{"id":895,"date":"2024-12-31T07:03:38","date_gmt":"2024-12-31T07:03:38","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2024\/12\/31\/from-default-python-line-chart-to-journal-quality-infographics-80e3949eacc3\/"},"modified":"2024-12-31T07:03:38","modified_gmt":"2024-12-31T07:03:38","slug":"from-default-python-line-chart-to-journal-quality-infographics-80e3949eacc3","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2024\/12\/31\/from-default-python-line-chart-to-journal-quality-infographics-80e3949eacc3\/","title":{"rendered":"From Default Python Line Chart to Journal-Quality Infographics"},"content":{"rendered":"

From Default Python Line Chart to Journal-Quality Infographics
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Transform boring default Matplotlib line charts into stunning, customized visualizations<\/h4>\n
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Cover, image by the\u00a0Author<\/figcaption><\/figure>\n

Everyone who has used Matplotlib knows how ugly the default charts look like. In this series of posts, I\u2019ll share some tricks to make your visualizations stand out and reflect your individual style.<\/p>\n

We\u2019ll start with a simple line chart, which is widely used. The main highlight will be adding a gradient fill below the plot\u200a\u2014\u200aa task that\u2019s not entirely straightforward.<\/p>\n

So, let\u2019s dive in and walk through all the key steps of this transformation!<\/p>\n

Let\u2019s make all the necessary imports\u00a0first.<\/p>\n

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 PathPatch

np.random.seed(38)<\/pre>\n
Now we need to generate sample data for our visualization. We will create something similar to what stock prices look\u00a0like.<\/p>\n
dates = pd.date_range(start='2024-02-01', periods=100, freq='D')
initial_rate = 75
drift = 0.003
volatility = 0.1
returns = np.random.normal(drift, volatility, len(dates))
rates = initial_rate * np.cumprod(1 + returns)

x, y = dates, rates<\/pre>\n
Let\u2019s check how it looks with the default Matplotlib settings.<\/p>\n
fix, ax = plt.subplots(figsize=(8, 4))
ax.plot(dates, rates)
ax.xaxis.set_major_locator(mdates.DayLocator(interval=30))
plt.show()<\/pre>\n
\"\"
Default plot, image by\u00a0Author<\/figcaption><\/figure>\n
Not really fascination, right? But we will gradually make it looking\u00a0better.<\/p>\n
    \n
  • set the\u00a0title<\/li>\n
  • set general chart parameters\u200a\u2014\u200asize and\u00a0font<\/li>\n
  • placing the Y ticks to the\u00a0right<\/li>\n
  • changing the main line color, style and\u00a0width<\/li>\n<\/ul>\n
    # General parameters
    fig, ax = plt.subplots(figsize=(10, 6))
    plt.title(\"Daily visitors\", fontsize=18, color=\"black\")
    rcParams['font.family'] = 'DejaVu Sans'
    rcParams['font.size'] = 14

    # Axis Y to the right
    ax.yaxis.tick_right()
    ax.yaxis.set_label_position(\"right\")

    # Plotting main line
    ax.plot(dates, rates, color='#268358', linewidth=2)<\/pre>\n
    \"\"
    General params applied, image by\u00a0Author<\/figcaption><\/figure>\n
    Alright, now it looks a bit\u00a0cleaner.<\/p>\n
    Now we\u2019d like to add minimalistic grid to the background, remove borders for a cleaner look and remove ticks from the Y\u00a0axis.<\/p>\n
    # Grid
    ax.grid(color=\"gray\", linestyle=(0, (10, 10)), linewidth=0.5, alpha=0.6)
    ax.tick_params(axis=\"x\", colors=\"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)

    # Remove ticks from axis Y
    ax.tick_params(axis='y', length=0)<\/pre>\n
    \"\"
    Grid added, image by\u00a0Author<\/figcaption><\/figure>\n
    Now we\u2019re adding a tine esthetic detail\u200a\u2014\u200ayear near the first tick on the axis X. Also we make the font color of tick labels more\u00a0pale.<\/p>\n
    # Add year to the first 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 color
    [t.set_color('#808079') for t in ax.yaxis.get_ticklabels()]
    [t.set_color('#808079') for t in ax.xaxis.get_ticklabels()]<\/pre>\n
    \"\"
    Year near first date, image by\u00a0Author<\/figcaption><\/figure>\n
    And we\u2019re getting closer to the trickiest moment\u200a\u2014\u200ahow to create a gradient under the curve. Actually there is no such option in Matplotlib, but we can simulate it creating a gradient image and then clipping it with the\u00a0chart.<\/p>\n
    # 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='lower',
                    alpha=0.3,
                    extent=[min(numeric_x), max(numeric_x), min(y_patch), max(y_patch) * 1.2], 
                    aspect=\"auto\", clip_path=patch, clip_on=True)<\/pre>\n
    \"\"
    Gradient added, image by\u00a0Author<\/figcaption><\/figure>\n
    Now it looks clean and nice. We just need to add several details using any editor (I prefer Google Slides)\u200a\u2014\u200atitle, round border corners and some numeric indicators.<\/p>\n
    \"\"
    Final visualization, image by\u00a0Author<\/figcaption><\/figure>\n
    The full code to reproduce the visualization is\u00a0below:<\/p>\n