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My aim is to show a bar chart with 3-dim data, x, categorical and y1, y2 as continuous series; the bars should have heights from y1 and color to indicate y2.
This does not seem to be particularly obscure to me, but I didn't find a simple / built-in way to use a bar chart to visualise three dimensions -- I'm thinking mostly for exploratory purposes, before investigating relationships more formally.
Am I missing a type of plot in the libraries? Is there a good alternative to showing 3d data?
Anyway here are some things that I've tried that aren't particularly satisfying:
Some data for these attempts
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Example data with explicit (-ve) correlation in the two series
n = 10; sd = 2.5
fruits = [ 'Lemon', 'Cantaloupe', 'Redcurrant', 'Raspberry', 'Papaya',
'Apricot', 'Cherry', 'Durian', 'Guava', 'Jujube']
np.random.seed(101)
cost = np.random.uniform(3, 15, n)
harvest = 50 - (np.random.randn(n) * sd + cost)
df = pd.DataFrame(data={'fruit':fruits, 'cost':cost, 'harvest':harvest})
df.sort_values(by="cost", inplace=True) # preferrable to sort during plot only
# set up several subplots to show progress.
n_colors = 5; cmap_base = "coolwarm" # a diverging map
fig, axs = plt.subplots(3,2)
ax = axs.flat
Attempt 1 uses hue for the 3rd dim data in barplot. However, this produces a single color for each value in the series, and also seems to do odd things with the bar width & spacing.
import seaborn as sns
sns.barplot(ax=ax[0], x='fruit', y='cost', hue='harvest',
data=df, palette=cmap_base)
# fix the sns barplot label orientation
ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=90)
Attempt 2 uses the pandas DataFrame.plot.bar, with a continuous color range, then adds a colorbar (need scalar mappable). I borrowed some techniques from medium post among others.
import matplotlib as mpl
norm = mpl.colors.Normalize(vmin=min(df.harvest), vmax=max(df.harvest), clip=True)
mapper1 = mpl.cm.ScalarMappable(norm=norm, cmap=cmap_base)
colors1 = [mapper1.to_rgba(x) for x in df.harvest]
df.plot.bar(ax=ax[1], x='fruit', y='cost', color=colors1, legend=False)
mapper1._A = []
plt.colorbar(mapper1, ax=ax[1], label='havest')
Attempt 3 builds on this, borrowing from https://gist.github.com/jakevdp/91077b0cae40f8f8244a to facilitate a discrete colormap.
def discrete_cmap(N, base_cmap=None):
"""Create an N-bin discrete colormap from the specified input map"""
# from https://gist.github.com/jakevdp/91077b0cae40f8f8244a
base = plt.cm.get_cmap(base_cmap)
color_list = base(np.linspace(0, 1, N))
cmap_name = base.name + str(N)
return base.from_list(cmap_name, color_list, N)
cmap_disc = discrete_cmap(n_colors, cmap_base)
mapper2 = mpl.cm.ScalarMappable(norm=norm, cmap=cmap_disc)
colors2 = [mapper2.to_rgba(x) for x in df.harvest]
df.plot.bar(ax=ax[2], x='fruit', y='cost', color=colors2, legend=False)
mapper2._A = []
cb = plt.colorbar(mapper2, ax=ax[2], label='havest')
cb.set_ticks(np.linspace(*cb.get_clim(), num=n_colors+1)) # indicate color boundaries
cb.set_ticklabels(["{:.0f}".format(t) for t in cb.get_ticks()]) # without too much precision
Finally, attempt 4 gives in to trying 3d in one plot and present in 2 parts.
sns.barplot(ax=ax[4], x='fruit', y='cost', data=df, color='C0')
ax[4].set_xticklabels(ax[4].get_xticklabels(), rotation=90)
sns.regplot(x='harvest', y='cost', data=df, ax=ax[5])
(1) is unusable - I'm clearly not using as intended. (2) is ok with 10 series but with more series is harder to tell whether a given sample is above/below average, for instance. (3) is quite nice and scales to 50 bars ok, but it is far from "out-of-the-box", too involved for a quick analysis. Moreover, the sm._A = [] seems like a hack but the code fails without it. Perhaps the solution in a couple of lines in (4) is a better way to go.
To come back to the question again: Is it possible easily produce a bar chart that displays 3d data? I've focused on using a small number of colors for the 3rd dimension for easier identification of trends, but I'm open to other suggestions.
