HowToSD-ComfyUI-Data-Analysis/docs/ug.md

User's Guide

For a complete list of nodes, refer to the Node reference.

Basic Concepts

How to connect nodes for DataFrame

To connect the DataFrame output from one Pandas node to another, move the wire toward the top-left of the text field. You may need to hover around the corner to find the correct connection point.

How to add a node

You can right-click, select Add Node, go to Data Analysis, and look for the following nodes:

Note This page was initially created using an older version of ComfyUI Data Analysis, so some of screenshots contain the names of deprecated nodes. Use the node name in text if there is any mismatch between text and screenshots.

List of nodes

Data loading, creation, saving

Node Name	Functionality
Pandas Load CSV	Load a CSV file
Pandas Load CSV With Encoding	Load a CSV file not encoded in utf-8
Pandas Load CSV With Index	Load a CSV file containing index
Pandas Load JSON	Load a JSON file
Pandas Save CSV	Save a DataFrame to a CSV file
Pandas Save JSON	Save a DataFrame to a JSON file
Pandas Create	Create a DataFrame from CSV text field
Pandas Create With Index	Create a DataFrame from CSV text field containing index
CDA JSON Create	Create JSON from a string entered in a text field

Selecting subset of data

Node Name	Functionality
Pandas Columns	Extract column labels
Pandas Index	Extract row labels (index)
Pandas Select Columns	Select specific columns from a DataFrame
Pandas Select Rows	Filter rows based on conditions
Pandas Iloc Row Series	Select a row by row integer position
Pandas Iloc Rows DataFrame	Select rows by the list of row integer positions
Pandas Loc Row Series	Select a row by row label (index)
Pandas Loc Cell Str	Select a cell by row label (index) and column label
Pandas Head	Extract the first few rows

Transforming data

Node Name	Functionality
Pandas Group By	Group rows and apply aggregation functions
Pandas Join	Join two DataFrames
Pandas Sort	Sort DataFrame by a column

Displaying data

Node Name	Functionality
Pandas Show DataFrame	Display DataFrame contents
Pandas Show Series	Display Series contents
Pandas Show Index	Display Index contents
Pandas Show Text	Display Text
Pandas Summary	Extract DataFrame statistics

Missing value processing

Node Name	Functionality
Pandas Is NA	Check DataFrame for missing values
Pandas Drop NA	Drop missing values from DataFrame

Summary statistics

Node Name	Functionality
Pandas Count	Number of rows
Pandas Max	Maximum value of row values
Pandas Mean	Mean of row values
Pandas Median	Median of row values
Pandas Min	Minimum value of row values
Pandas Mode	Mode of row values
Pandas Std	Standard deviation of row values
Pandas Sum	Sum of row values
Pandas Var	Variance of row values

Binary operators for two DataFrames

Node Name	Functionality
Pandas Add	Add two DataFrames
Pandas Div	Divide a DataFrame by another DataFrame
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
Pandas Add Series	Add a DataFrame and a Series
Pandas Div Series	Divide a DataFrame by a Series
Pandas Mul Series	Multiply a DataFrame by a Series
Pandas Pow Series	Raise a DataFrame to the power of a Series
Pandas Sub Series	Subtract a Series from another DataFrame

Converting to displayable string

Node Name	Functionality
Pandas Index To String	Convert Index to a displayable string
Pandas Series To String	Convert Series to a displayable string
Pandas To String	Convert DataFrame to a displayable string

Plot, chart

Node Name	Functionality
MPL Bar Chart	Generate a bar chart
MPL Line Plot	Generate a line plot
MPL Scatter Plot	Generate a scatter plot

A faster way is to double-click the canvas to open the node search dialog.

The examples directory contains workflows that load data from an example dataset included in this package. These workflows should give you a good idea about how to use these nodes.

Loading data

Use Pandas Load CSV node to load a CSV, or Pandas Load CSV With Index node if the CSV contains an index column. These nodes assume that the file is encoded in utf-8. If the file is not encoded in utf-8, use Pandas Load CSV With Encoding to specify the encoding of the file. This is necessary if the data was collected before utf-8 encoding became popular. For example, you can try "latin_1" for a file that contains accented characters in the US and Western Europe.

Use Pandas Load JSON node to load a JSON file.

These nodes emit a DataFrame object. CSV file path is relative to the ComfyUI installation directory unless you specify the absolute file path.

Saving data

Use Pandas Save CSV node to save the DataFrame to a CSV file. Use Pandas Save JSON node to save the DataFrame to a JSON file.

Creating data on the UI

To construct a DataFrame on the UI, use Pandas Create or Pandas Create with Index nodes. These nodes display the multiline text area where you can enter the data in CSV format. If the data contains an index column, use Pandas Create with Index and specify the index column.

Use CDA JSON Create to create JSON from a string entered in a text field. For example, if you want to create a list for a node that requires the list of integers for input, this node is ideal.

