Develop Reference

Customize legend labels in Geopandas

I would like to customize the labels on the geopandas plot legend.
fig, ax = plt.subplots(figsize = (8,5))
gdf.plot(column = "WF_CEREAL", ax = ax, legend=True, categorical=True, cmap='YlOrBr',legend_kwds = {"loc":"lower right"}, figsize =(10,6))
Adding "labels" in legend_kwds does not help.
I tried to add labels with legend_kwds in the following ways, but it didn't work-
legend_kwds = {"loc":"lower right", "labels":["low", "mid", "high", "strong", "severe"]
legend_labels:["low", "mid", "high", "strong", "severe"]
legend_labels=["low", "mid", "high", "strong", "severe"]

Since the question does not have reproducible code and data to work on. I will use the best possible approach to give a demo code that the general readers can follow and some of it may answer the question.
The code I provide below can run without the need of external data. Comments are inserted in various places to explain at important steps.
# Part 1
# Classifying the data of choice
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world['gdp_per_cap'] = world.gdp_md_est / world.pop_est
num_classes = 4 #quartile scheme has 4 classes
# You can use values derived from your preferred classification scheme here
num_qtiles = [0, .25, .5, .75, 1.] #class boundaries for quartiles
# Here is the categorical data to append to the dataframe
# They are also used as legend's label texts
qlabels = ["1st quartile","2nd quartile","3rd quartile","4th quartile"] #matching categorical data/labels
# Conditions
# len(num_qtiles)-1 == num_classes
# len(qlabels) == num_classes
# Create a new column for the categorical data mentioned above
world['gdp_quartile'] = pd.qcut(world['gdp_per_cap'], num_qtiles, labels=qlabels)
# Plotting the categorical data for checking
ax1 = world['gdp_quartile'].value_counts().plot(figsize=(5,4), kind='bar', xlabel='Quartile_Classes', ylabel='Countries', rot=45, legend=True)
The output of part1:-
# Part 2
# Plot world map using the categorical data
fig, ax = plt.subplots(figsize=(9,4))
# num_classes = 4 # already defined
#color_steps = plt.colormaps['Reds']._resample(num_classes) #For older version
color_steps = plt.colormaps['Reds'].resampled(num_classes) #Current version of matplotlib
# This plots choropleth map using categorical data as the theme
world.plot(column='gdp_quartile', cmap = color_steps,
legend=True,
legend_kwds={'loc':'lower left',
'bbox_to_anchor':(0, .2),
'markerscale':1.29,
'title_fontsize':'medium',
'fontsize':'small'},
ax=ax)
leg1 = ax.get_legend()
leg1.set_title("GDP per capita")
ax.title.set_text("World Map: GDP per Capita")
plt.show()
Output of part2:-
Edit
Additional code,
use it to replace the line plt.show() above.
This answers the question posted in the comment below.
# Part 3
# New categorical texts to use with legend
new_legtxt = ["low","mid","high","v.high"]
for ix,eb in enumerate(leg1.get_texts()):
print(eb.get_text(), "-->", new_legtxt[ix])
eb.set_text(new_legtxt[ix])
plt.show()

I am not sure if this will work but try:
gdf.plot(column = "WF_CEREAL", ax = ax, legend=True, categorical=True, cmap='YlOrBr',legend_kwds = {"loc":"lower right"}, figsize =(10,6), legend_labels=["low", "mid", "high", "strong", "severe"])

half (not split!) violin plots in seaborn

Currently seaborn offers functionality for split violinplots by setting split=True, according to a hue variable. I would like to make a 'half' violin plot, i.e. a plot where half of each violin is omitted. Such a plot depicts something similar to a pdf for each continuous variable, plotted on one side of each vertical line of each categorical variable only.
I have managed to trick seaborn to plot this with an extra data point outside the plotted range of values and an extra dummy hue, but I would like to know if this can be done without actually altering the dataset, e.g. within sns.violinplot() arguments.
For instance, this graph:
Was created by this snippet:
# imports
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# load dataset from seaborn
datalist = sns.get_dataset_names()
dataset_name = 'iris'
if dataset_name in datalist:
df = sns.load_dataset(dataset_name)
else:
print("Dataset with name: " + dataset_name + " was not found in the available datasets online by seaborn.")
# prepare data
df2 = df.append([-999,-999,-999,-999,'setosa'])
df2['huecol'] = 0.0
df2['huecol'].iloc[-1]= -999
# plot
fig = plt.figure(figsize=(6,6))
sns.violinplot(x='species',y="sepal_width",
split=True, hue ='huecol', inner = 'quartile',
palette="pastel", data=df2, legend=False)
plt.title('iris')
# remove hue legend
leg = plt.gca().legend()
leg.remove()
plt.ylim([1,5.0])
plt.show()

