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)
Concatenate Using Pandas with Examples
Example 1: Concatenate DataFrames in Python
In this example, we are concatenating two 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: Pandas combining two dataframes horizontally with index = 1
In this example, we create two Pandas Series (series1 and series2), and then concatenates them along the columns (axis=1) using pd.concat(). The resulting DataFrame contains both Series as columns, creating a new DataFrame with two columns.
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
creates two DataFrames (df1 and df2), and concatenates them along with keys assigned to each DataFrame using pd.concat(). The resulting DataFrame has a hierarchical index with keys ‘key1’ and ‘key2’, distinguishing the origin of each set of data.
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 DataFrames horizontally in Pandas with axis = 1
creates two DataFrames (df1 and df2), and concatenates them along the columns (axis=1) using pd.concat(). The resulting DataFrame combines columns from both df1 and df2, aligning them side by side.
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
creates two DataFrames (df1 and df2) with identical columns, and concatenates them vertically using pd.concat() with ignore_index=True. The resulting DataFrame has a continuous index, ignoring the original indices of df1 and df2.
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
creates a DataFrame (df) and a Series (series), then concatenates them along the columns (axis=1) using pd.concat(). The resulting DataFrame combines the columns from df and the Series, aligning them side by side. Note: There’s a typo in the display statement (df1 instead of df).
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
