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CRYPTOCURRENCY
8 February, 2025
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Turn the list to location control Python

As a trader of cryptocurrencies, it is very important to obtain accurate and organized information in order to make information -based decisions. In this article, we look at how to convert Binance Foucers API price list of pandas that can be used to manage drive.

Prerequisites:

  • Install binance_f library using PIP:PIP install binance_f

  • Set Binance API License Information

  • Bring the necessary libraries and set the API key

Code:

`Python

From binance_F import click, order book

Ethereum: How to convert list into DataFrame in Python (Binance Futures API)

Set Application Subscription Access Information and Customer Notifications

API_KEY = 'YOUR_API_KEY'

API_SECRET = 'Your_API_SECRET'

Request_client = RequestClient (API_KEY = API_KEY, API_SECRET = API_SECRET)

Def Convert_TO_DF (Prices):

"" ""

Turn the price list to pandas.

Parameters:

Prices (List): List of Convertible Prices

Return:

PD.Dataframe: Converted DATAFRAME

"" ""

Order_book = Request_client.get_orderbook ('BTCUSDT')

Create a dictionary to save price and volume data

Data = {

'Price': [],

'Volume': []

}

Entrance

If entry.price> record.Volume:

Information ['Price']. Appendix (record.Price)

Data ['Volume']. Appendix (Record.Volume)

df = pd.dataframe (data)

Return to DF

For use

Prices = [100.0, 120.0, 110.0, 130.0, 115.0]

BTC-SUSD examples

DF = Convert_to_df (prices)

Print (DF)

Explanation:

  • First we produce the necessary libraries and set up the application subscription access information.

  • We create “RequestClient” with the API key and mystery.

  • “Convert_to_DF ()” The action takes into account the rate price list and uses binance fuzers to apply for order books for the entry at all costs.

  • We connect the price and volume information to each entry with the dictionary (“data”).

  • We create a panda data frame from the dictionary and return it.

  • In the instrument section we show how “Convert_TO_DF () is the price list.

Tips and Variations:

  • You can edit the Convert_TO_DF () different types of price information (eg candle board).

  • If you need to process additional information (eg trend analysis), you may want to consider using a more modern library, such as Pandaas-Dataaser.

  • To optimize performance, a large amount of data for API processing requires a cache or a queue -based approach.

By following this article and adjusting it to your specific needs, you can effectively transform the price list to Panda’s data frame Python.

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