58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
from typing import Any, Dict
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import pandas as pd
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from .utils import column_labels_string_to_list
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class PandasCrosstab:
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"""
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Creates a contingency table (crosstab, or cross-tabulation) from two or more columns in a DataFrame.
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category: Categorical summary
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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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"index": ("STRING", {"default": ""}),
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"column_names": ("STRING", {"default": ""})
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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, index: str, column_names: str) -> tuple:
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"""
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Creates a contingency table (crosstab, or cross-tabulation) from two or more columns in a DataFrame.
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Args:
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dataframe (DataFrame): The DataFrame.
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index (str): A comma-separated list of index (row labels) containing categorical data.
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column_names (str): A comma-separated list of column names containing categorical data.
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Returns:
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tuple: A tuple containing the DataFrame.
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"""
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rows = column_labels_string_to_list(dataframe, index)
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cols = column_labels_string_to_list(dataframe, column_names)
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if len(rows) == 1:
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rows = dataframe[rows[0]]
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else:
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rows = [dataframe[e] for e in rows]
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if len(cols) == 1:
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cols = dataframe[cols[0]]
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else:
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cols = [dataframe[e] for e in cols]
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df2 = pd.crosstab(index=rows, columns=cols)
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return (df2,)
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