Remodel boring default Matplotlib line charts into gorgeous, 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 methods to make your visualizations stand out and mirror your particular person model.
We’ll begin with a easy line chart, which is extensively used. The primary spotlight might be including a gradient fill beneath the plot — a job that’s not solely simple.
So, let’s dive in and stroll via all the important thing steps of this transformation!
Let’s make all the mandatory 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 just like what inventory costs appear to be.
dates = pd.date_range(begin='2024-02-01', durations=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 verify the way it appears 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 step by step make it wanting higher.
- set the title
- set normal chart parameters — measurement and font
- putting the Y ticks to the precise
- altering the principle line colour, model and width
# Common parameters
fig, ax = plt.subplots(figsize=(10, 6))
plt.title("Each day guests", fontsize=18, colour="black")
rcParams['font.family'] = 'DejaVu Sans'
rcParams['font.size'] = 14# Axis Y to the precise
ax.yaxis.tick_right()
ax.yaxis.set_label_position("proper")
# Plotting essential line
ax.plot(dates, charges, colour='#268358', linewidth=2)
Alright, now it appears 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(colour="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 colour 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 colour
[t.set_color('#808079') for t in ax.yaxis.get_ticklabels()]
[t.set_color('#808079') for t in ax.xaxis.get_ticklabels()]
And we’re getting nearer to the trickiest second — learn how to create a gradient below the curve. Truly there isn’t any such possibility 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],
facet="auto", clip_path=patch, clip_on=True)
Now it appears clear and good. We simply want so as to add a number of particulars utilizing any editor (I want Google Slides) — title, spherical border corners and a few numeric indicators.
The complete code to breed the visualization is beneath: