---
title: Table Operations
slug: /dataframe-operations
---
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:::tip
Prior to Langflow 1.9.0, this component was named **DataFrame Operations**.
:::
The **Table Operations** component performs operations on [`Table`](/data-types#table) rows and columns, including schema changes, record changes, sorting, and filtering.
For all options, see [Table Operations parameters](#table-operations-parameters).
The output is a new `Table` containing the modified data after running the selected operation.
## Use the Table Operations component in a flow
The following steps explain how to configure a **Table Operations** component in a flow.
You can follow along with an example or use your own flow.
The only requirement is that the preceding component must create `Table` output that you can pass to the **Table Operations** component.
1. Create a new flow or use an existing flow.
Example: API response extraction flow
The following example flow uses five components to extract `JSON` from an API response, transform it to a `Table`, and then perform further processing on tabular data using a **Table Operations** component.
The sixth component, **Chat Output**, is optional in this example.
It only serves as a convenient way for you to view the final output in the **Playground**, rather than inspecting the component logs.
If you want to use this example to test the **Table Operations** component, do the following:
1. Create a flow with the following components:
* **API Request**
* **Language Model**
* **Smart Transform**
* **Type Convert**
2. Configure the [**Smart Transform** component](/smart-transform) and its dependencies:
* **API Request**: Configure the [**API Request** component](/api-request) to get JSON data from an endpoint of your choice, and then connect the **API Response** output to the **Smart Transform** component's **Data** input.
* **Language Model**: Select your preferred provider and model, and then enter a valid API key.
Change the output to **Language Model**, and then connect the `LanguageModel` output to the **Smart Transform** component's **Language Model** input.
* **Smart Transform**: In the **Instructions** field, enter natural language instructions to extract data from the API response.
Your instructions depend on the response content and desired outcome.
For example, if the response contains a large `result` field, you might provide instructions like `explode the result field out into a Data object`.
3. Convert the **Smart Transform** component's output from `JSON` to `Table`:
1. Connect the **Filtered Data** output to the **Type Convert** component's **JSON** input.
2. Set the **Type Convert** component's **Output Type** to **Table**.
Now the flow is ready for you to add the **Table Operations** component.
2. Add a **Table Operations** component to the flow, and then connect `Table` output from another component to the **Table** input.
All operations in the **Table Operations** component require at least one `Table` input from another component.
If a component doesn't produce `Table` output, you can use another component, such as the [**Type Convert** component](/type-convert), to reformat the data before passing it to the **Table Operations** component.
Alternatively, you could consider using a component that is designed to process the original data type, such as the [**Parser** component](/parser) or [**JSON Operations** component](/data-operations).
If you are following along with the example flow, connect the **Type Convert** component's **Table Output** port to the **Table** input.
3. In the **Operations** field, select the operation you want to perform on the incoming `Table`.
For example, the **Filter** operation filters the rows based on a specified column and value.
:::tip
You can select only one operation.
If you need to perform multiple operations on the data, you can chain multiple **Table Operations** components together to execute each operation in sequence.
For more complex multi-step operations, like dramatic schema changes or pivots, consider using an LLM-powered component, like the [**Structured Output** component](/structured-output) or [**Smart Transform** component](/smart-transform), as a replacement or preparation for the **Table Operations** component.
:::
If you're following along with the example flow, select any operation that you want to apply to the data that was extracted by the **Smart Transform** component.
To view the contents of the incoming `Table`, click **Run component** on the **Type Convert** component, and then **Inspect output**.
If the `Table` seems malformed, click **Inspect output** on each upstream component to determine where the error occurs, and then modify your flow's configuration as needed.
For example, if the **Smart Transform** component didn't extract the expected fields, modify your instructions or verify that the given fields are present in the **API Response** output.
4. Configure the operation's parameters.
The specific parameters depend on the selected operation.
For example, if you select the **Filter** operation, you must define a filter condition using the **Column Name**, **Filter Value**, and **Filter Operator** parameters.
For more information, see [Table Operations parameters](#table-operations-parameters)
5. To test the flow, click **Run component** on the **Table Operations** component, and then click **Inspect output** to view the new `Table` created from the **Filter** operation.
If you want to view the output in the **Playground**, connect the **Table Operations** component's output to a **Chat Output** component, rerun the **Table Operations** component, and then click **Playground**.
