I have this image from Matplotlib :
I would like to write for each category (cat i with i in [1-10] in the figure) the highest value and its corresponding legend on the graphic.
Below you can find visually what I would like to achieve :
The thing is the fact that I don't know if it is possible because of the way of plotting from matplotlib.
Basically, this is the part of the code for drawing multiple bars :
# create plot
fig, ax = plt.subplots(figsize = (9,9))
index = np.arange(len_category)
if multiple:
bar_width = 0.3
else :
bar_width = 1.5
opacity = 1.0
#test_array contains test1 and test2
cmap = get_cmap(len(test_array))
for i in range(len(test_array)):
count = count + 1
current_label = test_array[i]
rects = plt.bar(index-0.2+bar_width*i, score_array[i], bar_width, alpha=opacity, color=np.random.rand(3,1),label=current_label )
plt.xlabel('Categories')
plt.ylabel('Scores')
plt.title('Scores by Categories')
plt.xticks(index + bar_width, categories_array)
plt.legend()
plt.tight_layout()
plt.show()
and this is the part I have added in order to do what I would like to achieve. But it searches the max across all the bars in the graphics. For example, the max of test1 will be in cat10 and the max of test2 will be cat2. Instead, I would like to have the max for each category.
for i in range(len(test_array)):
count = count + 1
current_label = test_array[i]
rects = plt.bar(index-0.2+bar_width*i, score_array[i], bar_width,alpha=opacity,color=np.random.rand(3,1),label=current_label )
max_score_current = max(score_array[i])
list_rect = list()
max_height = 0
#The id of the rectangle who get the highest score
max_idx = 0
for idx,rect in enumerate(rects):
list_rect.append(rect)
height = rect.get_height()
if height > max_height:
max_height = height
max_idx = idx
highest_rect = list_rect[max_idx]
plt.text(highest_rect.get_x() + highest_rect.get_width()/2.0, max_height, str(test_array[i]),color='blue', fontweight='bold')
del list_rect[:]
Do you have an idea about how I can achieve that ?
Thank you
It usually better to keep data generation and visualization separate. Instead of looping through the bars themselves, just get the necessary data prior to plotting. This makes everything a lot more simple.
So first create a list of labels to use and then loop over the positions to annotate then. In the code below the labels are created by mapping the argmax of a column array to the test set via a dictionary.
import numpy as np
import matplotlib.pyplot as plt
test1 = [6,4,5,8,3]
test2 = [4,5,3,4,6]
labeldic = {0:"test1", 1:"test2"}
a = np.c_[test1,test2]
maxi = np.max(a, axis=1)
l = ["{} {}".format(i,labeldic[j]) for i,j in zip(maxi, np.argmax(a, axis=1))]
for i in range(a.shape[1]):
plt.bar(np.arange(a.shape[0])+(i-1)*0.3, a[:,i], width=0.3, align="edge",
label = labeldic[i])
for i in range(a.shape[0]):
plt.annotate(l[i], xy=(i,maxi[i]), xytext=(0,10),
textcoords="offset points", ha="center")
plt.margins(y=0.2)
plt.legend()
plt.show()
From your question it is not entirely clear what you want to achieve, but assuming that you want the relative height of each bar in one group printed above that bar, here is one way to achieve that:
from matplotlib import pyplot as plt
import numpy as np
score_array = np.random.rand(2,10)
index = np.arange(score_array.shape[1])
test_array=['test1','test2']
opacity = 1
bar_width = 0.25
for i,label in enumerate(test_array):
rects = plt.bar(index-0.2+bar_width*i, score_array[i], bar_width,alpha=opacity,label=label)
heights = [r.get_height() for r in rects]
print(heights)
rel_heights = [h/max(heights) for h in heights]
idx = heights.index(max(heights))
for i,(r,h, rh) in enumerate(zip(rects, heights, rel_heights)):
plt.text(r.get_x() + r.get_width()/2.0, h, '{:.2}'.format(rh), color='b', fontweight ='bold', ha='center')
plt.show()
The result looks like this:
Related
I was trying to reproduce this plot with Matplotlib:
So, by looking at the documentation, I found out that the closest thing is a grouped bar chart. The problem is that I have a different number of "bars" for each category (subject, illumination, ...) compared to the example provided by matplotlib that instead only has 2 classes (M, F) for each category (G1, G2, G3, ...). I don't know exactly from where to start, does anyone here has any clue? I think in this case the trick they made to specify bars location:
x = np.arange(len(labels)) # the label locations
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x - width/2, men_means, width, label='Men')
rects2 = ax.bar(x + width/2, women_means, width, label='Women')
does not work at all as in the second class (for example) there is a different number of bars. It would be awesome if anyone could give me an idea. Thank you in advance!
Supposing the data resides in a dataframe, the bars can be generated by looping through the categories:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# first create some test data, similar in structure to the question's
categories = ['Subject', 'Illumination', 'Location', 'Daytime']
df = pd.DataFrame(columns=['Category', 'Class', 'Value'])
for cat in categories:
for _ in range(np.random.randint(2, 7)):
df = df.append({'Category': cat,
'Class': "".join(np.random.choice([*'tuvwxyz'], 10)),
'Value': np.random.uniform(10, 17)}, ignore_index=True)
fig, ax = plt.subplots()
start = 0 # position for first label
gap = 1 # gap between labels
labels = [] # list for all the labels
label_pos = np.array([]) # list for all the label positions
# loop through the categories of the dataframe
# provide a list of colors (at least as long as the expected number of categories)
for (cat, df_cat), color in zip(df.groupby('Category', sort=False), ['navy', 'orange'] * len(df)):
num_in_cat = len(df_cat)
# add a text for the category, using "axes coordinates" for the y-axis
ax.text(start + num_in_cat / 2, 0.95, cat, ha='center', va='top', transform=ax.get_xaxis_transform())
# positions for the labels of the current category
this_label_pos = np.arange(start, start + num_in_cat)
# create bars at the desired positions
ax.bar(this_label_pos, df_cat['Value'], color=color)
# store labels and their positions
labels += df_cat['Class'].to_list()
label_pos = np.append(label_pos, this_label_pos)
start += num_in_cat + gap
# set the positions for the labels
ax.set_xticks(label_pos)
# set the labels
ax.set_xticklabels(labels, rotation=30)
# optionally set a new lower position for the y-axis
ax.set_ylim(ymin=9)
# optionally reduce the margin left and right
ax.margins(x=0.01)
plt.tight_layout()
plt.show()
I really don't understand what's going wrong with this... I've looked through what is pretty simple data several times and have restarted the kernel (running on Jupyter Notebook) and nothing seems to be solving it.
Here's the data frame I have (sorry I know the numbers look a bit silly, this is a really sparse dataset over a long time period, original is reindexed for 20 years):
DATE NODP NVP VP VDP
03/08/2002 0.083623 0.10400659 0.81235517 1.52458E-05
14/09/2003 0.24669167 0.24806379 0.5052293 1.52458E-05
26/07/2005 0.15553726 0.13324796 0.7111538 0.000060983
20/05/2006 0 0.23 0.315 0.455
05/06/2007 0.21280034 0.29139224 0.49579217 1.52458E-05
21/02/2010 0 0.55502195 0.4449628 1.52458E-05
09/04/2011 0.09531311 0.17514162 0.72954527 0
14/02/2012 0.19213217 0.12866237 0.67920546 0
17/01/2014 0.12438848 0.10297326 0.77263826 0
24/02/2017 0.01541347 0.09897548 0.88561105 0
Note that all of the rows add up to 1! I have triple, quadruple checked this...XD
I am trying to produce a stacked bar chart of this data, with the following code, which seems to have worked perfectly for everything else I have been using it for:
NODP = df['NODP']
NVP = df['NVP']
VDP = df['VDP']
VP = df['VP']
ind = np.arange(len(df.index))
width = 5.0
p1 = plt.bar(ind, NODP, width, label = 'NODP', bottom=NVP, color= 'grey')
p2 = plt.bar(ind, NVP, width, label = 'NVP', bottom=VDP, color= 'tan')
p3 = plt.bar(ind, VDP, width, label = 'VDP', bottom=VP, color= 'darkorange')
p4 = plt.bar(ind, VP, width, label = 'VP', color= 'darkgreen')
plt.ylabel('Ratio')
plt.xlabel('Year')
plt.title('Ratio change',x=0.06,y=0.8)
plt.xticks(np.arange(min(ind), max(ind)+1, 6.0), labels=xlabels) #the xticks were cumbersome so not included in this example code
plt.legend()
Which gives me the following plot:
As is evident, 1) NODP is not showing up at all, and 2) the remainder of them are being plotted with the wrong proportions...
I really don't understand what is wrong, it should be really simple, right?! I'm sorry if it is really simple, it's probably right under my nose. Any ideas greatly appreciated!
If you want to create stacked bars this way (so standard matplotlib without using pandas or seaborn for plotting), the bottom needs to be the sum of all the lower bars.
Here is an example with the given data.
