Appending two dataframes with same columns, different order
PythonJoinPandasAppendPython Problem Overview
I have two pandas dataframes.
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:
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.
Python Solutions
Solution 1 - Python
You could also use 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.append calls pd.concat.
DataFrame.append has 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.concat is a bit more direct.
Solution 2 - Python
For future users (sometime >pandas 0.23.0):
You may also need to add sort=True to 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 https://stackoverflow.com/questions/50501787/python-pandas-user-warning. The code below works and does not throw a warning.
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
Solution 3 - Python
You can use append for that
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
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