目前我有两个代表 excel 电子表格的数据框。我希望加入日期相等的数据。这是一对多连接,因为一个电子表格有一个日期,然后我需要添加具有相同日期的多行数据
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 的两行都将收到 B 的第 0 行和第 1 行,因为它们都在该范围内。
我试过用
df3 = pandas.merge(df2, df1, how='right', validate='1:m', left_on='Travel Date/Range', right_on='End')
完成此操作但收到此错误。
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
当然我可以根据需要添加更多信息
最佳答案
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']})
现在做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')
然后子集落在范围之间的日期:
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,因为我们使用的是 .str.split
,我们将得到 None
第二次约会。然后只需使用 .loc
适本地设置它。
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
其余不变
关于python - Pandas 在 `datetime` 或 `datetime` 合并到 `datetimeIndex`,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/51755268/