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Added Pandas Loc Cell Str node.

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HowToSD
2025-02-14 16:59:43 -08:00
parent a9736caea2
commit 21ccfa8178
6 changed files with 70 additions and 1 deletions
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__init__.py
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@@ -10,6 +10,7 @@ from .modules.pandas_wrapper.pandas_index import PandasIndex
from .modules.pandas_wrapper.pandas_join import PandasJoin
from .modules.pandas_wrapper.pandas_load_csv import PandasLoadCSV
from .modules.pandas_wrapper.pandas_load_csv_with_index import PandasLoadCSVWithIndex
from .modules.pandas_wrapper.pandas_loc_cell_str import PandasLocCellStr
from .modules.pandas_wrapper.pandas_save_csv import PandasSaveCSV
from .modules.pandas_wrapper.pandas_select_columns import PandasSelectColumns
from .modules.pandas_wrapper.pandas_select_rows import PandasSelectRows
@@ -33,6 +34,7 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
"PandasJoin": PandasJoin,
"PandasLoadCSV": PandasLoadCSV,
"PandasLoadCSVWithIndex": PandasLoadCSVWithIndex,
"PandasLocCellStr": PandasLocCellStr,
"PandasSaveCSV": PandasSaveCSV,
"PandasSelectColumns": PandasSelectColumns,
"PandasSelectRows": PandasSelectRows,
@@ -59,6 +61,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
"PandasJoin": "Pandas Join",
"PandasLoadCSV": "Pandas Load CSV",
"PandasLoadCSVWithIndex": "Pandas Load CSV with Index",
"PandasLocCellStr": "Pandas Loc Cell Str",
"PandasSaveCSV": "Pandas Save CSV",
"PandasSelectColumns": "Pandas Select Columns",
"PandasSelectRows": "Pandas Select Rows",
docs/images/loc_cell_str.png
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docs/ug.md
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@@ -22,6 +22,7 @@ You can right-click, select **Add Node**, go to **Data Analysis**, and look for
| **Pandas Index** | Extract row labels (index) |
| **Pandas Select Columns** | Select specific columns from a DataFrame |
| **Pandas Select Rows** | Filter rows based on conditions |
| **Pandas Loc Cell Str** | Select a cell by row label (index) and column label (index) |
| **Pandas Join** | Join two DataFrames |
| **Pandas Head** | Extract the first few rows |
| **Pandas Show DataFrame** | Display DataFrame contents |
@@ -63,6 +64,13 @@ Use **Pandas Select Columns** for selecting columns, **Pandas Select Rows** to s
![Pandas select subset](images/subset.jpg)
## Selecting a cell by row label (index) and column label (index)
Use **Pandas Loc Cell Str** for selecting a cell by row label (index) and column label (index). Data type of the labels needs to be specified as shown below:
![Pandas Loc Cell Str](images/loc_cell_str.png)
This node outputs the cell value as a string irrespective of the actual cell data type.
## Joining DataFrames
Use **Pandas Join**. You can select from inner, left, right, outer joins.
First, use two **Pandas Load CSV** nodes to load two CSVs for the left DataFrame and right DataFrame.
examples/workflows/loc_cell_str.json
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{"last_node_id":12,"last_link_id":8,"nodes":[{"id":8,"type":"PandasShowText","pos":[3.62848162651062,1602.7939453125],"size":[210,78.36762237548828],"flags":{},"order":6,"mode":0,"inputs":[{"name":"text","type":"STRING","link":5,"widget":{"name":"text"}}],"outputs":[{"name":"STRING","type":"STRING","links":null,"shape":6}],"properties":{"Node name for S&R":"PandasShowText"},"widgets_values":["","30"]},{"id":3,"type":"PandasCreateWithIndex","pos":[-607.1369018554688,1436.66015625],"size":[210,136.70285034179688],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"name":"STRING","type":"STRING","links":[2,4],"slot_index":0}],"properties":{"Node name for S&R":"PandasCreateWithIndex"},"widgets_values":["index,A,B\nUS,1,10\nFrance,2,20\nGermany,3,30",0]},{"id":7,"type":"PandasLocCellStr","pos":[-294.200927734375,1601.82275390625],"size":[267.4131774902344,156.59359741210938],"flags":{},"order":3,"mode":0,"inputs":[{"name":"dataframe_json","type":"STRING","link":4,"widget":{"name":"dataframe_json"}}],"outputs":[{"name":"STRING","type":"STRING","links":[5],"slot_index":0}],"properties":{"Node name for