How to change a String in Python to an Indexed List

Question

I normally use the python-binance library, however, it is not up to date for a particular call I need to make.

I'm therefore attempting to use the standard requests.get function as follows:

import requests

TakerRatio = requests.get('https://www.binance.com/futures/data/takerlongshortRatio','symbol=BTCUSDT&period=5m&limit=30')
TakerRatio = TakerRatio.text

This returns me all the data I need, however as one string:

[{"buySellRatio":"0.8314","sellVol":"418.6670","buyVol":"348.0720","timestamp":1591194900000},{"buySellRatio":"1.1938","sellVol":"284.8710","buyVol":"340.0660","timestamp":1591195200000},{"buySellRatio":"1.1901","sellVol":"377.4750","buyVol":"449.2250","timestamp":1591195500000}]

Could you guys help me with the simplest way to turn this string into an indexed list? I need to be able to reference the sellVol and buyVol values like you normally would, e.g. querying TakerRatio[0]['sellVol'] to return 418.6670.

Answer 1

It seems that what you get is already what you want, with the only difference that you are getting the numbers as strings. All you need to do is to convert them back to numbers. Also, the timestamp looks like a POSIX timestamp, but with milliseconds. We can use datetime.fromtimestamp to convert it to a proper date and time.

import requests
import datetime

TakerRatio = requests.get('https://www.binance.com/futures/data/takerlongshortRatio','symbol=BTCUSDT&period=5m&limit=30')
data = TakerRatio.json()

for i in data:
    i["buySellRatio"] = float(i["buySellRatio"])
    i["sellVol"] = float(i["sellVol"])
    i["buyVol"] = float(i["sellVol"])
    # The timestamp looks like a POSIX timestamp, but POSIX timestamps are in
    # seconds and the one returned looks like it is in milliseconds
    i["timestamp"] = datetime.datetime.fromtimestamp(i["timestamp"]/1000)


# This will print a number and not a string representation of the number
print(data[0]['sellVol'])

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Extra

You can also use pandas for an even easier way to work with this data. The previously converted data be easily turned into a pandas DataFrame.

import pandas as pd

df = pd.DataFrame(data)

# Print the first 5 rows in a nice tabular way
print(df.head(5))

This will show something like

   buySellRatio  sellVol   buyVol           timestamp
0        1.6940  406.295  406.295 2020-06-03 11:55:00
1        1.0489  333.205  333.205 2020-06-03 12:00:00
2        1.0623  209.696  209.696 2020-06-03 12:05:00
3        1.3297  240.111  240.111 2020-06-03 12:10:00
4        2.1152  272.227  272.227 2020-06-03 12:15:00

From this, you can do all kinds of manipulations very easily.