.concat()

The .concat() function is used to concatenate and combine multiple DataFrames or Series along a particular axis.

Syntax

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

• objs: Denotes the objects to concatenate, which can be a sequence or mapping of pandas Series or DataFrame objects. It must be specified and can be passed as a list, tuple, dictionary, Series, or DataFrame.
• axis: Specifies the axis to concatenate the objects. The default value is 0 for rows, while 1 represents columns.
• join: Determines how to handle indexes on other axes. Options include “outer” (default), “inner,” “left,” or “right.”
• ignore_index: If True, reset the index in the resulting DataFrame. The new axis will be labeled 0, …, n-1. The default value is False.
• keys: Constructs a hierarchical index using the provided keys as the outermost level. The default value is None.
• levels: Specific levels to use for constructing a MultiIndex if keys are provided. The default value is None.
• names: Names for the levels generated in the hierarchical index. The default value is None.
• verify_integrity: If True, checks whether the new concatenated axis contains duplicates. The default value is False.
• sort: If True, sorts the resulting Series or Dataframe by the keys. The default value is False.
• copy: If False, avoid copying data unnecessarily. The default value is True.

Note: Only the objs parameter is required; all other parameters are optional.

Example

The example below demonstrates the use of .concat() method:

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})

result = pd.concat([df1, df2])
print(result)

The example will result in a new DataFrame created by concatenating df1 and df2 along the rows. The output is as follows:

   A   B
0  1   3
1  2   4
0  5   7
1  6   8

Codebyte Example

The code demonstrates the .concat() function on two DataFrames, concatenating df1 and df2 column-wise (axis=1) and using keys to create a hierarchical column index:

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