问如何将JSON (从python请求)转换为pandas数据帧

EN

import pandas as pd
import json

j=json.loads(response.json())

data=[j['chart']['result'][0]['timestamp']]+ list(j['chart']['result'][0]['indicators']['quote'][0].values())

df=pd.DataFrame({'timestamp':data[0], 'close':data[1], 'open':data[2], 'high':data[3], 'low':data[4], 'volume':data[5]})

>>> print(df)

     timestamp   close   open   high    low   volume
0   1604578500  5.1200  5.120  5.120  5.120      0.0
1   1604580900  5.0600  5.060  5.060  5.060      0.0
2   1604581200  5.0500  5.060  5.060  5.050      0.0
3   1604581800  5.1000  5.100  5.100  5.100      0.0
4   1604582100  5.0200  5.120  5.120  5.000      0.0
..         ...     ...    ...    ...    ...      ...
71  1604602200  4.7100  4.655  4.710  4.655  38036.0
72  1604602500  4.6999  4.705  4.710  4.685  31368.0
73  1604602800  4.6950  4.700  4.700  4.690  24811.0
74  1604603100     NaN    NaN    NaN    NaN      NaN
75  1604603119  4.6950  4.695  4.695  4.695      0.0

[76 rows x 6 columns]

import pandas as pd

# from Code Pen (manually converted null -> None)
/*
* 提示：该行代码过长，系统自动注释不进行高亮。一键复制会移除系统注释 
* result = {"chart":{"result":[{"meta":{"currency":"USD","symbol":"AMRN","exchangeName":"NMS","instrumentType":"EQUITY","firstTradeDate":733674600,"regularMarketTime":1604603119,"gmtoffset":-18000,"timezone":"EST","exchangeTimezoneName":"America/New_York","regularMarketPrice":4.695,"chartPreviousClose":4.99,"previousClose":4.99,"scale":3,"priceHint":4,"currentTradingPeriod":{"pre":{"timezone":"EST","start":1604566800,"end":1604586600,"gmtoffset":-18000},"regular":{"timezone":"EST","start":1604586600,"end":1604610000,"gmtoffset":-18000},"post":{"timezone":"EST","start":1604610000,"end":1604624400,"gmtoffset":-18000}},"tradingPeriods":{"pre":[[{"timezone":"EST","start":1604566800,"end":1604586600,"gmtoffset":-18000}]],"post":[[{"timezone":"EST","start":1604610000,"end":1604624400,"gmtoffset":-18000}]],"regular":[[{"timezone":"EST","start":1604586600,"end":1604610000,"gmtoffset":-18000}]]},"dataGranularity":"5m","range":"1d","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1604578500,1604580900,1604581200,1604581800,1604582100,1604582400,1604582700,1604583000,1604583300,1604583600,1604583900,1604584200,1604584500,1604584800,1604585100,1604585400,1604585700,1604586000,1604586300,1604586600,1604586900,1604587200,1604587500,1604587800,1604588100,1604588400,1604588700,1604589000,1604589300,1604589600,1604589900,1604590200,1604590500,1604590800,1604591100,1604591400,1604591700,1604592000,1604592300,1604592600,1604592900,1604593200,1604593500,1604593800,1604594100,1604594400,1604594700,1604595000,1604595300,1604595600,1604595900,1604596200,1604596500,1604596800,1604597100,1604597400,1604597700,1604598000,1604598300,1604598600,1604598900,1604599200,1604599500,1604599800,1604600100,1604600400,1604600700,1604601000,1604601300,1604601600,1604601900,1604602200,1604602500,1604602800,1604603100,1604603119],"indicators":{"quote":[{"close":[5.12,5.06,5.05,5.1,5.02,4.99,4.8,4.7801,4.7999,4.8,4.8,4.8,4.79,4.82,4.81,4.82,4.86,4.86,4.95,4.6595001220703125,4.688799858093262,4.659999847412109,4.809999942779541,4.75,4.71999979019165,4.6819000244140625,4.651899814605713,4.658400058746338,4.652299880981445,4.650000095367432,4.679999828338623,4.740099906921387,4.760000228881836,4.724999904632568,4.71999979019165,4.755000114440918,4.769999980926514,4.755000114440918,4.735000133514404,4.74399995803833,4.755000114440918,4.735000133514404,4.735000133514404,4.710000038146973,4.71999979019165,4.724999904632568,4.718100070953369,4.730000019073486,4.704999923706055,4.699399948120117,4.690000057220459,4.679999828338623,4.679999828338623,4.679999828338623,4.670000076293945,4.652500152587891,4.659999847412109,4.664999961853027,4.675000190734863,4.675000190734863,4.679999828338623,4.659999847412109,4.644999980926514,4.659999847412109,4.650000095367432,4.65500020980835,4.65500020980835,4.644999980926514,4.639900207519531,4.650000095367432,4.65500020980835,4.710000038146973,4.699900150299072,4.695000171661377,None,4.695000171661377],"open":[5.12,5.06,5.06,5.1,5.12,5.0,4.9,4.7901,4.8,4.8,4.7999,4.8,4.79,4.82,4.81,4.82,4.82,4.86,4.87,4.840000152587891,4.659999847412109,4.6875,4.670000076293945,4.798099994659424,4.75,4.723800182342529,4.683899879455566,4.65500020980835,4.65500020980835,4.659999847412109,4.65500020980835,4.680099964141846,4.739999771118164,4.755000114440918,4.71999979019165,4.730000019073486,4.755000114440918,4.760000228881836,4.755000114440918,4.735000133514404,4.741600036621094,4.760000228881836,4.735000133514404,4.735000133514404,4.708099842071533,4.718100070953369,4.71999979019165,4.715000152587891,4.730000019073486,4.699999809265137,4.699999809265137,4.690000057220459,4.679999828338623,4.683800220489502,4.679999828338623,4.670100212097168,4.659900188446045,4.659999847412109,4.66480016708374,4.675000190734863,4.670000076293945,4.684999942779541,4.659999847412109,4.644999980926514,4.668700218200684,4.658899784088135,4.659999847412109,4.65500020980835,4.64109992980957,4.635000228881836,4.65500020980835,4.65500020980835,4.704999923706055,4.699999809265137,None,4.695000171661377],"high":[5.12,5.06,5.06,5.1,5.12,5.04,4.9,4.8,4.8,4.8,4.8,4.8,4.8001,4.82,4.81,4.83,4.86,4.86,4.95,4.889999866485596,4.724800109863281,4.690000057220459,4.815000057220459,4.809999942779541,4.789999961853027,4.730000019073486,4.6880998611450195,4.664999961853027,4.659999847412109,4.659999847412109,4.684999942779541,4.764999866485596,4.789999961853027,4.7581000328063965,4.739999771118164,4.760000228881836,4.769999980926514,4.764999866485596,4.760000228881836,4.75,4.760000228881836,4.768899917602539,4.75,4.735000133514404,4.71999979019165,4.739999771118164,4.724999904632568,4.730000019073486,4.735000133514404,4.704999923706055,4.699999809265137,4.695000171661377,4.690000057220459,4.690000057220459,4.687300205230713,4.679999828338623,4.670000076293945,4.675000190734863,4.679500102996826,4.678100109100342,4.690000057220459,4.690000057220459,4.670000076293945,4.670000076293945,4.669899940490723,4.659999847412109,4.659999847412109,4.659999847412109,4.650000095367432,4.659999847412109,4.664999961853027,4.710000038146973,4.710000038146973,4.699999809265137,None,4.695000171661377],"low":[5.12,5.06,5.05,5.1,5.0,4.99,4.77,4.7801,4.7999,4.7901,4.7999,4.79,4.79,4.82,4.81,4.82,4.82,4.85,4.86,4.619999885559082,4.630000114440918,4.593999862670898,4.670000076293945,4.744999885559082,4.71999979019165,4.65500020980835,4.639999866485596,4.639999866485596,4.639999866485596,4.650000095367432,4.614999771118164,4.680099964141846,4.730000019073486,4.71999979019165,4.71999979019165,4.704999923706055,4.75,4.730000019073486,4.730000019073486,4.7245001792907715,4.724999904632568,4.731100082397461,4.71999979019165,4.699999809265137,4.679999828338623,4.715000152587891,4.701099872589111,4.699999809265137,4.699999809265137,4.679999828338623,4.679999828338623,4.670000076293945,4.670000076293945,4.650000095367432,4.664999961853027,4.650000095367432,4.650000095367432,4.65500020980835,4.659999847412109,4.664999961853027,4.670000076293945,4.65500020980835,4.644999980926514,4.644999980926514,4.650000095367432,4.650000095367432,4.650000095367432,4.639999866485596,4.630000114440918,4.635000228881836,4.650000095367432,4.65500020980835,4.684999942779541,4.690000057220459,None,4.695000171661377],"volume":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,727514,383730,518259,375858,146610,136773,201506,187065,135156,109423,68829,269577,143004,176817,92720,73991,110323,80055,156048,59170,44408,56697,97600,74426,61372,120491,51939,58761,55472,60604,49009,104735,82872,61292,148882,95217,85067,64669,78016,63939,43058,37312,148380,123094,75770,32656,70807,43425,95237,72800,79580,61712,38036,31368,24811,None,0]}]}}],"error":None}}
*/