I've posted a solution as well, which uses a lot of custom code to achieve what I can't really believe is not built in some graphing library of python.
edit:
the following code, using R's ggplot gives a reasonable approximation to (2) with built-in commands.
ggplot(data = df, aes(x =reorder(fruit, +cost), y = cost, fill=harvest)) +
geom_bar(data=df, aes(fill=harvest), stat='identity') +
scale_fill_gradientn(colours=rev(brewer.pal(7,"RdBu")))
The first 2 lines are more or less the minimal code for barplot, and the third changes the color palette.
So if this ease were available in python I'd love to know about it!
I'm posting an answer that does solve my aims of being simple at the point of use, still being useful with ~100 bars, and by leveraging the Fisher-Jenks 1d classifier from PySAL ends up handling outliers quite well (post about d3 coloring)
-- but overall is quite involved (50+ lines in the BinnedColorScaler class, posted at the bottom).
# set up the color binner
quantizer = BinnedColorScaler(df.harvest, k=5, cmap='coolwarm' )
# and plot dataframe with it.
df.plot.bar(ax=ax, x='fruit', y='cost',
color=df.harvest.map(quantizer.map_by_class))
quantizer.add_legend(ax, title='harvest') # show meaning of bins in legend
Using the following class that uses a nice 1d classifier from PySAL and borrows ideas from geoplot/geopandas libraries.
from pysal.esda.mapclassify import Fisher_Jenks
class BinnedColorScaler(object):
'''
give this an array-like data set, a bin count, and a colormap name, and it
- quantizes the data
- provides a bin lookup and a color mapper that can be used by pandas for selecting artist colors
- provides a method for a legend to display the colors and bin ranges
'''
def __init__(self, values, k=5, cmap='coolwarm'):
self.base_cmap = plt.cm.get_cmap(cmap) # can be None, text, or a cmap instane
self.bin_colors = self.base_cmap(np.linspace(0, 1, k)) # evenly-spaced colors
# produce bins - see _discrete_colorize in geoplot.geoplot.py:2372
self.binning = Fisher_Jenks(np.array(values), k)
self.bin_edges = np.array([self.binning.yb.min()] + self.binning.bins.tolist())
# some text for the legend (as per geopandas approx)
self.categories = [
'{0:.2f} - {1:.2f}'.format(self.bin_edges[i], self.bin_edges[i + 1])
for i in xrange(len(self.bin_edges) - 1)]
def map_by_class(self, val):
''' return a color for a given data value '''
#bin_id = self.binning.find_bin(val)
bin_id = self.find_bin(val)
return self.bin_colors[bin_id]
def find_bin(self, x):
''' unfortunately the pysal implementation seems to fail on bin edge
cases :(. So reimplement with the way we expect here.
'''
# wow, subtle. just <= instead of < in the uptos
x = np.asarray(x).flatten()
uptos = [np.where(value <= self.binning.bins)[0] for value in x]
bins = [v.min() if v.size > 0 else len(self.bins)-1 for v in uptos] #bail upwards
bins = np.asarray(bins)
if len(bins) == 1:
return bins[0]
else:
return bins
def add_legend(self, ax, title=None, **kwargs):
''' add legend showing the discrete colors and the corresponding data range '''
# following the geoplot._paint_hue_legend functionality, approx.
# generate a patch for each color in the set
artists, labels = [], []
for i in xrange(len(self.bin_colors)):
labels.append(self.categories[i])
artists.append(mpl.lines.Line2D(
(0,0), (1,0), mfc='none', marker='None', ls='-', lw=10,
color=self.bin_colors[i]))
return ax.legend(artists, labels, fancybox=True, title=title, **kwargs)
I would like to replace part of my plot where the function dips down to '-1' with a dashed line carrying on from the previous point (see plots below).
Here's some code I've written, along with its output:
import numpy as np
import matplotlib.pyplot as plt
y = [5,6,8,3,5,7,3,6,-1,3,8,5]
plt.plot(np.linspace(1,12,12),y,'r-o')
plt.show()
for i in range(1,len(y)):
if y[i]!=-1:
plt.plot(np.linspace(i-1,i,2),y[i-1:i+1],'r-o')
else:
y[i]=y[i-1]
plt.plot(np.linspace(i-1,i,2),y[i-1:i+1],'r--o')
plt.ylim(-1,9)
plt.show()
Here's the original plot
Modified plot:
The code I've written works (it produces the desired output), but it's inefficient and takes a long time when I actually run it on my (much larger) dataset. Is there a smarter way to go about doing this?
