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Pandas: Data Manipulation - concat() function

concat() function

The concat() function is used to concatenate pandas objects along a particular axis with optional set logic along the other axes.

Syntax:

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

Parameters:

Name Description Type Default Required / Optional
objs

A sequence or mapping of Series or DataFrame objects. If a dict is passed, the sorted keys will be used as the keys argument, unless it is passed, in which case the values will be selected (see below).

Series or DataFrame objects   Required
axis The axis to concatenate along. {0/’index’, 1/’columns’} Default: 0 Optional
join Handle indexes on other axis (or axes).

{‘inner’, ‘outer’}

 

Default: ‘outer’ Optional
left_on Field name to join on in left DataFrame. label   Optional
ignore_index Useful to concate objects where the concatenation axis does not have meaningful indexing information. bool Default: False Optional
keys Construct hierarchical index using the passed keys as the outermost level. sequence Default: None Optional
levels Specific levels (unique values) to use for constructing a MultiIndex. Otherwise they will be inferred from the keys. list of sequences Default: None Optional
names Names for the levels in the resulting hierarchical index. list Default: None Optional
verify_integrity Field names to match on in the left DataFrame. bool Default: False Optional
sort Sort non-concatenation axis if it is not already aligned when join is "outer". bool Default: None Optional
copy Suffix to apply to overlapping column names in the left and right side, respectively. bool

Default: True

Optional

Returns: object
Ttype of objects:

  • When concatenating all Series along the index (axis=0), a Series is returned.
  • When objs contains at least one DataFrame, a DataFrame is returned.
  • When concatenating along the columns (axis=1), a DataFrame is returned.

Example:


Download the Pandas DataFrame Notebooks from here.

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