import.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests \n",
    "from bs4 import BeautifulSoup as bs \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "r=requests.get('https://cryptoslam.io/')\n",
    "content = bs(r.content)\n",
    "\n",
    "# print(content.prettify())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Collections</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Buyers</th>\n",
       "      <th>Txns</th>\n",
       "      <th>Owners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Axie Infinity</td>\n",
       "      <td>$3,391,483,898</td>\n",
       "      <td>1,105,095</td>\n",
       "      <td>9,995,287</td>\n",
       "      <td>2,682,388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CryptoPunks</td>\n",
       "      <td>$1,676,251,328</td>\n",
       "      <td>4,731</td>\n",
       "      <td>18,987</td>\n",
       "      <td>3,293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Art Blocks</td>\n",
       "      <td>$1,078,767,070</td>\n",
       "      <td>21,149</td>\n",
       "      <td>118,416</td>\n",
       "      <td>25,468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bored Ape Yacht Club</td>\n",
       "      <td>$789,907,069</td>\n",
       "      <td>8,306</td>\n",
       "      <td>22,612</td>\n",
       "      <td>5,861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NBA Top Shot</td>\n",
       "      <td>$785,818,188</td>\n",
       "      <td>375,813</td>\n",
       "      <td>11,965,958</td>\n",
       "      <td>605,878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Galaxy Fight Club</td>\n",
       "      <td>$18,018,304</td>\n",
       "      <td>4,242</td>\n",
       "      <td>13,935</td>\n",
       "      <td>2,465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Solsteads</td>\n",
       "      <td>$17,979,292</td>\n",
       "      <td>1,630</td>\n",
       "      <td>3,260</td>\n",
       "      <td>1,643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Forgotten Runes</td>\n",
       "      <td>$17,928,970</td>\n",
       "      <td>3,635</td>\n",
       "      <td>11,595</td>\n",
       "      <td>2,482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>CryptoArte</td>\n",
       "      <td>$17,819,905</td>\n",
       "      <td>4,123</td>\n",
       "      <td>9,712</td>\n",
       "      <td>3,351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Monkey Bet DAO</td>\n",
       "      <td>$17,644,948</td>\n",
       "      <td>6,019</td>\n",
       "      <td>13,259</td>\n",
       "      <td>3,683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Collections           Sales     Buyers        Txns     Owners\n",
       "0          Axie Infinity  $3,391,483,898  1,105,095   9,995,287  2,682,388\n",
       "1            CryptoPunks  $1,676,251,328      4,731      18,987      3,293\n",
       "2             Art Blocks  $1,078,767,070     21,149     118,416     25,468\n",
       "3   Bored Ape Yacht Club    $789,907,069      8,306      22,612      5,861\n",
       "4           NBA Top Shot    $785,818,188    375,813  11,965,958    605,878\n",
       "..                   ...             ...        ...         ...        ...\n",
       "95     Galaxy Fight Club     $18,018,304      4,242      13,935      2,465\n",
       "96             Solsteads     $17,979,292      1,630       3,260      1,643\n",
       "97       Forgotten Runes     $17,928,970      3,635      11,595      2,482\n",
       "98            CryptoArte     $17,819,905      4,123       9,712      3,351\n",
       "99        Monkey Bet DAO     $17,644,948      6,019      13,259      3,683\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table=content.select('table.table-hover.js-top-by-sales-table-all.summary-sales-table')[0]\n",
    "columns = table.find(\"thead\").find_all(\"th\")\n",
    "column_names = [c.string for c in columns]\n",
    "\n",
    "table_rows = table.find('tbody').find_all('tr')\n",
    "\n",
    "l = []\n",
    "for tr in table_rows:\n",
    "    td = tr.find_all('td')\n",
    "    row = [str(tr.get_text()).strip() for tr in td]\n",
    "    l.append(row)\n",
    "\n",
    "q = []\n",
    "for tr in table_rows:\n",
    "    td = tr.select('td span.summary-sales-table__column-product-name')\n",
    "    row = [str(tr.get_text()).strip() for tr in td]\n",
    "    q.append(row)\n",
    "\n",
    "\n",
    "df1 = pd.DataFrame(q, columns=['Collections'])\n",
    "df1 = df1.loc[~df1.index.duplicated(keep='first')]\n",
    "df2 = pd.DataFrame(l, columns=column_names)\n",
    "df2 = df2.loc[~df2.index.duplicated(keep='first')]\n",
    "\n",
    "nft_all_time = df1.join(df2)\n",
    "nft_all_time.drop(nft_all_time.columns[[2,3]],axis=1,inplace=True)\n",
    "nft_all_time.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nft_all_time.to_csv('nft_sales.csv',index=False)"
   ]
  }
 ],
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