FY 24 Budget Actuals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a00ee48",
   "metadata": {},
   "source": [
    "# Fiscal Year 2024 Budget Actuals\n",
    "\n",
    "Importing data using the [***tabula-py***](https://pypi.org/project/tabula-py/) package and creating a Sankey diagram using the [***matplotlib.sankey package***](https://matplotlib.org/stable/gallery/specialty_plots/sankey_basics.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c20f19cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing packages\n",
    "import tabula \n",
    "from tabula import read_pdf\n",
    "import os\n",
    "import pandas as pd\n",
    "import string as str\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.sankey import Sankey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c19d321c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting JAVA_HOME and PATH variables\n",
    "os.environ['JAVA_HOME'] = '/Users/scipio/Documents/FA_Budget_PDFs/jdk-22.0.2.jdk/Contents/Home'\n",
    "os.environ['PATH'] = '/Users/scipio/Documents/FA_Budget_PDFs/jdk-22.0.2.jdk/Contents/Home/lib/server'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe89a857",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing data from PDFs\n",
    "df1 = pd.DataFrame(tabula.read_pdf('/Users/scipio/Downloads/FY_24_General_Actuals_PDF.PDF', \n",
    "                             pages = 'all', pandas_options = {'header':None})[0])\n",
    "df2 = pd.DataFrame(tabula.read_pdf('/Users/scipio/Downloads/FY_24_General_Actuals_PDF.PDF', \n",
    "                             pages = 'all', pandas_options = {'header':None})[1])\n",
    "df3 = pd.DataFrame(tabula.read_pdf('/Users/scipio/Downloads/FY_24_General_Actuals_PDF.PDF', \n",
    "                             pages = 'all', pandas_options = {'header':None})[2])\n",
    "df4 = pd.DataFrame(tabula.read_pdf('/Users/scipio/Downloads/FY_24_General_Actuals_PDF.PDF', \n",
    "                             pages = 'all', pandas_options = {'header':None})[3])\n",
    "df5 = pd.DataFrame(tabula.read_pdf('/Users/scipio/Downloads/FY_24_General_Actuals_PDF.PDF', \n",
    "                             pages = 'all', pandas_options = {'header':None})[4])\n",
    "\n",
    "# combining dfs\n",
    "df = pd.concat([df1,df2,df3,df4,df5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d801e97",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>130-100-101-100-043</td>\n",
       "      <td>Teacher Salaries PS</td>\n",
       "      <td>1,249,986.00</td>\n",
       "      <td>(3,052.17)</td>\n",
       "      <td>1,246,933.83</td>\n",
       "      <td>1,246,933.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0                    1             2           3  \\\n",
       "0  130-100-101-100-043  Teacher Salaries PS  1,249,986.00  (3,052.17)   \n",
       "\n",
       "              4             5     6     7     8  \n",
       "0  1,246,933.83  1,246,933.83  0.00  0.00  0.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c531d52e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 191 entries, 0 to 7\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   0       191 non-null    object\n",
      " 1   1       53 non-null     object\n",
      " 2   2       191 non-null    object\n",
      " 3   3       191 non-null    object\n",
      " 4   4       191 non-null    object\n",
      " 5   5       191 non-null    object\n",
      " 6   6       191 non-null    object\n",
      " 7   7       191 non-null    object\n",
      " 8   8       191 non-null    object\n",
      "dtypes: object(9)\n",
      "memory usage: 14.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52ef55dc",
   "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>Expend. Account #</th>\n",
       "      <th>Account Title</th>\n",
       "      <th>Original Budget</th>\n",
       "      <th>New App/Trnsf</th>\n",
       "      <th>Revised Bgt</th>\n",
       "      <th>Expenditures</th>\n",
       "      <th>Encumbrances</th>\n",
       "      <th>Avail Balance</th>\n",
       "      <th>Refunds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>130-100-101-100-043</td>\n",
       "      <td>Teacher Salaries PS</td>\n",
       "      <td>1,249,986.00</td>\n",
       "      <td>(3,052.17)</td>\n",
       "      <td>1,246,933.83</td>\n",
       "      <td>1,246,933.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Expend. Account #        Account Title Original Budget New App/Trnsf  \\\n",
