Given the following setup:
from matplotlib import pyplot as plt
fig, ax = plt.subplots()
ax.plot([0,1,2,3,4,5,6], label='linear')
ax.plot([0,1,4,9,16,25,36], label='square')
lgd = ax.legend(loc='lower right')
If a function add_patch receives only lgd as an argument, can a custom legend item be added to the legend on top of the existing items, without changing the other properties of the legend?
I was able to add an item using:
def add_patch(legend):
from matplotlib.patches import Patch
ax = legend.axes
handles, labels = ax.get_legend_handles_labels()
handles.append(Patch(facecolor='orange', edgecolor='r'))
labels.append("Color Patch")
ax.legend(handles=handles, labels=labels)
But this does not preserve the properties of the legend like location. How can I add an item given only the legend object after lines have been plotted?
In principle a legend is not meant to be updated, but recreated instead.
The following would do what you're after, but beware that this is a hack which uses internal methods and is hence not guaranteed to work and might break in future releases. So don't use it in production code. Also, if you have set a title to the legend with a different font(size) than default, it will be lost upon updating. Also, if you have manipulated the order of handles and labels via markerfirst, this will be lost upon updating.
from matplotlib import pyplot as plt
fig, ax = plt.subplots()
ax.plot([0,1,2,3,4,5,6], label='linear')
ax.plot([0,1,4,9,16,25,36], label='square')
lgd = ax.legend(loc='lower right')
def add_patch(legend):
from matplotlib.patches import Patch
ax = legend.axes
handles, labels = ax.get_legend_handles_labels()
handles.append(Patch(facecolor='orange', edgecolor='r'))
labels.append("Color Patch")
legend._legend_box = None
legend._init_legend_box(handles, labels)
legend._set_loc(legend._loc)
legend.set_title(legend.get_title().get_text())
add_patch(lgd)
plt.show()
Is adding the color patch after the lines have been plotted but before adding the legend an option?
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
fig, ax = plt.subplots()
line1 = ax.plot([0,1,2,3,4,5,6], label='linear')
line2 = ax.plot([0,1,4,9,16,25,36], label='square')
patch = Patch(facecolor='orange', edgecolor='r', label='Color patch')
lgd = ax.legend(handles=[line1, line2, patch], loc='lower right')
Related
I have created a nested boxplot with an overlayed stripplot using the Seaborn package. I have seen answers on stackoverflow regarding how to edit box properties both for individual boxes and for all boxes using ax.artists generated by sns.boxplot.
Is there any way to edit whisker, cap, flier, etc. properties using a similar method? Currently I have to manually edit values in the restyle_boxplot method of the _BoxPlotter() class in the seaborn -> categorical.py file to get from the default plot to the desired plot:
Default Plot:
Desired Plot:
Here is my code for reference:
sns.set_style('whitegrid')
fig1, ax1 = plt.subplots()
ax1 = sns.boxplot(x="Facility", y="% Savings", hue="Analysis",
data=totalSavings)
plt.setp(ax1.artists,fill=False) # <--- Current Artist functionality
ax1 = sns.stripplot(x="Facility", y="% Savings", hue="Analysis",
data=totalSavings, jitter=.05,edgecolor = 'gray',
split=True,linewidth = 0, size = 6,alpha = .6)
ax1.tick_params(axis='both', labelsize=13)
ax1.set_xticklabels(['Test 1','Test 2','Test 3','Test 4','Test 5'], rotation=90)
ax1.set_xlabel('')
ax1.set_ylabel('Percent Savings (%)', fontsize = 14)
handles, labels = ax1.get_legend_handles_labels()
legend1 = plt.legend(handles[0:3], ['A','B','C'],bbox_to_anchor=(1.05, 1),
loc=2, borderaxespad=0.)
plt.setp(plt.gca().get_legend().get_texts(), fontsize='12')
fig1.set_size_inches(10,7)
EDIT: Note that this method appears to no longer work for matplotlib versions >=3.5. See the answer by #JohanC for an up to date answer
You need to edit the Line2D objects, which are stored in ax.lines.
Heres a script to create a boxplot (based on the example here), and then edit the lines and artists to the style in your question (i.e. no fill, all the lines and markers the same colours, etc.)
You can also fix the rectangle patches in the legend, but you need to use ax.get_legend().get_patches() for that.
I've also plotted the original boxplot on a second Axes, as a reference.
import matplotlib.pyplot as plt
import seaborn as sns
fig,(ax1,ax2) = plt.subplots(2)
sns.set_style("whitegrid")
tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", hue="smoker", data=tips, palette="Set1", ax=ax1)
sns.boxplot(x="day", y="total_bill", hue="smoker", data=tips, palette="Set1", ax=ax2)
for i,artist in enumerate(ax2.artists):
# Set the linecolor on the artist to the facecolor, and set the facecolor to None
col = artist.get_facecolor()
artist.set_edgecolor(col)
artist.set_facecolor('None')
# Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)
# Loop over them here, and use the same colour as above
for j in range(i*6,i*6+6):
line = ax2.lines[j]
line.set_color(col)
line.set_mfc(col)
line.set_mec(col)
# Also fix the legend
for legpatch in ax2.get_legend().get_patches():
col = legpatch.get_facecolor()
legpatch.set_edgecolor(col)
legpatch.set_facecolor('None')
plt.show()
For matplotlib 3.5 the rectangles for the boxes aren't stored anymore in ax2.artists, but in ax2.patches. As the background of the subplot is also stored as a rectangular patch, the list of patches needs to be filtered.
