Merge pull request #184 from siemdejong/issue-183

fix: changed Subplot to Axes, changed the minimum support version to Python 3.9, upgraded dependencies.

.github/workflows/test-pytest.yaml

@@ -8,7 +8,7 @@ jobs:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.9
           cache: "pip"
           cache-dependency-path: settings.ini
       - name: Run pytest

dabest/_dabest_object.py

@@ -8,6 +8,7 @@
 # %% ../nbs/API/dabest_object.ipynb 4
 # Import standard data science libraries
 from numpy import array, repeat, random, issubdtype, number
+import numpy as np
 import pandas as pd
 from scipy.stats import norm
 from scipy.stats import randint
@@ -479,7 +480,7 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
 
             # Handling str type condition
             if is_str_condition_met:
-                if len(pd.unique(idx).tolist()) != 2:
+                if len(np.unique(idx).tolist()) != 2:
                     err0 = "`mini_meta` is True, but `idx` ({})".format(idx)
                     err1 = "does not contain exactly 2 unique columns."
                     raise ValueError(err0 + err1)
@@ -667,7 +668,7 @@ def _get_plot_data(self, x, y, all_plot_groups):
                 all_plot_groups, ordered=True, inplace=True
             )
         else:
-            plot_data.loc[:, self.__xvar] = pd.Categorical(
+            plot_data[self.__xvar] = pd.Categorical(
                 plot_data[self.__xvar], categories=all_plot_groups, ordered=True
             )
 

dabest/_modidx.py

@@ -81,6 +81,8 @@
                                    'dabest.misc_tools.get_kwargs': ('API/misc_tools.html#get_kwargs', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.get_params': ('API/misc_tools.html#get_params', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.get_plot_groups': ('API/misc_tools.html#get_plot_groups', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.get_unique_categories': ( 'API/misc_tools.html#get_unique_categories',
+                                                                                'dabest/misc_tools.py'),
                                    'dabest.misc_tools.get_varname': ('API/misc_tools.html#get_varname', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.initialize_fig': ('API/misc_tools.html#initialize_fig', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.merge_two_dicts': ('API/misc_tools.html#merge_two_dicts', 'dabest/misc_tools.py'),

dabest/misc_tools.py

@@ -3,10 +3,11 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/misc_tools.ipynb.
 
 # %% auto 0
-__all__ = ['merge_two_dicts', 'unpack_and_add', 'print_greeting', 'get_varname', 'get_params', 'get_kwargs', 'get_color_palette',
-           'initialize_fig', 'get_plot_groups', 'add_counts_to_ticks', 'extract_contrast_plotting_ticks',
-           'set_xaxis_ticks_and_lims', 'show_legend', 'Gardner_Altman_Plot_Aesthetic_Adjustments',
-           'Cumming_Plot_Aesthetic_Adjustments', 'General_Plot_Aesthetic_Adjustments']
+__all__ = ['merge_two_dicts', 'unpack_and_add', 'print_greeting', 'get_varname', 'get_unique_categories', 'get_params',
+           'get_kwargs', 'get_color_palette', 'initialize_fig', 'get_plot_groups', 'add_counts_to_ticks',
+           'extract_contrast_plotting_ticks', 'set_xaxis_ticks_and_lims', 'show_legend',
+           'Gardner_Altman_Plot_Aesthetic_Adjustments', 'Cumming_Plot_Aesthetic_Adjustments',
+           'General_Plot_Aesthetic_Adjustments']
 
 # %% ../nbs/API/misc_tools.ipynb 4
 import datetime as dt
@@ -78,6 +79,19 @@ def get_varname(obj):
     if len(matching_vars) > 0:
         return matching_vars[0]
     return ""
+
+
+def get_unique_categories(names):
+    """
+    Extract unique categories from various input types.
+    """
+    if isinstance(names, np.ndarray):
+        return names  # numpy.unique() returns a sorted array
+    elif isinstance(names, (pd.Categorical, pd.Series)):
+        return names.cat.categories if hasattr(names, 'cat') else names.unique()
+    else:
+        # For dict_keys and other iterables
+        return np.unique(list(names))
 
