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 moduleimport pandas as pd# creating the Seriesseries1 = pd.Series([1, 2, 3])display('series1:', series1)series2 = pd.Series(['A', 'B', 'C'])display('series2:', series2)# concatenatingdisplay('After concatenating:')display(pd.concat([series1, series2])) |
Output:
Example 2: Concatenating 2 series horizontally with index = 1.
Python3
# importing the moduleimport pandas as pd# creating the Seriesseries1 = pd.Series([1, 2, 3])display('series1:', series1)series2 = pd.Series(['A', 'B', 'C'])display('series2:', series2)# concatenatingdisplay('After concatenating:')display(pd.concat([series1, series2], axis = 1)) |
Output:
Example 3: Concatenating 2 DataFrames and assigning keys.
Python3
# importing the moduleimport pandas as pd# creating the DataFramesdf1 = 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)# concatenatingdisplay('After concatenating:')display(pd.concat([df1, df2], keys = ['key1', 'key2'])) |
Output:
Example 4: Concatenating 2 DataFrames horizontally with axis = 1.
Python3
# importing the moduleimport pandas as pd# creating the DataFramesdf1 = 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)# concatenatingdisplay('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 moduleimport pandas as pd# creating the DataFramesdf1 = 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)# concatenatingdisplay('After concatenating:')display(pd.concat([df1, df2], ignore_index = True)) |
Output:
Example 6: Concatenating a DataFrame with a Series.
Python3
# importing the moduleimport pandas as pd# creating the DataFramedf = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3']})display('df:', df1)# creating the Seriesseries = pd.Series([1, 2, 3, 4])display('series:', series)# concatenatingdisplay('After concatenating:')display(pd.concat([df, series], axis = 1)) |
Output:
