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# Merge, join, and concatenate

pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.

## Concatenating objects

The `concat` function (in the main pandas namespace) does all of the heavy lifting of performing concatenation operations along an axis while performing optional set logic (union or intersection) of the indexes (if any) on the other axes. Note that I say “if any” because there is only a single possible axis of concatenation for Series.

Before diving into all of the details of `concat` and what it can do, here is a simple example:

    In [1]: df = DataFrame(np.random.randn(10, 4))

    In [2]: df
    Out[2]:
              0         1         2         3
    0  0.469112 -0.282863 -1.509059 -1.135632
    1  1.212112 -0.173215  0.119209 -1.044236
    2 -0.861849 -2.104569 -0.494929  1.071804
    3  0.721555 -0.706771 -1.039575  0.271860
    4 -0.424972  0.567020  0.276232 -1.087401
    5 -0.673690  0.113648 -1.478427  0.524988
    6  0.404705  0.577046 -1.715002 -1.039268
    7 -0.370647 -1.157892 -1.344312  0.844885
    8  1.075770 -0.109050  1.643563 -1.469388
    9  0.357021 -0.674600 -1.776904 -0.968914

    [10 rows x 4 columns]

    # break it into pieces
    In [3]: pieces = [df[:3], df[3:7], df[7:]]

    In [4]: concatenated = concat(pieces)

    In [5]: concatenated
    Out[5]:
              0         1         2         3
    0  0.469112 -0.282863 -1.509059 -1.135632
    1  1.212112 -0.173215  0.119209 -1.044236
    2 -0.861849 -2.104569 -0.494929  1.071804
    3  0.721555 -0.706771 -1.039575  0.271860
    4 -0.424972  0.567020  0.276232 -1.087401
    5 -0.673690  0.113648 -1.478427  0.524988
    6  0.404705  0.577046 -1.715002 -1.039268
    7 -0.370647 -1.157892 -1.344312  0.844885
    8  1.075770 -0.109050  1.643563 -1.469388
    9  0.357021 -0.674600 -1.776904 -0.968914

    [10 rows x 4 columns]

Like its sibling function on ndarrays, `numpy.concatenate`, `pandas.concat` takes a list or dict of homogeneously-typed objects and concatenates them with some configurable handling of “what to do with the other axes”:

    concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
           keys=None, levels=None, names=None, verify_integrity=False)

- `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)
- `axis`: {0, 1, ...}, default 0. The axis to concatenate along
- `join`: {‘inner’, ‘outer’}, default ‘outer’. How to handle indexes on other axis(es). Outer for union and inner for intersection
- `join_axes`: list of Index objects. Specific indexes to use for the other n - 1 axes instead of performing inner/outer set logic
- `keys`: sequence, default None. Construct hierarchical index using the passed keys as the outermost level If multiple levels passed, should contain tuples.
- `levels` : list of sequences, default None. If keys passed, specific levels to use for the resulting MultiIndex. Otherwise they will be inferred from the keys
- `names`: list, default None. Names for the levels in the resulting hierarchical index
- `verify_integrity`: boolean, default False. Check whether the new concatenated axis contains duplicates. This can be very expensive relative to the actual data concatenation
- `ignore_index` : boolean, default False. If True, do not use the index values on 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.

Without a little bit of context and example many of these arguments don’t make much sense. Let’s take the above example. Suppose we wanted to associate specific keys with each of the pieces of the chopped up DataFrame. We can do this using the `keys` argument:

    In [6]: concatenated = concat(pieces, keys=['first', 'second', 'third'])

    In [7]: concatenated
    Out[7]:
                     0         1         2         3
    first  0  0.469112 -0.282863 -1.509059 -1.135632
           1  1.212112 -0.173215  0.119209 -1.044236
           2 -0.861849 -2.104569 -0.494929  1.071804
    second 3  0.721555 -0.706771 -1.039575  0.271860
           4 -0.424972  0.567020  0.276232 -1.087401
           5 -0.673690  0.113648 -1.478427  0.524988
           6  0.404705  0.577046 -1.715002 -1.039268
    third  7 -0.370647 -1.157892 -1.344312  0.844885
           8  1.075770 -0.109050  1.643563 -1.469388
           9  0.357021 -0.674600 -1.776904 -0.968914

    [10 rows x 4 columns]

As you can see (if you’ve read the rest of the documentation), the resulting object’s index has a hierarchical index. This means that we can now do stuff like select out each chunk by key:

    In [8]: concatenated.ix['second']
    Out[8]:
              0         1         2         3
    3  0.721555 -0.706771 -1.039575  0.271860
    4 -0.424972  0.567020  0.276232 -1.087401
    5 -0.673690  0.113648 -1.478427  0.524988
    6  0.404705  0.577046 -1.715002 -1.039268

    [4 rows x 4 columns]

It’s not a stretch to see how this can be very useful. More detail on this functionality below.

