Python:Pandas join()

In Pandas, DataFrame.join() combines columns from another DataFrame (or multiple DataFrames) into the calling DataFrame based on the index or a key column. It’s mainly used for merging DataFrames with different sets of columns but shared row indices.

Syntax

DataFrame.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False, validate=None)

Parameters:

• other: Objects to join with the caller DataFrame.
• on: Column(s) in the caller DataFrame to join on; must match the index in other if provided.
• how: Type of join to perform — ‘left’, ‘right’, ‘outer’, or ‘inner’.
• lsuffix: Suffix to add to overlapping column names from the left DataFrame.
• rsuffix: Suffix to add to overlapping column names from the right DataFrame.
• sort: Sort the result DataFrame by the join keys if True.
• validate: Checks if the join is of a specific type (‘one_to_one’, ‘one_to_many’, ‘many_to_one’, ‘many_to_many’).

Return value:

Returns a new DataFrame with columns of both joined DataFrames combined according to the join logic.

Example

In this example, a list of words is joined into a single string separated by spaces:

words = ['Codecademy', 'is', 'awesome']
result = ' '.join(words)
print(result)

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This code produces the following output:

Codecademy is awesome

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Codebyte Example

In this example, a list of characters is joined into a single string separated by commas:

Visit us
Visit us
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