pandas 在"datetimeIndex"中的"datetime"或"datetime"上合并 [英] Pandas merge on `datetime` or `datetime` in `datetimeIndex`
问题描述
当前,我有两个表示excel电子表格的数据框.我希望加入日期相等的数据.这是一个一对多的联接,因为一个电子表格具有一个日期,然后我需要添加具有相同日期的多行数据
Currently I have two data frames representing excel spreadsheets. I wish to join the data where the dates are equal. This is a one to many join as one spread sheet has a date then I need to add data which has multiple rows with the same date
一个例子:
A B
date data date data
0 2015-0-1 ... 0 2015-0-1 to 2015-0-2 ...
1 2015-0-2 ... 1 2015-0-1 to 2015-0-2 ...
在这种情况下,A的两行都将接收B的行0和1,因为它们都在该范围内.
In this case both rows from A would recieve rows 0 and 1 from B because they are in that range.
我尝试使用
df3 = pandas.merge(df2, df1, how='right', validate='1:m', left_on='Travel Date/Range', right_on='End')
要完成此操作,但收到此错误.
to accomplish this but received this error.
Traceback (most recent call last):
File "<pyshell#61>", line 1, in <module>
df3 = pandas.merge(df2, df1, how='right', validate='1:m', left_on='Travel Date/Range', right_on='End')
File "C:\Users\M199449\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\reshape\merge.py", line 61, in merge
validate=validate)
File "C:\Users\M199449\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\reshape\merge.py", line 555, in __init__
self._maybe_coerce_merge_keys()
File "C:\Users\M199449\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\reshape\merge.py", line 990, in _maybe_coerce_merge_keys
raise ValueError(msg)
ValueError: You are trying to merge on object and datetime64[ns] columns. If you wish to proceed you should use pd.concat
我当然可以根据需要添加更多信息
I can add more information as needed of course
推荐答案
所以这是合并的选项:
假设您有两个数据框:
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']})
现在进行一些清洁工作以获取所需的所有日期,并确保它们为datetime
Now do some cleaning to get all of the dates you need and make sure they are 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')
现在将所有内容合并在一起.您将获得99x10K的行.
Now merge it all together. You'll get 99x10K rows.
df = df1.assign(dummy=1).merge(df2.assign(dummy=1), on='dummy').drop(columns='dummy')
以及属于这些范围之间的日期的子集:
And subset to the dates that fall in between the ranges:
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df2
中的某些日期是单个日期,由于我们使用的是.str.split
,因此第二个日期将得到None
.然后只需使用.loc
进行适当设置即可.
If for instance, some dates in df2
were a single date, since we're using .str.split
we will get None
for the second date. Then just use .loc
to set it appropriately.
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
现在其余的保持不变
这篇关于 pandas 在"datetimeIndex"中的"datetime"或"datetime"上合并的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!