Develop Reference

Python Plotly multiple charts in one single view

I want use a for cycle to call a charting function and then represent the outcome chart into a section of a multi charting pageExample single chart
expected outcome
I have a charting function (see below Charting Function Section) that i recall in a the main script with a for cycle to get several charts in sequence. Now I would like to represent all the charts, in compact size (2 columns 4 rows) in one single page. In literature I find that Subplot allows me to do so but I struggle to find the right command to represent the outcome from the charting function.
I thought something like the below in the Main Section would work but it is not
---------- Main Section ---------
import pandas as pd
import numpy as np
import plotly.graph_objs as go
from sklearn.cluster import KMeans
from plotly.subplots import make_subplots
for cont in range(8):
fig = charting_func(cont)
fig_all.add_trace(fig,
row=1, col=1
) #row and col incrementing function to be defined
fig_all.update_layout(height=600, width=800, title_text="Side By Side Subplots")
fig_all.show()
----- Charting Function ------
def charting_func(n_chrt):
# Arbitrarily 10 colors for up to 10 clusters
#colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet', 'purple','pink', 'silver']
# Create Scatter plot, assigning each point a color where
# point group = color index.
fig = btc.plot.scatter(
x=btc.index,
y="Adj Close",
color=[colors[i] for i in lists_clusters[n_chrt]],
title="k-values = {0}".format(n_chrt+2)
)
# Add horizontal lines
for cluster_avg in output[n_chrt][1:-1]:
fig.add_hline(y=cluster_avg, line_width=1, line_color="blue")
# Add a trace of the price for better clarity
fig.add_trace(go.Scatter(
x=btc.index,
y=btc['Adj Close'],
line_color="black",
line_width=1
))
# Make it pretty
layout = go.Layout(
plot_bgcolor='#D9D9D9',
showlegend=False,
# Font Families
font_family='Monospace',
font_color='#000000',
font_size=20,
xaxis=dict(
rangeslider=dict(
visible=False
))
)
fig.update_layout(layout)
return fig
type here

The basic form of a subplot is to add a location arrangement to the graph setup. So the functionalization needs to have matrix information or something like that. I have no data to present, so I have taken the stock prices of 4 companies and graphed them. As for the clustering by price, it is not included in the code, so the binning process is used to get the values and labels for the horizontal line. Please rewrite this part to your own logic. If you are good enough, the functionalization should work well.
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import itertools
import yfinance as yf
stock = ['TSLA','MSFT','AAPL','AMD']
#fig = go.Figure()
fig = make_subplots(rows=2, cols=2, subplot_titles=['MSFT','TSLA','AMD','AAPL'])
for s,rc in zip(stock, itertools.product([1,2],[2,1])):
#print(s, rc[0], rc[1])
df = yf.download(s, start="2017-09-01", end="2022-04-01", interval='1mo', progress=False)
colors = ['blue', 'red', 'green', 'purple', 'orange']
s_cut, bins = pd.cut(df['Adj Close'], 5, retbins=True, labels=colors)
fig.add_trace(go.Scatter(mode='markers+lines',
x=df.index,
y=df['Adj Close'],
marker=dict(
size=10,
color=s_cut.tolist()
)),
row=rc[0], col=rc[1]
)
for b in bins[1:-1]:
fig.add_hline(y=b, line_width=1, line_color="blue", row=rc[0], col=rc[1])
fig.update_layout(autosize=True, height=600, title_text="Side By Side Subplots")
fig.show()

Plotly: legend is not visible

Here is CDF visualization I have:
fig_cdf = px.ecdf(df['Timespan'], color_discrete_sequence=['blue'],ecdfnorm='probability', orientation='h')
fig_cdf.add_hline(y=90, line_width=2, line_color="red", name='90%', visible=True)
fig_cdf.add_hline(y=30, line_width=2, line_color="red", name='75%', visible=True)
fig_cdf.update_layout(width=500, height=500)
The problem here is that i want horizontal lines' names to be visible and appear as 2nd and 3rd legends. For this, I tried to add visible=True. However, it seems not to work. What's wrong?

This is one way of doing it...
Add the two lines to the dataframe as new columns
Use color_discrete_sequence to identify the colors you want
I am using some random dummy data, which you can replace with your data
import plotly.express as px
df = pd.DataFrame({'firstline': random.sample(range(1, 500), 20),'myX' : range(20)}) #My dummy data
#Add the two lines to dataframe
df['90%'] = [90] * 20
df['75%'] = [75] * 20
fig = px.line(df,
y = ['firstline', '90%', '75%'], x= 'myX', color_discrete_sequence=["blue", "red", "red"])
fig.update_layout(legend_title_text='Legend Heading') #Update Legend header if you dont like 'variable'
fig.show()
Output graph

This is my first experience with this graph, but to add it to the legend, you can use the line mode of the scatter plot. So I took the maximum x-axis value used in the first graph and set the legend name Average using the appropriate y-axis value. This example is taken from the official reference.
import plotly.express as px
import plotly.graph_objects as go
df = px.data.tips()
fig = px.ecdf(df, x=["total_bill", "tip"])
xmax = max(fig.data[0]['x'])
#print(xmax)
fig.add_trace(go.Scatter(
x=[0,xmax],
y=[0.6,0.6],
mode='lines',
line_color='red',
name='mean',
showlegend=True
))
fig.show()

