基于所选日期的python数据框连接答案

【问题标题】：python dataframe concatenate based on a chosen date基于所选日期的python数据框连接
【发布时间】：2020-08-06 16:48:54
【问题描述】：

假设我有以下变量和数据框：

a = '2020-04-23 14:00:00+00:00','2020-04-23 13:00:00+00:00','2020-04-23 12:00:00+00:00','2020-04-23 11:00:00+00:00','2020-04-23 10:00:00+00:00','2020-04-23 09:00:00+00:00','2020-04-23 08:00:00+00:00','2020-04-23 07:00:00+00:00','2020-04-23 06:00:00+00:00','2020-04-23 04:00:00+00:00'
b = '2020-04-23 10:00:00+00:00','2020-04-23 09:00:00+00:00','2020-04-23 08:00:00+00:00','2020-04-23 07:00:00+00:00','2020-04-23 06:00:00+00:00','2020-04-23 05:00:00+00:00','2020-04-23 04:00:00+00:00','2020-04-23 03:00:00+00:00','2020-04-23 02:00:00+00:00','2020-04-23 01:00:00+00:00'

aa = 7105.50,6923.50,6692.50,6523.00,6302.5,6081.5,6262.0,6451.50,6369.50,6110.00
bb = 6386.00,6221.00,6505.00,6534.70,6705.00,6535.00,7156.50,7422.00,7608.50,8098.00

df1 = pd.DataFrame()
df1['timestamp'] = a
df1['price'] = aa

df2 = pd.DataFrame()
df2['timestamp'] = b
df2['price'] = bb


print(df1)
print(df2)

我正在尝试连接以下行：

df1 顶行到 '2020-04-23 08:00:00+00:00'
'2020-04-23 07:00:00+00:00'到df2最后一行

出于说明目的，以下是数据框的外观：

c = '2020-04-23 14:00:00+00:00','2020-04-23 13:00:00+00:00','2020-04-23 12:00:00+00:00','2020-04-23 11:00:00+00:00','2020-04-23 10:00:00+00:00','2020-04-23 09:00:00+00:00','2020-04-23 08:00:00+00:00','2020-04-23 07:00:00+00:00','2020-04-23 06:00:00+00:00','2020-04-23 05:00:00+00:00','2020-04-23 04:00:00+00:00','2020-04-23 03:00:00+00:00','2020-04-23 02:00:00+00:00','2020-04-23 01:00:00+00:00'

cc = 7105.50,6923.50,6692.50,6523.00,6302.5,6081.5,6262.0,6534.70,6705.00,6535.00,7156.50,7422.00,7608.50,8098.00


df = pd.DataFrame()
df['timestamp'] = c
df['price'] = cc
print(df)

有什么想法吗？

【问题讨论】：

嘿伙计，我现在不能做，别担心，我会在早上检查。为响应干杯

标签： python pandas concatenation pandas-loc

【解决方案1】：

您可以将timestamp 列转换为pd.date_time 对象，然后使用布尔索引和pd.concat 选择并合并它们：

df1.timestamp = pd.to_datetime(df1.timestamp)
df2.timestamp = pd.to_datetime(df2.timestamp)

dfs = [df1.loc[df1.timestamp >= pd.to_datetime("2020-04-23 08:00:00+00:00"),:],
       df2.loc[df2.timestamp <= pd.to_datetime("2020-04-23 07:00:00+00:00"),:]
       ]

df_conc = pd.concat(dfs)

【讨论】：