71 lines
2.5 KiB
Python
71 lines
2.5 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 PandasSetIndex:
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"""
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Create index (row label) from existing column or columns.
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You can specify a single column or a comma-separated list of column labels in `column_names`.
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If `drop=True`, the specified columns are removed from column labels and become the index.
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If `drop=False`, the columns remain in the DataFrame column labels while also being used as the index.
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If `append=True`, the new index is added alongside the existing index.
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Below screenshots illustrate these options:
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This class internally calls `pandas.DataFrame.set_index()`.
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category: Transformation
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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 {
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"required": {
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"dataframe": ("DATAFRAME", {}),
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"column_names": ("STRING", {"default": ""}),
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"drop": ("BOOLEAN", {"default": True}),
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"append": ("BOOLEAN", {"default": False})
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}
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}
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RETURN_TYPES: tuple = ("DATAFRAME",)
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FUNCTION: str = "f"
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CATEGORY: str = "Data Analysis"
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def f(self, dataframe: pd.DataFrame, column_names: str, drop: bool, append: bool) -> tuple:
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"""
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Create index (row label) from existing column or columns.
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Args:
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dataframe (DataFrame): The DataFrame.
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column_names (str): A single column label or a comma-separated string of column names to select.
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drop (bool): If `drop=True`, the specified columns are removed from column labels and become the index.
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If `drop=False`, the columns remain in the DataFrame column labels while also being used as the index.
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append (bool): If `append=True`, the new index is added alongside the existing index.
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Returns:
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tuple: A tuple containing the DataFrame.
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"""
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columns = [col.strip() for col in column_names.split(",")]
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for c in columns:
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if c not in dataframe.columns:
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raise ValueError(f"Invalid column name {c}")
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if len(columns) > 1:
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arg = columns
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else:
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arg = columns[0]
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df2 = dataframe.set_index(arg, drop=drop, append=append)
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return (df2,)
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