S&R":"PandasLocCellStr"},"widgets_values":["","Germany","string","B","string"]},{"id":4,"type":"PandasShowDataFrame","pos":[-288.99395751953125,1434.0804443359375],"size":[210,116.62158966064453],"flags":{},"order":2,"mode":0,"inputs":[{"name":"dataframe_json","type":"STRING","link":2,"widget":{"name":"dataframe_json"}}],"outputs":[{"name":"STRING","type":"STRING","links":null,"shape":6}],"properties":{"Node name for S&R":"PandasShowDataFrame"},"widgets_values":[""," A B\nUS 1 10\nFrance 2 20\nGermany 3 30"]},{"id":10,"type":"PandasShowDataFrame","pos":[-293.0326232910156,1839.1920166015625],"size":[210,116.62158966064453],"flags":{},"order":4,"mode":0,"inputs":[{"name":"dataframe_json","type":"STRING","link":7,"widget":{"name":"dataframe_json"}}],"outputs":[{"name":"STRING","type":"STRING","links":null,"shape":6}],"properties":{"Node name for S&R":"PandasShowDataFrame"},"widgets_values":[""," A B\n0 1 100\n1 2 200\n2 3 300"]},{"id":9,"type":"PandasCreate","pos":[-610.7672119140625,1838.9556884765625],"size":[210,113.97640228271484],"flags":{},"order":1,"mode":0,"inputs":[],"outputs":[{"name":"STRING","type":"STRING","links":[7,8],"slot_index":0}],"properties":{"Node name for S&R":"PandasCreate"},"widgets_values":["A,B\n1,100\n2,200\n3,300"]},{"id":12,"type":"PandasShowText","pos":[8.03433609008789,2006.485107421875],"size":[210,78.36762237548828],"flags":{},"order":7,"mode":0,"inputs":[{"name":"text","type":"STRING","link":6,"widget":{"name":"text"}}],"outputs":[{"name":"STRING","type":"STRING","links":null,"shape":6}],"properties":{"Node name for S&R":"PandasShowText"},"widgets_values":["","300"]},{"id":11,"type":"PandasLocCellStr","pos":[-291.8468933105469,2007.802734375],"size":[267.4131774902344,156.59359741210938],"flags":{},"order":5,"mode":0,"inputs":[{"name":"dataframe_json","type":"STRING","link":8,"widget":{"name":"dataframe_json"}}],"outputs":[{"name":"STRING","type":"STRING","links":[6],"slot_index":0}],"properties":{"Node name for S&R":"PandasLocCellStr"},"widgets_values":["","2","int","B","string"]}],"links":[[2,3,0,4,0,"STRING"],[4,3,0,7,0,"STRING"],[5,7,0,8,0,"STRING"],[6,11,0,12,0,"STRING"],[7,9,0,10,0,"STRING"],[8,9,0,11,0,"STRING"]],"groups":[],"config":{},"extra":{"ds":{"scale":0.8639278591604531,"offset":[1001.9775968055815,-1355.3011187363343]},"node_versions":{"data-analysis":"265c91a0caf6a287657b6aa20bfd9ad97728bfe5"},"ue_links":[]},"version":0.4}
modules/pandas_wrapper/pandas_loc_cell_str.py
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@@ -0,0 +1,57 @@
from typing import Any, Dict
from io import StringIO
import pandas as pd
class PandasLocCellStr:
"""
PandasLocCellStr:
A class for selecting a cell from a pandas DataFrame and output as a string.
"""
@classmethod
def INPUT_TYPES(cls) -> Dict[str, Any]:
"""
Defines the input types for the `cell` function.
Returns:
Dict[str, Any]: A dictionary specifying required input types.
"""
return {
"required": {
"dataframe_json": ("STRING", {"multiline": True}),
"row_index": ("STRING", {"default": ""}),
"row_index_type":(("string", "int"),),
"column_index": ("STRING", {"default": ""}),
"column_index_type":(("string", "int"),)
}
}
RETURN_TYPES: tuple = ("STRING",)
FUNCTION: str = "cell"
CATEGORY: str = "Data Analysis"
def cell(self, dataframe_json: str,
row_index: str,
row_index_type: str,
column_index: str,
column_index_type: str) -> tuple:
"""
Selects specific cell from a pandas DataFrame.
Args:
dataframe_json (str): A JSON string representation of the DataFrame.
row_index (str): The row index (row label) for the cell.
row_index_type (str): The data type of the row index.
column_index (str): The column index (row label) for the cell.
column_index_type (str): The data type of the column index.
Returns:
tuple: A tuple containing the value of the cell in string.
"""
# Deserialize JSON string to DataFrame
df = pd.read_json(StringIO(dataframe_json))
row_index = int(row_index) if row_index_type == "int" else row_index
column_index = int(column_index) if column_index_type == "int" else column_index
value = df.loc[row_index, column_index]
return (str(value),)
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 = "1.0.8"
version = "1.0.9"
license = {file = "LICENSE"}
[project.urls]