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

Remodel boring default Matplotlib line charts into beautiful, personalized visualizations

Everybody who has used Matplotlib is aware of how ugly the default charts seem like. On this sequence of posts, I’ll share some tips to make your visualizations stand out and replicate your particular person type.

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

So, let’s dive in and stroll by way 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 are going to create one thing much like what inventory costs seem like.

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 examine the way it appears to be like 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 are going to step by step make it wanting higher.

set the title
set common chart parameters — measurement and font
putting the Y ticks to the fitting
altering the principle line shade, type and width

# Common parameters
fig, ax = plt.subplots(figsize=(10, 6))
plt.title("Each 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 foremost line
ax.plot(dates, charges, shade='#268358', linewidth=2)

Common params utilized, picture by Writer

Alright, now it appears to be like 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 — yr close to the primary tick on the axis X. Additionally we make the font shade of tick labels extra pale.

# Add yr 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 Writer

And we’re getting nearer to the trickiest second — create a gradient below the curve. Really there isn’t a such possibility in Matplotlib, however we will 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], 
facet="auto", clip_path=patch, clip_on=True)

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