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Concatenating a list of DataFrames in Pandas

schedule Aug 12, 2023
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To concatenate a list of DataFrames in Pandas either vertically or horizontally, use the concat(~) method.

Vertically concatenating DataFrames

Consider the following DataFrames:

df1 = pd.DataFrame({"A":[2,3],"B":[4,5]})
df2 = pd.DataFrame({"A":[6,7],"B":[8,9]})
A B | A B
0 2 4 | 0 6 8
1 3 5 | 1 7 9

To vertically concatenate the DataFrames:

pd.concat([df1, df2])
A B
0 2 4
1 3 5
0 6 8
1 7 9

If the column labels do not align, then some entries would be NaN:

df1 = pd.DataFrame({"A":[2,3],"B":[4,5]})
df2 = pd.DataFrame({"A":[6,7],"C":[8,9]})
pd.concat([df1, df2])
A B C
0 2 4.0 NaN
1 3 5.0 NaN
0 6 NaN 8.0
1 7 NaN 9.0

Horizontally concatenating DataFrames

Consider the following DataFrames:

df1 = pd.DataFrame({"A":[2,3],"B":[4,5]})
df2 = pd.DataFrame({"A":[6,7],"B":[8,9]})
A B | A B
0 2 4 | 0 6 8
1 3 5 | 1 7 9

To horizontally concatenate the DataFrames:

pd.concat([df1, df2], axis=1)
A B A C
0 2 4 6 8
1 3 5 7 9

Here, axis=1 is needed to perform concatenation horizontally, as opposed to vertically.

In the case when index (row labels) does not align, we end up with NaN for some entries:

df1 = pd.DataFrame({"A":[2,3],"B":[4,5]}, index=["a","b"])
df2 = pd.DataFrame({"A":[6,7],"B":[8,9]}, index=["a","c"])
pd.concat([df1, df2], axis=1)
A B A B
a 2.0 4.0 6.0 8.0
b 3.0 5.0 NaN NaN
c NaN NaN 7.0 9.0
robocat
Published by Isshin Inada
Edited by 0 others
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