pandas 如何合并/组合熊猫中的列？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/46563833/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
How to merge/combine columns in pandas?
提问by mati
I have a (example-) dataframe with 4 columns:
我有一个包含 4 列的（示例-）数据框：
data = {'A': ['a', 'b', 'c', 'd', 'e', 'f'],
'B': [42, 52, np.nan, np.nan, np.nan, np.nan],
'C': [np.nan, np.nan, 31, 2, np.nan, np.nan],
'D': [np.nan, np.nan, np.nan, np.nan, 62, 70]}
df = pd.DataFrame(data, columns = ['A', 'B', 'C', 'D'])
A B C D
0 a 42.0 NaN NaN
1 b 52.0 NaN NaN
2 c NaN 31.0 NaN
3 d NaN 2.0 NaN
4 e NaN NaN 62.0
5 f NaN NaN 70.0
I would now like to merge/combine columns B, C, and D to a new column E like in this example:
我现在想将 B、C 和 D 列合并/组合到一个新的 E 列，如本例所示：
data2 = {'A': ['a', 'b', 'c', 'd', 'e', 'f'],
'E': [42, 52, 31, 2, 62, 70]}
df2 = pd.DataFrame(data2, columns = ['A', 'E'])
A E
0 a 42
1 b 52
2 c 31
3 d 2
4 e 62
5 f 70
I found a quite similar question herebut this adds the merged colums B, C, and D at the end of column A:
我在这里发现了一个非常相似的问题，但这在 A 列的末尾添加了合并的列 B、C 和 D：
0 a
1 b
2 c
3 d
4 e
5 f
6 42
7 52
8 31
9 2
10 62
11 70
dtype: object
Thanks for help.
感谢帮助。
采纳答案by Zero
Option 1
Using assignand drop
选项 1
使用assign和drop
In [644]: cols = ['B', 'C', 'D']
In [645]: df.assign(E=df[cols].sum(1)).drop(cols, 1)
Out[645]:
A E
0 a 42.0
1 b 52.0
2 c 31.0
3 d 2.0
4 e 62.0
5 f 70.0
Option 2
Using assignment and drop
选项 2
使用赋值和drop
In [648]: df['E'] = df[cols].sum(1)
In [649]: df = df.drop(cols, 1)
In [650]: df
Out[650]:
A E
0 a 42.0
1 b 52.0
2 c 31.0
3 d 2.0
4 e 62.0
5 f 70.0
Option 3Lately, I like the 3rd option.
Using groupby
选项 3最近，我喜欢第三个选项。
使用groupby
In [660]: df.groupby(np.where(df.columns == 'A', 'A', 'E'), axis=1).first() #or sum max min
Out[660]:
A E
0 a 42.0
1 b 52.0
2 c 31.0
3 d 2.0
4 e 62.0
5 f 70.0
In [661]: df.columns == 'A'
Out[661]: array([ True, False, False, False], dtype=bool)
In [662]: np.where(df.columns == 'A', 'A', 'E')
Out[662]:
array(['A', 'E', 'E', 'E'],
dtype='|S1')
回答by Zero
The question as written asks for merge/combine as opposed to sum, so posting this to help folks who find this answer looking for help on coalescing with combine_first, which can be a bit tricky.
所写的问题要求合并/合并，而不是求和，因此发布此问题是为了帮助找到此答案的人寻求与 combine_first 合并的帮助，这可能有点棘手。
df2 = pd.concat([df["A"],
df["B"].combine_first(df["C"]).combine_first(df["D"])],
axis=1)
df2.rename(columns={"B":"E"}, inplace=True)
A E
0 a 42.0
1 b 52.0
2 c 31.0
3 d 2.0
4 e 62.0
5 f 70.0
What's so tricky about that? in this case there's no problem - but let's say you were pulling the B, C and D values from different dataframes, in which the a,b,c,d,e,f labels were present, but not necessarily in the same order. combine_first() aligns on the index, so you'd need to tack a set_index() on to each of your df references.
这有什么好纠结的？在这种情况下没有问题 - 但假设您从不同的数据帧中提取 B、C 和 D 值，其中存在 a、b、c、d、e、f 标签，但顺序不一定相同。combine_first() 在索引上对齐，因此您需要在每个 df 引用上添加 set_index() 。
df2 = pd.concat([df.set_index("A", drop=False)["A"],
df.set_index("A")["B"]\
.combine_first(df.set_index("A")["C"])\
.combine_first(df.set_index("A")["D"]).astype(int)],
axis=1).reset_index(drop=True)
df2.rename(columns={"B":"E"}, inplace=True)
A E
0 a 42
1 b 52
2 c 31
3 d 2
4 e 62
5 f 70
回答by jezrael
Use differencefor columns names without Aand then get sumor max:
使用difference了不列名A，然后得到sum或max：
cols = df.columns.difference(['A'])
df['E'] = df[cols].sum(axis=1).astype(int)
# df['E'] = df[cols].max(axis=1).astype(int)
df = df.drop(cols, axis=1)
print (df)
A E
0 a 42
1 b 52
2 c 31
3 d 2
4 e 62
5 f 70
If multiple values per rows:
如果每行有多个值：
data = {'A': ['a', 'b', 'c', 'd', 'e', 'f'],
'B': [42, 52, np.nan, np.nan, np.nan, np.nan],
'C': [np.nan, np.nan, 31, 2, np.nan, np.nan],
'D': [10, np.nan, np.nan, np.nan, 62, 70]}
df = pd.DataFrame(data, columns = ['A', 'B', 'C', 'D'])
print (df)
A B C D
0 a 42.0 NaN 10.0
1 b 52.0 NaN NaN
2 c NaN 31.0 NaN
3 d NaN 2.0 NaN
4 e NaN NaN 62.0
5 f NaN NaN 70.0
cols = df.columns.difference(['A'])
df['E'] = df[cols].apply(lambda x: ', '.join(x.dropna().astype(int).astype(str)), 1)
df = df.drop(cols, axis=1)
print (df)
A E
0 a 42, 10
1 b 52
2 c 31
3 d 2
4 e 62
5 f 70
回答by jpp
You can also use ffillwith iloc:
您还可以使用ffill带iloc：
df['E'] = df.iloc[:, 1:].ffill(1).iloc[:, -1].astype(int)
df = df.iloc[:, [0, -1]]
print(df)
A E
0 a 42
1 b 52
2 c 31
3 d 2
4 e 62
5 f 70
