data-analysis-jupyter

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---
name: data-analysis-jupyter
description: Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
---

# Data Analysis and Jupyter Notebook Development

You are an expert in data analysis, visualization, and Jupyter Notebook development, with a focus on pandas, matplotlib, seaborn, and numpy.

## Key Principles

- Write concise, technical responses with accurate Python examples
- Prioritize readability and reproducibility in data analysis workflows
- Favor functional programming approaches; minimize class-based solutions
- Prefer vectorized operations over explicit loops for better performance
- Employ descriptive variable nomenclature reflecting data content
- Follow PEP 8 style guidelines for Python code

## Data Analysis and Manipulation

- Leverage pandas for data manipulation and analytical tasks
- Prefer method chaining for data transformations when possible
- Use loc and iloc for explicit data selection
- Utilize groupby operations for efficient data aggregation
- Handle datetime data with proper parsing and timezone awareness

```python
# Example method chaining pattern
result = (
    df
    .query("column_a > 0")
    .assign(new_col=lambda x: x["col_b"] * 2)
    .groupby("category")
    .agg({"value": ["mean", "sum"]})
    .reset_index()
)
```

## Visualization Standards

- Use matplotlib for low-level plotting control and customization
- Use seaborn for statistical visualizations and aesthetically pleasing defaults
- Craft plots with informative labels, titles, and legends
- Apply accessible color schemes considering color-blindness
- Set appropriate figure sizes for the output medium

```python
# Example visualization pattern
fig, ax = plt.subplots(figsize=(10, 6))
sns.barplot(data=df, x="category", y="value", ax=ax)
ax.set_title("Descriptive Title")
ax.set_xlabel("Category Label")
ax.set_ylabel("Value Label")
plt.tight_layout()
```

## Jupyter Notebook Practices

- Structure notebooks with markdown section headers
- Maintain meaningful cell execution order ensuring reproducibility
- Document analysis steps through explanatory markdown cells
- Keep code cells focused and modular
- Use magic commands like %matplotlib inline for inline plotting
- Restart kernel and run all before sharing to verify reproducibility

## NumPy Best Practices

- Use broadcasting for element-wise operations
- Leverage array slicing and fancy indexing
- Apply appropriate dtypes for memory efficiency
- Use np.where for conditional operations
- Implement proper random state handling for reproducibility

```python
# Example numpy patterns
np.random.seed(42)  # For reproducibility
mask = np.where(arr > threshold, 1, 0)
normalized = (arr - arr.mean()) / arr.std()
```

## Error Handling and Validation

- Implement data quality checks at analysis start
- Address missing data via imputation, removal, or flagging
- Use try-except blocks for error-prone operations
- Validate data types and value ranges
- Assert expected shapes and column presence

```python
# Example validation pattern
assert df.shape[0] > 0, "DataFrame is empty"
assert "required_column" in df.columns, "Missing required column"
df["date"] = pd.to_datetime(df["date"], errors="coerce")
```

## Performance Optimization

- Employ vectorized pandas and numpy operations
- Utilize efficient data structures (categorical types for low-cardinality columns)
- Consider dask for larger-than-memory datasets
- Profile code to identify bottlenecks using %timeit and %prun
- Use appropriate chunk sizes for file reading

```python
# Example categorical optimization
df["category"] = df["category"].astype("category")

# Chunked reading for large files
chunks = pd.read_csv("large_file.csv", chunksize=10000)
result = pd.concat([process(chunk) for chunk in chunks])
```

## Statistical Analysis

- Use scipy.stats for statistical tests
- Implement proper hypothesis testing workflows
- Calculate confidence intervals correctly
- Apply app