Python Pandas：合并日期时间索引上的数据帧
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/36292959/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas: Merge data frames on datetime index
提问by Michael Perdue
I have the following two dataframes that I have set date to DateTime Index df.set_index(pd.to_datetime(df['date']), inplace=True)and would like to merge or join on date:
我有以下两个数据帧，我已将日期设置为 DateTime Indexdf.set_index(pd.to_datetime(df['date']), inplace=True)并希望在日期合并或加入：
df.head(5)
catcode_amt type feccandid_amt amount
date
1915-12-31 A5000 24K H6TX08100 1000
1916-12-31 T6100 24K H8CA52052 500
1954-12-31 H3100 24K S8AK00090 1000
1985-12-31 J7120 24E H8OH18088 36
1997-12-31 z9600 24K S6ND00058 2000
d.head(5)
catcode_disp disposition feccandid_disp bills
date
2007-12-31 A0000 support S4HI00011 1
2007-12-31 A1000 oppose S4IA00020', 'P20000741 1
2007-12-31 A1000 support S8MT00010 1
2007-12-31 A1500 support S6WI00061 2
2007-12-31 A1600 support S4IA00020', 'P20000741 3
I have tried the following two methods but both return a MemoryError:
我尝试了以下两种方法，但都返回 MemoryError：
df.join(d, how='right')
I use the code below on dataframes that dont have date set to index.
我在没有将日期设置为索引的数据帧上使用下面的代码。
merge=pd.merge(df,d, how='inner', on='date')
采纳答案by jezrael
You can add parameters left_index=Trueand right_index=Trueif you need merge by indexes in function merge:
您可以添加参数left_index=True，right_index=True如果您需要在函数中按索引合并merge：
merge=pd.merge(df,d, how='inner', left_index=True, right_index=True)
Sample (first value of index in dwas changed for matching):
示例（索引 in 的第一个值d已更改以进行匹配）：
print df
catcode_amt type feccandid_amt amount
date
1915-12-31 A5000 24K H6TX08100 1000
1916-12-31 T6100 24K H8CA52052 500
1954-12-31 H3100 24K S8AK00090 1000
1985-12-31 J7120 24E H8OH18088 36
1997-12-31 z9600 24K S6ND00058 2000
print d
catcode_disp disposition feccandid_disp bills
date
1997-12-31 A0000 support S4HI00011 1.0
2007-12-31 A1000 oppose S4IA00020', 'P20000741 1 NaN
2007-12-31 A1000 support S8MT00010 1.0
2007-12-31 A1500 support S6WI00061 2.0
2007-12-31 A1600 support S4IA00020', 'P20000741 3 NaN
merge=pd.merge(df,d, how='inner', left_index=True, right_index=True)
print merge
catcode_amt type feccandid_amt amount catcode_disp disposition \
date
1997-12-31 z9600 24K S6ND00058 2000 A0000 support
feccandid_disp bills
date
1997-12-31 S4HI00011 1.0
Or you can use concat:
或者你可以使用concat：
print pd.concat([df,d], join='inner', axis=1)
date
1997-12-31 z9600 24K S6ND00058 2000 A0000 support
feccandid_disp bills
date
1997-12-31 S4HI00011 1.0
EDIT: EdChumis right:
编辑：EdChum是对的：
I add duplicates to DataFrame df(last 2 values in index):
我向 DataFrame 添加重复项df（索引中的最后 2 个值）：
print df
catcode_amt type feccandid_amt amount
date
1915-12-31 A5000 24K H6TX08100 1000
1916-12-31 T6100 24K H8CA52052 500
1954-12-31 H3100 24K S8AK00090 1000
2007-12-31 J7120 24E H8OH18088 36
2007-12-31 z9600 24K S6ND00058 2000
print d
catcode_disp disposition feccandid_disp bills
date
1997-12-31 A0000 support S4HI00011 1.0
2007-12-31 A1000 oppose S4IA00020', 'P20000741 1 NaN
2007-12-31 A1000 support S8MT00010 1.0
2007-12-31 A1500 support S6WI00061 2.0
2007-12-31 A1600 support S4IA00020', 'P20000741 3 NaN
merge=pd.merge(df,d, how='inner', left_index=True, right_index=True)
print merge
catcode_amt type feccandid_amt amount catcode_disp disposition \
date
2007-12-31 J7120 24E H8OH18088 36 A1000 oppose
2007-12-31 J7120 24E H8OH18088 36 A1000 support
2007-12-31 J7120 24E H8OH18088 36 A1500 support
2007-12-31 J7120 24E H8OH18088 36 A1600 support
2007-12-31 z9600 24K S6ND00058 2000 A1000 oppose
2007-12-31 z9600 24K S6ND00058 2000 A1000 support
2007-12-31 z9600 24K S6ND00058 2000 A1500 support
2007-12-31 z9600 24K S6ND00058 2000 A1600 support
feccandid_disp bills
date
2007-12-31 S4IA00020', 'P20000741 1 NaN
2007-12-31 S8MT00010 1.0
2007-12-31 S6WI00061 2.0
2007-12-31 S4IA00020', 'P20000741 3 NaN
2007-12-31 S4IA00020', 'P20000741 1 NaN
2007-12-31 S8MT00010 1.0
2007-12-31 S6WI00061 2.0
2007-12-31 S4IA00020', 'P20000741 3 NaN
回答by dmb
It looks like your dates are your indices, in which case you would want to merge on the index, not column. If you have two dataframes, df_1and df_2:
看起来您的日期是您的索引，在这种情况下，您希望合并索引而不是列。如果您有两个数据框，df_1并且df_2：
df_1.merge(df_2, left_index=True, right_index=True, how='inner')
df_1.merge(df_2, left_index=True, right_index=True, how='inner')
回答by user1887071
I ran into similar problems. You most likely have a lot of NaTs.
I removed all my NaTs and then performed the join and was able to join it.
我遇到了类似的问题。您很可能有很多 NaT。
我删除了我所有的 NaT，然后执行了加入并能够加入它。
df = df[df['date'].notnull() == True].set_index('date')
d = d[d['date'].notnull() == True].set_index('date')
df.join(d, how='right')
