Python 附加具有相同列、不同顺序的两个数据框
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Appending two dataframes with same columns, different order
提问by redrubia
I have two pandas dataframes.
我有两个熊猫数据框。
noclickDF = DataFrame([[0,123,321],[0,1543,432]], columns=['click', 'id','location'])
clickDF = DataFrame([[1,123,421],[1,1543,436]], columns=['click', 'location','id'])
I simply want to join such that the final DF will look like:
我只是想加入这样的最终 DF 看起来像：
click | id | location
0 123 321
0 1543 432
1 421 123
1 436 1543
As you can see the column names of both original DF's are the same, but not in the same order. Also there is no join in a column.
如您所见，两个原始 DF 的列名相同，但顺序不同。列中也没有联接。
采纳答案by unutbu
You could also use pd.concat:
你也可以使用pd.concat：
In [36]: pd.concat([noclickDF, clickDF], ignore_index=True)
Out[36]:
click id location
0 0 123 321
1 0 1543 432
2 1 421 123
3 1 436 1543
Under the hood, DataFrame.appendcalls pd.concat.
DataFrame.appendhas code for handling various types of input, such as Series, tuples, lists and dicts. If you pass it a DataFrame, it passes straight through to pd.concat, so using pd.concatis a bit more direct.
在幕后，DataFrame.append调用pd.concat.
DataFrame.append具有处理各种类型输入的代码，例如系列、元组、列表和字典。如果你给它传递一个 DataFrame，它会直接传递到pd.concat，所以使用pd.concat会更直接一些。
回答by greole
You can use append for that
您可以为此使用 append
df = noclickDF.append(clickDF)
print df
click id location
0 0 123 321
1 0 1543 432
0 1 421 123
1 1 436 1543
and if you need you can reset the index by
如果您需要，您可以通过以下方式重置索引
df.reset_index(drop=True)
print df
click id location
0 0 123 321
1 0 1543 432
2 1 421 123
3 1 436 1543
回答by Beau Hilton
For future users (sometime >pandas 0.23.0):
对于未来的用户（有时>pandas 0.23.0）：
You may also need to add sort=Trueto sort the non-concatenation axis when it is not already aligned (i.e. to retain the OP's desired concatenation behavior). I used the code contributed above and got a warning, see Python Pandas User Warning. The code below works and does not throw a warning.
您可能还需要添加sort=True对尚未对齐的非串联轴进行排序（即保留 OP 所需的串联行为）。我使用了上面贡献的代码并收到了警告，请参阅Python Pandas 用户警告。下面的代码有效并且不会发出警告。
In [36]: pd.concat([noclickDF, clickDF], ignore_index=True, sort=True)
Out[36]:
click id location
0 0 123 321
1 0 1543 432
2 1 421 123
3 1 436 1543
