[ENH] Add DataFrame method to explode a list-like column (GH pandas-dev#16538)

Sometimes a values column is presented with list-like values on one row.
Instead we may want to split each individual value onto its own row,
keeping the same mapping to the other key columns. While it's possible
to chain together existing pandas operations (in fact that's exactly
what this implementation is) to do this, the sequence of operations
is not obvious. By contrast this is available as a built-in operation
in say Spark and is a fairly common use case.

Author: changhiskhan

Diff for: asv_bench/benchmarks/reshape.py

@@ -240,4 +240,22 @@ def time_qcut_datetime(self, bins):
         pd.qcut(self.datetime_series, bins)
 
 
+class Explode(object):
+    param_names = ['n_rows', 'max_list_length']
+    params = [[100, 1000, 10000], [3, 5, 10]]
+
+    def setup(self, n_rows, max_list_length):
+        import string
+        num_letters = np.random.randint(0, max_list_length, n_rows)
+        key_column = [','.join([np.random.choice(list(string.ascii_letters))
+                                for _ in range(k)])
+                      for k in num_letters]
+        value_column = np.random.randn(n_rows)
+        self.frame = pd.DataFrame({'key': key_column,
+                                   'value': value_column})
+
+    def time_explode(self, n_rows, max_list_length):
+        self.frame.explode('key', sep=',')
+
+
 from .pandas_vb_common import setup  # noqa: F401

Diff for: doc/source/user_guide/reshaping.rst

@@ -801,3 +801,34 @@ Note to subdivide over multiple columns we can pass in a list to the
 
    df.pivot_table(
        values=['val0'], index='row', columns=['item', 'col'], aggfunc=['mean'])
+
+.. _reshaping.explode:
+
+Exploding a List-like Column
+----------------------------
+
+Sometimes the value column is list-like:
+
+.. ipython:: python
+
+   keys = ['panda1', 'panda2', 'panda3']
+   values = [['eats', 'shoots'], ['shoots', 'leaves'], ['eats', 'leaves']]
+   df = pd.DataFrame({'keys': keys, 'values': values})
+   df
+
+But we actually want to put each value onto its own row.
+For this purpose we can use ``DataFrame.explode``:
+
+.. ipython:: python
+
+   df.explode('values')
+
+For convenience, we can use the optional keyword ``sep`` to automatically
+split a string column before exploding:
+
+.. ipython:: python
+
+   values = ['eats,shoots', 'shoots,leaves', 'eats,shoots,leaves']
+   df2 = pd.DataFrame({'keys': keys, 'values': values})
+   df2
+   df2.explode('values', sep=',')

Diff for: doc/source/whatsnew/v0.24.0.rst

@@ -15,7 +15,37 @@ This is a major release from 0.23.4 and includes a number of API changes, new
 features, enhancements, and performance improvements along with a large number
 of bug fixes.
 
