Parse Datetime (DP_PDT)

In data analysis and various applications, datetime information is a crucial component. To leverage this information effectively, we rely on the process of datetime data parsing.

DateTime data parsing involves extracting meaningful details from datetime values and converting them into formats that computers can understand. This process includes breaking down datetime strings into individual components such as year, month, day, hour, minute, and second.

By parsing datetime data, we enable our systems to recognize and use chronological patterns. For example, in financial analysis, parsing datetime information allows us to identify trading hours, weekdays, or specific times of the day.

The parsed datetime data can then be employed to align different datasets, create time-based features, and enable sophisticated chronological analyses. Whether it's for predicting trends, analyzing patterns, or understanding user interactions, datetime data parsing empowers us to unlock valuable insights embedded within time-related data.

Sample Request

{
    "project_id": 1,
    "parent_id": 5,
    "block_id": 6,
    "function_code": "DP_PDT",
    "args": {
        "index": 0,
        "drop": true
    }
}

Request Parameters

Parse Datetime

POST https://autogon.ai/api/v1/engine/start

Request Body

Name	Type	Description
project_id*	int	current project ID
parent_id*	int	parent block ID
block_id*	int	current block ID
function_code*	String	block's function code
drop*	bool	specifies if you want to drop the column after parsing the original date
args	object	block arguments
index*	int	column index to apply time stepping function

200: OK Data Encode Successful

{
    "status": "true",
    "message": {
        "id": 3,
        "project": 1,
        "block_id": 7,
        "parent_id": 6,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": ""
    }
}

Python

// Some code

Node