I was looking for a solution similar to this but did not find anything satisfactory. I ended up calling seaborn.kdeplot multiple times as violinplot is essentially a one-sided kernel density plot.
Example
Function definition for categorical_kde_plot below
categorical_kde_plot(
df,
variable="tip",
category="day",
category_order=["Thur", "Fri", "Sat", "Sun"],
horizontal=False,
)
with horizontal=True, the output would look like:
Code
import seaborn as sns
from matplotlib import pyplot as plt
def categorical_kde_plot(
df,
variable,
category,
category_order=None,
horizontal=False,
rug=True,
figsize=None,
):
"""Draw a categorical KDE plot
Parameters
----------
df: pd.DataFrame
The data to plot
variable: str
The column in the `df` to plot (continuous variable)
category: str
The column in the `df` to use for grouping (categorical variable)
horizontal: bool
If True, draw density plots horizontally. Otherwise, draw them
vertically.
rug: bool
If True, add also a sns.rugplot.
figsize: tuple or None
If None, use default figsize of (7, 1*len(categories))
If tuple, use that figsize. Given to plt.subplots as an argument.
"""
if category_order is None:
categories = list(df[category].unique())
else:
categories = category_order[:]
figsize = (7, 1.0 * len(categories))
fig, axes = plt.subplots(
nrows=len(categories) if horizontal else 1,
ncols=1 if horizontal else len(categories),
figsize=figsize[::-1] if not horizontal else figsize,
sharex=horizontal,
sharey=not horizontal,
)
for i, (cat, ax) in enumerate(zip(categories, axes)):
sns.kdeplot(
data=df[df[category] == cat],
x=variable if horizontal else None,
y=None if horizontal else variable,
# kde kwargs
bw_adjust=0.5,
clip_on=False,
fill=True,
alpha=1,
linewidth=1.5,
ax=ax,
color="lightslategray",
)
keep_variable_axis = (i == len(fig.axes) - 1) if horizontal else (i == 0)
if rug:
sns.rugplot(
data=df[df[category] == cat],
x=variable if horizontal else None,
y=None if horizontal else variable,
ax=ax,
color="black",
height=0.025 if keep_variable_axis else 0.04,
)
_format_axis(
ax,
cat,
horizontal,
keep_variable_axis=keep_variable_axis,
)
plt.tight_layout()
plt.show()
def _format_axis(ax, category, horizontal=False, keep_variable_axis=True):
# Remove the axis lines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
if horizontal:
ax.set_ylabel(None)
lim = ax.get_ylim()
ax.set_yticks([(lim[0] + lim[1]) / 2])
ax.set_yticklabels([category])
if not keep_variable_axis:
ax.get_xaxis().set_visible(False)
ax.spines["bottom"].set_visible(False)
else:
ax.set_xlabel(None)
lim = ax.get_xlim()
ax.set_xticks([(lim[0] + lim[1]) / 2])
ax.set_xticklabels([category])
if not keep_variable_axis:
ax.get_yaxis().set_visible(False)
ax.spines["left"].set_visible(False)
if __name__ == "__main__":
df = sns.load_dataset("tips")
categorical_kde_plot(
df,
variable="tip",
category="day",
category_order=["Thur", "Fri", "Sat", "Sun"],
horizontal=True,
)

The answer is simply, no, it's not possible with seaborn without tricking it into thinking there is a hue present.
This answer shows how to do it in matplotlib and in principle the same can be applied to seaborn violinplots as well, namely to cut out half of the violin path.