The code below further makes a few adjustments:
the exact number of lines belonging to one boxplot is counted, as depending on the boxplot options there can be a different number of lines
saturation=1 is used; seaborn prefers to add some desaturation to larger areas, but lines will be better visible with full saturation
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))
sns.set_style("whitegrid")
tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", hue="smoker", data=tips, palette="Set1", ax=ax1)
sns.boxplot(x="day", y="total_bill", hue="smoker", data=tips, palette="Set1", saturation=1, ax=ax2)
box_patches = [patch for patch in ax2.patches if type(patch) == matplotlib.patches.PathPatch]
if len(box_patches) == 0: # in matplotlib older than 3.5, the boxes are stored in ax2.artists
box_patches = ax2.artists
num_patches = len(box_patches)
lines_per_boxplot = len(ax2.lines) // num_patches
for i, patch in enumerate(box_patches):
# Set the linecolor on the patch to the facecolor, and set the facecolor to None
col = patch.get_facecolor()
patch.set_edgecolor(col)
patch.set_facecolor('None')
# Each box has associated Line2D objects (to make the whiskers, fliers, etc.)
# Loop over them here, and use the same color as above
for line in ax2.lines[i * lines_per_boxplot: (i + 1) * lines_per_boxplot]:
line.set_color(col)
line.set_mfc(col) # facecolor of fliers
line.set_mec(col) # edgecolor of fliers
# Also fix the legend
for legpatch in ax2.legend_.get_patches():
col = legpatch.get_facecolor()
legpatch.set_edgecolor(col)
legpatch.set_facecolor('None')
sns.despine(left=True)
plt.show()
I need to create the legend as a separate figure, and more importantly separate instance that can be saved in a new file. My plot consists of lines and a filled in segment.
The problem is the fill_between element, I can not add it to the external figure/legend.
I realise, this is a different type of object, it is a PolyCollection, while to line-plots are Line2D elements.
How do I handle the PolyCollection so that I can use it in the external legend?
INFO: matplotlib version 3.3.2
import matplotlib.pyplot as plt
import numpy as np
# Dummy data
x = np.linspace(1, 100, 1000)
y = np.log(x)
y1 = np.sin(x)
# Create regular plot and plot everything
fig = plt.figure('Line plot')
ax = fig.add_subplot(111)
line1, = ax.plot(x, y)
line2, = ax.plot(x, y1)
fill = ax.fill_between(x, y, y1)
ax.legend([line1, line2, fill],['Log','Sin','Area'])
ax.plot()
# create new plot only for legend
legendFig = plt.figure('Legend plot')
legendFig.legend([line1, line2],['Log','Sin']) <----- This works
# legendFig.legend([line1, line2, fill],['Log','Sin', 'Area']) <----- This does not work
You forgot to mention what does not work means here.
Apparently, you get an error message: RuntimeError: Can not put single artist in more than one figure.
Matplotlib doesn't allow elements placed in one figure to be reused in another. It is just a lucky coincidence that the line don't give an error.
To use an element in another figure, you can create a new element, and that copy the style from the original element:
from matplotlib.lines import Line2D
from matplotlib.collections import PolyCollection
legendFig = plt.figure('Legend plot')
handle_line1 = Line2D([], [])
handle_line1.update_from(line1)
handle_line2 = Line2D([], [])
handle_line2.update_from(line2)
handle_fill = PolyCollection([])
handle_fill.update_from(fill)
legendFig.legend([handle_line1, handle_line2, handle_fill], ['Log', 'Sin', 'Area'])
I have a parallel coordinates plot with lots of data points so I'm trying to use a continuous colour bar to represent that, which I think I have worked out. However, I haven't been able to remove the default key that is put in when creating the plot, which is very long and hinders readability. Is there a way to remove this table to make the graph much easier to read?
This is the code I'm currently using to generate the parallel coordinates plot:
parallel_coordinates(data[[' male_le','
female_le','diet','activity','obese_perc','median_income']],'median_income',colormap = 'rainbow',
alpha = 0.5)
fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cmap = mpl.cm.rainbow
bounds = [0.00,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N,)
plt.colorbar(mpl.cm.ScalarMappable(norm = norm, cmap=cmap),cax = ax, orientation = 'horizontal',
label = 'normalised median income', alpha = 0.5)
plt.show()
Current Output:
I want my legend to be represented as a color bar, like this:
Any help would be greatly appreciated. Thanks.
