I'm trying to generate 'violin'-like bar charts, however i'm running in several difficulties described bellow...
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# init data
label = ['aa', 'b', 'cc', 'd']
data1 = [5, 7, 6, 9]
data2 = [7, 3, 6, 1]
data1_minus = np.array(data1)*-1
gs = gridspec.GridSpec(1, 2, top=0.95, bottom=0.07,)
fig = plt.figure(figsize=(7.5, 4.0))
# adding left bar chart
ax1 = fig.add_subplot(gs[0])
ax1.barh(pos, data1_minus)
ax1.yaxis.tick_right()
ax1.yaxis.set_label(label)
# adding right bar chart
ax2 = fig.add_subplot(gs[1], sharey=ax1)
ax2.barh(pos, data2)
Trouble adding 'label' as labels for both charts to share.
Centering the labels between the both plots (as well as vertically in the center of each bar)
Keeping just the ticks on the outer yaxis (not inner, where the labels would go)
If I understand the question correctly, I believe these changes accomplish what you're looking for:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# init data
label = ['aa', 'b', 'cc', 'd']
data1 = [5, 7, 6, 9]
data2 = [7, 3, 6, 1]
data1_minus = np.array(data1)*-1
gs = gridspec.GridSpec(1, 2, top=0.95, bottom=0.07,)
fig = plt.figure(figsize=(7.5, 4.0))
pos = np.arange(4)
# adding left bar chart
ax1 = fig.add_subplot(gs[0])
ax1.barh(pos, data1_minus, align='center')
# set tick positions and labels appropriately
ax1.yaxis.tick_right()
ax1.set_yticks(pos)
ax1.set_yticklabels(label)
ax1.tick_params(axis='y', pad=15)
# adding right bar chart
ax2 = fig.add_subplot(gs[1], sharey=ax1)
ax2.barh(pos, data2, align='center')
# turn off the second axis tick labels without disturbing the originals
[lbl.set_visible(False) for lbl in ax2.get_yticklabels()]
plt.show()
This yields this plot:
As for keeping the actual numerical ticks (if you want those), the normal matplotlib interface ties the ticks pretty closely together when the axes are shared (or twinned). However, the axes_grid1 toolkit can allow you more control, so if you want some numerical ticks you can replace the entire ax2 section above with the following:
from mpl_toolkits.axes_grid1 import host_subplot
ax2 = host_subplot(gs[1], sharey=ax1)
ax2.barh(pos, data2, align='center')
par = ax2.twin()
par.set_xticklabels('')
par.set_yticks(pos)
par.set_yticklabels([str(x) for x in pos])
[lbl.set_visible(False) for lbl in ax2.get_yticklabels()]
which yields:
Related
I have 2 sets of boxplots, one set in blue color and another in red color. I want the legend to show the label for each set of boxplots, i.e.
Legend:
-blue box- A, -red box- B
Added labels='A' and labels='B' within sns.boxplot(), but didn't work with error message "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument". How do I add the labels?
enter image description here
code for the inserted image:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
x = list(range(1,13))
n = 40
index = [item for item in x for i in range(n)]
np.random.seed(123)
df = pd.DataFrame({'A': np.random.normal(30, 2, len(index)),
'B': np.random.normal(10, 2, len(index))},
index=index)
red_diamond = dict(markerfacecolor='r', marker='D')
blue_dot = dict(markerfacecolor='b', marker='o')
plt.figure(figsize=[10,5])
ax = plt.gca()
ax1 = sns.boxplot( x=df.index, y=df['A'], width=0.5, color='red', \
boxprops=dict(alpha=.5), flierprops=red_diamond, labels='A')
ax2 = sns.boxplot( x=df.index, y=df['B'], width=0.5, color='blue', \
boxprops=dict(alpha=.5), flierprops=blue_dot, labels='B')
plt.ylabel('Something')
plt.legend(loc="center", fontsize=8, frameon=False)
plt.show()
Here are the software versions I am using: seaborn version 0.11.2. matplotlib version 3.5.1. python version 3.10.1
The following approach sets a label via the boxprops, and creates a legend using part of ax.artists. (Note that ax, ax1 and ax2 of the question's code are all pointing to the same subplot, so here only ax is used.)
