Additional Methods

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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.

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]

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Parameters

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Returns

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(...),
}

​

Example usage

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,
)

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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 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.

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

​

Returns

response = "path/to/plot.png"

​

Example usage

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",
)

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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 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.

insights(
    user_input: str,
    insights_context: str = None,
    n_insights: int = 3,
) -> str

​

Parameters

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Returns

response = "- The average sale price is $123.45.\n-The highest sale price is $456.78.\n-The lowest sale price is $12.34."

​

Example usage

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,
)

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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 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:

           [{
               "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.

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

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Parameters

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Returns

response = "..."

​

Example usage

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",
)

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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 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.

tasks(
    user_input: str,
    tasks_context: str = None,
    n_tasks: int = 3,
) -> str

​

Parameters

​

Returns

response = "..."

​

Example usage

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,
)