How to annotate end of lines using python and matplotlib?

In order to annotate a point use ax.annotate(). In this case it makes sense to specify the coordinates to annotate separately. I.e. the y coordinate is the data coordinate of the last point of the line (which you can get from line.get_ydata()[-1]) while the x coordinate is independent of the data and should be the right hand side of the axes (i.e. 1 in axes coordinates). You may then also want to offset the text a bit such that it does not overlap with the axes.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

rows = 75
df = pd.DataFrame(np.random.randint(-4,5,size=(rows, 3)), columns=['A', 'B', 'C'])
datelist = pd.date_range(pd.datetime(2017, 1, 1).strftime('%Y-%m-%d'), periods=rows).tolist()
df['dates'] = datelist 
df = df.set_index(['dates'])
df.index = pd.to_datetime(df.index)
df = df.cumsum()

ax = df.plot()

for line, name in zip(ax.lines, df.columns):
    y = line.get_ydata()[-1]
    ax.annotate(name, xy=(1,y), xytext=(6,0), color=line.get_color(), 
                xycoords = ax.get_yaxis_transform(), textcoords="offset points",
                size=14, va="center")

plt.show()

Method 1

Here is one way, or at least a method, which you can adapt to aesthetically fit in whatever way you want, using the plt.annotate method:

[EDIT]: If you're going to use a method like this first one, the method outlined in ImportanceOfBeingErnest's answer is better than what I've proposed.

df.plot()

for col in df.columns:
    plt.annotate(col,xy=(plt.xticks()[0][-1]+0.7, df[col].iloc[-1]))

plt.show()

For the xy argument, which is the x and y coordinates of the text, I chose the last x coordinate in plt.xticks(), and added 0.7 so that it is outside of your x axis, but you can coose to make it closer or further as you see fit.

METHOD 2:

You could also just use the right y axis, and label it with your 3 lines. For example:

fig, ax = plt.subplots()
df.plot(ax=ax)
ax2 = ax.twinx()
ax2.set_ylim(ax.get_ylim())
ax2.set_yticks([df[col].iloc[-1] for col in df.columns])
ax2.set_yticklabels(df.columns)

plt.show()

This gives you the following plot: