Try using plt.tight_layout As a quick example: import matplotlib.pyplot as plt fig, axes = plt.subplots(nrows=4, ncols=4) fig.tight_layout() # Or equivalently, “plt.tight_layout()” plt.show() Without Tight Layout With Tight Layout
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
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
I eventually found a trick (edit: see below for using seaborn and longform dataframe): Solution with pandas and matplotlib Here it is with a more complete example : import pandas as pd import matplotlib.cm as cm import numpy as np import matplotlib.pyplot as plt def plot_clustered_stacked(dfall, labels=None, title=”multiple stacked bar plot”, H=”/”, **kwargs): “””Given a … Read more
Unfortunately matplotlib 3.1.1 broke seaborn heatmaps; and in general inverted axes with fixed ticks. This is fixed in the current development version; you may hence revert to matplotlib 3.1.0 use matplotlib 3.1.2 or higher set the heatmap limits manually (ax.set_ylim(bottom, top) # set the ylim to bottom, top)
It seems none of the other answers here actually answer the question. So here is a code that uses a scatter and shows an annotation upon hovering over the scatter points. import matplotlib.pyplot as plt import numpy as np; np.random.seed(1) x = np.random.rand(15) y = np.random.rand(15) names = np.array(list(“ABCDEFGHIJKLMNO”)) c = np.random.randint(1,5,size=15) norm = plt.Normalize(1,4) … Read more
There are several ways to do it. The subplots method creates the figure along with the subplots that are then stored in the ax array. For example: import matplotlib.pyplot as plt x = range(10) y = range(10) fig, ax = plt.subplots(nrows=2, ncols=2) for row in ax: for col in row: col.plot(x, y) plt.show() However, something … Read more