Centralize and modify variable type inference (#2084)

* Centralize and modify variable type inference

* Use core.variable_type elsewhere in the library

* Bump pinned pandas to avoid bug

* Test coverage

* Move orientation inference to core and improve error handling

* Parameterize necessity of numeric variable by plot type

* Tweak language

* Shorten parameter name

* Correct comment

* Tweak docs

doc/installing.rst

@@ -34,18 +34,18 @@ Dependencies
 Mandatory dependencies
 ^^^^^^^^^^^^^^^^^^^^^^
 
--  `numpy <https://numpy.org/>`__ (>= 1.13.3)
+-  `numpy <https://numpy.org/>`__
 
--  `scipy <https://www.scipy.org/>`__ (>= 1.0.1)
+-  `scipy <https://www.scipy.org/>`__
 
--  `pandas <https://pandas.pydata.org/>`__ (>= 0.22.0)
+-  `pandas <https://pandas.pydata.org/>`__
 
--  `matplotlib <https://matplotlib.org>`__ (>= 2.1.2)
+-  `matplotlib <https://matplotlib.org>`__
 
 Recommended dependencies
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
--  `statsmodels <https://www.statsmodels.org/>`__ (>= 0.8.0)
+-  `statsmodels <https://www.statsmodels.org/>`__
 
