Data Viz

Create publication-quality data visualizations from tabular data files. Uses qsv to profile and prepare data, then generates Python charts with best practices for clarity, accuracy, and design.

Cowork note: If relative paths don't resolve, call

mcp__qsv__qsv_get_working_dir

and

mcp__qsv__qsv_set_working_dir

to sync the working directory.

Steps

1. Understand the Request

Determine:

• Data source: CSV/TSV/Excel file, or query results from a prior analysis
• Chart type: Explicitly requested or needs to be recommended
• Purpose: Exploration, presentation, report, dashboard component
• Audience: Technical team, executives, external stakeholders

2. Profile the Data with qsv

a. Index and detect: Run

mcp__qsv__qsv_index

mcp__qsv__qsv_sniff

b. Understand structure: Run

mcp__qsv__qsv_headers

mcp__qsv__qsv_count

c. Profile columns: Run

mcp__qsv__qsv_stats

cardinality: true, stats_jsonl: true

.stats.csv

• type

  → choose appropriate axis type (numeric, categorical, date)

• min

  /

  max

  → set axis ranges

• cardinality

  → determine if column is categorical (low) or continuous (high)

• nullcount

  → note missing data that could affect the chart

d. Check distributions: Run

mcp__qsv__qsv_frequency

limit: 20

e. Run moarstats for visualization hints: Run

mcp__qsv__qsv_moarstats

advanced: true

.stats.csv

skewness

pearson_skewness

bimodality_coefficient

kurtosis

outliers_percentage

q1

q3

iqr

q1 - 1.5*iqr

q3 + 1.5*iqr

cv

sparsity

mode

mode_count

f. Preview data: Run

mcp__qsv__qsv_slice

len: 5

3. Prepare the Data

Use qsv to prepare visualization-ready data:

• Filter:

  mcp__qsv__qsv_search

  or

  mcp__qsv__qsv_sqlp

  to subset rows
• Aggregate:

  mcp__qsv__qsv_sqlp

  for GROUP BY, window functions, computed columns
• Select columns:

  mcp__qsv__qsv_select

  to keep only what's needed
• Sort:

  mcp__qsv__qsv_sqlp

  with ORDER BY for ordered categories or time series

Export the prepared data to a CSV file for Python to read.

4. Select Chart Type

If the user didn't specify, recommend based on data and question:

stats

frequency

cardinality

frequency

stats

moarstats

stats

frequency

--limit

cardinality

5. Generate the Visualization

Write Python code using matplotlib + seaborn (default) or plotly (if interactive requested):

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# Load the prepared CSV
df = pd.read_csv('prepared_data.csv')

# Set professional style
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")

# Create figure with appropriate size
fig, ax = plt.subplots(figsize=(10, 6))

# [chart-specific code]

# Always include:
ax.set_title('Clear, Descriptive Title', fontsize=14, fontweight='bold')
ax.set_xlabel('X-Axis Label', fontsize=11)
ax.set_ylabel('Y-Axis Label', fontsize=11)

# Format numbers appropriately
# - Percentages: '45.2%' not '0.452'
# - Currency: '$1.2M' not '1200000'
# - Large numbers: '2.3K' or '1.5M' not '2300' or '1500000'

# Remove chart junk
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.savefig('chart_name.png', dpi=150, bbox_inches='tight')
plt.show()

6. Apply Design Best Practices

Color:

• Use a consistent, colorblind-friendly palette
• Highlight the key data point or trend with a contrasting color
• Grey out less important reference data

Typography:

• Descriptive title that states the insight, not just the metric (e.g., "Revenue grew 23% YoY" not "Revenue by Month")
• Readable axis labels (not rotated 90 degrees if avoidable)
• Data labels on key points when they add clarity

Layout:

• Appropriate whitespace and margins
• Legend placement that doesn't obscure data
• Sort categories by value (not alphabetically) unless there's a natural order

Accuracy:

• Y-axis starts at zero for bar charts
• No misleading axis breaks without clear notation
• Consistent scales when comparing panels
• Appropriate precision (don't show 10 decimal places)

7. Save and Present

1. Save the chart as PNG with descriptive name
2. Display the chart to the user
3. Provide the Python code so they can modify it
4. Suggest variations (different chart type, different grouping, zoomed time range)

Data Preparation Recipes

Time Series

qsv_sqlp: SELECT date_col, SUM(value) as total
  FROM data GROUP BY date_col ORDER BY date_col

Top-N Categories

qsv_frequency: --select category_col --limit 10

Or for aggregated values:

qsv_sqlp: SELECT category, SUM(amount) as total
  FROM data GROUP BY category ORDER BY total DESC LIMIT 10

Distribution

qsv_stats: Check min, max, mean, stddev, cardinality
qsv_moarstats: --advanced for kurtosis, bimodality
qsv_sqlp: SELECT FLOOR(value/10)*10 as bin, COUNT(*) as cnt
  FROM data GROUP BY bin ORDER BY bin

Correlation

qsv_select: Pick the two numeric columns
qsv_stats: Verify both are numeric types with reasonable ranges

Comparison Across Groups

qsv_sqlp: SELECT group_col, AVG(metric) as avg_metric, COUNT(*) as n
  FROM data GROUP BY group_col ORDER BY avg_metric DESC

Notes

• Always profile with qsv first —

  stats

  and

  frequency

  reveal the right chart type and catch data issues before plotting
• For large files, use

  mcp__qsv__qsv_sqlp

  to aggregate before passing to Python — don't load millions of rows into pandas
• If interactive charts are requested (hover, zoom), use plotly instead of matplotlib
• Specify "presentation" for larger fonts and higher contrast
• Multiple charts can be created at once (e.g., "create a 2x2 grid")
• Charts are saved to the current directory as PNG files
• For data quality issues discovered during profiling, recommend

  /data-clean

  before visualizing