{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd75b6ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa90abb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "al= pd.read_csv('alloted.csv')\n",
    "paid= pd.read_csv('paid.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cffb9f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=pd.DataFrame(al.groupby('Product').sum().iloc[:,-1].sort_values(ascending=False).astype(int))\n",
    "b= pd.DataFrame(al.groupby('Product').count().iloc[:,-1].sort_values(ascending=False).astype(int))\n",
    "a=pd.merge(a,b, left_index=True, right_index=True)\n",
    "a.to_csv('F.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac6d9933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "CA    465497605\n",
       "TW    222507193\n",
       "UT     36260589\n",
       "CD     11344560\n",
       "CT      3125076\n",
       "TT       290151\n",
       "CP        26382\n",
       "Name: TOTAL OD ( OD + CHARGES ), dtype: int32"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al.groupby('Product').sum().iloc[:,-1].sort_values(ascending=False).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d24669f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4cfb8ff",
   "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>AMOUNT COLLECTED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3661</th>\n",
       "      <td>2450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3662</th>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3663</th>\n",
       "      <td>3415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3664</th>\n",
       "      <td>4500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3665</th>\n",
       "      <td>3555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3666 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AMOUNT COLLECTED\n",
       "0               2577.0\n",
       "1               2165.0\n",
       "2               5000.0\n",
       "3               1825.0\n",
       "4               2300.0\n",
       "...                ...\n",
       "3661            2450.0\n",
       "3662          150000.0\n",
       "3663            3415.0\n",
       "3664            4500.0\n",
       "3665            3555.0\n",
       "\n",
       "[3666 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b7ac5af",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No objects to concatenate",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-daf84f6405d6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpaid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'object'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdescribe\u001b[1;34m(self, percentiles, include, exclude, datetime_is_numeric)\u001b[0m\n\u001b[0;32m  10408\u001b[0m                     \u001b[0mnames\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m  10409\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m> 10410\u001b[1;33m         \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mldesc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m  10411\u001b[0m         \u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m  10412\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m    283\u001b[0m     \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mIndexes\u001b[0m \u001b[0mhave\u001b[0m \u001b[0moverlapping\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m     \"\"\"\n\u001b[1;32m--> 285\u001b[1;33m     op = _Concatenator(\n\u001b[0m\u001b[0;32m    286\u001b[0m         \u001b[0mobjs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    287\u001b[0m         \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[0;32m    340\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No objects to concatenate\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: No objects to concatenate"
     ]
    }
   ],
   "source": [
    "paid.describe(include=['object'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2b4088fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19757477768795473"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(paid)/len(al)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "61f6bf30",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[1,5,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0dbce46f",
   "metadata": {},
   "outputs": [],
   "source": [
    "a.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b90271b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 5]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c785427",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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