Python Data Visualisation for Business Analyst – How to plot Diverging Lollipop Chart with Markers in Python

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Python Data Visualisation for Business Analyst – How to plot Diverging Lollipop Chart with Markers in Python

In this data visualisation tutorial, you will learn How to plot Diverging Lollipop Chart with Markers in Python.

Lollipop with markers provides a flexible way of visualizing the divergence by laying emphasis on any significant datapoints you want to bring attention to and give reasoning within the chart appropriately.

 

Setup

Run this once before the plot’s code. The individual charts, however, may redefine its own aesthetics.

/* !pip install brewer2mpl */
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings; warnings.filterwarnings(action='once')

large = 22; med = 16; small = 12
params = {'axes.titlesize': large,
          'legend.fontsize': med,
          'figure.figsize': (16, 10),
          'axes.labelsize': med,
          'axes.titlesize': med,
          'xtick.labelsize': med,
          'ytick.labelsize': med,
          'figure.titlesize': large}
plt.rcParams.update(params)
plt.style.use('seaborn-whitegrid')
sns.set_style("white")
%matplotlib inline

/* Version */
print(mpl.__version__)  
print(sns.__version__)
Data: mtcars

 

How to plot Diverging Lollipop Chart with Markers in Python

Download Code:

/* Prepare Data */
df = pd.read_csv("mtcars.csv")
x = df.loc[:, ['mpg']]
df['mpg_z'] = (x - x.mean())/x.std()
df['colors'] = 'black'

/* color fiat differently */
df.loc[df.cars == 'Fiat X1-9', 'colors'] = 'darkorange'
df.sort_values('mpg_z', inplace=True)
df.reset_index(inplace=True)


/* Draw plot */
import matplotlib.patches as patches

plt.figure(figsize=(14,16), dpi= 80)
plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z, color=df.colors, alpha=0.4, linewidth=1)
plt.scatter(df.mpg_z, df.index, color=df.colors, s=[600 if x == 'Fiat X1-9' else 300 for x in df.cars], alpha=0.6)
plt.yticks(df.index, df.cars)
plt.xticks(fontsize=12)

/* Annotate */
plt.annotate('Mercedes Models', xy=(0.0, 11.0), xytext=(1.0, 11), xycoords='data', 
            fontsize=15, ha='center', va='center',
            bbox=dict(boxstyle='square', fc='firebrick'),
            arrowprops=dict(arrowstyle='-[, widthB=2.0, lengthB=1.5', lw=2.0, color='steelblue'), color='white')

/* Add Patches */
p1 = patches.Rectangle((-2.0, -1), width=.3, height=3, alpha=.2, facecolor='red')
p2 = patches.Rectangle((1.5, 27), width=.8, height=5, alpha=.2, facecolor='green')
plt.gca().add_patch(p1)
plt.gca().add_patch(p2)

/* Decorate */
plt.title('Diverging Bars of Car Mileage', fontdict={'size':20})
plt.grid(linestyle='--', alpha=0.5)
plt.show()

 

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