{
"cells": [
{
"cell_type": "markdown",
"id": "d68a1604-1d1c-4a04-95eb-ca0e0f9d4da1",
"metadata": {},
"source": [
"# Waterfall Plot\n",
"\n",
"A waterfall plot shows the cumulative effect of sequentially introduced positive or negative values.\n",
"\n",
"To use it, you need to import the 'bistro' module."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7b926beb-92f0-405e-8425-cef02e49285e",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"from lets_plot import *\n",
"from lets_plot.bistro import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "67f97700-ee2e-4a27-8eee-296b4307c79e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0a5272f5-a594-4ce6-84e5-941528ac115d",
"metadata": {},
"outputs": [],
"source": [
"data = {\n",
" \"Accounts\": [\"Product revenue\", \"Services revenue\", \"Fixed costs\", \"Variable costs\"],\n",
" \"Values\": [830_000, 290_000, -360_000, -150_000],\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "1858f51f-2da3-4f37-be27-f659db1e9752",
"metadata": {},
"source": [
"## Default View"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ec40036a-b909-4f7f-8324-231dc2197885",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"waterfall_plot(data, \"Accounts\", \"Values\")"
]
},
{
"cell_type": "markdown",
"id": "6ab3c679-ae24-474b-b651-b208d8b9b839",
"metadata": {},
"source": [
"## Improved View"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5c6f023a-fc3a-4e10-a147-3c019c40aeab",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"waterfall_plot(data, \"Accounts\", \"Values\", \\\n",
" size=.75, alpha=.15, width=.8, total_title=\"Profit\", \\\n",
" hline=element_line(linetype='solid', size=1), \\\n",
" connector=element_line(linetype='dotted'), \\\n",
" label=element_text(size=10, family=\"Courier\", face='bold', color='flow_type'), \\\n",
" label_format=\"$,.1~s\") + \\\n",
" scale_y_continuous(name=\"Values\", format=\"$,.1~s\") + \\\n",
" ggtitle(\"Company Profit (in USD)\") + \\\n",
" ggsize(1000, 500) + \\\n",
" theme_minimal() + \\\n",
" theme(plot_title=element_text(size=20, face='bold', hjust=.5))"
]
},
{
"cell_type": "markdown",
"id": "a184b578-1ad1-44cc-8b5b-b7e4272a74cc",
"metadata": {},
"source": [
"## Additional Parameters"
]
},
{
"cell_type": "markdown",
"id": "a1769b18-83c1-4188-bd10-1bbd04824e76",
"metadata": {},
"source": [
"### `measure` and `group`"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3e168567-84d9-4fe7-8378-f1994ba9ae91",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({\n",
" \"Company\": [\"Badgersoft\"] * 7 + [\"AIlien Co.\"] * 7,\n",
" \"Accounts\": [\"initial\", \"revenue\", \"costs\", \"Q1\", \"revenue\", \"costs\", \"Q2\"] * 2,\n",
" \"Values\": [200, 200, -100, None, 250, -100, None, \\\n",
" 150, 50, -100, None, 100, -100, None],\n",
" \"Measure\": ['absolute', 'relative', 'relative', 'total', 'relative', 'relative', 'total'] * 2,\n",
"})\n",
"\n",
"waterfall_plot(df, \"Accounts\", \"Values\", measure=\"Measure\", group=\"Company\") + \\\n",
" facet_grid(x=\"Company\", scales='free_x')"
]
},
{
"cell_type": "markdown",
"id": "2bbe2c0c-1ac5-484b-b500-11a021618da2",
"metadata": {},
"source": [
"### `calc_total`\n",
"\n",
"`calc_total=False` disables the calculation of the total.\n",
"\n",
"If the `measure` serie is specified however, the `calc_total` setting has no effect."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dcdec1d7-b879-43cc-ab00-4e51515e27b6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gggrid([\n",
" waterfall_plot(data, \"Accounts\", \"Values\", calc_total=False),\n",
" waterfall_plot(df[df[\"Company\"] == \"Badgersoft\"], \"Accounts\", \"Values\", measure=\"Measure\", calc_total=False),\n",
"])"
]
},
{
"cell_type": "markdown",
"id": "e674cb66-a316-4d12-a69f-d934e229a9b0",
"metadata": {},
"source": [
"### Tooltips\n",
"\n",
"Tooltips for relative and absolute measures should be specified independently."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5e4b5e87-ddf4-44a9-be53-76854fc70e85",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"relative_tooltips = layer_tooltips().title(\"Account: @..xlabel..\")\\\n",
" .format(\"@..initial..\", \" $,.3~s\")\\\n",
" .format(\"@..value..\", \" $,.3~s\")\\\n",
" .line(\"@{..flow_type..}d from @..initial.. to @..value..\")\\\n",
" .disable_splitting()\n",
"absolute_tooltips = 'none'\n",
"\n",
"gggrid([\n",
" waterfall_plot(data, \"Accounts\", \"Values\", relative_tooltips='detailed', absolute_tooltips='detailed') + ggtitle(\"'detailed' tooltips\"),\n",
" waterfall_plot(data, \"Accounts\", \"Values\", relative_tooltips=relative_tooltips, absolute_tooltips=absolute_tooltips) + ggtitle(\"Custom tooltips\"),\n",
"])"
]
},
{
"cell_type": "markdown",
"id": "d784f373-fd3d-4bcf-9c1d-ca2c526902e2",
"metadata": {},
"source": [
"### `sorted_value`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e6a1bf56-2bba-4bc2-8e14-53e0ed2a405d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"waterfall_plot(data, \"Accounts\", \"Values\", sorted_value=True)"
]
},
{
"cell_type": "markdown",
"id": "df771bf1-bb42-4694-bcb2-81be2df25097",
"metadata": {},
"source": [
"### `threshold`/`max_values`"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "efd0ce82-8f0f-4b38-8836-892d1f84345f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gggrid([\n",
" waterfall_plot(data, \"Accounts\", \"Values\") + ggtitle(\"Default\"),\n",
" waterfall_plot(data, \"Accounts\", \"Values\", threshold=300_000) + ggtitle(\"Specified threshold\"),\n",
" waterfall_plot(data, \"Accounts\", \"Values\", max_values=2) + ggtitle(\"Specified max_values\"),\n",
"])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}