问题：如何合并两个数据帧？
我正在使用Pandas数据框。我说有一个初始数据框D。我从中提取两个数据帧，如下所示：
A = D[D.label == k]
B = D[D.label != k]
然后我更改标签中A和B
A.label = 1
B.label = -1
我想将A和B结合起来，这样我就可以将它们作为一个数据帧使用，类似于联合操作。数据的顺序并不重要。但是，当我们从D采样A和B时，它们保留了D的索引。
I’m using Pandas data frames. I have a initial data frame, say D. I extract two data frames from it like this:
A = D[D.label == k]
B = D[D.label != k]
I want to combine A and B so I can have them as one DataFrame, something like a union operation. The order of the data is not important. However, when we sample A and B from D, they retain their indexes from D.
回答 0
我相信你可以使用该append方法
bigdata = data1.append(data2, ignore_index=True)
保持索引只是不使用ignore_index关键字…
I believe you can use the append method
bigdata = data1.append(data2, ignore_index=True)
to keep their indexes just dont use the ignore_index keyword …
回答 1
您还可以使用pd.concat，当您连接两个以上数据框时，这特别有用：
bigdata = pd.concat([data1, data2], ignore_index=True, sort=False)
You can also use pd.concat, which is particularly helpful when you are joining more than two dataframes:
bigdata = pd.concat([data1, data2], ignore_index=True, sort=False)
回答 2
如果有人发现它有用，可以考虑在此处添加它。@ostrokach已经提到了如何合并跨行的数据框，即
df_row_merged = pd.concat([df_a, df_b], ignore_index=True)
要跨列合并，可以使用以下语法：
df_col_merged = pd.concat([df_a, df_b], axis=1)
Thought to add this here in case someone finds it useful. @ostrokach already mentioned how you can merge the data frames across rows which is
df_row_merged = pd.concat([df_a, df_b], ignore_index=True)
To merge across columns, you can use the following syntax:
df_col_merged = pd.concat([df_a, df_b], axis=1)
回答 3
对于正在处理大数据并需要连接多个数据集的情况，还有另一种解决方案。concat可能会提高性能，因此，如果您不想每次都创建新的df，则可以使用列表推导：
frames = [ process_file(f) for f in dataset_files ]
result = pd.append(frames)
（如本节底部文档中的此处所指出）：
注意：但是，值得注意的是，concat（并因此append）制作了数据的完整副本，并且不断地重用此功能可能会严重影响性能。如果需要对多个数据集使用该操作，请使用列表推导。
There’s another solution for the case that you are working with big data and need to concatenate multiple datasets. concat can get performance-intensive, so if you don’t want to create a new df each time, you can instead use a list comprehension:
frames = [ process_file(f) for f in dataset_files ]
result = pd.append(frames)
(as pointed out here in the docs at the bottom of the section):
Note: It is worth noting however, that concat (and therefore append)
makes a full copy of the data, and that constantly reusing this
function can create a significant performance hit. If you need to use
the operation over several datasets, use a list comprehension.
回答 4
如果要用df1第二个数据帧的值更新/替换第一个数据帧的值df2。您可以按照以下步骤进行操作-
步骤1：设置第一个数据帧（df1）的索引
df1.set_index('id')
步骤2：设置第二个数据帧（df2）的索引
df2.set_index('id')
最后使用以下代码段更新数据框-
df1.update(df2)
If you want to update/replace the values of first dataframe df1 with the values of second dataframe df2. you can do it by following steps —
Step 1: Set index of the first dataframe (df1)
df1.set_index('id')
Step 2: Set index of the second dataframe (df2)
df2.set_index('id')
and finally update the dataframe using the following snippet —
df1.update(df2)
回答 5
第一个数据帧
train.shape
结果：-
(31962, 3)
第二个数据帧
test.shape
结果：-
(17197, 2)
结合
new_data=train.append(test,ignore_index=True)
检查一下
new_data.shape
结果：-
(49159, 3)
1st dataFrame
train.shape
result:-
(31962, 3)
2nd dataFrame
test.shape
result:-
(17197, 2)
Combine
new_data=train.append(test,ignore_index=True)
Check
new_data.shape
result:-
(49159, 3)
