CBI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Ingestion step A\n",
    "# -------------------------------\n",
    "\n",
    "#add libraries required for final project\n",
    "\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import sqlite3\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from plotnine import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = './Tbl1_01_CS_Plate.csv'\n",
    "\n",
    "# open the file for reading and automatically close it when exiting the \"with\" block\n",
    "with open(file_path, 'r') as file:\n",
    "    \n",
    "    df_plate = pd.read_csv(file)\n",
    "        \n",
    "#print(df_plate.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################\n",
    "### Do Not Edit, Just Run   ####\n",
    "################################\n",
    "\n",
    "\n",
    "def dataframe2CreateTable(dataFrame, tableName = \"WHATS_MY_NAME\",useFirstColumnAsPK=True):\n",
    "    '''\n",
    "    This function inspects a Panda Dataframe and converts it to \n",
    "    a SQL Create Table Statement String\n",
    "    \n",
    "    Arguments:\n",
    "       dataFrame : a panda dataframe with column headers\n",
    "       tableName : a valid SQL table name\n",
    "       useFirstColumnAsPK : Use the first column as a primary key, default=True\n",
    "    \n",
    "    Returns : a Create Table tableName string\n",
    "    '''\n",
    "    createTableStmt = \"CREATE TABLE {} (\\n\".format(tableName)  # used the format to splice in the table name \n",
    "    dtype2SQL = {'object' : 'TEXT', 'float64' : 'REAL', 'int64' : \"INTEGER\"}\n",
    "    columnList = list(dataFrame)  # Replaced players from code with function variable\n",
    "    \n",
    "    for columnName in columnList:\n",
    "        # NOTE: Some of the columns start with a number, this is not valid column naming\n",
    "        # in most databases;  so the next four lines detect and fix\n",
    "        if (columnName[0].isdigit()):\n",
    "            sqlColumnName = \"n\"+columnName   # we will just prepend the letter 'n' (for number)\n",
    "        else:\n",
    "            sqlColumnName = columnName\n",
    "\n",
    "        pandaType = str(dataFrame[columnName].dtype) # Note, we need to force the conversion of the type name to a string\n",
    "        sqlDataTypeStr = dtype2SQL[pandaType]      # Then we look up the SQL type Desired\n",
    "        createTableStmt += \"\\t{} {},\\n\".format(sqlColumnName, sqlDataTypeStr)\n",
    "    # END OF FOR EACH COLUMN\n",
    "    \n",
    "    # Close off the Create Table Statement with the PK\n",
    "    if (useFirstColumnAsPK):\n",
    "        createTableStmt += \"\\tPRIMARY KEY({})\\n\".format(columnList[0])\n",
    "    else: # replace last comma with a space, note it's minus 2 because -1 is the newline\n",
    "                                          # This is the substring access \n",
    "                                          # see : https://docs.python.org/3/tutorial/introduction.html#strings\n",
    "        createTableStmt = createTableStmt[:len(createTableStmt) -2] + \"\\n\"\n",
    "    createTableStmt += \");\"\n",
    "    \n",
    "    return  createTableStmt\n",
    "# ------- END OF dataframe2CreateTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plates\n",
      "(1, 191167, 191167, 'Precontract', 'Plate - Carbon Steel (Non-sour)', '191167-MR-01302A', '191167 Outer Tank Bills Rev. C', '44.000 m Dia x 45.552 m HT Ethylene Tank (46.105k m3)', 1, 'MT', 1.0, 'Girder Plate3 ~ Horz', 16.0, 2128.0, 6936.0, 'A537 CL1', '191167-000-PS-SP-00003', 'Hyundai', 'USD', 1.0, '4/13/21', 'South Korea', 'South Korea', 'CFR', 'Saudi Arabia', 15.0, 16.0, 2128.0, 6936.0, 1.0, 1059.0, 76.0, 0.0, 120.0, 0, 0, 0, 0, 0.0, 0, 1.853831885, 1.853831885, 1059.0, 76.0, 0.0, 120.0, 1963.21, 140.89, 0.0, 222.46, 2021, 'Apr', 'Qtr2', 44.0, 45.552, 'Ethylene')\n",
      "(2, 191167, 191167, 'Precontract', 'Plate - Carbon Steel (Non-sour)', '191167-MR-01302A', '191167 Outer Tank Bills Rev. C', '44.000 m Dia x 45.552 m HT Ethylene Tank (46.105k m3)', 1, 'MT', 1.0, 'Special Girder2 ~ Vert', 16.0, 702.0, 7100.0, 'A537 CL1', '191167-000-PS-SP-00003', 'Hyundai', 'USD', 1.0, '4/13/21', 'South Korea', 'South Korea', 'CFR', 'Saudi Arabia', 15.0, 16.0, 1500.0, 7100.0, 1.0, 1079.0, 76.0, 0.0, 120.0, 0, 0, 0, 0, 0.0, 0, 0.62601552, 1.33764, 1079.0, 76.0, 0.0, 120.0, 1443.31, 101.66, 0.0, 160.52, 2021, 'Apr', 'Qtr2', 44.0, 45.552, 'Ethylene')\n",
      "(3, 191167, 191167, 'Precontract', 'Plate - Carbon Steel (Non-sour)', '191167-MR-01302A', '191167 Outer Tank Bills Rev. C', '44.000 m Dia x 45.552 m HT Ethylene Tank (46.105k m3)', 1, 'MT', 1.0, 'Special Girder2 ~ Horz', 16.0, 927.0, 6913.0, 'A537 CL1', '191167-000-PS-SP-00003', 'Hyundai', 'USD', 1.0, '4/13/21', 'South Korea', 'South Korea', 'CFR', 'Saudi Arabia', 15.0, 16.0, 1500.0, 6913.0, 1.0, 1079.0, 76.0, 0.0, 120.0, 0, 0, 0, 0, 0.0, 0, 0.804888886, 1.3024091999999998, 1079.0, 76.0, 0.0, 120.0, 1405.3, 98.98, 0.0, 156.29, 2021, 'Apr', 'Qtr2', 44.0, 45.552, 'Ethylene')\n",
      "(4, 191167, 191167, 'Precontract', 'Plate - Carbon Steel (Non-sour)', '191167-MR-01302A', '191167 Outer Tank Bills Rev. C', '44.000 m Dia x 45.552 m HT Ethylene Tank (46.105k m3)', 1, 'MT', 1.0, 'Rafter Clips', 10.0, 2910.0, 1464.0, 'A36-MOD', None, 'Hyundai', 'USD', 1.0, '4/13/21', 'South Korea', 'South Korea', 'CFR', 'Saudi Arabia', 15.0, 10.0, 2910.0, 3000.0, 1.0, 965.0, 76.0, 0.0, 120.0, 0, 0, 0, 0, 0.0, 0, 0.33442884, 0.685305, 965.0, 76.0, 0.0, 120.0, 661.32, 52.08, 0.0, 82.24, 2021, 'Apr', 'Qtr2', 44.0, 45.552, 'Ethylene')\n",
      "(5, 191167, 251428, 'Precontract', 'Plate - Carbon Steel (Non-sour)', '251428-MR-01302A', '251428 Sphere Plate List', '21.50 m Dia Sphere (Ethylene)', 3, 'MT', 2.0, 'TOP HEAD PLATES-7', 46.1, 3207.0, 9462.0, 'SA537 CL2', '251428-000-SP-SP-00101', 'Hyundai', 'USD', 1.0, '4/13/21', 'South Korea', 'South Korea', 'CFR', 'Saudi Arabia', 15.0, 46.1, 3207.0, 9462.0, 6.0, 1123.0, 76.0, 0.0, 120.0, 0, 0, 0, 0, 0.0, 0, 65.88760725, 197.66282180000002, 1123.0, 76.0, 0.0, 120.0, 221975.35, 15022.37, 0.0, 23719.54, 2021, 'Apr', 'Qtr2', 21.5, 0.0, 'Sphere')\n"
     ]
    }
   ],
   "source": [
    "### Ingestion step C\n",
    "# -------------------------------\n",
    "## Step below take the information from the CSV dataframe and convert it into a database file\n",
    "\n",
    "table_decl = dataframe2CreateTable(df_plate, 'plates', False)\n",
    "\n",