       "0  130-100-101-100-043  Teacher Salaries PS    1,249,986.00    (3,052.17)   \n",
       "\n",
       "    Revised Bgt  Expenditures Encumbrances Avail Balance Refunds  \n",
       "0  1,246,933.83  1,246,933.83         0.00          0.00    0.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating rename object to rename columns\n",
    "rename = {\n",
    "    0:'Expend. Account #',\n",
    "    1:'Account Title',\n",
    "    2:'Original Budget',\n",
    "    3:'New App/Trnsf',\n",
    "    4:'Revised Bgt',\n",
    "    5:'Expenditures',\n",
    "    6:'Encumbrances',\n",
    "    7:'Avail Balance',\n",
    "    8:'Refunds'\n",
    "}\n",
    "\n",
    "# renaming columns\n",
    "df.rename(columns = rename, inplace = True)\n",
    "\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5799f819",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data cleaning\n",
    "df['Expenditures'] = df['Expenditures'].str.replace(',','').astype('float') #--> casting column as float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31e39ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating campus column\n",
    "df['Campus'] = df['Expend. Account #'].str.split('-').str[3]\n",
    "\n",
    "# data cleaning\n",
    "def agg_campus(row):\n",
    "    if row['Campus'] in ['100','101']:\n",
    "        return 100\n",
    "    elif row['Campus'] in ['200','201']:\n",
    "        return 200\n",
    "    elif row['Campus'] in ['300','301']:\n",
    "        return 300\n",
    "    elif row['Campus'] in ['500','501']:\n",
    "        return 500\n",
    "    else:\n",
    "        return row['Campus']\n",
    "\n",
    "df['Campus_Agg'] = df.apply(agg_campus, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "819c3445",
   "metadata": {},
   "outputs": [],
   "source": [
    "# aggregating actuals based on campus\n",
    "fy_24_actuals = df.groupby('Campus_Agg')['Expenditures'].sum().reset_index()\n",
    "\n",
    "# finding percentage of expenditures for each campus\n",
    "fy_24_actuals['%'] = round(fy_24_actuals['Expenditures']/fy_24_actuals['Expenditures'].sum(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7cccd40",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expenditures for fiscal year 2024 was $19,529,428.44\n"
     ]
    }
   ],
   "source": [
    "print(\"The expenditures for fiscal year 2024 was\",f\"${fy_24_actuals['Expenditures'].sum():,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2e0d239c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data cleaning\n",
    "fy_24_actuals['%'] = round(fy_24_actuals['Expenditures']/fy_24_actuals['Expenditures'].sum(),2) #--> finding percentage\n",
    "\n",
    "fy_24_actuals['Expenditures'] = round(fy_24_actuals['Expenditures'],3).apply(lambda x: f'${x:,}') #--> adding comma separator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "929ac034",
   "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>Campus_Agg</th>\n",
       "      <th>Expenditures</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>$1,834,405.69</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>$1,397,904.33</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>$2,095,660.48</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500</td>\n",
       "      <td>$2,827,902.3</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000</td>\n",
       "      <td>$11,373,555.64</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Campus_Agg    Expenditures     %\n",
       "0        100   $1,834,405.69  0.09\n",
       "1        200   $1,397,904.33  0.07\n",
       "2        300   $2,095,660.48  0.11\n",
       "3        500    $2,827,902.3  0.14\n",
       "4        000  $11,373,555.64  0.58"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying data\n",
    "fy_24_actuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7afd4ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating Sankey Diagram\n",
    "\n",
    "Sankey(\n",
    "    flows = [19.52,-1.39,-1.83,-2.10,-2.82,-11.37], #--> data\n",
    "    labels = ['Total','(IS)','(PS)','(MS)','(HS)','(HO)'], #--> labels\n",
    "    scale = 0.03, #--> scaling factor for flow paths\n",
    "    orientations = [0,1,1,1,1,-1],#--> orientation of the flow arrows\n",
    "      ).finish()\n",
    "\n",
    "plt.title('FY 24 Campus Actuals (Millions)') #--> title\n",
    "plt.figure(figsize = (15,15))\n",
    "\n",
    "# displaying plot\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}