Merge branch 'v0.9'

examples/different_scatter_variables.py

@@ -9,7 +9,7 @@
 import matplotlib.pyplot as plt
 sns.set(style="whitegrid")
 
-# Load the example iris dataset
+# Load the example diamonds dataset
 diamonds = sns.load_dataset("diamonds")
 
 # Draw a scatter plot while assigning point colors and sizes to different

examples/distplot_options.py

@@ -26,7 +26,7 @@
 # Plot a filled kernel density estimate
 sns.distplot(d, hist=False, color="g", kde_kws={"shade": True}, ax=axes[1, 0])
 
-# Plot a historgram and kernel density estimate
+# Plot a histogram and kernel density estimate
 sns.distplot(d, color="m", ax=axes[1, 1])
 
 plt.setp(axes, yticks=[])

examples/heatmap_annotation.py

@@ -7,7 +7,7 @@
 import seaborn as sns
 sns.set()
 
-# Load the example flights dataset and conver to long-form
+# Load the example flights dataset and convert to long-form
 flights_long = sns.load_dataset("flights")
 flights = flights_long.pivot("month", "year", "passengers")
 

examples/logistic_regression.py

@@ -7,13 +7,13 @@
 import seaborn as sns
 sns.set(style="darkgrid")
 
-# Load the example titanic dataset
+# Load the example Titanic dataset
 df = sns.load_dataset("titanic")
 
 # Make a custom palette with gendered colors
 pal = dict(male="#6495ED", female="#F08080")
 
-# Show the survival proability as a function of age and sex
+# Show the survival probability as a function of age and sex
 g = sns.lmplot(x="age", y="survived", col="sex", hue="sex", data=df,
                palette=pal, y_jitter=.02, logistic=True)
 g.set(xlim=(0, 80), ylim=(-.05, 1.05))

examples/many_pairwise_correlations.py

@@ -21,8 +21,7 @@
 corr = d.corr()
 
 # Generate a mask for the upper triangle
-mask = np.zeros_like(corr, dtype=np.bool)
-mask[np.triu_indices_from(mask)] = True
+mask = np.triu(np.ones_like(corr, dtype=np.bool))
 
 # Set up the matplotlib figure
 f, ax = plt.subplots(figsize=(11, 9))

examples/multiple_regression.py

@@ -10,7 +10,7 @@
 # Load the iris dataset
 iris = sns.load_dataset("iris")
 
-# Plot sepal with as a function of sepal_length across days
+# Plot sepal width as a function of sepal_length across days
 g = sns.lmplot(x="sepal_length", y="sepal_width", hue="species",
                truncate=True, height=5, data=iris)
 

examples/scatterplot_sizes.py

@@ -9,7 +9,7 @@
 import seaborn as sns
 sns.set()
 
-# Load the example iris dataset
+# Load the example planets dataset
 planets = sns.load_dataset("planets")
 
 cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)

seaborn/axisgrid.py

@@ -1668,7 +1668,7 @@ def __init__(self, x, y, data=None, height=6, ratio=5, space=.2,
         if size is not None:
             height = size
             msg = ("The `size` parameter has been renamed to `height`; "
-                   "pleaes update your code.")
+                   "please update your code.")
             warnings.warn(msg, UserWarning)
 
         # Set up the subplot grid
@@ -2061,7 +2061,7 @@ def pairplot(data, hue=None, hue_order=None, palette=None,
     if size is not None:
         height = size
         msg = ("The `size` parameter has been renamed to `height`; "
-               "pleaes update your code.")
+               "please update your code.")
         warnings.warn(msg, UserWarning)
 
     if not isinstance(data, pd.DataFrame):

seaborn/categorical.py

@@ -3035,6 +3035,9 @@ def swarmplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None,
         of the points.
     {linewidth}
     {ax_in}
+    kwargs : key, value mappings
+        Other keyword arguments are passed through to ``plt.scatter`` at draw
+        time.
 
     Returns
     -------
@@ -3667,7 +3670,7 @@ def factorplot(*args, **kwargs):
 
     if "size" in kwargs:
         kwargs["height"] = kwargs.pop("size")
-        msg = ("The `size` paramter has been renamed to `height`; "
+        msg = ("The `size` parameter has been renamed to `height`; "
                "please update your code.")
         warnings.warn(msg, UserWarning)
 
@@ -3687,7 +3690,7 @@ def catplot(x=None, y=None, hue=None, data=None, row=None, col=None,
     # Handle deprecations
     if "size" in kwargs:
         height = kwargs.pop("size")
-        msg = ("The `size` paramter has been renamed to `height`; "
+        msg = ("The `size` parameter has been renamed to `height`; "
                "please update your code.")
         warnings.warn(msg, UserWarning)
 

seaborn/distributions.py

@@ -81,9 +81,9 @@ def distplot(a, bins=None, hist=True, kde=True, rug=False, fit=None,
         Name for the support axis label. If None, will try to get it
         from a.namel if False, do not set a label.
     label : string, optional
-        Legend label for the relevent component of the plot
+        Legend label for the relevant component of the plot.
     ax : matplotlib axis, optional
-        if provided, plot on this axis
+        If provided, plot on this axis.
 
     Returns
     -------
@@ -288,8 +288,8 @@ def _univariate_kdeplot(data, shade, vertical, kernel, bw, gridsize, cut,
             msg = "Kernel other than `gau` requires statsmodels."
             warnings.warn(msg, UserWarning)
         if cumulative:
-            raise ImportError("Cumulative distributions are currently"
-                              "only implemented in statsmodels."
+            raise ImportError("Cumulative distributions are currently "
+                              "only implemented in statsmodels. "
                               "Please install statsmodels.")
         x, y = _scipy_univariate_kde(data, bw, gridsize, cut, clip)
 

seaborn/regression.py

@@ -541,7 +541,7 @@ def lmplot(x, y, data, hue=None, col=None, row=None, palette=None,
     # Handle deprecations
     if size is not None:
         height = size
-        msg = ("The `size` paramter has been renamed to `height`; "
+        msg = ("The `size` parameter has been renamed to `height`; "
                "please update your code.")
         warnings.warn(msg, UserWarning)
 
@@ -819,7 +819,7 @@ def regplot(x, y, data=None, x_estimator=None, x_bins=None, x_ci="ci",
     {truncate}
     {xy_jitter}
     label : string
-        Label to apply to ether the scatterplot or regression line (if
+        Label to apply to either the scatterplot or regression line (if
         ``scatter`` is ``False``) for use in a legend.
     color : matplotlib color
         Color to apply to all plot elements; will be superseded by colors

seaborn/relational.py

@@ -1136,7 +1136,7 @@ def lineplot(x=None, y=None, hue=None, size=None, style=None, data=None,
     err_style : "band" or "bars", optional
         Whether to draw the confidence intervals with translucent error bands
         or discrete error bars.
-    err_band : dict of keyword arguments
+    err_kws : dict of keyword arguments
         Additional paramters to control the aesthetics of the error bars. The
         kwargs are passed either to ``ax.fill_between`` or ``ax.errorbar``,
         depending on the ``err_style``.