How to Combine Two DataFrames with Equal Number of Rows in Pandas?

Concatenate Rows of Two Dataframes in Pandas

To horizontally concatenate two dataframes, df_a and df_b, with an equal number of rows without considering keys, use the concat function with the axis parameter set to 1:

<code class="python">pd.concat([df_a, df_b], axis=1)</code>

For example, consider the following two dataframes:

<code class="python">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)</code>

Using the concat function with axis=1, we can concatenate these dataframes column-wise:

<code class="python">pd.concat([df_a, df_b], axis=1)</code>

This will result in a dataframe with the same number of rows (3) and the number of columns equal to the sum of the columns in both dataframes (9):

        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  

Note that since there are no overlapping columns, the merged dataframe directly combines the columns into a single table.

Additionally, you can merge the dataframes using their indices since they have the same number of rows, which provides the same result:

<code class="python">df_a.merge(df_b, left_index=True, right_index=True)</code>

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