    "databaseFilename = './cs_plate_data.db'\n",
    "\n",
    "# This code removes the db file in case you re-run the cell\n",
    "if os.path.exists(databaseFilename):\n",
    "    os.remove(databaseFilename)\n",
    "    \n",
    "# Open / Create the database file.\n",
    "connection = sqlite3.connect(databaseFilename)\n",
    "\n",
    "# SQLite uses a cursor to track and manage group operations.\n",
    "cursor = connection.cursor()\n",
    "\n",
    "# Create table\n",
    "cursor.execute(table_decl)\n",
    "\n",
    "# Save (commit) the changes\n",
    "connection.commit()\n",
    "\n",
    "# Close the connection\n",
    "connection.close()\n",
    "\n",
    "connection = sqlite3.connect(databaseFilename)\n",
    "\n",
    "# Select the list of tables from the SQLite Engine Catalog for the database file\n",
    "cursor = connection.cursor()\n",
    "cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
    "tables = cursor.fetchall()\n",
    "\n",
    "# Iterate through all the rows back, where the first column is the table name\n",
    "for table_name in tables:\n",
    "    print(table_name[0])\n",
    "\n",
    "# Create a SQL string to insert the values\n",
    "ncol = len(df_plate.columns)\n",
    "\n",
    "# the question marks are placeholds that will be replace by the values\n",
    "cmd = 'INSERT INTO plates VALUES({})'.format(','.join('?'*ncol))\n",
    "\n",
    "for row in df_plate.itertuples(index=False, name=None):\n",
    "    #The question marks in cmd will be replaced with the values in row\n",
    "    # and the statement will be executed\n",
    "    cursor.execute(cmd,row)\n",
    "\n",
    "# Save (commit) the changes\n",
    "connection.commit()\n",
    "\n",
    "cur = connection.cursor()\n",
    "cur.execute(\"SELECT * FROM plates limit 5;\")\n",
    "\n",
    "rows = cur.fetchall()\n",
    "\n",
    "for row in rows:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>Project_number</th>\n",
       "      <th>Sub_CSS_Number</th>\n",
       "      <th>Contract_Type</th>\n",
       "      <th>Package_Description</th>\n",
       "      <th>MR_Number</th>\n",
       "      <th>MTO_Name</th>\n",
       "      <th>Tank_Design</th>\n",
       "      <th>Number_of_Tanks</th>\n",
       "      <th>UOM</th>\n",
       "      <th>Engg_Qty_Per_Tank_EA</th>\n",
       "      <th>...</th>\n",
       "      <th>BP_Line_USD</th>\n",
       "      <th>Fab_Line_USD</th>\n",
       "      <th>Freight_Line_USD</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Quarters</th>\n",
       "      <th>Tank_Dia_m</th>\n",
       "      <th>Tank_Ht_m</th>\n",
       "      <th>Type_of_Tank</th>\n",
       "      <th>Vendor_Ton_per_Tank_MT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>191167</td>\n",
       "      <td>191167</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>Plate - Carbon Steel (Non-sour)</td>\n",
       "      <td>191167-MR-01302A</td>\n",
       "      <td>191167 Outer Tank Bills Rev. C</td>\n",
       "      <td>44.000 m Dia x 45.552 m HT Ethylene Tank (46.1...</td>\n",
       "      <td>1</td>\n",
       "      <td>MT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>140.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>222.46</td>\n",
       "      <td>2021</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Qtr2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.552</td>\n",
       "      <td>Ethylene</td>\n",
       "      <td>1.853832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191167</td>\n",
       "      <td>191167</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>Plate - Carbon Steel (Non-sour)</td>\n",
       "      <td>191167-MR-01302A</td>\n",
       "      <td>191167 Outer Tank Bills Rev. C</td>\n",
       "      <td>44.000 m Dia x 45.552 m HT Ethylene Tank (46.1...</td>\n",
       "      <td>1</td>\n",
       "      <td>MT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>101.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.52</td>\n",
       "      <td>2021</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Qtr2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.552</td>\n",
       "      <td>Ethylene</td>\n",
       "      <td>1.337640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191167</td>\n",
       "      <td>191167</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>Plate - Carbon Steel (Non-sour)</td>\n",
       "      <td>191167-MR-01302A</td>\n",
       "      <td>191167 Outer Tank Bills Rev. C</td>\n",
       "      <td>44.000 m Dia x 45.552 m HT Ethylene Tank (46.1...</td>\n",
       "      <td>1</td>\n",
       "      <td>MT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>98.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.29</td>\n",
       "      <td>2021</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Qtr2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.552</td>\n",
       "      <td>Ethylene</td>\n",
       "      <td>1.302409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191167</td>\n",
       "      <td>191167</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>Plate - Carbon Steel (Non-sour)</td>\n",
       "      <td>191167-MR-01302A</td>\n",
       "      <td>191167 Outer Tank Bills Rev. C</td>\n",
       "      <td>44.000 m Dia x 45.552 m HT Ethylene Tank (46.1...</td>\n",
       "      <td>1</td>\n",
       "      <td>MT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>52.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.24</td>\n",
       "      <td>2021</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Qtr2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.552</td>\n",
       "      <td>Ethylene</td>\n",
       "      <td>0.685305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>191167</td>\n",
       "      <td>251428</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>Plate - Carbon Steel (Non-sour)</td>\n",
       "      <td>251428-MR-01302A</td>\n",
       "      <td>251428 Sphere Plate List</td>\n",
       "      <td>21.50 m Dia Sphere (Ethylene)</td>\n",
       "      <td>3</td>\n",
       "      <td>MT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15022.37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23719.54</td>\n",
       "      <td>2021</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Qtr2</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>65.887607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Project_number  Sub_CSS_Number Contract_Type  \\\n",