+<<<<<<< HEAD
 Highlights include:
+=======
+These are the changes in pandas 0.24.0. See :ref:`release` for a full changelog
+including other versions of pandas.
+
+.. _whatsnew_0240.enhancements:
+
+New features
+~~~~~~~~~~~~
+- :func:`merge` now directly allows merge between objects of type ``DataFrame`` and named ``Series``, without the need to convert the ``Series`` object into a ``DataFrame`` beforehand (:issue:`21220`)
+- ``ExcelWriter`` now accepts ``mode`` as a keyword argument, enabling append to existing workbooks when using the ``openpyxl`` engine (:issue:`3441`)
+- ``FrozenList`` has gained the ``.union()`` and ``.difference()`` methods. This functionality greatly simplifies groupby's that rely on explicitly excluding certain columns. See :ref:`Splitting an object into groups <groupby.split>` for more information (:issue:`15475`, :issue:`15506`).
+- :func:`DataFrame.to_parquet` now accepts ``index`` as an argument, allowing
+  the user to override the engine's default behavior to include or omit the
+  dataframe's indexes from the resulting Parquet file. (:issue:`20768`)
+- :meth:`DataFrame.corr` and :meth:`Series.corr` now accept a callable for generic calculation methods of correlation, e.g. histogram intersection (:issue:`22684`)
+- :func:`DataFrame.to_string` now accepts ``decimal`` as an argument, allowing the user to specify which decimal separator should be used in the output. (:issue:`23614`)
+- :func:`read_feather` now accepts ``columns`` as an argument, allowing the user to specify which columns should be read. (:issue:`24025`)
+- :func:`DataFrame.to_html` now accepts ``render_links`` as an argument, allowing the user to generate HTML with links to any URLs that appear in the DataFrame.
+  See the :ref:`section on writing HTML <io.html>` in the IO docs for example usage. (:issue:`2679`)
+- :func:`DataFrame.explode` to split list-like values onto individual rows. See :ref:`section on Exploding list-like column <reshaping.html>` in docs for more information (:issue:`16538`)
+
+.. _whatsnew_0240.values_api:
+
+Accessing the values in a Series or Index
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:attr:`Series.array` and :attr:`Index.array` have been added for extracting the array backing a
+``Series`` or ``Index``. (:issue:`19954`, :issue:`23623`)
+>>>>>>> 2138ef063... [ENH] Add DataFrame method to explode a list-like column (GH #16538)
 
 * :ref:`Optional Integer NA Support <whatsnew_0240.enhancements.intna>`
 * :ref:`New APIs for accessing the array backing a Series or Index <whatsnew_0240.values_api>`

Diff for: pandas/core/frame.py

@@ -6423,6 +6423,57 @@ def melt(
             col_level=col_level,
         )
 
+    def explode(self, col_name, sep=None, dtype=None):
+        """
+        Create new DataFrame expanding a list-like column.
+
+        .. versionadded:: 0.24.0
+
+        Parameters
+        ----------
+        col_name : str
+            Name of the column to be exploded.
+        sep : str, default None
+            Convenience to split a string `col_name` before exploding.
+        dtype : str or dtype, default None
+            Optionally coerce the dtype of exploded column.
+
+        Returns
+        -------
+        exploded: DataFrame
+
+        See Also
+        --------
+        Series.str.split: Split string values on specified separator.
+        Series.str.extract: Extract groups from the first regex match.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'k': ['a,b', 'c,d'], 'v': [0, 1]})
+        >>> df.explode('k', sep=',')
+           k  v
+        0  a  0
+        0  b  0
+        1  c  1
+        1  d  1
+        """
+        col = self[col_name]
+        if len(self) == 0:
+            return self.copy()
+        if sep:
+            col_expanded = col.str.split(sep, expand=True)
+        else:
+            col_expanded = col.apply(Series)
+        col_stacked = (col_expanded
+                       .stack()
+                       .reset_index(level=-1, drop=True)
+                       .rename(col_name))
+        if dtype:
+            col_stacked = col_stacked.astype(dtype)
+        return (col_stacked.to_frame()
+                .join(self.drop(col_name, axis=1))
+                .reindex(self.columns, axis=1))
+
     # ----------------------------------------------------------------------
     # Time series-related
 

Diff for: pandas/tests/frame/test_reshape.py

@@ -1043,6 +1043,101 @@ def test_unstack_swaplevel_sortlevel(self, level):
         tm.assert_frame_equal(result, expected)
 
