79 lines
2.4 KiB
Python
79 lines
2.4 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 .common import common_input_types_y_only_bin
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from .util import plot_post_steps
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from .util import set_up_sns_style_palette
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from .util import process_y_columns_and_labels
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class SNSHistogram:
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"""
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SNS Histogram:
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Generates a histogram from a Pandas DataFrame using Seaborn.
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Currently, plotting multiple series of data is not supported.
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For style and palette parameters, refer to [SNS Line Plot documentation](sns_line.md).
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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 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 common_input_types_y_only_bin()
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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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y_column_name: str,
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title: str,
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y_axis_label: str,
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# legend_label: str,
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bins: int,
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style: str=None,
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palette: str=None) -> tuple:
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"""
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Generates a histogram from a Pandas DataFrame using Seaborn.
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Args:
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dataframe (DataFrame): The DataFrame.
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y_column_name (str): The column name for the y-axis.
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title (str): The title of the plot.
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y_axis_label (str): The label for the y-axis data.
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bins (int): Number of bins.
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style (str): Style 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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df = dataframe
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set_up_sns_style_palette(style, palette)
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# TODO: For future enhancement, restore legend_label as the last arg.
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y_columns, labels = process_y_columns_and_labels(df, y_column_name, "")
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# Create the plot
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fig, ax = plt.subplots()
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for i in range(len(y_columns)):
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sns.histplot(df[y_columns[i]], bins=bins, ax=ax, label=labels[i])
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break # TODO: Remove this if we decide support multiple plots
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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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y_axis_label,
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None)
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