试图从eod api eod日内api 获取几年的日内历史数据
from datetime import datetime
dates=[ '01-01-2021','01-04-2021','01-07-2021','01-10-2021',
'01-01-2022','01-04-2022','01-07-2022']
# Convert your strings to datetime, using `datetime` library
dates = [datetime.strptime(date, "%d-%m-%Y") for date in dates]
def create_df(pair,dates):
df = []
for index, elem in enumerate(dates):
if index== 0:
curr_date = str(elem.timestamp())
next_date = str(dates[index+1].timestamp())
df = client.get_prices_intraday(pair, interval = '1m', from_ = curr_date, to = next_date)
elif ((index>0) & (index+1 < len(dates))):
curr_date = str(elem.timestamp())
next_date = str(dates[index+1].timestamp())
df2 = client.get_prices_intraday(pair, interval = '1m', from_ = curr_date, to = next_date)
df.append(df2)
return df
from eod import EodHistoricalData
# create the instance of the SDK
api_key = 'my_api_key'
client = EodHistoricalData(api_key)
GBPAUD = create_df('GBPAUD.FOREX',dates)
是什么给了我这样的东西：
GBPAUD
[{'timestamp': 1609693200,
'gmtoffset': 0,
'datetime': '2021-01-03 17:00:00',
'open': 1.77086,
'high': 1.77086,
'low': 1.77086,
'close': 1.77086,
'volume': 1},
{'timestamp': 1609693260,
'gmtoffset': 0,
'datetime': '2021-01-03 17:01:00',
'open': 1.77086,
'high': 1.77086,
'low': 1.77086,
'close': 1.77086,
'volume': 1},
{'timestamp': 1609693320,
'gmtoffset': 0,
'datetime': '2021-01-03 17:02:00',
'open': 1.77086,
'high': 1.77086,
'low': 1.77086,
'close': 1.77086,
'volume': 1},
{'timestamp': 1609693380,
'gmtoffset': 0,
'datetime': '2021-01-03 17:03:00',
'open': 1.77086,
'high': 1.77222,
'low': 1.77086,
'close': 1.77199,
'volume': 14},
{'timestamp': 1609693440,
'gmtoffset': 0,
'datetime': '2021-01-03 17:04:00',
'open': 1.77203,
'high': 1.77348,
'low': 1.77176,
'close': 1.77199,
'volume': 23},
它被存储为一个列表，但当我试图转换为pandas数据帧时：
GBPAUD = pd.DataFrame(GBPAUD)
----------------------------------------------------------------------------AttributeError Traceback(最近调用最后(输入In[39]，在<细胞系：1>((---->1 GBPAUD=pd.DataFrame(GBPAUD(
文件~/anconda3/envs/rapids-22.02/lib/python3.9/site packages/pandas/core/frameter.py:694，在DataFrame中init(self、data、index、columns、dtype、copy(689如果列不是"无"：690#错误：参数1到"；ensure_index"；具有不兼容的类型691#"；集合[Any]"；；预期的"；Union[Unination[Unination[ExtensionArray，692"ndarray]，Index，Series]，Sequence[Any]]"；693 columns=ensure_index(columns(#type:ignore[arg type]-->694个数组，列，索引=嵌套数据_数组(695#错误：参数3为"0"；nested_data_to_arrays"；具有不兼容696#型"；Optional[Collection[Any]]"；；预期的"；可选[索引]"；697个数据，698列，699索引，#type:ignore[arg-type]700d型，701(702 mgr=阵列_to_mgr(703阵列，704列，(…(708 typ=管理器，709(710其他：
文件~/anconda3/envs/rapids-22.02/lib/python3.9/site packages/pandas/core/internites/construction.py:483，在nested_data_to_arrays中(数据、列、索引、数据类型(480如果is_named_tuple(data[0](和columns为None:481列=ensure_index(data[0].fields(-->483个数组，列=到数组(数据，列，数据类型=数据类型(484列=ensure_index(列(486如果索引为None:
文件~/anconda3/envs/rapids-22.02/lib/python3.9/site packages/pandas/core/internites/construction.py:799，在to_arrays(数据、列、数据类型(中797 arr=_list_to_arrays(数据(798 elif isistance(数据[0]，abc.Mapping(：-->799 arr，columns=_list_of_dict_to_arrays(数据，列(800 elif isistance(数据[0]，ABC系列(：801 arr，columns=_list_of_series_to_arrays(数据，columns(
文件~/anconda3/envs/rapids-22.02/lib/python3.9/site packages/pandas/core/internites/construction.py:884，在_list_of_dict_to_arrays(数据，列(中882 gen=(数据中x的list(x.keys((((883 sort=没有(数据中d的isistance(d，dict((-->884 pre_cols=lib.fast_unique_multiple_list_gen(gen，sort=sort(885列=ensure_index(pre_cols(887#确保它们属于基本dict类，而不是派生的888#类
文件~/anconda3/envs/rapids-22.02/lib/python3.9/site packages/pandas/libs/lib.pyx:400，大熊猫_libs.lib.fast_unique_multiple_list_gen((
文件~/anconda3/envs/rapids-22.02/lib/python3.9/site packages/pandas/core/internites/construction.py:882，在(.0(862〃"quot；863将dicts列表转换为numpy数组864(…(879列：索引880〃"quot；881如果列为"无"：-->882 gen=(数据中x的list(x.keys((((883 sort=没有(数据中d的isistance(d，dict((884 pre_cols=lib.fast_unique_multiple_list_gen(gen，sort=sort(
AttributeError:"list"对象没有属性"keys">
有谁能用更优雅的方法从该api中获取大量数据，或者修复错误？
感谢
