If you don’t want to change the spacing globally (by editing your rcParams), and want a cleaner approach, try this: ax.tick_params(axis=”both”, which=”major”, pad=15) or for just x axis ax.tick_params(axis=”x”, which=”major”, pad=15) or the y axis ax.tick_params(axis=”y”, which=”major”, pad=15)
IIUC you can format the xticks and set these: In[60]: #generate some psuedo data df = pd.DataFrame({‘num’:[50000, 75000, 100000, 125000], ‘Rent/Sqft’:np.random.randn(4), ‘Region’:list(‘abcd’)}) df Out[60]: num Rent/Sqft Region 0 50000 0.109196 a 1 75000 0.566553 b 2 100000 -0.274064 c 3 125000 -0.636492 d In[61]: import matplotlib.pyplot as plt import matplotlib.ticker as ticker import seaborn as … Read more
Nevermind, I figured it out. ax.tick_params(axis=”x”, which=”minor”, bottom=False)
There is a simpler way actually. I just found: import matplotlib.pyplot as plt # We prepare the plot fig, ax = plt.subplots() # We change the fontsize of minor ticks label ax.tick_params(axis=”both”, which=”major”, labelsize=10) ax.tick_params(axis=”both”, which=”minor”, labelsize=8) This only answers to the size of label part of your question though.
I had exactly the same problem, and these lines fixed the problem: from matplotlib.ticker import ScalarFormatter y_formatter = ScalarFormatter(useOffset=False) ax.yaxis.set_major_formatter(y_formatter)
There are several problems in your code. First the big ones: You are creating a new figure and a new axes in every iteration of your loop → put fig = plt.figure and ax = fig.add_subplot(1,1,1) outside of the loop. Don’t use the Locators. Call the functions ax.set_xticks() and ax.grid() with the correct keywords. With … Read more
This should be simpler: (from https://scivision.co/matplotlib-force-integer-labeling-of-axis/) import matplotlib.pyplot as plt from matplotlib.ticker import MaxNLocator #… ax = plt.figure().gca() #… ax.xaxis.set_major_locator(MaxNLocator(integer=True)) Read the official docs: https://matplotlib.org/stable/api/ticker_api.html#matplotlib.ticker.MaxNLocator
You can use the Axes.set_yscale method. That allows you to change the scale after the Axes object is created. That would also allow you to build a control to let the user pick the scale if you needed to. The relevant line to add is: ax.set_yscale(‘log’) You can use ‘linear’ to switch back to a … Read more