### Set logic on the other axes

When gluing together multiple DataFrames (or Panels or...), for example, you have a choice of how to handle the other axes (other than the one being concatenated). This can be done in three ways:

- Take the (sorted) union of them all, `join='outer'`. This is the default option as it results in zero information loss.
- Take the intersection, `join='inner'`.
- Use a specific index (in the case of DataFrame) or indexes (in the case of Panel or future higher dimensional objects), i.e. the `join_axes` argument

Here is a example of each of these methods. First, the default `join='outer'` behavior:

    In [9]: from pandas.util.testing import rands

    In [10]: df = DataFrame(np.random.randn(10, 4), columns=['a', 'b', 'c', 'd'],
       ....:                index=[rands(5) for _ in range(10)])
       ....:

    In [11]: df
    Out[11]:
                  a         b         c         d
    6I74i -1.294524  0.413738  0.276662 -0.472035
    RP8O8 -0.013960 -0.362543 -0.006154 -0.923061
    lTKuy  0.895717  0.805244 -1.206412  2.565646
    BmVOx  1.431256  1.340309 -1.170299 -0.226169
    qp7p7  0.410835  0.813850  0.132003 -0.827317
    k3K2f -0.076467 -1.187678  1.130127 -1.436737
    HGqMS -1.413681  1.607920  1.024180  0.569605
    Xby44  0.875906 -2.211372  0.974466 -2.006747
    PL69Z -0.410001 -0.078638  0.545952 -1.219217
    AZAf4 -1.226825  0.769804 -1.281247 -0.727707

    [10 rows x 4 columns]

    In [12]: concat([df.ix[:7, ['a', 'b']], df.ix[2:-2, ['c']],
       ....:         df.ix[-7:, ['d']]], axis=1)
       ....:
    Out[12]:
                  a         b         c         d
    6I74i -1.294524  0.413738       NaN       NaN
    AZAf4       NaN       NaN       NaN -0.727707
    BmVOx  1.431256  1.340309 -1.170299 -0.226169
    HGqMS -1.413681  1.607920  1.024180  0.569605
    PL69Z       NaN       NaN       NaN -1.219217
    RP8O8 -0.013960 -0.362543       NaN       NaN
    Xby44       NaN       NaN  0.974466 -2.006747
    k3K2f -0.076467 -1.187678  1.130127 -1.436737
    lTKuy  0.895717  0.805244 -1.206412       NaN
    qp7p7  0.410835  0.813850  0.132003 -0.827317

    [10 rows x 4 columns]

Note that the row indexes have been unioned and sorted. Here is the same thing with `join='inner'`:

    In [13]: concat([df.ix[:7, ['a', 'b']], df.ix[2:-2, ['c']],
       ....:         df.ix[-7:, ['d']]], axis=1, join='inner')
       ....:
    Out[13]:
                  a         b         c         d
    BmVOx  1.431256  1.340309 -1.170299 -0.226169
    qp7p7  0.410835  0.813850  0.132003 -0.827317
    k3K2f -0.076467 -1.187678  1.130127 -1.436737
    HGqMS -1.413681  1.607920  1.024180  0.569605

    [4 rows x 4 columns]

Lastly, suppose we just wanted to reuse the exact index from the original DataFrame:

    In [14]: concat([df.ix[:7, ['a', 'b']], df.ix[2:-2, ['c']],
       ....:         df.ix[-7:, ['d']]], axis=1, join_axes=[df.index])
       ....:
    Out[14]:
                  a         b         c         d
    6I74i -1.294524  0.413738       NaN       NaN
    RP8O8 -0.013960 -0.362543       NaN       NaN
    lTKuy  0.895717  0.805244 -1.206412       NaN
    BmVOx  1.431256  1.340309 -1.170299 -0.226169
    qp7p7  0.410835  0.813850  0.132003 -0.827317
    k3K2f -0.076467 -1.187678  1.130127 -1.436737
    HGqMS -1.413681  1.607920  1.024180  0.569605
    Xby44       NaN       NaN  0.974466 -2.006747
    PL69Z       NaN       NaN       NaN -1.219217
    AZAf4       NaN       NaN       NaN -0.727707

    [10 rows x 4 columns]

### Concatenating using `append`

A useful shortcut to `concat` are the `append` instance methods on Series and DataFrame. These methods actually predated `concat`. They concatenate along `axis=0`, namely the index:

    In [15]: s = Series(randn(10), index=np.arange(10))

    In [16]: s1 = s[:5] # note we're slicing with labels here, so 5 is included

    In [17]: s2 = s[6:]

    In [18]: s1.append(s2)
    Out[18]:
    0   -0.121306
    1   -0.097883
    2    0.695775
    3    0.341734
    4    0.959726
    6   -0.619976
    7    0.149748
    8   -0.732339
    9    0.687738
    dtype: float64

In the case of DataFrame, the indexes must be disjoint but the columns do not need to be:

    In [19]: df = DataFrame(randn(6, 4), index=date_range('1/1/2000', periods=6),
       ....:                columns=['A', 'B', 'C', 'D'])
       ....:

    In [20]: df1 = df.ix[:3]

    In [21]: df2 = df.ix[3:, :3]

    In [22]: df1
    Out[22]:
                       A         B         C         D
    2000-01-01  0.176444  0.403310 -0.154951  0.301624
    2000-01-02 -2.179861 -1.369849 -0.954208  1.462696
    2000-01-03 -1.743161 -0.826591 -0.345352  1.314232

    [3 rows x 4 columns]

    In [23]: df2
    Out[23]:
                       A         B         C
    2000-01-04  0.690579  0.995761  2.396780
    2000-01-05  3.357427 -0.317441 -1.236269
    2000-01-06 -0.487602 -0.082240 -2.182937

    [3 rows x 3 columns]

