[英]Merging multiple pandas Dataframes
我正在努力解決以下問題。 我有多個單獨的數據框(50),每個數據框都包含許多股票的一種特定特征(例如價格,標准差等),因此如下所示:
import pandas as pd
import numpy as np
dates = pd.date_range('20130101',periods=6)
df1 = pd.DataFrame(np.random.randn(6,4),index=dates,\
columns('AAPL','MSFT','TSLA','GE'))
df2 = pd.DataFrame(np.random.randn(6,4),index=dates,\
columns=('AAPL','MSFT','TSLA','GE'))
df3 = pd.DataFrame(np.random.randn(6,4),index=dates,\
columns=('AAPL','MSFT','TSLA','GE'))
df4 = pd.DataFrame(np.random.randn(6,4),index=dates,\
columns=('AAPL','MSFT','TSLA','GE'))
現在,我想以一種方式合並它們,以便為每個股票獲得一個數據框,其中包含該特定股票的所有特征,因此如下所示:
aapl = pd.DataFrame(np.random.randn(6,4),index=dates,\
columns=('AAPL1','AAPL2','AAPL3','AAPL4'))
msft = pd.DataFrame(np.random.randn(6,4),index=dates,\
columns=('MSFT1','MSFT2','MSFT3','MSFT4'))
tsla = pd.DataFrame(np.random.randn(6,4),index=dates,\
columns=('TSLA1','TSLA2','TSLA3','TSLA4'))
ge = pd.DataFrame(np.random.randn(6,4),index=dates,\
columns=('GE1','GE2','GE3','GE4'))
我會用concat:
In [11]: res = pd.concat([df1, df2, df3, df4], keys=[1, 2, 3, 4], axis=1)
In [12]: res
Out[12]:
1 2 3 4
AAPL MSFT TSLA GE AAPL MSFT TSLA GE AAPL MSFT TSLA GE AAPL MSFT TSLA GE
2013-01-01 0.144764 1.292692 -1.303908 -0.843892 -1.104683 -1.178507 0.898648 -0.626209 0.492292 0.147169 1.814729 0.562406 -0.121656 0.865116 0.430813 -0.326225
2013-01-02 -0.163063 0.019601 -2.565271 0.708233 0.317464 -2.574969 -0.080129 -1.176806 0.045253 0.684745 -1.062797 -0.483389 -0.579194 0.401920 -0.393240 0.113734
2013-01-03 0.213592 -0.732072 -0.942323 0.191418 -0.962551 -0.027296 0.665155 2.775983 -0.627107 -0.015927 0.939107 0.239057 0.548166 -1.753082 -0.007525 1.771812
2013-01-04 1.067464 -0.331888 0.638843 -1.197937 0.925848 2.273798 0.646925 -2.910974 0.531653 -0.748255 0.262995 0.077923 -0.867982 1.174089 0.183573 0.263749
2013-01-05 0.873720 -0.816305 0.270330 -1.543169 0.116701 -1.392711 1.519368 -0.601046 -0.154348 -0.345653 -0.785385 -0.095604 1.351421 0.192520 0.802445 2.107376
2013-01-06 -0.781975 1.007111 -2.555165 -1.866207 1.480997 0.212057 1.053570 -0.798790 -0.785660 -0.853178 -2.274432 0.481971 -1.555876 -0.928069 -0.408319 0.270534
那么您可以使用xs拉出APPL:
In [13]: res.xs("AAPL", level=1, axis=1)
Out[13]:
1 2 3 4
2013-01-01 0.144764 -1.104683 0.492292 -0.121656
2013-01-02 -0.163063 0.317464 0.045253 -0.579194
2013-01-03 0.213592 -0.962551 -0.627107 0.548166
2013-01-04 1.067464 0.925848 0.531653 -0.867982
2013-01-05 0.873720 0.116701 -0.154348 1.351421
2013-01-06 -0.781975 1.480997 -0.785660 -1.555876
也許更好的事情是獲得各組的意見:
In [21]: d = dict(iter(res.groupby(level=1, axis=1)))
In [22]: d["AAPL"]
Out[22]:
1 2 3 4
AAPL AAPL AAPL AAPL
2013-01-01 0.144764 -1.104683 0.492292 -0.121656
2013-01-02 -0.163063 0.317464 0.045253 -0.579194
2013-01-03 0.213592 -0.962551 -0.627107 0.548166
2013-01-04 1.067464 0.925848 0.531653 -0.867982
2013-01-05 0.873720 0.116701 -0.154348 1.351421
2013-01-06 -0.781975 1.480997 -0.785660 -1.555876
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