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Combining multiple DataFrames into one DataFrame in Pandas

Pandas
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Multi-index Operations Cookbook
schedule Jul 1, 2022
Last updated
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To combine multiple DataFrames into a single DataFrame, use the pd.concat(~) method.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,3],"B":[4,5]})
df
   A  B
0  2  4
1  3  5

Here's the other DataFrame we want to concatenate:

df_other = pd.DataFrame({"A":[6,7],"B":[8,9]})
df_other
   A  B
0  6  8
1  7  9

Notice how df and df_other have matching column labels.

Concatenating DataFrames vertically

To concatenate multiple DataFrames vertically:

pd.concat([df, df_other])   # axis=0
   A  B
0  2  4
1  3  5
0  6  8
1  7  9

Concatenating DataFrames horizontally

To concatenate multiple DataFrames horizontally, pass in axis=1 like so:

pd.concat([df, df_other], axis=1)
   A  B  A  B
0  2  4  6  8
1  3  5  7  9

Case when column labels differ

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,3],"B":[4,5]})
df
   A  B
0  2  4
1  3  5

Here's the other DataFrame we want to concatenate:

df_other = pd.DataFrame({"B":[6,7],"C":[8,9]})
df_other
   B  C
0  6  8
1  7  9

Concatenating them vertically:

pd.concat([df, df_other])
   A    B  C
0  2.0  4  NaN
1  3.0  5  NaN
0  NaN  6  8.0
1  NaN  7  9.0

Observe how the two DataFrames got vertically stacked with shared column (B) aligned. The newly introduced gaps are then filled using nan.

NOTE

To learn more about concat(~), check our our full documentation.

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Published by Isshin Inada
Edited by 0 others
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