For a complete list of nodes, refer to the Node reference.
This page was originally created using an older version of ComfyUI Data Analysis, so some screenshots and references may include deprecated node names and outdated socket data types. Additionally, it covers only a portion of the current node set. Refer to the Node Reference for specific functionality.
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.
You can right-click, select Add Node, go to Data Analysis, and look for the following nodes:
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.
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.
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.
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.
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:
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.
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.
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:
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.
Specify the common column in the "on column name" field, and set the join method to inner in the Pandas Join node.
Specify the common column in the "on column name" field, and set the join method to left in the Pandas Join node.
Specify the common column in the "on column name" field, and set the join method to right in the Pandas Join node.
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.
Use Pandas Head.
Use Pandas Is NA for checking DataFrame for missing values, and Pandas Drop NA for dropping missing values from DataFrame.
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.
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.
Use Pandas Show Series node.
Use Pandas Summary to extract statistics then connect the output to Pandas Show Text for display.
Use Pandas Sort.
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.
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 |
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.
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.
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 |