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
x = np.arange(1, 13)
index = np.repeat(x, 40)
np.random.seed(123)
df = pd.DataFrame({'A': np.random.normal(30, 2, len(index)),
'B': np.random.normal(10, 2, len(index))},
index=index)
red_diamond = dict(markerfacecolor='r', marker='D')
blue_dot = dict(markerfacecolor='b', marker='o')
plt.figure(figsize=[10, 5])
ax = sns.boxplot(data=df, x=df.index, y='A', width=0.5, color='red',
boxprops=dict(alpha=.5, label='A'), flierprops=red_diamond)
sns.boxplot(data=df, x=df.index, y='B', width=0.5, color='blue',
boxprops=dict(alpha=.5, label='B'), flierprops=blue_dot, ax=ax)
ax.set_ylabel('Something')
handles, labels = ax.get_legend_handles_labels()
handles = [h for h, lbl, prev in zip(handles, labels, [None] + labels) if lbl != prev]
ax.legend(handles=handles, loc="center", fontsize=8, frameon=False)
plt.show()
Alternative approaches could be:
pd.melt the dataframe to long form, so hue could be used; a problem here is that then the legend wouldn't take the alpha from the boxprops into account; also setting different fliers wouldn't be supported
create a legend from custom handles
Suppose something comes up in my plot that mandates that I change the height ratio between two subplots that I've generated within my plot. I've tried changing GridSpec's height ratio to no avail.
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig = plt.figure()
gs = GridSpec(2, 1, height_ratios=[2, 1])
ax1 = fig.add_subplot(gs[0])
ax1 = fig.axes[0]
ax2 = fig.add_subplot(gs[1])
ax2 = fig.axes[1]
ax1.plot([0, 1], [0, 1])
ax2.plot([0, 1], [1, 0])
gs.height_ratios = [2, 5]
The last line has no effect on the plot ratio.
In my actual code, it is not feasible without major reworking to set the height_ratios to 2:5 ahead of time.
How do I get this to update like I want?
The axes of relevant subplots can be manipulated and adjusted to get new height ratios.
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig = plt.figure()
gs = GridSpec(2, 1, height_ratios=[2, 1]) #nrows, ncols
ax1 = fig.add_subplot(gs[0])
ax1 = fig.axes[0]
ax2 = fig.add_subplot(gs[1])
ax2 = fig.axes[1]
ax1.plot([0, 1], [0, 1])
ax2.plot([0, 1], [1, 0])
# new height ratio: 2:5 is required for the 2 subplots
rw, rh = 2, 5
# get dimensions of the 2 axes
box1 = ax1.get_position()
box2 = ax2.get_position()
# current dimensions
w1,h1 = box1.x1-box1.x0, box1.y1-box1.y0
w2,h2 = box2.x1-box2.x0, box2.y1-box2.y0
top1 = box1.y0+h1
#top2 = box2.y0+h2
full_h = h1+h2 #total height
# compute new heights for each axes
new_h1 = full_h*rw/(rw + rh)
new_h2 = full_h*rh/(rw + rh)
#btm1,btm2 = box1.y0, box2.y0
new_bottom1 = top1-new_h1
# finally, set new location/dimensions of the axes
ax1.set_position([box1.x0, new_bottom1, w1, new_h1])
ax2.set_position([box2.x0, box2.y0, w2, new_h2])
plt.show()
The output for ratio: (2, 5):
The output for (2, 10):
I am using subplots side by side
plt.subplot(1, 2, 1)
# plot 1
plt.xlabel('MEM SET')
plt.ylabel('Memory Used')
plt.bar(inst_memory['MEMORY_SET_TYPE'], inst_memory['USED_MB'], alpha = 0.5, color = 'r')
# pol 2
plt.subplot(1, 2, 2)
plt.xlabel('MEM POOL')
plt.ylabel('Memory Used')
plt.bar(set_memory['POOL_TYPE'], set_memory['MEMORY_POOL_USED'], alpha = 0.5, color = 'g')
they have identical size - but is it possible to define the width for each subplot, so the right one could be wider as it has more entries and text would not squeeze or would it be possible to replace the bottom x-text by a number and have a legend with 1:means xx 2:means yyy
I find GridSpec helpful for subplot arrangements, see this demo at matplotlib.
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import pandas as pd
N=24
inst_memory = pd.DataFrame({'MEMORY_SET_TYPE': np.random.randint(0,3,N),
'USED_MB': np.random.randint(0,1000,N)})
set_memory = pd.DataFrame({'MEMORY_POOL_USED': np.random.randint(0,1000,N),
'POOL_TYPE': np.random.randint(0,10,N)})
fig = plt.figure()
gs = GridSpec(1, 2, width_ratios=[1, 2],wspace=0.3)
ax1 = fig.add_subplot(gs[0])
ax2 = fig.add_subplot(gs[1])
ax1.bar(inst_memory['MEMORY_SET_TYPE'], inst_memory['USED_MB'], alpha = 0.5, color = 'r')
ax2.bar(set_memory['POOL_TYPE'], set_memory['MEMORY_POOL_USED'], alpha = 0.5, color = 'g')
You may need to adjust width_ratios and wspace to get the desired layout.
Also, rotating the text in x-axis might help, some info here.
So when I try to graph multiple subplots using pyplot.subplots I get something like:
How can I have:
Multiple independent axes for every subplot
Axes for every subplot
Overlay plots in every subplot axes using subplots. I tried to do ((ax1,ax2),(ax3,ax4)) = subplots and then do ax1.plot twice, but as a result, neither of the two showed.
Code for the picture:
import string
import matplotlib
matplotlib.use('WX')
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import numpy as np
from itertools import izip,chain
f,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2,sharex='col',sharey='row')
ax1.plot(range(10),2*np.arange(10))
ax2.plot(range(10),range(10))
ax3.plot(range(5),np.arange(5)*1000)
#pyplot.yscale('log')
#ax2.set_autoscaley_on(False)
#ax2.set_ylim([0,10])
plt.show()
Questions 1 & 2:
To accomplish this, explicitly set the subplots options sharex and sharey=False.
replace this line in the code for the desired results.
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=False, sharey=False)
Alternatively, those two options can be omitted altogether, as False is the default. (as noted by rubenvb below)
Question 3:
Here are two examples of adding secondary plots to two of the subplots:
(add this snippet before plt.show())
# add an additional line to the lower left subplot
ax3.plot(range(5), -1*np.arange(5)*1000)
# add a bar chart to the upper right subplot
width = 0.75 # the width of the bars
x = np.arange(2, 10, 2)
y = [3, 7, 2, 9]
rects1 = ax2.bar(x, y, width, color='r')
Don't tell it to share axes:
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.plot(range(10),2*np.arange(10))
ax2.plot(range(10),range(10))
ax3.plot(range(5),np.arange(5)*1000)
doc
I'm plotting a series of data points with x and y error but do NOT want the errorbars to be included in the legend (only the marker). Is there a way to do so?
Example:
import matplotlib.pyplot as plt
import numpy as np
subs=['one','two','three']
x=[1,2,3]
y=[1,2,3]
yerr=[2,3,1]
xerr=[0.5,1,1]
fig,(ax1)=plt.subplots(1,1)
for i in np.arange(len(x)):
ax1.errorbar(x[i],y[i],yerr=yerr[i],xerr=xerr[i],label=subs[i],ecolor='black',marker='o',ls='')
ax1.legend(loc='upper left', numpoints=1)
fig.savefig('test.pdf', bbox_inches=0)
You can modify the legend handler. See the legend guide of matplotlib.
Adapting your example, this could read:
import matplotlib.pyplot as plt
import numpy as np
subs=['one','two','three']
x=[1,2,3]
y=[1,2,3]
yerr=[2,3,1]
xerr=[0.5,1,1]
fig,(ax1)=plt.subplots(1,1)
for i in np.arange(len(x)):
ax1.errorbar(x[i],y[i],yerr=yerr[i],xerr=xerr[i],label=subs[i],ecolor='black',marker='o',ls='')
# get handles
handles, labels = ax1.get_legend_handles_labels()
# remove the errorbars
handles = [h[0] for h in handles]
# use them in the legend
ax1.legend(handles, labels, loc='upper left',numpoints=1)
plt.show()
This produces
Here is an ugly patch:
pp = []
colors = ['r', 'b', 'g']
for i, (y, yerr) in enumerate(zip(ys, yerrs)):
p = plt.plot(x, y, '-', color='%s' % colors[i])
pp.append(p[0])
plt.errorbar(x, y, yerr, color='%s' % colors[i])
plt.legend(pp, labels, numpoints=1)
Here is a figure for example:
The accepted solution works in simple cases but not in general. In particular, it did not work in my own more complex situation.
I found a more robust solution, which tests for ErrorbarContainer, which did work for me. It was proposed by Stuart W D Grieve and I copy it here for completeness
import matplotlib.pyplot as plt
from matplotlib import container
label = ['one', 'two', 'three']
color = ['red', 'blue', 'green']
x = [1, 2, 3]
y = [1, 2, 3]
yerr = [2, 3, 1]
xerr = [0.5, 1, 1]
fig, (ax1) = plt.subplots(1, 1)
for i in range(len(x)):
ax1.errorbar(x[i], y[i], yerr=yerr[i], xerr=xerr[i], label=label[i], color=color[i], ecolor='black', marker='o', ls='')
handles, labels = ax1.get_legend_handles_labels()
handles = [h[0] if isinstance(h, container.ErrorbarContainer) else h for h in handles]
ax1.legend(handles, labels)
plt.show()
It produces the following plot (on Matplotlib 3.1)
I works for me if I set the label argument as a None type.
plt.errorbar(x, y, yerr, label=None)