Getting Around Overlapping Data Labels With Python

[intapiuser]

A common hazard when adding data labels to a chart, is that often times the data labels will overlap when there are two points located relatively close to one another. Fortunately, the flexibility of python allows us a way around overlapping data labels. 

Here is my original chart as a Sisense for Cloud Data Teams scatter plot with data labels applied,

We can see the the data labels overlap in some areas making it hard to read and visually unappealing. 

Instead we can create a Python chart and write a custom function that checks the location of the data-labels and adjusts any that overlap. 

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

# source: https://stackoverflow.com/questions/8850142/matplotlib-overlapping-annotations

def get_text_positions(x_data, y_data, txt_width, txt_height):
    a = zip(y_data, x_data)
    text_positions = y_data.copy()
    for index, (y, x) in enumerate(a):
        local_text_positions = [i for i in a if i[0] > (y - txt_height)
                            and (abs(i[1] - x) < txt_width * 2) and i != (y,x)]
        if local_text_positions:
            sorted_ltp = sorted(local_text_positions)
            if abs(sorted_ltp[0][0] - y) < txt_height: #True == collision
                differ = np.diff(sorted_ltp, axis=0)
                a[index] = (sorted_ltp[-1][0] + txt_height, a[index][1])
                text_positions[index] = sorted_ltp[-1][0] + txt_height
                for k, (j, m) in enumerate(differ):
                    #j is the vertical distance between words
                    if j > txt_height * 1.5: #if True then room to fit a word in
                        a[index] = (sorted_ltp[k][0] + txt_height, a[index][1])
                        text_positions[index] = sorted_ltp[k][0] + txt_height
                        break
    return text_positions


def text_plotter(x_data, y_data, text_positions, axis,txt_width,txt_height):
    for x,y,t in zip(x_data, y_data, text_positions):
        axis.text(x - .03, 1.02*t, '%d'%int(y),rotation=0, color='blue', fontsize=13)
        if y != t:
            axis.arrow(x, t+20,0,y-t, color='blue',alpha=0.2, width=txt_width*0.0,
                       head_width=.02, head_length=txt_height*0.5,
                       zorder=0,length_includes_head=True)

x_data = df['avg']
y_data = df['count']


fig = plt.figure(figsize=(15,8))
ax = fig.add_subplot(111)
ax.scatter(x_data, y_data, alpha = 0.4)
txt_height = 0.04*(plt.ylim()[1] - plt.ylim()[0])
txt_width = 0.02*(plt.xlim()[1] - plt.xlim()[0])
text_positions = get_text_positions(x_data, y_data, txt_width, txt_height)
text_plotter(x_data, y_data, text_positions, ax, txt_width, txt_height)

plt.ylim(0,3610)
plt.xlim(4.3,6.5)


periscope.output(plt)

Here is our new python scatter plot: