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VistasHow to add a shared x-label and y-label to a plot created with pandas plot
One can create subplots easily from a dataframe using pandas:
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'A': [0.3, 0.2, 0.5, 0.2], 'B': [0.1, 0.0, 0.3, 0.1], 'C': [0.2, 0.5, 0.0, 0.7], 'D': [0.6, 0.3, 0.4, 0.6]}, index=list('abcd'))
ax = df.plot(kind="bar", subplots=True, layout=(2, 2), sharey=True, sharex=True, rot=0, fontsize=20)
How would one now add the x- and y-labels to the resulting plot? Here it is explained for a single plot. So if I wanted to add labels to a particular subplot I could do:
ax[1][0].set_xlabel('my_general_xlabel')
ax[0][0].set_ylabel('my_general_ylabel')
plt.show()
That gives:
How would one add the labels so that they are centred and do not just refer to a one row/column?
3 Respuestas
Responde la pregunta0
X and y labels are bound to an axes in matplotlib. So it makes little sense to use xlabel or ylabel commands for the purpose of labeling several subplots.
What is possible though, is to create a simple text and place it at the desired position. fig.text(x,y, text) places some text at coordinates x and y in figure coordinates, i.e. the lower left corner of the figure has coordinates (0,0) the upper right one (1,1).
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'A': [0.3, 0.2, 0.5, 0.2], 'B': [0.1, 0.0, 0.3, 0.1], 'C': [0.2, 0.5, 0.0, 0.7], 'D': [0.6, 0.3, 0.4, 0.6]}, index=list('abcd'))
axes = df.plot(kind="bar", subplots=True, layout=(2,2), sharey=True, sharex=True)
fig=axes[0,0].figure
fig.text(0.5,0.04, "Some very long and even longer xlabel", ha="center", va="center")
fig.text(0.05,0.5, "Some quite extensive ylabel", ha="center", va="center", rotation=90)
plt.show()
The drawback of this solution is that the coordinates of where to place the text need to be set manually and may depend on the figure size.
0
Another solution: create a big subplot and then set the common labels. Here is what I got.
The source code is below.
import pandas as pd
import matplotlib.pyplot as plt
fig = plt.figure()
axarr = fig.add_subplot(221)
df = pd.DataFrame({'A': [0.3, 0.2, 0.5, 0.2], 'B': [0.1, 0.0, 0.3, 0.1], 'C': [0.2, 0.5, 0.0, 0.7], 'D': [0.6, 0.3, 0.4, 0.6]}, index=list('abcd'))
axes = df.plot(kind="bar", ax=axarr, subplots=True, layout=(2, 2), sharey=True, sharex=True, rot=0, fontsize=20)
# Create a big subplot
ax = fig.add_subplot(111, frameon=False)
# hide tick and tick label of the big axes
plt.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
ax.set_xlabel('my_general_xlabel', labelpad=10) # Use argument `labelpad` to move label downwards.
ax.set_ylabel('my_general_ylabel', labelpad=20)
plt.show()
0
This will create an invisible 111 axis where you can set the general x and y labels:
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'A': [0.3, 0.2, 0.5, 0.2], 'B': [0.1, 0.0, 0.3, 0.1],
'C': [0.2, 0.5, 0.0, 0.7], 'D': [0.6, 0.3, 0.4, 0.6]},
index=list('abcd'))
ax = df.plot(kind="bar", subplots=True, layout=(2, 2), sharey=True,
sharex=True, rot=0, fontsize=12)
fig = ax[0][0].get_figure() # getting the figure
ax0 = fig.add_subplot(111, frame_on=False) # creating a single axes
ax0.set_xticks([])
ax0.set_yticks([])
ax0.set_xlabel('my_general_xlabel', labelpad=25)
ax0.set_ylabel('my_general_ylabel', labelpad=45)
# Part of a follow up question: Modifying the fontsize of the titles:
for i,axi in np.ndenumerate(ax):
axi.set_title(axi.get_title(),{'size' : 16})
