import easyquotation
import pandas as pd

quotation = easyquotation.use("timekline")
data = quotation.real(['300692'], prefix=True)

data 有3部分
1. data dictionary
2. 300692 dictionary
3. time_data -list

怎样提取第3部分的value
0  ['0930', '16.78', '2819']
1  ['0931', '17.07', '4094']
2  ['0932', '17.08', '4678']
...
240 ['1459', '17.54', '92371']
241 ['1500', '17.55', '93846']
242 ['1506', '17.55', '93846']
243 ['1507', '17.55', '93846']
244 ['1508', '17.55', '93846']
转成DataFrame，['time'，'price'，'volume']
time = 将第2部分的日期 date  如：20210430 加在时分前面， 202104300930 202104300931 ...202104301500
price = 16.78  17.07 ... 17.55
volume = 2819  (4094-2819)  (4678-4094) ...(93846-92371) , 1500后面的数据可以删除,volume==0
最终保存为 300692.csv

import easyquotation
import pandas as pd

quotation = easyquotation.use("timekline")
data = quotation.real(['300852'], prefix=True)
a=data['sz300852.js']['time_data']
df = pd.DataFrame(a, columns=['time', 'price', 'volume'])
b=data['sz300852.js']['date']
df['time']=b+df['time']
df['volume']=pd.to_numeric(df['volume'])
df['volume_1']=df['volume']-df['volume'].shift(1)
df.loc[:0,'volume_1']=df.head(1).volume
df['price']=pd.to_numeric(df['price'])

自己先回答下，剩怎样把1500后的数据删除了