From e00579f97d0d186123f27236700753335860e251 Mon Sep 17 00:00:00 2001
From: LeonceNsh <nshutl0@sewanee.edu>
Date: Sat, 8 Jun 2024 03:38:44 -0500
Subject: [PATCH] add defense specific notebook

---
 app/__pycache__/app.cpython-311.pyc   | Bin 11845 -> 11769 bytes
 eda/explore_largest_contractors.ipynb | 101 ++++++++++++++++----------
 2 files changed, 61 insertions(+), 40 deletions(-)

diff --git a/app/__pycache__/app.cpython-311.pyc b/app/__pycache__/app.cpython-311.pyc
index 330d97c843963b8d555d6085b3da6e8be4fd0fc4..03586a25d132354cc103a979ecda134c82c9f73f 100644
GIT binary patch
delta 102
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zs)DXUaDHB8j7w^9c1eCgOipTIQC>`Gae984ZgPHJNl{{QNwIEGVtPzsK|#!Bdloh=
E04u&Ek^lez

delta 179
zcmXwuF%AJi6hI|HA)!=HqLWPiAt)(TG_&JlVs`$T|5s?1Q%EFwwKxL@;RvG8*+{(R
z<>h_Gw@K^VY&I$-HTT4O=h8Y=cke^lFk*}hKJX$J2LqQx6Spv=#CcSGvU!R@7%ZiQ
z$uf^c&r?1v2SXouDU|)Kv>G4<i;w-0)zDa#y5JzAC^PboAm=%)wWT1@SZTA4W)6ZI
P(L$P6{@2%6wch;#@3lfg

diff --git a/eda/explore_largest_contractors.ipynb b/eda/explore_largest_contractors.ipynb
index 0ed1355..e95b673 100644
--- a/eda/explore_largest_contractors.ipynb
+++ b/eda/explore_largest_contractors.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -221,7 +221,7 @@
        "[5 rows x 23 columns]"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,34 +245,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "office\n",
-       "Dla Land And Maritime                 1.977653e+13\n",
-       "Dla Aviation                          1.426716e+13\n",
-       "Navsup Weapon Systems Support Mech    9.680362e+12\n",
-       "Dla Troop Support                     5.925161e+12\n",
-       "Navsup Weapon Systems Support         3.755201e+12\n",
-       "Naval Research Laboratory             1.390334e+12\n",
-       "Dla Aviation At Philadelphia, Pa      9.095642e+11\n",
-       "Navair Warfare Ctr Aircraft Div       8.056140e+11\n",
-       "Dla Energy                            8.056140e+11\n",
-       "Dla Land Warren                       7.796265e+11\n",
-       "Naval Air Systems Command             7.796265e+11\n",
-       "Msc Norfolk                           5.847199e+11\n",
-       "Dla Maritime - Puget Sound            5.457385e+11\n",
-       "Nswc Crane                            5.327448e+11\n",
-       "W6Qm Micc-Ft Drum                     5.327448e+11\n",
-       "Name: award_amount, dtype: float64"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "KeyError",
+     "evalue": "'Column not found: award_amount'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[7], line 7\u001b[0m\n\u001b[0;32m      4\u001b[0m df_clean[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moffice\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df_clean[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moffice\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mtitle()\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Group by 'office' and calculate the sum of 'award_amount' for each group\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m df_clean_grouped \u001b[38;5;241m=\u001b[39m \u001b[43mdf_clean\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43moffice\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43maward_amount\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39msum()\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Sort the groups by 'award_amount' in descending order and get the top 15\u001b[39;00m\n\u001b[0;32m     10\u001b[0m df_sorted \u001b[38;5;241m=\u001b[39m df_clean_grouped\u001b[38;5;241m.\u001b[39msort_values(ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m15\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\LNshuti\\AppData\\Local\\miniconda3\\envs\\data-tourism\\Lib\\site-packages\\pandas\\core\\groupby\\generic.py:1951\u001b[0m, in \u001b[0;36mDataFrameGroupBy.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1944\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(key) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1945\u001b[0m     \u001b[38;5;66;03m# if len == 1, then it becomes a SeriesGroupBy and this is actually\u001b[39;00m\n\u001b[0;32m   1946\u001b[0m     \u001b[38;5;66;03m# valid syntax, so don't raise\u001b[39;00m\n\u001b[0;32m   1947\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1948\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot subset columns with a tuple with more than one element. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1949\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse a list instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1950\u001b[0m     )\n\u001b[1;32m-> 1951\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\LNshuti\\AppData\\Local\\miniconda3\\envs\\data-tourism\\Lib\\site-packages\\pandas\\core\\base.py:244\u001b[0m, in \u001b[0;36mSelectionMixin.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    242\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    243\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj:\n\u001b[1;32m--> 244\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumn not found: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    245\u001b[0m     ndim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj[key]\u001b[38;5;241m.\u001b[39mndim\n\u001b[0;32m    246\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gotitem(key, ndim\u001b[38;5;241m=\u001b[39mndim)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Column not found: award_amount'"
+     ]
     }
    ],
    "source": [
@@ -292,20 +279,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1800x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import squarify\n",
@@ -329,6 +305,51 @@
     "\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "# Loaded variable 'df' from URI: c:\\Users\\LNshuti\\OneDrive - Sony\\Desktop\\learn\\usgov-contracts-rag\\data\\sample_contract_df.parquet\n",
+    "df = pd.read_parquet(r'c:\\\\Users\\\\LNshuti\\\\OneDrive - Sony\\\\Desktop\\\\learn\\\\usgov-contracts-rag\\\\data\\\\sample_contract_df.parquet')\n",
+    "\n",
+    "df_clean = clean_data(df.copy())\n",
+    "df_clean.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_data(df):\n",
+    "    # List of columns to drop\n",
+    "    columns_to_drop = ['cgac', 'fpds_code', 'sub_tier', 'basetype', 'popstreetaddress', \n",
+    "                       'popstate', 'popzip', 'popcountry', 'awardnumber', 'awarddate', \n",
+    "                       'award', 'awardee', 'state', 'zipcode', 'countrycode']\n",
+    "\n",
+    "    # Drop specified columns\n",
+    "    df = df.drop(columns=columns_to_drop)\n",
+    "\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_clean = clean_data(df.copy())\n",
+    "#df = df[df['office'].str.title() == \"Usaid/Zambia\"]\n",
+    "df_clean.head()"
+   ]
   }
  ],
  "metadata": {