Here you will find an article about the problem of the website edition in a pandas data frame with Binance:
The Problem: Infinite Loop of the Data Output to Pandas Dataframe
Since you have successfully integrated your website connection into your script in Binance, it is important to tackle another common challenge that results from this integration. The problem lies in the way data is collected and saved in a pandas data frame.
When using a webocket -API like Binance of webocets, every message received by the client is usally saved as a separate element in the “Date” attribute of an object returned by the Web Socket Connection. This can lead to exponential data growth in your pandas data frame, which leads to an infinite loop of the data output.
Why Does That Happen?
In weboctets -API, messages in pieces are sent with a time stamp and news content. If you subscribe to several streams (e.g. for Bitcoin Price and Couple Volues), Each Stream Receives its own separate sentence from messages. Since the website connection is carried out indefinitely, it continues to receive new messages from every stream and creates an infinite loop.
The Solution: Dealing with Infinite Data Expenses With Pandas
In order to avoid this infinite data output and prevent the memory of your script from flows, you can use severe strategies:
1. Use dask
Dask is a parallel computer library with which you can scale your calculation of large data records without having to use a full cluster. By using Dask, you can disassemble the massive amount of data into smaller pieces and process it in parallel, which reduces the memory consumption.
`Python
Import Thek.
Create An Empty Data Frame With 1000 Lines (An Appropriate Pieces of Pieces)
d = dd.from_pandas (pd.datataframe ({'price': np.random.rand (1000)}), nartitions = 10)
Create calculations of the data in pieces of 100 lines at the same time
d.compute ()
2. Use the “number” buffer
If you work with large binary data sets, you should use numpy’s buffer -Based approach to save and manipulate more efficiently.
`Python
Import nump as an np
By io import bytesio
Create An Empty List To Keep The Data (As A Numpy Buffer)
Data = []
Process Every Data Block In A Loop
For I within Reach (1000):
Read 10,000 bytes from the website connection to the buffer
chunk = np.fromberffer (with chunk_data * 10, dtype = np.int32) .tobytes ()
Take the Chunk to the List (As Numpy Buffer)
Date.Append (np.befajertenager (buffer = bytesio (chunk))))))
Combine the buffers to a single data frame
df = pd.concat (data)
Now you can carry out calculations on the entire data record with dask or pandas
3 .. Use a streaming data processing library
There are libraries such as “Starlette” that provide streaming data processing functions for Binance’s website API.
`Python
By Starlette Import Web, httpview
Import asyncio
Class Web SocketProcessor (HTMLVIEW):
Async Def Call (Self, Request):
Get the Message from the Website Connection
Message = Act Request.json ()
Process the message and save it in a data frame (use dask for efficient processing).
df = dd.from_pandas (pd.datataframe ({'content': [message ['data']}), nartitions = 10)
Calculate Calculates of the Data in Parallel with Dask
Result = waait dask.com Pute (DF). Compute ()
Return Web.json_Response (Result)
Start the Server to Edit Incoming Requirements
App = Web.Application ([widebocetical processor))
web.run_app (app, host = '0.0.0.0', port = 8000)
Diploma
In summary, it can be said that the problem of the output of infinity data can be addressed in a pandas data frame from Binance’s website by using strategies such as the dask or the use of number buffer for efficient processing and storage.