Data API

doc/Tutorials/Pandas_Conversion.ipynb

@@ -140,24 +140,6 @@
     "HTML(df.reset_index().to_html())"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For now though, the a, b, and c columns is all we'll need. To confirm the dataframe was converted correctly we can call the `.info` method on Table:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "example_table.info"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -267,7 +249,7 @@
    },
    "outputs": [],
    "source": [
-    "macro_df = pd.read_csv('http://ioam.github.com/holoviews/Tutorials/macro.csv', sep='\\t')"
+    "macro_df = pd.read_csv('http://ioam.github.com/holoviews/Tutorials/macro.csv', '\\t')"
    ]
   },
   {
@@ -404,7 +386,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that we've extracted the gdp_curves we can apply some operations to them. The collapse method applies some function across the data along the supplied dimensions. This lets us quickly compute the mean GDP Growth by year for example, but it also allows us to map a function with parameters to the data and visualize the resulting samples. A simple example is computing a curve for each percentile and embedding it in an NdOverlay.\n",
+    "Now that we've extracted the gdp_curves we can apply some operations to them. The collapse method applies some function across the data along the supplied dimensions. This let's us quickly compute a the mean GDP Growth by year for example, but it also allows us to map a function with parameters to the data and visualize the resulting samples. A simple example is computing a curve for each percentile and embedding it in an NdOverlay.\n",
     "\n",
     "Additionally we can apply a Palette to visualize the range of percentiles."
    ]
@@ -462,9 +444,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using the .select method we can pull out the data for just a few countries and specific years. We can also make more advanced use of the Palettes.\n",
+    "Using the .select method we can pull out the data for just a few countries and specific years. We can also make more advanced use the Palettes.\n",
     "\n",
-    "Palettes can be customized by selecting only a subrange of the underlying cmap to draw the colors from. The Palette draws samples from the colormap using the supplied sample_fn, which by default just draws linear samples but may be overriden with any function that draws samples in the supplied ranges. By slicing the Set1 colormap we draw colors only from the upper half of the palette and then reverse it."
+    "Palettes can customized by selecting only a subrange of the underlying cmap to draw the colors from. The Palette draws samples from the colormap using the supplied sample_fn, which by default just draws linear samples but may be overriden with any function that draws samples in the supplied ranges. By slicing the Set1 colormap we draw colors only from the upper half of the palette and then reverse it."
    ]
   },
   {

doc/Tutorials/Sampling_Data.ipynb

@@ -274,7 +274,7 @@
    "outputs": [],
    "source": [
     "raster = hv.Raster(np.random.rand(3, 3))\n",
-    "raster + hv.Points(raster.table().keys())[-1:3, -1:3] + raster.table()"
+    "raster + hv.Points(raster)[-1:3, -1:3] + raster.table()"
    ]
   },
   {
@@ -316,7 +316,7 @@
    "source": [
     "extents = (0, 0, 3, 3)\n",
     "img = hv.Image(np.random.rand(3, 3), bounds=extents)\n",
-    "img + hv.Points(img.table().keys(), extents=extents) + img.table()"
+    "img + hv.Points(img, extents=extents) + img.table()"
    ]
   },
   {
@@ -462,7 +462,7 @@
     "extents = (0, 0, 10, 10)\n",
     "img = hv.Image(np.random.rand(10, 10), bounds=extents)\n",
     "img_coords = hv.Points(img.table(), extents=extents)\n",
-    "img + img * img_coords * hv.Points([img.closest((5,5))])(style=dict(color='r')) + img.sample([(5, 5)])"
+    "img + img * img_coords * hv.Points([img.closest([(5,5)])])(style=dict(color='r')) + img.sample([(5, 5)])"
    ]
   },
   {
@@ -481,7 +481,7 @@
    "outputs": [],
    "source": [
     "sampled = img.sample(y=5)\n",
-    "img + img * img_coords * hv.Points(zip(sampled.table().keys(), [img.closest((5,5))[1]]*10)) + sampled"
+    "img + img * img_coords * hv.Points(zip(sampled['x'], [img.closest(y=5)]*10)) + sampled"
    ]
   },
   {
@@ -610,8 +610,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "scrolled": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [

doc/Tutorials/Showcase.ipynb

@@ -246,7 +246,7 @@
    "source": [
     "sample_pos = (0,0.25)\n",
     "annotated = circular_wave * hv.Points([sample_pos])\n",
-    "sample = circular_wave.sample(samples=[sample_pos]).reindex().to.curve('Phase', 'Amplitude')\n",
+    "sample = circular_wave.sample(samples=[sample_pos]).to.curve('Phase', 'Amplitude', ['Frequency'])\n",
     "annotated + sample"
    ]
   },

holoviews/core/__init__.py

@@ -1,4 +1,5 @@
 from .boundingregion import *  # pyflakes:ignore (API import)
+from .data import *            # pyflakes:ignore (API import)
 from .dimension import *       # pyflakes:ignore (API import)
 from .element import *         # pyflakes:ignore (API import)
 from .layout import *          # pyflakes:ignore (API import)