所以这是合并的选项:
假设您有两个 DataFrame:
import pandas as pd
df1 = pd.DataFrame({'date': ['2015-01-01', '2015-01-02', '2015-01-03'],
'data': ['A', 'B', 'C']})
df2 = pd.DataFrame({'date': ['2015-01-01 to 2015-01-02', '2015-01-01 to 2015-01-02', '2015-01-02 to 2015-01-03'],
'data': ['E', 'F', 'G']})
现在进行一些清理以获取您需要的所有日期并确保它们是datetime
df1['date'] = pd.to_datetime(df1.date)
df2[['start', 'end']] = df2['date'].str.split(' to ', expand=True)
df2['start'] = pd.to_datetime(df2.start)
df2['end'] = pd.to_datetime(df2.end)
# No need for this anymore
df2 = df2.drop(columns='date')
现在将它们全部合并在一起。您将获得 99x10K 行。
df = df1.assign(dummy=1).merge(df2.assign(dummy=1), on='dummy').drop(columns='dummy')
以及落在范围之间的日期的子集:
df[(df.date >= df.start) & (df.date <= df.end)]
# date data_x data_y start end
#0 2015-01-01 A E 2015-01-01 2015-01-02
#1 2015-01-01 A F 2015-01-01 2015-01-02
#3 2015-01-02 B E 2015-01-01 2015-01-02
#4 2015-01-02 B F 2015-01-01 2015-01-02
#5 2015-01-02 B G 2015-01-02 2015-01-03
#8 2015-01-03 C G 2015-01-02 2015-01-03
例如,如果某些日期df2
是单个日期,因为我们正在使用.str.split
我们将得到None
第二次约会。然后只需使用.loc
来适当地设置它。
df2 = pd.DataFrame({'date': ['2015-01-01 to 2015-01-02', '2015-01-01 to 2015-01-02', '2015-01-02 to 2015-01-03',
'2015-01-03'],
'data': ['E', 'F', 'G', 'H']})
df2[['start', 'end']] = df2['date'].str.split(' to ', expand=True)
df2.loc[df2.end.isnull(), 'end'] = df2.loc[df2.end.isnull(), 'start']
# data start end
#0 E 2015-01-01 2015-01-02
#1 F 2015-01-01 2015-01-02
#2 G 2015-01-02 2015-01-03
#3 H 2015-01-03 2015-01-03
现在其余部分保持不变