You can achieve something similar without the loops:
import pandas as pd
import matplotlib.pyplot as plt
# Create a data frame from the list
a = pd.DataFrame([5,6,-1,-1, 8,3,5,7,3,6,-1,3,8,5])
# Prepare a boolean mask
mask = a > 0
# New data frame with missing values filled with the last element of
# the previous segment. Choose 'bfill' to use the first element of
# the next segment.
a_masked = a[mask].fillna(method = 'ffill')
# Prepare the plot
fig, ax = plt.subplots()
line, = ax.plot(a_masked, ls = '--', lw = 1)
ax.plot(a[mask], color=line.get_color(), lw=1.5, marker = 'o')
plt.show()
You can also highlight the negative regions by choosing a different colour for the lines:
My answer is based on a great post from July, 2017. The latter also tackles the case when the first element is NaN or in your case a negative number:
Dotted lines instead of a missing value in matplotlib
I would use numpy functionality to cut your line into segments and then plot all solid and dashed lines separately. In the example below I added two additional -1s to your data to see that this works universally.
import numpy as np
import matplotlib.pyplot as plt
Y = np.array([5,6,-1,-1, 8,3,5,7,3,6,-1,3,8,5])
X = np.arange(len(Y))
idxs = np.where(Y==-1)[0]
sub_y = np.split(Y,idxs)
sub_x = np.split(X,idxs)
fig, ax = plt.subplots()
##replacing -1 values and plotting dotted lines
for i in range(1,len(sub_y)):
val = sub_y[i-1][-1]
sub_y[i][0] = val
ax.plot([sub_x[i-1][-1], sub_x[i][0]], [val, val], 'r--')
##plotting rest
for x,y in zip(sub_x, sub_y):
ax.plot(x, y, 'r-o')
plt.show()
The result looks like this:
Note, however, that this will fail if the first value is -1, as then your problem is not well defined (no previous value to copy from). Hope this helps.
Not too elegant, but here's something that doesn't use loops which I came up with (based on the above answers) which works. #KRKirov and #Thomas Kühn , thank you for your answers, I really appreciate them
import pandas as pd
import matplotlib.pyplot as plt
# Create a data frame from the list
a = pd.DataFrame([5,6,-1,-1, 8,3,5,7,3,6,-1,3,8,5])
b=a.copy()
b[2]=b[0].shift(1,axis=0)
b[4]=(b[0]!=-1) & (b[2]==-1)
b[5]=b[4].shift(-1,axis=0)
b[0] = (b[5] | b[4])
c=b[0]
d=pd.DataFrame(c)
# Prepare a boolean mask
mask = a > 0
# New data frame with missing values filled with the last element of
# the previous segment. Choose 'bfill' to use the first element of
# the next segment.
a_masked = a[mask].fillna(method = 'ffill')
# Prepare the plot
fig, ax = plt.subplots()
line, = ax.plot(a_masked, 'b:o', lw = 1)
ax.plot(a[mask], color=line.get_color(), lw=1.5, marker = 'o')
ax.plot(a_masked[d], color=line.get_color(), lw=1.5, marker = 'o')
plt.show()
I am trying to make two sets of box plots using Matplotlib. I want each set of box plot filled (and points and whiskers) in a different color. So basically there will be two colors on the plot
My code is below, would be great if you can help make these plots in color. d0 and d1 are each list of lists of data. I want the set of box plots made with data in d0 in one color, and the set of box plots with data in d1 in another color.
plt.boxplot(d0, widths = 0.1)
plt.boxplot(d1, widths = 0.1)
To colorize the boxplot, you need to first use the patch_artist=True keyword to tell it that the boxes are patches and not just paths. Then you have two main options here:
set the color via ...props keyword argument, e.g.
boxprops=dict(facecolor="red"). For all keyword arguments, refer to the documentation
Use the plt.setp(item, properties) functionality to set the properties of the boxes, whiskers, fliers, medians, caps.
obtain the individual items of the boxes from the returned dictionary and use item.set_<property>(...) on them individually. This option is detailed in an answer to the following question: python matplotlib filled boxplots, where it allows to change the color of the individual boxes separately.