    In [24]: df1.append(df2)
    Out[24]:
                       A         B         C         D
    2000-01-01  0.176444  0.403310 -0.154951  0.301624
    2000-01-02 -2.179861 -1.369849 -0.954208  1.462696
    2000-01-03 -1.743161 -0.826591 -0.345352  1.314232
    2000-01-04  0.690579  0.995761  2.396780       NaN
    2000-01-05  3.357427 -0.317441 -1.236269       NaN
    2000-01-06 -0.487602 -0.082240 -2.182937       NaN

    [6 rows x 4 columns]

`append` may take multiple objects to concatenate:

    In [25]: df1 = df.ix[:2]

    In [26]: df2 = df.ix[2:4]

    In [27]: df3 = df.ix[4:]

    In [28]: df1.append([df2,df3])
    Out[28]:
                       A         B         C         D
    2000-01-01  0.176444  0.403310 -0.154951  0.301624
    2000-01-02 -2.179861 -1.369849 -0.954208  1.462696
    2000-01-03 -1.743161 -0.826591 -0.345352  1.314232
    2000-01-04  0.690579  0.995761  2.396780  0.014871
    2000-01-05  3.357427 -0.317441 -1.236269  0.896171
    2000-01-06 -0.487602 -0.082240 -2.182937  0.380396

    [6 rows x 4 columns]

Note

Unlike list.append method, which appends to the original list and returns nothing, `append` here does not modify `df1` and returns its copy with `df2` appended.

### Ignoring indexes on the concatenation axis

For DataFrames which don’t have a meaningful index, you may wish to append them and ignore the fact that they may have overlapping indexes:

    In [29]: df1 = DataFrame(randn(6, 4), columns=['A', 'B', 'C', 'D'])

    In [30]: df2 = DataFrame(randn(3, 4), columns=['A', 'B', 'C', 'D'])

    In [31]: df1
    Out[31]:
              A         B         C         D
    0  0.084844  0.432390  1.519970 -0.493662
    1  0.600178  0.274230  0.132885 -0.023688
    2  2.410179  1.450520  0.206053 -0.251905
    3 -2.213588  1.063327  1.266143  0.299368
    4 -0.863838  0.408204 -1.048089 -0.025747
    5 -0.988387  0.094055  1.262731  1.289997

    [6 rows x 4 columns]

    In [32]: df2
    Out[32]:
              A         B         C         D
    0  0.082423 -0.055758  0.536580 -0.489682
    1  0.369374 -0.034571 -2.484478 -0.281461
    2  0.030711  0.109121  1.126203 -0.977349

    [3 rows x 4 columns]

To do this, use the `ignore_index` argument:

    In [33]: concat([df1, df2], ignore_index=True)
    Out[33]:
              A         B         C         D
    0  0.084844  0.432390  1.519970 -0.493662
    1  0.600178  0.274230  0.132885 -0.023688
    2  2.410179  1.450520  0.206053 -0.251905
    3 -2.213588  1.063327  1.266143  0.299368
    4 -0.863838  0.408204 -1.048089 -0.025747
    5 -0.988387  0.094055  1.262731  1.289997
    6  0.082423 -0.055758  0.536580 -0.489682
    7  0.369374 -0.034571 -2.484478 -0.281461
    8  0.030711  0.109121  1.126203 -0.977349

    [9 rows x 4 columns]

This is also a valid argument to `DataFrame.append`:

    In [34]: df1.append(df2, ignore_index=True)
    Out[34]:
              A         B         C         D
    0  0.084844  0.432390  1.519970 -0.493662
    1  0.600178  0.274230  0.132885 -0.023688
    2  2.410179  1.450520  0.206053 -0.251905
    3 -2.213588  1.063327  1.266143  0.299368
    4 -0.863838  0.408204 -1.048089 -0.025747
    5 -0.988387  0.094055  1.262731  1.289997
    6  0.082423 -0.055758  0.536580 -0.489682
    7  0.369374 -0.034571 -2.484478 -0.281461
    8  0.030711  0.109121  1.126203 -0.977349

    [9 rows x 4 columns]

### More concatenating with group keys

Let’s consider a variation on the first example presented:

    In [35]: df = DataFrame(np.random.randn(10, 4))

    In [36]: df
    Out[36]:
              0         1         2         3
    0  1.474071 -0.064034 -1.282782  0.781836
    1 -1.071357  0.441153  2.353925  0.583787
    2  0.221471 -0.744471  0.758527  1.729689
    3 -0.964980 -0.845696 -1.340896  1.846883
    4 -1.328865  1.682706 -1.717693  0.888782
    5  0.228440  0.901805  1.171216  0.520260
    6 -1.197071 -1.066969 -0.303421 -0.858447
    7  0.306996 -0.028665  0.384316  1.574159
    8  1.588931  0.476720  0.473424 -0.242861
    9 -0.014805 -0.284319  0.650776 -1.461665

    [10 rows x 4 columns]

    # break it into pieces
    In [37]: pieces = [df.ix[:, [0, 1]], df.ix[:, [2]], df.ix[:, [3]]]

    In [38]: result = concat(pieces, axis=1, keys=['one', 'two', 'three'])

