================ by Jawad Haider

Chpt 4 - Visualization with Matplotlib¶

07 - Customizing Plot Legends¶

Customizing Plot Legends
Choosing Elements for the Legend
Legend for Size of Points
Multiple Legends

Customizing Plot Legends¶

Plot legends give meaning to a visualization, assigning labels to the various plot ele‐ ments. We previously saw how to create a simple legend; here we’ll take a look at cus‐ tomizing the placement and aesthetics of the legend in Matplotlib. The simplest legend can be created with the plt.legend() command, which auto‐ matically creates a legend for any labeled plot elements

import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
import numpy as np

x=np.linspace(0,10,10000)
fig,ax=plt.subplots()
ax.plot(x, np.sin(x),'-b',label='Sine')
ax.plot(x, np.cos(x),'--r',label='Cosine')

ax.axis('equal')
leg=ax.legend();

#many wasy we can customize the legend,
# we can specify the location and turn off the frame 
ax.legend(loc='upper left', frameon=False)
fig

#We can use the ncol command to specify the number of columns in the legend

ax.legend(frameon=False, loc='lower center',ncol=2)
fig

# We can use a rounded box (fancybox) or add a shadow, change the transparency
#(alpha value) of the frame, or change the padding around the text
ax.legend(fancybox=True, framealpha=0.8, shadow=True, borderpad=0.5)
fig

Choosing Elements for the Legend¶

As we’ve already seen, the legend includes all labeled elements by default. If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands. The plt.plot() command is able to create multiple lines at once, and returns a list of created line instances. Passing any of these to plt.legend() will tell it which to identify, along with the labels we’d like to specify

y=np.sin(x[:,np.newaxis]+np.pi*np.arange(0,2,0.5))
lines=plt.plot(x,y)

# line is a list of plt.line2D instance
plt.legend(lines[:2],['first','second'])

<matplotlib.legend.Legend at 0x7fba9d7a2c10>

I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you’d like to show on the legend

plt.plot(x,y[:,0],label='first')
plt.plot(x,y[:,1],label='second')
plt.plot(x,y[:,2:])
plt.legend(framealpha=1, frameon=True);

Legend for Size of Points¶

Sometimes the legend defaults are not sufficient for the given visualization. For exam‐ ple, perhaps you’re using the size of points to mark certain features of the data, and want to create a legend reflecting this. Here is an example where we’ll use the size of points to indicate populations of California cities. We’d like a legend that specifies the scale of the sizes of the points, and we’ll accomplish this by plotting some labeled data with no entries

import pandas as pd
cities=pd.read_csv('../data/california_cities.csv')

# extract the data we'er interestd in
lat, lon = cities['latd'], cities['longd']
population, area=cities['population_total'], cities['area_total_km2']

# scatter the points, using size and color but no label
plt.scatter(lon,lat,label=None,
           c=np.log10(population), cmap='viridis',
           s=area,linewidth=0, alpha=0.5)

#plt.axis(aspect='equal')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.colorbar(label='log$_{10}$(population)')
plt.clim(3,7)

# scatter the points, using size and color but no label
plt.scatter(lon,lat,label=None,
           c=np.log10(population), cmap='viridis',
           s=area,linewidth=0, alpha=0.5)

#plt.axis(aspect='equal')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.colorbar(label='log$_{10}$(population)')
plt.clim(3,7)

# here we created a legend:
# we'll plot empty lists with the desired size and label
for area in [100,300,500]:
    plt.scatter([],[],c='k',alpha=0.3,s=area,
               label=str(area)+' km$^2$')
plt.legend(scatterpoints=1, frameon=False,
              title='City Area')
plt.title('California cities: Area and Population')

Text(0.5, 1.0, 'California cities: Area and Population')

Multiple Legends¶

Sometimes when designing a plot you’d like to add multiple legends to the same axes. Unfortunately, Matplotlib does not make this easy: via the standard legend interface, it is only possible to create a single legend for the entire plot. If you try to create a second legend using plt.legend() or ax.legend(), it will simply override the first one. We can work around this by creating a new legend artist from scratch, and then using the lower-level ax.add_artist() method to manually add the second artist to the plot

fig, ax = plt.subplots()
lines = []
styles = ['-', '--', '-.', ':']
x = np.linspace(0, 10, 1000)
for i in range(4):
    lines += ax.plot(x, np.sin(x - i * np.pi / 2),
                     styles[i], color='black')
ax.axis('equal')

# specify the lines and labels of the first legend
ax.legend(lines[:2], ['line A', 'line B'],
loc='upper right', frameon=False)

# Create the second legend and add the artist manually.
from matplotlib.legend import Legend
leg = Legend(ax, lines[2:], ['line C', 'line D'],
loc='lower right', frameon=False)
ax.add_artist(leg);