index.md

title	Matplotlib Cyberpunk Style
date	2020-03-27 20:26:07 +0100
draft	false
description	Futuristic neon glow for your next data visualization
tags	tutorials matplotlib
displayInList	true
authors	Dominik Haitz
resources	name src params featuredImage figures/5.png showOnTop

1 - The Basis

Let's make up some numbers, put them in a Pandas dataframe and plot them:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'A': [1, 3, 9, 5, 2, 1, 1],
                   'B': [4, 5, 5, 7, 9, 8, 6]})

df.plot(marker='o')
plt.show()

2 - The Darkness

Not bad, but somewhat ordinary. Let's customize it by using Seaborn's dark style, as well as changing background and font colors:

plt.style.use("seaborn-dark")

for param in ['figure.facecolor', 'axes.facecolor', 'savefig.facecolor']:
    plt.rcParams[param] = '#212946'  # bluish dark grey

for param in ['text.color', 'axes.labelcolor', 'xtick.color', 'ytick.color']:
    plt.rcParams[param] = '0.9'  # very light grey

ax.grid(color='#2A3459')  # bluish dark grey, but slightly lighter than background

3 - The Light

It looks more interesting now, but we need our colors to shine more against the dark background:

fig, ax = plt.subplots()
colors = [
    '#08F7FE',  # teal/cyan
    '#FE53BB',  # pink
    '#F5D300',  # yellow
    '#00ff41', # matrix green
]
df.plot(marker='o', ax=ax, color=colors)

4 - The Glow

Now, how to get that neon look? To make it shine, we redraw the lines multiple times, with low alpha value and slightly increasing linewidth. The overlap creates the glow effect.

n_lines = 10
diff_linewidth = 1.05
alpha_value = 0.03

for n in range(1, n_lines+1):

    df.plot(marker='o',
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            legend=False,
            ax=ax,
            color=colors)

5 - The Finish

For some more fine tuning, we color the area below the line (via ax.fill_between) and adjust the axis limits.

Here's the full code:

import pandas as pd
import matplotlib.pyplot as plt


plt.style.use("dark_background")

for param in ['text.color', 'axes.labelcolor', 'xtick.color', 'ytick.color']:
    plt.rcParams[param] = '0.9'  # very light grey

for param in ['figure.facecolor', 'axes.facecolor', 'savefig.facecolor']:
    plt.rcParams[param] = '#212946'  # bluish dark grey

colors = [
    '#08F7FE',  # teal/cyan
    '#FE53BB',  # pink
    '#F5D300',  # yellow
    '#00ff41',  # matrix green
]


df = pd.DataFrame({'A': [1, 3, 9, 5, 2, 1, 1],
                   'B': [4, 5, 5, 7, 9, 8, 6]})

fig, ax = plt.subplots()

df.plot(marker='o', color=colors, ax=ax)

# Redraw the data with low alpha and slightly increased linewidth:
n_shades = 10
diff_linewidth = 1.05
alpha_value = 0.3 / n_shades

for n in range(1, n_shades+1):

    df.plot(marker='o',
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            legend=False,
            ax=ax,
            color=colors)

# Color the areas below the lines:
for column, color in zip(df, colors):
    ax.fill_between(x=df.index,
                    y1=df[column].values,
                    y2=[0] * len(df),
                    color=color,
                    alpha=0.1)

ax.grid(color='#2A3459')

ax.set_xlim([ax.get_xlim()[0] - 0.2, ax.get_xlim()[1] + 0.2])  # to not have the markers cut off
ax.set_ylim(0)

plt.show()

If this helps you or if you have constructive criticism, I'd be happy to hear about it! Please contact me via here or here. Thanks!