    In [39]: result
    Out[39]:
            one                 two     three
              0         1         2         3
    0  1.474071 -0.064034 -1.282782  0.781836
    1 -1.071357  0.441153  2.353925  0.583787
    2  0.221471 -0.744471  0.758527  1.729689
    3 -0.964980 -0.845696 -1.340896  1.846883
    4 -1.328865  1.682706 -1.717693  0.888782
    5  0.228440  0.901805  1.171216  0.520260
    6 -1.197071 -1.066969 -0.303421 -0.858447
    7  0.306996 -0.028665  0.384316  1.574159
    8  1.588931  0.476720  0.473424 -0.242861
    9 -0.014805 -0.284319  0.650776 -1.461665

    [10 rows x 4 columns]

You can also pass a dict to `concat` in which case the dict keys will be used for the `keys` argument (unless other keys are specified):

    In [40]: pieces = {'one': df.ix[:, [0, 1]],
       ....:           'two': df.ix[:, [2]],
       ....:           'three': df.ix[:, [3]]}
       ....:

    In [41]: concat(pieces, axis=1)
    Out[41]:
            one               three       two
              0         1         3         2
    0  1.474071 -0.064034  0.781836 -1.282782
    1 -1.071357  0.441153  0.583787  2.353925
    2  0.221471 -0.744471  1.729689  0.758527
    3 -0.964980 -0.845696  1.846883 -1.340896
    4 -1.328865  1.682706  0.888782 -1.717693
    5  0.228440  0.901805  0.520260  1.171216
    6 -1.197071 -1.066969 -0.858447 -0.303421
    7  0.306996 -0.028665  1.574159  0.384316
    8  1.588931  0.476720 -0.242861  0.473424
    9 -0.014805 -0.284319 -1.461665  0.650776

    [10 rows x 4 columns]

    In [42]: concat(pieces, keys=['three', 'two'])
    Out[42]:
                    2         3
    three 0       NaN  0.781836
          1       NaN  0.583787
          2       NaN  1.729689
          3       NaN  1.846883
          4       NaN  0.888782
          5       NaN  0.520260
          6       NaN -0.858447
          7       NaN  1.574159
          8       NaN -0.242861
          9       NaN -1.461665
    two   0 -1.282782       NaN
          1  2.353925       NaN
          2  0.758527       NaN
          3 -1.340896       NaN
          4 -1.717693       NaN
                  ...       ...

    [20 rows x 2 columns]

The MultiIndex created has levels that are constructed from the passed keys and the columns of the DataFrame pieces:

    In [43]: result.columns.levels
    Out[43]: FrozenList([[u'one', u'two', u'three'], [0, 1, 2, 3]])

If you wish to specify other levels (as will occasionally be the case), you can do so using the `levels` argument:

    In [44]: result = concat(pieces, axis=1, keys=['one', 'two', 'three'],
       ....:                 levels=[['three', 'two', 'one', 'zero']],
       ....:                 names=['group_key'])
       ....:

    In [45]: result
    Out[45]:
    group_key       one                 two     three
                      0         1         2         3
    0          1.474071 -0.064034 -1.282782  0.781836
    1         -1.071357  0.441153  2.353925  0.583787
    2          0.221471 -0.744471  0.758527  1.729689
    3         -0.964980 -0.845696 -1.340896  1.846883
    4         -1.328865  1.682706 -1.717693  0.888782
    5          0.228440  0.901805  1.171216  0.520260
    6         -1.197071 -1.066969 -0.303421 -0.858447
    7          0.306996 -0.028665  0.384316  1.574159
    8          1.588931  0.476720  0.473424 -0.242861
    9         -0.014805 -0.284319  0.650776 -1.461665

    [10 rows x 4 columns]

    In [46]: result.columns.levels
    Out[46]: FrozenList([[u'three', u'two', u'one', u'zero'], [0, 1, 2, 3]])

Yes, this is fairly esoteric, but is actually necessary for implementing things like GroupBy where the order of a categorical variable is meaningful.

### Appending rows to a DataFrame

While not especially efficient (since a new object must be created), you can append a single row to a DataFrame by passing a Series or dict to `append`, which returns a new DataFrame as above.

    In [47]: df = DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])

    In [48]: df
    Out[48]:
              A         B         C         D
    0 -1.137707 -0.891060 -0.693921  1.613616
    1  0.464000  0.227371 -0.496922  0.306389
    2 -2.290613 -1.134623 -1.561819 -0.260838
    3  0.281957  1.523962 -0.902937  0.068159
    4 -0.057873 -0.368204 -1.144073  0.861209
    5  0.800193  0.782098 -1.069094 -1.099248
    6  0.255269  0.009750  0.661084  0.379319
    7 -0.008434  1.952541 -1.056652  0.533946

    [8 rows x 4 columns]

    In [49]: s = df.xs(3)

    In [50]: df.append(s, ignore_index=True)
    Out[50]:
              A         B         C         D
    0 -1.137707 -0.891060 -0.693921  1.613616
    1  0.464000  0.227371 -0.496922  0.306389
    2 -2.290613 -1.134623 -1.561819 -0.260838
    3  0.281957  1.523962 -0.902937  0.068159
    4 -0.057873 -0.368204 -1.144073  0.861209
    5  0.800193  0.782098 -1.069094 -1.099248
    6  0.255269  0.009750  0.661084  0.379319
    7 -0.008434  1.952541 -1.056652  0.533946
    8  0.281957  1.523962 -0.902937  0.068159

    [9 rows x 4 columns]

You should use `ignore_index` with this method to instruct DataFrame to discard its index. If you wish to preserve the index, you should construct an appropriately-indexed DataFrame and append or concatenate those objects.