The complete example, showing options 1 and 2:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.normal(0.1, size=(100,6))
data[76:79,:] = np.ones((3,6))+0.2
plt.figure(figsize=(4,3))
# option 1, specify props dictionaries
c = "red"
plt.boxplot(data[:,:3], positions=[1,2,3], notch=True, patch_artist=True,
boxprops=dict(facecolor=c, color=c),
capprops=dict(color=c),
whiskerprops=dict(color=c),
flierprops=dict(color=c, markeredgecolor=c),
medianprops=dict(color=c),
)
# option 2, set all colors individually
c2 = "purple"
box1 = plt.boxplot(data[:,::-2]+1, positions=[1.5,2.5,3.5], notch=True, patch_artist=True)
for item in ['boxes', 'whiskers', 'fliers', 'medians', 'caps']:
plt.setp(box1[item], color=c2)
plt.setp(box1["boxes"], facecolor=c2)
plt.setp(box1["fliers"], markeredgecolor=c2)
plt.xlim(0.5,4)
plt.xticks([1,2,3], [1,2,3])
plt.show()
You can change the color of a box plot using setp on the returned value from boxplot(). This example defines a box_plot() function that allows the edge and fill colors to be specified:
import matplotlib.pyplot as plt
def box_plot(data, edge_color, fill_color):
bp = ax.boxplot(data, patch_artist=True)
for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:
plt.setp(bp[element], color=edge_color)
for patch in bp['boxes']:
patch.set(facecolor=fill_color)
return bp
example_data1 = [[1,2,0.8], [0.5,2,2], [3,2,1]]
example_data2 = [[5,3, 4], [6,4,3,8], [6,4,9]]
fig, ax = plt.subplots()
bp1 = box_plot(example_data1, 'red', 'tan')
bp2 = box_plot(example_data2, 'blue', 'cyan')
ax.legend([bp1["boxes"][0], bp2["boxes"][0]], ['Data 1', 'Data 2'])
ax.set_ylim(0, 10)
plt.show()
This would display as follows:
This question seems to be similar to that one (Face pattern for boxes in boxplots)
I hope this code solves your problem
import matplotlib.pyplot as plt
# fake data
d0 = [[4.5, 5, 6, 4],[4.5, 5, 6, 4]]
d1 = [[1, 2, 3, 3.3],[1, 2, 3, 3.3]]
# basic plot
bp0 = plt.boxplot(d0, patch_artist=True)
bp1 = plt.boxplot(d1, patch_artist=True)
for box in bp0['boxes']:
# change outline color
box.set(color='red', linewidth=2)
# change fill color
box.set(facecolor = 'green' )
# change hatch
box.set(hatch = '/')
for box in bp1['boxes']:
box.set(color='blue', linewidth=5)
box.set(facecolor = 'red' )
plt.show()
Change the color of a boxplot
import numpy as np
import matplotlib.pyplot as plt
#generate some random data
data = np.random.randn(200)
d= [data, data]
#plot
box = plt.boxplot(d, showfliers=False)
# change the color of its elements
for _, line_list in box.items():
for line in line_list:
line.set_color('r')
plt.show()
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
Here is a very simplified example:
xvalues = [2,3,4,6]
for x in xvalues:
plt.axvline(x,color='b',label='xvalues')
plt.legend()
The legend will now show 'xvalues' as a blue line 4 times in the legend.
Is there a more elegant way of fixing this than the following?
for i,x in enumerate(xvalues):
if not i:
plt.axvline(x,color='b',label='xvalues')
else:
plt.axvline(x,color='b')
plt.legend takes as parameters
A list of axis handles which are Artist objects
A list of labels which are strings
These parameters are both optional defaulting to plt.gca().get_legend_handles_labels().
You can remove duplicate labels by putting them in a dictionary before calling legend. This is because dicts can't have duplicate keys.