 
+class TestDataFrameExplode(object):
+    # GH 16538
+    columns = ['a', 'b', 'c']
+
+    def test_sep(self):
+        # Automatically do str.split
+        df = pd.DataFrame([['foo,bar', 'x', 42],
+                           ['fizz,buzz', 'y', 43]],
+                          columns=self.columns)
+        rs = df.explode('a', sep=',')
+        xp = pd.DataFrame({'a': ['foo', 'bar', 'fizz', 'buzz'],
+                           'b': ['x', 'x', 'y', 'y'],
+                           'c': [42, 42, 43, 43]},
+                          index=[0, 0, 1, 1])
+        tm.assert_frame_equal(rs, xp)
+
+    def test_dtype(self):
+        # Coerce dtype
+        df = pd.DataFrame([[[0, 1, 4], 'x', 42],
+                           [[2, 3], 'y', 43]],
+                          columns=self.columns)
+        rs = df.explode('a', dtype='int')
+        xp = pd.DataFrame({'a': np.array([0, 1, 4, 2, 3], dtype='int'),
+                           'b': ['x', 'x', 'x', 'y', 'y'],
+                           'c': [42, 42, 42, 43, 43]},
+                          index=[0, 0, 0, 1, 1])
+        tm.assert_frame_equal(rs, xp)
+
+    def test_na(self):
+        # NaN's and empty lists are omitted
+        # TODO: option to preserve explicit NAs instead
+        df = pd.DataFrame([[[], 'x', 42],
+                           [[2.0, np.nan], 'y', 43]],
+                          columns=self.columns)
+        rs = df.explode('a')
+        xp = pd.DataFrame({'a': [2.0],
+                           'b': ['y'],
+                           'c': [43]},
+                          index=[1])
+        tm.assert_frame_equal(rs, xp)
+
+    def test_nonuniform_type(self):
+        # Not everything is a list
+        df = pd.DataFrame([[[0, 1, 4], 'x', 42],
+                           [3, 'y', 43]],
+                          columns=self.columns)
+        rs = df.explode('a', dtype='int')
+        xp = pd.DataFrame({'a': np.array([0, 1, 4, 3], dtype='int'),
+                           'b': ['x', 'x', 'x', 'y'],
+                           'c': [42, 42, 42, 43]},
+                          index=[0, 0, 0, 1])
+        tm.assert_frame_equal(rs, xp)
+
+    def test_all_scalars(self):
+        # Nothing is a list
+        df = pd.DataFrame([[0, 'x', 42],
+                           [3, 'y', 43]],
+                          columns=self.columns)
+        rs = df.explode('a')
+        xp = pd.DataFrame({'a': [0, 3],
+                           'b': ['x', 'y'],
+                           'c': [42, 43]},
+                          index=[0, 1])
+        tm.assert_frame_equal(rs, xp)
+
+    def test_empty(self):
+        # Empty frame
+        rs = pd.DataFrame(columns=['a', 'b']).explode('a')
+        xp = pd.DataFrame(columns=['a', 'b'])
+        tm.assert_frame_equal(rs, xp)
+
+    def test_missing_column(self):
+        # Bad column name
+        df = pd.DataFrame([[0, 'x', 42],
+                           [3, 'y', 43]],
+                          columns=self.columns)
+        pytest.raises(KeyError, df.explode, 'badcolumnname')
+
+    def test_multi_index(self):
+        # Multi-index
+        idx = pd.MultiIndex.from_tuples([(0, 'a'), (1, 'b')])
+        df = pd.DataFrame([['foo,bar', 'x', 42],
+                           ['fizz,buzz', 'y', 43]],
+                          columns=self.columns,
+                          index=idx)
+        rs = df.explode('a', sep=',')
+        idx = pd.MultiIndex.from_tuples(
+            [(0, 'a'), (0, 'a'), (1, 'b'), (1, 'b')])
+        xp = pd.DataFrame({'a': ['foo', 'bar', 'fizz', 'buzz'],
+                           'b': ['x', 'x', 'y', 'y'],
+                           'c': [42, 42, 43, 43]},
+                          index=idx)
+        tm.assert_frame_equal(rs, xp)
+
+
 def test_unstack_fill_frame_object():
     # GH12815 Test unstacking with object.
     data = pd.Series(["a", "b", "c", "a"], dtype="object")