       "0          191167          191167   Precontract   \n",
       "1          191167          191167   Precontract   \n",
       "2          191167          191167   Precontract   \n",
       "3          191167          191167   Precontract   \n",
       "4          191167          251428   Precontract   \n",
       "\n",
       "               Package_Description         MR_Number  \\\n",
       "0  Plate - Carbon Steel (Non-sour)  191167-MR-01302A   \n",
       "1  Plate - Carbon Steel (Non-sour)  191167-MR-01302A   \n",
       "2  Plate - Carbon Steel (Non-sour)  191167-MR-01302A   \n",
       "3  Plate - Carbon Steel (Non-sour)  191167-MR-01302A   \n",
       "4  Plate - Carbon Steel (Non-sour)  251428-MR-01302A   \n",
       "\n",
       "                         MTO_Name  \\\n",
       "0  191167 Outer Tank Bills Rev. C   \n",
       "1  191167 Outer Tank Bills Rev. C   \n",
       "2  191167 Outer Tank Bills Rev. C   \n",
       "3  191167 Outer Tank Bills Rev. C   \n",
       "4        251428 Sphere Plate List   \n",
       "\n",
       "                                         Tank_Design  Number_of_Tanks UOM  \\\n",
       "0  44.000 m Dia x 45.552 m HT Ethylene Tank (46.1...                1  MT   \n",
       "1  44.000 m Dia x 45.552 m HT Ethylene Tank (46.1...                1  MT   \n",
       "2  44.000 m Dia x 45.552 m HT Ethylene Tank (46.1...                1  MT   \n",
       "3  44.000 m Dia x 45.552 m HT Ethylene Tank (46.1...                1  MT   \n",
       "4                      21.50 m Dia Sphere (Ethylene)                3  MT   \n",
       "\n",
       "   Engg_Qty_Per_Tank_EA  ... BP_Line_USD  Fab_Line_USD  Freight_Line_USD  \\\n",
       "0                   1.0  ...      140.89           0.0            222.46   \n",
       "1                   1.0  ...      101.66           0.0            160.52   \n",
       "2                   1.0  ...       98.98           0.0            156.29   \n",
       "3                   1.0  ...       52.08           0.0             82.24   \n",
       "4                   2.0  ...    15022.37           0.0          23719.54   \n",
       "\n",
       "   Year Month Quarters Tank_Dia_m Tank_Ht_m  Type_of_Tank  \\\n",
       "0  2021   Apr     Qtr2       44.0    45.552      Ethylene   \n",
       "1  2021   Apr     Qtr2       44.0    45.552      Ethylene   \n",
       "2  2021   Apr     Qtr2       44.0    45.552      Ethylene   \n",
       "3  2021   Apr     Qtr2       44.0    45.552      Ethylene   \n",
       "4  2021   Apr     Qtr2       21.5     0.000        Sphere   \n",
       "\n",
       "  Vendor_Ton_per_Tank_MT  \n",
       "0               1.853832  \n",
       "1               1.337640  \n",
       "2               1.302409  \n",
       "3               0.685305  \n",
       "4              65.887607  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pull in database from Task 1 and convert to a DataFrame for use in the analysis\n",
    "\n",
    "connection2 = sqlite3.connect('cs_plate_data.db')\n",
    "\n",
    "df_plate = pd.read_sql_query(\"SELECT * FROM plates\", connection2)\n",
    "\n",
    "# there is a column named 'id' and 'date' we want to delete from the dataframe\n",
    "del df_plate['ID']\n",
    "\n",
    "df_plate['Vendor_Ton_per_Tank_MT'] = df_plate['Vendor_Line_Tonnage_MT'] / df_plate['Number_of_Tanks']\n",
    "\n",
    "df_plate.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Project_number</th>\n",
       "      <th>Sub_CSS_Number</th>\n",
       "      <th>Contract_Type</th>\n",
       "      <th>Material_Grade</th>\n",
       "      <th>Vendor</th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarters</th>\n",
       "      <th>Point_of_Manufacture</th>\n",
       "      <th>Vendor_Line_Tonnage_MT</th>\n",
       "      <th>Matl_Line_USD</th>\n",
       "      <th>BP_Line_USD</th>\n",
       "      <th>Fab_Line_USD</th>\n",
       "      <th>Freight_Line_USD</th>\n",
       "      <th>Matl_USD/MT</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>179308</td>\n",
       "      <td>179308</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>A36</td>\n",
       "      <td>Plate Solutions</td>\n",
       "      <td>2017</td>\n",
       "      <td>Qtr1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2,038.79</td>\n",
       "      <td>1,618,248.30</td>\n",
       "      <td>308,891.00</td>\n",
       "      <td>467,051.55</td>\n",
       "      <td>116,550.00</td>\n",
       "      <td>793.73</td>\n",
       "      <td>2017 Qtr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>182160</td>\n",
       "      <td>182160</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>A516-70N</td>\n",
       "      <td>Posco</td>\n",
       "      <td>2016</td>\n",
       "      <td>Qtr4</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>474.07</td>\n",
       "      <td>333,241.87</td>\n",
       "      <td>37,694.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30,814.49</td>\n",
       "      <td>702.94</td>\n",
       "      <td>2016 Qtr4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>182228</td>\n",
       "      <td>182228</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>A36</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>2019</td>\n",
       "      <td>Qtr2</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>5,211.49</td>\n",
       "      <td>3,581,276.30</td>\n",
       "      <td>113,354.27</td>\n",
       "      <td>420,435.31</td>\n",
       "      <td>792,474.97</td>\n",
       "      <td>687.19</td>\n",
       "      <td>2019 Qtr2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>182228</td>\n",
       "      <td>182228</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>A537 CL1</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>2019</td>\n",
       "      <td>Qtr2</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>21.93</td>\n",
       "      <td>27,412.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,052.63</td>\n",
       "      <td>1,250.00</td>\n",
       "      <td>2019 Qtr2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>182228</td>\n",
       "      <td>182228</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>A572-50</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>2019</td>\n",
       "      <td>Qtr2</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>84.66</td>\n",
       "      <td>59,398.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,910.43</td>\n",
