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HowToSD-ComfyUI-Data-Analysis/modules/pandas_wrapper/pandas_strftime.py
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1.8 KiB
Python

from typing import Any, Dict
import pandas as pd
import pandas.api.types as ptypes
class PandasStrftime:
"""
Converts one or more datetime columns in a DataFrame to string columns.
Refer to [the Python documentation](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) for date/time format strings.
category: Date and time processing
"""
@classmethod
def INPUT_TYPES(cls) -> Dict[str, Any]:
"""
Defines the input types for the function.
Returns:
Dict[str, Any]: A dictionary specifying required input types.
"""
return {
"required": {
"dataframe": ("DATAFRAME", {}),
"column_names": ("STRING", {"default": ""}),
"date_format": ("STRING", {"default": ""}),
}
}
RETURN_TYPES: tuple = ("DATAFRAME",)
FUNCTION: str = "f"
CATEGORY: str = "Data Analysis"
def f(self, dataframe: pd.DataFrame, column_names: str, date_format: str) -> tuple:
"""
Converts one or more datetime columns in a DataFrame to string columns.
Args:
dataframe (DataFrame): The DataFrame.
column_names (str): A comma-separated string of column names containing datetime values.
date_format (str): A date format string for strftime.
Returns:
tuple: A tuple containing the DataFrame with formatted date columns.
"""
date_columns = [col.strip() for col in column_names.split(",")]
df2 = dataframe.copy()
df2[date_columns] = df2[date_columns].apply(
# Keep the datetime column check in case the user specifies the non-date column
lambda col: col.dt.strftime(date_format) if ptypes.is_datetime64_any_dtype(col) else col
)
return (df2,)