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Move seaborn plot legend to a different position

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 … Read more

How to add percentages on top of bars in seaborn

The seaborn.catplot organizing function returns a FacetGrid, which gives you access to the fig, the ax, and its patches. If you add the labels when nothing else has been plotted you know which bar-patches came from which variables. From @LordZsolt’s answer I picked up the order argument to catplot: I like making that explicit because … Read more

How to manually create a legend

Have you checked the Legend Guide? For practicality, I quote the example from the guide. Not all handles can be turned into legend entries automatically, so it is often necessary to create an artist which can. Legend handles don’t have to exists on the Figure or Axes in order to be used. Suppose we wanted … Read more

Color by Column Values in Matplotlib

Imports and Data import numpy import pandas import matplotlib.pyplot as plt import seaborn seaborn.set(style=”ticks”) numpy.random.seed(0) N = 37 _genders= [‘Female’, ‘Male’, ‘Non-binary’, ‘No Response’] df = pandas.DataFrame({ ‘Height (cm)’: numpy.random.uniform(low=130, high=200, size=N), ‘Weight (kg)’: numpy.random.uniform(low=30, high=100, size=N), ‘Gender’: numpy.random.choice(_genders, size=N) }) Update August 2021 With seaborn 0.11.0, it’s recommended to use new figure level functions … Read more

How to plot multiple Seaborn Jointplot in Subplot

Moving axes in matplotlib is not as easy as it used to be in previous versions. The below is working with the current version of matplotlib. As has been pointed out at several places (this question, also this issue) several of the seaborn commands create their own figure automatically. This is hardcoded into the seaborn … Read more

plot different color for different categorical levels using matplotlib

Imports and Sample DataFrame import matplotlib.pyplot as plt import pandas as pd import seaborn as sns # for sample data from matplotlib.lines import Line2D # for legend handle # DataFrame used for all options df = sns.load_dataset(‘diamonds’) carat cut color clarity depth table price x y z 0 0.23 Ideal E SI2 61.5 55.0 326 … Read more

Python matplotlib multiple bars

import matplotlib.pyplot as plt from matplotlib.dates import date2num import datetime x = [ datetime.datetime(2011, 1, 4, 0, 0), datetime.datetime(2011, 1, 5, 0, 0), datetime.datetime(2011, 1, 6, 0, 0) ] x = date2num(x) y = [4, 9, 2] z = [1, 2, 3] k = [11, 12, 13] ax = plt.subplot(111) ax.bar(x-0.2, y, width=0.2, color=”b”, align=’center’) … Read more

How to change the figure size of a seaborn axes or figure level plot

You can also set figure size by passing dictionary to rc parameter with key ‘figure.figsize’ in seaborn set method: import seaborn as sns sns.set(rc={‘figure.figsize’:(11.7,8.27)}) Other alternative may be to use figure.figsize of rcParams to set figure size as below: from matplotlib import rcParams # figure size in inches rcParams[‘figure.figsize’] = 11.7,8.27 More details can be … Read more

Plotting with seaborn using the matplotlib object-oriented interface

It depends a bit on which seaborn function you are using. The plotting functions in seaborn are broadly divided into two classes “Axes-level” functions, including regplot, boxplot, kdeplot, and many others “Figure-level” functions, including relplot, catplot, displot, pairplot, jointplot and one or two others The first group is identified by taking an explicit ax argument … Read more

Stacked Bar Chart with Centered Labels

The following method is more succinct, and easily scales. Putting the data into a pandas.DataFrame is the easiest way to plot a stacked bar plot. Using pandas.DataFrame.plot.bar(stacked=True), or pandas.DataFrame.plot(kind=’bar’, stacked=True), is the easiest way to plot a stacked bar plot. This method returns a matplotlib.axes.Axes or a numpy.ndarray of them. Since seaborn is just a … Read more