       "      <td>701.59</td>\n",
       "      <td>2019 Qtr2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Project_number  Sub_CSS_Number Contract_Type Material_Grade  \\\n",
       "0          179308          179308   Precontract            A36   \n",
       "1          182160          182160   Precontract       A516-70N   \n",
       "2          182228          182228   Precontract            A36   \n",
       "3          182228          182228   Precontract       A537 CL1   \n",
       "4          182228          182228   Precontract        A572-50   \n",
       "\n",
       "            Vendor  Year Quarters Point_of_Manufacture  \\\n",
       "0  Plate Solutions  2017     Qtr1         South Africa   \n",
       "1            Posco  2016     Qtr4          South Korea   \n",
       "2          Hyundai  2019     Qtr2          South Korea   \n",
       "3          Hyundai  2019     Qtr2          South Korea   \n",
       "4          Hyundai  2019     Qtr2          South Korea   \n",
       "\n",
       "   Vendor_Line_Tonnage_MT  Matl_Line_USD  BP_Line_USD  Fab_Line_USD  \\\n",
       "0                2,038.79   1,618,248.30   308,891.00    467,051.55   \n",
       "1                  474.07     333,241.87    37,694.30          0.00   \n",
       "2                5,211.49   3,581,276.30   113,354.27    420,435.31   \n",
       "3                   21.93      27,412.20         0.00          0.00   \n",
       "4                   84.66      59,398.21         0.00          0.00   \n",
       "\n",
       "   Freight_Line_USD  Matl_USD/MT     period  \n",
       "0        116,550.00       793.73  2017 Qtr1  \n",
       "1         30,814.49       702.94  2016 Qtr4  \n",
       "2        792,474.97       687.19  2019 Qtr2  \n",
       "3          1,052.63     1,250.00  2019 Qtr2  \n",
       "4          4,910.43       701.59  2019 Qtr2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pricing = df_plate.groupby(['Project_number','Sub_CSS_Number','Contract_Type','Material_Grade','Vendor',\n",
    "                               'Year','Quarters','Point_of_Manufacture'])['Vendor_Line_Tonnage_MT','Matl_Line_USD',\n",
    "                                'BP_Line_USD', 'Fab_Line_USD','Freight_Line_USD'].sum()\n",
    "\n",
    "df_pricing = df_pricing.reset_index() \n",
    "\n",
    "df_pricing['Matl_USD/MT'] = df_pricing['Matl_Line_USD']/df_pricing['Vendor_Line_Tonnage_MT']\n",
    "df_pricing['period'] = df_pricing['Year'].astype(str) + \" \" + df_pricing['Quarters']\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "\n",
    "df_pricing.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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22H9N8f2/Fa/f4P1qKouXdurH90SSvquTYu3PImIhcCNwHfAvE+4rOySZ+dWI+BxwK+3Lql+nPSL2BeDqiPhAse0TxY98AvjLiLgX2EJ7koM0Y17aqR/fE0maWidLd7y6WK7jFcDrgcsi4gHahdt1h7ruWma+H3j/AZvvA146yb7fAd54KDmSJEn9rNOlO75DUZwBRMRzgbNpL6FxUmY+o8CSpF5ptVrk9q2lPSYqR7fS8jG3knqko2INICIWAGcU3347Mz8GfCwijupKzyRJktTR0h1HA/8f8DrgP2g/E/Q5EfF54L9m5u7udlGSZmZoaIjRIyn1Qe5Di4em31GSumBOB/tcAhwJPDszfzgzXwQspV3o/Y9udk6SJGm26+Qy6BuAl2bmzn0bMnNHRPwycBMWbJI6VOUjmnJ027T3rOW2JwCI+cdNvd/oNnBkTVKPdFKsjU8s1PbJzCciwrXOJHWs/YimO2DR1MURtO/mv23zNJPNNz8x6eZly5Z11p/t7aJw2XSF2OKhZ7TZnsSwrbQnD+ToFlp79pbSlqRm6aRYy+LxUDHJa+Ml90dS0y06jrnnvriUpsaunfxpd50+5cD13CT1g06Ktfm0H7I+WbHmyJqkWak9ieGIUp8NOrR4SSltSWqWThbFPa2CfkhSV012v9zB7o3zGaSS6qSTpTueA2zNzG3F9z9FexmP+4E/dekOSf1qYGCg112QpGl1chn0GtqPmdoWES8CPgt8EHgR8DHgF7vXPUkqhyNlzdeN2cbgSKt6r5NibSAzW8XXPw/8eWZeHhFzgG90r2uSpCabrLhqtdr/3AwN7T9Dt5OCaWRkhG/ddQ/HLVo65X57aD94Z8Pmp6bt4xPTzUiWKtBJsTZxYsGZwPsAMnM8YrI5B5I0uVarBdt3HHQW54xt3kFrd2v6/fpUe3mQ7Tz1d58vrc0c3UxrT33vXtm1a9dh/fxxi5bygnPfV1Jv4LZrP1haW9Kh6qRYuyEirgEeAU4EbgCIiJOB+p7xkqTa6PQS5cGMjIw4EUSzVifF2q8BPwecDPx4Zu4ptp9E+1FUkrpoJv/IlbHyfzcNDQ2x+aixUtdZG1rU3CcLtJcHOYqjX/v60tp86u8+z9DiRaW116n2gsjf5ojBU6bcbzzb/yx9a9OTU+63d/Th0vom1V0nS3ckcPUk27/elR5J2s/IyAjr77qNYwan33d3sfLhvZtum3K/74yW0DGRo1umfYJBbtsBQMw/ftq2aPg6a0cMnsJx57yzlLaeWPOnpbQj9YNOlu7Ywf6L3yawGfgn4D2Z6V/7UpcdMwjPO2dOae3dt8aHjxyuyR5p1Wq1nnHP1a7vfAeAgQPu8R0YGNj/JvrFSzp+TFY/arVa7N3+RGlF1t7Rh2ntme6xZVIzdDKy9oz/DhaPn3oH8HHgjeV3S1IdlD1br0kmO1b/vCR1Qyf3rD1DZj4OfDgi3lZyfyTV3OHO1muyfi2+urE+2YHF6NDQEI8f+WSpl0GHFh9bSltS3R1SsQYQEUcezs9Lqr/Jig8fft487Zv/72bO4NT3zI1n+1L8+k1bp95vdGNpfZPU2T1rb5hk84m0Z4h+rvQeSZIqN2dwCQPn/Hwpbe1a81eltCOprZORsdce8H0Co8BHMvML5XdJkiRJ+3QyweD8ThqKiPdlpks9S5Iklai8tQCcFSpJklS6Mos1HxQqSZJUsjKLtZx+F0mSJM2EI2uSJEk1VuY6aZ8tsS1JEpCjm3nq7z4/9T7btgEQ8+d31B49eJC7pEPXyTprf8wUlzgz813F50tL7JckzXqdPit0ZHu7WFvWSRG2eFGjn0EqNVEnI2s3d70XkqRn6PTxVT5VQmq2TtZZuxIgIt6Ymftd6owIl+uQJEnqopncs/Y+nnlf2mTbphURzwc+M2HT84D/CVxVbD8NuB94U2Y+HhEBfAR4NbATeEdm3jrTXEkHV8XDvCVJM9fJPWtn0y6STomIj0546QRg7FBCM/Nu4EVF+0cADwOfB94LrMvMD0XEe4vv3wOcDZxRfLwMWFV8llSS9sO818Oio6bZcw8At23+9tS7bd5dTsckaZbrZGStRfu+tXOAWyZs3wH8egl9WAGMZOaGiDgXeEWx/UrgRtrF2rnAVZmZwE0RsSAiTs7MR0rIl7TPoqM44vVDpTS19/OtUtpR97VaLca37yjtAezjoxtp7dlZSluSOrtn7ZvANyPiU5m5pwt9eDPw6eLrJRMKsEeBJcXXpwAPTviZh4ptFmuSJKnRZnLP2mkR8UHg+4Fj9m3MzOcdanhEHEV7xO59B76WmRkRM3oqQkRcCFwIsHTp0kPtlqRu2vwEY9dOc8vptmJUZv68advCJcMO29DQEFuO3MrAOT9fSnu71vwVQ4sXlNKWpJkVa58E3g98GPgp4HwO/wkIZwO3ZubG4vuN+y5vRsTJwGPF9oeBZ0/4uVOLbfvJzCuAKwCWL1+e0L5peu3atfvtt3PnTtpXVKcXEcybt/8/GCtXrvSmaekQdLxu2Lb2JIZli6b5T9eiztuUpH41k2JtIDPXRURk5gbgdyLiFtqzOA/VW/juJVCANcB5wIeKz9dO2P4rEXE17YkF27xfTeo/rhsmSTM3k2LtqYiYA9wTEb9Ce2TruEMNjohjgZ8GfmnC5g8B10TEBcAG4E3F9i/SnpF6L+2lO87vNOeiiy5yFGyWqmIpCpe7kCR120yKtV8F5gHvAn4POJP26NchycwngcEDto3Snh164L4JvPNQszQ7jYyMcOedt3HiiVPvNz7e/vzoo7dNud/jj0+eccedt3HCwqkzxoqr7g9tnDpj+5ap25EkzT4dF2uZ+bXiyyeYwciW1Esnngg//cpy2vrSP06+/YSF8LKzo5SMr/7DjObUSJJmgU4WxV0z1euZeU553ZEkSdJEnYys/QjtNc4+DXwVKGcIQZIkSdPqpFg7ifZEgLcAbwW+AHw6M7/VzY5Jkppl7+jDPLHmT6fcZ3zbZgDmzJ96Ab29ow/D4u8prW9SnXXyBIO9wHXAdRFxNO2i7caI+F+Z+Sfd7qCkarRaLdj+VHmPidr8FK3dPnKqbJPNQD7YbOM6zSzueI297Y+291987NQ7Lv4e19jTrNHRBIOiSPsZ2oXaacBHaT94XZLUYwMDA73uwrRcY086dJ1MMLgK+EHaa539r8xc3/VeSarc0NAQm496otQHuQ8tKqctfVddRsokVaeTkbWfB56kvc7auyKenl8QtJdAO6FLfZMkSZr1Orln7XCf/ympX2zePf09a9v2tD/PP3LatnzIuqrUarV4YvuT3HbtB0tr84nND9DaPc39c1KXzeQJBpJ6oNVq8Z3tcN+a8dLa/M4otPbsX5TN/CHr0+zvQ9alUk02uaTVap/HQ0P733JQp8klOnwWa5IAbwBX/xsaGmLPUU/xgnPfV1qbt137QYYWHV1ae506WGG2a9eu/bbt+/7A7a1W6xk/bwHXvyzWpJobGhpi55Gbed455d2RcN+acYYWT3/zf78uE6GZGx/dyK41fzX1PtvaD8idM3/qB+6Oj26ExQtK69tsNDIywt133suShc95etu8IxYz77j993s820udnHjcSc9oY+vGPU9/vXHLhu50VJWwWJM0I/2wTIRmpvM10Ebb+09XiC1e4CXwEixZ+BzevvK/l9LWVWs/UEo76g2LNe2n1WqxY2vyyRv3TL9zBx7dmjyJC6P2K0fKZgcvgUv15kxPSZKkGnNkTfsZGhpiG6Oc/4pplmXo0Cdv3MP8IRdG1ew1OjrKpZdeyiWXXMLChQt73R31