Require keyword arguments almost everywhere

doc/releases/v0.11.0.txt

@@ -4,7 +4,7 @@ v0.11.0 (Unreleased)
 
 - TODO stub for explaining improvements to variable specificiation. Make this good!  GH2017
 
-- Enforced keyword-only arguments for most parameters of most functions and classes.  GH2052
+- TODO stub for enforced keyword-only arguments for most parameters of most functions and classes.  GH2052, GH2081
 
 - Standardized the parameter names for the oldest functions (:func:`distplot`, :func:`kdeplot`, and :func:`rugplot`) to be `x` and `y`, as in other functions. Using the old names will warn now and break in the future.  GH2060
 

doc/tutorial/axis_grids.ipynb

@@ -170,7 +170,7 @@
    "outputs": [],
    "source": [
     "g = sns.FacetGrid(tips, col=\"day\", height=4, aspect=.5)\n",
-    "g.map(sns.barplot, \"sex\", \"total_bill\");"
+    "g.map(sns.barplot, \"sex\", \"total_bill\", order=[\"Male\", \"Female\"]);"
    ]
   },
   {

doc/tutorial/categorical.ipynb

@@ -155,8 +155,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sns.catplot(x=\"size\", y=\"total_bill\", kind=\"swarm\",\n",
-    "            data=tips.query(\"size != 3\"));"
+    "sns.catplot(x=\"size\", y=\"total_bill\", data=tips);"
    ]
   },
   {
@@ -526,7 +525,7 @@
    "outputs": [],
    "source": [
     "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"smoker\",\n",
-    "            col=\"time\", aspect=.6,\n",
+    "            col=\"time\", aspect=.7,\n",
     "            kind=\"swarm\", data=tips);"
    ]
   },
@@ -562,7 +561,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3.6  (seaborn-py37-latest)",
+   "display_name": "seaborn-py37-latest",
    "language": "python",
    "name": "seaborn-py37-latest"
   },
@@ -580,5 +579,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

doc/tutorial/color_palettes.ipynb

@@ -459,7 +459,7 @@
    "source": [
     "x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T\n",
     "cmap = sns.cubehelix_palette(light=1, as_cmap=True)\n",
-    "sns.kdeplot(x, y, cmap=cmap, shade=True);"
+    "sns.kdeplot(x=x, y=y, cmap=cmap, shade=True);"
    ]
   },
   {
@@ -527,7 +527,7 @@
    "outputs": [],
    "source": [
     "pal = sns.dark_palette(\"palegreen\", as_cmap=True)\n",
-    "sns.kdeplot(x, y, cmap=pal);"
+    "sns.kdeplot(x=x, y=y, cmap=pal);"
    ]
   },
   {
@@ -738,7 +738,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3.6  (seaborn-py37-latest)",
+   "display_name": "seaborn-py37-latest",
    "language": "python",
    "name": "seaborn-py37-latest"
   },
@@ -756,5 +756,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

