Why set_ylim(bottom=0) Seems Ignored in Matplotlib Bar Charts in Shiny for Python—and How to Fix It

Matplotlib bar charts in Shiny for Python: when set_ylim(bottom=0) won’t anchor the y-axis. Learn why mixed arrays break limits—and how to fix, step-by-step.

Getting a Matplotlib bar chart inside Shiny for Python to anchor the y-axis at zero seems straightforward: call set_ylim(bottom=0). Yet sometimes the axis stubbornly starts at the first data point, like 4.1, and ignores your limits. When that happens, it usually signals that the plot isn’t receiving the data in the shape you think it is.

Problem: y-axis won’t start at zero

Below is a minimal Shiny Express example that exhibits the issue. The chart renders, but the y-axis starts around 4.1, and set_ylim(bottom=0) has no visible effect.

from shiny.express import input, render, ui
import matplotlib.pyplot as plt
import numpy as np

series_map = {
    "Maturity": ["1Y", "2Y", "3Y", "4Y", "5Y", "6Y", "7Y", "8Y"],
    "Yield": [4.1, 4.3, 4.5, 4.7, 4.8, 4.9, 5.0, 5.1],
}
bundle = np.array([series_map["Maturity"], series_map["Yield"]])
print(bundle[1])

def make_bars():
    x_vals = bundle[0]
    y_vals = bundle[1]
    fig, ax = plt.subplots()
    ax.bar(x_vals, y_vals)
    ax.set_xlabel("Maturity")
    ax.yaxis.set_label_text("Yield (%)")
    ax.set_ylim(bottom=0)
    return fig

@render.plot
def show_chart():
    return make_bars()

Why set_ylim looks ignored

The axis call itself is fine. The unexpected behavior stems from how the dataset is processed before plotting. By transforming the original structure into a single array that mixes labels and numeric values, the plotting step no longer sees a plain numeric series on the y-axis. That change affects how the axis is computed and makes the y-limit instruction ineffective.

Fix: avoid the array conversion

Keep the data structure simple and pass the x labels and numeric y values directly to Matplotlib. In practice, avoiding the array conversion resolves the axis behavior.

from shiny.express import render
import matplotlib.pyplot as plt

curve = {
    "1Y": 4.1, "2Y": 4.3, "3Y": 4.5, "4Y": 4.7,
    "5Y": 4.8, "6Y": 4.9, "7Y": 5.0, "8Y": 5.1,
}

@render.plot
def draw_bars():
    cats = list(curve.keys())
    vals = list(curve.values())
    fig, ax = plt.subplots()
    ax.bar(cats, vals)
    ax.set_xlabel("Maturity")
    ax.yaxis.set_label_text("Yield (%)")
    ax.set_ylim(bottom=0)
    return fig

Why this matters

In reactive dashboards, small data-shaping steps can quietly change how plotting libraries interpret inputs. Once the structure ceases to be a straightforward pair of label and numeric sequences, axis operations may stop working as expected. Keeping the pipeline predictable is the simplest way to ensure chart controls—like y-axis limits—consistently apply.

Takeaways

If set_ylim(bottom=0) doesn’t move the baseline to zero in a Shiny for Python + Matplotlib bar chart, look at the data path. Feed Matplotlib plain lists of category labels for x and numeric values for y, and avoid bundling them into a single array. That small change restores the intended axis behavior and keeps the chart honest.