 def get_params(effectsize_df, plot_kwargs):
     """
@@ -369,6 +383,7 @@ def get_color_palette(plot_kwargs, plot_data, xvar, show_pairs, idx, all_plot_gr
                 raise ValueError(err1 + err2)
 
     if custom_pal is None and color_col is None:
+        categories = get_unique_categories(names)
         swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors]
         contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]
         bar_color = [sns.desaturate(c, bar_desat) for c in unsat_colors]
@@ -382,9 +397,9 @@ def get_color_palette(plot_kwargs, plot_data, xvar, show_pairs, idx, all_plot_gr
                     plot_palette_raw[names_i] = swarm_colors[i]
                     plot_palette_contrast[names_i] = contrast_colors[i]
         else:
-            plot_palette_raw = dict(zip(names.categories, swarm_colors))
-            plot_palette_contrast = dict(zip(names.categories, contrast_colors))
-            plot_palette_bar = dict(zip(names.categories, bar_color))
+            plot_palette_raw = dict(zip(categories, swarm_colors))
+            plot_palette_contrast = dict(zip(categories, contrast_colors))
+            plot_palette_bar = dict(zip(categories, bar_color))
 
         # For Sankey Diagram plot, no need to worry about the color, each bar will have the same two colors
         # default color palette will be set to "hls"
@@ -541,7 +556,7 @@ def get_plot_groups(is_paired, idx, proportional, all_plot_groups):
 
 def add_counts_to_ticks(plot_data, xvar, yvar, rawdata_axes, plot_kwargs):
     # Add the counts to the rawdata axes xticks.
-    counts = plot_data.groupby(xvar).count()[yvar]
+    counts = plot_data.groupby(xvar, observed=False).count()[yvar]
 
     def lookup_value(text):
         try:
@@ -695,19 +710,19 @@ def Gardner_Altman_Plot_Aesthetic_Adjustments(effect_size_type, plot_data, xvar,
     # Check that the effect size is within the swarm ylims.
     if effect_size_type in ["mean_diff", "cohens_d", "hedges_g", "cohens_h"]:
         control_group_summary = (
-            plot_data.groupby(xvar)
+            plot_data.groupby(xvar, observed=False)
             .mean(numeric_only=True)
             .loc[current_control, yvar]
         )
         test_group_summary = (
-            plot_data.groupby(xvar).mean(numeric_only=True).loc[current_group, yvar]
+            plot_data.groupby(xvar, observed=False).mean(numeric_only=True).loc[current_group, yvar]
         )
     elif effect_size_type == "median_diff":
         control_group_summary = (
-            plot_data.groupby(xvar).median().loc[current_control, yvar]
+            plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_control, yvar]
         )
         test_group_summary = (
-            plot_data.groupby(xvar).median().loc[current_group, yvar]
+            plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_group, yvar]
         )
 
     if swarm_ylim is None:
@@ -751,7 +766,7 @@ def Gardner_Altman_Plot_Aesthetic_Adjustments(effect_size_type, plot_data, xvar,
             pooled_sd = stds[0]
 
         if effect_size_type == "hedges_g":
-            gby_count = plot_data.groupby(xvar).count()
+            gby_count = plot_data.groupby(xvar, observed=False).count()
             len_control = gby_count.loc[current_control, yvar]
             len_test = gby_count.loc[current_group, yvar]
 

dabest/plot_tools.py

@@ -120,25 +120,25 @@ def error_bar(
     else:
         group_order = pd.unique(data[x])
 
-    means = data.groupby(x)[y].mean().reindex(index=group_order)
+    means = data.groupby(x, observed=False)[y].mean().reindex(index=group_order)
 
     if method in ["proportional_error_bar", "sankey_error_bar"]:
         g = lambda x: np.sqrt(
             (np.sum(x) * (len(x) - np.sum(x))) / (len(x) * len(x) * len(x))
         )
-        sd = data.groupby(x)[y].apply(g)
+        sd = data.groupby(x, observed=False)[y].apply(g)
     else:
-        sd = data.groupby(x)[y].std().reindex(index=group_order)
+        sd = data.groupby(x, observed=False)[y].std().reindex(index=group_order)
 
     lower_sd = means - sd
     upper_sd = means + sd
 
     if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any():
         kwargs["clip_on"] = True
 