For another example, see [Conditional looping](/loop#conditional-looping).
## Table Operations parameters
Most **Table Operations** parameters are conditional because they only apply to specific operations.
The only permanent parameters are **Table** (`df`), which is the `Table` input, and **Operation** (`operation`), which is the operation to perform on the `Table`.
Once you select an operation, the conditional parameters for that operation appear on the **Table Operations** component.
The **Add Column** operation allows you to add a new column to the `Table` with a constant value.
The parameters are **New Column Name** (`new_column_name`) and **New Column Value** (`new_column_value`).
The **Concatenate** operation combines multiple input `Table` objects into a single `Table` by stacking their rows vertically.
For example, if you have Table A and Table B, they are combined into one table with all rows from Table A, and then all rows from Table B.
This operation uses the **Table** (`df`) input.
Connect multiple `Table` outputs to the same input to concatenate them.
The output is a single `Table` containing the combined rows from all connected inputs.
The **Drop Column** operation allows you to remove a column from the `Table`, specified by **Column Name** (`column_name`).
The **Filter** operation allows you to filter the `Table` based on a specified condition.
The output is a `Table` containing only the rows that matched the filter condition.
Provide the following parameters:
* **Column Name** (`column_name`): The name of the column to filter on.
* **Filter Value** (`filter_value`): The value to filter on.
* **Filter Operator** (`filter_operator`): The operator to use for filtering, one of `equals` (default), `not equals`, `contains`, `not contains`, `starts with`, `ends with`, `greater than`, or `less than`.
The **Head** operation allows you to retrieve the first `n` rows of the `Table`, where `n` is set in **Number of Rows** (`num_rows`).
The default is `5`.
The output is a `Table` containing only the selected rows.
The **Merge** operation combines two input `Table` objects by matching rows that share the same value in a selected column.
For example, if one table has `id` and `name`, and another has `id` and `department`, you can merge both tables on `id` to produce one table with `id`, `name`, and `department`.
Provide the following parameters:
* **Left Table** (`left_dataframe`): The primary table in the merge.
* **Right Table** (`right_dataframe`): The secondary table in the merge.
* **Merge On Column** (`merge_on_column`): The shared column used to match rows. This column must exist in both tables.
* **Merge Type** (`merge_how`): Controls which matched and unmatched rows are kept in the output. Use `inner` to keep only matching rows, `left` to keep all rows from the left table, `right` to keep all rows from the right table, or `outer` to keep all rows from both tables.
The output is a `Table` containing matched records from both inputs.
The **Rename Column** operation allows you to rename an existing column in the `Table`.
The parameters are **Column Name** (`column_name`), which is the current name, and **New Column Name** (`new_column_name`).
The **Replace Value** operation allows you to replace values in a specific column of the `Table`.
This operation replaces a target value with a new value.
All cells matching the target value are replaced with the new value in the new `Table` output.
Provide the following parameters:
* **Column Name** (`column_name`): The name of the column to modify.
* **Value to Replace** (`replace_value`): The value that you want to replace.
* **Replacement Value** (`replacement_value`): The new value to use.
The **Select Columns** operation allows you to select one or more specific columns from the `Table`.
Provide a list of column names in **Columns to Select** (`columns_to_select`).
In the visual editor, click **Add More** to add multiple fields, and then enter one column name in each field.
The output is a `Table` containing only the specified columns.
The **Sort** operation allows you to sort the `Table` on a specific column in ascending or descending order.
Provide the following parameters:
* **Column Name** (`column_name`): The name of the column to sort on.
* **Sort Ascending** (`ascending`): Whether to sort in ascending or descending order. If enabled (`true`), sorts in ascending order; if disabled (`false`), sorts in descending order. Default: Enabled (`true`)
The **Tail** operation allows you to retrieve the last `n` rows of the `Table`, where `n` is set in **Number of Rows** (`num_rows`).
The default is `5`.
The output is a `Table` containing only the selected rows.
The **Drop Duplicates** operation removes rows from the `Table` by identifying all duplicate values within a single column.
The only parameter is the **Column Name** (`column_name`).
When the flow runs, all rows with duplicate values in the given column are removed.
The output is a `Table` containing all columns from the original `Table`, but only rows with non-duplicate values.