doc/tutorial/distributions.ipynb

@@ -81,7 +81,7 @@
    "outputs": [],
    "source": [
     "x = np.random.normal(size=100)\n",
-    "sns.distplot(x);"
+    "sns.distplot(x=x);"
    ]
   },
   {
@@ -102,7 +102,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sns.distplot(x, kde=False, rug=True);"
+    "sns.distplot(x=x, kde=False, rug=True);"
    ]
   },
   {
@@ -118,7 +118,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sns.distplot(x, bins=20, kde=False, rug=True);"
+    "sns.distplot(x=x, bins=20, kde=False, rug=True);"
    ]
   },
   {
@@ -137,7 +137,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sns.distplot(x, hist=False, rug=True);"
+    "sns.distplot(x=x, hist=False, rug=True);"
    ]
   },
   {
@@ -164,7 +164,7 @@
     "    kernels.append(kernel)\n",
     "    plt.plot(support, kernel, color=\"r\")\n",
     "\n",
-    "sns.rugplot(x, color=\".2\", linewidth=3);"
+    "sns.rugplot(x=x, color=\".2\", linewidth=3);"
    ]
   },
   {
@@ -199,7 +199,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sns.kdeplot(x, shade=True);"
+    "sns.kdeplot(x=x, shade=True);"
    ]
   },
   {
@@ -215,9 +215,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sns.kdeplot(x)\n",
-    "sns.kdeplot(x, bw=.2, label=\"bw: 0.2\")\n",
-    "sns.kdeplot(x, bw=2, label=\"bw: 2\")\n",
+    "sns.kdeplot(x=x)\n",
+    "sns.kdeplot(x=x, bw=.2, label=\"bw: 0.2\")\n",
+    "sns.kdeplot(x=x, bw=2, label=\"bw: 2\")\n",
     "plt.legend();"
    ]
   },
@@ -234,8 +234,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sns.kdeplot(x, shade=True, cut=0)\n",
-    "sns.rugplot(x);"
+    "sns.kdeplot(x=x, shade=True, cut=0)\n",
+    "sns.rugplot(x=x);"
    ]
   },
   {
@@ -255,7 +255,7 @@
    "outputs": [],
    "source": [
     "x = np.random.gamma(6, size=200)\n",
-    "sns.distplot(x, kde=False, fit=stats.gamma);"
+    "sns.distplot(x=x, kde=False, fit=stats.gamma);"
    ]
   },
   {
@@ -352,9 +352,9 @@
    "outputs": [],
    "source": [
     "f, ax = plt.subplots(figsize=(6, 6))\n",
-    "sns.kdeplot(df.x, df.y, ax=ax)\n",
-    "sns.rugplot(df.x, color=\"g\", ax=ax)\n",
-    "sns.rugplot(df.y, vertical=True, ax=ax);"
+    "sns.kdeplot(x=df.x, y=df.y, ax=ax)\n",
+    "sns.rugplot(x=df.x, color=\"g\", ax=ax)\n",
+    "sns.rugplot(x=df.y, vertical=True, ax=ax);"
    ]
   },
   {
@@ -372,7 +372,7 @@
    "source": [
     "f, ax = plt.subplots(figsize=(6, 6))\n",
     "cmap = sns.cubehelix_palette(as_cmap=True, dark=0, light=1, reverse=True)\n",
-    "sns.kdeplot(df.x, df.y, cmap=cmap, n_levels=60, shade=True);"
+    "sns.kdeplot(x=df.x, y=df.y, cmap=cmap, n_levels=60, shade=True);"
    ]
   },
   {
@@ -461,7 +461,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3.6  (seaborn-py37-latest)",
+   "display_name": "seaborn-py37-latest",
    "language": "python",
    "name": "seaborn-py37-latest"
   },
@@ -479,5 +479,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

doc/tutorial/regression.ipynb

@@ -201,9 +201,7 @@
   },
   {
    "cell_type": "raw",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
     "In the presence of these kind of higher-order relationships, :func:`lmplot` and :func:`regplot` can fit a polynomial regression model to explore simple kinds of nonlinear trends in the dataset:"
    ]
@@ -517,7 +515,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3.6  (seaborn-py37-latest)",
+   "display_name": "seaborn-py37-latest",
    "language": "python",
    "name": "seaborn-py37-latest"
   },
@@ -535,5 +533,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

doc/tutorial/relational.ipynb

@@ -404,9 +404,7 @@
   },
   {
    "cell_type": "raw",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
     "The default colormap and handling of the legend in :func:`lineplot` also depends on whether the hue semantic is categorical or numeric:"
    ]
@@ -616,7 +614,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3.6  (seaborn-py37-latest)",
+   "display_name": "seaborn-py37-latest",
    "language": "python",
    "name": "seaborn-py37-latest"
   },
@@ -634,5 +632,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/cubehelix_palette.py

@@ -22,7 +22,7 @@
 
     # Generate and plot a random bivariate dataset
     x, y = rs.randn(2, 50)
-    sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=ax)
+    sns.kdeplot(x=x, y=y, cmap=cmap, shade=True, cut=5, ax=ax)
     ax.set(xlim=(-3, 3), ylim=(-3, 3))
 
 f.tight_layout()

examples/distplot_options.py

@@ -15,19 +15,20 @@
 sns.despine(left=True)
 
