68 lines
1.8 KiB
Python
68 lines
1.8 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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import seaborn as sns
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from .util import plot_post_steps
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class SNSHeatmap:
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"""
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SNS Heatmap:
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Generates a heatmap from a Pandas DataFrame using Seaborn.
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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 `bar_chart` 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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"title": ("STRING", {"default": ""}),
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"palette": ("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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title: str,
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palette: str=None) -> tuple:
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"""
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Generates a heatmap from a Pandas DataFrame using Seaborn.
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Args:
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dataframe (DataFrame): The DataFrame.
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title (str): The title of the plot.
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palette (str): Palette 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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# set_up_sns_style_palette(style, palette) won't work for heatmap
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# Create the plot
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fig, ax = plt.subplots()
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corr = dataframe.corr()
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if palette:
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sns.heatmap(corr, annot=True, ax=ax, cmap=sns.color_palette(palette, as_cmap=True))
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else:
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sns.heatmap(corr, annot=True, ax=ax)
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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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None,
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None,
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None)
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