# PyAutoPlot Functions

**Available functions:**

* `AutoPlot.`[`auto_plot`](#auto-plot)`(output_file=None, theme="light", excludes=None, **kwargs)`: Automatically generate plots based on a given dataset.
* `AutoPlot.`[`plot`](#plot)`(plot_type, x=None, y=None, **kwargs)`: Manually plot data from your dataset.
* `AutoPlot.`[`cleanup`](#cleanup)`(strategy='mean', fill_value=None, columns=None, drop_na=False)`: Perform basic data cleanup and handle missing values.
* `AutoPlot.`[`export_report`](#export-report)`(output_file, format='html')`: Export a comprehensive report of the analysis and visualizations.
* `AutoPlot.`[`export_json`](#export-json)`(output_file)`: Export statistical analysis and metadata to a JSON file.

***

### Auto plot

Automatically generate plots based on a given dataset.

```python
This method produces:
1. Detailed analysis summary as a text-based plot.
2. Numeric visualizations: Histograms, Boxplots, and Pairwise Scatter Matrix.
3. Categorical visualizations: Enhanced Bar Plots and Pie Charts.
4. Time-series visualizations: Line and Stacked Area Plots.

Parameters:
    output_file (str, optional): Base filename for saving plots. Default is None (no saving).
    theme (str, optional): Plot theme ("light", "dark", or a custom dictionary). Default is "light".
    excludes (list, optional): Sections to exclude from plotting, e.g., ["histograms"]. Default is None.
    **kwargs: Additional keyword arguments passed to plot methods.

Examples:
    autoplot.auto_plot(output_file="dataset_output", theme="dark", excludes=["pie_charts"])
```

{% hint style="info" %}
Scatter plots on large datasets can cause performance issues; use \`excludes\` to exclude them if necessary.
{% endhint %}

#### Function arguments:

`output_file`: The output file, and format (optional). E.g. `output.png` will result in a plot with the prefix `output` and the format `png`.

`theme`: Either `light` or `dark`, else defaults to `light`. You can also pass in a custom RCParam dictionary for custom themes.

`excludes`: A list of plots to exclude. Available plots are: `detailed_analysis`, `numeric`, `categorical`, `pairwise_scatter`, `pie_charts`, `line_plots`, `correlation`, `violin` and `stacked_area`.

`**kwargs`: Other supported keyword arguments for `matplotlib`. E.g. `color='orange'`.

### Plot

Manually create a plot based on data within your dataset.

```python
Parameters:
    plot_type (str): Type of plot to generate. Options include:
        - "scatter": Scatter plot (requires x and y).
        - "distribution": Histogram (requires x).
        - "boxplot": Boxplot (requires x).
        - "bar": Bar chart (requires x).
    x (str, optional): Name of the column for the x-axis or primary data. Default is None.
    y (str, optional): Name of the column for the y-axis (for scatter plots). Default is None.
    **kwargs: Additional keyword arguments for the plot.

Examples:
    autoplot.plot(plot_type="scatter", x="age", y="income")
    autoplot.plot(plot_type="distribution", x="salary", bins=20)
```

### Cleanup

Perform basic data cleanup and handle missing values.

```python
Parameters:
    strategy (str): Strategy to fill missing values ('mean', 'median', 'mode', 'constant'). Default is 'mean'.
    fill_value (any): Value to use if strategy is 'constant'.
    columns (list): List of columns to clean. If None, cleans all columns.
    drop_na (bool): If True, drops rows with missing values instead of filling them.
```

### Export Report

Export a comprehensive report of the analysis and visualizations.

```python
Parameters:
    output_file (str): Path to the output file.
    format (str): Format of the report ('html' or 'pdf'). Default is 'html'.
```

### Export JSON

Export statistical analysis and metadata to a JSON file.

```python
Parameters:
    output_file (str): Path to the output file.
```


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