您的示例数据正确读取到panda中(关闭列表后(。在for循环中，在不查看数据的情况下(请共享一个片段而不是一个链接来注册服务(，您将以两种不同的方式将数据添加到df变量中，这将在单个列表和无键之后创建一个列表列表列表。这就是熊猫所抱怨的。在下面的例子中，它有一个最小化版本的代码，并使用您的数据作为输入，观察以下输出：
data = [{'timestamp': 1609693200,
'gmtoffset': 0,
'datetime': '2021-01-03 17:00:00',
'open': 1.77086,
'high': 1.77086,
'low': 1.77086,
'close': 1.77086,
'volume': 1},
{'timestamp': 1609693260,
'gmtoffset': 0,
'datetime': '2021-01-03 17:01:00',
'open': 1.77086,
'high': 1.77086,
'low': 1.77086,
'close': 1.77086,
'volume': 1},
{'timestamp': 1609693320,
'gmtoffset': 0,
'datetime': '2021-01-03 17:02:00',
'open': 1.77086,
'high': 1.77086,
'low': 1.77086,
'close': 1.77086,
'volume': 1},
{'timestamp': 1609693380,
'gmtoffset': 0,
'datetime': '2021-01-03 17:03:00',
'open': 1.77086,
'high': 1.77222,
'low': 1.77086,
'close': 1.77199,
'volume': 14},
{'timestamp': 1609693440,
'gmtoffset': 0,
'datetime': '2021-01-03 17:04:00',
'open': 1.77203,
'high': 1.77348,
'low': 1.77176,
'close': 1.77199,
'volume': 23}]
df = []
dates = [1,2,3]
for index, elem in enumerate(dates):
if index== 0:
# df = client.get_prices_intraday(pair, interval = '1m', from_ = curr_date, to = next_date)
df = data
elif ((index>0) & (index+1 < len(dates))):
# df2 = client.get_prices_intraday(pair, interval = '1m', from_ = curr_date, to = next_date)
df2 = data
df.append(df2)
print(df)
你的输出是：
[{'timestamp': 1609693200, 'gmtoffset': 0, 'datetime': '2021-01-03 17:00:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693260, 'gmtoffset': 0, 'datetime': '2021-01-03 17:01:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693320, 'gmtoffset': 0, 'datetime': '2021-01-03 17:02:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693380, 'gmtoffset': 0, 'datetime': '2021-01-03 17:03:00', 'open': 1.77086, 'high': 1.77222, 'low': 1.77086, 'close': 1.77199, 'volume': 14}, {'timestamp': 1609693440, 'gmtoffset': 0, 'datetime': '2021-01-03 17:04:00', 'open': 1.77203, 'high': 1.77348, 'low': 1.77176, 'close': 1.77199, 'volume': 23}, [...], [...]]
看到最后的两个列表了吗？我想你想要的是，使用相同的列表数据，这个
df = []
dates = [1,2,3]
for date in dates:
df += data
print(df)
它输出这个：
[{'timestamp': 1609693200, 'gmtoffset': 0, 'datetime': '2021-01-03 17:00:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693260, 'gmtoffset': 0, 'datetime': '2021-01-03 17:01:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693320, 'gmtoffset': 0, 'datetime': '2021-01-03 17:02:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693380, 'gmtoffset': 0, 'datetime': '2021-01-03 17:03:00', 'open': 1.77086, 'high': 1.77222, 'low': 1.77086, 'close': 1.77199, 'volume': 14}, {'timestamp': 1609693440, 'gmtoffset': 0, 'datetime': '2021-01-03 17:04:00', 'open': 1.77203, 'high': 1.77348, 'low': 1.77176, 'close': 1.77199, 'volume': 23}, {'timestamp': 1609693200, 'gmtoffset': 0, 'datetime': '2021-01-03 17:00:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693260, 'gmtoffset': 0, 'datetime': '2021-01-03 17:01:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693320, 'gmtoffset': 0, 'datetime': '2021-01-03 17:02:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693380, 'gmtoffset': 0, 'datetime': '2021-01-03 17:03:00', 'open': 1.77086, 'high': 1.77222, 'low': 1.77086, 'close': 1.77199, 'volume': 14}, {'timestamp': 1609693440, 'gmtoffset': 0, 'datetime': '2021-01-03 17:04:00', 'open': 1.77203, 'high': 1.77348, 'low': 1.77176, 'close': 1.77199, 'volume': 23}, {'timestamp': 1609693200, 'gmtoffset': 0, 'datetime': '2021-01-03 17:00:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693260, 'gmtoffset': 0, 'datetime': '2021-01-03 17:01:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693320, 'gmtoffset': 0, 'datetime': '2021-01-03 17:02:00', 'open': 1.77086, 'high': 1.77086, 'low': 1.77086, 'close': 1.77086, 'volume': 1}, {'timestamp': 1609693380, 'gmtoffset': 0, 'datetime': '2021-01-03 17:03:00', 'open': 1.77086, 'high': 1.77222, 'low': 1.77086, 'close': 1.77199, 'volume': 14}, {'timestamp': 1609693440, 'gmtoffset': 0, 'datetime': '2021-01-03 17:04:00', 'open': 1.77203, 'high': 1.77348, 'low': 1.77176, 'close': 1.77199, 'volume': 23}]
读熊猫或大棒都很好。