我的第一个数据框 df_gammask 看起来像这样:
distance breakEvenDistance min max
0 2.1178 2.0934 NaN 0.000955
1 2.0309 2.1473 0.000955 0.001041
2 1.9801 1.7794 0.001041 0.001124
3 1.9282 2.1473 0.001124 0.001199
4 1.8518 1.5885 0.001199 0.001259
5 1.8518 1.5151 0.001259 0.001319
我的第二个 df_gammabid:
distance breakEvenDistance min max
0 1.9999 1.9329 NaN 0.001034
1 1.9251 2.0670 0.001034 0.001118
2 1.8802 1.6758 0.001118 0.001193
3 1.8802 1.5956 0.001193 0.001252
4 1.7542 1.5181 0.001252 0.001317
5 1.7542 1.4541 0.001317 0.001374
我需要的是像这样的 json 文件:
{
"buy": [
{
"distance": 0.6278,
"breakEvenDistance": 0.6261,
"max": 0.0031920626236615754
},
{
"distance": 0.6224,
"breakEvenDistance": 0.6199,
"min": 0.0031920626236615754,
"max": 0.003223405873670448
},
{
"distance": 0.6202,
"breakEvenDistance": 0.6142,
"min": 0.003223405873670448,
"max": 0.003253791039488344
},
{
"distance": 0.6174,
"breakEvenDistance": 0.6081,
"min": 0.003253791039488344,
"max": 0.003285709011703031}],
"sell": [
{
"distance": 0.8012,
"breakEvenDistance": 0.8005,
"max": 0.0024962095663052064
},
{
"distance": 0.7996,
"breakEvenDistance": 0.7939,
"min": 0.0024962095663052064,
"max": 0.002516799325547373
},
{
"distance": 0.794,
"breakEvenDistance": 0.7877,
"min": 0.002516799325547373,
"max": 0.0025370182220432014
},
{
"distance": 0.7927,
"breakEvenDistance": 0.7807,
"min": 0.0025370182220432014,
"max": 0.0025605480833123294
}]
我知道有函数 pd.DataFrame.to_json 但它适用于一个数据帧，关于如何使用 2 个数据帧和上述格式执行此操作的任何线索？我必须合并它们吗？ buy 端是 df_gammask，sell 端是 dg_gammabid!谢谢
最佳答案
使用DataFrame.to_dict在嵌套字典理解中删除缺失值，然后创建 dictionary 并转换为 json:
import json
L1 = [{k: v for k, v in x.items() if pd.notnull(v)} for x in df_gammask.to_dict('r')]
L2 = [{k: v for k, v in x.items() if pd.notnull(v)} for x in df_gammabid.to_dict('r')]
with open('file.json', 'w') as file:
json.dump({ "buy": L1, "sell": L2}, file)
关于python - 使用 pandas 将两个不同的数据帧转换为一个 json 文件，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/55119103/