Iterate through each subplot / FacetGrid with for ax in g.axes.flat:. ax.patches contains matplotlib.patches.Rectangle and matplotlib.patches.PathPatch, so the correct ones must be used. Caveat: all hues must appear for each group in each Facet, otherwise the patches and hatches will not match. In this case, manual or conditional code will probably be required to correctly … Read more
Since I spent some time on this, I thought I share this so that others can easily adapt this approach: from matplotlib import pyplot as plt import pandas as pd import seaborn as sns import numpy as np x_list = [1, 2, 3, 4, 6, 7, 9, 9, 9, 10] df = pd.DataFrame({“X”: x_list, “Y”: … Read more
I spent a few days to build a more general solution, PyWaffle. You can install it through pip install pywaffle The source code: https://github.com/gyli/PyWaffle PyWaffle does not use matshow() method, but builds those squares one by one. That makes it easier for customization. Besides, what it provides is a custom Figure class, which returns a … Read more
There isn’t a clear and quick answer to this at the top of search engine results so I provide simple examples here: .1e = scientific notation with 1 decimal point (standard form) .2f = 2 decimal places .3g = 3 significant figures .4% = percentage with 4 decimal places A more detailed explanation on the … Read more
Considering your question mentions barplot I thought I would add in a solution for that type of plot also as it differs from the factorplot in @Jules solution. import matplotlib.pyplot as plt import seaborn as sns sns.set(style=”whitegrid”) xs = [“First”, “First”, “Second”, “Second”, “Third”, “Third”] hue = [“Female”, “Male”] * 3 ys = [1988, 301, … Read more
I would suggest not to use seaborn pointplot for plotting. This makes things unnecessarily complicated. Instead use matplotlib plot_date. This allows to set labels to the plots and have them automatically put into a legend with ax.legend(). import matplotlib.pyplot as plt import pandas as pd import seaborn as sns import numpy as np date = … Read more
We create the figure with the subplots: f, axes = plt.subplots(1, 2) Where axes is an array with each subplot. Then we tell each plot in which subplot we want them with the argument ax. sns.boxplot( y=”b”, x= “a”, data=df, orient=”v” , ax=axes[0]) sns.boxplot( y=”c”, x= “a”, data=df, orient=”v” , ax=axes[1]) And the result is:
Seaborn favors the “long format” as input. The key ingredient to convert your DataFrame from its “wide format” (one column per measurement type) into long format (one column for all measurement values, one column to indicate the type) is pandas.melt. Given a data_preproc structured like yours, filled with random values: num_rows = 20 years = … Read more
You can loop through the containers and call ax.bar_label(…) for each of them. Note that seaborn creates one set of bars for each hue value. The following example uses the titanic dataset and sets ci=None to avoid the error bars overlapping with the text (if error bars are needed, one could set a lighter color, … Read more
Update for the latest versions of matplotlib (3.3.4) and seaborn (0.11.1): the kdeplot with shade=True now doesn’t create a line object anymore. To get the same outcome as before, setting shade=False will still create the line object. The curve can then be filled with ax.fill_between(). The code below is changed accordingly. (Use the revision history … Read more