in this commands;


import requests

import pandas as pd

url = "https://api.binance.com/api/v3/depth?symbol=BNBUSDT&limit=100"

payload = {}

headers = {
'Content-Type': 'application/json'

}

response = requests.request("GET", url, headers=headers, data=payload).json()

depth = pd.DataFrame(response, columns=["bids","asks"])

print(depth)

outputs :

and first i need to change the table this format

and then turn the columns values float so that be able to calculate in a specific range values quantity (e.g 0bidsValue 400.00 and ?bidsValue 380.00 ("?" because of i don't know row number) first i must find row number of bidsValue 380.00 (for example it is 59) then calculate 0bidsQuantity to 59bidsQuantity) and last write the results in rangeTotalbidsQuantity column.

I've been trying for hours, I tried many commands from "pandas.Dataframe" but I couldn't do it. Thank you!

CodePudding user response：

You can use this solution. In your case this would look something like this:

depth['bidsValue'], depth['bidsQuantity'] = zip(*list(depth['bids'].values))
depth['asksValue'], depth['asksQuantity'] = zip(*list(depth['asks'].values))
depth = depth.drop(columns=['bids', 'asks'])

For the second part look at this tutorial.

CodePudding user response：

You can use for example:

pd.DataFrame(df['bids'].tolist())

Then concatenate that to the original dataframe.

CodePudding user response：

You can apply() pandas.Series and assign to new columns

depth[ ['bidsValue', 'bidsQuantity'] ] = depth['bids'].apply(pd.Series)
depth[ ['asksValue', 'asksQuantity'] ] = depth['asks'].apply(pd.Series)

and later you have to remove original columns bids,asks

depth = depth.drop(columns=['bids', 'asks'])

Full working code with other changes

import requests
import pandas as pd

url = "https://api.binance.com/api/v3/depth"

payload = {
    'symbol': 'BNBUSDT',
    'limit': 100,
}

response = requests.get(url, params=payload)
#print(response.status_code)

data = response.json()

depth = pd.DataFrame(data, columns=["bids","asks"])

#print(depth)

depth[ ['bidsValue', 'bidsQuantity'] ] = depth['bids'].apply(pd.Series)
depth[ ['asksValue', 'asksQuantity'] ] = depth['asks'].apply(pd.Series)

depth = depth.drop(columns=['bids', 'asks'])

print(depth.to_string())