HelloWorld.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16598, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('vgsales.csv')\n",
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print('hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 'Wii Sports', 'Wii', ..., 3.77, 8.46, 82.74],\n",
       "       [2, 'Super Mario Bros.', 'NES', ..., 6.81, 0.77, 40.24],\n",
       "       [3, 'Mario Kart Wii', 'Wii', ..., 3.79, 3.31, 35.82],\n",
       "       ...,\n",
       "       [16598, 'SCORE International Baja 1000: The Official Game', 'PS2',\n",
       "        ..., 0.0, 0.0, 0.01],\n",
       "       [16599, 'Know How 2', 'DS', ..., 0.0, 0.0, 0.01],\n",
       "       [16600, 'Spirits & Spells', 'GBA', ..., 0.0, 0.0, 0.01]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Rank</th>\n",
       "      <th>Year</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16598.000000</td>\n",
       "      <td>16327.000000</td>\n",
       "      <td>16598.000000</td>\n",
       "      <td>16598.000000</td>\n",
       "      <td>16598.000000</td>\n",
       "      <td>16598.000000</td>\n",
       "      <td>16598.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8300.605254</td>\n",
       "      <td>2006.406443</td>\n",
       "      <td>0.264667</td>\n",
       "      <td>0.146652</td>\n",
       "      <td>0.077782</td>\n",
       "      <td>0.048063</td>\n",
       "      <td>0.537441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4791.853933</td>\n",
       "      <td>5.828981</td>\n",
       "      <td>0.816683</td>\n",
       "      <td>0.505351</td>\n",
       "      <td>0.309291</td>\n",
       "      <td>0.188588</td>\n",
       "      <td>1.555028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4151.250000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8300.500000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12449.750000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16600.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>41.490000</td>\n",
       "      <td>29.020000</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>10.570000</td>\n",
       "      <td>82.740000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Rank          Year      NA_Sales      EU_Sales      JP_Sales  \\\n",
       "count  16598.000000  16327.000000  16598.000000  16598.000000  16598.000000   \n",
       "mean    8300.605254   2006.406443      0.264667      0.146652      0.077782   \n",
       "std     4791.853933      5.828981      0.816683      0.505351      0.309291   \n",
       "min        1.000000   1980.000000      0.000000      0.000000      0.000000   \n",
       "25%     4151.250000   2003.000000      0.000000      0.000000      0.000000   \n",
       "50%     8300.500000   2007.000000      0.080000      0.020000      0.000000   \n",
       "75%    12449.750000   2010.000000      0.240000      0.110000      0.040000   \n",
       "max    16600.000000   2020.000000     41.490000     29.020000     10.220000   \n",
       "\n",
       "        Other_Sales  Global_Sales  \n",
       "count  16598.000000  16598.000000  \n",
       "mean       0.048063      0.537441  \n",
       "std        0.188588      1.555028  \n",
       "min        0.000000      0.010000  \n",
       "25%        0.000000      0.060000  \n",
       "50%        0.010000      0.170000  \n",
       "75%        0.040000      0.470000  \n",
       "max       10.570000     82.740000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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