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Merge pull request #31 from tdhopper/new-plots
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‎Examples.ipynb‎

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"sns.histplot(mpg, x='cty', binwidth=2);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Box Plot of a Continuous Value by Category"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:ggplot"
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]
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},
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"outputs": [],
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"source": [
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"%%R -w 10 -h 10 -u in\n",
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"ggplot(data=mpg) +\n",
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" aes(x=class, y=hwy) +\n",
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" geom_boxplot()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:pandas"
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]
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},
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"outputs": [],
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"source": [
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"mpg.boxplot(column='hwy', by='class',\n",
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" rot=45);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:plotnine"
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]
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},
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"outputs": [],
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"source": [
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"(ggplot(mpg) +\n",
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" aes(x=\"class\", y=\"hwy\") +\n",
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" geom_boxplot())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:lets-plot"
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]
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},
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"outputs": [],
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"source": [
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"(lp.ggplot(mpg) +\n",
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" lp.aes(x=\"class\", y=\"hwy\") +\n",
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" lp.geom_boxplot())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:plotly"
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]
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},
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"outputs": [],
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"source": [
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"px.box(\n",
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" mpg, x=\"class\", y=\"hwy\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:altair"
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]
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},
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"outputs": [],
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"source": [
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"(\n",
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" alt.Chart(mpg)\n",
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" .mark_boxplot()\n",
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" .encode(x=\"class\", y=\"hwy\")\n",
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" .properties(width=400)\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:hvplot"
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]
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},
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"outputs": [],
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"source": [
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"mpg.hvplot.box(y=\"hwy\", by=\"class\",\n",
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" rot=45)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:matplotlib"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"`boxplot` takes a list of arrays, so the\n",
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"groups are split by hand.\n",
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"\"\"\"\n",
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"groups = mpg.groupby('class')['hwy']\n",
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"fig, ax = pyplot.subplots()\n",
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"ax.boxplot([v for _, v in groups],\n",
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" tick_labels=list(groups.groups))\n",
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"ax.set_xlabel('class')\n",
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"ax.set_ylabel('hwy')\n",
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"pyplot.xticks(rotation=45);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:seaborn"
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]
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},
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"outputs": [],
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"source": [
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"ax = sns.boxplot(mpg, x='class', y='hwy')\n",
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"ax.tick_params(axis='x', rotation=45);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"sns.lineplot(ts, x='date', y='value');"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Heatmap of Counts by Two Categories"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:ggplot"
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]
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},
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"outputs": [],
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"source": [
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"%%R -w 10 -h 10 -u in\n",
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"counts <- as.data.frame(\n",
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" table(cut=diamonds$cut,\n",
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" clarity=diamonds$clarity),\n",
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" responseName=\"count\")\n",
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"ggplot(data=counts) +\n",
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" aes(x=cut, y=clarity, fill=count) +\n",
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" geom_tile()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:plotnine"
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]
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},
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"outputs": [],
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"source": [
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"counts = (diamonds\n",
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" .groupby([\"cut\", \"clarity\"])\n",
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" .size()\n",
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" .reset_index(name=\"count\"))\n",
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"(ggplot(counts) +\n",
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" aes(x=\"cut\", y=\"clarity\", fill=\"count\") +\n",
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" geom_tile())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:lets-plot"
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]
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},
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"outputs": [],
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"source": [
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"counts = (diamonds\n",
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" .groupby([\"cut\", \"clarity\"])\n",
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" .size()\n",
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" .reset_index(name=\"count\"))\n",
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"(lp.ggplot(counts) +\n",
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" lp.aes(x=\"cut\", y=\"clarity\",\n",
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" fill=\"count\") +\n",
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" lp.geom_tile())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:plotly"
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]
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},
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"outputs": [],
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"source": [
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"px.density_heatmap(\n",
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" diamonds, x=\"cut\", y=\"clarity\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:altair"
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]
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},
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"outputs": [],
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"source": [
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"alt.data_transformers.disable_max_rows()\n",
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"(\n",
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" alt.Chart(diamonds)\n",
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" .mark_rect()\n",
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" .encode(\n",
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" x=\"cut\", y=\"clarity\",\n",
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" color=\"count()\",\n",
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" )\n",
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" .properties(width=400, height=400)\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:hvplot"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"hvPlot needs the counts up front; `C` names\n",
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"the column that colors each tile.\n",
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"\"\"\"\n",
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"(diamonds\n",
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" .groupby([\"cut\", \"clarity\"])\n",
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" .size()\n",
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" .reset_index(name=\"count\")\n",
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" .hvplot.heatmap(x=\"cut\", y=\"clarity\",\n",
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" C=\"count\", rot=45))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:matplotlib"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"Matplotlib has no heatmap function, so the\n",
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"counts are pivoted and drawn with `pcolormesh`.\n",
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"\"\"\"\n",
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"counts = (diamonds\n",
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" .groupby(['clarity', 'cut'])\n",
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" .size()\n",
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" .unstack())\n",
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"fig, ax = pyplot.subplots()\n",
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"mesh = ax.pcolormesh(counts.values)\n",
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"ax.set_xticks(\n",
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" np.arange(len(counts.columns)) + .5,\n",
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" counts.columns, rotation=45)\n",
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"ax.set_yticks(\n",
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" np.arange(len(counts.index)) + .5,\n",
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" counts.index)\n",
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"ax.set_xlabel('cut')\n",
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"ax.set_ylabel('clarity')\n",
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"fig.colorbar(mesh, label='count');"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:seaborn"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"`heatmap` colors a matrix, so the counts are\n",
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"pivoted into one first.\n",
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"\"\"\"\n",
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"counts = (diamonds\n",
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" .groupby(['clarity', 'cut'])\n",
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" .size()\n",
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" .unstack())\n",
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"ax = sns.heatmap(\n",
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" counts, cbar_kws=dict(label='count'))\n",
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"ax.tick_params(axis='y', rotation=0);"
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]
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}
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],
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"metadata": {

