diff --git a/pandas/tests/io/formats/test_info.py b/pandas/tests/io/formats/test_info.py
index 8c2155aec7248..329215d41fd41 100644
--- a/pandas/tests/io/formats/test_info.py
+++ b/pandas/tests/io/formats/test_info.py
@@ -16,39 +16,13 @@
     Series,
     date_range,
     option_context,
-    reset_option,
-    set_option,
 )
-import pandas._testing as tm
 
 
 @pytest.fixture
-def datetime_frame():
-    """
-    Fixture for DataFrame of floats with DatetimeIndex
-
-    Columns are ['A', 'B', 'C', 'D']
-
-                       A         B         C         D
-    2000-01-03 -1.122153  0.468535  0.122226  1.693711
-    2000-01-04  0.189378  0.486100  0.007864 -1.216052
-    2000-01-05  0.041401 -0.835752 -0.035279 -0.414357
-    2000-01-06  0.430050  0.894352  0.090719  0.036939
-    2000-01-07 -0.620982 -0.668211 -0.706153  1.466335
-    2000-01-10 -0.752633  0.328434 -0.815325  0.699674
-    2000-01-11 -2.236969  0.615737 -0.829076 -1.196106
-    ...              ...       ...       ...       ...
-    2000-02-03  1.642618 -0.579288  0.046005  1.385249
-    2000-02-04 -0.544873 -1.160962 -0.284071 -1.418351
-    2000-02-07 -2.656149 -0.601387  1.410148  0.444150
-    2000-02-08 -1.201881 -1.289040  0.772992 -1.445300
-    2000-02-09  1.377373  0.398619  1.008453 -0.928207
-    2000-02-10  0.473194 -0.636677  0.984058  0.511519
-    2000-02-11 -0.965556  0.408313 -1.312844 -0.381948
-
-    [30 rows x 4 columns]
-    """
-    return DataFrame(tm.getTimeSeriesData())
+def duplicate_columns_frame():
+    """Dataframe with duplicate column names."""
+    return DataFrame(np.random.randn(1500, 4), columns=["a", "a", "b", "b"])
 
 
 def test_info_empty():
@@ -65,9 +39,7 @@ def test_info_empty():
     assert result == expected
 
 
-def test_info_categorical_column():
-
-    # make sure it works
+def test_info_categorical_column_smoke_test():
     n = 2500
     df = DataFrame({"int64": np.random.randint(100, size=n)})
     df["category"] = Series(
@@ -82,18 +54,48 @@ def test_info_categorical_column():
     df2.info(buf=buf)
 
 
-def test_info(float_frame, datetime_frame):
-    io = StringIO()
-    float_frame.info(buf=io)
-    datetime_frame.info(buf=io)
+@pytest.mark.parametrize(
+    "fixture_func_name",
+    [
+        "int_frame",
+        "float_frame",
+        "datetime_frame",
+        "duplicate_columns_frame",
+    ],
+)
+def test_info_smoke_test(fixture_func_name, request):
+    frame = request.getfixturevalue(fixture_func_name)
+    buf = StringIO()
+    frame.info(buf=buf)
+    result = buf.getvalue().splitlines()
+    assert len(result) > 10
 
-    frame = DataFrame(np.random.randn(5, 3))
 
-    frame.info()
-    frame.info(verbose=False)
+@pytest.mark.parametrize(
+    "num_columns, max_info_columns, verbose",
+    [
+        (10, 100, True),
+        (10, 11, True),
+        (10, 10, True),
+        (10, 9, False),
+        (10, 1, False),
+    ],
+)
+def test_info_default_verbose_selection(num_columns, max_info_columns, verbose):
+    frame = DataFrame(np.random.randn(5, num_columns))
+    with option_context("display.max_info_columns", max_info_columns):
+        io_default = StringIO()
+        frame.info(buf=io_default)
+        result = io_default.getvalue()
 
+        io_explicit = StringIO()
+        frame.info(buf=io_explicit, verbose=verbose)
+        expected = io_explicit.getvalue()
 
-def test_info_verbose():
+        assert result == expected
+
+
+def test_info_verbose_check_header_separator_body():
     buf = StringIO()
     size = 1001
     start = 5
@@ -202,33 +204,23 @@ def test_info_wide():
 
     io = StringIO()
     df.info(buf=io, max_cols=101)
-    rs = io.getvalue()
-    assert len(rs.splitlines()) > 100
-    xp = rs
-
-    set_option("display.max_info_columns", 101)
-    io = StringIO()
-    df.info(buf=io)
-    assert rs == xp
-    reset_option("display.max_info_columns")
-
+    result = io.getvalue()
+    assert len(result.splitlines()) > 100
 
-def test_info_duplicate_columns():
-    io = StringIO()
-
-    # it works!
-    frame = DataFrame(np.random.randn(1500, 4), columns=["a", "a", "b", "b"])
-    frame.info(buf=io)
+    expected = result
+    with option_context("display.max_info_columns", 101):
+        io = StringIO()
+        df.info(buf=io)
+        result = io.getvalue()
+        assert result == expected
 
 
 def test_info_duplicate_columns_shows_correct_dtypes():
     # GH11761
     io = StringIO()
-
     frame = DataFrame([[1, 2.0]], columns=["a", "a"])
     frame.info(buf=io)
-    io.seek(0)
-    lines = io.readlines()
+    lines = io.getvalue().splitlines(True)
     assert " 0   a       1 non-null      int64  \n" == lines[5]
     assert " 1   a       1 non-null      float64\n" == lines[6]
 
@@ -272,7 +264,6 @@ def test_info_max_cols():
             assert len(res.strip().split("\n")) == len_
 
     for len_, verbose in [(12, None), (5, False), (12, True)]:
-
         # max_cols not exceeded
         with option_context("max_info_columns", 5):
             buf = StringIO()
@@ -418,7 +409,6 @@ def test_usage_via_getsizeof():
 
 
 def test_info_memory_usage_qualified():
-
     buf = StringIO()
     df = DataFrame(1, columns=list("ab"), index=[1, 2, 3])
     df.info(buf=buf)
@@ -454,7 +444,8 @@ def memory_usage(f):
     N = 100
     M = len(uppercase)
     index = MultiIndex.from_product(
-        [list(uppercase), date_range("20160101", periods=N)], names=["id", "date"]
+        [list(uppercase), date_range("20160101", periods=N)],
+        names=["id", "date"],
     )
     df = DataFrame({"value": np.random.randn(N * M)}, index=index)