Alternatively, if you want to simply set the number of ticks while allowing matplotlib to position them (currently only with MaxNLocator), there is pyplot.locator_params, pyplot.locator_params(nbins=4) You can specify specific axis in this method as mentioned below, default is both: # To specify the number of ticks on both or any single axes pyplot.locator_params(axis=”y”, nbins=6) pyplot.locator_params(axis=”x”, … Read more
Perhaps use plt.annotate: import numpy as np import matplotlib.pyplot as plt N = 10 data = np.random.random((N, 4)) labels = [‘point{0}’.format(i) for i in range(N)] plt.subplots_adjust(bottom = 0.1) plt.scatter( data[:, 0], data[:, 1], marker=”o”, c=data[:, 2], s=data[:, 3] * 1500, cmap=plt.get_cmap(‘Spectral’)) for label, x, y in zip(labels, data[:, 0], data[:, 1]): plt.annotate( label, xy=(x, y), … Read more
For Linux Debian based distros: sudo apt-get install python3-tk RPM based distros: sudo yum install python3-tkinter For windows: For Windows, I think the problem is you didn’t install complete Python package. Since Tkinter should be shipped with Python out of box. See: http://www.tkdocs.com/tutorial/install.html . Good python distributions for Windows can be found by the companies … Read more
pip will make your life easy! Step 1: Install pip – Check if you have pip already simply by writing pip in the python console. If you don’t have pip, get a python script called get-pip.py , via here: https://pip.pypa.io/en/latest/installing.html or directly here: https://bootstrap.pypa.io/get-pip.py (You may have to use Save As ..) Step 2: Take … Read more
The imshow() function with parameters interpolation=’nearest’ and cmap=’hot’ should do what you want. import matplotlib.pyplot as plt import numpy as np a = np.random.random((16, 16)) plt.imshow(a, cmap=’hot’, interpolation=’nearest’) plt.show()
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
Seaborn’s barplot returns an axis-object (not a figure). This means you can do the following: import pandas as pd import seaborn as sns import matplotlib.pyplot as plt fake = pd.DataFrame({‘cat’: [‘red’, ‘green’, ‘blue’], ‘val’: [1, 2, 3]}) ax = sns.barplot(x = ‘val’, y = ‘cat’, data = fake, color=”black”) ax.set(xlabel=”common xlabel”, ylabel=”common ylabel”) plt.show()
Matplotlib does this by default. E.g.: import matplotlib.pyplot as plt import numpy as np x = np.arange(10) plt.plot(x, x) plt.plot(x, 2 * x) plt.plot(x, 3 * x) plt.plot(x, 4 * x) plt.show() And, as you may already know, you can easily add a legend: import matplotlib.pyplot as plt import numpy as np x = np.arange(10) … Read more
Matplotlib doesn’t work with pixels directly, but rather physical sizes and DPI. If you want to display a figure with a certain pixel size, you need to know the DPI of your monitor. For example this link will detect that for you. If you have an image of 3841×7195 pixels it is unlikely that you … Read more
If you prefer a non-object-oriented approach, move plt.xticks(rotation=70) to right before the two avail_plot calls, eg plt.xticks(rotation=70) avail_plot(axs[0], dates, s1, ‘testing’, ‘green’) avail_plot(axs[1], dates, s1, ‘testing2’, ‘red’) This sets the rotation property before setting up the labels. Since you have two axes here, plt.xticks gets confused after you’ve made the two plots. At the point … Read more