You can also pass a list of dicts or Series:

    In [51]: df = DataFrame(np.random.randn(5, 4),
       ....:                columns=['foo', 'bar', 'baz', 'qux'])
       ....:

    In [52]: dicts = [{'foo': 1, 'bar': 2, 'baz': 3, 'peekaboo': 4},
       ....:          {'foo': 5, 'bar': 6, 'baz': 7, 'peekaboo': 8}]
       ....:

    In [53]: result = df.append(dicts, ignore_index=True)

    In [54]: result
    Out[54]:
            bar       baz       foo  peekaboo       qux
    0  0.040403 -0.507516 -1.226970       NaN -0.230096
    1 -1.934370 -1.652499  0.394500       NaN  1.488753
    2  0.576897  1.146000 -0.896484       NaN  1.487349
    3  2.121453  0.597701  0.604603       NaN  0.563700
    4 -1.057909  1.375020  0.967661       NaN -0.928797
    5  2.000000  3.000000  1.000000         4       NaN
    6  6.000000  7.000000  5.000000         8       NaN

    [7 rows x 5 columns]

## Database-style DataFrame joining/merging

pandas has full-featured, high performance in-memory join operations idiomatically very similar to relational databases like SQL. These methods perform significantly better (in some cases well over an order of magnitude better) than other open source implementations (like `base::merge.data.frame` in R). The reason for this is careful algorithmic design and internal layout of the data in DataFrame.

See the cookbook for some advanced strategies.

Users who are familiar with SQL but new to pandas might be interested in a comparison with SQL.

pandas provides a single function, `merge`, as the entry point for all standard database join operations between DataFrame objects:

    merge(left, right, how='left', on=None, left_on=None, right_on=None,
          left_index=False, right_index=False, sort=True,
          suffixes=('_x', '_y'), copy=True)

Here’s a description of what each argument is for:

> - `left`: A DataFrame object
> - `right`: Another DataFrame object
> - `on`: Columns (names) to join on. Must be found in both the left and right DataFrame objects. If not passed and `left_index` and `right_index` are `False`, the intersection of the columns in the DataFrames will be inferred to be the join keys
> - `left_on`: Columns from the left DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrame
> - `right_on`: Columns from the right DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrame
> - `left_index`: If `True`, use the index (row labels) from the left DataFrame as its join key(s). In the case of a DataFrame with a MultiIndex (hierarchical), the number of levels must match the number of join keys from the right DataFrame
> - `right_index`: Same usage as `left_index` for the right DataFrame
> - `how`: One of `'left'`, `'right'`, `'outer'`, `'inner'`. Defaults to `inner`. See below for more detailed description of each method
> - `sort`: Sort the result DataFrame by the join keys in lexicographical order. Defaults to `True`, setting to `False` will improve performance substantially in many cases
> - `suffixes`: A tuple of string suffixes to apply to overlapping columns. Defaults to `('_x', '_y')`.
> - `copy`: Always copy data (default `True`) from the passed DataFrame objects, even when reindexing is not necessary. Cannot be avoided in many cases but may improve performance / memory usage. The cases where copying can be avoided are somewhat pathological but this option is provided nonetheless.

`merge` is a function in the pandas namespace, and it is also available as a DataFrame instance method, with the calling DataFrame being implicitly considered the left object in the join.

The related `DataFrame.join` method, uses `merge` internally for the index-on-index and index-on-column(s) joins, but joins on indexes by default rather than trying to join on common columns (the default behavior for `merge`). If you are joining on index, you may wish to use `DataFrame.join` to save yourself some typing.

### Brief primer on merge methods (relational algebra)

Experienced users of relational databases like SQL will be familiar with the terminology used to describe join operations between two SQL-table like structures (DataFrame objects). There are several cases to consider which are very important to understand:

> - one-to-one joins: for example when joining two DataFrame objects on their indexes (which must contain unique values)
> - many-to-one joins: for example when joining an index (unique) to one or more columns in a DataFrame
> - many-to-many joins: joining columns on columns.

Note

When joining columns on columns (potentially a many-to-many join), any indexes on the passed DataFrame objects will be discarded.

It is worth spending some time understanding the result of the many-to-many join case. In SQL / standard relational algebra, if a key combination appears more than once in both tables, the resulting table will have the Cartesian product of the associated data. Here is a very basic example with one unique key combination:

    In [55]: left = DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})

    In [56]: right = DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})

    In [57]: left
    Out[57]:
       key  lval
    0  foo     1
    1  foo     2

    [2 rows x 2 columns]

    In [58]: right
    Out[58]:
       key  rval
    0  foo     4
    1  foo     5

    [2 rows x 2 columns]

    In [59]: merge(left, right, on='key')
    Out[59]:
       key  lval  rval
    0  foo     1     4
    1  foo     1     5
    2  foo     2     4
    3  foo     2     5

    [4 rows x 3 columns]

Here is a more complicated example with multiple join keys:

    In [60]: left = DataFrame({'key1': ['foo', 'foo', 'bar'],
       ....:                   'key2': ['one', 'two', 'one'],
       ....:                   'lval': [1, 2, 3]})
       ....:

    In [61]: right = DataFrame({'key1': ['foo', 'foo', 'bar', 'bar'],
       ....:                    'key2': ['one', 'one', 'one', 'two'],
       ....:                    'rval': [4, 5, 6, 7]})
       ....:

    In [62]: merge(left, right, how='outer')
    Out[62]:
      key1 key2  lval  rval
    0  foo  one     1     4
    1  foo  one     1     5
    2  foo  two     2   NaN
    3  bar  one     3     6
    4  bar  two   NaN     7

    [5 rows x 4 columns]

    In [63]: merge(left, right, how='inner')
    Out[63]:
      key1 key2  lval  rval
    0  foo  one     1     4
    1  foo  one     1     5
    2  bar  one     3     6

    [3 rows x 4 columns]

The `how` argument to `merge` specifies how to determine which keys are to be included in the resulting table. If a key combination does not appear in either the left or right tables, the values in the joined table will be `NA`. Here is a summary of the `how` options and their SQL equivalent names:

Merge method | SQL Join Name | Description
--- | --- | ---
`left` | `LEFT OUTER JOIN` | Use keys from left frame only
`right` | `RIGHT OUTER JOIN` | Use keys from right frame only
`outer` | `FULL OUTER JOIN` | Use union of keys from both frames
`inner` | `INNER JOIN` | Use intersection of keys from both frames

### Joining on index

`DataFrame.join` is a convenient method for combining the columns of two potentially differently-indexed DataFrames into a single result DataFrame. Here is a very basic example:

    In [64]: df = DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])

    In [65]: df1 = df.ix[1:, ['A', 'B']]

    In [66]: df2 = df.ix[:5, ['C', 'D']]

    In [67]: df1
    Out[67]:
              A         B
    1 -2.461467 -1.553902
    2  1.771740 -0.670027
    3 -3.201750  0.792716
    4 -0.747169 -0.309038
    5  0.936527  1.255746
    6  0.062297 -0.110388
    7  0.077849  0.629498

    [7 rows x 2 columns]

    In [68]: df2
    Out[68]:
              C         D
    0  0.377953  0.493672
    1  2.015523 -1.833722
    2  0.049307 -0.521493
    3  0.146111  1.903247
    4  0.393876  1.861468
    5 -2.655452  1.219492

    [6 rows x 2 columns]

    In [69]: df1.join(df2)
    Out[69]:
              A         B         C         D
    1 -2.461467 -1.553902  2.015523 -1.833722
    2  1.771740 -0.670027  0.049307 -0.521493
    3 -3.201750  0.792716  0.146111  1.903247
    4 -0.747169 -0.309038  0.393876  1.861468
    5  0.936527  1.255746 -2.655452  1.219492
    6  0.062297 -0.110388       NaN       NaN
    7  0.077849  0.629498       NaN       NaN

    [7 rows x 4 columns]

    In [70]: df1.join(df2, how='outer')
    Out[70]:
              A         B         C         D
    0       NaN       NaN  0.377953  0.493672
    1 -2.461467 -1.553902  2.015523 -1.833722
    2  1.771740 -0.670027  0.049307 -0.521493
    3 -3.201750  0.792716  0.146111  1.903247
    4 -0.747169 -0.309038  0.393876  1.861468
    5  0.936527  1.255746 -2.655452  1.219492
    6  0.062297 -0.110388       NaN       NaN
    7  0.077849  0.629498       NaN       NaN

    [8 rows x 4 columns]

    In [71]: df1.join(df2, how='inner')
    Out[71]:
              A         B         C         D
    1 -2.461467 -1.553902  2.015523 -1.833722
    2  1.771740 -0.670027  0.049307 -0.521493
    3 -3.201750  0.792716  0.146111  1.903247
    4 -0.747169 -0.309038  0.393876  1.861468
    5  0.936527  1.255746 -2.655452  1.219492

    [5 rows x 4 columns]

The data alignment here is on the indexes (row labels). This same behavior can be achieved using `merge` plus additional arguments instructing it to use the indexes:

    In [72]: merge(df1, df2, left_index=True, right_index=True, how='outer')
    Out[72]:
              A         B         C         D
    0       NaN       NaN  0.377953  0.493672
    1 -2.461467 -1.553902  2.015523 -1.833722
    2  1.771740 -0.670027  0.049307 -0.521493
    3 -3.201750  0.792716  0.146111  1.903247
    4 -0.747169 -0.309038  0.393876  1.861468
    5  0.936527  1.255746 -2.655452  1.219492
    6  0.062297 -0.110388       NaN       NaN
    7  0.077849  0.629498       NaN       NaN

    [8 rows x 4 columns]

### Joining key columns on an index

`join` takes an optional `on` argument which may be a column or multiple column names, which specifies that the passed DataFrame is to be aligned on that column in the DataFrame. These two function calls are completely equivalent:

    left.join(right, on=key_or_keys)
    merge(left, right, left_on=key_or_keys, right_index=True,
          how='left', sort=False)

Obviously you can choose whichever form you find more convenient. For many-to-one joins (where one of the DataFrame’s is already indexed by the join key), using `join` may be more convenient. Here is a simple example:

    In [73]: df['key'] = ['foo', 'bar'] * 4

    In [74]: to_join = DataFrame(randn(2, 2), index=['bar', 'foo'],
       ....:                     columns=['j1', 'j2'])
       ....:

