PDH - A2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import dtale\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales = pd.read_csv(\"sales_predictions_a_1.csv\", sep=',', header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales[sales['sales'] < -50] = -50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales[sales['sales'] > 1000] = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id       77354.4\n",
       "sales        0.0\n",
       "Name: 0.1, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.quantile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.88119477463852"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.percentileofscore(sales['sales'],1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>id</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>773535.000000</td>\n",
       "      <td>773535.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>386339.402473</td>\n",
       "      <td>42.991514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>223563.213885</td>\n",
       "      <td>78.277097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-50.000000</td>\n",
       "      <td>-50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>192744.500000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>386343.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>579941.500000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>773535.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id          sales\n",
       "count  773535.000000  773535.000000\n",
       "mean   386339.402473      42.991514\n",
       "std    223563.213885      78.277097\n",
       "min       -50.000000     -50.000000\n",
       "25%    192744.500000       8.000000\n",
       "50%    386343.000000      22.000000\n",
       "75%    579941.500000      48.000000\n",
       "max    773535.000000    1000.000000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,8.27)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp = sns.distplot(sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales.to_csv('sales_predictions_a_2_1.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "rms = mean_squared_error(a, a, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list(sales.iloc[:,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = b.pop(0)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "b.append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       " ...]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111.00109619637566"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rms = mean_squared_error(a, b, squared=False)\n",
    "rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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