# Analytics and data analysis Human Guide

## What This Is For
Best practices for analytics, data analysis, and visualization using Python, pandas, matplotlib, seaborn, and Jupyter notebooks. It gives the agent a clearer input/output frame for marketing analytics: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Analytics and data analysis agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for analytics and data analysis.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Analytics and data analysis guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- **Load and inspect** — Read data with `pd.read_csv()` or appropriate loader, check `.shape`, `.dtypes`, `.describe()`, and `.isnull().sum()`
- **Clean and transform** — Handle missing values, fix dtypes, rename columns, filter outliers using vectorized pandas operations
- **Explore relationships** — Use `.groupby()`, `.corr()`, and cross-tabulations to identify patterns
- **Visualize findings** — Create targeted plots with matplotlib/seaborn; label axes, add titles, use colorblind-friendly palettes
- **Validate results** — Run statistical tests, report confidence intervals, verify assumptions
- **Document and share** — Structure notebook with markdown sections, clear outputs before sharing, pin dependencies
- Write concise, technical code with accurate Python examples
- Emphasize readability and reproducibility in data analysis workflows
- Use functional programming patterns; minimize class usage
- Leverage vectorized operations over explicit loops for performance
- Use descriptive variable naming conventions (e.g., `is_valid`, `has_data`, `total_count`)
- Adhere to PEP 8 style guidelines

## Decision Points And Nuance
The original skill emphasizes: Workflow: Exploratory Data Analysis Pipeline, Key Principles, Quick Start Example, Data Analysis with Pandas, Data Manipulation Best Practices, Performance Optimization, Data Validation, Visualization Standards, Matplotlib Guidelines, Seaborn for Statistical Visualizations.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- Starting without a clear audience or goal.
- Asking for a final artifact before sharing examples or constraints.
- Accepting a generic first draft without checking it against the intended use.

## Copy-And-Paste Prompt
```text
Use the Analytics and data analysis human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the mindrally/skills skill entry for `analytics-data-analysis`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Analytics and Data Analysis

Guidelines for data analysis, visualization, and Jupyter-based workflows using pandas, matplotlib, seaborn, and numpy. Prioritize readability, reproducibility, and vectorized operations.

## Workflow: Exploratory Data Analysis Pipeline

1. **Load and inspect** — Read data with `pd.read_csv()` or appropriate loader, check `.shape`, `.dtypes`, `.describe()`, and `.isnull().sum()`
2. **Clean and transform** — Handle missing values, fix dtypes, rename columns, filter outliers using vectorized pandas operations
3. **Explore relationships** — Use `.groupby()`, `.corr()`, and cross-tabulations to identify patterns
4. **Visualize findings** — Create targeted plots with matplotlib/seaborn; label axes, add titles, use colorblind-friendly palettes
5. **Validate results** — Run statistical tests, report confidence intervals, verify assumptions
6. **Document and share** — Structure notebook with markdown sections, clear outputs before sharing, pin dependencies

## Key Principles

- Write concise, technical code with accurate Python examples
- Emphasize readability and reproducibility in data analysis workflows
- Use functional programming patterns; minimize class usage
- Leverage vectorized operations over explicit loops for performance
- Use descriptive variable naming conventions (e.g., `is_valid`, `has_data`, `total_count`)
- Adhere to PEP 8 style guidelines

## Quick Start Example

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

# Load and inspect
df = pd.read_csv("data.csv", parse_dates=["timestamp"])
print(f"Shape: {df.shape}, Missing: {df.isnull().sum().sum()}")

# Clean: drop rows missing target, fill numeric gaps with median
df = (
    df.dropna(subset=["revenue"])
    .assign(category=lambda x: x["category"].astype("category"))
    .fillna(df.select_dtypes("number").median())
)

# Analyze: revenue by category
summary = df.groupby("category")["revenue"].agg(["mean", "median", "std"])

# Visualize
fig, ax = plt.subplots(figsize=(10, 6))
sns.boxplot(data=df, x="category", y="revenue", palette="colorblind", ax=ax)
ax.set_title("Revenue Distribution by Category")
ax.set_ylabel("Revenue ($)")
plt.tight_layout()
plt.savefig("revenue_by_category.png", dpi=150)
plt.show()
```

## Data Analysis with Pandas

### Data Manipulation Best Practices
- Use pandas for all data manipulation and analysis tasks
- Apply method chaining for clean, readable transformations
- Utilize `loc` and `iloc` for explicit data selection
- Employ `groupby` for efficient data aggregation
- Use `merge` and `join` appropriately for combining datasets

### Performance Optimization
- Use vectorized operations instead of loops
- Utilize efficient data structures like categorical data types for low-cardinality string columns
- Consider dask for larger-than-memory datasets
- Profile code to identify and optimize bottlenecks
- Use appropriate dtypes to minimize memory usage

### Data Validation
- Validate data types and ranges to ensure data integrity
- Use try-except blocks for error-prone operations when reading external data
- Check for missing values and handle appropriately
- Verify data shape and structure after transformations

## Visualization Standards

### Matplotlib Guidelines
- Use matplotlib for fine-grained customization control
- Create clear, informative plots with proper labeling