include bin counts and edges in tablereport json output

CHANGES.rst

@@ -40,6 +40,10 @@ New Features
   Additionally, negative numbers indicated with parentheses can be converted to the
   regular numeric format (``(432)`` becomes ``-432``). :pr:`1772` by :user:`Gabriela
   Gómez Jiménez <gabrielapgomezji>`.
+- :meth:`TableReport.json` now includes histogram data for numeric and datetime
+  columns (the bin count and edges, and numbers of low and high outliers). Now
+  ``json()`` contains all the information shown in the report html rendering,
+  including the plots. :pr:`2164` by :user:`Jérôme Dockès <jeromedockes>`.
 
 Changes
 -------

skrub/_reporting/_data/templates/column-summary.html

@@ -4,10 +4,10 @@
     data-name-repr="{{ column.name.__repr__() }}"
     data-column-name="{{ column.name }}"
     data-column-idx="{{ column.idx }}"
-     {% if column['n_low_outliers'] %}
+     {% if column['histogram_data'] and column['histogram_data']['n_low_outliers'] %}
      data-has-low-outliers
      {% endif %}
-     {% if column['n_high_outliers'] %}
+     {% if column['histogram_data'] and column['histogram_data']['n_high_outliers'] %}
      data-has-high-outliers
      {% endif %}
     data-manager="FilterableColumn {% if in_sample_tab %}SampleColumnSummary{% endif %}"

skrub/_reporting/_plotting.py

@@ -11,8 +11,8 @@
 import numpy as np
 from matplotlib import pyplot as plt
 
-from skrub import _dataframe as sbd
-
+from .. import _dataframe as sbd
+from .. import _datetime_encoder
 from . import _utils
 
 __all__ = ["COLORS", "COLOR_0", "histogram", "line", "value_counts"]
@@ -192,15 +192,35 @@ def _get_range(values, frac=0.2, factor=3.0):
     return low, high
 
 
-def _robust_hist(values, ax, color):
+def _robust_hist(col, ax=None, color=None):
+    col = sbd.drop_nulls(col)
+    if sbd.is_float(col):
+        # avoid any issues with pandas nullable dtypes
+        # (to_numpy can yield a numpy array with object dtype in old pandas
+        # version if there are inf or nan)
+        col = sbd.to_float32(col)
+    values = sbd.to_numpy(col)
+    if sbd.is_any_date(col):
+        # numpy histogram does not handle datetimes but matplotlib does
+        np_histogram_values = sbd.to_numpy(
+            _datetime_encoder.DatetimeEncoder(resolution=None).fit_transform(col)
+        ).ravel()
+    else:
+        np_histogram_values = values
     low, high = _get_range(values)
-    inliers = values[(low <= values) & (values <= high)]
+    inlier_mask = (low <= values) & (values <= high)
     n_low_outliers = (values < low).sum()
     n_high_outliers = (high < values).sum()
-    n, bins, patches = ax.hist(inliers)
+    result = {"n_low_outliers": n_low_outliers, "n_high_outliers": n_high_outliers}
+    result["bin_counts"], result["bin_edges"] = np.histogram(
+        np_histogram_values[inlier_mask]
+    )
+    if ax is None:
+        return result
+    n, bins, patches = ax.hist(values[inlier_mask])
     n_out = n_low_outliers + n_high_outliers
     if not n_out:
-        return 0, 0
+        return result
     width = bins[1] - bins[0]
     start, stop = bins[0], bins[-1]
     line_params = dict(color=_RED, linestyle="--", ymax=0.95)
@@ -229,28 +249,25 @@ def _robust_hist(values, ax, color):
         color=_RED,
     )
     ax.set_xlim(start, stop)
-    return n_low_outliers, n_high_outliers
+    return result
+
+
+def histogram_data(col):
+    return _robust_hist(col, ax=None, color=None)
 
 
 @_plot
 def histogram(col, duration_unit=None, color=COLOR_0):
     """Histogram for a numeric column."""
-    col = sbd.drop_nulls(col)
-    if sbd.is_float(col):
-        # avoid any issues with pandas nullable dtypes
-        # (to_numpy can yield a numpy array with object dtype in old pandas
-        # version if there are inf or nan)
-        col = sbd.to_float32(col)
-    values = sbd.to_numpy(col)
     fig, ax = plt.subplots()
     _despine(ax)
-    n_low_outliers, n_high_outliers = _robust_hist(values, ax, color=color)
+    histogram_data = _robust_hist(col, ax=ax, color=color)
     if duration_unit is not None:
         ax.set_xlabel(f"{duration_unit.capitalize()}s")
     if sbd.is_any_date(col):
         _rotate_ticklabels(ax)
     _adjust_fig_size(fig, ax, 2.0, 1.0)
-    return _serialize(fig), n_low_outliers, n_high_outliers
+    return _serialize(fig), histogram_data
 
