Add HTML repr for groupby dataframe and series

doc/source/whatsnew/v1.4.0.rst

@@ -29,6 +29,7 @@ enhancement2
 
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
+- Added HTML representation for grouped DataFrame and Series (:issue:`34926`)
 -
 -
 

pandas/core/config_init.py

@@ -110,6 +110,12 @@ def use_numba_cb(key):
     correct auto-detection.
 """
 
+pc_max_groups_doc = """
+: int
+    If max_groups is exceeded, switch to truncate groupby view. 'None' value
+    means unlimited.
+"""
+
 pc_min_rows_doc = """
 : int
     The numbers of rows to show in a truncated view (when `max_rows` is
@@ -355,6 +361,9 @@ def is_terminal() -> bool:
         validator=is_instance_factory((int, type(None))),
     )
     cf.register_option("max_rows", 60, pc_max_rows_doc, validator=is_nonnegative_int)
+    cf.register_option(
+        "max_groups", 10, pc_max_groups_doc, validator=is_nonnegative_int
+    )
     cf.register_option(
         "min_rows",
         10,

pandas/core/groupby/groupby.py

@@ -113,6 +113,8 @@ class providing the base-class of operations.
 if TYPE_CHECKING:
     from typing import Literal
 
+from pandas.io.formats.format import repr_html_groupby
+
 _common_see_also = """
         See Also
         --------
@@ -601,6 +603,9 @@ def __repr__(self) -> str:
         # TODO: Better repr for GroupBy object
         return object.__repr__(self)
 
+    def _repr_html_(self) -> str:
+        return repr_html_groupby(self)
+
     @final
     @property
     def groups(self) -> dict[Hashable, np.ndarray]:

pandas/io/formats/format.py

@@ -2086,3 +2086,42 @@ def buffer_put_lines(buf: IO[str], lines: list[str]) -> None:
     if any(isinstance(x, str) for x in lines):
         lines = [str(x) for x in lines]
     buf.write("\n".join(lines))
+
+
+def repr_html_groupby(group_obj) -> str:
+    """
+    Create an HTML representation for a grouped DataFrame or Series.
+
+    Parameters
+    ----------
+    group_obj : [DataFrameGroupBy, SeriesGroupBy]
+        Object to make HTML representation of.
+    Returns
+    -------
+    str :
+        HTML representation of the input object.
+    """
+    max_groups = get_option("display.max_groups")
+    max_rows = max(
+        1, get_option("display.max_rows") // min(max_groups, group_obj.ngroups)
+    )
+    group_names = list(group_obj.groups.keys())
+    truncated = max_groups < group_obj.ngroups
+    if truncated:
+        n_start = (max_groups + 1) // 2
+        n_end = max_groups - n_start
+        group_names = group_names[:n_start] + group_names[-n_end:]
+    repr_html_list = list()
+    for group_name in group_names:
+        if not isinstance(group_name, tuple):
+            group = group_obj.get_group((group_name, ))
+        else:
+            group = group_obj.get_group(group_name)
+        if not hasattr(group, "to_html"):
+            group = group.to_frame()
+        repr_html_list.append(
+            f"<H3>Group Key: {group_name}<H3/>\n{group.to_html(max_rows=max_rows)}"
+        )
+    if truncated:
+        repr_html_list.insert(max_groups // 2, "<H3>...<H3/>")
+    return "\n".join(repr_html_list)

pandas/tests/groupby/test_groupby.py

@@ -7,6 +7,7 @@
 
 from pandas.compat import IS64
 from pandas.errors import PerformanceWarning
+import pandas.util._test_decorators as td
 
 import pandas as pd
 from pandas import (
@@ -2226,6 +2227,45 @@ def test_groups_repr_truncates(max_seq_items, expected):
         assert result == expected
 
 
+@td.skip_if_no("lxml")
+@pytest.mark.parametrize(
+    "n_groups,n_rows,check_n_groups,check_n_rows",
+    [
+        (10, 60, 5, 3),  # All groups and all rows in the groups are shown
+        (25, 100, 5, 2),  # Not all groups are shown
+        (4, 400, 2, 7),  # Not all rows are shown in the groups
+        (20, 400, 5, 3),  # Not all groups and not all rows in the groups are shown
+    ],
+)
+def test_groupby_repr(n_groups, n_rows, check_n_groups, check_n_rows):
+    # GH 34926
+    df = DataFrame(
+        {
+            "A": range(n_rows),
+            "B": range(0, n_rows * 2, 2),
+            "C": list(range(n_groups)) * (n_rows // n_groups),
+        }
+    )
+
+    gb = df.groupby("C")
+
+    df_from_html = pd.concat(pd.read_html(StringIO(gb._repr_html_()), index_col=0))
+
+    # Drop "..." rows and convert index and data to int
+    df_from_html = df_from_html[df_from_html.index != "..."].astype(int)
+    df_from_html.index = df_from_html.index.astype(int)
+
+    # Iterate over the first and last "check_n_groups" groups
+    gb_iter = list(gb)[:check_n_groups] + list(gb)[-check_n_groups:]
+    for group_name, df_group in gb_iter:
+        # Iterate over the first and last "check_n_rows" of every group
+        df_iter = pd.concat(
+            [df_group.iloc[:check_n_rows], df_group.iloc[-check_n_rows:]]
+        ).iterrows()
+        for index, row in df_iter:
+            tm.assert_series_equal(row, df_from_html.loc[index])
+
+
 def test_group_on_two_row_multiindex_returns_one_tuple_key():
     # GH 18451
     df = DataFrame([{"a": 1, "b": 2, "c": 99}, {"a": 1, "b": 2, "c": 88}])