The value of hjust and vjust are only defined between 0 and 1: 0 means left-justified 1 means right-justified Source: ggplot2, Hadley Wickham, page 196 (Yes, I know that in most cases you can use it beyond this range, but don’t expect it to behave in any specific way. This is outside spec.) hjust controls … Read more
If you find yourself doing things like this regularly it may be worth investigating the object-oriented interface to matplotlib. In your case: import matplotlib.pyplot as plt import numpy as np x = np.arange(5) y = np.exp(x) fig1, ax1 = plt.subplots() ax1.plot(x, y) ax1.set_title(“Axis 1 title”) ax1.set_xlabel(“X-label for axis 1”) z = np.sin(x) fig2, (ax2, ax3) … Read more
There is a clear figure command, and it should do it for you: plt.clf() If you have multiple subplots in the same figure plt.cla() clears the current axes.
Update: This answer is outdated since matplotlib version 3.5. The plot function now handles datetime data directly. See https://matplotlib.org/3.5.1/api/_as_gen/matplotlib.pyplot.plot_date.html The use of plot_date is discouraged. This method exists for historic reasons and may be deprecated in the future. datetime-like data should directly be plotted using plot. If you need to plot plain numeric data as … Read more
This wording is no longer in the documentation. Use of the pylab import is now discouraged and the OO interface is recommended for most non-interactive usage. From the documentation, the emphasis is mine: Matplotlib is the whole package; pylab is a module in matplotlib that gets installed alongside matplotlib; and matplotlib.pyplot is a module in … Read more
This is a few months late, but I have created PR#6251 with matplotlib to add a new PercentFormatter class. With this class you just need one line to reformat your axis (two if you count the import of matplotlib.ticker): import … import matplotlib.ticker as mtick ax = df[‘myvar’].plot(kind=’bar’) ax.yaxis.set_major_formatter(mtick.PercentFormatter()) PercentFormatter() accepts three arguments, xmax, decimals, … Read more
No one has mentioned using negative inset values for legend. Here is an example, where the legend is to the right of the plot, aligned to the top (using keyword “topright”). # Random data to plot: A <- data.frame(x=rnorm(100, 20, 2), y=rnorm(100, 20, 2)) B <- data.frame(x=rnorm(100, 21, 1), y=rnorm(100, 21, 1)) # Add extra … Read more
You could use Series.reindex: import pandas as pd idx = pd.date_range(’09-01-2013′, ’09-30-2013′) s = pd.Series({’09-02-2013′: 2, ’09-03-2013′: 10, ’09-06-2013′: 5, ’09-07-2013′: 1}) s.index = pd.DatetimeIndex(s.index) s = s.reindex(idx, fill_value=0) print(s) yields 2013-09-01 0 2013-09-02 2 2013-09-03 10 2013-09-04 0 2013-09-05 0 2013-09-06 5 2013-09-07 1 2013-09-08 0 …
I spent a long time looking for solutions, and found this answer. It looks like, in order to get what you (and I) want, you need the combination of plt.ion(), plt.show() (not with block=False) and, most importantly, plt.pause(.001) (or whatever time you want). The pause is needed because the GUI events happen while the main … Read more
Use geom_text , with aes label. You can play with hjust, vjust to adjust text position. ggplot(nba, aes(x= MIN, y= PTS, colour=”green”, label=Name))+ geom_point() +geom_text(hjust=0, vjust=0) EDIT: Label only values above a certain threshold: ggplot(nba, aes(x= MIN, y= PTS, colour=”green”, label=Name))+ geom_point() + geom_text(aes(label=ifelse(PTS>24,as.character(Name),”)),hjust=0,vjust=0)