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Stacked bar plot in python / plotly (express): grouping / ordering of bars

I have data in a dataframe that I want to plot with a stacked bar plot:
test_df = pd.DataFrame([[1, 5, 1, 'A'], [2, 10, 1, 'B'], [3, 3, 1, 'A']], columns = ('ID', 'Value', 'Bucket', 'Type'))
if I do the plot with Plotly Express I get bars stacked on each other and correctly ordered (based on the index):
fig = px.bar(test_df, x='Bucket', y='Value', barmode='stack')
However, I want to color the data based on Type, hence I go for
fig = px.bar(test_df, x='Bucket', y='Value', barmode='stack', color='Type')
This works, except now the ordering is messed up, because all bars are now grouped by Type. I looked through the docs of Plotly Express and couldn't find a way to specify the ordering of the bars independently. Any tips on how to do this?
I found this one here, but the scenario is a bit different and the options mentioned there don't seem to help me:
How to disable plotly express from grouping bars based on color?
Edit: This goes into the right direction, but not with using Plotly Express, but rather Plotly graph_objects:
import plotly.graph_objects as go
test_df = pd.DataFrame([[1, 5, 1, 'A', 'red'], [2, 10, 1, 'B', 'blue'], [3, 3, 1, 'A', 'red']], columns = ('ID', 'Value', 'Bucket', 'Type', 'Color'))
fig = go.Figure()
fig.add_trace(go.Bar(x=test_df["Bucket"], y=test_df["Value"], marker_color=test_df["Color"]))
Output:
Still, I'd prefer the Express version, because so many things are easier to handle there (Legend, Hover properties etc.).

The only way I can understand your question is that you don't want B to be stacked on top of A, but rather the opposite. If that's the case, then you can get what you want through:
fig.data = fig.data[::-1]
fig.layout.legend.traceorder = 'reversed'
Some details:
fig.data = fig.data[::-1] simply reverses the order that the traces appear in fig.data and ultimately in the plotted figure itself. This will however reverse the order of the legend as well. So without fig.layout.legend.traceorder = 'reversed' the result would be:
And so it follows that the complete work-around looks like this:
fig.data = fig.data[::-1]
fig.layout.legend.traceorder = 'reversed'
Complete code:
import pandas as px
import plotly.express as px
test_df = pd.DataFrame([[1, 5, 1, 'A'], [2, 10, 1, 'B'], [3, 3, 1, 'A']], columns = ('ID', 'Value', 'Bucket', 'Type'))
fig = px.bar(test_df, x='Bucket', y='Value', barmode='stack', color='Type')
fig.data = fig.data[::-1]
fig.layout.legend.traceorder = 'reversed'
fig.show()

Ok, sorry for the long delay on this, but I finally got around to solving this.
My solution is possibly not the most straight forward one, but it does work.
The basic idea is to use graph_objects instead of express and then iterate over the dataframe and add each bar as a separate trace. This way, each trace can get a name that can be grouped in a certain way (which is not possible if adding all bars in a single trace, or at least I could not find a way).
Unfortunately, the ordering of the legend is messed up (if you have more then 2 buckets) and there is no way in plotly currently to sort it. But that's a minor thing.
The main thing that bothers me is that this could've been so much easier if plotly.express allowed for manual ordering of the bars by a certain column.
Maybe I'll submit that as a suggestion.
import pandas as pd
import plotly.graph_objects as go
import plotly.io as pio
pio.renderers.default = "browser"
test_df = pd.DataFrame(
[[1, 5, 1, 'B'], [3, 3, 1, 'A'], [5, 10, 1, 'B'],
[2, 8, 2, 'B'], [4, 5, 2, 'A'], [6, 3, 2, 'A']],
columns = ('ID', 'Value', 'Bucket', 'Type'))
# add named colors to the dataframe based on type
test_df.loc[test_df['Type'] == 'A', 'Color'] = 'Crimson'
test_df.loc[test_df['Type'] == 'B', 'Color'] = 'ForestGreen'
# ensure that the dataframe is sorted by the values
test_df.sort_values('ID', inplace=True)
fig = go.Figure()
# it's tedious to iterate over each item, but only this way we can ensure that everything is correctly ordered and labelled
# Set up legend_show_dict to check if an item should be shown or not. This should be only done for the first occurrence to avoid duplication.
legend_show_dict = {}
for i, row in test_df.iterrows():
if row['Type'] in legend_show_dict:
legend_show = legend_show_dict[row['Type']]
else:
legend_show = True
legend_show_dict[row['Type']] = False
fig.add_trace(
go.Bar(
x=[row['Bucket']],
y=[row['Value']],
marker_color=row['Color'],
name=row['Type'],
legendgroup=row['Type'],
showlegend=legend_show,
hovertemplate="<br>".join([
'ID: ' + str(row['ID']),
'Value: ' + str(row['Value']),
'Bucket: ' + str(row['Value']),
'Type: ' + row['Type'],
])
))
fig.update_layout(
xaxis={'categoryorder': 'category ascending', 'title': 'Bucket'},
yaxis={'title': 'Value'},
legend={'traceorder': 'normal'}
)
fig.update_layout(barmode='stack', font_size=20)
fig.show()
This is what it should look like then:

