Pandas equivalent rbind operation

Question

Basically, I am looping through a bunch of CSV files and in the end would like to append each dataframe into one. Actually, all I need is an rbind type function. So, I did some search and followed the guide. However, I still could not get the ideal solution.

A sample code is attached below. For instance shape of data1 is always 47 by 42. But shape of data_out_final becomes (47, 42), (47, 84), and (47, 126) after the first three files. Idealy, it should be (141, 42). In addition, I check index of data1, which is RangeIndex(start=0, stop=47, step=1). Appreciate any suggestions!

My pandas version is 0.18.1

code

appended_data = []
for csv_each in csv_pool:
    data1 = pd.read_csv(csv_each, header=0)
    # do something here
    appended_data.append(data2) 
data_out_final = pd.concat(appended_data, axis=1)

If using data_out_final = pd.concat(appended_data, axis=1), shape of data_out_final becomes (141, 94)

PS

kind of figure it out. Actually, you have to standardize column names before pd.concat.

Answer 1

>>> df1
          a         b
0 -1.417866 -0.828749
1  0.212349  0.791048
2 -0.451170  0.628584
3  0.612671 -0.995330
4  0.078460 -0.322976
5  1.244803  1.576373
6  1.169629 -1.135926
7 -0.652443  0.506388
8  0.549604 -0.691054
9 -0.512829 -0.959398

>>> df2
          a         b
0 -0.652161  0.940932
1  2.495067  0.004833
2 -2.187792  1.692402
3  1.900738  0.372425
4  0.245976  1.894527
5  0.627297  0.029331
6 -0.828628 -1.600014
7 -0.991835 -0.061202
8  0.543389  0.703457
9 -0.755059  1.239968

>>> pd.concat([df1, df2])
          a         b
0 -1.417866 -0.828749
1  0.212349  0.791048
2 -0.451170  0.628584
3  0.612671 -0.995330
4  0.078460 -0.322976
5  1.244803  1.576373
6  1.169629 -1.135926
7 -0.652443  0.506388
8  0.549604 -0.691054
9 -0.512829 -0.959398
0 -0.652161  0.940932
1  2.495067  0.004833
2 -2.187792  1.692402
3  1.900738  0.372425
4  0.245976  1.894527
5  0.627297  0.029331
6 -0.828628 -1.600014
7 -0.991835 -0.061202
8  0.543389  0.703457
9 -0.755059  1.239968

Unless I'm misinterpreting what you need, this is what you need.

Answer 2

Try: http://pandas.pydata.org/pandas-docs/stable/10min.html?highlight=concat#concat

"pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations."