This is the suggested Matplotlib 3 solution from the official website HERE: import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2*np.pi, 100) y = np.sin(x) ax = plt.subplot(111) ax.plot(x, y) # Hide the right and top spines ax.spines.right.set_visible(False) ax.spines.top.set_visible(False) # Only show ticks on the left and bottom spines ax.yaxis.set_ticks_position(‘left’) ax.xaxis.set_ticks_position(‘bottom’) plt.show()
You need to dig a bit deeper into the api to do this: from matplotlib import pyplot as plt plt.plot(range(5)) plt.xlim(-3, 3) plt.ylim(-3, 3) plt.gca().set_aspect(‘equal’, adjustable=”box”) plt.draw() doc for set_aspect
Update: This answer is outdated since matplotlib version 3.5. The plot function now handles datetime data directly. See https://matplotlib.org/3.5.1/api/_as_gen/matplotlib.pyplot.plot_date.html The use of plot_date is discouraged. This method exists for historic reasons and may be deprecated in the future. datetime-like data should directly be plotted using plot. If you need to plot plain numeric data as … Read more
You essentially have two options: Do exactly what you’re currently doing, but call graph1.clear() and graph2.clear() before replotting the data. This is the slowest, but most simplest and most robust option. Instead of replotting, you can just update the data of the plot objects. You’ll need to make some changes in your code, but this … Read more
This wording is no longer in the documentation. Use of the pylab import is now discouraged and the OO interface is recommended for most non-interactive usage. From the documentation, the emphasis is mine: Matplotlib is the whole package; pylab is a module in matplotlib that gets installed alongside matplotlib; and matplotlib.pyplot is a module in … Read more
A Seaborn box plot returns a Matplotlib axes instance. Unlike pyplot itself, which has a method plt.title(), the corresponding argument for an axes is ax.set_title(). Therefore you need to call sns.boxplot(‘Day’, ‘Count’, data=gg).set_title(‘lalala’) A complete example would be: import seaborn as sns import matplotlib.pyplot as plt tips = sns.load_dataset(“tips”) sns.boxplot(x=tips[“total_bill”]).set_title(“LaLaLa”) plt.show() Of course you could … Read more
This is a few months late, but I have created PR#6251 with matplotlib to add a new PercentFormatter class. With this class you just need one line to reformat your axis (two if you count the import of matplotlib.ticker): import … import matplotlib.ticker as mtick ax = df[‘myvar’].plot(kind=’bar’) ax.yaxis.set_major_formatter(mtick.PercentFormatter()) PercentFormatter() accepts three arguments, xmax, decimals, … Read more
I don’t know what you mean by ‘manually’. You can choose a colourmap and make a colour array easily enough: import numpy as np import matplotlib.pyplot as plt import matplotlib.cm as cm x = np.arange(10) ys = [i+x+(i*x)**2 for i in range(10)] colors = cm.rainbow(np.linspace(0, 1, len(ys))) for y, c in zip(ys, colors): plt.scatter(x, y, … Read more
You can manually create the subplots with matplotlib, and then plot the dataframes on a specific subplot using the ax keyword. For example for 4 subplots (2×2): import matplotlib.pyplot as plt fig, axes = plt.subplots(nrows=2, ncols=2) df1.plot(ax=axes[0,0]) df2.plot(ax=axes[0,1]) … Here axes is an array which holds the different subplot axes, and you can access one … Read more
I spent a long time looking for solutions, and found this answer. It looks like, in order to get what you (and I) want, you need the combination of plt.ion(), plt.show() (not with block=False) and, most importantly, plt.pause(.001) (or whatever time you want). The pause is needed because the GUI events happen while the main … Read more