query bokeh multi_line with markers

I would like to know if there is a way to add markers to multiline for bokeh. I can get the multiple lines but then p.circle() doesn't seem to work on list of lists. Here is a sample:
from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource
from bokeh.palettes import Spectral6, Spectral11
numlines = 2
mypalette = Spectral6[0:numlines]
data = {'x_values': [[1, 2, 3], [1,2,3]],
'y_values': [[1, 2, 3], [4 ,5, 6]], 'labels': ['a', 'b'], 'line_color': mypalette}
source = ColumnDataSource(data=data)
p = figure()
p.multi_line(xs='x_values', ys='y_values', line_color='line_color', source=source)
show(p)

As of Bokeh 0.13.0 there is not. You would need to call p.circle, p.square, etc. for each "sub" line in the multi-line.

Bar chart showing count of each category month-wise using Bokeh

I have data as shown below:
So, from this, I need to display the count in each category year_month_id wise. Since I have 12 months there will be 12 sub-divisions and under each count of
ID within each class.
Something like the image below is what I am looking for.
Now the examples in Bokeh use ColumnDataSource and dictionary mapping, but how do I do this for my dataset.
Can someone please help me with this?
Below is the expected output in tabular and chart format.

I believe the pandas Python package would come in handy for preparing your data for plotting. It's useful for manipulating table-like data structures.
Here is how I went about your problem:
from pandas import DataFrame
from bokeh.io import show
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from bokeh.palettes import Viridis5
# Your sample data
df = DataFrame({'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 1],
'year_month_id': [201612, 201612, 201612, 201612, 201612, 201612, 201612, 201612, 201612, 201701],
'class': ['A', 'D', 'B', 'other', 'other', 'other', 'A', 'other', 'A', 'B']
})
# Get counts of groups of 'class' and fill in 'year_month_id' column
df2 = DataFrame({'count': df.groupby(["year_month_id", "class"]).size()}).reset_index()
df2 now looks like this:
# Create new column to make plotting easier
df2['class-date'] = df2['class'] + "-" + df2['year_month_id'].map(str)
# x and y axes
class_date = df2['class-date'].tolist()
count = df2['count'].tolist()
# Bokeh's mapping of column names and data lists
source = ColumnDataSource(data=dict(class_date=class_date, count=count, color=Viridis5))
# Bokeh's convenience function for creating a Figure object
p = figure(x_range=class_date, y_range=(0, 5), plot_height=350, title="Counts",
toolbar_location=None, tools="")
# Render and show the vbar plot
p.vbar(x='class_date', top='count', width=0.9, color='color', source=source)
show(p)
So the Bokeh plot looks like this:
Of course you can alter it to suit your needs. The first thing I thought of was making the top of the y_range variable so it could accommodate data better, though I have not tried it myself.

