如何按列和索引连接 Pandas DataFrames？
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How can I concatenate Pandas DataFrames by column and index?
提问by user2950747
I've got four Pandas DataFrames with numerical columns and indices:
我有四个带有数字列和索引的 Pandas DataFrame：
A = pd.DataFrame(data={"435000": [9.792, 9.795], "435002": [9.825, 9.812]}, index=[119000, 119002])
B = pd.DataFrame(data={"435004": [9.805, 9.783], "435006": [9.785, 9.78]}, index=[119000, 119002])
C = pd.DataFrame(data={"435000": [9.778, 9.743], "435002": [9.75, 9.743]}, index=[119004, 119006])
D = pd.DataFrame(data={"435004": [9.743, 9.743], "435006": [9.762, 9.738]}, index=[119004, 119006])
I want to concatenate them into one DataFrame like this, matching on both column names and indices:
我想将它们连接成一个像这样的 DataFrame，同时匹配列名和索引：
If I try to pd.concatthe four dfs, they are stacked (either above and below, or to the side, depending on axis) and I end up with NaNvalues in the df:
如果我尝试pd.concat使用四个 dfs，它们会堆叠在一起（在上方和下方，或在侧面，具体取决于axis），并且我最终会NaN在 df 中得到值：
result = pd.concat([A, B, C, D], axis=0)
How can I use pd.concat(or merge, joinetc.) to get the right result?
如何使用pd.concat（或merge，join等等），以获得正确的结果呢？
采纳答案by jezrael
You need concat in pairs:
您需要成对连接：
result = pd.concat([pd.concat([A, C], axis=0), pd.concat([B, D], axis=0)], axis=1)
print (result)
435000 435002 435004 435006
119000 9.792 9.825 9.805 9.785
119002 9.795 9.812 9.783 9.780
119004 9.778 9.750 9.743 9.762
119006 9.743 9.743 9.743 9.738
Better is stack+ concat+ unstack:
result = pd.concat([A.stack(), B.stack(), C.stack(), D.stack()], axis=0).unstack()
print (result)
435000 435002 435004 435006
119000 9.792 9.825 9.805 9.785
119002 9.795 9.812 9.783 9.780
119004 9.778 9.750 9.743 9.762
119006 9.743 9.743 9.743 9.738
More dynamic:
更动态：
dfs = [A,B,C,D]
result = pd.concat([df.stack() for df in dfs], axis=0).unstack()
print (result)
435000 435002 435004 435006
119000 9.792 9.825 9.805 9.785
119002 9.795 9.812 9.783 9.780
119004 9.778 9.750 9.743 9.762
119006 9.743 9.743 9.743 9.738
回答by ayhan
You can use join too:
您也可以使用 join：
pd.concat((A.join(B), C.join(D)))
Out:
435000 435002 435004 435006
119000 9.792 9.825 9.805 9.785
119002 9.795 9.812 9.783 9.780
119004 9.778 9.750 9.743 9.762
119006 9.743 9.743 9.743 9.738