    In [75]: df
    Out[75]:
              A         B         C         D  key
    0 -0.308853 -0.681087  0.377953  0.493672  foo
    1 -2.461467 -1.553902  2.015523 -1.833722  bar
    2  1.771740 -0.670027  0.049307 -0.521493  foo
    3 -3.201750  0.792716  0.146111  1.903247  bar
    4 -0.747169 -0.309038  0.393876  1.861468  foo
    5  0.936527  1.255746 -2.655452  1.219492  bar
    6  0.062297 -0.110388 -1.184357 -0.558081  foo
    7  0.077849  0.629498 -1.035260 -0.438229  bar

    [8 rows x 5 columns]

    In [76]: to_join
    Out[76]:
               j1        j2
    bar  0.503703  0.413086
    foo -1.139050  0.660342

    [2 rows x 2 columns]

    In [77]: df.join(to_join, on='key')
    Out[77]:
              A         B         C         D  key        j1        j2
    0 -0.308853 -0.681087  0.377953  0.493672  foo -1.139050  0.660342
    1 -2.461467 -1.553902  2.015523 -1.833722  bar  0.503703  0.413086
    2  1.771740 -0.670027  0.049307 -0.521493  foo -1.139050  0.660342
    3 -3.201750  0.792716  0.146111  1.903247  bar  0.503703  0.413086
    4 -0.747169 -0.309038  0.393876  1.861468  foo -1.139050  0.660342
    5  0.936527  1.255746 -2.655452  1.219492  bar  0.503703  0.413086
    6  0.062297 -0.110388 -1.184357 -0.558081  foo -1.139050  0.660342
    7  0.077849  0.629498 -1.035260 -0.438229  bar  0.503703  0.413086

    [8 rows x 7 columns]

    In [78]: merge(df, to_join, left_on='key', right_index=True,
       ....:       how='left', sort=False)
       ....:
    Out[78]:
              A         B         C         D  key        j1        j2
    0 -0.308853 -0.681087  0.377953  0.493672  foo -1.139050  0.660342
    1 -2.461467 -1.553902  2.015523 -1.833722  bar  0.503703  0.413086
    2  1.771740 -0.670027  0.049307 -0.521493  foo -1.139050  0.660342
    3 -3.201750  0.792716  0.146111  1.903247  bar  0.503703  0.413086
    4 -0.747169 -0.309038  0.393876  1.861468  foo -1.139050  0.660342
    5  0.936527  1.255746 -2.655452  1.219492  bar  0.503703  0.413086
    6  0.062297 -0.110388 -1.184357 -0.558081  foo -1.139050  0.660342
    7  0.077849  0.629498 -1.035260 -0.438229  bar  0.503703  0.413086

    [8 rows x 7 columns]

To join on multiple keys, the passed DataFrame must have a `MultiIndex`:

    In [79]: index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
       ....:                            ['one', 'two', 'three']],
       ....:                    labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
       ....:                            [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
       ....:                    names=['first', 'second'])
       ....:

    In [80]: to_join = DataFrame(np.random.randn(10, 3), index=index,
       ....:                     columns=['j_one', 'j_two', 'j_three'])
       ....:

    # a little relevant example with NAs
    In [81]: key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
       ....:         'qux', 'snap']
       ....:

    In [82]: key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
       ....:         'three', 'one']
       ....:

    In [83]: data = np.random.randn(len(key1))

    In [84]: data = DataFrame({'key1' : key1, 'key2' : key2,
       ....:                   'data' : data})
       ....:

    In [85]: data
    Out[85]:
           data  key1   key2
    0 -1.004168   bar    two
    1 -1.377627   bar    one
    2  0.499281   bar  three
    3 -1.405256   foo    one
    4  0.162565   foo    two
    5 -0.067785   baz    one
    6 -1.260006   baz    two
    7 -1.132896   qux    two
    8 -2.006481   qux  three
    9  0.301016  snap    one

    [10 rows x 3 columns]

    In [86]: to_join
    Out[86]:
                     j_one     j_two   j_three
    first second
    foo   one     0.464794 -0.309337 -0.649593
          two     0.683758 -0.643834  0.421287
          three   1.032814 -1.290493  0.787872
    bar   one     1.515707 -0.276487 -0.223762
          two     1.397431  1.503874 -0.478905
    baz   two    -0.135950 -0.730327 -0.033277
          three   0.281151 -1.298915 -2.819487
    qux   one    -0.851985 -1.106952 -0.937731
          two    -1.537770  0.555759 -2.277282
          three  -0.390201  1.207122  0.178690

    [10 rows x 3 columns]

Now this can be joined by passing the two key column names:

    In [87]: data.join(to_join, on=['key1', 'key2'])
    Out[87]:
           data  key1   key2     j_one     j_two   j_three
    0 -1.004168   bar    two  1.397431  1.503874 -0.478905
    1 -1.377627   bar    one  1.515707 -0.276487 -0.223762
    2  0.499281   bar  three       NaN       NaN       NaN
    3 -1.405256   foo    one  0.464794 -0.309337 -0.649593
    4  0.162565   foo    two  0.683758 -0.643834  0.421287
    5 -0.067785   baz    one       NaN       NaN       NaN
    6 -1.260006   baz    two -0.135950 -0.730327 -0.033277
    7 -1.132896   qux    two -1.537770  0.555759 -2.277282
    8 -2.006481   qux  three -0.390201  1.207122  0.178690
    9  0.301016  snap    one       NaN       NaN       NaN

