Feature reference gallery

examples/reference/pandas/kde.ipynb

@@ -80,5 +80,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/reference/pandas/line.ipynb

@@ -44,5 +44,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/reference/xarray/bar.ipynb

@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d0ed0f8-ce26-4c54-9161-d34ca73ae716",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.xarray  # noqa\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3f7e297-9576-4c36-9a9f-a4200ccf2d36",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "A `bar` plot represents **categorical data** with rectangular bars with heights proportional to the **numerical values** that they represent.\n",
+    "The x-axis represents the categories and the y axis represents the numerical value scale.\n",
+    "The bars are of equal width which allows for instant comparison of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af429bbb-9a65-4b5d-b447-ce50b35dfedc",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "Let's load some data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8db8fff7-b85f-49ea-b281-4a4d86e0f3ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.tutorial.open_dataset('air_temperature').load()\n",
+    "air = ds.air\n",
+    "air1d = air.sel(lon=285.,lat=40.).groupby('time.month').mean()\n",
+    "air1d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "629c5172-9cba-4f5f-bf3b-67e0a390385b",
+   "metadata": {},
+   "source": [
+    "## Basic Bar Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfd1afa2-e310-4630-bd30-96e390078caf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air1d.hvplot.bar(y='air', height=500, title=\"Air Temperature by Month\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/reference/xarray/hist.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.xarray  # noqa\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`hist` is often a good way to start looking at data to get a sense of the distribution. Similar methods include [`kde`](kde.ipny) (also available as `density`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.tutorial.open_dataset('air_temperature').load()\n",
+    "air = ds.air\n",
+    "air1d = air.sel(lon=285.,lat=40.)\n",
+    "air1d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air1d.hvplot.hist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Customize the plot by changing the title and bar color."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air1d.hvplot.hist(title=\"Air Temperature over time at lat=40,lon285\", color='gray')"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/reference/xarray/kde.ipynb

@@ -0,0 +1,53 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ccf9fd5-9d10-4522-961d-7e8d236213b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.xarray  # noqa\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7ed6c65-2280-4741-b89b-721110628547",
+   "metadata": {},
+   "source": [
+    "Kernel density estimate (`kde`) provides a mechanism for showing the distribution and spread of the data. In `hvplot` the method is exposed both as `kde` and `density`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8680d48-02b1-4480-96e3-d0fd7807a804",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.tutorial.open_dataset('air_temperature').load()\n",
+    "air = ds.air\n",
+    "air1d = air.sel(lat=[25, 50, 75])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f33d3355-deaa-4bdf-befa-af20d2e46d7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air1d.hvplot.kde('air', by='lat', alpha=0.5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/reference/xarray/line.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.xarray  # noqa\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`line` is useful when data is continuous and has a continuous axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.tutorial.open_dataset('air_temperature').load()\n",
+    "air = ds.air\n",
+    "air1d = air.sel(lon=285.,lat=40.)\n",
+    "air1d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air1d.hvplot.line()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Customize the plot by changing the title and line color."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air1d.hvplot.line(title=\"Air Temperature over time at lat=40,lon285\",line_color='gray')"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/reference/xarray/violin.ipynb

@@ -0,0 +1,53 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ccf9fd5-9d10-4522-961d-7e8d236213b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.xarray  # noqa\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7ed6c65-2280-4741-b89b-721110628547",
+   "metadata": {},
+   "source": [
+    "`violin` plots are similar to box plots, but provide a better sense of the distribution of data. Note that `violin` plots depend on the `scipy` library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0f6ecc7-0f6a-4590-9c98-da10a674b9b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.tutorial.open_dataset('air_temperature').load()\n",
+    "air = ds.air\n",
+    "air"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a137994-0a9d-4a75-8192-1e4bb4ead6c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air.hvplot.violin(y='air', by='lat', color='lat', cmap='Category20', title=\"Air Temperature vs. latitude\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}