Python 将列附加到 Pandas 数据框
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Append column to pandas dataframe
提问by BenDundee
This is probably easy, but I have the following data:
这可能很容易，但我有以下数据：
In data frame 1:
在数据框 1 中：
index dat1
0 9
1 5
In data frame 2:
在数据框 2 中：
index dat2
0 7
1 6
I want a data frame with the following form:
我想要一个具有以下形式的数据框：
index dat1 dat2
0 9 7
1 5 6
I've tried using the appendmethod, but I get a cross join (i.e. cartesian product).
我试过使用该append方法，但我得到了一个交叉连接（即笛卡尔积）。
What's the right way to do this?
这样做的正确方法是什么？
采纳答案by U2EF1
It seems in general you're just looking for a join:
一般来说，您似乎只是在寻找加入：
> dat1 = pd.DataFrame({'dat1': [9,5]})
> dat2 = pd.DataFrame({'dat2': [7,6]})
> dat1.join(dat2)
dat1 dat2
0 9 7
1 5 6
回答by BenDundee
Just a matter of the right google search:
只是一个正确的谷歌搜索问题：
data = dat_1.append(dat_2)
data = data.groupby(data.index).sum()
回答by Ella Cohen
You can also use:
您还可以使用：
dat1 = pd.concat([dat1, dat2], axis=1)
回答by Jeremy Z
Both join()and concat()way could solve the problem. However, there is one warning I have to mention: Reset the index before you join()or concat()if you trying to deal with some data frame by selecting some rows from another DataFrame.
无论join()和concat()方法可以解决这个问题。但是，我必须提到一个警告：在您之前重置索引，join()或者concat()如果您尝试通过从另一个 DataFrame 中选择一些行来处理某个数据框。
One example below shows some interesting behavior of join and concat:
下面的一个例子展示了 join 和 concat 的一些有趣的行为：
dat1 = pd.DataFrame({'dat1': range(4)})
dat2 = pd.DataFrame({'dat2': range(4,8)})
dat1.index = [1,3,5,7]
dat2.index = [2,4,6,8]
# way1 join 2 DataFrames
print(dat1.join(dat2))
# output
dat1 dat2
1 0 NaN
3 1 NaN
5 2 NaN
7 3 NaN
# way2 concat 2 DataFrames
print(pd.concat([dat1,dat2],axis=1))
#output
dat1 dat2
1 0.0 NaN
2 NaN 4.0
3 1.0 NaN
4 NaN 5.0
5 2.0 NaN
6 NaN 6.0
7 3.0 NaN
8 NaN 7.0
#reset index
dat1 = dat1.reset_index(drop=True)
dat2 = dat2.reset_index(drop=True)
#both 2 ways to get the same result
print(dat1.join(dat2))
dat1 dat2
0 0 4
1 1 5
2 2 6
3 3 7
print(pd.concat([dat1,dat2],axis=1))
dat1 dat2
0 0 4
1 1 5
2 2 6
3 3 7
回答by Raj Stha
Just as a matter of fact:
事实上：
data_joined = dat1.join(dat2)
print(data_joined)
