DataFrames Python 的 DataFrame

如何解决DataFrames Python 的 DataFrame

我有这个 Json 文件，

{
  "timezone": "UTC","serverTime": 1565246363776,"rateLimits": […… ],"exchangeFilters": [….. ],"symbols": [
    {
      "symbol": "ETHBTC","status": "TradING","baseAsset": "ETH","baseAssetPrecision": 8,"quoteAsset": "BTC","quotePrecision": 8,"quoteAssetPrecision": 8,"baseCommissionPrecision": 8,"quoteCommissionPrecision": 8,"filters": [
        {"filterType": "PRICE_FILTER","minPrice": "0.00000100","maxPrice": "100000.00000000","tickSize": "0.00000100"},{"filterType": "PERCENT_PRICE","multiplierUp": "1.3000","multiplierDown": "0.7000","avgPriceMins": 5}
        {"filterType": "LOT_SIZE","minQty": "0.00100000","maxQty": "100000.00000000","stepSize": “0.00100000"}]
    }

我已将 Json 文件中的“符号”转换为 DataFrame：

with open('exchangeInfo.json','r') as fp:
    exchangeInfo = json.load(fp)


symbolsDF = pd.DataFrame(client.get_exchange_info(['symbols'])

我想将“过滤器”列从“符号”转换为这样的列

"symbols": [
    {
      "symbol": "ETHBTC","tickSize": "0.00000100","stepSize": "0.00100000"}]
    }

所以我的最终 DataFrame 将由符号组成，列将是

["symbol","status","baseAsset","baseAssetPrecision","quoteAsset","quotePrecision","quoteAssetPrecision","baseCommissionPrecision","quoteCommissionPrecision","minPrice","maxPrice","tickSize","minQty","maxQty","stepSize"]

谢谢

解决方法

试试：

 /            (containing the project)
 /src/        (containing the JS)
 /static/     (containing the static files)

打印：

exchangeInfo = {
    "timezone": "UTC","serverTime": 1565246363776,"rateLimits": [],"exchangeFilters": [],"symbols": [
        {
            "symbol": "ETHBTC","status": "TRADING","baseAsset": "ETH","baseAssetPrecision": 8,"quoteAsset": "BTC","quotePrecision": 8,"quoteAssetPrecision": 8,"baseCommissionPrecision": 8,"quoteCommissionPrecision": 8,"filters": [
                {
                    "filterType": "PRICE_FILTER","minPrice": "0.00000100","maxPrice": "100000.00000000","tickSize": "0.00000100",},{
                    "filterType": "PERCENT_PRICE","multiplierUp": "1.3000","multiplierDown": "0.7000","avgPriceMins": 5,{
                    "filterType": "LOT_SIZE","minQty": "0.00100000","maxQty": "100000.00000000","stepSize": "0.00100000",],}

df = pd.json_normalize(exchangeInfo["symbols"])
df = pd.concat(
    [
        df,df.pop("filters")
        .apply(lambda x: dict(i for d in x for i in d.items()))
        .apply(pd.Series),axis=1,).drop(columns="filterType")
print(df)