 # Generate a random univariate dataset
-d = rs.normal(size=100)
+x = rs.normal(size=100)
 
 # Plot a simple histogram with binsize determined automatically
-sns.distplot(d, kde=False, color="b", ax=axes[0, 0])
+sns.distplot(x=x, kde=False, color="b", ax=axes[0, 0])
 
 # Plot a kernel density estimate and rug plot
-sns.distplot(d, hist=False, rug=True, color="r", ax=axes[0, 1])
+sns.distplot(x=x, hist=False, rug=True, color="r", ax=axes[0, 1])
 
 # Plot a filled kernel density estimate
-sns.distplot(d, hist=False, color="g", kde_kws={"shade": True}, ax=axes[1, 0])
+sns.distplot(x=x, hist=False, color="g",
+             kde_kws={"shade": True}, ax=axes[1, 0])
 
 # Plot a histogram and kernel density estimate
-sns.distplot(d, color="m", ax=axes[1, 1])
+sns.distplot(x=x, color="m", ax=axes[1, 1])
 
 plt.setp(axes, yticks=[])
-plt.tight_layout()
+f.tight_layout()

examples/hexbin_marginals.py

@@ -12,4 +12,4 @@
 x = rs.gamma(2, size=1000)
 y = -.5 * x + rs.normal(size=1000)
 
-sns.jointplot(x, y, kind="hex", color="#4CB391")
+sns.jointplot(x=x, y=y, kind="hex", color="#4CB391")

examples/joint_kde.py

@@ -18,4 +18,4 @@
 x2 = pd.Series(x2, name="$X_2$")
 
 # Show the joint distribution using kernel density estimation
-g = sns.jointplot(x1, x2, kind="kde", height=7, space=0)
+g = sns.jointplot(x=x1, y=x2, kind="kde", height=7, space=0)

examples/marginal_ticks.py

@@ -15,6 +15,6 @@
 x, y = rs.multivariate_normal(mean, cov, 100).T
 
 # Use JointGrid directly to draw a custom plot
-grid = sns.JointGrid(x, y, space=0, height=6, ratio=20)
+grid = sns.JointGrid(x=x, y=y, space=0, height=6, ratio=20)
 grid.plot_joint(sns.scatterplot, color="g")
 grid.plot_marginals(sns.rugplot, height=1, color="g")

examples/regression_marginals.py

@@ -8,7 +8,7 @@
 sns.set(style="darkgrid")
 
 tips = sns.load_dataset("tips")
-g = sns.jointplot("total_bill", "tip", data=tips,
+g = sns.jointplot(x="total_bill", y="tip", data=tips,
                   kind="reg", truncate=False,
                   xlim=(0, 60), ylim=(0, 12),
                   color="m", height=7)

examples/residplot.py

@@ -13,4 +13,4 @@
 y = 2 + 1.5 * x + rs.normal(0, 2, 75)
 
 # Plot the residuals after fitting a linear model
-sns.residplot(x, y, lowess=True, color="g")
+sns.residplot(x=x, y=y, lowess=True, color="g")

seaborn/_decorators.py

@@ -31,16 +31,17 @@ def _deprecate_positional_args(f):
     def inner_f(*args, **kwargs):
         extra_args = len(args) - len(all_args)
         if extra_args > 0:
-            # ignore first 'self' argument for instance methods
-            args_msg = ['{}={}'.format(name, arg)
-                        for name, arg in zip(kwonly_args[:extra_args],
-                                             args[-extra_args:])]
-            plural = "s" if len(args_msg) > 1 else ""
-            warnings.warn("Pass {} as keyword arg{}. From version 0.12, "
-                          "passing these as positional arguments will "
-                          "result in an error"
-                          .format(", ".join(args_msg), plural),
-                          FutureWarning)
+            plural = "s" if extra_args > 1 else ""
+            article = "" if plural else "a "
+            warnings.warn(
+                "Pass the following variable{} as {}keyword arg{}: {}. "
+                "From version 0.12, the only valid positional argument "
+                "will be `data`, and passing other arguments without an "
+                "explcit keyword will result in an error or misinterpretation."
+                .format(plural, article, plural,
+                        ", ".join(kwonly_args[:extra_args])),
+                FutureWarning
+            )
         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
         return f(**kwargs)
     return inner_f