-    medians = data.groupby(x)[y].median().reindex(index=group_order)
+    medians = data.groupby(x, observed=False)[y].median().reindex(index=group_order)
     quantiles = (
-        data.groupby(x)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order)
+        data.groupby(x, observed=False)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order)
     )
     lower_quartiles = quantiles[0.25]
     upper_quartiles = quantiles[0.75]
@@ -978,7 +978,7 @@ def swarm_bars_plotter(plot_data: object, xvar: str, yvar: str, ax: object,
     else:
         swarm_bars_order = pd.unique(plot_data[xvar])
 
-    swarm_means = plot_data.groupby(xvar)[yvar].mean().reindex(index=swarm_bars_order)
+    swarm_means = plot_data.groupby(xvar, observed=False)[yvar].mean().reindex(index=swarm_bars_order)
     swarm_bars_colors = (
         [swarm_bars_kwargs.get('color')] * (max(swarm_bars_order) + 1) 
         if swarm_bars_kwargs.get('color') is not None 
@@ -1199,7 +1199,7 @@ def slopegraph_plotter(dabest_obj, plot_data, xvar, yvar, color_col, plot_palett
             if color_col is None:
                 slopegraph_kwargs["color"] = ytick_color
             else:
-                color_key = observation[color_col][0]
+                color_key = observation[color_col].iloc[0]
                 if isinstance(color_key, (str, np.int64, np.float64)):
                     slopegraph_kwargs["color"] = plot_palette_raw[color_key]
                     slopegraph_kwargs["label"] = color_key
@@ -1497,7 +1497,7 @@ def swarmplot(
     data: pd.DataFrame,
     x: str,
     y: str,
-    ax: axes.Subplot,
+    ax: axes.Axes,
     order: List = None,
     hue: str = None,
     palette: Union[Iterable, str] = "black",
@@ -1521,8 +1521,8 @@ def swarmplot(
         The column in the DataFrame to be used as the x-axis.
     y : str
         The column in the DataFrame to be used as the y-axis.
-    ax : axes._subplots.Subplot | axes._axes.Axes
-        Matplotlib AxesSubplot object for which the plot would be drawn on. Default is None.
+    ax : axes.Axes
+        Matplotlib axes.Axes object for which the plot would be drawn on. Default is None.
     order : List
         The order in which x-axis categories should be displayed. Default is None.
     hue : str
@@ -1552,8 +1552,8 @@ def swarmplot(
 
     Returns
     -------
-    axes._subplots.Subplot | axes._axes.Axes
-        Matplotlib AxesSubplot object for which the swarm plot has been drawn on.
+    axes.Axes
+        Matplotlib axes.Axes object for which the swarm plot has been drawn on.
     """
     s = SwarmPlot(data, x, y, ax, order, hue, palette, zorder, size, side, jitter)
     ax = s.plot(is_drop_gutter, gutter_limit, ax, filled, **kwargs)
@@ -1566,7 +1566,7 @@ def __init__(
         data: pd.DataFrame,
         x: str,
         y: str,
-        ax: axes.Subplot,
+        ax: axes.Axes,
         order: List = None,
         hue: str = None,
         palette: Union[Iterable, str] = "black",
@@ -1586,8 +1586,8 @@ def __init__(
             The column in the DataFrame to be used as the x-axis.
         y : str
             The column in the DataFrame to be used as the y-axis.
-        ax : axes.Subplot
-            Matplotlib AxesSubplot object for which the plot would be drawn on.
+        ax : axes.Axes
+            Matplotlib axes.Axes object for which the plot would be drawn on.
         order : List
             The order in which x-axis categories should be displayed. Default is None.
         hue : str
@@ -1674,7 +1674,7 @@ def __init__(
         self.__dsize = dsize
 
     def _check_errors(
-        self, data: pd.DataFrame, ax: axes.Subplot, size: float, side: str
+        self, data: pd.DataFrame, ax: axes.Axes, size: float, side: str
     ) -> None:
         """
         Check the validity of input parameters. Raises exceptions if detected.
@@ -1683,8 +1683,8 @@ def _check_errors(
         ----------
         data : pd.Dataframe
             Input data used for generation of the swarmplot.
-        ax : axes.Subplot
-            Matplotlib AxesSubplot object for which the plot would be drawn on.
+        ax : axes.Axes
+            Matplotlib axes.Axes object for which the plot would be drawn on.
         size : int | float
             scalar value determining size of dots of the swarmplot.
         side: str
@@ -1697,9 +1697,9 @@ def _check_errors(
         # Type enforcement
         if not isinstance(data, pd.DataFrame):
             raise ValueError("`data` must be a Pandas Dataframe.")
-        if not isinstance(ax, (axes._subplots.Subplot, axes._axes.Axes)):
+        if not isinstance(ax, axes.Axes):
             raise ValueError(
-                f"`ax` must be a Matplotlib AxesSubplot. The current `ax` is a {type(ax)}"
+                f"`ax` must be a Matplotlib axes.Axes. The current `ax` is a {type(ax)}"
             )
         if not isinstance(size, (int, float)):
             raise ValueError("`size` must be a scalar or float.")
@@ -1859,9 +1859,10 @@ def _swarm(
             raise ValueError("`dsize` must be a scalar or float.")
 
         # Sorting algorithm based off of: https://github.com/mgymrek/pybeeswarm
-        points_data = pd.DataFrame(
-            {"y": [yval * 1.0 / dsize for yval in values], "x": [0] * len(values)}
-        )
+        points_data = pd.DataFrame({
+            "y": [yval * 1.0 / dsize for yval in values],
+            "x": np.zeros(len(values), dtype=float)  # Initialize with float zeros
+        })
         for i in range(1, points_data.shape[0]):
             y_i = points_data["y"].values[i]
             points_placed = points_data[0:i]
@@ -1968,7 +1969,7 @@ def plot(
         ax: axes.Subplot,
         filled: Union[bool, List, Tuple],
         **kwargs,
-    ) -> axes.Subplot:
+    ) -> axes.Axes:
         """
         Generate a swarm plot.
 
@@ -1978,7 +1979,7 @@ def plot(
             If True, drop points that hit the gutters; otherwise, readjust them.
         gutter_limit : int | float
             The limit for points hitting the gutters.
-        ax : axes.Subplot
+        ax : axes.Axes
             The matplotlib figure object to which the swarm plot will be added.
         filled : bool | List | Tuple
             Determines whether the dots in the swarmplot are filled or not. If set to False,
@@ -1990,8 +1991,8 @@ def plot(
 
         Returns
         -------
-        axes.Subplot:
-            The matplotlib figure containing the swarm plot.
+        axes.Axes:
+            The matplotlib axes containing the swarm plot.
         """
         # Input validation
         if not isinstance(is_drop_gutter, bool):
@@ -2019,8 +2020,7 @@ def plot(
             0  # x-coordinate of center of each individual swarm of the swarm plot
         )
         x_tick_tabels = []
-
-        for group_i, values_i in self.__data_copy.groupby(self.__x):
+        for group_i, values_i in self.__data_copy.groupby(self.__x, observed=False):
             x_new = []
             values_i_y = values_i[self.__y]
             x_offset = self._swarm(

nbs/API/dabest_object.ipynb

@@ -57,6 +57,7 @@
     "#| export\n",
     "# Import standard data science libraries\n",
     "from numpy import array, repeat, random, issubdtype, number\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
     "from scipy.stats import norm\n",
     "from scipy.stats import randint"
@@ -547,7 +548,7 @@
     "\n",
     "            # Handling str type condition\n",
     "            if is_str_condition_met:\n",
-    "                if len(pd.unique(idx).tolist()) != 2:\n",
+    "                if len(np.unique(idx).tolist()) != 2:\n",
     "                    err0 = \"`mini_meta` is True, but `idx` ({})\".format(idx)\n",
     "                    err1 = \"does not contain exactly 2 unique columns.\"\n",
     "                    raise ValueError(err0 + err1)\n",
@@ -735,7 +736,7 @@
     "                all_plot_groups, ordered=True, inplace=True\n",
     "            )\n",
     "        else:\n",
-    "            plot_data.loc[:, self.__xvar] = pd.Categorical(\n",
+    "            plot_data[self.__xvar] = pd.Categorical(\n",
     "                plot_data[self.__xvar], categories=all_plot_groups, ordered=True\n",
     "            )\n",
     "\n",