‎INTRO.md‎

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#### Matplotlib-Based Libraries
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[Pandas plotting](https://pandas.pydata.org/docs/user_guide/visualization.html "pandas user guide: Chart Visualization") provides "the basics ... to easily create decent looking plots" from data frames. That is about 70% of what I do day-to-day. It has no faceting, no categorical color mapping, and no smoothing, so five of the examples below have no pandas column.
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[Pandas plotting](https://pandas.pydata.org/docs/user_guide/visualization.html "pandas user guide: Chart Visualization") provides "the basics ... to easily create decent looking plots" from data frames. That is about 70% of what I do day-to-day. It has no faceting, no categorical color mapping, no smoothing, and no heatmap, so six of the examples below have no pandas column.
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Seaborn calls itself "[statistical data visualization](https://seaborn.pydata.org/ "seaborn: statistical data visualization")." Its classic interface is a set of named functions (`histplot`, `scatterplot`, `countplot`, `lmplot`, `kdeplot`) plus [FacetGrid](http://seaborn.pydata.org/tutorial/axis_grids.html), which I use for faceting more than anything else in the library. It covers every plot below, once you know which function to reach for.
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Seaborn 0.12 added [seaborn.objects](https://seaborn.pydata.org/tutorial/objects_interface.html), a second interface built on the grammar of graphics. It composes a plot from marks and statistical transforms instead of dispatching to a named plotting function. The interface has no loess smoother and no regression confidence band, so those two examples are missing.
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Seaborn 0.12 added [seaborn.objects](https://seaborn.pydata.org/tutorial/objects_interface.html), a second interface built on the grammar of graphics. It composes a plot from marks and statistical transforms instead of dispatching to a named plotting function. The interface has no loess smoother, no regression confidence band, and neither a box-plot nor a rectangle mark, so four of the examples are missing.
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"[plotnine](https://plotnine.org/) is a data visualization package for Python based on the grammar of graphics." It tracks ggplot2 closely enough that most R code translates line for line, down to the `+` for layering. I reach for it when I want ggplot2 semantics without leaving Python.
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