autoviz-4-feature-analysis-and-distribution-plots
Sub-skill of autoviz: 4. Feature Analysis and Distribution Plots.
Best use case
autoviz-4-feature-analysis-and-distribution-plots is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of autoviz: 4. Feature Analysis and Distribution Plots.
Teams using autoviz-4-feature-analysis-and-distribution-plots should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/4-feature-analysis-and-distribution-plots/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How autoviz-4-feature-analysis-and-distribution-plots Compares
| Feature / Agent | autoviz-4-feature-analysis-and-distribution-plots | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of autoviz: 4. Feature Analysis and Distribution Plots.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 4. Feature Analysis and Distribution Plots
## 4. Feature Analysis and Distribution Plots
**Understanding Feature Distributions:**
```python
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
# Create dataset with various distributions
np.random.seed(42)
df = pd.DataFrame({
# Normal distribution
"normal": np.random.normal(100, 15, 1000),
# Skewed distribution
"skewed": np.random.exponential(50, 1000),
# Bimodal distribution
"bimodal": np.concatenate([
np.random.normal(30, 5, 500),
np.random.normal(70, 5, 500)
]),
# Uniform distribution
"uniform": np.random.uniform(0, 100, 1000),
# Categorical with different frequencies
"category_balanced": np.random.choice(["A", "B", "C"], 1000),
"category_imbalanced": np.random.choice(
["Common", "Rare", "Very Rare"],
1000,
p=[0.8, 0.15, 0.05]
),
# Target variable
"target": np.random.choice([0, 1], 1000, p=[0.7, 0.3])
})
AV = AutoViz_Class()
# AutoViz will automatically:
# 1. Detect distribution types
# 2. Create appropriate histograms
# 3. Show box plots for numerical features
# 4. Create bar charts for categorical features
# 5. Highlight potential outliers
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar="target",
verbose=2,
chart_format="svg"
)
```
**Categorical Feature Analysis:**
```python
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
# Dataset with multiple categorical features
df = pd.DataFrame({
"product_category": np.random.choice(
["Electronics", "Clothing", "Food", "Home", "Sports"],
1000
),
"customer_segment": np.random.choice(
["Premium", "Standard", "Budget"],
1000,
p=[0.2, 0.5, 0.3]
),
"region": np.random.choice(
["North", "South", "East", "West"],
1000
),
"channel": np.random.choice(
["Online", "Store", "Mobile"],
1000
),
"revenue": np.random.exponential(500, 1000),
"quantity": np.random.randint(1, 20, 1000)
})
AV = AutoViz_Class()
# AutoViz creates:
# - Bar charts for each categorical variable
# - Cross-tabulation visualizations
# - Category vs numerical variable plots
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar="revenue",
verbose=1,
chart_format="png"
)
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