从 pandas python 中的列表数据重新采样时间数据

Question

我有一个像下面这样的列表数据，列表中的每个元素都有 6 个东西

"timestamp , open , high , low , close, volume"

['1606470300000,244.25,244.25,244.00,244.00,489269', 
 '1606470360000,244.05,244.30,244.00,244.15,452854', 
 '1606470420000,244.15,244.20,244.00,244.10,403043', 
 '1606470480000,244.15,244.15,243.95,244.00,370182', 
 '1606470540000,244.05,244.20,244.00,244.15,445286', 
 '1606470600000,244.15,244.25,244.05,244.20,473342', 
 '1606470660000,244.25,244.35,244.00,244.05,491117', 
 '1606470720000,244.05,244.20,244.00,244.20,298261', 
 '1606470780000,244.20,244.25,244.10,244.25,344172', 
 '1606470840000,244.20,244.35,244.20,244.30,347080', 
 '1606470900000,244.30,244.40,244.25,244.30,447630', 
 '1606470960000,244.30,244.30,244.00,244.00,360666', 
 '1606471020000,244.05,244.15,243.95,243.95,467724',
 '1606471080000,243.95,244.10,243.70,244.00,386080', 
 '1606471140000,244.00,244.20,243.70,244.20,166559']

这是一个 1 分钟的数据，我想把它转换成 15 分钟的数据

请帮忙

Answer 1

这是使用 pandas 的一种方法。

import pandas as pd
list_data = ['1606470300000,244.25,244.25,244.00,244.00,489269',
 '1606470360000,244.05,244.30,244.00,244.15,452854',
 '1606470420000,244.15,244.20,244.00,244.10,403043',
 '1606470480000,244.15,244.15,243.95,244.00,370182',
 '1606470540000,244.05,244.20,244.00,244.15,445286',
 '1606470600000,244.15,244.25,244.05,244.20,473342',
 '1606470660000,244.25,244.35,244.00,244.05,491117',
 '1606470720000,244.05,244.20,244.00,244.20,298261',
 '1606470780000,244.20,244.25,244.10,244.25,344172',
 '1606470840000,244.20,244.35,244.20,244.30,347080',
 '1606470900000,244.30,244.40,244.25,244.30,447630',
 '1606470960000,244.30,244.30,244.00,244.00,360666',
 '1606471020000,244.05,244.15,243.95,243.95,467724',
 '1606471080000,243.95,244.10,243.70,244.00,386080',
 '1606471140000,244.00,244.20,243.70,244.20,166559']

# Convert to usable a dataframe  
df = pd.DataFrame(list_data)\
         .loc[:, 0]\
         .str.rsplit(",", expand=True)
df.columns = ["timestamp" , "open" , "high" , "low" , "close", "volume"]
df.loc[:, "timestamp"] = pd.to_datetime(df.timestamp.astype(int)*1e6)
df = df.set_index("timestamp")
df = df.astype(float)

# That is how it looks like
df.head()
                       open    high     low   close    volume
timestamp                                                    
2020-11-27 09:45:00  244.25  244.25  244.00  244.00  489269.0
2020-11-27 09:46:00  244.05  244.30  244.00  244.15  452854.0
2020-11-27 09:47:00  244.15  244.20  244.00  244.10  403043.0
2020-11-27 09:48:00  244.15  244.15  243.95  244.00  370182.0
2020-11-27 09:49:00  244.05  244.20  244.00  244.15  445286.0


# resample to 15 min data
df.resample("15min").mean()

从 pandas python 中的列表数据重新采样时间数据

resample time data from list data in pandas python

python

pandas

pandas-groupby

pandas-resample