Python 如何按列和索引连接数据帧?
我有四个带有数字列和索引的数据帧:Python 如何按列和索引连接数据帧?,python,pandas,Python,Pandas,我有四个带有数字列和索引的数据帧: 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":
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])
我想将它们连接成这样一个数据帧,在列名和索引上都匹配:
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])
如果我尝试对四个df进行pd.concat
运算,它们会堆叠在一起(根据轴的不同,可以是上下堆叠,也可以是侧面堆叠),最后在df中得到NaN
值:
result = pd.concat([A, B, C, D], axis=0)
如何使用pd.concat
(或merge
,join
等)获得正确的结果?您需要concat成对:
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
更好的是++:
更具活力:
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
您也可以使用“加入”:
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
很高兴能帮上忙,天气真好!顺便说一句,问题很好,色彩丰富;)