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
columns = ['DATE', 'NODP', 'NVP', 'VP', 'VDP']
data = [['03/08/2002', 0.083623, 0.10400659, 0.81235517, 1.52458E-05],
['14/09/2003', 0.24669167, 0.24806379, 0.5052293, 1.52458E-05],
['26/07/2005', 0.15553726, 0.13324796, 0.7111538, 0.000060983],
['20/05/2006', 0, 0.23, 0.315, 0.455],
['05/06/2007', 0.21280034, 0.29139224, 0.49579217, 1.52458E-05],
['21/02/2010', 0, 0.55502195, 0.4449628, 1.52458E-05],
['09/04/2011', 0.09531311, 0.17514162, 0.72954527, 0],
['14/02/2012', 0.19213217, 0.12866237, 0.67920546, 0],
['17/01/2014', 0.12438848, 0.10297326, 0.77263826, 0],
['24/02/2017', 0.01541347, 0.09897548, 0.88561105, 0]]
df = pd.DataFrame(data=data, columns=columns)
ind = pd.to_datetime(df.DATE)
NODP = df.NODP.to_numpy()
NVP = df.NVP.to_numpy()
VP = df.VP.to_numpy()
VDP = df.VDP.to_numpy()
width = 120
p1 = plt.bar(ind, NODP, width, label='NODP', bottom=NVP+VDP+VP, color='grey')
p2 = plt.bar(ind, NVP, width, label='NVP', bottom=VDP+VP, color='tan')
p3 = plt.bar(ind, VDP, width, label='VDP', bottom=VP, color='darkorange')
p4 = plt.bar(ind, VP, width, label='VP', color='darkgreen')
plt.ylabel('Ratio')
plt.xlabel('Year')
plt.title('Ratio change')
plt.yticks(np.arange(0, 1.001, 0.1))
plt.legend()
plt.show()
Note that in this case the x-axis measured in days, and each bar is located at its date. This helps to know the relative spacing between the dates, in case this is important. If it isn't important, the x-positions could be chosen equidistant and labeled via the dates column.
To do so with standard matplotlib, following code would change:
ind = range(len(df))
width = 0.8
plt.xticks(ind, df.DATE, rotation=20)
plt.tight_layout() # needed to show the full labels of the x-axis
Plot the dataframe
# using your data above
df.DATE = pd.to_datetime(df.DATE)
df.set_index('DATE', inplace=True)
ax = df.plot(stacked=True, kind='bar', figsize=(12, 8))
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
# sets the tick labels so time isn't included
ax.xaxis.set_major_formatter(plt.FixedFormatter(df.index.to_series().dt.strftime("%Y-%m-%d")))
plt.show()
Add labels for clarity
By adding the following code before plt.show() you can add text annotations to the bars
# .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 width of the bar is the data value and can used as the label
label_text = f'{height:.2f}' # f'{height:.2f}' if you have decimal values as labels
label_x = x + width - 0.125
label_y = y + height / 2
# don't include label if it's equivalently 0
if height > 0.001:
ax.text(label_x, label_y, label_text, ha='right', va='center', fontsize=8)
plt.show()
I am creating a figure with a title, 16 subplots, and a legend. I cannot for the life of me get it to save nicely. I am going to try my best to explain my predicament but my vocabulary may not be correct, so I apologize in advanced.
If I run my code (end) I receive the following output:
That is not pretty, everything is overlapping or cut off. If I were to add plt.savefig() that is what I get.
I can drag the corners of the pop-up window and that gives me a very nicely spaced figure, and is precisely what I want:
However, the save function at the bottom of that pop up window does not always work, and I would much rather be able to create a nice figure in my code that i send to the plt.savefig() function.
In all my searches I keep seeing tight_layout being recommended as a fix to this. The issue with that is it adjusts my plot sizes rather than the spacing between plots, so my titles overlap and my data isn't as visible:
I have also tried constrained_layout() with zero success
I am really hoping there is an obvious solution I am missing, as taking screen shots of the plot isn't really working for me.