It's not necessary to modify the data:
ax = sns.violinplot(
data=tips,
x="day", y="total_bill", hue=True,
hue_order=[True, False], split=True,
)
ax.legend_ = None

Wrap text in seaborn barplot x-axis python

I'm trying to plot a dataframe and I want the x-axis labels to not override each other like here
When dealing with one plot, adding .set_xticklabels(rotation=30) does solve the issue:
sns.catplot("foo", data=boo, aspect=5, kind="count" ).set_xticklabels(rotation=30)
but when I add the ax argument as here:
sns.catplot("foo", data=boo, aspect=5, kind="count" , ax=fig.add_subplot(211) ),
it doesn't (see the first snapshot).
I tried sns.catplot("foo", data=boo, aspect=5, kind="count", ax=ax1).set_xticklabels(rotation=30)
I'm looking for some kind of excel's "wrap text". I tried looking in seaborn attributes and had no success. I also tried to make the x-axis values smaller, but it was still overriding each over in some of the labels.

Disc - This is my first post on 'StackOF'. Please excuse me if it is not up to your expectations. To wrap the x-axis labels in Seaborn is not possible (afaik) but even with Dexplot there are too many limitations. Here is the logic for achieving this using Matplotlib (using python).
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
raw_data = {'plan_type': ['this is big ticks number one', 'this is also a big ticks number two Group B', 'and this as well a very big ticks number three Group C', 'this is no exception than a very big ticks number four Group D','Group E'],
'Total': [56,24,18,49,67],
'Total_PY': [12,82,33,60,16]
}
df = pd.DataFrame(raw_data,
columns = ['plan_type', 'Total', 'Total_PY'])
fig, ax = plt.subplots(figsize=(14,9))
ax = df.plot.bar(rot=0, ax=ax, color=['#FF5733','#2323ff'],
width = 0.7 )
for p in ax.patches[1:]:
h = p.get_height()
x = p.get_x()+p.get_width()/2.
if h != 0 :
ax.annotate("%g" % p.get_height(), xy=(x,h), xytext=(0,4), rotation=0,
textcoords="offset points", ha="center", va="bottom")
ax.set_xlim(-0.5, None)
ax.set_ylim(-0.5, 120)
ax.margins(y=0)
ax.legend(ncol = len(df.columns), loc="lower right", bbox_to_anchor=(0,1.02,1,0.08),
borderaxespad=0, title= 'Year' )
ax = plt.gca();
labels = df["plan_type"].tolist()
new_labels = []
for tick in labels:
word_list = tick.split();
new_labels.append(word_list);
new_ticks = []
for label_tick in new_labels:
if(len(label_tick) >= 2):
joined = ' \n'.join(label_tick);
new_ticks.append(joined) ;
else:
joined = ' '.join(label_tick);
new_ticks.append(joined) ;
plt.xticks(ticks = df.index, labels = new_ticks)
plt.xlabel('Type of Plan')
plt.ylabel('Count of Numbers')

This isn't possible with seaborn, but it is with dexplot, a library similar to seaborn, but with many more features.
Below, is a bar chart with long labels. They are automatically wrapped, but can be wrapped at a specific position with x_textwrap and y_textwrap.
In seaborn, this same plot looks like this:

Matplotlib bar chart show x-ticks only at non-zero bars

I have to make a (stacked) bar plot that has ~3000 positions on the x axis. However, many of these positions do not contain bars but are still labeled on the x-axis, making reading the plot difficult. Is there any way to only show x-ticks for existing (stacked) bars? The spaces between the bars based on the x-tick values are necessary. How would one tackle this in matplotlib? Is there a more fitting plot than a stacked bar chart? I'm constructing the plots from a pandas cross-table (pd.crosstab()).
link to image of plot:
https://i.stack.imgur.com/qk99z.png
as an example of what my dataframe would look like (thanks gepcel):
import pandas as pd
import numpy as np
N = 3200
df = pd.DataFrame(np.random.randint(1, 5, size=(N, 3)))
df.loc[np.random.choice(df.index, size=3190, replace=False), :] = 0
df_select = df[df.sum(axis=1)>0]

