The matplotlib documentation says Add a table to the current axes. Returns a matplotlib.table.Table instance. For finer grained control over tables, use the Table class and add it to the axes with add_table(). You could do is the following, look at the properties of your table (it’s and object belonging to that class Table): print … Read more
You are facing this issue since you are reading the image with opencv and opencv reads and displays an image as BGR format instead of RGB color format. Whereas matplotlib uses RGB color format to display image. Try using: image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) pixels = np.array(image) plt.imshow(pixels) plt.show()
Whenever you set the x-ticklabels manually, you should try to first set the corresponding ticks, and then specify the labels. In your case, therefore you should do g = sns.lineplot(data=df) g.set_xticks(range(len(df))) # <— set the ticks first g.set_xticklabels([‘2011′,’2012′,’2013′,’2014′,’2015′,’2016′,’2017′,’2018’])
If you just want to set them to a single color, use the error_kw kwarg (expected to be a dict of keyword arguments that’s passed on to ax.errorbar). Also, just so you know, you can pass a sequence of facecolors directly to bar, though this won’t change the errorbar color. As a quick example: import … 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 finally found a proper solution to this long-standing problem (currently in Matplotlib 3), which does not require multiple calls to contour or rasterizing the figure. Note that the problem illustrated in the question appears only in saved publication-quality figures formats like PDF, not in lower-quality raster files like PNG. My solution was inspired by … Read more
Use scipy.signal.spectrogram. import matplotlib.pyplot as plt from scipy import signal from scipy.io import wavfile sample_rate, samples = wavfile.read(‘path-to-mono-audio-file.wav’) frequencies, times, spectrogram = signal.spectrogram(samples, sample_rate) plt.pcolormesh(times, frequencies, spectrogram) plt.imshow(spectrogram) plt.ylabel(‘Frequency [Hz]’) plt.xlabel(‘Time [sec]’) plt.show() Be sure that your wav file is mono (single channel) and not stereo (dual channel) before trying to do this. I highly … Read more
Although you can iterate through the axes and set the titles individually using matplotlib commands, it is cleaner to use seaborn’s built-in tools to control the title. For example: # Add a column of appropriate labels df_reduced[‘measure’] = df_reduced[‘ActualExternal’].replace({0: ‘Internal’, 1: ‘External’} g = sns.FacetGrid(df_reduced, col=”measure”, margin_titles=True) g.map(plt.hist, “ActualDepth”, color=”steelblue”, bins=bins, width=4.5) # Adjust title … Read more
I am no expert in this, but here is code that uses the pydot library and its graph_viz dependency. These libraries come with Anaconda Python but are not installed by default, so first do this from the command prompt: conda install pydot Then here is code adapted from Circular Tree. import matplotlib.pyplot as plt import … Read more
Just iterate through the axes tied to the figure, set the active axes to the iterated object, and modify: for ax in fig.axes: matplotlib.pyplot.sca(ax) plt.xticks(rotation=90)