pandas.concat() function in Python

The pandas.concat() function does all the heavy lifting of performing concatenation operations along with an axis of Pandas objects while performing optional set logic (union or intersection) of the indexes (if any) on the other axes.

Pandas concat() function Syntax

Syntax: concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)

Parameters:

• objs: Series or DataFrame objects
• axis: axis to concatenate along; default = 0
• join: way to handle indexes on other axis; default = ‘outer’
• ignore_index: if True, do not use the index values along the concatenation axis; default = False
• keys: sequence to add an identifier to the result indexes; default = None
• levels: specific levels (unique values) to use for constructing a MultiIndex; default = None
• names: names for the levels in the resulting hierarchical index; default = None
• verify_integrity: check whether the new concatenated axis contains duplicates; default = False
• sort: sort non-concatenation axis if it is not already aligned when join is ‘outer’; default = False
• copy: if False, do not copy data unnecessarily; default = True

Returns: type of objs (Series of DataFrame)

Pandas concat() Examples

Example 1: Concatenating 2 Series with default parameters in Pandas.

Python3

# importing the module
import pandas as pd

# creating the Series
series1 = pd.Series([1, 2, 3])
display('series1:', series1)
series2 = pd.Series(['A', 'B', 'C'])
display('series2:', series2)

# concatenating
display('After concatenating:')
display(pd.concat([series1, series2]))

Output:

Example 2: Concatenating 2 series horizontally with index = 1.

Python3

# importing the module
import pandas as pd

# creating the Series
series1 = pd.Series([1, 2, 3])
display('series1:', series1)
series2 = pd.Series(['A', 'B', 'C'])
display('series2:', series2)

# concatenating
display('After concatenating:')
display(pd.concat([series1, series2],
                  axis = 1))

Output:

Example 3: Concatenating 2 DataFrames and assigning keys.

Python3

# importing the module
import pandas as pd

# creating the DataFrames
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})
display('df1:', df1)
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7']})
display('df2:', df2)

# concatenating
display('After concatenating:')
display(pd.concat([df1, df2],
                  keys = ['key1', 'key2']))

Output:

Example 4: Concatenating 2 DataFrames horizontally with axis = 1.

Python3

# importing the module
import pandas as pd

# creating the DataFrames
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})
display('df1:', df1)
df2 = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']})
display('df2:', df2)

# concatenating
display('After concatenating:')
display(pd.concat([df1, df2],
                  axis = 1))

Output:

Example 5: Concatenating 2 DataFrames with ignore_index = True so that new index values are displayed in the concatenated DataFrame.

Python3

# importing the module
import pandas as pd

# creating the DataFrames
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})
display('df1:', df1)
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7']})
display('df2:', df2)

# concatenating
display('After concatenating:')
display(pd.concat([df1, df2],
                  ignore_index = True))

Output:

Example 6: Concatenating a DataFrame with a Series.

Python3

# importing the module
import pandas as pd

# creating the DataFrame
df = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})
display('df:', df1)
# creating the Series
series = pd.Series([1, 2, 3, 4])
display('series:', series)

# concatenating
display('After concatenating:')
display(pd.concat([df, series],
                  axis = 1))

Output: