flow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flow\n",
    "Show the reader volumes or intensity of movement between two or more states or conditions. These might be logical sequences or geographical locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#ggplot equivalent: plotnine\n",
    "from plotnine import *\n",
    "#scales package equivalent: mizani\n",
    "from mizani.breaks import *\n",
    "from mizani.formatters import *\n",
    "import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sankey\n",
    "\n",
    "Shows changes in flows from one condition to at least one other; good for tracing the eventual outcome of a complex process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Waterfall\n",
    "\n",
    "Designed to show the sequencing of data through a flow process, typically budgets. Can include +/- components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>kind</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Revenue</td>\n",
       "      <td>net</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other</td>\n",
       "      <td>out</td>\n",
       "      <td>-400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales Expense</td>\n",
       "      <td>out</td>\n",
       "      <td>-2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>out</td>\n",
       "      <td>-600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other Income</td>\n",
       "      <td>in</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Profit</td>\n",
       "      <td>net</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable kind  value\n",
       "0        Revenue  net   4000\n",
       "1          Other  out   -400\n",
       "2  Sales Expense  out  -2000\n",
       "3            R&D  out   -600\n",
       "4   Other Income   in    100\n",
       "5         Profit  net   1100"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'variable':['Revenue','Other','Sales Expense','R&D','Other Income','Profit'],\n",
    "                   'kind':['net','out','out','out','in','net'],\n",
    "                   'value':[4000,-400,-2000,-600,100,1100]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>kind</th>\n",
       "      <th>value</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Revenue</td>\n",
       "      <td>net</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other</td>\n",
       "      <td>out</td>\n",
       "      <td>-400</td>\n",
       "      <td>4000</td>\n",
       "      <td>3600</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales Expense</td>\n",
       "      <td>out</td>\n",
       "      <td>-2000</td>\n",
       "      <td>3600</td>\n",
       "      <td>1600</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>out</td>\n",
       "      <td>-600</td>\n",
       "      <td>1600</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other Income</td>\n",
       "      <td>in</td>\n",
       "      <td>100</td>\n",
       "      <td>1000</td>\n",
       "      <td>1100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Profit</td>\n",
       "      <td>net</td>\n",
       "      <td>1100</td>\n",
       "      <td>0</td>\n",
       "      <td>1100</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable kind  value  start   end  id\n",
       "0        Revenue  net   4000      0  4000   1\n",
       "1          Other  out   -400   4000  3600   2\n",
       "2  Sales Expense  out  -2000   3600  1600   3\n",
       "3            R&D  out   -600   1600  1000   4\n",
       "4   Other Income   in    100   1000  1100   5\n",
       "5         Profit  net   1100      0  1100   6"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#order cats\n",
    "ordered_cats = df['variable']\n",
    "df['variable'] = pd.Categorical(df['variable'], categories=ordered_cats, ordered=True)\n",
    "#create the water fall\n",
    "df['start'] = 0\n",
    "df['end'] = 0\n",
    "df['id'] = range(1,df.shape[0]+1)\n",
    "for i in range(df.shape[0]):\n",
    "    if df.iloc[i,1]=='net':\n",
    "        df.iloc[i,4] = df.iloc[i,2] #set end\n",
    "    else:\n",
    "        df.iloc[i,3] = df.iloc[i-1,4] #set start equals to previous end\n",
    "        df.iloc[i,4] = df.iloc[i,3] + df.iloc[i,2] #set end equals to start + value\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363242664415800)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df,aes(fill='kind')) +\n",
    "     #draw rectangles\n",
    "     geom_rect(aes(x='variable',xmin='id-0.45',xmax='id+0.45',\n",
    "              ymin='end',ymax='start')) +\n",
    "     geom_text(aes(x='variable',y='start+100',label='value')) +\n",
    "     scale_fill_manual(values=['green','lightblue','red']) +\n",
    "     xlab('Things') + ylab('Amount') +\n",
    "     theme(legend_title=element_blank())\n",
    "    )\n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chord\n",
    "A complex but powerful diagram which can illustrate 2-way flows (and net winner) in a matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network\n",
    "\n",
    "Used for showing the complexity and inter-connectdness of relationships of varying types."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}