From matplotlib 2.1 on you can plot categorical variables. This allows to plot plt.bar([“apple”,”cherry”,”banana”], [1,2,3]). However in matplotlib 2.1 the output will be sorted by category, hence alphabetically. This was considered as bug and is changed in matplotlib 2.2 (see this PR). In matplotlib 2.2 the bar plot would hence preserve the order. In matplotlib … Read more
The problem is that sympy Plot creates its own figure and axes. It is not meant to draw to an existing axes. You may however replace the axes the plot is drawn to by an existing axes prior to showing the sympy plot. from sympy import Symbol,plot import matplotlib.pyplot as plt def move_sympyplot_to_axes(p, ax): backend … Read more
The additional ticklabels that overlap originate from some minor ticklabels, which are present in the plot. To get rid of them, one can set the minor formatter to the NullFormatter: plt.gca().xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter()) The complete code from the question might then look like import matplotlib.pyplot as plt import matplotlib.ticker import numpy as np x = np.linspace(0,2.5) y … Read more
I usually use the second one of these: from matplotlib.pyplot import cm import numpy as np #variable n below should be number of curves to plot #version 1: color = cm.rainbow(np.linspace(0, 1, n)) for i, c in zip(range(n), color): plt.plot(x, y, c=c) #or version 2: color = iter(cm.rainbow(np.linspace(0, 1, n))) for i in range(n): c … Read more
In matplotlib versions >= 1.5, you can print the rcParam called axes.prop_cycle: print(plt.rcParams[‘axes.prop_cycle’].by_key()[‘color’]) # [u’#1f77b4′, u’#ff7f0e’, u’#2ca02c’, u’#d62728′, u’#9467bd’, u’#8c564b’, u’#e377c2′, u’#7f7f7f’, u’#bcbd22′, u’#17becf’] Or equivalently, in python2: print plt.rcParams[‘axes.prop_cycle’].by_key()[‘color’] In versions < 1.5, this was called color_cycle: print plt.rcParams[‘axes.color_cycle’] # [u’b’, u’g’, u’r’, u’c’, u’m’, u’y’, u’k’] Note that the default color cycle changed … Read more
Here’s a (very) slightly slicker version of Dan Allan’s answer: import matplotlib.pyplot as plt import pandas as pd import numpy as np import string df = pd.DataFrame({‘x’:np.random.rand(10), ‘y’:np.random.rand(10)}, index=list(string.ascii_lowercase[:10])) Which gives: x y a 0.541974 0.042185 b 0.036188 0.775425 c 0.950099 0.888305 d 0.739367 0.638368 e 0.739910 0.596037 f 0.974529 0.111819 g 0.640637 0.161805 h … Read more
I wrote a function very similar to plt.axes. You could use it for plotting yours sub-subplots. There is an example… import matplotlib.pyplot as plt import numpy as np #def add_subplot_axes(ax,rect,facecolor=”w”): # matplotlib 2.0+ def add_subplot_axes(ax,rect,axisbg=’w’): fig = plt.gcf() box = ax.get_position() width = box.width height = box.height inax_position = ax.transAxes.transform(rect[0:2]) transFigure = fig.transFigure.inverted() infig_position = … Read more
Use the xticks command. import matplotlib.pyplot as plt t11 = [’00’, ’01’, ’02’, ’03’, ’04’, ’05’, ’10’, ’11’, ’12’, ’13’, ’14’, ’15’, ’20’, ’21’, ’22’, ’23’, ’24’, ’25’, ’30’, ’31’, ’32’, ’33’, ’34’, ’35’, ’40’, ’41’, ’42’, ’43’, ’44’, ’45’, ’50’, ’51’, ’52’, ’53’, ’54’, ’55’] t12 = [173, 135, 141, 148, 140, 149, 152, … Read more
Figure size (figsize) determines the size of the figure in inches. This gives the amount of space the axes (and other elements) have inside the figure. The default figure size is (6.4, 4.8) inches in matplotlib 2. A larger figure size will allow for longer texts, more axes or more ticklabels to be shown. Dots … Read more
Copying the axes The inital answer here does not work, we keep it for future reference and also to see why a more sophisticated approach is needed. #There are some pitfalls on the way with the initial approach. #Adding an `axes` to a figure can be done via `fig.add_axes(axes)`. However, at this point, #the axes’ … Read more