 
 @_plot

skrub/_reporting/_summarize.py

@@ -255,9 +255,10 @@ def _add_datetime_summary(summary, column, with_plots):
     if with_plots:
         (
             summary["histogram_plot"],
-            summary["n_low_outliers"],
-            summary["n_high_outliers"],
+            summary["histogram_data"],
         ) = _plotting.histogram(column, color=_plotting.COLORS[0])
+    else:
+        summary["histogram_data"] = _plotting.histogram_data(column)
 
 
 def _add_numeric_summary(
@@ -289,13 +290,10 @@ def _add_numeric_summary(
     summary["value_is_constant"] = False
     summary["quantiles"] = quantiles
     if not with_plots:
+        summary["histogram_data"] = _plotting.histogram_data(column)
         return
     if order_by_column is None:
-        (
-            summary["histogram_plot"],
-            summary["n_low_outliers"],
-            summary["n_high_outliers"],
-        ) = _plotting.histogram(
+        summary["histogram_plot"], summary["histogram_data"] = _plotting.histogram(
             column, duration_unit=duration_unit, color=_plotting.COLORS[0]
         )
     else:

skrub/_reporting/_utils.py

@@ -115,6 +115,8 @@ def default(self, value):
                 return int(value)
             if isinstance(value, np.floating):
                 return float(value)
+            if isinstance(value, np.ndarray):
+                return value.tolist()
             raise
 
 

skrub/_reporting/tests/test_plotting.py

@@ -10,21 +10,21 @@ def test_histogram():
     o = rng.uniform(-100, 100, size=10)
 
     data = pd.Series(np.concatenate([x, o]))
-    _, n_low, n_high = _plotting.histogram(data)
-    assert (n_low, n_high) == (5, 4)
+    _, hist = _plotting.histogram(data)
+    assert (hist["n_low_outliers"], hist["n_high_outliers"]) == (5, 4)
 
     data = pd.Series(np.concatenate([x, o - 1000]))
-    _, n_low, n_high = _plotting.histogram(data)
-    assert (n_low, n_high) == (10, 0)
+    _, hist = _plotting.histogram(data)
+    assert (hist["n_low_outliers"], hist["n_high_outliers"]) == (10, 0)
 
     data = pd.Series(np.concatenate([x, o + 1000]))
-    _, n_low, n_high = _plotting.histogram(data)
-    assert (n_low, n_high) == (0, 10)
+    _, hist = _plotting.histogram(data)
+    assert (hist["n_low_outliers"], hist["n_high_outliers"]) == (0, 10)
 
     data = pd.Series(x)
-    _, n_low, n_high = _plotting.histogram(data)
-    assert (n_low, n_high) == (0, 0)
+    _, hist = _plotting.histogram(data)
+    assert (hist["n_low_outliers"], hist["n_high_outliers"]) == (0, 0)
 
     data = pd.Series([0.0])
-    _, n_low, n_high = _plotting.histogram(data)
-    assert (n_low, n_high) == (0, 0)
+    _, hist = _plotting.histogram(data)
+    assert (hist["n_low_outliers"], hist["n_high_outliers"]) == (0, 0)

skrub/_reporting/tests/test_summarize.py

@@ -84,6 +84,9 @@ def test_summarize(
         0.75: 33.6,
         1.0: 78.3,
     }
+    if order_by is None:
+        assert len(summary["columns"][5]["histogram_data"]["bin_counts"]) == 10
+        assert len(summary["columns"][5]["histogram_data"]["bin_edges"]) == 11
     assert summary["columns"][7]["null_count"] == 9
     assert summary["columns"][7]["nulls_level"] == "warning"
     assert summary["columns"][8]["null_count"] == 17

skrub/_reporting/tests/test_utils.py

@@ -104,7 +104,7 @@ def test_json_encoder():
     d = {"a": x[0], "b": y[0]}
     assert json.dumps(d, cls=_utils.JSONEncoder) == '{"a": 1, "b": 1.0}'
     with pytest.raises(TypeError, match=".*JSON serializable"):
-        json.dumps({"a": x}, cls=_utils.JSONEncoder)
+        json.dumps({"a": np}, cls=_utils.JSONEncoder)
 
 
 def test_svg_to_img_src():