Release updates (#697)

* Silenced Pandas and Numpy warnings
* Ugly workarounds for markdown-rendering bugs in Jupyter
* Updated to match changes in HoloViews
* Fixed typos and wording

datashader/transfer_functions.py

@@ -145,7 +145,7 @@ def eq_hist(data, mask=None, nbins=256*256):
     if not isinstance(data, np.ndarray):
         raise TypeError("data must be np.ndarray")
     data2 = data if mask is None else data[~mask]
-    if np.issubdtype(data2.dtype, np.integer):
+    if data2.dtype == bool or np.issubdtype(data2.dtype, np.integer):
         hist = np.bincount(data2.ravel())
         bin_centers = np.arange(len(hist))
         idx = np.nonzero(hist)[0][0]
@@ -210,16 +210,17 @@ def _interpolate(agg, cmap, how, alpha, span, min_alpha, name):
 
         interp = data - offset
 
-    data = interpolater(interp, mask)
-    if span is None:
-        span = [np.nanmin(data), np.nanmax(data)]
-    else:
-        if how == 'eq_hist':
-            # For eq_hist to work with span, we'll need to store the histogram
-            # from the data and then apply it to the span argument.
-            raise ValueError("span is not (yet) valid to use with eq_hist")
-
-        span = interpolater([0, span[1] - span[0]], 0)
+    with np.errstate(invalid="ignore", divide="ignore"):
+        data = interpolater(interp, mask)
+        if span is None:
+            span = [np.nanmin(data), np.nanmax(data)]
+        else:
+            if how == 'eq_hist':
+                # For eq_hist to work with span, we'll need to store the histogram
+                # from the data and then apply it to the span argument.
+                raise ValueError("span is not (yet) valid to use with eq_hist")
+
+            span = interpolater([0, span[1] - span[0]], 0)
 
     if isinstance(cmap, Iterator):
         cmap = list(cmap)

examples/getting_started/2_Pipeline.ipynb

@@ -33,14 +33,15 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
+    "from collections import OrderedDict as odict\n",
     "\n",
     "num=10000\n",
     "np.random.seed(1)\n",
     "\n",
-    "dists = {cat: pd.DataFrame.from_items([('x',np.random.normal(x,s,num)), \n",
-    "                                       ('y',np.random.normal(y,s,num)), \n",
-    "                                       ('val',val), \n",
-    "                                       ('cat',cat)])      \n",
+    "dists = {cat: pd.DataFrame(odict([('x',np.random.normal(x,s,num)), \n",
+    "                                  ('y',np.random.normal(y,s,num)), \n",
+    "                                  ('val',val), \n",
+    "                                  ('cat',cat)]))      \n",
     "         for x,  y,  s,  val, cat in \n",
     "         [(  2,  2, 0.03, 10, \"d1\"), \n",
     "          (  2, -2, 0.10, 20, \"d2\"), \n",

examples/getting_started/3_Interactivity.ipynb

@@ -21,14 +21,15 @@
     "import numpy as np\n",
     "import datashader as ds\n",
     "import datashader.transfer_functions as tf\n",
+    "from collections import OrderedDict as odict\n",
     "\n",
     "num=100000\n",
     "np.random.seed(1)\n",
     "\n",
-    "dists = {cat: pd.DataFrame.from_items([('x',np.random.normal(x,s,num)), \n",
-    "                                       ('y',np.random.normal(y,s,num)), \n",
-    "                                       ('val',val), \n",
-    "                                       ('cat',cat)])      \n",
+    "dists = {cat: pd.DataFrame(odict([('x',np.random.normal(x,s,num)), \n",
+    "                                  ('y',np.random.normal(y,s,num)), \n",
+    "                                  ('val',val), \n",
+    "                                  ('cat',cat)]))      \n",
     "         for x,  y,  s,  val, cat in \n",
     "         [(  2,  2, 0.03, 10, \"d1\"), \n",
     "          (  2, -2, 0.10, 20, \"d2\"), \n",

examples/nyc_taxi-nongeo.ipynb

@@ -245,7 +245,7 @@
    "source": [
     "Here datashader is aggregating over both fare_amount and trip_distance, but trip_distance was specified to have only a height of 2, because it will be further collapsed to create the histogram being displayed.  You can now use the wheel zoom tool when hovering over the x axis, and the plot will zoom in or out, dynamically resampling at the given location to make a new histogram (as long as there is a live Python server running). \n",
     "\n",
-    "In this particular plot, there is a very wide range of fare amounts, with an implausibly high maximum fare of over \\$4000, but you can easily zoom in to the bulk of the data to show that nearly all fares are between \\$4 and \\$20, following something like a gamma distribution, and they are discretized to the nearest $0.50 in this dataset."
+    "In this particular plot, there is a very wide range of fare amounts, with an implausibly high maximum fare of over 4000 dollars, but you can easily zoom in to the bulk of the data to show that nearly all fares are between 4 and 20 dollars, following something like a gamma distribution, and they are discretized to the nearest 50 cents in this dataset."
    ]
   }
  ],

examples/topics/nyc_taxi.ipynb

@@ -470,5 +470,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

examples/user_guide/10_Performance.ipynb

@@ -24,8 +24,8 @@
     "```\n",
     "\n",
     "If your data includes categorical values that take on a limited, fixed\n",
-    "number of possible values (e.g. \"Male\", \"Female\"), With parquet,\n",
-    "categorical columns use a more memory-efficient data representation and\n",
+    "number of possible values (e.g. \"Male\", \"Female\"),\n",
+    "Parquet's categorical columns use a more memory-efficient data representation and\n",
     "are optimized for common operations such as sorting and finding uniques.\n",
     "Before saving, just convert the column as follows:\n",
     "\n",