I am not sure if this is the prettiest way to do it, but it does fix it with one line: import matplotlib.pyplot as plt import seaborn as sns import numpy as np import pandas as pd np.random.seed(0) fig = plt.figure() ax1 = fig.add_subplot(111) ax1.plot(pd.Series(np.random.uniform(0, 1, size=10))) ax2 = ax1.twinx() ax2.plot(pd.Series(np.random.uniform(10, 20, size=10)), color=”r”) # … Read more
You can use matplotlib for this. matplotlib has a mplot3d module that will do exactly what you want. import matplotlib.pyplot as plt import random fig = plt.figure(figsize=(12, 12)) ax = fig.add_subplot(projection=’3d’) sequence_containing_x_vals = list(range(0, 100)) sequence_containing_y_vals = list(range(0, 100)) sequence_containing_z_vals = list(range(0, 100)) random.shuffle(sequence_containing_x_vals) random.shuffle(sequence_containing_y_vals) random.shuffle(sequence_containing_z_vals) ax.scatter(sequence_containing_x_vals, sequence_containing_y_vals, sequence_containing_z_vals) plt.show() The code above generates a … Read more
Does plt.tricontourf(x,y,z) satisfy your requirements? It will plot filled contours for irregularly spaced data (non-rectilinear grid). You might also want to look into plt.tripcolor(). import numpy as np import matplotlib.pyplot as plt x = np.random.rand(100) y = np.random.rand(100) z = np.sin(x)+np.cos(y) f, ax = plt.subplots(1,2, sharex=True, sharey=True) ax[0].tripcolor(x,y,z) ax[1].tricontourf(x,y,z, 20) # choose 20 contour levels, … Read more
To add arrow patches to a 3D plot, the simple solution is to use FancyArrowPatch class defined in /matplotlib/patches.py. However, it only works for 2D plot (at the time of writing), as its posA and posB are supposed to be tuples of length 2. Therefore we create a new arrow patch class, name it Arrow3D, … Read more
use labelpad parameter: pl.xlabel(“…”, labelpad=20) or set it after: ax.xaxis.labelpad = 20
Aside from cleaning up your data into a tidy format, you need to reformat the text data (percentages) into numeric data types. Since that has nothing to do with barplots, I’ll assume you can take care of that on your own and focus on the plotting and data structures instead: df = pandas.DataFrame({ ‘Factor’: [‘Growth’, … Read more