53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
from typing import Any, Dict
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from io import StringIO
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import json
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import pandas as pd
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from .utils import series_to_jsons
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class PandasIlocRowsDataFrame:
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"""
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PandasIlocRowsDataFrame:
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A class for selecting rows from a pandas DataFrame and returning it as a DataFrame.
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Rows are specified by the list of integer positions.
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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 `select_rows` 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_json": ("STRING", {"multiline": True}),
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"row_int_pos_list_json": ("STRING", {"multiline": False})
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}
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}
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RETURN_TYPES: tuple = ("STRING",)
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FUNCTION: str = "select_rows"
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CATEGORY: str = "Data Analysis"
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def select_rows(self, dataframe_json: str,
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row_int_pos_list_json: str) -> tuple:
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"""
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Select rows from a pandas DataFrame and returning it as a DataFrame.
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Rows are specified by the list of integer positions and returned as
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the JSON serialized DataFrame object.
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Args:
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dataframe_json (str): A JSON string representation of the DataFrame.
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row_int_pos_list_json (str): A list containing row integer positions.
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Returns:
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tuple: A tuple containing the value of the row in string. The string is
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a JSON string containing the serialized Pandas Series object.
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"""
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# Deserialize JSON string to DataFrame
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df = pd.read_json(StringIO(dataframe_json))
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row_int_pos_list = json.loads(row_int_pos_list_json)
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df2 = df.iloc[row_int_pos_list]
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return (df2.to_json(),)
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