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Update pandas to 2.1.4
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@Jacobluke-
Jacobluke- committed Sep 17, 2024
1 parent 17d1014 commit 9a42975
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Showing 7 changed files with 25 additions and 25 deletions.
2 changes: 1 addition & 1 deletion dabest/_dabest_object.py
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Original file line number Diff line number Diff line change
Expand Up @@ -667,7 +667,7 @@ 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
)

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10 changes: 5 additions & 5 deletions dabest/plot_tools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,25 +117,25 @@ 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]
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12 changes: 6 additions & 6 deletions dabest/plotter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -780,7 +780,7 @@ def effectsize_df_plotter(effectsize_df, **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]
ticks_with_counts = []
ticks_loc = rawdata_axes.get_xticks()
rawdata_axes.xaxis.set_major_locator(matplotlib.ticker.FixedLocator(ticks_loc))
Expand Down Expand Up @@ -1076,19 +1076,19 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
# 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(numeric_only=True).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(numeric_only=True).loc[current_group, yvar]
plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_group, yvar]
)

if swarm_ylim is None:
Expand Down Expand Up @@ -1132,7 +1132,7 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
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]

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2 changes: 1 addition & 1 deletion nbs/API/dabest_object.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -735,7 +735,7 @@
" 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",
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10 changes: 5 additions & 5 deletions nbs/API/plot_tools.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -170,25 +170,25 @@
" else:\n",
" group_order = pd.unique(data[x])\n",
"\n",
" means = data.groupby(x)[y].mean().reindex(index=group_order)\n",
" means = data.groupby(x, observed=False)[y].mean().reindex(index=group_order)\n",
"\n",
" if method in [\"proportional_error_bar\", \"sankey_error_bar\"]:\n",
" g = lambda x: np.sqrt(\n",
" (np.sum(x) * (len(x) - np.sum(x))) / (len(x) * len(x) * len(x))\n",
" )\n",
" sd = data.groupby(x)[y].apply(g)\n",
" sd = data.groupby(x, observed=False)[y].apply(g)\n",
" else:\n",
" sd = data.groupby(x)[y].std().reindex(index=group_order)\n",
" sd = data.groupby(x, observed=False)[y].std().reindex(index=group_order)\n",
"\n",
" lower_sd = means - sd\n",
" upper_sd = means + sd\n",
"\n",
" if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any():\n",
" kwargs[\"clip_on\"] = True\n",
"\n",
" medians = data.groupby(x)[y].median().reindex(index=group_order)\n",
" medians = data.groupby(x, observed=False)[y].median().reindex(index=group_order)\n",
" quantiles = (\n",
" data.groupby(x)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order)\n",
" data.groupby(x, observed=False)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order)\n",
" )\n",
" lower_quartiles = quantiles[0.25]\n",
" upper_quartiles = quantiles[0.75]\n",
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12 changes: 6 additions & 6 deletions nbs/API/plotter.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -837,7 +837,7 @@
" )\n",
"\n",
" # Add the counts to the rawdata axes xticks.\n",
" counts = plot_data.groupby(xvar).count()[yvar]\n",
" counts = plot_data.groupby(xvar, observed=False).count()[yvar]\n",
" ticks_with_counts = []\n",
" ticks_loc = rawdata_axes.get_xticks()\n",
" rawdata_axes.xaxis.set_major_locator(matplotlib.ticker.FixedLocator(ticks_loc))\n",
Expand Down Expand Up @@ -1133,19 +1133,19 @@
" # Check that the effect size is within the swarm ylims.\n",
" if effect_size_type in [\"mean_diff\", \"cohens_d\", \"hedges_g\", \"cohens_h\"]:\n",
" control_group_summary = (\n",
" plot_data.groupby(xvar)\n",
" plot_data.groupby(xvar, observed=False)\n",
" .mean(numeric_only=True)\n",
" .loc[current_control, yvar]\n",
" )\n",
" test_group_summary = (\n",
" plot_data.groupby(xvar).mean(numeric_only=True).loc[current_group, yvar]\n",
" plot_data.groupby(xvar, observed=False).mean(numeric_only=True).loc[current_group, yvar]\n",
" )\n",
" elif effect_size_type == \"median_diff\":\n",
" control_group_summary = (\n",
" plot_data.groupby(xvar).median(numeric_only=True).loc[current_control, yvar]\n",
" plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_control, yvar]\n",
" )\n",
" test_group_summary = (\n",
" plot_data.groupby(xvar).median(numeric_only=True).loc[current_group, yvar]\n",
" plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_group, yvar]\n",
" )\n",
"\n",
" if swarm_ylim is None:\n",
Expand Down Expand Up @@ -1189,7 +1189,7 @@
" pooled_sd = stds[0]\n",
"\n",
" if effect_size_type == \"hedges_g\":\n",
" gby_count = plot_data.groupby(xvar).count()\n",
" gby_count = plot_data.groupby(xvar, observed=False).count()\n",
" len_control = gby_count.loc[current_control, yvar]\n",
" len_test = gby_count.loc[current_group, yvar]\n",
"\n",
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2 changes: 1 addition & 1 deletion settings.ini
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ language = English
status = 3
user = acclab

requirements = fastcore pandas~=1.5.3 numpy~=1.26 matplotlib~=3.8.4 seaborn~=0.12.2 scipy~=1.12 datetime statsmodels lqrt
requirements = fastcore pandas~=2.1.4 numpy~=1.26 matplotlib~=3.8.4 seaborn~=0.12.2 scipy~=1.12 datetime statsmodels lqrt
dev_requirements = pytest~=7.2.1 pytest-mpl~=0.16.1

### Optional ###
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