 Bugs
 ~~~~

doc/releases/v0.11.0.txt

@@ -23,3 +23,7 @@ v0.11.0 (Unreleased)
 - Made :meth:`FacetGrid.set_axis_labels` clear labels from "interior" axes.  GH2046
 
 - Improved :meth:`FacetGrid.set_titles` with `margin titles=True`, such that texts representing the original row titles are removed before adding new ones. GH2083
+
+- Changed how functions that use different representations for numeric and categorical data handle vectors with an `object` data type. Previously, data was considered numeric if it could be coerced to a float representation without error. Now, object-typed vectors are considered numeric only when their contents are themselves numeric. As a consequence, numbers that are encoded as strings will now be treated as categorical data.  GH2084
+
+- Improved the error messages produced when categorical plots process the orientation parameter.

seaborn/axisgrid.py

@@ -10,6 +10,7 @@
 import matplotlib.pyplot as plt
 
 from . import utils
+from .core import variable_type
 from .palettes import color_palette, blend_palette
 from .distributions import distplot, kdeplot, _freedman_diaconis_bins
 from ._decorators import _deprecate_positional_args
@@ -1611,15 +1612,10 @@ def _add_axis_labels(self):
 
     def _find_numeric_cols(self, data):
         """Find which variables in a DataFrame are numeric."""
-        # This can't be the best way to do this, but  I do not
-        # know what the best way might be, so this seems ok
         numeric_cols = []
         for col in data:
-            try:
-                data[col].astype(np.float)
+            if variable_type(data[col]) == "numeric":
                 numeric_cols.append(col)
-            except (ValueError, TypeError):
-                pass
         return numeric_cols
 
 

seaborn/categorical.py

@@ -11,6 +11,7 @@
 from distutils.version import LooseVersion
 
 from . import utils
+from .core import variable_type, infer_orient
 from .utils import iqr, categorical_order, remove_na
 from .algorithms import bootstrap
 from .palettes import color_palette, husl_palette, light_palette, dark_palette
@@ -30,6 +31,7 @@ class _CategoricalPlotter(object):
 
     width = .8
     default_palette = "light"
+    require_numeric = True
 
     def establish_variables(self, x=None, y=None, hue=None, data=None,
                             orient=None, order=None, hue_order=None,
@@ -68,11 +70,8 @@ def establish_variables(self, x=None, y=None, hue=None, data=None,
                     order = []
                     # Reduce to just numeric columns
                     for col in data:
-                        try:
-                            data[col].astype(np.float)
+                        if variable_type(data[col]) == "numeric":
                             order.append(col)
-                        except ValueError:
-                            pass
                 plot_data = data[order]
                 group_names = order
                 group_label = data.columns.name
@@ -153,7 +152,9 @@ def establish_variables(self, x=None, y=None, hue=None, data=None,
                     raise ValueError(err)
 
             # Figure out the plotting orientation
-            orient = self.infer_orient(x, y, orient)
+            orient = infer_orient(
+                x, y, orient, require_numeric=self.require_numeric
+            )
 
             # Option 2a:
             # We are plotting a single set of data
@@ -321,43 +322,6 @@ def establish_colors(self, color, palette, saturation):
         self.colors = rgb_colors
         self.gray = gray
 
-    def infer_orient(self, x, y, orient=None):
-        """Determine how the plot should be oriented based on the data."""
-        orient = str(orient)
-
-        def is_categorical(s):
-            return pd.api.types.is_categorical_dtype(s)
-
-        def is_not_numeric(s):
-            try:
-                np.asarray(s, dtype=np.float)
-            except ValueError:
-                return True
-            return False
-
-        no_numeric = "Neither the `x` nor `y` variable appears to be numeric."
-
-        if orient.startswith("v"):
-            return "v"
-        elif orient.startswith("h"):
-            return "h"
-        elif x is None:
-            return "v"
-        elif y is None:
-            return "h"
-        elif is_categorical(y):
-            if is_categorical(x):
-                raise ValueError(no_numeric)
-            else:
-                return "h"
-        elif is_not_numeric(y):
-            if is_not_numeric(x):
-                raise ValueError(no_numeric)
-            else:
-                return "h"
-        else:
-            return "v"
-
     @property
     def hue_offsets(self):
         """A list of center positions for plots when hue nesting is used."""
@@ -1080,6 +1044,7 @@ def plot(self, ax):
 class _CategoricalScatterPlotter(_CategoricalPlotter):
 
     default_palette = "dark"
+    require_numeric = False
 
     @property
     def point_colors(self):
@@ -1456,6 +1421,8 @@ def plot(self, ax, kws):
 
 class _CategoricalStatPlotter(_CategoricalPlotter):
 
+    require_numeric = True
+
     @property
     def nested_width(self):
         """A float with the width of plot elements when hue nesting is used."""
@@ -1819,6 +1786,10 @@ def plot(self, ax):
             ax.invert_yaxis()
 
 
+class _CountPlotter(_BarPlotter):
+    require_numeric = False
+
+
 class _LVPlotter(_CategoricalPlotter):
 
     def __init__(self, x, y, hue, data, order, hue_order,
@@ -2148,9 +2119,9 @@ def plot(self, ax, boxplot_kws):
     orient=dedent("""\
     orient : "v" | "h", optional
         Orientation of the plot (vertical or horizontal). This is usually
-        inferred from the dtype of the input variables, but can be used to
-        specify when the "categorical" variable is a numeric or when plotting
-        wide-form data.\
+        inferred based on the type of the input variables, but it can be used
+        to resolve ambiguitiy when both `x` and `y` are numeric or when
+        plotting wide-form data.\
     """),
     color=dedent("""\
     color : matplotlib color, optional
@@ -3607,14 +3578,14 @@ def countplot(
         orient = "v"
         y = x
     elif x is not None and y is not None:
-        raise TypeError("Cannot pass values for both `x` and `y`")
-    else:
-        raise TypeError("Must pass values for either `x` or `y`")
+        raise ValueError("Cannot pass values for both `x` and `y`")
 
-    plotter = _BarPlotter(x, y, hue, data, order, hue_order,
-                          estimator, ci, n_boot, units, seed,
-                          orient, color, palette, saturation,
-                          errcolor, errwidth, capsize, dodge)
+    plotter = _CountPlotter(
+        x, y, hue, data, order, hue_order,
+        estimator, ci, n_boot, units, seed,
+        orient, color, palette, saturation,
+        errcolor, errwidth, capsize, dodge
+    )
 
     plotter.value_label = "count"
 
@@ -3778,7 +3749,7 @@ def catplot(
         elif y is None and x is not None:
             x_, y_, orient = x, x, "v"
         else:
-            raise ValueError("Either `x` or `y` must be None for count plots")
+            raise ValueError("Either `x` or `y` must be None for kind='count'")
     else:
         x_, y_ = x, y
 
@@ -3791,7 +3762,19 @@ def catplot(
 
     # Determine the order for the whole dataset, which will be used in all
     # facets to ensure representation of all data in the final plot
+    plotter_class = {
+        "box": _BoxPlotter,
+        "violin": _ViolinPlotter,
+        "boxen": _LVPlotter,
+        "lv": _LVPlotter,
+        "bar": _BarPlotter,
+        "point": _PointPlotter,
+        "strip": _StripPlotter,
+        "swarm": _SwarmPlotter,
+        "count": _CountPlotter,
+    }[kind]
     p = _CategoricalPlotter()
+    p.require_numeric = plotter_class.require_numeric
     p.establish_variables(x_, y_, hue, data, orient, order, hue_order)
     order = p.group_names
     hue_order = p.hue_names

seaborn/conftest.py

@@ -143,6 +143,7 @@ def long_df(rng):
         f=rng.choice([0.2, 0.3], n),
     ))
     df["s_cat"] = df["s"].astype("category")
+    df["s_str"] = df["s"].astype(str)
     return df