eq_csv = r'/here/is/the/file.csv'
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
eq_df = pd.read_csv(eq_csv)
eq_data = eq_df[['LON', 'LAT', 'DEPTH', 'MAG']] # retrieve only the columns I need
eq_data = eq_data.sort_values(['MAG'], ascending=False)
# Get the NSEW boundaries and the Magnitude min and max
nbound = max(eq_data.LAT) + 0.05
sbound = min(eq_data.LAT) - 0.05
ebound = max(eq_data.LON) + 0.01
wbound = min(eq_data.LON)
xlimit = (wbound, ebound)
ylimit = (sbound, nbound)
magmin = min(eq_data.MAG)
magmax = max(eq_data.MAG)
# Loop through depth slices and create a 4 x 4 figure of subplots
fig, axes = plt.subplots(4,4)
for ax in axes.flat:
for n in list(range(1, 17)):
km = eq_data[(eq_data.DEPTH > n - 1) & (eq_data.DEPTH <= n)]
km = km.sort_values(['MAG'], ascending=True)
plt.subplot(4, 4, n) # plot a 4x4 sub plot at the nth location
scatter = plt.scatter(km["LON"], km['LAT'], s=10, c=km['MAG'], vmin=magmin, vmax=magmax, alpha = 0.5)
plt.ylim(sbound, nbound)
plt.xlim(wbound, ebound)
plt.tick_params(axis='both', which='major', labelsize=4)
plt.yticks(rotation = 90)
plt.ylabel('Latitude', rotation = 90, size = 6)
plt.xlabel('Longitude', size = 6)
plt.subplots_adjust(hspace=0.65, wspace=0.25)
plt.gca().set_title('Depth = ' + str(n - 1) + 'km to ' + str(n) + 'km', size=8, fontweight = 'bold') # set title of subplots
plt.suptitle('Magnitude of Events at Different Depth Slices, 1950 to Today', size = 20, fontweight = 'bold')
plt.tight_layout()
fig.subplots_adjust(right=0.8) #adust location of plot
cbar_ax = fig.add_axes([0.85, 0.15, 0.01, 0.7]) #location of color bar
cbar = fig.colorbar(scatter, cax=cbar_ax)
cbar.set_alpha(1)
cbar.set_label('Magnitude', rotation = 270, labelpad = 10)
cbar.draw_all()
plt.show()
plt.savefig('save/location')
I want to change the colour of the boxplots according to what they represent, this are grouped in pairs, so my question is:
How can i change the colour of the boxplots when they are paired?
Considering that the first boxplot of each pair should be blue and the second one red.
This is the code, sorry if it's messy:
def obtenerBoxplotsAnuales(self, directorioEntrada, directorioSalida):
meses = ["Enero","Febrero","Marzo","Abril","Mayo","Junio", "Julio", "Agosto","Septie.","Octubre","Noviem.","Diciem."]
ciudades = ["CO","CR"]
anios = ["2011", "2012", "2013"]
boxPlotMensual = []
fig = plt.figure()
fig.set_size_inches(14.3, 9)
ax = plt.axes()
plt.hold(True)
for anio in anios:
boxPlotAnual = []
i=0
ticks = []
for mes in range(len(meses)):
data1 = getSomeData()
data2 = getSomeData()
data = [ [int(float(data1[2])), int(float(data1[0])), int(float(data1[1]))],
[int(float(data2[2])), int(float(data2[0])), int(float(data2[1]))] ]
plt.boxplot(data, positions=[i,i+1], widths=0.5)
ticks.append(i+0.5)
i=i+2
hB, = plt.plot([1,1],'b-')
hR, = plt.plot([1,1],'r-')
plt.legend((hB, hR),('Caleta', 'Comodoro'))
hB.set_visible(False)
hR.set_visible(False)
ax.set_xticklabels(meses)
ax.set_xticks(ticks)
plt.savefig(directorioSalida+"/asdasd"+str(anio)+".ps", orientation='landscape', papertype='A4' )
This is what i get:
I've read that the solution is related with the fact that plt.boxplot(...) returns a kind of dict object that contains a list of the lines created so the way to modify the colour of each boxplot would be access to the indexes? How for this case?
You can set the colour of the return dict from boxplot as follows,
import matplotlib.pyplot as plt
import numpy as np
nboxes = 10
# fake up some data
spread= np.random.rand(50,nboxes) * 100
center = np.ones((25,nboxes)) * 50
flier_high = np.random.rand(10,nboxes) * 100 + 100
flier_low = np.random.rand(10,nboxes) * -100
data =np.concatenate((spread, center, flier_high, flier_low), 0)
# plot figure
plt.figure()
bp = plt.boxplot(data)
for i, box in enumerate(bp['boxes']):
#Colour alternate boxes blue and red
if i%2:
box.set_color('blue')
else:
box.set_color('red')
plt.show()
Where you loop through all boxes in bp['boxes'] and use the method set_color (you can also box.set_markerfacecolor and other standard matplotlib artist attributes). The bp dict also contains ['boxes', 'fliers', 'medians', 'means', 'whiskers', 'caps'] which can also be changed as required.
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