You could explicitly set where you want to tick marks with plt.xticks: plt.xticks(np.arange(min(x), max(x)+1, 1.0)) For example, import numpy as np import matplotlib.pyplot as plt x = [0,5,9,10,15] y = [0,1,2,3,4] plt.plot(x,y) plt.xticks(np.arange(min(x), max(x)+1, 1.0)) plt.show() (np.arange was used rather than Python’s range function just in case min(x) and max(x) are floats instead of ints.) … Read more
Just place the colorbar in its own axis and use subplots_adjust to make room for it. As a quick example: import numpy as np import matplotlib.pyplot as plt fig, axes = plt.subplots(nrows=2, ncols=2) for ax in axes.flat: im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1) fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im, cax=cbar_ax) plt.show() Note that the … Read more
The data is read in as strings. In matplotlib 2.0 those were automatically converted to floating point numbers such that they can be plotted. In matplotlib 2.1, categorical plots have been introduced. This now allows for something like plt.plot([“apple”, “banana”, “cherry”], [2,1,3]) While this is of course great for certain applications, it breaks the previous … Read more
Firstly freq_series.plot returns an axis not a figure so to make my answer a little more clear I’ve changed your given code to refer to it as ax rather than fig to be more consistent with other code examples. You can get the list of the bars produced in the plot from the ax.patches member. … Read more
figure tells you the call signature: from matplotlib.pyplot import figure figure(figsize=(8, 6), dpi=80) figure(figsize=(1,1)) would create an inch-by-inch image, which would be 80-by-80 pixels unless you also give a different dpi argument.
There are several ways to do it. The subplots method creates the figure along with the subplots that are then stored in the ax array. For example: import matplotlib.pyplot as plt x = range(10) y = range(10) fig, ax = plt.subplots(nrows=2, ncols=2) for row in ax: for col in row: col.plot(x, y) plt.show() However, something … Read more
There are a number of ways to do what you want. To add to what @inalis and @Navi already said, you can use the bbox_to_anchor keyword argument to place the legend partially outside the axes and/or decrease the font size. Before you consider decreasing the font size (which can make things awfully hard to read), … Read more
figure tells you the call signature: from matplotlib.pyplot import figure figure(figsize=(8, 6), dpi=80) figure(figsize=(1,1)) would create an inch-by-inch image, which would be 80-by-80 pixels unless you also give a different dpi argument.
Ok so here is what you want to do import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # for 3d plotting # constants h = 1.34e-34 m = 1.6e-19 a = 2e-9 # define our function (python standard uses lowercase for funcs/vars) def t(e, u): k2 = np.sqrt(2 * m * … Read more
df = pd.DataFrame(dict(Subsidy=[3, 3, 3], Bonus=[1, 1, 1], Expense=[2, 2, 2]), list(‘ABC’)) df ax = df[[‘Subsidy’, ‘Bonus’]].plot.bar(stacked=True, position=1, width=.2, ylim=[0, 8], color=[‘orange’, ‘red’]) df[[‘Expense’]].plot.bar(ax=ax, position=0, width=.2, color=[‘green’])