169 lines
6.0 KiB
Python
169 lines
6.0 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 PandasMelt:
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"""
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Performs an unpivot operation (melt) on a pandas DataFrame.
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## Understanding Melt (Unpivot)
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The melt operation converts a DataFrame from **wide format** (where multiple columns represent separate variables) into **long format** (where values are stacked under a single column).
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### Example: Frozen Food Prices Table
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Given the following DataFrame:
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```
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Frozen Food Prices
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Manufacturer Pizza Burger
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---------------------------------------------------
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Aimee Italian Foods 10 12
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Olivia Cuisine 11 8
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Maddie Foods 12 7
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```
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To demonstrate **melting a single column**, we will **only transform the `"Pizza"` column** and **temporarily exclude `"Burger"`** from the output. `"Burger"` is not affected in this step because we are only selecting `"Pizza"` for melting.
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```
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Frozen Food Prices
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Manufacturer X Y
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---------------------------------------------------
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Aimee Italian Foods Pizza 10
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Olivia Cuisine Pizza 11
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Maddie Foods Pizza 12
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```
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Here:
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- Column `X` is assigned to `"Item"` (`var_name`).
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- Column `Y` is assigned to `"Price"` (`value_name`).
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- `"Pizza"` is explicitly specified in `value_vars` as the column being unpivoted.
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- `"Manufacturer"` remains unchanged and is included in `id_vars` to preserve row identity.
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The final transformed DataFrame:
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```
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Frozen Food Prices
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Manufacturer Item Price
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----------------------------------------------------
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Aimee Italian Foods Pizza 10
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Olivia Cuisine Pizza 11
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Maddie Foods Pizza 12
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```
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### Expanding the Melt Operation
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If we want to **melt both `"Pizza"` and `"Burger"` together**, we specify both columns in `value_vars`:
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```
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Frozen Food Prices
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Manufacturer Item Price
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----------------------------------------------------
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Aimee Italian Foods Pizza 10
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Olivia Cuisine Pizza 11
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Maddie Foods Pizza 12
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Aimee Italian Foods Burger 12
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Olivia Cuisine Burger 8
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Maddie Foods Burger 7
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```
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Here, we explicitly define:
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- `"Pizza"` and `"Burger"` in `value_vars`.
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- A new `"Item"` column (`var_name`), which holds the original column names (`"Pizza"`, `"Burger"`).
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- A `"Price"` column, which stores the corresponding values.
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To specify both columns in this node, provide:
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```
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Pizza,Burger
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```
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This node automatically converts the string into a list and passes it to `pandas.DataFrame.melt()`.
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Refer to the [Pandas documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.melt.html) for more information.
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## Limitation
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MultiIndex columns are not supported.
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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 `select_columns` 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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"id_vars": ("STRING", {"default": ""}),
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"value_vars": ("STRING", {"default": ""}),
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"var_name": ("STRING", {"default": ""}),
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"value_name": ("STRING", {"default": ""}),
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"ignore_index": ("BOOLEAN", {"default": True})
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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,
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id_vars: str,
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value_vars: str,
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var_name: str,
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value_name: str,
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ignore_index: bool) -> tuple:
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"""
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Selects specific columns from a pandas DataFrame.
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Args:
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dataframe (DataFrame): The input DataFrame.
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id_vars (str): Comma-separated string to specify ID variable columns.
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value_vars (str): Comma-separated string to specify column(s) to be unpivoted.
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var_name (str): New column label for the column that contains original column labels as row values.
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value_name (str): Name of the new column that holds the values for columns specified in `value_vars`.
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ignore_index (bool): Flag that specifies whether to keep the original index.
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If ``True``, a new sequential index is generated.
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If ``False``, the original index is retained and repeated as needed.
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Returns:
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tuple: A tuple containing the DataFrame.
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"""
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id_vars = column_labels_string_to_list(dataframe, id_vars)
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value_vars = column_labels_string_to_list(dataframe, value_vars)
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var_name = column_labels_string_to_list(dataframe, var_name, no_check=True)
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value_name = column_labels_string_to_list(dataframe, value_name, no_check=True)
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if len(id_vars) == 1:
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id_vars = id_vars[0]
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if len(value_vars) == 1:
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value_vars = value_vars[0]
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if len(var_name) == 0:
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var_name = None
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elif len(var_name) == 1:
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var_name = var_name[0]
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if len(value_name) == 0:
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value_name = None
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elif len(value_name) == 1:
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value_name = value_name[0]
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df2 = dataframe.melt(
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id_vars=id_vars,
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value_vars=value_vars,
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var_name=var_name,
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value_name=value_name,
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ignore_index=ignore_index)
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return (df2,)
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