Concatenate pandas objects along a particular axis.
Allows 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.
If a mapping 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 a ValueError will be raised.
The axis to concatenate along.
How to handle indexes on other axis (or axes).
If True, do not use the index values along the concatenation axis. The resulting axis will be labeled 0, â¦, n - 1. This is useful if you are concatenating objects where the concatenation axis does not have meaningful indexing information. Note the index values on the other axes are still respected in the join.
If multiple levels passed, should contain tuples. Construct hierarchical index using the passed keys as the outermost level.
Specific levels (unique values) to use for constructing a MultiIndex. Otherwise they will be inferred from the keys.
Names for the levels in the resulting hierarchical index.
Check whether the new concatenated axis contains duplicates. This can be very expensive relative to the actual data concatenation.
Sort non-concatenation axis if it is not already aligned. One exception to this is when the non-concatentation axis is a DatetimeIndex and join=âouterâ and the axis is not already aligned. In that case, the non-concatenation axis is always sorted lexicographically.
If False, do not copy data unnecessarily.
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.
Notes
The keys, levels, and names arguments are all optional.
A walkthrough of how this method fits in with other tools for combining pandas objects can be found here.
It is not recommended to build DataFrames by adding single rows in a for loop. Build a list of rows and make a DataFrame in a single concat.
Examples
Combine two Series.
>>> s1 = pd.Series(['a', 'b']) >>> s2 = pd.Series(['c', 'd']) >>> pd.concat([s1, s2]) 0 a 1 b 0 c 1 d dtype: object
Clear the existing index and reset it in the result by setting the ignore_index option to True.
>>> pd.concat([s1, s2], ignore_index=True) 0 a 1 b 2 c 3 d dtype: object
Add a hierarchical index at the outermost level of the data with the keys option.
>>> pd.concat([s1, s2], keys=['s1', 's2'])
s1 0 a
1 b
s2 0 c
1 d
dtype: object
Label the index keys you create with the names option.
>>> pd.concat([s1, s2], keys=['s1', 's2'],
... names=['Series name', 'Row ID'])
Series name Row ID
s1 0 a
1 b
s2 0 c
1 d
dtype: object
Combine two DataFrame objects with identical columns.
>>> df1 = pd.DataFrame([['a', 1], ['b', 2]], ... columns=['letter', 'number']) >>> df1 letter number 0 a 1 1 b 2 >>> df2 = pd.DataFrame([['c', 3], ['d', 4]], ... columns=['letter', 'number']) >>> df2 letter number 0 c 3 1 d 4 >>> pd.concat([df1, df2]) letter number 0 a 1 1 b 2 0 c 3 1 d 4
Combine DataFrame objects with overlapping columns and return everything. Columns outside the intersection will be filled with NaN values.
>>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']], ... columns=['letter', 'number', 'animal']) >>> df3 letter number animal 0 c 3 cat 1 d 4 dog >>> pd.concat([df1, df3], sort=False) letter number animal 0 a 1 NaN 1 b 2 NaN 0 c 3 cat 1 d 4 dog
Combine DataFrame objects with overlapping columns and return only those that are shared by passing inner to the join keyword argument.
>>> pd.concat([df1, df3], join="inner") letter number 0 a 1 1 b 2 0 c 3 1 d 4
Combine DataFrame objects horizontally along the x axis by passing in axis=1.
>>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']], ... columns=['animal', 'name']) >>> pd.concat([df1, df4], axis=1) letter number animal name 0 a 1 bird polly 1 b 2 monkey george
Prevent the result from including duplicate index values with the verify_integrity option.
>>> df5 = pd.DataFrame([1], index=['a'])
>>> df5
0
a 1
>>> df6 = pd.DataFrame([2], index=['a'])
>>> df6
0
a 2
>>> pd.concat([df5, df6], verify_integrity=True)
Traceback (most recent call last):
...
ValueError: Indexes have overlapping values: ['a']
Append a single row to the end of a DataFrame object.
>>> df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])
>>> df7
a b
0 1 2
>>> new_row = pd.Series({'a': 3, 'b': 4})
>>> new_row
a 3
b 4
dtype: int64
>>> pd.concat([df7, new_row.to_frame().T], ignore_index=True)
a b
0 1 2
1 3 4
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