Basically, without an example, you should select out the ticks that the total value (aka, stacked value) greater than zero. And then set the xticks and xticklabels manually.
Let's say you have a dataframe like the following:
import pandas as pd
import numpy as np
N = 3200
df = pd.DataFrame(np.random.randint(1, 5, size=(N, 3)))
df.loc[np.random.choice(df.index, size=3190, replace=False), :] = 0
Then the selected data should be something like this:
df_select = df[df.sum(axis=1)>0]
And then you can plot a stacked bar plot like:
# set width=20, the bar is not too thin to show
plt.bar(df_select.index, df_select[0], width=20, label='0')
plt.bar(df_select.index, df_select[1], width=20, label='1',
bottom=df_select[0])
plt.bar(df_select.index, df_select[2], width=20, label='2',
bottom=df_select[0]+df_select[1])
# Only show the selected ticks, it'll be a little tricky if
# you want ticklabels to be different than ticks
# And still hard to avoid ticklabels overlapping
plt.xticks(df_select.index)
plt.legend()
plt.show()
The result should be something like this:
UPDATE:
It's easy to put texts on top of bars by:
for n, row in df_select.iterrows():
plt.text(n, row.sum()+0.2, n, ha='center', rotation=90, va='bottom')
It's to calculate the position of the top of each bar, and put text there, and maybe add some offset (like +0.2), and use rotation=90 to control the rotation. Full codes will be:
df_select = df[df.sum(axis=1)>0]
plt.bar(df_select.index, df_select[0], width=20, label='0')
plt.bar(df_select.index, df_select[1], width=20, label='1',
bottom=df_select[0])
plt.bar(df_select.index, df_select[2], width=20, label='2',
bottom=df_select[0]+df_select[1])
# Here is the part to put text:
for n, row in df_select.iterrows():
plt.text(n, row.sum()+0.2, n, ha='center', rotation=90, va='bottom')
plt.xticks(df_select.index)
plt.legend()
plt.show()
And a result:

Here's a twist on gepcel's answer that adapts to a dataframe with a varying number of columns:
# in this case I'm creating the dataframe with 3 columns
# but the code is meant to adapt to dataframes with varying column numbers
df = pd.DataFrame(np.random.randint(1, 5, size=(3200, 3)))
df.loc[np.random.choice(df.index, size=3190, replace=False), :] = 0
df_select = df[df.sum(axis=1)>1]
fig, ax = plt.subplots()
ax.bar(df_select.index, df_select.iloc[:,0], label = df_select.columns[0])
if df_select.shape[1] > 1:
for i in range(1, df_select.shape[1]):
bottom = df_select.iloc[:,np.arange(0,i,1)].sum(axis=1)
ax.bar(df_select.index, df_select.iloc[:,i], bottom=bottom, label =
df_select.columns[i])
ax.set_xticks(df_select.index)
plt.legend(loc='best', bbox_to_anchor=(1, 0.5))
plt.xticks(rotation=90, fontsize=8)

Horizontal stacked bar plot and add labels to each section

I am trying to replicate the following image in matplotlib and it seems barh is my only option. Though it appears that you can't stack barh graphs so I don't know what to do
If you know of a better python library to draw this kind of thing, please let me know.
This is all I could come up with as a start:
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
people = ('A','B','C','D','E','F','G','H')
y_pos = np.arange(len(people))
bottomdata = 3 + 10 * np.random.rand(len(people))
topdata = 3 + 10 * np.random.rand(len(people))
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.barh(y_pos, bottomdata,color='r',align='center')
ax.barh(y_pos, topdata,color='g',align='center')
ax.set_yticks(y_pos)
ax.set_yticklabels(people)
ax.set_xlabel('Distance')
plt.show()
I would then have to add labels individually using ax.text which would be tedious. Ideally I would like to just specify the width of the part to be inserted then it updates the center of that section with a string of my choosing. The labels on the outside (e.g. 3800) I can add myself later, it is mainly the labeling over the bar section itself and creating this stacked method in a nice way I'm having problems with. Can you even specify a 'distance' i.e. span of color in any way?

