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Publication-quality data visualization with matplotlib, seaborn, and plotly
Passed 19 of 22 test cases with the skill, and 17 of 22 without it.
2 fixed17 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am building a grouped bar chart with Seaborn comparing BLEU and ROUGE metrics across three model variants (Baseline, Ours, Ours+FT). I want custom slate gray, blue, and red hex colors ('#94A3B8', '#3B82F6', '#EF4444'), a legend titled 'Method', and no frame border around the legend. Write the Seaborn barplot code snippet.
| Checked | Without skill | With skill |
|---|---|---|
| The barplot code sets palette to ['#94A3B8', '#3B82F6', '#EF4444']. | Missed | Passed |
| The legend function sets frameon to False. | Passed | Passed |
The instructions your model reads when the skill loads.
Data visualization is how researchers communicate quantitative findings. A well-designed figure can convey complex relationships instantly, while a poor one buries the signal in clutter. Python's visualization ecosystem -- anchored by matplotlib, seaborn, and plotly -- provides everything needed to produce publication-quality figures for journals, conferences, and presentations.
This guide covers the three major Python visualization libraries, their strengths and trade-offs, and concrete recipes for the chart types researchers use most frequently. Each example is designed to be copy-paste ready and customizable for your specific dataset and venue requirements.
The emphasis is on producing figures that meet journal standards: correct DPI, appropriate font sizes, accessible color palettes, and vector-format exports. We also cover interactive visualization with plotly for exploratory analysis and supplementary materials.
Matplotlib is the most flexible Python plotting library. Nearly every other visualization tool in the Python ecosystem builds on it.
import matplotlib.pyplot as plt
import matplotlib as mpl
# Publication-quality defaults
plt.rcParams.update({
'figure.figsize': (6, 4),
'figure.dpi': 150,
'savefig.dpi': 300,
'savefig.bbox': 'tight',
'font.size': 11,
'font.family': 'serif',
'font.serif': ['Times New Roman'],
'axes.labelsize': 12,
'axes.titlesize': 13,
'xtick.labelsize': 10,
'ytick.labelsize': 10,
'legend.fontsize': 10,
'lines.linewidth': 1.5,
'lines.markersize': 6,
'axes.grid': True,
'grid.alpha': 0.3,
})
test cases passed, vs 17 / 22 without
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