Concatenate rows of two dataframes in pandas

Question

I need to concatenate two dataframes df_a anddf_b having equal number of rows (nRow) one after another without any consideration of keys. This function is similar to cbind in R programming language. The number of columns in each dataframe may be different.

The resultant dataframe will have the same number of rows nRow and number of columns equal to the sum of number of columns in both the dataframes. In othe words, this is a blind columnar concatenation of two dataframes.

import pandas as pd dict_data = {'Treatment': ['C', 'C', 'C'], 'Biorep': ['A', 'A', 'A'], 'Techrep': [1, 1, 1], 'AAseq': ['ELVISLIVES', 'ELVISLIVES', 'ELVISLIVES'], 'mz':[500.0, 500.5, 501.0]} df_a = pd.DataFrame(dict_data) dict_data = {'Treatment1': ['C', 'C', 'C'], 'Biorep1': ['A', 'A', 'A'], 'Techrep1': [1, 1, 1], 'AAseq1': ['ELVISLIVES', 'ELVISLIVES', 'ELVISLIVES'], 'inte1':[1100.0, 1050.0, 1010.0]} df_b = pd.DataFrame(dict_data) 

Answer 1

call concat and pass param axis=1 to concatenate column-wise:

In [5]:  pd.concat([df_a,df_b], axis=1) Out[5]:         AAseq Biorep  Techrep Treatment     mz      AAseq1 Biorep1  Techrep1  \ 0  ELVISLIVES      A        1         C  500.0  ELVISLIVES       A         1    1  ELVISLIVES      A        1         C  500.5  ELVISLIVES       A         1    2  ELVISLIVES      A        1         C  501.0  ELVISLIVES       A         1       Treatment1  inte1   0          C   1100   1          C   1050   2          C   1010   

There is a useful guide to the various methods of merging, joining and concatenating online.

For example, as you have no clashing columns you can merge and use the indices as they have the same number of rows:

In [6]:  df_a.merge(df_b, left_index=True, right_index=True) Out[6]:         AAseq Biorep  Techrep Treatment     mz      AAseq1 Biorep1  Techrep1  \ 0  ELVISLIVES      A        1         C  500.0  ELVISLIVES       A         1    1  ELVISLIVES      A        1         C  500.5  ELVISLIVES       A         1    2  ELVISLIVES      A        1         C  501.0  ELVISLIVES       A         1       Treatment1  inte1   0          C   1100   1          C   1050   2          C   1010   

And for the same reasons as above a simple join works too:

In [7]:  df_a.join(df_b) Out[7]:         AAseq Biorep  Techrep Treatment     mz      AAseq1 Biorep1  Techrep1  \ 0  ELVISLIVES      A        1         C  500.0  ELVISLIVES       A         1    1  ELVISLIVES      A        1         C  500.5  ELVISLIVES       A         1    2  ELVISLIVES      A        1         C  501.0  ELVISLIVES       A         1       Treatment1  inte1   0          C   1100   1          C   1050   2          C   1010