Edit 2: for more heterogeneous data. (I've left the above method since I find it more usual to work with the same number of records per series)
Answering the two parts of the question:
a) barh returns a container of handles to all the patches that it drew. You can use the coordinates of the patches to aid the text positions.
b) Following these two answers to the question that I noted before (see Horizontal stacked bar chart in Matplotlib), you can stack bar graphs horizontally by setting the 'left' input.
and additionally c) handling data that is less uniform in shape.
Below is one way you could handle data that is less uniform in shape is simply to process each segment independently.
import numpy as np
import matplotlib.pyplot as plt
# some labels for each row
people = ('A','B','C','D','E','F','G','H')
r = len(people)
# how many data points overall (average of 3 per person)
n = r * 3
# which person does each segment belong to?
rows = np.random.randint(0, r, (n,))
# how wide is the segment?
widths = np.random.randint(3,12, n,)
# what label to put on the segment (xrange in py2.7, range for py3)
labels = range(n)
colors ='rgbwmc'
patch_handles = []
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
left = np.zeros(r,)
row_counts = np.zeros(r,)
for (r, w, l) in zip(rows, widths, labels):
print r, w, l
patch_handles.append(ax.barh(r, w, align='center', left=left[r],
color=colors[int(row_counts[r]) % len(colors)]))
left[r] += w
row_counts[r] += 1
# we know there is only one patch but could enumerate if expanded
patch = patch_handles[-1][0]
bl = patch.get_xy()
x = 0.5*patch.get_width() + bl[0]
y = 0.5*patch.get_height() + bl[1]
ax.text(x, y, "%d%%" % (l), ha='center',va='center')
y_pos = np.arange(8)
ax.set_yticks(y_pos)
ax.set_yticklabels(people)
ax.set_xlabel('Distance')
plt.show()
Which produces a graph like this , with a different number of segments present in each series.
Note that this is not particularly efficient since each segment used an individual call to ax.barh. There may be more efficient methods (e.g. by padding a matrix with zero-width segments or nan values) but this likely to be problem-specific and is a distinct question.
Edit: updated to answer both parts of the question.
import numpy as np
import matplotlib.pyplot as plt
people = ('A','B','C','D','E','F','G','H')
segments = 4
# generate some multi-dimensional data & arbitrary labels
data = 3 + 10* np.random.rand(segments, len(people))
percentages = (np.random.randint(5,20, (len(people), segments)))
y_pos = np.arange(len(people))
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
colors ='rgbwmc'
patch_handles = []
left = np.zeros(len(people)) # left alignment of data starts at zero
for i, d in enumerate(data):
patch_handles.append(ax.barh(y_pos, d,
color=colors[i%len(colors)], align='center',
left=left))
# accumulate the left-hand offsets
left += d
# go through all of the bar segments and annotate
for j in range(len(patch_handles)):
for i, patch in enumerate(patch_handles[j].get_children()):
bl = patch.get_xy()
x = 0.5*patch.get_width() + bl[0]
y = 0.5*patch.get_height() + bl[1]
ax.text(x,y, "%d%%" % (percentages[i,j]), ha='center')
ax.set_yticks(y_pos)
ax.set_yticklabels(people)
ax.set_xlabel('Distance')
plt.show()
You can achieve a result along these lines (note: the percentages I used have nothing to do with the bar widths, as the relationship in the example seems unclear):
See Horizontal stacked bar chart in Matplotlib for some ideas on stacking horizontal bar plots.