Extracting column and row labels

Use Pandas Columns and Pandas Index nodes. As these nodes output JSON-serialized Index object, to display the value, use Pandas Show Text node as shown below:

Selecting columns and rows

Use Pandas Select Columns for selecting columns. For rows, there are multiple options. Use Pandas Select Rows to select rows by a filter condition.

You can use Pandas Loc Row Series to select a row by row label (index). You can use Pandas Iloc Row Series to select a row by row integer position. These nodes returns a JSON-serialized Series object.

You can use Pandas Iloc Rows DataFrame to select rows by the list of row integer positions. This node returns a JSON-serialized DataFrame object. You need to pass the list of integers to this node. This can be done by using the CDA JSON Create node.

For Pandas Loc Row Series, data type of the labels needs to be specified as shown below:

For Pandas Iloc Row Series, you only need to specify the row integer position:

Note that in Pandas, integer position is an equivalent of an index in other software, while "index" means a unique label assigned to each row. See pandas.DataFrame.iloc.html.

Selecting a cell by row label (index) and column label

Use Pandas Loc Cell Str for selecting a cell by row label (index) and column label. Data type of the labels needs to be specified as shown below:

This node outputs the cell value as a string irrespective of the actual cell data type.

Grouping Rows

Use Pandas Group By to group rows and apply aggregation functions. Currently below aggregation functions are supported:

• sum
• mean
• count
• std
• min
• max

Below shows an example of using sum, mean, count functions:

Below shows an example of using std, min, max functions:

You can also have multiple columns to apply aggregation functions. In the below example, Qty and Price columns are aggregated for the Fruit column:

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.

Inner join

Specify the common column in the "on column name" field, and set the join method to inner in the Pandas Join node.

Left join

Specify the common column in the "on column name" field, and set the join method to left in the Pandas Join node.

Right join

Specify the common column in the "on column name" field, and set the join method to right in the Pandas Join node.

Outer join

Specify the common column in the "on column name" field, and set the join method to outer in the Pandas Join node.

The workflow file examples/workflows/join.json contains all of the above cases.

Extract the first few rows

Use Pandas Head.

Missing value processing

Use Pandas Is NA for checking DataFrame for missing values, and Pandas Drop NA for dropping missing values from DataFrame.

Display text

Use Pandas Show Text node.

Currently this node is using the code taken from ComfyUI-Custom-Scripts's Show Text custom node ( https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/py/show_text.py). This node has been copied to display text without requiring the user to install the above custom nodes package. However, if you already have ComfyUI-Custom-Scripts, you can also use the Show Text node from the package.

Displaying DataFrame contents

Use Pandas Show DataFrame node. When the number of rows is large, This node automatically hides middle rows during string conversion, so the display size should be always manageable.

Displaying Series contents

Use Pandas Show Series node.

Extracting DataFrame statistics

Use Pandas Summary to extract statistics then connect the output to Pandas Show Text for display.

Sorting DataFrame by a column

Use Pandas Sort.

Summary statistics

You can compute summary statistics for each column in a DataFrame using below nodes:

• Pandas Count to get the number of rows for the column.
• Pandas Min to get the minimum row value within a column.
• Pandas Max to get the maximum value of row values within a column.
• Pandas Mean to get the mean of row values within a column.
• Pandas Median to get the median of row values within a column.
• Pandas Mode to get the mode of row values within a column.
• Pandas Sum to get the sum of row values within a column.
• Pandas Std to get the standard deviation of row values within a column.
• Pandas Var to get the variance of row values within a column.

All nodes return a Series except for Pandas Mode, which returns a DataFrame. This is necessary because multiple values may occur with the highest frequency. For example:

apple
apple
apple
banana
donut
coconut
coconut
coconut

In this case, both apple and coconut are the mode.

Binary operators for DataFrames and Series

For binary operation for two DataFrames, you can use below operators:

Node Name	Functionality
Pandas Add	Add two DataFrames
Pandas Div	Divide a DataFrame by another DataFrame
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)

For binary operation for a DataFrame and a Series, you can use below operators:

Node Name	Functionality
Pandas Add Series	Add a DataFrame and a Series
Pandas Div Series	Divide a DataFrame by a Series
Pandas Mul Series	Multiply a DataFrame by a Series
Pandas Pow Series	Raise a DataFrame to the power of a Series
Pandas Sub Series	Subtract a Series from another DataFrame

Convert DataFrame to a string

Use Pandas To String. This node is to convert the DataFrame to a displayable string, and this truncates the data when the data is large. This can be used to display the data content in a text display node (e.g. Show Text node). However, you can use Show DataFrame nodes to display the DataFrame content, so you do not normally need to use this.

Convert Series to a string

Use Pandas Series To String. This node converts a Pandas Series to a displayable string. You can use this as an input to the Pandas Show Text node.

Displaying charts or plots

Use one of the below:

Node	Purpose
MPL Bar Chart	Generate a bar chart
MPL Line Plot	Generate a line plot
MPL Scatter Plot	Generate a scatter plot