You can use ax.legend_.remove() to remove the legend.
The cax parameter of plt.colorbar indicates the subplot where to put the colorbar. If you leave it out, matplotlib will create a new subplot, "stealing" space from the current subplot (subplots are often referenced to by ax in matplotlib). So, here leaving out cax (adding ax=ax isn't necessary, as here ax is the current subplot) will create the desired colorbar.
The code below uses seaborn's penguin dataset to create a standalone example.
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import numpy as np
from pandas.plotting import parallel_coordinates
penguins = sns.load_dataset('penguins')
fig, ax = plt.subplots(figsize=(10, 4))
cmap = plt.get_cmap('rainbow')
bounds = np.arange(penguins['body_mass_g'].min(), penguins['body_mass_g'].max() + 200, 200)
norm = mpl.colors.BoundaryNorm(bounds, 256)
penguins = penguins.dropna(subset=['body_mass_g'])
parallel_coordinates(penguins[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']],
'body_mass_g', colormap=cmap, alpha=0.5, ax=ax)
ax.legend_.remove()
plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
ax=ax, orientation='horizontal', label='body mass', alpha=0.5)
plt.show()
I have a errorbar plot with only one data point (i.e. one errorbar) per data set. Therefore I would like to have a single errorbar symbol in the legend as well.
The single one can be achieved by legend(numpoints=1). Using this in the following code:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.errorbar(x=[0.3], y=[0.7], xerr=[0.2], marker='+', markersize=10, label='horizontal marker line')
ax.errorbar(x=[0.7], y=[0.3], yerr=[0.2], marker='+', markersize=10, label='is too long')
ax.set_xlim([0,1])
ax.set_ylim([0,1])
ax.legend(numpoints=1) # I want only one symbol
plt.show()
results in these symbols in the legend:
As you see, the errorbars are mixed up with horizontal lines, that make sense when there are more than one error bars to be connected (using legend(numpoints=2) or higher), but look ugly in my case.
How can I get rid of the lines in the legend markers without loosing the errorbars?
This is due to the default settings in matplotlib. At the start of your code you can change them by changing the settings using rcParams:
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['legend.handlelength'] = 0
mpl.rcParams['legend.markerscale'] = 0
fig, ax = plt.subplots()
ax.errorbar(x=[0.3], y=[0.7], xerr=[0.2], marker='+', markersize=10, label='horizontal marker')
ax.errorbar(x=[0.7], y=[0.3], yerr=[0.2], marker='+', markersize=10, label='is gone')
ax.set_xlim([0,1])
ax.set_ylim([0,1])
ax.legend(numpoints=1)
plt.show()
Note: This changes the settings for all the graphs that will be plotted in the code.
I have plotted my data with factorplot in seaborn and get facetgrid object, but still cannot understand how the following attributes could be set in such a plot:
Legend size: when I plot lots of variables, I get very small legends, with small fonts.
Font sizes of y and x labels (a similar problem as above)
You can scale up the fonts in your call to sns.set().
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
x = np.random.normal(size=37)
y = np.random.lognormal(size=37)
# defaults
sns.set()
fig, ax = plt.subplots()
ax.plot(x, y, marker='s', linestyle='none', label='small')
ax.legend(loc='upper left', bbox_to_anchor=(0, 1.1))
sns.set(font_scale=5) # crazy big
fig, ax = plt.subplots()
ax.plot(x, y, marker='s', linestyle='none', label='big')
ax.legend(loc='upper left', bbox_to_anchor=(0, 1.3))
The FacetGrid plot does produce pretty small labels. While #paul-h has described the use of sns.set as a way to the change the font scaling, it may not be the optimal solution since it will change the font_scale setting for all plots.
You could use the seaborn.plotting_context to change the settings for just the current plot:
with sns.plotting_context(font_scale=1.5):
sns.factorplot(x, y ...)
I've made some modifications to #paul-H code, such that you can independently set the font size for the x/y axes and legend:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
x = np.random.normal(size=37)
y = np.random.lognormal(size=37)
# defaults
sns.set()
fig, ax = plt.subplots()
ax.plot(x, y, marker='s', linestyle='none', label='small')
ax.legend(loc='upper left', fontsize=20,bbox_to_anchor=(0, 1.1))
ax.set_xlabel('X_axi',fontsize=20);
ax.set_ylabel('Y_axis',fontsize=20);
plt.show()
This is the output:
For the legend, you can use this
plt.setp(g._legend.get_title(), fontsize=20)
Where g is your facetgrid object returned after you call the function making it.
This worked for me
g = sns.catplot(x="X Axis", hue="Class", kind="count", legend=False, data=df, height=5, aspect=7/4)
g.ax.set_xlabel("",fontsize=30)
g.ax.set_ylabel("Count",fontsize=20)
g.ax.tick_params(labelsize=15)
What did not work was to call set_xlabel directly on g like g.set_xlabel() (then I got a "Facetgrid has no set_xlabel" method error)