For example:
For Python versions < 3.7
from collections import OrderedDict
import matplotlib.pyplot as plt
handles, labels = plt.gca().get_legend_handles_labels()
by_label = OrderedDict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
For Python versions > 3.7
As of Python 3.7, dictionaries retain input order by default. Thus, there is no need for OrderedDict form the collections module.
import matplotlib.pyplot as plt
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
Docs for plt.legend
handles, labels = ax.get_legend_handles_labels()
handle_list, label_list = [], []
for handle, label in zip(handles, labels):
if label not in label_list:
handle_list.append(handle)
label_list.append(label)
plt.legend(handle_list, label_list)
I don't know if this can be considered "elegant", but you can have your label a variable that gets set to "_nolegend_" after first usage:
my_label = "xvalues"
xvalues = [2,3,4,6]
for x in xvalues:
plt.axvline(x, color='b', label=my_label)
my_label = "_nolegend_"
plt.legend()
This can be generalized using a dictionary of labels if you have to put several labels:
my_labels = {"x1" : "x1values", "x2" : "x2values"}
x1values = [1, 3, 5]
x2values = [2, 4, 6]
for x in x1values:
plt.axvline(x, color='b', label=my_labels["x1"])
my_labels["x1"] = "_nolegend_"
for x in x2values:
plt.axvline(x, color='r', label=my_labels["x2"])
my_labels["x2"] = "_nolegend_"
plt.legend()
(Answer inspired by https://stackoverflow.com/a/19386045/1878788)
Problem - 3D Array
Questions: Nov 2012, Oct 2013
import numpy as np
a = np.random.random((2, 100, 4))
b = np.random.random((2, 100, 4))
c = np.random.random((2, 100, 4))
Solution - dict uniqueness
For my case _nolegend_ (bli and DSM) would not work, nor would label if i==0. ecatmur's answer uses get_legend_handles_labels and reduces the legend down with collections.OrderedDict. Fons demonstrates this is possible without an import.
Inline with these answers, I suggest using dict for unique labels.
# Step-by-step
ax = plt.gca() # Get the axes you need
a = ax.get_legend_handles_labels() # a = [(h1 ... h2) (l1 ... l2)] non unique
b = {l:h for h,l in zip(*a)} # b = {l1:h1, l2:h2} unique
c = [*zip(*b.items())] # c = [(l1 l2) (h1 h2)]
d = c[::-1] # d = [(h1 h2) (l1 l2)]
plt.legend(*d)
Or
plt.legend(*[*zip(*{l:h for h,l in zip(*ax.get_legend_handles_labels())}.items())][::-1])
Maybe less legible and memorable than Matthew Bourque's solution. Code golf welcome.
Example
import numpy as np
a = np.random.random((2, 100, 4))
b = np.random.random((2, 100, 4))
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1)
ax.plot(*a, 'C0', label='a')
ax.plot(*b, 'C1', label='b')
ax.legend(*[*zip(*{l:h for h,l in zip(*ax.get_legend_handles_labels())}.items())][::-1])
# ax.legend() # Old, ^ New
plt.show()
Based on answer https://stackoverflow.com/a/13589144/9132798 and https://stackoverflow.com/a/19386045/9132798
plt.gca().get_legend_handles_labels()[1] gives a list of names, it is possible to check if the label is already in the list while in the loop plotting (label= name[i] if name[i] not in plt.gca().get_legend_handles_labels()[1] else '').
For the given example this solution would look like:
import matplotlib.pyplot as plt
xvalues = [2,3,4,6]
for x in xvalues:
plt.axvline(x,color='b',\
label= 'xvalues' if 'xvalues' \
not in plt.gca().get_legend_handles_labels()[1] else '')
plt.legend()
Which is much shorter than https://stackoverflow.com/a/13589144/9132798 and more flexible than https://stackoverflow.com/a/19386045/9132798 as it could be use for any kind of loop any plot function in the loop individually.
However, for many cycles it probably slower than https://stackoverflow.com/a/13589144/9132798.
These code snippets didn't work for me personally. I was plotting two different groups in two different colors. The legend would show two red markers and two blue markers, when I only wanted to see one per color. I'll paste a simplified version of what did work for me:
Import statements
import matplotlib.pyplot as plt
from matplotlib.legend_handler import HandlerLine2D
Plot data
points_grp, = plt.plot(x[grp_idx], y[grp_idx], color=c.c[1], marker=m, ms=4, lw=0, label=leglab[1])
points_ctrl, = plt.plot(x[ctrl_idx], y[ctrl_idx], color=c.c[0], marker=m, ms=4, lw=0, label=leglab[0])
Add legend
points_dict = {points_grp: HandlerLine2D(numpoints=1),points_ctrl: HandlerLine2D(numpoints=1)}
leg = ax.legend(fontsize=12, loc='upper left', bbox_to_anchor=(1, 1.03),handler_map=points_dict)