iVarxY5tO0u7fLlxywZ27p1XSluqniNrktRFq1evZv369axevbrXXZHUpxxZk9RTTZ5xOjo6ytq1a8lMrr/+eoaHhx1dU0eGhobYesSeUicYLFhSzhUTVc+RNUm1MzAw0IhZp6tXr2Z8vL2Y8fj4uKNrkg6JI2uSeqqfRspm6oYbbmBsbAyAsbEx1q1bx8UXX9zjXknqN46sSVKXnHnmmcyd2/4/8dy5c1mxYkWPeySpHzmyJkldMjw8zNq1awGYM2cOw8PDPe5R8z2x+YFpH+S+a9tjAAzMf1ZH7bHojFL6Jh0qizVJ6pLBwUFWrlzJF77wBc466ywnF3RZx09i2LYbgOd08szPRWf4NAb1nMWaJHXR8PAwGzZscFStApPd/zjZbOOp9NuMY80OFmuS1EWDg4Ncfvnlve6GJmjCTGPNLhZrkqSeqGKNPUfJ1AQWa5Kk2nDUS3omizXpMLRaLbZvg6/+Q5bS3vYt0NrbKqUtqe4c9ZI64zprkiRJNebImnQYhoaGGD9iMy87O0pp76v/kAwtGSqlLUmqq8nuV2y12lcVhob2/zvQGboWa1Jf+M4o3LdmfNr9dm9rfz5q/vTtsfjw+yWpezZu2cBVaz/w9PeP73iU3Xue6uhnjzryaE48/qT92lqw5PTS+1imXbt29boLtWWxJtXcTBbkHNne/p/qssXT/MzimbUrqVqTnZ879x7B+K7ORvGPHjiCBUuOfPr7BUtOr9U5P9lI2b4ZwJdddlnV3am9nhVrEbEA+DPgB4EEfgG4G/gMcBpwP/CmzHw8IgL4CPBqYCfwjsy8tQfdlio3k+F//7KTmmG2X/bT/no5weAjwHWZ+b3AC4E7gfcC6zLzDGBd8T3A2cAZxceFwKrquytJklS9nhRrETEf+M/AJwAyc3dmbgXOBa4sdrsSeF3x9bnAVdl2E7AgIk6uuNuSJEmV69XI2nOBTcAnI+LrEfFnEXEssCQzHyn2eRRYUnx9CvDghJ9/qNi2n4i4MCJujoibN23a1MXuS5IkVaNX96zNBV4MXJyZX42Ij/DdS54AZGZGxIxWGs3MK4ArAJYvX17OKqWSJOmQTbZMx2QO9qixycy25Tx6Vaw9BDyUmV8tvv8c7WJtY0ScnJmPFJc5Hytefxh49oSfP7XYJkmSamxkZIR777iHpcefOuV+R421Z6/ufnDqJTwe2PFQaX3rFz0p1jLz0Yh4MCKen5l3AyuAO4qP84APFZ+vLX5kDfArEXE18DJg24TLpZIk9ZSLvE5t6fGn8tv/6TdLaevSr11eSjv9pJfrrF0MrI6Io4D7gPNp30N3TURcAGwA3lTs+0Xay3bcS3vpjvOr764kSZ1zkVeVpWfFWmZ+A1g+yUsrJtk3gXd2vVOSJB0CF3lVN/kEAzVWq9Vi2zb40j+W097jj8P4eKucxiRJ6pDFmiRJ6ppWq8WTO54s7V6zDTse4tjWsaW01S8s1tRYQ0NDzJmzmZ9+ZTntfekf4aSThqbfUWqAyW6YP9jSCrPxhnmpShZrkqSODAwM9LoL6kNDQ0Ps3rur1NmgRw3Nrt9FizVJ0jM4UnZwLvKqqlmsSZI0AyMjI9x1170sHHzOlPtlHgXAY5v2TLnfltENpfVNzWSxJknSDC0cfA6vOed/lNLW36/5vVLaUXNZrEmSpK56YMdD084G3bhzEwBL5i2etq3TOaO0vvUDizU12uOPT7/O2o4d7c/HHz99WyedVE6/JGm2WLZsWUf77R5pXy4+6tlTTx44nTM6brMpLNbUWJ2ezE8+2b4J+KSTpt7/pJM6b1OS1NbpxAmf+HBwFmtqrKb/BeE6WFJvtFottm3fWdq9ZqOjGxjbM6+UttRMFmtSg7gOliQ1j8Van5tsdKXVaj+/cmho/9X2HV3pju1b4Kv/kFPu82RxX9yx09wXt30LsKSzXN9LqTeGhoaYe+SeUmeDPmvxkaW0pWayWGugXbt29boLs0an97CNPNEuqE9dMs3+S7wvTpK0P4u1PjfZ6Eq/3oPVj5p+X5wkdYP33M6MxZokSeo577k9OIs1SZJUqdk+UjZTFmuqhZlMlIBDHxZ36F1Sk4yOjnLppZdyySWXsHDhwl53R10yp9cdkA5m165dlUyWGBgYcPhdUl9avXo169evZ/Xq1b3uirrIkTXVQlUTJRwpk9QUo6OjrF27lszk+uuvZ3h42NG1hnJkTZKkPrR69WrGx8cBGB8fd3StwRxZ6xIXq5UkddMNN9zA2NgYAGNjY6xbt46LL764x71SNziyVqGq7sGSJDXfmWeeydy57TGXuXPnsmLFih73SN3iyGjUPhAAABgvSURBVFqXdOMerMlG6yZzsNmNk3FUT5Jmbsvohmkf5L5926MAnDD/pGnbetbi02fch+HhYdauXQvAnDlzGB4ennEb6g8Wa31kZGSEe+64jVPnTz0geuTe9j0Mux5eP+V+D20bL61vkjRbdPpIuB3bdwNM+9zPZy0+/ZAeMzc4OMjKlSv5whe+wFlnneXkggazWOszp86fw2/86NGltPWH//pUKe3MVDdGCKE+o4Su5SY1W50eMzc8PMyGDRscVWs4izVVbmRkhLvuvI3FC6bZsRj4G33ktmnb3LT18PvVTa7jJqkbBgcHufzyy3vdDXWZxZp6YvEC+C8rjiitvb9et7e0tg6XI2WSpDJZrPWRVqvFk9vGS7t8+dC2cY6NViltSZKk7nDpDkmSpBpzZK2PDA0Ncc/Wzftt2/TkOE+NdfbzR8+FxcfuX59P9pB0SZJUHz0r1iLifmAHsBcYy8zlEbEQ+AxwGnA/8KbMfDwiAvgI8GpgJ/COzLy1F/3upcmmds9ttdjT4UK7cwcGGJhQnJ1xSudT0CVJUm/0emTtpzJz4lDRe4F1mfmhiHhv8f17gLOBM4qPlwGris+zijeuS5I0+/S6WDvQucAriq+vBG6kXaydC1yVmQncFBELIuLkzHykJ73UYWm1WmzfVu4Mzk1b4al0soQkqXl6OcEggbURcUtEXFhsWzKhAHsUWFJ8fQrw4ISffajYJkmS1Gi9HFn78cx8OCKeBXwpIu6a+GJmZkTkTBosir4LAZYuXVpeT1WqoaEhjo7Npa+zNniykyUkSc3Ts2ItMx8uPj8WEZ8HXgps3Hd5MyJOBh4rdn8YePaEHz+12HZgm1cAVwAsX758RoWeJEmHysfMqZt6chk0Io6NiOP3fQ2sBNYDa4Dzit3OA64tvl4DvD3aXg5s8341SVKdDQwM+Kg5laJXI2tLgM+3V+RgLvCpzLwuIr4GXBMRFwAbgDcV+3+R9rId99JeuuP86rssSdLkHClTN/WkWMvM+4AXTrJ9FFgxyfYE3llB1w7JZMPfkznYkPjBOFQuSZLqtnRHXxoZGeHeO+9g6fzjptzvqL17ANjdemDaNh/Y9kQpfZMkSf3NYq0kS+cfxyU/8eLS2vv9f551D2iQJEmT8EHukiRJNebImp7h0W3JJ2/cM+U+W55or4yy8LiYtq35Ln8mSdIhs1jTfjp9sPvmYrLE/KGp958/5MPiJUk6HBZr2k+ns0/3zWi97LLLutkdSZJmPe9ZkyRJqjGLNUmSpBqzWJMkSaoxizVJkqQac4JBCVqtFk9u3VHqQrYbtu7gWFqltSdJkvqTI2uSJEk15shaCYaGhtjNWOmPmzpqqLmryW7aCn+9bu+U+2wtHo+6YOpHrj7d3uDJJXRMkqSasVhT5TpdJHdrsfDu4MnT7z94sovvSpKayWJNlXPhXUmSOuc9a5IkSTVmsSZJklRjFmuSJEk1ZrEmSZJUYxZrkiRJNeZs0AYaHR3l0ksv5ZJLLmHhwoWH3d6qVasYKZbR2Gff9/tmbO6zbNmyjmd7SpKk6VmsleSBbU9M+7ipjU/sBGDJcfM6au/0Q1wTd/Xq1axfv57Vq1dz8cUXH1oj0xgYGOhKu5IkaX8WayXodDHW3cVo1FFDS6fd9/ShQ1vkdXR0lLVr15KZXH/99QwPDx/26JojZZIk9Y7FWgnqtMjr6tWrGR8fB2B8fLyro2uSJKn7nGDQMDfccANjY2MAjI2NsW7duh73SJIkHQ6LtYY588wzmTu3PWA6d+5cVqxY0eMeSZKkw2Gx1jDDw8PMmdN+W+fMmcPw8HCPeyRJkg6HxVrDDA4OsnLlSiKCs846q5SlOyRJUu84waCBhoeH2bBhg6NqkiQ1gMVaAw0ODnL55Zf3uhuSJKkEXgaVJEmqsZ6OrEXEEcDNwMOZ+ZqIeC5wNTAI3AK8LTN3R8TRwFXAS4BR4Ocy8/4edVtdMJNHWoGPtZIkzR69Hln7VeDOCd//AfDhzDwdeBy4oNh+AfB4sf3DxX5quIGBAR9rJUma9Xo2shYRpwI/A/w+8BsREcCZwFuLXa4EfgdYBZxbfA3wOeBPIiIyM6vss7rHUTJJkibXy5G1PwJ+Cxgvvh8EtmbmWPH9Q8ApxdenAA8CFK9vK/aXJElqtJ4UaxHxGuCxzLyl5HYvjIibI+LmTZs2ldm0JElST/RqZO3HgHMi4n7aEwrOBD4CLIiIfZdmTwUeLr5+GHg2QPH6fNoTDfaTmVdk5vLMXL548eLuHoEkSVIFelKsZeb7MvPUzDwNeDNwQ2YOA/8E/Gyx23nAtcXXa4rvKV6/wfvVJEnSbNDr2aAHeg/tyQb30r4n7RPF9k8Ag8X23wDe26P+SZIkVarnTzDIzBuBG4uv7wNeOsk+3wHeWGnHJEmSaqDnxVpTzWSRVxd4lSRJB2OxViEXeJUkSTNlsdYljpRJkqQy1G2CgSRJkiawWJMkSaoxizVJkqQas1iTJEmqMYs1SZKkGrNYkyRJqjGLNUmSpBqzWJMkSaoxizVJkqQas1iTJEmqMYs1SZKkGrNYkyRJqjGLNUmSpBqLzOx1H7oiIjYBG2b4Y4uAzV3oTtUZVeU0JaOqHI+lfhlV5TQlo6qcpmRUleOx1C/jUHKek5mLJ3uhscXaoYiImzNzeb9nVJXTlIyqcjyW+mVUldOUjKpympJRVY7HUr+MsnO8DCpJklRjFmuSJEk1ZrG2vysaklFVTlMyqsrxWOqXUVVOUzKqymlKRlU5Hkv9MkrN8Z41SZKkGnNkTZIkqcYs1iRJkmrMYk2SJKnGLNYOEBH/s4KM8m46jDgiIn4pIn4vIn7sgNf+e0kZ8yLityLiv0XEMRHxjohYExH/OyKOKyPjILnf7lbbB8kr5X2p4j0p2mr8+1L2+RgRZ0XEBRFx2gHbf6Gk9iMi3hQRbyy+XhERH42IX46Irv19GxE3dKvtg+T1zbnSq/OkyPZcOXj7XT9XmvR3sRMMDhARD2Tm0hLaWXiwl4BvZuaph5tR5PwZMA/4d+BtwJcz8zeK127NzBeXkHEN8CAwADwfuBP4DHAOcFJmvq2EjB3Avl/GKD7PA3YCmZknHG5GkdP196WK96RoqzHvyxT5pZyPRVuXAj8O3Aq8FvijzPzj4rWyzpWPAc8CjgK2A0cDa4CfATZm5q+WkHHbgZuA7wHuBsjMFxxuRpHTiHOlivOkyPFcmVlGFedKc/4uno3FWkRsP9hLwEBmzi0hYy/tx13FhM1ZfH9KZh51uBlFzm37/nKOiLnAx2g/4uItwE2Z+cMlZHwjM18UEQE8ApycmVl8/80y/nGIiI8CC4D/lpkbi23/kZnPPdy2D8jp+vtSxXtStN2I96WK87HIuR344cwci4gFwKeAuzPz1yPi6yWdK7dn5g9FxJHAo7Tfk93F78GtJb0na2j/4/YBYBftP6d/pv2PK5k508fsHSynEedKFedJkeO5MsOMCs6VxvxdPFsvg24FzsjMEw74OJ72H3QZ7gNekZnPnfDxvOLE3VhSBrT/VwJAZo5l5oXAN4AbgFKH+LNd2X+x+Lzv+1Kq/cx8F/AR4NMR8a5iGLwb/5Oo4n2p7D0pMvr9fanifASYm5ljAJm5lfaIwQkR8VkmvGeHaV/7e4CvZebu4vsxYLyMgMw8B/hr2ms4vTAz7wf2ZOaGsgq1QqPOlW6eJ0V7nisz0/VzhQb9XTxbi7WrgOcc5LVPlZTxR8CJB3ntf5eUAXBzRLxq4obM/F3gk8BpJWYcV7T99P0KEbEM2FFSBpl5C/DK4tsvA8eU1fYEVbwvVbwn+3Ka8L5UcT4CjETET+77JjP3ZuYFtC8ffl9JGY9OeE+e/h2IiJOA3SVlkJmfB84GXhER11LeP6ATNeVcqeQ8Kdr3XOlcFedKY/4unpWXQaF9cyNwamY+2MWMOcDLM/Nfu5VRVc7BMiIisqRfookZEXEy7WH4L5bR9sFyym67yoypcvrtfanofBwovlx0YE5EnJKZD5eU84xjiYhjgWMz87GyMyLihcCPZObHy2j7gJxGnCtVnCcH5niudJRTxbnSiL+LZ22xBt+9Zt7ljFKu79chpykZVeV4LDPO6Pr5WFVOUzKKnKb8fjXpfPRcmVlG37/3s/Uy6D63RsR/6nLGuoj4L8X/IPo9pykZVeV4LDNTxflYVU5TMqA5v19NOh89V2am79/72T6ydhdwOu0ZT0/SnlGTWdLsoCJjB3As7ZspvzMho9Rp3FXkNCWjqhyPZcYZXT8fq8ppSkaR05Tfryadj54rM8vo+/d+thdrk96omeXOqJLUgarOxypympKhevJcmX1m+2XQD2Qx3T2/O+39A2UGRMS6Trb1Q05TMqrK8VhmrOvnY4U5TclozO9Xk85HPFdmpAnvfSkL6PWxH5j4TUQcAbykjIYj4hjaKycviogT4emFJU8ATikjo6qcpmRUleOxHLKunY89yOn7jKb8fjXpfJzAc6UDTXrvZ2WxFhHvA34bGCiuMe+zm/ZCk2X4JeDXgCHaj+zYZzvwJyVlVJXTlIyqcjyWGajofKwkpykZhUb8flWUUUmO58qMNea9JzNn7QfwwQoyLq7oWLqe05QMj6W2GV0/H6vKaUpGhe99IzIqPBbPlZq9J93OmZUja8C+Rf/ui4g/LDbdDHwui0delJRxMnBSRPzNhIyPZ+aWsjKqymlKRlU5HsuMM7p+PlaV05SMIqcpv19NOh89V2aW0Yj3flZOMIiIHwLuoP3g4/uLj7OA/xsRCyLisG9ujPajOv6d9hTevyg+jgb+KSKeGxF/ebgZVeU0JaOqHI9lxhldPx+rymlKRpHTlN+vJp2Pniszy2jMe9/1YcE6fgD/BPz0JNtfCTwEXF9Cxr/TftTIgdtfRPs69pUlHUvXc5qS4bHUNqPr52NVOU3JaNjvV5POR8+Vmr0nlb33ZXS03z6Au6Z47T+AeSVk3DHFa/cAc0o6lq7nNCXDY6ltRtfPx6pympJR4XvfiIwKj8VzpWbvSVU5s/IyKDAnIo4+cGO0p9/uycydJWREtKfwHrhxITCWmeMlZFSV05SMqnI8lpmp4nysKqcpGUWTjfj9atL56LkyM41572drsXYV8NcxYeXkiDgNuAYo5Ro28GFgbUT8ZEQcX3y8AviH4rWyVJHTlIyqcjyWmanifKwqpykZ0Jzfryadj54rM9Oc976MIcB+/AB+BXgA2Fx8bKDkabfAa4CvAKNFxleA13bhWLqe05QMj6W2GV0/H6vKaUpGw36/mnQ+eq7U7D2pImdWPxsUICKOB8jMHdPtK6m7qjofq8hpSobqyXNldpn1xZokSVKdzdZ71iRJkvqCxZokSVKNzebHTb0UyMz8WkR8P/Aq2uu+fLGk9l8G3JmZ26P9SI33Ai+mvWLzpZm5rV9ympJR5LwL+HxmPlhGe73KqCqnooyjgDcDrcz8x4h4K/CjwJ3AFZm5p19ympIxIet5wBuAZwN7gW8Dn8rM7Wb0Jicivhc4Fzil2PQwsCYz7ywro6qcqo7lINnnZ+Yn+yVnVt6zFhHvB86mXax+CXgZxWrKtFdN/v0SMr4FvDAzxyLiCmAn8DlgRbH9DYebUVVOUzKKnG3Ak8AI8Gngs5m5qYy2q8yoKqeijNW0z8V5wFbgOOBvaL/3kZnn9UtOUzKKnHfx3Rlurwa+XuS9HvjlzLzRjGpzIuI9wFuAq2mv8g9wKu3i/erM/NDhZlSVU9WxTJH/QGYu7WZGqTllT1/thw/gduAI2n/ZbQdOKLYPALeVlHHnhK9vPeC1b5R4LF3PaUpG0dbXaV/+Xwl8AtgEXAecBxzfLxlNOpZ95xztAmQjcETxfZR1PlaV05SMor3bJ7Q9D7ix+Hop8HUzenIs3waOnGT7UcA9JR5L13MqyrjtIB+3A0+V+OfV9ZzZes/aWGbuzfYKySNZDFFn5i6grBWN10fE+cXX34yI5QAR8T1AaZcpKsppSga0L32PZ+bazLwAGAI+Rvsy+H19lFFVThUZc4pLe8fT/kdufrH9aODIkjKqymlKxj77bpU5mvYIHpn5QMk5TcmoImec9jl4oJMp79+uqnKqyFgCvB147SQfoyVlVJIzW+9Z2x0R84pi7SX7NkbEfMr7JflF4CMR8d9pL5D3bxHxIPBg8VpZqshpSga0Rx+elu37e9YAayJiXh9lVJVTRcYngLtoj3ZfAnw2Iu4DXk77EklZqshpSgbAnwFfi4ivAj8B/AFARCwGtpjRk5xfA9ZFxD20/26E9sjd6bQXmC1LFTlVZPw9cFxmfuPAFyLixpIyKsmZrfesHZ2ZT02yfRFwcmbeXmLWCcBzaRfGD2XmxrLarjqnCRkR8T2Z+e0y2+xFRlU5FR7LEEBmtiJiAfBK4IHM/Pd+y2lKRpHzA8D3Aesz864y225aRlU5ETEHeCn735T/tczc2285VR1LE8zKYm0qEXFcZj7R7xlV5TQlo6ocj6V+GVXlNCWjqpymZFSZo+aarfesTeWOhmRUldOUjKpyPJb6ZVSV05SMqnKaklFaTkS8ICJuiogHI+KKiDhxwmtljtx2PacpGVXlzMp71iLiNw72EsVNof2QUVVOUzKqyvFY6pdRVU5TMqrKaUpGhTkfA34HuIn2vbz/EhHnZOYI5U6WqCKnKRmV5MzWkbVLgRNpz6aa+HEc5f2ZVJFRVU5TMqrK8Vjql1FVTlMyqsppSkZVOcdn5nWZuTUzL6N9I/51EfFyoMx7mqrIaUpGNTllrP/Rbx/AvwIvOchrD/ZLRpOOxT+veuY0JaNJx+KfV/0yKjyWbwLzD9j2AuAeYLTEY+l6TlMyKjuWsjrbTx/A84HFB3ltSb9kNOlY/POqZ05TMpp0LP551S+jwmN5K/DySbYvBf5PicfS9ZymZFSV42xQSZKkGpuV96xFxPyI+FBE3BURWyJiNCLuLLYt6JeMqnKaklFVjsdSv4yqcpqSUVVOUzKqyvFY6pdRVc6sLNaAa4DHgVdk5sLMHAR+qth2TR9lVJXTlIyqcjyW+mVUldOUjKpympJRVY7HUr+ManLKumbbTx/A3YfyWt0ymnQs/nnVM6cpGU06Fv+86pfhsczejKpyZuvI2oaI+K2IWLJvQ0QsiYj38N1nlPVDRlU5TcmoKsdjqV9GVTlNyagqpykZVeV4LPXLqCRnthZrPwcMAl8uri9vAW4EFgJv6qOMqnKaklFVjsdSv4yqcpqSUVVOUzKqyvFY6pdRSY6zQSVJkmpsto6sERHfGxErIuLYA7a/qp8yqsppSkZVOR5L/TKqymlKRlU5TcmoKsdjqV9GJTll3WDXTx/Au4C7gb8F7gfOnfDarf2S0aRj8c+rnjlNyWjSsfjnVb8Mj2X2ZlR2LGV1tp8+gNuB44qvTwNuBn61+P7r/ZLRpGPxz6ueOU3JaNKx+OdVvwyPZfZmVJUzl9lpTmY+AZCZ90fEK4DPRcRzgOijjKpympJRVY7HUr+MqnKaklFVTlMyqsrxWOqXUUnObL1nbWNEvGjfN8Uf8muARcAP9VFGVTlNyagqx2OpX0ZVOU3JqCqnKRlV5Xgs9cuoJGdWzgaNiFOBscx8dJLXfiwz/28/ZFSV05SMqnI8lvplVJXTlIyqcpqSUVWOx1K/jKpyZmWxJkmS1C9m62VQSZKkvmCxJkmSVGMWa5I0QxHxXyPi7TPY/7SIWN/NPklqrtm6dIckHZKImJuZH+91PyTNHhZrkmadiDgNuA64BXgx8C3g7cD3AX8IHAdsBt6RmY9ExI3AN4AfBz4dEccDT2TmZcWU/Y8D84AR4Bcy8/GIeAnw50Xk2ooOTVIDeRlU0mz1fOBjmfl9wHbgncAfAz+bmfsKrd+fsP9Rmbk8My8/oJ2rgPdk5gtor2T+/mL7J4GLM/OF3TwISc3nyJqk2erBCesf/RXw28APAl+KCIAjgEcm7P+ZAxuIiPnAgsz8crHpSuCzEbGg2P6VYvtfAmeXfwiSZgOLNUmz1YGLTO4AvpWZP3KQ/Z/scn8kaVJeBpU0Wy2NiH2F2VuBm4DF+7ZFxJER8QNTNZCZ24DHI+Inik1vA76cmVuBrRHx48X24fK7L2m2sFiTNFvdDbwzIu4ETqS4Xw34g4j4Ju0JBT/aQTvnAf9vRNwGvAj43WL7+cCfRsQ3KPeh0ZJmGR83JWnWKWaD/n1m/mCPuyJJ03JkTZIkqcYcWZMkSaoxR9YkSZJqzGJNkiSpxizWJEmSasxiTZIkqcYs1iRJkmrMYk2SJKnG/n8Ez6MpDpgeagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_a36 = df_pricing[df_pricing['Material_Grade'] == \"A36\"]\n",
    "\n",
    "df_a36 = df_a36.sort_values(['period'])\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "a36 = sbn.boxplot(x=\"period\", y=\"Matl_USD/MT\", data=df_a36)\n",
    "a36 = a36.set_xticklabels(a36.get_xticklabels(), rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ton_a36 = df_a36[['Vendor_Line_Tonnage_MT','period']]   # Subset the columns of the dataframe\n",
    "ton_a36_group = ton_a36.groupby('period')        # Define groups by name\n",
    "ton_totals = ton_a36_group.sum()               # Sum ext price by name\n",
    "\n",
    "my_plot = ton_totals.plot.bar(stacked=True,title=\"Bid Totals - Tonnage\",figsize=(10, 6), grid=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'South Korea A36 Price Variation')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_a36 = df_pricing[df_pricing['Material_Grade'] == \"A36\"]\n",
    "df_a36 = df_pricing[df_pricing['Point_of_Manufacture'] == \"South Korea\"]\n",
    "\n",
    "df_a36 = df_a36.sort_values(['period'])\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "a36 = sbn.boxplot(x=\"period\", y=\"Matl_USD/MT\", data=df_a36)\n",
    "a36 = a36.set_xticklabels(a36.get_xticklabels(), rotation=90)\n",
    "plt.title(\"South Korea A36 Price Variation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Project_number</th>\n",
       "      <th>Sub_CSS_Number</th>\n",
       "      <th>Contract_Type</th>\n",
       "      <th>Number_of_Tanks</th>\n",
       "      <th>Material_Grade</th>\n",
       "      <th>Vendor</th>\n",
       "      <th>Type_of_Tank</th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarters</th>\n",
       "      <th>Point_of_Manufacture</th>\n",
       "      <th>Tank_Dia_m</th>\n",
       "      <th>Tank_Ht_m</th>\n",
       "      <th>Vendor_Ton_per_Tank_MT</th>\n",
       "      <th>Vendor_Line_Tonnage_MT</th>\n",
       "      <th>Matl_Line_USD</th>\n",
       "      <th>BP_Line_USD</th>\n",
       "      <th>Fab_Line_USD</th>\n",
       "      <th>Freight_Line_USD</th>\n",
       "      <th>dia_group</th>\n",
       "      <th>ht_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>179308</td>\n",
       "      <td>179308</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>1</td>\n",
       "      <td>A36</td>\n",
