HW2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import scipy as sp \n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read in\n",
    "pd_data = pd.read_csv('Reviews.csv')\n",
    "#For this homework, please use the first 10000 rows\n",
    "pd_data = pd_data[:10000]\n",
    "pd_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserId\n",
       "A1Z54EM24Y40LL    17\n",
       "A2SZLNSI5KOQJT    10\n",
       "A35R32TA60XD57     9\n",
       "A281NPSIMI1C2R     8\n",
       "A3OXHLG6DIBRW8     7\n",
       "A3NHUQ33CFH3VM     7\n",
       "A3PJZ8TU8FDQ1K     7\n",
       "A3TX6CWSZL02PW     6\n",
       "AQQLWCMRNDFGI      6\n",
       "A2R6RA8FRBS608     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top-10 Reviewer\n",
    "#Sort by number of reviews (\"UserId\")\n",
    "userid=pd_data.groupby(\"UserId\")\n",
    "userid_sort_10=userid.size().sort_values(ascending=False).head(10)\n",
    "userid_sort_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new DataFrame to show answer\n",
    "new=pd.DataFrame(columns=['UserId','ProfileName','Scorecount','score mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    ppl=userid_sort_10.index[i]  #UserId\n",
    "    data=userid.get_group(ppl)   #get group\n",
    "    new_row={'UserId':[ppl],\n",
    "                'ProfileName':[data.iloc[0,3]],\n",
    "                'Scorecount':[userid_sort_10[i]],\n",
    "                'score mean':[data.mean()[3]]}     #one's data\n",
    "    add=pd.DataFrame(new_row)    #turn to dataframe\n",
    "    new=new.append(add)          #append\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>index</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>Scorecount</th>\n",
       "      <th>score mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\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>0</td>\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>0</td>\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>0</td>\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>0</td>\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>5</th>\n",
       "      <td>0</td>\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>0</td>\n",
       "      <td>A3PJZ8TU8FDQ1K</td>\n",
       "      <td>Jared Castle</td>\n",
       "      <td>7</td>\n",
       "      <td>4.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>A3TX6CWSZL02PW</td>\n",
       "      <td>LaVerne \":LaVerne\"</td>\n",
       "      <td>6</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\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>0</td>\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": [
       "   index          UserId                                 ProfileName  \\\n",
       "0      0  A1Z54EM24Y40LL                                          c2   \n",
       "1      0  A2SZLNSI5KOQJT  Carrie, \"Formerly &#34;Sister Carrie&#34;\"   \n",
       "2      0  A35R32TA60XD57                                    M. Torma   \n",
       "3      0  A281NPSIMI1C2R      Rebecca of Amazon \"The Rebecca Review\"   \n",
       "4      0  A3OXHLG6DIBRW8                            C. F. Hill \"CFH\"   \n",
       "5      0  A3NHUQ33CFH3VM                                Citizen John   \n",
       "6      0  A3PJZ8TU8FDQ1K                                Jared Castle   \n",
       "7      0  A3TX6CWSZL02PW                          LaVerne \":LaVerne\"   \n",
       "8      0   AQQLWCMRNDFGI                          Steven A. Peterson   \n",
       "9      0  A2R6RA8FRBS608                          Matthew G. Sherwin   \n",
       "\n",
       "  Scorecount  score mean  \n",
       "0         17    3.882353  \n",
       "1         10    3.800000  \n",
       "2          9    4.111111  \n",
       "3          8    5.000000  \n",
       "4          7    4.428571  \n",
       "5          7    3.142857  \n",
       "6          7    4.714286  \n",
       "7          6    5.000000  \n",
       "8          6    4.166667  \n",
       "9          6    5.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1.Answer\n",
    "new.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. Plot score distribution for the user with the most number of reviews\n",
    "first=userid_sort_10.index[0]  #userid with the most number of reviews\n",
    "first_data=userid.get_group(first)     #user\n",
    "first_score=first_data[\"Score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(first_score.value_counts().index,first_score.value_counts())\n",
    "plt.xlabel('score')\n",
    "plt.ylabel('times')\n",
    "#2.Answer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3. Plot pandas Series DataFrame (Time->Date)\n",
    "time=pd_data[\"Time\"]\n",
    "#transfer column \"Time\" -> Date\n",
    "plt.hist(pd.to_datetime(time,unit='s').dt.year,bins=13)\n",
    "plt.title('Review Count by Year')\n",
    "plt.xlabel('review years')\n",
    "plt.ylabel('review count')\n",
    "#3.Answer\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4. Plot HeatMap using seaborn\n",
    "sns.set()\n",
    "#Correlation between numeric features (Id, HelpfulnessNumerator, HelpfulnessDenominator, Score, Time)\n",
    "heat_data=pd_data[['Id','HelpfulnessNumerator','HelpfulnessDenominator','Score','Time']]\n",
    "corrmat = heat_data.corr()\n",
    "ax=sns.heatmap(corrmat,annot=True,vmax=0.6,vmin=-0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5. Helpful percent\n",
    "H=pd_data\n",
    "\n",
    "def weird_division(n, d):\n",
    "    if d>0:\n",
    "        return n/d\n",
    "    elif n>d:\n",
    "        return -2\n",
    "    else:\n",
    "        return -1\n",
    "def get(df):\n",
    "    hn=df['HelpfulnessNumerator']\n",
    "    hd=df['HelpfulnessDenominator']\n",
    "    result=weird_division(hn, hd)    \n",
    "    return result\n",
    "    \n",
    "helpful_data= H.apply(get,axis=1)\n",
    "for i in range(len(helpful_data)):\n",
    "    if helpful_data[i]==-2:\n",
    "        del helpful_data[i]\n",
    "\n",
    "helpful_data.hist(bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "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
}