    [10 rows x 6 columns]

The default for `DataFrame.join` is to perform a left join (essentially a “VLOOKUP” operation, for Excel users), which uses only the keys found in the calling DataFrame. Other join types, for example inner join, can be just as easily performed:

    In [88]: data.join(to_join, on=['key1', 'key2'], how='inner')
    Out[88]:
           data key1   key2     j_one     j_two   j_three
    0 -1.004168  bar    two  1.397431  1.503874 -0.478905
    1 -1.377627  bar    one  1.515707 -0.276487 -0.223762
    3 -1.405256  foo    one  0.464794 -0.309337 -0.649593
    4  0.162565  foo    two  0.683758 -0.643834  0.421287
    6 -1.260006  baz    two -0.135950 -0.730327 -0.033277
    7 -1.132896  qux    two -1.537770  0.555759 -2.277282
    8 -2.006481  qux  three -0.390201  1.207122  0.178690

    [7 rows x 6 columns]

As you can see, this drops any rows where there was no match.

### Overlapping value columns

The merge `suffixes` argument takes a tuple of list of strings to append to overlapping column names in the input DataFrames to disambiguate the result columns:

    In [89]: left = DataFrame({'key': ['foo', 'foo'], 'value': [1, 2]})

    In [90]: right = DataFrame({'key': ['foo', 'foo'], 'value': [4, 5]})

    In [91]: merge(left, right, on='key', suffixes=['_left', '_right'])
    Out[91]:
       key  value_left  value_right
    0  foo           1            4
    1  foo           1            5
    2  foo           2            4
    3  foo           2            5

    [4 rows x 3 columns]

`DataFrame.join` has `lsuffix` and `rsuffix` arguments which behave similarly.

### Merging Ordered Data

New in v0.8.0 is the ordered_merge function for combining time series and other ordered data. In particular it has an optional `fill_method` keyword to fill/interpolate missing data:

    In [92]: A
    Out[92]:
      group key  lvalue
    0     a   a       1
    1     a   c       2
    2     a   e       3
    3     b   a       1
    4     b   c       2
    5     b   e       3

    [6 rows x 3 columns]

    In [93]: B
    Out[93]:
      key  rvalue
    0   b       1
    1   c       2
    2   d       3

    [3 rows x 2 columns]

    In [94]: ordered_merge(A, B, fill_method='ffill', left_by='group')
    Out[94]:
      group key  lvalue  rvalue
    0     a   a       1     NaN
    1     a   b       1       1
    2     a   c       2       2
    3     a   d       2       3
    4     a   e       3       3
    5     b   a       1     NaN
    6     b   b       1       1
    7     b   c       2       2
    8     b   d       2       3
    9     b   e       3       3

    [10 rows x 4 columns]

### Joining multiple DataFrame or Panel objects

A list or tuple of DataFrames can also be passed to `DataFrame.join` to join them together on their indexes. The same is true for `Panel.join`.

    In [95]: df1 = df.ix[:, ['A', 'B']]

    In [96]: df2 = df.ix[:, ['C', 'D']]

    In [97]: df3 = df.ix[:, ['key']]

    In [98]: df1
    Out[98]:
              A         B
    0 -0.308853 -0.681087
    1 -2.461467 -1.553902
    2  1.771740 -0.670027
    3 -3.201750  0.792716
    4 -0.747169 -0.309038
    5  0.936527  1.255746
    6  0.062297 -0.110388
    7  0.077849  0.629498

    [8 rows x 2 columns]

    In [99]: df1.join([df2, df3])
    Out[99]:
              A         B         C         D  key
    0 -0.308853 -0.681087  0.377953  0.493672  foo
    1 -2.461467 -1.553902  2.015523 -1.833722  bar
    2  1.771740 -0.670027  0.049307 -0.521493  foo
    3 -3.201750  0.792716  0.146111  1.903247  bar
    4 -0.747169 -0.309038  0.393876  1.861468  foo
    5  0.936527  1.255746 -2.655452  1.219492  bar
    6  0.062297 -0.110388 -1.184357 -0.558081  foo
    7  0.077849  0.629498 -1.035260 -0.438229  bar

    [8 rows x 5 columns]

### Merging together values within Series or DataFrame columns

Another fairly common situation is to have two like-indexed (or similarly indexed) Series or DataFrame objects and wanting to “patch” values in one object from values for matching indices in the other. Here is an example:

    In [100]: df1 = DataFrame([[nan, 3., 5.], [-4.6, np.nan, nan],
       .....:                  [nan, 7., nan]])
       .....:

    In [101]: df2 = DataFrame([[-42.6, np.nan, -8.2], [-5., 1.6, 4]],
       .....:                 index=[1, 2])
       .....:

For this, use the `combine_first` method:

    In [102]: df1.combine_first(df2)
    Out[102]:
         0   1    2
    0  NaN   3  5.0
    1 -4.6 NaN -8.2
    2 -5.0   7  4.0

    [3 rows x 3 columns]

Note that this method only takes values from the right DataFrame if they are missing in the left DataFrame. A related method, `update`, alters non-NA values inplace:

    In [103]: df1.update(df2)

    In [104]: df1
    Out[104]:
          0    1    2
    0   NaN  3.0  5.0
    1 -42.6  NaN -8.2
    2  -5.0  1.6  4.0

    [3 rows x 3 columns]

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