From Default Python Line Chart to Journal-High quality Infographics | by Vladimir Zhyvov

Remodel boring default Matplotlib line charts into beautiful, custom-made visualizations

Everybody who has used Matplotlib is aware of how ugly the default charts appear to be. On this collection of posts, I’ll share some tips to make your visualizations stand out and mirror your particular person fashion.

We’ll begin with a easy line chart, which is extensively used. The primary spotlight will likely be including a gradient fill beneath the plot — a process that’s not fully simple.

So, let’s dive in and stroll by means of all the important thing steps of this transformation!

Let’s make all the required imports first.

import pandas as pd
import numpy as np
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib import rcParams
from matplotlib.path import Path
from matplotlib.patches import PathPatchnp.random.seed(38)

Now we have to generate pattern information for our visualization. We’ll create one thing much like what inventory costs appear to be.

dates = pd.date_range(begin='2024-02-01', intervals=100, freq='D')
initial_rate = 75
drift = 0.003
volatility = 0.1
returns = np.random.regular(drift, volatility, len(dates))
charges = initial_rate * np.cumprod(1 + returns)x, y = dates, charges

Let’s test the way it seems with the default Matplotlib settings.

repair, ax = plt.subplots(figsize=(8, 4))
ax.plot(dates, charges)
ax.xaxis.set_major_locator(mdates.DayLocator(interval=30))
plt.present()

Probably not fascination, proper? However we’ll regularly make it trying higher.

set the title
set common chart parameters — measurement and font
inserting the Y ticks to the fitting
altering the primary line shade, fashion and width

# Normal parameters
fig, ax = plt.subplots(figsize=(10, 6))
plt.title("Every day guests", fontsize=18, shade="black")
rcParams['font.family'] = 'DejaVu Sans'
rcParams['font.size'] = 14# Axis Y to the fitting
ax.yaxis.tick_right()
ax.yaxis.set_label_position("proper")
# Plotting most important line
ax.plot(dates, charges, shade='#268358', linewidth=2)

Normal params utilized, picture by Creator

Alright, now it seems a bit cleaner.

Now we’d like so as to add minimalistic grid to the background, take away borders for a cleaner look and take away ticks from the Y axis.

# Grid
ax.grid(shade="grey", linestyle=(0, (10, 10)), linewidth=0.5, alpha=0.6)
ax.tick_params(axis="x", colours="black")
ax.tick_params(axis="y", left=False, labelleft=False) # Borders
ax.spines["top"].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines["bottom"].set_color("black")
ax.spines['left'].set_color('white')
ax.spines['left'].set_linewidth(1)
# Take away ticks from axis Y
ax.tick_params(axis='y', size=0)

Now we’re including a tine esthetic element — 12 months close to the primary tick on the axis X. Additionally we make the font shade of tick labels extra pale.

# Add 12 months to the primary date on the axis
def custom_date_formatter(t, pos, dates, x_interval):
date = dates[pos*x_interval]
if pos == 0:
return date.strftime('%d %b '%y')  
else:
return date.strftime('%d %b')  
ax.xaxis.set_major_formatter(ticker.FuncFormatter((lambda x, pos: custom_date_formatter(x, pos, dates=dates, x_interval=x_interval))))# Ticks label shade
[t.set_color('#808079') for t in ax.yaxis.get_ticklabels()]
[t.set_color('#808079') for t in ax.xaxis.get_ticklabels()]

12 months close to first date, picture by Creator

And we’re getting nearer to the trickiest second — learn how to create a gradient underneath the curve. Really there isn’t a such choice in Matplotlib, however we are able to simulate it making a gradient picture after which clipping it with the chart.

# Gradient
numeric_x = np.array([i for i in range(len(x))])
numeric_x_patch = np.append(numeric_x, max(numeric_x))
numeric_x_patch = np.append(numeric_x_patch[0], numeric_x_patch)
y_patch = np.append(y, 0)
y_patch = np.append(0, y_patch)path = Path(np.array([numeric_x_patch, y_patch]).transpose())
patch = PathPatch(path, facecolor='none')
plt.gca().add_patch(patch)
ax.imshow(numeric_x.reshape(len(numeric_x), 1),  interpolation="bicubic",
cmap=plt.cm.Greens, 
origin='decrease',
alpha=0.3,
extent=[min(numeric_x), max(numeric_x), min(y_patch), max(y_patch) * 1.2], 
side="auto", clip_path=patch, clip_on=True)

Now it seems clear and good. We simply want so as to add a number of particulars utilizing any editor (I choose Google Slides) — title, spherical border corners and a few numeric indicators.