HW1-part1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import scipy as sp \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"./Reviews.csv\"\n",
    "pd_data = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_data = pd_data[:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Top10_Id = pd_data['UserId'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def get_name(text):\n",
    "    return pd_data[pd_data.UserId == text]['ProfileName'].values[0]\n",
    "PN = pd.Series([get_name(text)for text in Top10_Id.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_score(text):\n",
    "    score = 0\n",
    "    for num in pd_data[pd_data.UserId == text]['Score'].values:\n",
    "        score += num\n",
    "    return score/Top10_Id[text]\n",
    "scores = pd.Series([get_score(text)for text in Top10_Id.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>Score count</th>\n",
       "      <th>Score mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1Z54EM24Y40LL</td>\n",
       "      <td>c2</td>\n",
       "      <td>17</td>\n",
       "      <td>3.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2SZLNSI5KOQJT</td>\n",
       "      <td>Carrie, \"Formerly &amp;#34;Sister Carrie&amp;#34;\"</td>\n",
       "      <td>10</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A35R32TA60XD57</td>\n",
       "      <td>M. Torma</td>\n",
       "      <td>9</td>\n",
       "      <td>4.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A281NPSIMI1C2R</td>\n",
       "      <td>Rebecca of Amazon \"The Rebecca Review\"</td>\n",
       "      <td>8</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3PJZ8TU8FDQ1K</td>\n",
       "      <td>Jared Castle</td>\n",
       "      <td>7</td>\n",
       "      <td>4.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A3NHUQ33CFH3VM</td>\n",
       "      <td>Citizen John</td>\n",
       "      <td>7</td>\n",
       "      <td>3.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A3OXHLG6DIBRW8</td>\n",
       "      <td>C. F. Hill \"CFH\"</td>\n",
       "      <td>7</td>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A10H24TDLK2VDP</td>\n",
       "      <td>William Jens Jensen</td>\n",
       "      <td>6</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AQQLWCMRNDFGI</td>\n",
       "      <td>Steven A. Peterson</td>\n",
       "      <td>6</td>\n",
       "      <td>4.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A2R6RA8FRBS608</td>\n",
       "      <td>Matthew G. Sherwin</td>\n",
       "      <td>6</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           UserId                                 ProfileName  Score count  \\\n",
       "0  A1Z54EM24Y40LL                                          c2           17   \n",
       "1  A2SZLNSI5KOQJT  Carrie, \"Formerly &#34;Sister Carrie&#34;\"           10   \n",
       "2  A35R32TA60XD57                                    M. Torma            9   \n",
       "3  A281NPSIMI1C2R      Rebecca of Amazon \"The Rebecca Review\"            8   \n",
       "4  A3PJZ8TU8FDQ1K                                Jared Castle            7   \n",
       "5  A3NHUQ33CFH3VM                                Citizen John            7   \n",
       "6  A3OXHLG6DIBRW8                            C. F. Hill \"CFH\"            7   \n",
       "7  A10H24TDLK2VDP                         William Jens Jensen            6   \n",
       "8   AQQLWCMRNDFGI                          Steven A. Peterson            6   \n",
       "9  A2R6RA8FRBS608                          Matthew G. Sherwin            6   \n",
       "\n",
       "   Score mean  \n",
       "0    3.882353  \n",
       "1    3.800000  \n",
       "2    4.111111  \n",
       "3    5.000000  \n",
       "4    4.714286  \n",
       "5    3.142857  \n",
       "6    4.428571  \n",
       "7    3.000000  \n",
       "8    4.166667  \n",
       "9    5.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. Top-10 Reviewer\n",
    "Top10_dict = {\n",
    "    \"UserId\":Top10_Id.index,\n",
    "    \"ProfileName\":PN,\n",
    "    \"Score count\":Top10_Id.values,\n",
    "    \"Score mean\":scores\n",
    "}\n",
    "Top10 = pd.DataFrame(Top10_dict)\n",
    "Top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f7b9743cc0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2. Plot score distribution for the user with the most number of reviews\n",
    "Top_score = pd_data[pd_data.UserId == Top10_Id.index[0]]['Score'].values\n",
    "sns.countplot(Top_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3. Plot pandas Series DataFrame (Time->Date)\n",
    "import time\n",
    "years = []\n",
    "year = {}\n",
    "for i in range(10000):\n",
    "    years.append(time.gmtime(pd_data['Time'][i]).tm_year)\n",
    "for i in years:\n",
    "    year[i] = 0\n",
    "for i in years:\n",
    "    year[i] += 1\n",
    "year.keys()\n",
    "year.values()\n",
    "plt.xlabel('Review year')\n",
    "plt.ylabel('Review count')\n",
    "plt.title('Review count by year')\n",
    "plt.bar(year.keys(),year.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f7b9b6add8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4. Plot HeatMap using seaborn\n",
    "sns.heatmap(pd_data.drop(columns = ['ProductId', 'UserId', 'ProfileName', 'Summary', 'Text']).corr(), vmax=0.6, square=True, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4878.,    0.,    0.,    0.,    0.,  689.,  225.,  422.,  317.,\n",
       "        3469.]),\n",
       " array([-1. , -0.8, -0.6, -0.4, -0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5. Helpful percent\n",
    "#HelpfulnessNumerator HelpfulnessDenominator\n",
    "percent = []\n",
    "for i in range(10000):\n",
    "    if pd_data['HelpfulnessNumerator'][i] <= pd_data['HelpfulnessDenominator'][i]:\n",
    "        if pd_data['HelpfulnessDenominator'][i] != 0:\n",
    "            percent.append((pd_data['HelpfulnessNumerator'][i] / pd_data['HelpfulnessDenominator'][i]))\n",
    "        elif pd_data['HelpfulnessNumerator'][i] == 0 and pd_data['HelpfulnessDenominator'][i] == 0:\n",
    "            percent.append(-1)\n",
    "plt.grid()\n",
    "plt.hist(percent)"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}