autoviz-1-basic-one-line-eda
Sub-skill of autoviz: 1. Basic One-Line EDA.
Best use case
autoviz-1-basic-one-line-eda is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of autoviz: 1. Basic One-Line EDA.
Teams using autoviz-1-basic-one-line-eda 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/1-basic-one-line-eda/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How autoviz-1-basic-one-line-eda Compares
| Feature / Agent | autoviz-1-basic-one-line-eda | 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: 1. Basic One-Line EDA.
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
# 1. Basic One-Line EDA
## 1. Basic One-Line EDA
**Simplest Usage:**
```python
from autoviz import AutoViz_Class
# Initialize AutoViz
AV = AutoViz_Class()
# Automatic visualization with one line
# Returns a dataframe and generates all charts
df_analyzed = AV.AutoViz(
filename="data.csv",
sep=",",
depVar="", # Target variable (optional)
dfte=None, # Pass DataFrame directly instead of filename
header=0,
verbose=1, # 0=minimal, 1=medium, 2=detailed output
lowess=False,
chart_format="svg",
max_rows_analyzed=150000,
max_cols_analyzed=30
)
print(f"Analyzed {df_analyzed.shape[0]} rows, {df_analyzed.shape[1]} columns")
```
**From DataFrame:**
```python
from autoviz import AutoViz_Class
import pandas as pd
# Load your data
df = pd.read_csv("sales_data.csv")
# Or create sample data
df = pd.DataFrame({
"revenue": [100, 200, 150, 300, 250, 400, 350, 500],
"units": [10, 20, 15, 30, 25, 40, 35, 50],
"category": ["A", "B", "A", "B", "A", "B", "A", "B"],
"region": ["North", "South", "East", "West", "North", "South", "East", "West"],
"profit": [20, 40, 30, 60, 50, 80, 70, 100],
"customer_age": [25, 35, 45, 55, 30, 40, 50, 60]
})
# Initialize and visualize
AV = AutoViz_Class()
# Pass DataFrame directly using dfte parameter
df_result = AV.AutoViz(
filename="", # Empty when using dfte
sep=",",
depVar="profit", # Optional: specify target variable
dfte=df,
header=0,
verbose=1,
chart_format="png"
)
```
**With Target Variable Analysis:**
```python
from autoviz import AutoViz_Class
import pandas as pd
# Classification dataset
df_classification = pd.DataFrame({
"feature_1": [1.2, 2.3, 1.5, 3.4, 2.1, 4.5, 3.2, 5.1],
"feature_2": [0.5, 1.2, 0.8, 2.1, 1.0, 3.2, 2.4, 4.0],
"feature_3": ["low", "medium", "low", "high", "medium", "high", "medium", "high"],
"target": [0, 0, 0, 1, 0, 1, 1, 1]
})
AV = AutoViz_Class()
# Specify target variable for focused analysis
df_analyzed = AV.AutoViz(
filename="",
sep=",",
depVar="target", # Target variable for classification
dfte=df_classification,
header=0,
verbose=2, # More detailed output
chart_format="svg"
)
# Regression dataset
df_regression = pd.DataFrame({
"size": [1000, 1500, 1200, 2000, 1800, 2500, 2200, 3000],
"bedrooms": [2, 3, 2, 4, 3, 4, 4, 5],
"location": ["urban", "suburban", "urban", "rural", "suburban", "rural", "suburban", "rural"],
"age": [5, 10, 3, 15, 8, 20, 12, 25],
"price": [200000, 280000, 220000, 350000, 300000, 380000, 340000, 420000]
})
# Analyze with continuous target
df_analyzed = AV.AutoViz(
filename="",
sep=",",
depVar="price", # Continuous target
dfte=df_regression,
header=0,
verbose=1,
chart_format="png"
)
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