langflow/docs/versioned_docs/version-1.8.0/Components/dataframe-operations.mdx

---
title: DataFrame Operations
slug: /dataframe-operations
---

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The **DataFrame Operations** component performs operations on [`DataFrame`](/data-types#dataframe) (table) rows and columns, including schema changes, record changes, sorting, and filtering.
For all options, see [DataFrame Operations parameters](#dataframe-operations-parameters).

The output is a new `DataFrame` containing the modified data after running the selected operation.

## Use the DataFrame Operations component in a flow

The following steps explain how to configure a **DataFrame 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 `DataFrame` output that you can pass to the **DataFrame Operations** component.

1. Create a new flow or use an existing flow.

    <details>
    <summary>Example: API response extraction flow</summary>

    The following example flow uses five components to extract `Data` from an API response, transform it to a `DataFrame`, and then perform further processing on the tabular data using a **DataFrame 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.

    ![A flow that ingests an API response, extracts it to a DataFrame with a Smart Transform component, and the processes it through a DataFrame Operations component](/img/component-dataframe-operations.png)

    If you want to use this example to test the **DataFrame 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 `Data` output to `DataFrame`:

        1. Connect the **Filtered Data** output to the **Type Convert** component's **Data** input.
        2. Set the **Type Convert** component's **Output Type** to **DataFrame**.

    Now the flow is ready for you to add the **DataFrame Operations** component.

    </details>

2. Add a **DataFrame Operations** component to the flow, and then connect `DataFrame` output from another component to the **DataFrame** input.

    All operations in the **DataFrame Operations** component require at least one `DataFrame` input from another component.
    If a component doesn't produce `DataFrame` output, you can use another component, such as the [**Type Convert** component](/type-convert), to reformat the data before passing it to the **DataFrame 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 [**Data Operations** component](/data-operations).

    If you are following along with the example flow, connect the **Type Convert** component's **DataFrame Output** port to the **DataFrame** input.

3. In the **Operations** field, select the operation you want to perform on the incoming `DataFrame`.
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 **DataFrame 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 **DataFrame 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 `DataFrame`, click <Icon name="Play" aria-hidden="true" /> **Run component** on the **Type Convert** component, and then <Icon name="TextSearch" aria-hidden="true" /> **Inspect output**.
    If the `DataFrame` seems malformed, click <Icon name="TextSearch" aria-hidden="true" /> **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 [DataFrame Operations parameters](#dataframe-operations-parameters)

5. To test the flow, click <Icon name="Play" aria-hidden="true" /> **Run component** on the **DataFrame Operations** component, and then click <Icon name="TextSearch" aria-hidden="true" /> **Inspect output** to view the new `DataFrame` created from the **Filter** operation.

   If you want to view the output in the **Playground**, connect the **DataFrame Operations** component's output to a **Chat Output** component, rerun the **DataFrame Operations** component, and then click **Playground**.

For another example, see [Conditional looping](/loop#conditional-looping).

## DataFrame Operations parameters

Most **DataFrame Operations** parameters are conditional because they only apply to specific operations.

The only permanent parameters are **DataFrame** (`df`), which is the `DataFrame` input, and **Operation** (`operation`), which is the operation to perform on the `DataFrame`.
Once you select an operation, the conditional parameters for that operation appear on the **DataFrame Operations** component.

<Tabs>
<TabItem value="addcolumn" label="Add Column" default>

The **Add Column** operation allows you to add a new column to the `DataFrame` with a constant value.

The parameters are **New Column Name** (`new_column_name`) and **New Column Value** (`new_column_value`).

</TabItem>
<TabItem value="dropcolumn" label="Drop Column">

The **Drop Column** operation allows you to remove a column from the `DataFrame`, specified by **Column Name** (`column_name`).

</TabItem>
<TabItem value="filter" label="Filter">

The **Filter** operation allows you to filter the `DataFrame` based on a specified condition.
The output is a `DataFrame` 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`.

</TabItem>
<TabItem value="head" label="Head">

The **Head** operation allows you to retrieve the first `n` rows of the `DataFrame`, where `n` is set in **Number of Rows** (`num_rows`).
The default is `5`.

The output is a `DataFrame` containing only the selected rows.

</TabItem>
<TabItem value="renamecolumn" label="Rename Column">

The **Rename Column** operation allows you to rename an existing column in the `DataFrame`.

The parameters are **Column Name** (`column_name`), which is the current name, and **New Column Name** (`new_column_name`).

</TabItem>
<TabItem value="replacevalue" label="Replace Value">

The **Replace Value** operation allows you to replace values in a specific column of the `DataFrame`.
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 `DataFrame` 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.

</TabItem>
<TabItem value="selectcolumns" label="Select Columns">

The **Select Columns** operation allows you to select one or more specific columns from the `DataFrame`.

Provide a list of column names in **Columns to Select** (`columns_to_select`).
In the visual editor, click <Icon name="Plus" aria-hidden="true"/> **Add More** to add multiple fields, and then enter one column name in each field.

The output is a `DataFrame` containing only the specified columns.

</TabItem>
<TabItem value="sort" label="Sort">

The **Sort** operation allows you to sort the `DataFrame` 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`)

</TabItem>
<TabItem value="tail" label="Tail">

The **Tail** operation allows you to retrieve the last `n` rows of the `DataFrame`, where `n` is set in **Number of Rows** (`num_rows`).
The default is `5`.

The output is a `DataFrame` containing only the selected rows.

</TabItem>
<TabItem value="dropduplicates" label="Drop Duplicates">

The **Drop Duplicates** operation removes rows from the `DataFrame` 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 `DataFrame` containing all columns from the original `DataFrame`, but only rows with non-duplicate values.

</TabItem>
</Tabs>