'How to convert API stock data to table using pandas?
my API response is like this
{
'1644205500': {'date': '06-02-2022', 'open': 501.85, 'high': 504.2, 'low': 500.75, 'close': 503.05},
'1644205800': {'date': '06-02-2022', 'open': 502.85, 'high': 503.05, 'low': 497.45, 'close': 498.55},
'1644206100': {'date': '06-02-2022', 'open': 498.85, 'high': 498.85, 'low': 495.3, 'close': 498.75},
'1644206400': {'date': '06-02-2022', 'open': 498.7, 'high': 498.75, 'low': 497.2, 'close': 498.05},
'1644206700': {'date': '06-02-2022', 'open': 497.95, 'high': 498.85, 'low': 496.45, 'close': 496.45},
'1644207000': {'date': '06-02-2022', 'open': 496.45, 'high': 498, 'low': 496.45, 'close': 497.5}
}
When I use pd.json_normalize(data) there is only one row and so many columns. Like this:
1644205500.date 1644205500.open 1644205500.high 1644205500.low 1644205500.close 1644205800.date 1644205800.open 1644205800.high 1644205800.low 1644205800.close 1644206100.date 1644206100.open 1644206100.high 1644206100.low 1644206100.close
How can I make it like a table like this?
date open high low close
06-02-2022 500 501 490 501
06-02-2022 501 502 480 470
....
Sources
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