# Pandas Query Component

The **Pandas query** component is designed to filter the data by applying pandas query on it. 

All component configurations are classified broadly into the following sections:

* ​[​Basic Information​](/data-pipeline-1/components/component-base-configuration.md)​
* Meta Information
* ​[Resource Configuration​](/data-pipeline-1/components/resource-configuration.md)​

{% hint style="success" %}
*Follow the steps given in the demonstration to configure the Pandas Query component.*
{% endhint %}

{% embed url="<https://files.gitbook.com/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fuq3RSHHup7fjHYaspk7y%2Fuploads%2FzlUuuTBMgpM47EpTLdaQ%2Fpandas%20query%20component%20(online-video-cutter.com).mp4?alt=media&token=b9c3282a-d21c-4f63-a1f9-53bc5cf34b46>" %}
Configuring the Pandas Query Component
{% endembed %}

## **Steps to Configure the Pandas Query Component**  <a href="#steps-to-configure-the-pandas-query-component" id="steps-to-configure-the-pandas-query-component"></a>

This component helps the users to get data as per the entered query.

* Drag and Drop the Pandas Query component to the Workflow Editor.

<figure><img src="https://859511478-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGDmsjfjJBNqow7Fo97cO%2Fuploads%2FjwJshWI8bl4QBbw09u2K%2Fimage.png?alt=media&#x26;token=acdedb49-04f6-4b3a-905c-5041650eda4c" alt=""><figcaption><p>Drag the Transformation group</p></figcaption></figure>

* The transformation component requires an input event (to get the data) and sends the data to an output event.
* Create two Events and drag them to the Workspace.
* Connect the input event and the output event to the component (The data in the input event can come from any Ingestion, Reader, or shared events).

<figure><img src="https://859511478-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGDmsjfjJBNqow7Fo97cO%2Fuploads%2FFDbBgS4IaoZdy36SX6ib%2Fimage.png?alt=media&#x26;token=131c3b19-1be3-445a-a41b-27df76c92507" alt=""><figcaption><p>Pandas Query Component</p></figcaption></figure>

* Click the Pandas Query component to get the component properties tabs.

### Basic Information Tab <a href="#basic-information-tab" id="basic-information-tab"></a>

The **Basic Information** tab opens by default while clicking the dragged component.

* Select an Invocation type from the drop-down menu to confirm the running mode of the Pandas Query component. Select ‘**Real-Time**’ or ‘**Batch**’ from the drop-down menu.
* **Deployment Type:** It displays the deployment type for the component. This field comes pre-selected.
* **Container Image Version**: It displays the image version for the docker container. This field comes pre-selected.
* **Failover Event**: Select a failover Event from the drop-down menu.
* **Batch Size (min 10)**: Provide the maximum number of records to be processed in one execution cycle (Min limit for this field is 10).

<figure><img src="https://859511478-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGDmsjfjJBNqow7Fo97cO%2Fuploads%2FAc96fKKJFx4t0l0K4DHL%2Fimage.png?alt=media&#x26;token=25d690f4-93e1-46cf-968e-06fd8ca714df" alt=""><figcaption></figcaption></figure>

### Meta Information Tab <a href="#meta-information-tab" id="meta-information-tab"></a>

Open the **Meta Information** tab and provide the connection-specific detail&#x73;**.**

* Enter a valid data query to fetch data.
* Provide the Table Name.

Note: The table name and query DF should be the same.

### Saving the Component Configuration <a href="#saving-the-component-configuration" id="saving-the-component-configuration"></a>

* Click the **Save Component in Storage** icon to save the component properties.
* A Notification message appears to notify the successful update of the component.

![](https://859511478-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGDmsjfjJBNqow7Fo97cO%2Fuploads%2F8VuodkH0vV6yYl6XIX87%2Fimage.png?alt=media\&token=42f31951-c63a-4dcf-8abe-bd5d404d262f)​

{% hint style="info" %}
*<mark style="color:green;">Please Note</mark><mark style="color:green;">:</mark> The samples of Pandas Query are given below together with the SQL query for the same statements.*
{% endhint %}

### **Samples Query Examples**&#x20;

| **SQL Query**                                                                                                                | **Pandas Query**                                                                                                                                         |
| ---------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| select id from airports where ident = 'KLAX'                                                                                 | airports \[airports.ident == 'KLAX'].id                                                                                                                  |
| select \* from airport\_freq where airport\_ident = 'KLAX' order by type                                                     | airports\[(airports.iso\_region == 'US-CA') & (airports.type == 'seaplane\_base')]                                                                       |
| select type, count(\*) from airports where iso\_country = 'US' group by type having count(\*) > 1000 order by count(\*) desc | <p>airports\[airports.iso\_country == 'US'].groupby('type').filter(lambda g: len(g) > 1000).groupby('type').size().sort\_values(ascending=False)<br></p> |


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