nodes/HowToSD-ComfyUI-Data-Analysis
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Added Pandas Horizontal Concat and Pandas Vertical Concat nodes.

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HowToSD
2025-02-18 01:36:09 -08:00
parent d6955792b6
commit 754a9705d5
7 changed files with 99 additions and 2 deletions
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__init__.py
+7 -1
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@@ -17,6 +17,7 @@ from .modules.pandas_wrapper.pandas_div_series import PandasDivSeries
from .modules.pandas_wrapper.pandas_dropna import PandasDropNA
from .modules.pandas_wrapper.pandas_group_by import PandasGroupBy
from .modules.pandas_wrapper.pandas_head import PandasHead
from .modules.pandas_wrapper.pandas_horizontal_concat import PandasHorizontalConcat
from .modules.pandas_wrapper.pandas_iloc_row_series import PandasIlocRowSeries
from .modules.pandas_wrapper.pandas_iloc_rows_dataframe import PandasIlocRowsDataFrame
from .modules.pandas_wrapper.pandas_index import PandasIndex
@@ -55,6 +56,7 @@ from .modules.pandas_wrapper.pandas_sum import PandasSum
from .modules.pandas_wrapper.pandas_summary import PandasSummary
from .modules.pandas_wrapper.pandas_to_string import PandasToString
from .modules.pandas_wrapper.pandas_var import PandasVar
from .modules.pandas_wrapper.pandas_vertical_concat import PandasVerticalConcat
T = TypeVar("T")
@@ -78,6 +80,7 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
"PandasDropNA": PandasDropNA,
"PandasGroupBy": PandasGroupBy,
"PandasHead": PandasHead,
"PandasHorizontalConcat": PandasHorizontalConcat,
"PandasIlocRowSeries": PandasIlocRowSeries,
"PandasIlocRowsDataFrame": PandasIlocRowsDataFrame,
"PandasIndex": PandasIndex,
@@ -115,7 +118,8 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
"PandasSum": PandasSum,
"PandasSummary": PandasSummary,
"PandasToString": PandasToString,
"PandasVar": PandasVar
"PandasVar": PandasVar,
"PandasVerticalConcat": PandasVerticalConcat
}
"""
NODE_CLASS_MAPPINGS (Dict[str, Type[T]]):
@@ -141,6 +145,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
"PandasDropNA": "Pandas Drop NA",
"PandasGroupBy": "Pandas Group By",
"PandasHead": "Pandas Head",
"PandasHorizontalConcat": "Pandas Horizontal Concat",
"PandasIlocRowSeries": "Pandas Iloc Row Series",
"PandasIlocRowsDataFrame": "Pandas Iloc Rows DataFrame",
"PandasIndex": "Pandas Index",
@@ -179,6 +184,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
"PandasSummary": "Pandas Summary",
"PandasToString": "Pandas To String",
"PandasVar": "Pandas Var",
"PandasVerticalConcat": "Pandas Vertical Concat"
}
"""
NODE_DISPLAY_NAME_MAPPINGS (Dict[str, str]):
docs/images/concat.png
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docs/ug.md
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@@ -79,6 +79,8 @@ This page was initially created using an older version of ComfyUI Data Analysis,
| **Pandas Mul** | Multiply two DataFrames |
| **Pandas Pow** | Raise a DataFrame to the power of another DataFrame |
| **Pandas Sub** | Subtract a DataFrame from another DataFrame |
| **Pandas Horizontal Concat** | Concatenate two DataFrames horizontally (axis=1) |
| **Pandas Vertical Concat** | Concatenate two DataFrames vertically (axis=0) |
