72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
from typing import Any, Dict
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from matplotlib import pyplot as plt
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import pandas as pd
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from .util import plot_post_steps
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from ..pandas_wrapper.utils import column_label_string_to_target_type
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class MPLPieChart:
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"""
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MPL Pie Chart:
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Generates a pie chart from a pandas DataFrame.
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Below screenshot shows a workflow to generate a pie chart from categorical data.
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category: Plot
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"""
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@classmethod
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def INPUT_TYPES(cls) -> Dict[str, Any]:
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"""
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Defines the input types for the `line_plot` function.
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Returns:
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Dict[str, Any]: A dictionary specifying required input types.
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"""
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return {
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"required": {
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"dataframe": ("DATAFRAME", {}),
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"label_column_name": ("STRING", {"default": ""}),
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"data_column_name": ("STRING", {"default": ""}),
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"title": ("STRING", {"default": ""})
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}
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}
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RETURN_TYPES: tuple = ("IMAGE",)
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FUNCTION: str = "f"
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CATEGORY: str = "Data Analysis"
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def f(self,
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dataframe: pd.DataFrame,
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label_column_name: str,
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data_column_name: str,
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title: str) -> tuple:
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"""
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Generates a pie chart from a Pandas DataFrame.
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Args:
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dataframe (DataFrame): The DataFrame.
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label_column_name (str): The column name for the labels.
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data_column_name (str): The column name for the data.
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title (str): The title of the plot.
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Returns:
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tuple: A tuple containing the image tensor representation of the plot.
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"""
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df = dataframe
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label_column_name = column_label_string_to_target_type(df, label_column_name)
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data_column_name = column_label_string_to_target_type(df, data_column_name)
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# Create the plot
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fig, ax = plt.subplots()
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ax.pie(df[data_column_name], labels=df[label_column_name], autopct='%1.1f%%', startangle=140)
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return plot_post_steps(
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fig,
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ax,
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plt,
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title,
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"",
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"",
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False)
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