64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
from typing import Any, Dict
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from io import StringIO
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import pandas as pd
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class PandasGroupBy:
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"""
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Computes aggregate values for groups in a DataFrame.
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category: Aggregation
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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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See https://pandas.pydata.org/docs/reference/groupby.html for aggragate functions.
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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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"column_name": ("STRING", {"default": ""}),
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"aggragate_function":(("sum", "mean", "count", "std", "min", "max"),)
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}
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}
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RETURN_TYPES: tuple = ("DATAFRAME",)
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FUNCTION: str = "groupby_dataframes"
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CATEGORY: str = "Data Analysis"
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def groupby_dataframes(self,
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dataframe: pd.DataFrame,
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column_name: str,
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aggragate_function: str) -> tuple:
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"""
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Compute aggregate values for groups in a DataFrame
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Args:
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dataframe (DataFrame): The DataFrame.
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column_name (str): The name of the column for use as groups.
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aggregate_function (str): The type of aggregate function to perform.
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Returns:
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tuple: A tuple containing the merged DataFrame.
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"""
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if aggragate_function == "max":
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df_out = dataframe.groupby(column_name).max()
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elif aggragate_function == "min":
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df_out = dataframe.groupby(column_name).min()
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elif aggragate_function == "std":
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df_out = dataframe.groupby(column_name).std()
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elif aggragate_function == "count":
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df_out = dataframe.groupby(column_name).count()
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elif aggragate_function == "sum":
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df_out = dataframe.groupby(column_name).sum()
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elif aggragate_function == "mean":
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df_out = dataframe.groupby(column_name).mean()
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else:
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raise ValueError("Unsupported aggregate function.")
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return (df_out,)
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