> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Additional Methods

> Additional methods for the DataAnalyzr class in the lyzr library

## `analysis`

This method performs data analysis based on the specified analysis type and user input.
It also sets the `analysis_guide`, `analysis_output` and `analysis_code` attributes of the `DataAnalyzr` instance.

1. If the analysis type is set to `skip`, the method sets `analysis_guide` to `"No analysis performed"` and `analysis_output` and `analysis_code` to `None`.
2. If the analysis type is set to `sql`, it generates a SQL query and performs SQL-based analysis using the provided user input and context.
3. If the analysis type is set to `ml`, it generates Python code and performs pandas and machine learning-based analysis using the provided user input and context.

```python theme={null}
analysis(
    user_input: str,
    analysis_context: str = None,
    time_limit: int = None,
    max_retries: int = None,
    auto_train: bool = None,
) -> Union[str, pd.DataFrame, dict[str, pd.DataFrame], None]
```

### Parameters

<ParamField path="user_input" type="string" required>
  User input for generating the analysis.
</ParamField>

<ParamField path="analysis_context" type="string">
  Context for the analysis.
</ParamField>

<ParamField path="time_limit" type="integer">
  Time limit in seconds for the analysis. Default is 45.
</ParamField>

<ParamField path="max_retries" type="integer">
  Maximum number of retries for the analysis. Default is 10.
</ParamField>

<ParamField path="auto_train" type="boolean">
  Whether to automatically add questions with their SQL query or Python code to the vector store. Default is True.
</ParamField>

### Returns

<ResponseField name="response" type="string or pandas.DataFrame or dictionary or None">
  Analysis output generated based on the user input and context.

  * Depending on the given input, the output may sometimes be a single number or string. It is then returned as a string.
  * When the output is a dictionary, at least one of the values is a pandas DataFrame.
  * If no analysis is performed, or the analysis fails, the output is `None`.

  ```python theme={null}
  user_input = "What is the average sale price?"
  response = "123.456"
  # or ---
  user_input = "Which country has the highest GDP?"
  response = "United States"
  # or ---
  user_input = "How do sales vary with time?"
  response = pd.DataFrame(...)
  # or ---
  user_input = "What are the top 5 countries by GDP? What are their top industries?"
  response = {
      "key1": pd.DataFrame(...),
      "key2": pd.DataFrame(...),
  }
  ```
</ResponseField>

### Example usage

```python theme={null}
analysis_output = data_analyzr.analysis(
    user_input="What is the average sale price?",
    analysis_context="Today is {date}. The column {column_name} refers to the listed sale price.",
    time_limit=60,
    max_retries=5,
    auto_train=True,
)
```

## `visualisation`

This method generates visualizations based on the user input and plot context.
These visualisations are then saved to the `plot_path`.
It is recommended that to generate visualisations, the [`ask` method](/pre-built-agents/data-analyzr/api-reference/ask.mdx) should be used instead of this method.

1. If `plot_path` is not provided, the generated plot is saved to `generated_plots/<random-image-name>.png`.
2. Sets the `plot_code` and `visualisation_output` attribute of the `DataAnalyzr` instance.

```python theme={null}
visualisation(
    user_input: str,
    plot_context: str = None,
    time_limit: int = None,
    max_retries: int = None,
    auto_train: bool = None,
    plot_path: str = None,
) -> str
```

### Parameters

<ParamField path="user_input" type="string" required>
  User input for generating the visualisation.
</ParamField>

<ParamField path="plot_context" type="string">
  Context for the plot.
</ParamField>

<ParamField path="time_limit" type="integer">
  Time limit in seconds for the visualisation process. Default is 60.
</ParamField>

<ParamField path="max_retries" type="integer">
  Maximum number of retries for the visualisation process. Default is 10.
</ParamField>

<ParamField path="auto_train" type="boolean">
  Whether to automatically add the visualisation to training data. Default is True.
</ParamField>

<ParamField path="plot_path" type="string">
  Path to save the generated plot. Defaults to `generated_plots/<random-image-name>.png`.
</ParamField>

### Returns

<ResponseField name="response" type="string">
  Path to the saved plot.

  ```python theme={null}
  response = "path/to/plot.png"
  ```
</ResponseField>

### Example usage

```python theme={null}
plot_path = data_analyzr.visualisation(
    user_input="Visualize the sales data",
    plot_context="Use the `ggplot` style with `plt.style.use('ggplot')`.",
    time_limit=60,
    max_retries=5,
    auto_train=True,
    plot_path="path/to/save/plot.png",
)
```

## `insights`

This method generates insights based on the user input, analysis output, and specified insights context.
It is recommended that to generate insights, the [`ask` method](/pre-built-agents/data-analyzr/api-reference/ask.mdx) should be used instead of this method.