How to shift the tick labels between two ticks python plotly

I have created a plot bar using plotly. An xticklabel is under each bar. Is it possible to shift the xticklabels a bit to the right or the left or even in the middle between two ticks?
import plotly
import pandas as pd
from plotly.graph_objs import *
json_file = {'y': [0, 1, 2, 3, 1]}
df = pd.DataFrame(json_file, index=['a', 'b', 'c', 'd', 'e'])
trace1 = Bar(
x=df.index,
y=df['y'])
layout = Layout(
xaxis=XAxis(
ticks=df.index,
tickvals=df.index))
data = Data([trace1])
fig = Figure(data=data, layout=layout)
plotly.offline.plot(fig)
A part of the result is the following:
Is there a way to place b between the two bars?

How can create Python iplot graph, colors changes with value?

Here you are part of my data.
I count my data
count_interests = interests.count()
then made a graph
count_interests.iplot(kind = 'bar', xTitle='Interests', yTitle='Number of Person', colors='Red')
I tried many times to find a function change columns color with values so bigger and smaller columns looks different colors.
I know there is colorscale and color functions and I tried many times I couldn't find. Does anyone know any function?

You could define a function which returns a color for each value and then pass the colors for each bar in a list.
import pandas as pd
import plotly
def color(val, median, std):
if val > median + std:
return 'darkgreen'
if val < median - std:
return 'darkred'
return 'orange'
df = pd.DataFrame({'cinema': [1, 2, 5, 3, 3, None],
'theatre': [3, 0, 8, 4, 0, 4],
'wine': [3, 2, 5, None, 1, None],
'beer': [4, 8, 2, None, None, None]})
med = df.count().median()
std = df.count().std()
colors = [color(i, med, std) for i in df.count()]
fig = plotly.graph_objs.Bar(x=df.columns,
y=df.count(),
marker=dict(color=colors))
plotly.offline.plot([fig])

The bars could be also colored either by pd.pivot_table() the rows to columns or by creating a separate list of traces for bars. Here, each column was aggregated by taking a sum() as an example. Code below:
# Import libraries
import datetime
from datetime import date
import pandas as pd
import numpy as np
from plotly import __version__
%matplotlib inline
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
init_notebook_mode(connected=True)
cf.go_offline()
import plotly.graph_objs as go
import plotly.offline as pyo
# Create dataframe
INT_M_PUB = [0,0,0,0,0,1,0,0,0,0]
INT_M_CINEMA = [1,1,1,0,0,0,0,0,0,1]
INT_M_THEATRE = [1,0,1,0,0,1,0,1,0,1]
INT_M_GYM = [0,0,0,0,0,1,0,0,0,1]
INT_M_ENTERTAIN = [0,0,1,1,0,1,0,1,0,1]
INT_M_EATOUT = [0,1,1,0,0,1,0,0,1,1]
INT_M_WINE = [0,0,0,0,0,1,0,0,0,1]
interests = pd.DataFrame({'INT_M_PUB':INT_M_PUB, 'INT_M_CINEMA':INT_M_CINEMA, 'INT_M_THEATRE':INT_M_THEATRE,
'INT_M_GYM':INT_M_GYM, 'INT_M_ENTERTAIN':INT_M_ENTERTAIN, 'INT_M_EATOUT':INT_M_EATOUT,
'INT_M_WINE':INT_M_WINE
})
interests.head(2)
dfm = interests.sum().reset_index().rename(columns={'index':'interests', 0:'value'})
dfm
# Re-creating the plot similar to that in question (note: y-axis scales are different)
df = dfm.copy()
col_list = df.columns
df.iplot(kind = 'bar', x='interests', y='value', xTitle='Interests', yTitle='Number of Person', title='These bars need to be colored', color='red')
# Color plots by creating traces
# Initialize empty list named data to collect traces for each bar
data = []
for col_name in col_list:
trace = go.Bar(
x=[col_name],
y=df[col_name],
name=col_name
)
data.append(trace)
data = data
layout = go.Layout(
barmode='group',
title='Interests',
xaxis=dict(title='Interests'),
yaxis=dict(title='Number of Person')
)
fig = go.Figure(data=data, layout=layout)
pyo.iplot(fig, filename='grouped-bar')
# Creating plot by pivoting the table
df = pd.pivot_table(dfm, values='value', columns='interests')
df.iplot(kind = 'bar',xTitle='Interests', yTitle='Number of Person')