script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NEWCOMERS_JSON = './data/newcomers.json'\n",
    "newcomers = pd.read_json(NEWCOMERS_JSON)\n",
    "newcomers['reveal_date'] = pd.to_datetime(newcomers['reveal_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "HEADERS = ['week', 'popularity']\n",
    "\n",
    "def calculate_popularity_diff(newcomer):\n",
    "    df = pd.read_csv(\n",
    "        newcomer['popularity_file'],\n",
    "        names = HEADERS,\n",
    "        skiprows = 3\n",
    "    )\n",
    "    df['week'] = pd.to_datetime(df['week'])\n",
    "    \n",
    "    before = df[df['week'] < newcomer['reveal_date']]['popularity'].mean()\n",
    "    after = df[df['week'] >= newcomer['reveal_date']]['popularity'].mean()\n",
    "    \n",
    "    return after - before\n",
    "    \n",
    "\n",
    "newcomers['popularity_gain'] = newcomers.apply(\n",
    "    lambda x: calculate_popularity_diff(x),\n",
    "    axis=1\n",
    ")\n",
    "newcomers = newcomers.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1128fcef0>"
      ]
     },
     "execution_count": 5,
     "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": [
    "newcomers.plot.barh(x='name', y='popularity_gain', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUTPUT = './output/popularity_gains.csv'\n",
    "COLUMNS = ['name', 'series', 'popularity_gain']\n",
    "newcomers.to_csv(OUTPUT, columns=COLUMNS)"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>series</th>\n",
       "      <th>reveal_date</th>\n",
       "      <th>popularity_file</th>\n",
       "      <th>popularity_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inkling</td>\n",
       "      <td>Splatoon</td>\n",
       "      <td>2018-03-08</td>\n",
       "      <td>./data/splatoon.csv</td>\n",
       "      <td>-1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Daisy</td>\n",
       "      <td>Mario</td>\n",
       "      <td>2018-06-12</td>\n",
       "      <td>./data/mario.csv</td>\n",
       "      <td>-2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ridley</td>\n",
       "      <td>Metroid</td>\n",
       "      <td>2018-06-12</td>\n",
       "      <td>./data/metroid.csv</td>\n",
       "      <td>-3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dark Samus</td>\n",
       "      <td>Metroid</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>./data/metroid.csv</td>\n",
       "      <td>-4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chrom</td>\n",
       "      <td>Fire Emblem</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>./data/fire emblem.csv</td>\n",
       "      <td>-1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Simon</td>\n",
       "      <td>Castlevania</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>./data/castlevania.csv</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Richter</td>\n",
       "      <td>Castlevania</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>./data/castlevania.csv</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>King K. Rool</td>\n",
       "      <td>Donkey Kong</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>./data/donkey kong.csv</td>\n",
       "      <td>-6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Isabelle</td>\n",
       "      <td>Animal Crossing</td>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>./data/animal crossing.csv</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ken</td>\n",
       "      <td>Street Fighter</td>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>./data/street fighter.csv</td>\n",
       "      <td>-7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Incineroar</td>\n",
       "      <td>Pokémon</td>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>./data/pokémon.csv</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Piranha Plant</td>\n",
       "      <td>Mario</td>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>./data/mario.csv</td>\n",
       "      <td>-2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Joker</td>\n",
       "      <td>Persona</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>./data/persona.csv</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hero</td>\n",
       "      <td>Dragon Quest</td>\n",
       "      <td>2019-07-11</td>\n",
       "      <td>./data/dragon quest.csv</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Banjo-Kazooie</td>\n",
       "      <td>Banjo-Kazooie</td>\n",
       "      <td>2019-07-11</td>\n",
       "      <td>./data/banjo-kazooie.csv</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Terry</td>\n",
       "      <td>Fatal Fury</td>\n",
       "      <td>2019-09-04</td>\n",
       "      <td>./data/fatal fury.csv</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Byleth</td>\n",
       "      <td>Fire Emblem</td>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>./data/fire emblem.csv</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name           series reveal_date             popularity_file  \\\n",
       "0         Inkling         Splatoon  2018-03-08         ./data/splatoon.csv   \n",
       "1           Daisy            Mario  2018-06-12            ./data/mario.csv   \n",
       "2          Ridley          Metroid  2018-06-12          ./data/metroid.csv   \n",
       "3      Dark Samus          Metroid  2018-08-08          ./data/metroid.csv   \n",
       "4           Chrom      Fire Emblem  2018-08-08      ./data/fire emblem.csv   \n",
       "5           Simon      Castlevania  2018-08-08      ./data/castlevania.csv   \n",
       "6         Richter      Castlevania  2018-08-08      ./data/castlevania.csv   \n",
       "7    King K. Rool      Donkey Kong  2018-08-08      ./data/donkey kong.csv   \n",
       "8        Isabelle  Animal Crossing  2018-09-13  ./data/animal crossing.csv   \n",
       "9             Ken   Street Fighter  2018-11-01   ./data/street fighter.csv   \n",
       "10     Incineroar          Pokémon  2018-11-01          ./data/pokémon.csv   \n",
       "11  Piranha Plant            Mario  2018-11-01            ./data/mario.csv   \n",
       "12          Joker          Persona  2018-12-07          ./data/persona.csv   \n",
       "13           Hero     Dragon Quest  2019-07-11     ./data/dragon quest.csv   \n",
       "14  Banjo-Kazooie    Banjo-Kazooie  2019-07-11    ./data/banjo-kazooie.csv   \n",
       "15          Terry       Fatal Fury  2019-09-04       ./data/fatal fury.csv   \n",
       "16         Byleth      Fire Emblem  2020-01-16      ./data/fire emblem.csv   \n",
       "\n",
       "    popularity_gain  \n",
       "0              -1.8  \n",
       "1              -2.6  \n",
       "2              -3.9  \n",
       "3              -4.4  \n",
       "4              -1.1  \n",
       "5               5.1  \n",
       "6               5.1  \n",
       "7              -6.2  \n",
       "8               0.7  \n",
       "9              -7.2  \n",
       "10              0.3  \n",
       "11             -2.2  \n",
       "12              5.8  \n",
       "13             18.2  \n",
       "14              2.3  \n",
       "15              8.0  \n",
       "16              1.2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcomers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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