1. The method utilizes a language model to generate insights based on the provided input and context.
2. The generated insights are influenced by the analysis output, user input, and insights context.
3. The generated insights are formatted and returned as a string.
4. Sets the `insights_output` attribute of the `DataAnalyzr` instance.

```python theme={null}
insights(
    user_input: str,
    insights_context: str = None,
    n_insights: int = 3,
) -> str
```

### Parameters

<ParamField path="user_input" type="string" required>
  User input for generating the insights.
</ParamField>

<ParamField path="insights_context" type="string">
  Context for the insights generation.
</ParamField>

<ParamField path="n_insights" type="integer">
  Number of insights to generate. Default is 3.
</ParamField>

### Returns

<ResponseField name="response" type="string">
  Generated insights based on the user input and context.

  ```python theme={null}
  response = "- The average sale price is $123.45.\n-The highest sale price is $456.78.\n-The lowest sale price is $12.34."
  ```
</ResponseField>

### Example usage

```python theme={null}
insights_output = data_analyzr.insights(
    user_input="What is the average sale price?",
    insights_context="Today is {date}. The column {column_name} refers to the listed sale price.",
    n_insights=3,
)
```

## `recommendations`

This method generates recommendations based on the user input, analysis insights, and specified recommendations context.
It is recommended that to generate recommendations, the [`ask` method](/pre-built-agents/data-analyzr/api-reference/ask.mdx) should be used instead of this method.

1. If the parameter `from_insights` is `False`, the method generates recommendations without using the insights output.

2. The format of the recommendations output can be customized using the `recs_format` parameter.

   * If the `output_type` is `text`, this parameter is ignored.
   * If `recs_format` is `None`, the default format will be used:

   ```json theme={null}
           [{
               "Recommendation": "string",
               "Basis of the Recommendation": "string",
               "Impact if implemented": "string",
           }]
   ```

3. The output type of the recommendations can be either text or JSON.

4. Sets the `recommendations_output` attribute of the `DataAnalyzr` instance.

```python theme={null}
recommendations(
    user_input: str,
    from_insights: bool = True,
    recs_format: dict = None,
    recommendations_context: str = None,
    n_recommendations: int = 3,
    output_type: Literal["text", "json"] = "text",
) -> str
```

### Parameters

<ParamField path="user_input" type="string" required>
  User input for generating the recommendations.
</ParamField>

<ParamField path="from_insights" type="boolean">
  Whether to generate recommendations from insights. Default is True.
</ParamField>

<ParamField path="recs_format" type="dictionary">
  Format for the recommendations output. Default is `None`.
</ParamField>

<ParamField path="recommendations_context" type="string">
  Context for the recommendations generation.
</ParamField>

<ParamField path="n_recommendations" type="integer">
  Number of recommendations to generate. Default is 3.
</ParamField>

<ParamField path="output_type" type="Literal['text', 'json']">
  Type of output for recommendations. Default is `text`.
</ParamField>

### Returns

<ResponseField name="response" type="string">
  Generated recommendations based on the user input and context.

  ```python theme={null}
  response = "..."
  ```
</ResponseField>

### Example usage

```python theme={null}
recommendations_output = data_analyzr.recommendations(
    user_input="What is the average sale price?",
    from_insights=True,
    recs_format={
        "Recommendation": "Recommendation text",
        "Reason": "Reason for the recommendation",
        "Impact": "Impact of the recommendation",
    },
    recommendations_context="Today is {date}. The average sale price is $123.45.",
    n_recommendations=3,
    output_type="json",
)
```

## `tasks`

This method generates tasks based on the user input, analysis output, and specified tasks context.
It is recommended that to generate tasks, the [`ask` method](/pre-built-agents/data-analyzr/api-reference/ask.mdx) should be used instead of this method.

1. The method incorporates user input, insights, and recommendations into the generated tasks.
2. The number of tasks to generate can be customized using the `n_tasks` parameter.
3. Sets the `tasks_output` attribute of the `DataAnalyzr` instance.

```python theme={null}
tasks(
    user_input: str,
    tasks_context: str = None,
    n_tasks: int = 3,
) -> str
```

### Parameters

<ParamField path="user_input" type="string" required>
  User input for generating the tasks.
</ParamField>

<ParamField path="tasks_context" type="string">
  Context for the tasks generation.
</ParamField>

<ParamField path="n_tasks" type="integer">
  Number of tasks to generate. Default is 3.
</ParamField>

### Returns

<ResponseField name="response" type="string">
  Generated tasks based on the user input and context.

  ```python theme={null}
  response = "..."
  ```
</ResponseField>

### Example usage

```python theme={null}
tasks_output = data_analyzr.tasks(
    user_input="What is the average sale price?",
    tasks_context="Today is {date}. The average sale price is $123.45.",
    n_tasks=3,
)
```