Nested Dict To DataFrame - pandas

Question

CAN ANYONE HELP
msg = {'e': 'kline',
'E': 1672157513375,
's': 'BTCUSDT',
'k': {
't': 1672157460000, #REQUIRE, CONVERT MS TO DATETIME,
#RENAME AS TIME, AS INDEX
'T': 1672157519999,
's': 'BTCUSDT',
'i': '1m',
'f': 2388965371,
'L': 2388969270,
'o': '16787.32000000', #REQUIRE RENAME AS OPEN
'c': '16783.23000000', #REQUIRE RENAME AS CLOSE
'h': '16789.41000000', #REQUIRE RENAME AS HIGH
'l': '16782.69000000', #REQUIRE RENAME AS LOW
'v': '149.27507000', #REQUIRE RENAME AS VOLUME
'n': 3900,
'x': False,
'q': '2505669.98288240',
'V': '59.70465000',
'Q': '1002207.92308370',
'B': '0'
}
}
Time = k(t),datetime
Open = k(o),dtype float
High = k(h),dtype float
Low = k(l), dtype float
Close = k(c), dtype float
Volume = k (v),dtype float
index give as,
k(t) convert this millisecond to datetime,
and converted give as index
language python
WHAT I TRYING:
def getdata(msg):
frame = pd.DataFrame(msg)
#DONT UNDERSTOOD
frame = frame.loc[frame['k']['t'],frame['k']['t'],frame['k']['t'],
frame['k']['t'],frame['k']['t'],frame['k']['t']]
#SOME UNDERSTOOD
frame.columns = ["Time","Open","High","Low","Close","Volume"]
frame.set_index("Time",inplace=True)
frame.index = pd.to_datetime(frame.index,unit='ms')
frame = frame.astype(float)
return frame
getdata(msg)
REQUIRE OUTPUT:
Time Open High Low Close Volume
2022-12-27 16:11:00 16787.7 16789.4 16782.6 16783.2 149
<3

Answer 1

Using json_normalize():
df = (pd
.json_normalize(data=msg["k"])[["t", "o", "h", "l", "c", "v"]]
.rename(columns={"t": "Time", "o": "Open", "h": "High", "l": "Low", "c": "Close", "v": "Volume"})
)
df["Time"] = (
pd.to_datetime(df["Time"], unit="ms")
.dt.tz_localize("UTC")
.dt.tz_localize(None)
.dt.floor("S")
)
print(df)
Output:
Time Open High Low Close Volume
0 2022-12-27 16:11:00 16787.32000000 16789.41000000 16782.69000000 16783.23000000 149.27507000