**Binary operators for a DataFrame and a Series**
| Node Name | Functionality |
@@ -272,6 +274,10 @@ For binary operation for two DataFrames, you can use below operators:
| **Pandas Mul** | Multiply two DataFrames |
| **Pandas Pow** | Raise a DataFrame to the power of another DataFrame |
| **Pandas Sub** | Subtract a DataFrame from another DataFrame |
| **Pandas Horizontal Concat** | Concatenate two DataFrames horizontally (axis=1) |
| **Pandas Vertical Concat** | Concatenate two DataFrames vertically (axis=0) |
![Pandas Vertical Concat & Horizontal Concat](images/concat.png)
For binary operation for a DataFrame and a Series, you can use below operators:
| Node Name | Functionality |
examples/workflows/concat.json
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@@ -0,0 +1 @@
{"last_node_id":16,"last_link_id":14,"nodes":[{"id":5,"type":"PandasCreate","pos":[340.3745422363281,2836.533935546875],"size":[210,94.9962158203125],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"name":"DATAFRAME","type":"DATAFRAME","links":[11],"slot_index":0,"localized_name":"DATAFRAME"}],"properties":{"Node name for S&R":"PandasCreate"},"widgets_values":["A,B\n500,5000\n600,6000"]},{"id":15,"type":"PandasCreate","pos":[340.7720031738281,2668.344970703125],"size":[210,106.02879333496094],"flags":{},"order":1,"mode":0,"inputs":[],"outputs":[{"name":"DATAFRAME","type":"DATAFRAME","links":[13],"slot_index":0,"localized_name":"DATAFRAME"}],"properties":{"Node name for S&R":"PandasCreate"},"widgets_values":["A,B\n1,10\n2,20"]},{"id":14,"type":"PandasCreate","pos":[340.77191162109375,2421.255126953125],"size":[210,94.9962158203125],"flags":{},"order":2,"mode":0,"inputs":[],"outputs":[{"name":"DATAFRAME","type":"DATAFRAME","links":[14],"slot_index":0,"localized_name":"DATAFRAME"}],"properties":{"Node name for S&R":"PandasCreate"},"widgets_values":["C,D\n500,5000\n600,6000"]},{"id":4,"type":"PandasCreate","pos":[342.41656494140625,2254.481201171875],"size":[210,106.02879333496094],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[{"name":"DATAFRAME","type":"DATAFRAME","links":[4],"slot_index":0,"localized_name":"DATAFRAME"}],"properties":{"Node name for S&R":"PandasCreate"},"widgets_values":["A,B\n1,10\n2,20"]},{"id":6,"type":"PandasHorizontalConcat","pos":[682.4112548828125,2363.37939453125],"size":[302.4000244140625,46],"flags":{},"order":5,"mode":0,"inputs":[{"name":"a_dataframe","type":"DATAFRAME","link":4,"localized_name":"a_dataframe"},{"name":"b_dataframe","type":"DATAFRAME","link":14,"localized_name":"b_dataframe"}],"outputs":[{"name":"DATAFRAME","type":"DATAFRAME","links":[6],"slot_index":0,"localized_name":"DATAFRAME"}],"properties":{"Node name for S&R":"PandasHorizontalConcat"},"widgets_values":[]},{"id":2,"type":"PandasShowDataFrame","pos":[1043.4501953125,2363.7451171875],"size":[210,104.60636138916016],"flags":{},"order":7,"mode":0,"inputs":[{"name":"dataframe","type":"DATAFRAME","link":6,"localized_name":"dataframe"}],"outputs":[{"name":"STRING","type":"STRING","links":[],"slot_index":0,"shape":6,"localized_name":"STRING"}],"properties":{"Node name for S&R":"PandasShowDataFrame"},"widgets_values":[" A B C D\n0 1 10 500 5000\n1 2 20 600 