Pandas concat() Examples [Latest]

1. Pandas concat() Syntax

The concat() method syntax is:

concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
           keys=None, levels=None, names=None, verify_integrity=False,
           sort=None, copy=True)

• objs: a sequence of pandas objects to concatenate.
• join: optional parameter to define how to handle the indexes on the other axis. The valid values are ‘inner’ and ‘outer’.
• join_axes: deprecated in version 0.25.0.
• ignore_index: if True, the indexes from the source objects will be ignored and a sequence of indexes from 0,1,2..n will be assigned to the result.
• keys: a sequence to add an identifier to the result indexes. It’s helpful in marking the source objects in the output.
• levels: a sequence to specify the unique levels to create multiindex.
• names: names for the levels in the resulting hierarchical index.
• verify_integrity: Check whether the new concatenated axis contains duplicates. It’s an expensive operation.
• sort: Sort non-concatenation axis if it is not already aligned when join is ‘outer’. Added in version 0.23.0
• copy: if False, don’t copy data unnecessarily.

2. Pandas concat() Example

Let’s look at a simple example to concatenate two DataFrame objects.

import pandas
d1 = {"Name": ["Pankaj", "Lisa"], "ID": [1, 2]}
d2 = {"Name": "David", "ID": 3}
df1 = pandas.DataFrame(d1, index={1, 2})
df2 = pandas.DataFrame(d2, index={3})
print('********n', df1)
print('********n', df2)
df3 = pandas.concat([df1, df2])
print('********n', df3)

Output:

********
      Name  ID
1  Pankaj   1
2    Lisa   2
********
     Name  ID
3  David   3
********
      Name  ID
1  Pankaj   1
2    Lisa   2
3   David   3

Notice that the concatenation is performed row-wise i.e. 0-axis. Also, the indexes from the source DataFrame objects are preserved in the output.

3. Concatenating Along Column i.e. 1-axis

d1 = {"Name": ["Pankaj", "Lisa"], "ID": [1, 2]}
d2 = {"Role": ["Admin", "Editor"]}
df1 = pandas.DataFrame(d1, index={1, 2})
df2 = pandas.DataFrame(d2, index={1, 2})
df3 = pandas.concat([df1, df2], axis=1)
print('********n', df3)

Output:

********
      Name  ID    Role
1  Pankaj   1   Admin
2    Lisa   2  Editor

The concatenation along column makes sense when the source objects contain different kinds of data of an object.

4. Assigning Keys to the Concatenated DataFrame Indexes

d1 = {"Name": ["Pankaj", "Lisa"], "ID": [1, 2]}
d2 = {"Name": "David", "ID": 3}
df1 = pandas.DataFrame(d1, index={1, 2})
df2 = pandas.DataFrame(d2, index={3})
df3 = pandas.concat([df1, df2], keys=["DF1", "DF2"])
print('********n', df3)

Output:

********
          Name  ID
DF1 1  Pankaj   1
    2    Lisa   2
DF2 3   David   3

5. Ignore Source DataFrame Objects in Concatenation

d1 = {"Name": ["Pankaj", "Lisa"], "ID": [1, 2]}
d2 = {"Name": "David", "ID": 3}
df1 = pandas.DataFrame(d1, index={10, 20})
df2 = pandas.DataFrame(d2, index={30})
df3 = pandas.concat([df1, df2], ignore_index=True)
print('********n', df3)

Output:

********
      Name  ID
0  Pankaj   1
1    Lisa   2
2   David   3

This is useful when the indexes in the source objects don’t make much sense. So we can ignore them and assign the default indexes to the output DataFrame.

6. References