I'm using factorplot(kind="bar")
with seaborn.
The plot is fine except the legend is misplaced: too much to the right, text goes out of the plot's shaded area.
How do I make seaborn place the legend somewhere else, such as in top-left instead of middle-right?
Building on @user308827's answer: you can use legend=False
in factorplot and specify the legend through matplotlib:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid")
titanic = sns.load_dataset("titanic")
g = sns.factorplot("class", "survived", "sex",
data=titanic, kind="bar",
size=6, palette="muted",
legend=False)
g.despine(left=True)
plt.legend(loc='upper left')
g.set_ylabels("survival probability")
plt
acts on the current axes. To get axes from a FacetGrid
use fig.
g.fig.get_axes()[0].legend(loc='lower left')
Modifying the example here :
You can use legend_out = False
import seaborn as sns
sns.set(style="whitegrid")
titanic = sns.load_dataset("titanic")
g = sns.factorplot("class", "survived", "sex",
data=titanic, kind="bar",
size=6, palette="muted",
legend_out=False)
g.despine(left=True)
g.set_ylabels("survival probability")
Check out the docs here: https://matplotlib.org/users/legend_guide.html#legend-location
adding this simply worked to bring legend out of the plot:
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
This is how I was able to move the legend to a particular place inside the plot and change the aspect and size of the plot:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
import seaborn as sns
sns.set(style="ticks")
figure_name = 'rater_violinplot.png'
figure_output_path = output_path + figure_name
viol_plot = sns.factorplot(x="Rater",
y="Confidence",
hue="Event Type",
data=combo_df,
palette="colorblind",
kind='violin',
size = 10,
aspect = 1.5,
legend=False)
viol_plot.ax.legend(loc=2)
viol_plot.fig.savefig(figure_output_path)
This worked for me to change the size and aspect of the plot as well as move the legend outside the plot area.
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
import seaborn as sns
sns.set(style="ticks")
figure_name = 'rater_violinplot.png'
figure_output_path = output_path + figure_name
viol_plot = sns.factorplot(x="Rater",
y="Confidence",
hue="Event Type",
data=combo_df,
palette="colorblind",
kind='violin',
size = 10,
aspect = 1.5,
legend_out=True)
viol_plot.fig.savefig(figure_output_path)
I figured this out from mwaskom's answer here and Fernando Hernandez's answer here .
it seems you can directly call:
g = sns.factorplot("class", "survived", "sex",
data=titanic, kind="bar",
size=6, palette="muted",
legend_out=False)
g._legend.set_bbox_to_anchor((.7, 1.1))
seaborn 0.11.2
, there is the option to use seaborn.move_legend
, which applies to Axes and Figure level plots.sns.factorplot
, which has been renamed toseaborn.catplot
, a figure-level plot.matplotlib.axes.Axes.legend
and How to put the legend out of the plot for parameters and their usage.import matplotlib.pyplot as plt
import seaborn as sns
# load the data
penguins = sns.load_dataset('penguins', cache=False)
g = sns.displot(penguins, x="bill_length_mm", hue="species", col="island", col_wrap=2, height=2)
sns.move_legend(g, "upper left", bbox_to_anchor=(.55, .45))
plt.show()
ax = sns.histplot(penguins, x="bill_length_mm", hue="species")
sns.move_legend(ax, "lower center", bbox_to_anchor=(.5, 1), ncol=3, title=None, frameon=False)
plt.show()
If you wish to customize your legend, just use the add_legend
method. It takes the same parameters as matplotlib plt.legend
.
import seaborn as sns
sns.set(style="whitegrid")
titanic = sns.load_dataset("titanic")
g = sns.factorplot("class", "survived", "sex",
data=titanic, kind="bar",
size=6, palette="muted",
legend_out=False)
g.despine(left=True)
g.set_ylabels("survival probability")
g.add_legend(bbox_to_anchor=(1.05, 0), loc=2, borderaxespad=0.)
Using object oriented API:
fig,ax = plt.subplots(1,1)
sns.someplot(...,ax=ax)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels,loc="upper left")
source: https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html
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