       "      <td>Plate Solutions</td>\n",
       "      <td>AGDR</td>\n",
       "      <td>2017</td>\n",
       "      <td>Qtr1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>36.80</td>\n",
       "      <td>20.00</td>\n",
       "      <td>384.16</td>\n",
       "      <td>384.16</td>\n",
       "      <td>302,230.73</td>\n",
       "      <td>55,177.46</td>\n",
       "      <td>86,258.53</td>\n",
       "      <td>21,898.56</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>179308</td>\n",
       "      <td>179308</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>1</td>\n",
       "      <td>A36</td>\n",
       "      <td>Plate Solutions</td>\n",
       "      <td>DRT</td>\n",
       "      <td>2017</td>\n",
       "      <td>Qtr1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>16.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>75.69</td>\n",
       "      <td>75.69</td>\n",
       "      <td>64,947.86</td>\n",
       "      <td>16,240.46</td>\n",
       "      <td>20,110.92</td>\n",
       "      <td>4,325.45</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>179308</td>\n",
       "      <td>179308</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>2</td>\n",
       "      <td>A36</td>\n",
       "      <td>Plate Solutions</td>\n",
       "      <td>AGDR</td>\n",
       "      <td>2017</td>\n",
       "      <td>Qtr1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>26.20</td>\n",
       "      <td>20.00</td>\n",
       "      <td>213.22</td>\n",
       "      <td>426.44</td>\n",
       "      <td>344,377.55</td>\n",
       "      <td>71,940.74</td>\n",
       "      <td>101,906.44</td>\n",
       "      <td>24,630.34</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>179308</td>\n",
       "      <td>179308</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>3</td>\n",
       "      <td>A36</td>\n",
       "      <td>Plate Solutions</td>\n",
       "      <td>AGDR</td>\n",
       "      <td>2017</td>\n",
       "      <td>Qtr1</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>36.80</td>\n",
       "      <td>20.00</td>\n",
       "      <td>384.16</td>\n",
       "      <td>1,152.49</td>\n",
       "      <td>906,692.16</td>\n",
       "      <td>165,532.34</td>\n",
       "      <td>258,775.66</td>\n",
       "      <td>65,695.65</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>182160</td>\n",
       "      <td>182160</td>\n",
       "      <td>Precontract</td>\n",
       "      <td>1</td>\n",
       "      <td>A516-70N</td>\n",
       "      <td>Posco</td>\n",
       "      <td>CRT</td>\n",
       "      <td>2016</td>\n",
       "      <td>Qtr4</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.90</td>\n",
       "      <td>9.85</td>\n",
       "      <td>9.85</td>\n",
       "      <td>7,383.48</td>\n",
       "      <td>828.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>640.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Project_number  Sub_CSS_Number Contract_Type  Number_of_Tanks  \\\n",
       "0          179308          179308   Precontract                1   \n",
       "1          179308          179308   Precontract                1   \n",
       "2          179308          179308   Precontract                2   \n",
       "3          179308          179308   Precontract                3   \n",
       "4          182160          182160   Precontract                1   \n",
       "\n",
       "  Material_Grade           Vendor Type_of_Tank  Year Quarters  \\\n",
       "0            A36  Plate Solutions         AGDR  2017     Qtr1   \n",
       "1            A36  Plate Solutions          DRT  2017     Qtr1   \n",
       "2            A36  Plate Solutions         AGDR  2017     Qtr1   \n",
       "3            A36  Plate Solutions         AGDR  2017     Qtr1   \n",
       "4       A516-70N            Posco          CRT  2016     Qtr4   \n",
       "\n",
       "  Point_of_Manufacture  Tank_Dia_m  Tank_Ht_m  Vendor_Ton_per_Tank_MT  \\\n",
       "0         South Africa       36.80      20.00                  384.16   \n",
       "1         South Africa       16.00      12.50                   75.69   \n",
       "2         South Africa       26.20      20.00                  213.22   \n",
       "3         South Africa       36.80      20.00                  384.16   \n",
       "4          South Korea        4.00       4.90                    9.85   \n",
       "\n",
       "   Vendor_Line_Tonnage_MT  Matl_Line_USD  BP_Line_USD  Fab_Line_USD  \\\n",
       "0                  384.16     302,230.73    55,177.46     86,258.53   \n",
       "1                   75.69      64,947.86    16,240.46     20,110.92   \n",
       "2                  426.44     344,377.55    71,940.74    101,906.44   \n",
       "3                1,152.49     906,692.16   165,532.34    258,775.66   \n",
       "4                    9.85       7,383.48       828.71          0.00   \n",
       "\n",
       "   Freight_Line_USD  dia_group  ht_group  \n",
       "0         21,898.56          6         4  \n",
       "1          4,325.45          4         3  \n",
       "2         24,630.34          5         4  \n",
       "3         65,695.65          6         4  \n",
       "4            640.30          1         1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dia = df_plate.groupby(['Project_number','Sub_CSS_Number','Contract_Type','Number_of_Tanks','Material_Grade','Vendor','Type_of_Tank',\n",
    "                               'Year','Quarters','Point_of_Manufacture','Tank_Dia_m','Tank_Ht_m'])['Vendor_Ton_per_Tank_MT',\n",
    "                                'Vendor_Line_Tonnage_MT', 'Matl_Line_USD','BP_Line_USD','Fab_Line_USD','Freight_Line_USD'].sum()\n",
    "\n",
    "df_dia = df_dia.reset_index()\n",
    "\n",
    "df_dia['dia_group'] = \"\"\n",
    "df_dia['ht_group'] = \"\"\n",
    "\n",
    "conditions1 = [(df_dia['Tank_Dia_m']<=5.5), #1\n",
    "               (df_dia['Tank_Dia_m']<= 10), #2\n",
    "               (df_dia['Tank_Dia_m']<= 15), #3\n",
    "               (df_dia['Tank_Dia_m']<= 20), #4\n",
    "               (df_dia['Tank_Dia_m']<= 30), #5\n",
    "               (df_dia['Tank_Dia_m']<= 40), #6\n",
    "               (df_dia['Tank_Dia_m']<= 50),\n",
    "               (df_dia['Tank_Dia_m']<= 60),\n",
    "               (df_dia['Tank_Dia_m']<= 70),\n",
    "               (df_dia['Tank_Dia_m']<= 80),\n",
    "               (df_dia['Tank_Dia_m']<= 90),\n",