Imports and Test DataFrame
Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2
For vertical stacked bars see Stacked Bar Chart with Centered Labels
import pandas as pd
import numpy as np
# create sample data as shown in the OP
np.random.seed(365)
people = ('A','B','C','D','E','F','G','H')
bottomdata = 3 + 10 * np.random.rand(len(people))
topdata = 3 + 10 * np.random.rand(len(people))
# create the dataframe
df = pd.DataFrame({'Female': bottomdata, 'Male': topdata}, index=people)
# display(df)
Female Male
A 12.41 7.42
B 9.42 4.10
C 9.85 7.38
D 8.89 10.53
E 8.44 5.92
F 6.68 11.86
G 10.67 12.97
H 6.05 7.87
Updated with matplotlib v3.4.2
Use matplotlib.pyplot.bar_label
See How to add value labels on a bar chart for additional details and examples with .bar_label.
labels = [f'{v.get_width():.2f}%' if v.get_width() > 0 else '' for v in c ] for python < 3.8, without the assignment expression (:=).
Plotted using pandas.DataFrame.plot with kind='barh'
ax = df.plot(kind='barh', stacked=True, figsize=(8, 6))
for c in ax.containers:
# customize the label to account for cases when there might not be a bar section
labels = [f'{w:.2f}%' if (w := v.get_width()) > 0 else '' for v in c ]
# set the bar label
ax.bar_label(c, labels=labels, label_type='center')
# uncomment and use the next line if there are no nan or 0 length sections; just use fmt to add a % (the previous two lines of code are not needed, in this case)
# ax.bar_label(c, fmt='%.2f%%', label_type='center')
# move the legend
ax.legend(bbox_to_anchor=(1.025, 1), loc='upper left', borderaxespad=0.)
# add labels
ax.set_ylabel("People", fontsize=18)
ax.set_xlabel("Percent", fontsize=18)
plt.show()
Using seaborn
sns.barplot does not have an option for stacked bar plots, however, sns.histplot and sns.displot can be used to create horizontal stacked bars.
seaborn typically requires the dataframe to be in a long, instead of wide, format, so use pandas.DataFrame.melt to reshape the dataframe.
Reshape dataframe
# convert the dataframe to a long form
df = df.reset_index()
df = df.rename(columns={'index': 'People'})
dfm = df.melt(id_vars='People', var_name='Gender', value_name='Percent')
# display(dfm)
People Gender Percent
0 A Female 12.414557
1 B Female 9.416027
2 C Female 9.846105
3 D Female 8.885621
4 E Female 8.438872
5 F Female 6.680709
6 G Female 10.666258
7 H Female 6.050124
8 A Male 7.420860
9 B Male 4.104433
10 C Male 7.383738
11 D Male 10.526158
12 E Male 5.916262
13 F Male 11.857227
14 G Male 12.966913
15 H Male 7.865684
sns.histplot: axes-level plot
fig, axe = plt.subplots(figsize=(8, 6))
sns.histplot(data=dfm, y='People', hue='Gender', discrete=True, weights='Percent', multiple='stack', ax=axe)
# iterate through each set of containers
for c in axe.containers:
# add bar annotations
axe.bar_label(c, fmt='%.2f%%', label_type='center')
axe.set_xlabel('Percent')
plt.show()
sns.displot: figure-level plot
g = sns.displot(data=dfm, y='People', hue='Gender', discrete=True, weights='Percent', multiple='stack', height=6)
# iterate through each facet / supbplot
for axe in g.axes.flat:
# iteate through each set of containers
for c in axe.containers:
# add the bar annotations
axe.bar_label(c, fmt='%.2f%%', label_type='center')
axe.set_xlabel('Percent')
plt.show()
Original Answer - before matplotlib v3.4.2
The easiest way to plot a horizontal or vertical stacked bar, is to load the data into a pandas.DataFrame
This will plot, and annotate correctly, even when all categories ('People'), don't have all segments (e.g. some value is 0 or NaN)
Once the data is in the dataframe:
It's easier to manipulate and analyze
It can be plotted with the matplotlib engine, using:
pandas.DataFrame.plot.barh
label_text = f'{width}' for annotations
pandas.DataFrame.plot.bar
label_text = f'{height}' for annotations
SO: Vertical Stacked Bar Chart with Centered Labels
These methods return a matplotlib.axes.Axes or a numpy.ndarray of them.
Using the .patches method unpacks a list of matplotlib.patches.Rectangle objects, one for each of the sections of the stacked bar.
Each .Rectangle has methods for extracting the various values that define the rectangle.
Each .Rectangle is in order from left the right, and bottom to top, so all the .Rectangle objects, for each level, appear in order, when iterating through .patches.
The labels are made using an f-string, label_text = f'{width:.2f}%', so any additional text can be added as needed.
Plot and Annotate
Plotting the bar, is 1 line, the remainder is annotating the rectangles
# plot the dataframe with 1 line
ax = df.plot.barh(stacked=True, figsize=(8, 6))
# .patches is everything inside of the chart
for rect in ax.patches:
# Find where everything is located
height = rect.get_height()
width = rect.get_width()
x = rect.get_x()
y = rect.get_y()
# The height of the bar is the data value and can be used as the label
label_text = f'{width:.2f}%' # f'{width:.2f}' to format decimal values
# ax.text(x, y, text)
label_x = x + width / 2
label_y = y + height / 2
# only plot labels greater than given width
if width > 0:
ax.text(label_x, label_y, label_text, ha='center', va='center', fontsize=8)
# move the legend
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
# add labels
ax.set_ylabel("People", fontsize=18)
ax.set_xlabel("Percent", fontsize=18)
plt.show()
Example with Missing Segment
# set one of the dataframe values to 0
df.iloc[4, 1] = 0
Note the annotations are all in the correct location from df.