Plotly Stacked Bar Chart text annotation issue

So, I have this simplified data frame and I'm using plotly.graph_objects to plot a stacked bar chart with text annotations.
I got the text as I wanted from the Salary column but I can't get the same for the Age column where the values are significantly lower. I would like these annotations to be the same size and on top of each bar.
How can I get the text annotations to be visible for the Age column as well?
Please find my code below:
data = {'Name':['Tom', 'Nick', 'Jack'],
'Age':[18, 21, 19],
'Salary':[500, 700, 900]}
df_new=pd.DataFrame(data)
fig = go.Figure(go.Bar(x = df_new["Name"],
y = df_new["Age"],name='Age',text=df_new["Age"],
textposition='outside'))
fig.add_bar(x = df_new["Name"],
y = df_new["Salary"],name='Salary',text=df_new["Salary"],
textposition='outside')
fig.update_layout(barmode='stack',
title = 'Age - Salary',
xaxis_title="Name",
yaxis_title="Age / Salary")
Thanks in advance!

I think you have to choose from 2 possible solutions. First of all, by using the barmode = stack argument, you are stacking and thus summing the values of age and salary. The height of bars will be age + salary, such that the height of Tom's bar will be 500 + 18 = 518. I'd advise against this, as the height should reflect the callout value in my opinion.
Solution 1 - grouped bars
This solution is based on changing the barmode to barmode = group. This will make two separate bars, which have their own callout and heights reflecting their values.
I've also added the width argument to make prettier aspect ratios.
fig = go.Figure()
fig.add_bar(x = df_new["Name"],
y = df_new["Age"],name='Age',text=df_new["Age"],
width = [0.3]*len(df_new),
)
fig.add_bar(x = df_new["Name"],
y = df_new["Salary"],name='Salary',text=df_new["Salary"],
width = [0.3]*len(df_new)
)
fig.update_layout(barmode='group',
title = 'Age - Salary',
xaxis_title="Name",
yaxis_title="Age / Salary"
)
fig.update_traces(
textposition='outside'
)
fig.update_yaxes(range=[0,1000])
Solution 2 - add secondary y-axis
I prefer this solution, as the relative size of the two categories can each be scaled to their own domain; which makes the chart a lot more readable. This uses make_subplots to create two axes and the secondary_y argument. I've made both bars visible by playing around with the widths and ranges of the axes.
Based on the data you'd have to manually rescale to your liking. You could also incorporate opacity for look-through bars, but you'd still have the risk of overlapping data callouts.
from plotly.subplots import make_subplots
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_bar(
x=df_new["Name"],
y=df_new["Age"],
name="Age",
text=df_new["Age"],
width=[0.3] * len(df_new),
secondary_y=True,
textposition="outside"
)
fig.add_bar(
x=df_new["Name"],
y=df_new["Salary"],
name="Salary",
text=df_new["Salary"],
width=[0.5] * len(df_new),
secondary_y=False,
textposition="outside"
)
fig.update_yaxes(range=[0, 1000], title='Salary', secondary_y=False)
fig.update_yaxes(range=[0, 45], title='Age', secondary_y=True)
fig.update_layout(title="Age and Salary", xaxis_title="Name")

How do I resize my Plotly bar height and show only bar’s edge (in subplot)?

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()

I can't figure out how to make my Plotly charts show whole numbers only

image of plotly chart
Hello, I'm really struggling to figure out how to format the axes on this chart. I've gone through the documentation and tried all sorts of different formatting suggestions from here and elsewhere but really not getting it. As you can see, the bottom chart has a .5 number, I want that to be skipped altogether and only have whole numbers along the axis.
I've seen ,d as a tickformat option to do this in about every answer, but I can't get that to work or I'm not seeing how to apply it to the second chart.
Can anyone with some Plotly charting experience help me out?
Here's the pertinent code:
def create_chart():
#Put data together into an interactive chart
fig.update_layout(height=500, width=800, yaxis_tickprefix = '$', hovermode='x unified', xaxis_tickformat =',d',
template=symbol_template, separators=".", title_text=(df.columns[DATA_COL_1]) + " & Units 2015-2019"
)

I believe what is happening is that the xaxis_tickformat parameter is affecting only the first subplot, but not the second one. To modify the formatting for each subplot, you can pass a dictionary with the tickformat parameter to yaxis, yaxis2, .... and so on for however many subplots you have (in your case, you only have 2 subplots).
import pandas as pd
from plotly.subplots import make_subplots
import plotly.graph_objects as go
## recreate the df
df = pd.DataFrame({'Year':[2015,2016,2017,2018,2019],
'Sales':[8.8*10**7,8.2*10**7,8.5*10**7,9.1*10**7,9.6*10**7],
'Units':[36200,36500,36900,37300,37700]})
def create_chart():
#Put data together into an interactive chart
fig = make_subplots(rows=2, cols=1)
fig.add_trace(go.Scatter(
x=df.Year,
y=df.Sales,
name='Sales',
mode='lines+markers'
), row=1, col=1)
fig.add_trace(go.Scatter(
x=df.Year,
y=df.Units,
name='Units',
mode='lines+markers'
), row=2, col=1)
fig.update_layout(
title_x=0.5,
height=500,
width=800,
yaxis_tickprefix = '$',
hovermode='x unified',
xaxis_tickformat =',d',
## this will change the formatting for BOTH subplots
yaxis=dict(tickformat ='d'),
yaxis2=dict(tickformat ='d'),
# template=symbol_template,
separators=".",
title={
'text':"MCD Sales & Units 2015-2019",
'x':0.5
}
)
fig.show()
create_chart()