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Concatenating a list of DataFrames in Pandas
schedule Aug 12, 2023
Last updated local_offer
Tags Python●Pandas
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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 B0 2 4 | 0 6 81 3 5 | 1 7 9
To vertically concatenate the DataFrames:
pd.concat([df1, df2])
A B0 2 41 3 50 6 81 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 C0 2 4.0 NaN1 3 5.0 NaN0 6 NaN 8.01 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 B0 2 4 | 0 6 81 3 5 | 1 7 9
To horizontally concatenate the DataFrames:
pd.concat([df1, df2], axis=1)
A B A C0 2 4 6 81 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 Ba 2.0 4.0 6.0 8.0b 3.0 5.0 NaN NaNc NaN NaN 7.0 9.0
Published by Isshin Inada
Edited by 0 others
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