How to avoid overlapping of labels & autopct in a pie chart

Question

My Python code is:

values = [234, 64, 54,10, 0, 1, 0, 9, 2, 1, 7, 7]
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
          'Jul','Aug','Sep','Oct', 'Nov','Dec']

colors = ['yellowgreen', 'red', 'gold', 'lightskyblue', 
          'white','lightcoral','blue','pink', 'darkgreen', 
          'yellow','grey','violet','magenta','cyan']

plt.pie(values, labels=labels, autopct='%1.1f%%', shadow=True, 
        colors=colors, startangle=90, radius=1.2)

plt.show()

Is it possible to show the labels "Jan", "Feb", "Mar", etc. and the percentages, either:

• without overlapping, or
• using an arrow mark?

Answer 1

Alternatively you can put the legends beside the pie graph:

import matplotlib.pyplot as plt
import numpy as np

x = np.char.array(['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct', 'Nov','Dec'])
y = np.array([234, 64, 54,10, 0, 1, 0, 9, 2, 1, 7, 7])
colors = ['yellowgreen','red','gold','lightskyblue','white','lightcoral','blue','pink', 'darkgreen','yellow','grey','violet','magenta','cyan']
porcent = 100.*y/y.sum()

patches, texts = plt.pie(y, colors=colors, startangle=90, radius=1.2)
labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(x, porcent)]

sort_legend = True
if sort_legend:
    patches, labels, dummy =  zip(*sorted(zip(patches, labels, y),
                                          key=lambda x: x[2],
                                          reverse=True))

plt.legend(patches, labels, loc='left center', bbox_to_anchor=(-0.1, 1.),
           fontsize=8)

plt.savefig('piechart.png', bbox_inches='tight')

---

EDIT: if you want to keep the legend in the original order, as you mentioned in the comments, you can set sort_legend=False in the code above, giving:

Answer 2

If anyone just wants to offset the labels automatically, and not use a legend, I wrote this function that does it (yup I'm a real try-hard). It uses numpy but could easily be re-written in pure python.

import numpy as np

def fix_labels(mylabels, tooclose=0.1, sepfactor=2):
    vecs = np.zeros((len(mylabels), len(mylabels), 2))
    dists = np.zeros((len(mylabels), len(mylabels)))
    for i in range(0, len(mylabels)-1):
        for j in range(i+1, len(mylabels)):
            a = np.array(mylabels[i].get_position())
            b = np.array(mylabels[j].get_position())
            dists[i,j] = np.linalg.norm(a-b)
            vecs[i,j,:] = a-b
            if dists[i,j] < tooclose:
                mylabels[i].set_x(a[0] + sepfactor*vecs[i,j,0])
                mylabels[i].set_y(a[1] + sepfactor*vecs[i,j,1])
                mylabels[j].set_x(b[0] - sepfactor*vecs[i,j,0])
                mylabels[j].set_y(b[1] - sepfactor*vecs[i,j,1])

So use it like:

wedges, labels, autopct = ax1.pie(sizes, labels=groups, autopct='%1.1f%%',
                                  shadow=False, startangle=90)

fix_labels(autopct, sepfactor=3)
fix_labels(labels, sepfactor=2)

This works well as-written if you only have a few labels overlapping. If you have a whole bunch like OP, you might want to add a random direction vector to the vecs[i,j,:] = a-b line. That would probably work well.

Answer 3

Try tightlayout.

plt.tight_layout()

at the end of your code. It may prevent the overlap a little bit.

Answer 4

First of all; avoid pie charts whenever you can!

Secondly, have a think about how objects work in python. I believe this example should be self-explaining, however, you obviously don't need to move labels manually.

from matplotlib import pyplot as plt

fig, ax = plt.subplots()
ax.axis('equal')

patches, texts, autotexts = ax.pie([12,6,2,3], 
                              labels=['A', 'B', 'C', 'no data'],
                              autopct='%1.1f%%', 
                              pctdistance=0.5, 
                              labeldistance=1.1)



# Move a label
texts[1]._x =-0.5
texts[1]._y =+0.5

# E.g. change some formatting
texts[-1]._color = 'blue'

There are some options to modify the labels:

# Check all options
print(texts[0].__dict__)

returns

{'_stale': False,
 'stale_callback': <function matplotlib.artist._stale_axes_callback(self, val)>,
 '_axes': <AxesSubplot:>,
 'figure': <Figure size 432x288 with 1 Axes>,
 '_transform': <matplotlib.transforms.CompositeGenericTransform at 0x7fe09bedf210>,
 '_transformSet': True,
 '_visible': True,
 '_animated': False,
 '_alpha': None,
 'clipbox': <matplotlib.transforms.TransformedBbox at 0x7fe065d3dd50>,
 '_clippath': None,
 '_clipon': False,
 '_label': '',
 '_picker': None,
 '_contains': None,
 '_rasterized': None,
 '_agg_filter': None,
 '_mouseover': False,
 'eventson': False,
 '_oid': 0,
 '_propobservers': {},
 '_remove_method': <function list.remove(value, /)>,
 '_url': None,
 '_gid': None,
 '_snap': None,
 '_sketch': None,
 '_path_effects': [],
 '_sticky_edges': _XYPair(x=[], y=[]),
 '_in_layout': True,
 '_x': -0.07506663683168735,
 '_y': 1.097435647331897,
 '_text': 'A',
 '_color': 'black',
 '_fontproperties': <matplotlib.font_manager.FontProperties at 0x7fe065d3db90>,
 '_usetex': False,
 '_wrap': False,
 '_verticalalignment': 'center',
 '_horizontalalignment': 'right',
 '_multialignment': None,
 '_rotation': 'horizontal',
 '_bbox_patch': None,
 '_renderer': <matplotlib.backends.backend_agg.RendererAgg at 0x7fe08b01fd90>,
 '_linespacing': 1.2,
 '_rotation_mode': None}