Fix heatmap x-axis label alignment in Python in Excel: right-align rotated week labels in Seaborn/Matplotlib

Learn why heatmap x-axis labels look shifted in Python in Excel and how to fix them: right-align rotated ticks in Seaborn/Matplotlib for clear, accurate charts.

Heatmaps are great for scanning patterns across names and weeks, but sometimes axis labels don’t cooperate. In a Python-in-Excel setup, you might expect Week1, Week2, and so on on the x-axis, yet see numbers or labels that look shifted. The good news: when your data and labels are set up correctly, this is often just a visual alignment quirk rather than a data or plotting error.

Example setup

Consider a heatmap built from a range that includes a first column with names and subsequent columns for weeks. The goal is to plot week columns across the x-axis and names on the y-axis.

import pandas as pnd
import matplotlib.pyplot as mplt
import seaborn as sbrn
# Read the Excel range (names in the first column, weeks across)
data_block = xl("W142:CH166")
# Use the first column as the index (name labels)
data_block.set_index(data_block.columns[0], inplace=True)
# Coerce cells to numeric values
data_block = data_block.apply(pnd.to_numeric, errors='coerce')
# Remove empty rows and rows that are all zeros
data_block = data_block.dropna(how='all')
data_block = data_block[(data_block != 0).any(axis=1)]
# Ensure column labels are strings for plotting
data_block.columns = data_block.columns.astype(str)
# Build the heatmap
mplt.figure(figsize=(50, 5), dpi=100)
sbrn.heatmap(data_block, cmap='Blues', annot=False, linewidths=0.5, linecolor='white')
# Set x-axis labels
mplt.xticks(ticks=range(len(data_block.columns)), labels=data_block.columns, rotation=45)
mplt.yticks(rotation=0)
mplt.title('Heat Map of Wine Sales Over Time')
mplt.show()

What’s actually going on

The axis isn’t misaligned and your weeks aren’t missing. The labels are there, but when text is centered on each tick, the eye can read them as if they belong to the next grid cell. That “shifted” feel creates the impression the axis shows numbers or the wrong labels, even though the data and ticks are in place.

The fix

Make the x-axis labels right-aligned. Rotated labels that are right-aligned sit visually under their intended grid cells, removing the illusion of an offset. No other structural changes are needed.

import pandas as pnd
import matplotlib.pyplot as mplt
import seaborn as sbrn
data_block = xl("W142:CH166")
data_block.set_index(data_block.columns[0], inplace=True)
data_block = data_block.apply(pnd.to_numeric, errors='coerce')
data_block = data_block.dropna(how='all')
data_block = data_block[(data_block != 0).any(axis=1)]
data_block.columns = data_block.columns.astype(str)
mplt.figure(figsize=(50, 5), dpi=100)
sbrn.heatmap(data_block, cmap='Blues', annot=False, linewidths=0.5, linecolor='white')
# Align labels to the right to remove the visual offset
mplt.xticks(ticks=range(len(data_block.columns)), labels=data_block.columns, rotation=45, ha='right')
mplt.yticks(rotation=0)
mplt.title('Heat Map of Wine Sales Over Time')
mplt.show()

Why this matters

Data visualizations are only as useful as they are readable. When labels look off, trust in the chart drops, even if the underlying data is correct. In compact views like heatmaps, small formatting details such as tick label alignment and rotation make the difference between a quick insight and confusion. Keeping the pipeline stable—indexing by the first column, coercing to numeric, dropping empty rows, and using string column names—helps ensure the plot logic stays consistent while you refine the presentation.

Conclusion

If your heatmap’s x-axis appears to show numbers instead of week names, check the label alignment before changing your data or plot structure. Adjusting horizontal alignment to right on rotated ticks restores the expected visual mapping between labels and cells. Keep the rest of the transformation steps intact and focus on the label placement to get a clear, trustworthy view of weekly patterns.