', 'arrowhead': 7, 'yref': 'y2', 'text': 'dict Text', 'ay': -40, 'ax': 10, 'y': -0.0041268527747384542, 'x': 1.1132422279202281, 'showarrow': True}]
Hope this could help ;)

it also works with update(),
if you adress the subplot as an element inside the annotations list.
from plotly.subplots import make_subplots
import plotly.graph_objects as go
# create figure with subplots
fig = make_subplots(rows=1, cols=2, subplot_titles = ['title1','title2'])
fig.add_trace(
go.Scatter(x=[1, 2, 3], y=[4, 5, 6]),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=[20, 30, 40], y=[50, 60, 70]),
row=1, col=2
)
fig.update_layout(height=600, width=800, title_text="Subplots")
fig.show()
# to change subtitle, address subplot
fig['layout']['annotations'][0].update(text='your text here');
fig.show()