6000"]},{"id":13,"type":"PandasVerticalConcat","pos":[684.0742797851562,2751.436767578125],"size":[267,46],"flags":{},"order":4,"mode":0,"inputs":[{"name":"a_dataframe","type":"DATAFRAME","link":13,"localized_name":"a_dataframe"},{"name":"b_dataframe","type":"DATAFRAME","link":11,"localized_name":"b_dataframe"}],"outputs":[{"name":"DATAFRAME","type":"DATAFRAME","links":[12],"slot_index":0,"localized_name":"DATAFRAME"}],"properties":{"Node name for S&R":"PandasVerticalConcat"},"widgets_values":[]},{"id":12,"type":"PandasShowDataFrame","pos":[1046.1783447265625,2746.99560546875],"size":[210,178.95204162597656],"flags":{},"order":6,"mode":0,"inputs":[{"name":"dataframe","type":"DATAFRAME","link":12,"localized_name":"dataframe"}],"outputs":[{"name":"STRING","type":"STRING","links":[],"slot_index":0,"shape":6,"localized_name":"STRING"}],"properties":{"Node name for S&R":"PandasShowDataFrame"},"widgets_values":[" A B\n0 1 10\n1 2 20\n2 500 5000\n3 600 6000"]}],"links":[[4,4,0,6,0,"DATAFRAME"],[6,6,0,2,0,"DATAFRAME"],[11,5,0,13,1,"DATAFRAME"],[12,13,0,12,0,"DATAFRAME"],[13,15,0,13,0,"DATAFRAME"],[14,14,0,6,1,"DATAFRAME"]],"groups":[],"config":{},"extra":{"ds":{"scale":0.7622054019658013,"offset":[41.88950958615887,-1970.1509315749547]},"node_versions":{"data-analysis":"63c4dadeb8ece016f51f3beca9266a33a508d313"},"ue_links":[]},"version":0.4}
modules/pandas_wrapper/pandas_horizontal_concat.py
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from typing import Any, Dict
import pandas as pd
class PandasHorizontalConcat:
"""
PandasHorizontalConcat:
A class for horizontally concatenating two pandas DataFrames.
"""
@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": {
"a_dataframe": ("DATAFRAME", {}),
"b_dataframe": ("DATAFRAME", {})
}
}
RETURN_TYPES: tuple = ("DATAFRAME",)
FUNCTION: str = "f"
CATEGORY: str = "Data Analysis"
def f(self, a_dataframe: pd.DataFrame, b_dataframe: pd.DataFrame) -> tuple:
"""
Horizontally concatenate two pandas DataFrames.
Args:
a_dataframe (DataFrame): The first DataFrame.
b_dataframe (DataFrame): The second DataFrame.
Returns:
tuple: A tuple containing the concatenated DataFrame.
"""
df_out = pd.concat([a_dataframe, b_dataframe], axis=1)
return (df_out,)
modules/pandas_wrapper/pandas_vertical_concat.py
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@@ -0,0 +1,42 @@
from typing import Any, Dict
import pandas as pd
class PandasVerticalConcat:
"""
PandasVerticalConcat:
A class for vertically concatenating two pandas DataFrames.
"""
@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": {
"a_dataframe": ("DATAFRAME", {}),
"b_dataframe": ("DATAFRAME", {})
}
}
RETURN_TYPES: tuple = ("DATAFRAME",)
FUNCTION: str = "f"
CATEGORY: str = "Data Analysis"
def f(self, a_dataframe: pd.DataFrame, b_dataframe: pd.DataFrame) -> tuple:
"""
Vertically concatenate two pandas DataFrames.
Args:
a_dataframe (DataFrame): The first DataFrame.
b_dataframe (DataFrame): The second DataFrame.
Returns:
tuple: A tuple containing the concatenated DataFrame.
"""
df_out = pd.concat([a_dataframe, b_dataframe], axis=0, ignore_index=True)
return (df_out,)
pyproject.toml
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@@ -1,7 +1,7 @@
[project]
name = "data-analysis"
description = "Data analysis custom modules for ComfyUI - Use Pandas & Matplotlib within ComfyUI"
version = "1.1.1"
version = "1.1.2"
license = {file = "LICENSE"}
[project.urls]