    "               (df_dia['Tank_Dia_m']<= 100),\n",
    "               (df_dia['Tank_Dia_m']<= 120),\n",
    "               (df_dia['Tank_Dia_m'] > 120)]\n",
    "\n",
    "values1 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "\n",
    "df_dia['dia_group'] = np.select(conditions1, values1)\n",
    "\n",
    "conditions2 = [(df_dia['Tank_Ht_m']<=5.5), #1\n",
    "               (df_dia['Tank_Ht_m']<= 10), #2\n",
    "               (df_dia['Tank_Ht_m']<= 15), #3\n",
    "               (df_dia['Tank_Ht_m']<= 20), #4\n",
    "               (df_dia['Tank_Ht_m']<= 30), #5\n",
    "               (df_dia['Tank_Ht_m']<= 40), #6\n",
    "               (df_dia['Tank_Ht_m']<= 50),\n",
    "               (df_dia['Tank_Ht_m']<= 60),\n",
    "               (df_dia['Tank_Ht_m']<= 70),\n",
    "               (df_dia['Tank_Ht_m']<= 80),\n",
    "               (df_dia['Tank_Ht_m']<= 90),\n",
    "               (df_dia['Tank_Ht_m']<= 100),\n",
    "               (df_dia['Tank_Ht_m']<= 120),\n",
    "               (df_dia['Tank_Ht_m'] > 120)]\n",
    "\n",
    "values2 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "\n",
    "df_dia['ht_group'] = np.select(conditions2, values2)\n",
    "\n",
    "df_dia.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3039797466536207"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dia.dia_group.cov(df_dia.ht_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5206265592286752"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dia.dia_group.corr(df_dia.ht_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
       "<ggplot: (-9223363276453325894)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(aes(x='dia_group', y = 'ht_group'), data = df_dia) + geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Contract_Type</th>\n",
       "      <th>Type_of_Tank</th>\n",
       "      <th>Tank_Dia_m</th>\n",
       "      <th>Tank_Ht_m</th>\n",
       "      <th>dia_group</th>\n",
       "      <th>ht_group</th>\n",
       "      <th>Vendor_Ton_per_Tank_MT</th>\n",
       "      <th>Matl_Line_USD</th>\n",
       "      <th>Dia_Ht</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Precontract</td>\n",
       "      <td>DRT</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.42</td>\n",
       "      <td>9,602.19</td>\n",
       "      <td>1-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Precontract</td>\n",
       "      <td>DRT</td>\n",
       "      <td>4.57</td>\n",
       "      <td>6.10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16.70</td>\n",
       "      <td>9,455.30</td>\n",
       "      <td>1-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Precontract</td>\n",
       "      <td>DRT</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.40</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>39.55</td>\n",
       "      <td>28,160.32</td>\n",
       "      <td>2-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Precontract</td>\n",
       "      <td>DRT</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>30.65</td>\n",
       "      <td>14,512.93</td>\n",
       "      <td>2-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Precontract</td>\n",
       "      <td>DRT</td>\n",
       "      <td>8.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>56.48</td>\n",
       "      <td>62,123.00</td>\n",
       "      <td>2-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Contract_Type Type_of_Tank  Tank_Dia_m  Tank_Ht_m  dia_group  ht_group  \\\n",
       "1   Precontract          DRT        5.00       5.00          1         1   \n",
       "0   Precontract          DRT        4.57       6.10          1         2   \n",
       "2   Precontract          DRT        6.60       6.40          2         2   \n",
       "3   Precontract          DRT        8.00       9.00          2         2   \n",
       "4   Precontract          DRT        8.00      10.00          2         2   \n",
       "\n",
       "   Vendor_Ton_per_Tank_MT  Matl_Line_USD Dia_Ht  \n",
       "1                   13.42       9,602.19    1-1  \n",
       "0                   16.70       9,455.30    1-2  \n",
       "2                   39.55      28,160.32    2-2  \n",
       "3                   30.65      14,512.93    2-2  \n",
       "4                   56.48      62,123.00    2-2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dia2 = df_dia[df_dia['Type_of_Tank'] == \"DRT\"]\n",
    "\n",
    "df_dia2 = df_dia2.groupby(['Contract_Type','Type_of_Tank',\n",
    "                            'Tank_Dia_m','Tank_Ht_m','dia_group','ht_group'])['Vendor_Ton_per_Tank_MT','Matl_Line_USD'].sum()\n",
    "\n",
    "df_dia2 = df_dia2.reset_index()\n",
    "\n",
    "df_dia2['Dia_Ht'] = df_dia2['dia_group'].astype(str) + \"-\" + df_dia2['ht_group'].astype(str)\n",
    "\n",
    "df_dia2 = df_dia2.sort_values(['Dia_Ht'])\n",
    "\n",
    "df_dia2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dia2.plot(kind=\"scatter\", x=\"dia_group\", y=\"ht_group\", c=\"Vendor_Ton_per_Tank_MT\", cmap=cm.hsv, \n",
    "             sharex = False, figsize=(10,7))\n",
    "plt.title(\"DRT Tanks; Grouping - 1:<5.5m, 2:<10m, 3:<15m, 4:<20m, 5-14 in increments of 10m\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Dia_Ht</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4-3</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-3</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-4</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-2</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-2</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-4</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-5</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6-3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dia_Ht\n",
       "4-3      13\n",
       "3-3       8\n",
       "4-4       6\n",
       "3-2       5\n",
       "2-2       5\n",
       "5-4       3\n",
       "3-5       3\n",
       "5-3       3\n",
       "2-3       2\n",
       "4-2       2\n",
       "1-1       1\n",
       "3-4       1\n",
       "4-5       1\n",
       "6-3       1\n",
       "5-5       1\n",
       "1-2       1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "dia_comp = sbn.boxplot(x=\"Dia_Ht\", y=\"Vendor_Ton_per_Tank_MT\", data=df_dia2)\n",
    "plt.title(\"DRT: Grouping - 1:<5.5m, 2:<10m, 3:<15m, 4:<20m, 5-14 in increments of 10m\")\n",
    "df_dia2['Dia_Ht'].value_counts().to_frame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}