d = result['chart']['result'][0]['indicators']['quote'][0]
closes  = d['close']
opens   = d['open']
lows    = d['low']
highs   = d['high']
volumes = d['volume']
timestamps = result['chart']['result'][0]['timestamp']

df = pd.DataFrame({'timestamp': timestamps, 
                   'open': opens, 
                   'close': closes, 
                   'high': highs, 
                   'low': lows, 
                   'volume': volumes,})
print(df.shape)
print(df.head(10))

(76, 6)
    timestamp    open   close  high     low  volume
0  1604578500  5.1200  5.1200  5.12  5.1200     0.0
1  1604580900  5.0600  5.0600  5.06  5.0600     0.0
2  1604581200  5.0600  5.0500  5.06  5.0500     0.0
3  1604581800  5.1000  5.1000  5.10  5.1000     0.0
4  1604582100  5.1200  5.0200  5.12  5.0000     0.0
5  1604582400  5.0000  4.9900  5.04  4.9900     0.0
6  1604582700  4.9000  4.8000  4.90  4.7700     0.0
7  1604583000  4.7901  4.7801  4.80  4.7801     0.0
8  1604583300  4.8000  4.7999  4.80  4.7999     0.0
9  1604583600  4.8000  4.8000  4.80  4.7901     0.0

json = response.json()
df = pd.DataFrame.from_dict(json)

df = pd.DataFrame.from_dict(json, orient='index')

df = pd.DataFrame.from_dict(json['chart']['result'][0]['indicators']['quote'][0])