我有一个json列表，我不能转换为Pandas Dataframe （各行和19列）
回复链接：https://www.byma.com.ar/wp-admin/admin-ajax.php?action=get_historico_simbolo&simbolo=INAG&fecha=01-02-2018
json响应：
[
{"Apertura":35,"Apertura_Homogeneo":35,"Cantidad_Operaciones":1,"Cierre":35,"Cierre_Homogeneo":35,"Denominacion":"INSUMOS AGROQUIMICOS S.A.","Fecha":"02\/02\/2018","Maximo":35,"Maximo_Homogeneo":35,"Minimo":35,"Minimo_Homogeneo":35,"Monto_Operado_Pesos":175,"Promedio":35,"Promedio_Homogeneo":35,"Simbolo":"INAG","Variacion":-5.15,"Variacion_Homogeneo":0,"Vencimiento":"48hs","Volumen_Nominal":5},
{"Apertura":34.95,"Apertura_Homogeneo":34.95,"Cantidad_Operaciones":2,"Cierre":34.95,"Cierre_Homogeneo":34.95,"Denominacion":"INSUMOS AGROQUIMICOS S.A.","Fecha":"05\/02\/2018","Maximo":34.95,"Maximo_Homogeneo":34.95,"Minimo":34.95,"Minimo_Homogeneo":34.95,"Monto_Operado_Pesos":5243,"Promedio":-79228162514264337593543950335,"Promedio_Homogeneo":-79228162514264337593543950335,"Simbolo":"INAG","Variacion":-0.14,"Variacion_Homogeneo":-0.14,"Vencimiento":"48hs","Volumen_Nominal":150},
{"Apertura":32.10,"Apertura_Homogeneo":32.10,"Cantidad_Operaciones":2,"Cierre":32.10,"Cierre_Homogeneo":32.10,"Denominacion":"INSUMOS AGROQUIMICOS S.A.","Fecha":"07\/02\/2018","Maximo":32.10,"Maximo_Homogeneo":32.10,"Minimo":32.10,"Minimo_Homogeneo":32.10,"Monto_Operado_Pesos":98756,"Promedio":32.10,"Promedio_Homogeneo":32.10,"Simbolo":"INAG","Variacion":-8.16,"Variacion_Homogeneo":-8.88,"Vencimiento":"48hs","Volumen_Nominal":3076}
]我使用下一段代码将此json转换为dataframe：
def getFinanceHistoricalStockFromByma(tickerList):
dataFrameHistorical = pd.DataFrame()
for item in tickerList:
url = 'https://www.byma.com.ar/wp-admin/admin-ajax.php?action=get_historico_simbolo&simbolo=' + item + '&fecha=01-02-2018'
response = requests.get(url)
if response.content : print 'ok info Historical Stock'
data = response.json()
dfItem = jsonToDataFrame(data)
dataFrameHistorical = dataFrameHistorical.append(dfItem, ignore_index=True)
return dataFrameHistorical
def jsonToDataFrame(jsonStr):
return json_normalize(jsonStr)json_normalize的结果是1行和很多列，我如何将这个json响应转换为每个列表1行？
2条答案
按热度按时间jdgnovmf1#
如果在函数中更改此行：
dfItem = jsonToDataFrame(data)至：dfItem = pd.DataFrame.from_records(data)我测试了你的函数，替换了这一行，使用['INAG']作为传递给
getFinanceHistoricalStockFromByma函数的参数，它返回了一个DataFrame。tnkciper2#
您可以直接在字典列表上调用
pd.DataFrame()，如OP中的示例所示（.from_records()不是必需的）。对于OP中的函数，由于
pd.DataFrame.append()已被弃用，当前（panda〉= 1.4.0）编写它的最佳方法是在Python列表中收集json响应，并在循环结束时创建一个DataFrame。