Python Pandas Merge(pd.Merge)如何设置索引和连接
我有两个数据帧:dfLeft和dfRight,以日期作为索引 左:Python Pandas Merge(pd.Merge)如何设置索引和连接,python,pandas,Python,Pandas,我有两个数据帧:dfLeft和dfRight,以日期作为索引 左: cusip factorL date 2012-01-03 XXXX 4.5 2012-01-03 YYYY 6.2 .... 2012-01-04 XXXX 4.7 2012-01-04 YYYY 6.1 .... 右: idc__id factorR date 2012-01-03 XX
cusip factorL
date
2012-01-03 XXXX 4.5
2012-01-03 YYYY 6.2
....
2012-01-04 XXXX 4.7
2012-01-04 YYYY 6.1
....
右:
idc__id factorR
date
2012-01-03 XXXX 5.0
2012-01-03 YYYY 6.0
....
2012-01-04 XXXX 5.1
2012-01-04 YYYY 6.2
两者的形状接近(121900,3)
我尝试了以下合并:
test = pd.merge(dfLeft, dfRight, left_index=True, right_index=True, left_on='cusip', right_on='idc__id', how = 'inner')
这使得测试的形状为(60643500,6)
对这里的问题有什么建议吗?我希望它能够根据日期和cusip/idc_id进行合并。注意:在本例中,cusip是对齐的,但实际上可能不是这样
谢谢
预期产量
测试:
重置索引,然后在多个(列)键上合并: 然后可以将“日期”重置为索引:
dfMerged.set_index('date', inplace=True)
这里有一个例子:
raw1 = '''
2012-01-03 XXXX 4.5
2012-01-03 YYYY 6.2
2012-01-04 XXXX 4.7
2012-01-04 YYYY 6.1
'''
raw2 = '''
2012-01-03 XYXX 45.
2012-01-03 YYYY 62.
2012-01-04 XXXX -47.
2012-01-05 YYYY 61.
'''
import pandas as pd
from StringIO import StringIO
df1 = pd.read_table(StringIO(raw1), header=None,
delim_whitespace=True, parse_dates=[0], skiprows=1)
df2 = pd.read_table(StringIO(raw2), header=None,
delim_whitespace=True, parse_dates=[0], skiprows=1)
df1.columns = ['date', 'cusip', 'factorL']
df2.columns = ['date', 'idc__id', 'factorL']
print pd.merge(df1, df2,
left_on=['date', 'cusip'],
right_on=['date', 'idc__id'],
how='inner')
给
date cusip factorL_x idc__id factorL_y
0 2012-01-03 00:00:00 YYYY 6.2 YYYY 62
1 2012-01-04 00:00:00 XXXX 4.7 XXXX -47
您可以将
'cuspin'
和'idc\u id'
作为索引添加到您面前的数据帧中(以下是它在前几行上的工作方式):
这也很好,因为您不必创建数据帧的副本。
raw1 = '''
2012-01-03 XXXX 4.5
2012-01-03 YYYY 6.2
2012-01-04 XXXX 4.7
2012-01-04 YYYY 6.1
'''
raw2 = '''
2012-01-03 XYXX 45.
2012-01-03 YYYY 62.
2012-01-04 XXXX -47.
2012-01-05 YYYY 61.
'''
import pandas as pd
from StringIO import StringIO
df1 = pd.read_table(StringIO(raw1), header=None,
delim_whitespace=True, parse_dates=[0], skiprows=1)
df2 = pd.read_table(StringIO(raw2), header=None,
delim_whitespace=True, parse_dates=[0], skiprows=1)
df1.columns = ['date', 'cusip', 'factorL']
df2.columns = ['date', 'idc__id', 'factorL']
print pd.merge(df1, df2,
left_on=['date', 'cusip'],
right_on=['date', 'idc__id'],
how='inner')
date cusip factorL_x idc__id factorL_y
0 2012-01-03 00:00:00 YYYY 6.2 YYYY 62
1 2012-01-04 00:00:00 XXXX 4.7 XXXX -47
In [10]: dfL
Out[10]:
cuspin factorL
date
2012-01-03 XXXX 4.5
2012-01-03 YYYY 6.2
In [11]: dfL1 = dfLeft.set_index('cuspin', append=True)
In [12]: dfR1 = dfRight.set_index('idc_id', append=True)
In [13]: dfL1
Out[13]:
factorL
date cuspin
2012-01-03 XXXX 4.5
YYYY 6.2
In [14]: dfL1.join(dfR1)
Out[14]:
factorL factorR
date cuspin
2012-01-03 XXXX 4.5 5
YYYY 6.2 6