Perform time series analysis including trend detection, seasonality, and statistics.

Args:
    file_path: Path to CSV or SQLite file
    date_column: Name of the date/datetime column
    value_column: Name of the numeric column to analyze
    freq: Frequency for resampling - 'D' (daily), 'W' (weekly), 'M' (monthly), 'Q' (quarterly), 'Y' (yearly)
    include_forecast: If True, include simple moving average forecast

Returns:
    Dictionary containing:
    - trend: Overall trend direction and statistics
    - statistics: Time series statistics
    - moving_averages: 7, 30, 90 period moving averages
    - seasonality: Day of week / month patterns
    - forecast: Simple forecast if requested

Automatically generate interesting insights about a dataset.
Perfect for quick data exploration and understanding.

Args:
    file_path: Path to CSV or SQLite file
    max_insights: Maximum number of insights to generate (default 10)

Returns:
    Dictionary containing automatically discovered insights

Compute correlation matrix between numeric columns.

Args:
    file_path: Path to CSV or SQLite file
    columns: List of columns to include (default: all numeric columns)
    method: Correlation method - 'pearson' (default), 'spearman', or 'kendall'

Returns:
    Dictionary containing:
    - method: Correlation method used
    - correlation_matrix: Full correlation matrix
    - top_correlations: Top 10 strongest correlations (excluding self-correlations)

Create a pivot table from tabular data - the most common business analysis operation.

Args:
    file_path: Path to CSV or SQLite file
    index: Column(s) to use as row labels (grouping)
    columns: Column(s) to use as column headers (optional)
    values: Column to aggregate (default: first numeric column)
    aggfunc: Aggregation function - 'sum', 'mean', 'count', 'min', 'max', 'median', 'std'
    fill_value: Value to replace missing entries (default: None = show as null)

Returns:
    Dictionary containing the pivot table data and metadata

Example:
    create_pivot_table(
        file_path="data/sales.csv",
        index=["region"],
        columns=["category"],
        values="revenue",
        aggfunc="sum"
    )

Generate a comprehensive data quality assessment report.
Essential for understanding data health before analysis.

Args:
    file_path: Path to CSV or SQLite file

Returns:
    Dictionary containing:
    - completeness: Missing value analysis per column
    - uniqueness: Duplicate detection
    - validity: Data type consistency and outlier counts
    - overall_score: Data quality score (0-100)

Generate comprehensive statistics for a tabular dataset.

Args:
    file_path: Path to CSV or SQLite file
    include_all: If True, include statistics for all columns (not just numeric)

Returns:
    Dictionary containing:
    - shape: (rows, columns)
    - columns: List of column names with their types
    - numeric_stats: Descriptive statistics for numeric columns
    - missing_values: Count of missing values per column
    - sample: First 5 rows as preview

Detect anomalies/outliers in a numeric column.

Args:
    file_path: Path to CSV or SQLite file
    column: Name of the numeric column to analyze
    method: Detection method - 'zscore' (default), 'iqr', or 'isolation_forest'
    threshold: Threshold for anomaly detection (default 3.0 for zscore, 1.5 for IQR)

Returns:
    Dictionary containing:
    - method: Detection method used
    - anomaly_count: Number of anomalies found
    - anomaly_indices: Row indices of anomalies
    - anomalies: The anomalous rows
    - statistics: Column statistics

Export filtered/transformed data to a new CSV file.

Args:
    file_path: Path to source CSV or SQLite file
    output_name: Name for output file (without extension, saved to data/ folder)
    filter_column: Optional column to filter on
    filter_operator: Filter operator - 'eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'contains'
    filter_value: Value to filter by
    columns: List of columns to include (default: all)
    sort_by: Column to sort by
    sort_ascending: Sort direction (default: ascending)
    limit: Maximum rows to export

Returns:
    Dictionary containing export details and file path

Filter rows based on a condition.

Args:
    file_path: Path to CSV or SQLite file
    column: Column name to filter on
    operator: Comparison operator - 'eq', 'ne', 'gt', 'gte', 'lt', 'lte', 'contains', 'startswith', 'endswith'
    value: Value to compare against
    limit: Maximum number of rows to return (default 100)

Returns:
    Dictionary containing:
    - filter_applied: Description of the filter
    - original_count: Number of rows before filtering
    - filtered_count: Number of rows after filtering
    - rows: Filtered rows (up to limit)

Generate a chart/visualization from tabular data.
Returns chart as base64-encoded PNG for display.

Args:
    file_path: Path to CSV or SQLite file
    chart_type: Type of chart - 'bar', 'line', 'scatter', 'histogram', 'pie', 'box'
    x_column: Column for X-axis (not needed for histogram/pie)
    y_column: Column for Y-axis values
    group_by: Optional column for grouping/coloring
    title: Chart title (auto-generated if not provided)
    output_format: 'base64' (default) or 'file' (saves to data/charts/)

Returns:
    Dictionary containing chart data as base64 or file path

Group data and compute aggregations.

Args:
    file_path: Path to CSV or SQLite file
    group_by: Columns to group by
    aggregations: Dictionary mapping column names to list of aggregation functions
                 (e.g., {"sales": ["sum", "mean"], "quantity": ["count", "max"]})
                 Supported: sum, mean, median, min, max, count, std, var

Returns:
    Dictionary containing grouped and aggregated data

List available data files in the project data directory.

Args:
    data_dir: Relative path to data directory (default: "data")

Returns:
    Dictionary containing list of available CSV and SQLite files

List all tables in a SQLite database.

Args:
    db_path: Path to SQLite database file

Returns:
    Dictionary containing table names and their schemas

Merge/join two datasets together - essential for combining data sources.

Args:
    file_path_left: Path to left/primary dataset
    file_path_right: Path to right/secondary dataset
    on: Column(s) to join on (if same name in both datasets)
    left_on: Column name in left dataset to join on
    right_on: Column name in right dataset to join on
    how: Join type - 'inner', 'left', 'right', 'outer'
    preview_limit: Number of rows to return in preview

Returns:
    Dictionary containing merged data preview and statistics

Execute a SQL query on a SQLite database.

Args:
    db_path: Path to SQLite database file
    query: SQL query to execute (SELECT queries only for safety)
    limit: Maximum number of rows to return (default 100)

Returns:
    Dictionary containing:
    - query: The executed query
    - row_count: Number of rows returned
    - columns: List of column names
    - rows: Query results

Perform statistical hypothesis tests on data.

Args:
    file_path: Path to CSV or SQLite file
    test_type: Type of test:
        - 'ttest_ind': Independent samples t-test (compare 2 groups)
        - 'ttest_paired': Paired samples t-test
        - 'chi_squared': Chi-squared test for categorical independence
        - 'anova': One-way ANOVA (compare 3+ groups)
        - 'mann_whitney': Non-parametric alternative to t-test
        - 'pearson': Pearson correlation test
        - 'spearman': Spearman correlation test
    column1: First column for analysis
    column2: Second column (required for correlation, optional for t-test)
    group_column: Column defining groups (for t-test, ANOVA)
    alpha: Significance level (default 0.05)

Returns:
    Dictionary containing test statistic, p-value, and interpretation