For this case, the above answers work perfectly. The issue I had, and didn't find a plug-and-play solution online, was that I often have to plot stacked bars in multi-subplot figures, with many values, which tend to have very non-homogenous amplitudes.
(Note: I work usually with pandas dataframes, and matplotlib. I couldn't make the bar_label() method of matplotlib to work all the times.)
So, I just give a kind of ad-hoc, but easily generalizable solution. In this example, I was working with single-row dataframes (for power-exchange monitoring purposes per hour), so, my dataframe (df) had just one row.
(I provide an example figure to show how this can be useful in very densely-packed plots)
[enter image description here][1]
[1]: https://i.stack.imgur.com/9akd8.png
'''
This implementation produces a stacked, horizontal bar plot.
df --> pandas dataframe. Columns are used as the iterator, and only the firs value of each column is used.
waterfall--> bool: if True, apart from the stack-direction, also a perpendicular offset is added.
cyclic_offset_x --> list (of any length) or None: loop through these values to use as x-offset pixels.
cyclic_offset_y --> list (of any length) or None: loop through these values to use as y-offset pixels.
ax --> matplotlib Axes, or None: if None, creates a new axis and figure.
'''
def magic_stacked_bar(df, waterfall=False, cyclic_offset_x=None, cyclic_offset_y=None, ax=None):
if isinstance(cyclic_offset_x, type(None)):
cyclic_offset_x = [0, 0]
if isinstance(cyclic_offset_y, type(None)):
cyclic_offset_y = [0, 0]
ax0 = ax
if isinstance(ax, type(None)):
fig, ax = plt.subplots()
fig.set_size_inches(19, 10)
cycler = 0;
prev = 0 # summation variable to make it stacked
for c in df.columns:
if waterfall:
y = c ; label = "" # bidirectional stack
else:
y = 0; label = c # unidirectional stack
ax.barh(y=y, width=df[c].values[0], height=1, left=prev, label = label)
prev += df[c].values[0] # add to sum-stack
offset_x = cyclic_offset_x[divmod(cycler, len(cyclic_offset_x))[1]]
offset_y = cyclic_offset_y[divmod(cycler, len(cyclic_offset_y))[1]]
ax.annotate(text="{}".format(int(df[c].values[0])), xy=(prev - df[c].values / 2, y),
xytext=(offset_x, offset_y), textcoords='offset pixels',
ha='center', va='top', fontsize=8,
arrowprops=dict(facecolor='black', shrink=0.01, width=0.3, headwidth=0.3),
bbox=dict(boxstyle='round', facecolor='grey', alpha=0.5))
cycler += 1
if not waterfall:
ax.legend() # if waterfall, the index annotates the columns. If
# waterfall ==False, the legend annotates the columns
if isinstance(ax0, type(None)):
ax.set_title("Voi la")
ax.set_xlabel("UltraWatts")
plt.show()
else:
return ax
''' (Sometimes, it is more tedious and requires some custom functions to make the labels look alright.
'''
A, B = 80,80
n_units = df.shape[1]
cyclic_offset_x = -A*np.cos(2*np.pi / (2*n_units) *np.arange(n_units))
cyclic_offset_y = B*np.sin(2*np.pi / (2*n_units) * np.arange(n_units)) + B/2