pandas.concat¶
- pandas.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False)¶
Concatenate pandas objects along a particular axis with optional set logic along the other axes. Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number
Parameters: objs : list or dict of Series, DataFrame, or Panel 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). Any None objects will be dropped silently unless they are all None in which case an Exception will be raised
axis : {0, 1, ...}, default 0
The axis to concatenate along
join : {‘inner’, ‘outer’}, default ‘outer’
How to handle indexes on other axis(es)
join_axes : list of Index objects
Specific indexes to use for the other n - 1 axes instead of performing inner/outer set logic
verify_integrity : boolean, default False
Check whether the new concatenated axis contains duplicates. This can be very expensive relative to the actual data concatenation
keys : sequence, default None
If multiple levels passed, should contain tuples. Construct hierarchical index using the passed keys as the outermost level
levels : list of sequences, default None
Specific levels (unique values) to use for constructing a MultiIndex. Otherwise they will be inferred from the keys
names : list, default None
Names for the levels in the resulting hierarchical index
ignore_index : boolean, default False
If True, do not use the index values along the concatenation axis. The resulting axis will be labeled 0, ..., n - 1. This is useful if you are concatenating objects where the concatenation axis does not have meaningful indexing information. Note the the index values on the other axes are still respected in the join.
Returns: concatenated : type of objects
Notes
The keys, levels, and names arguments are all optional