average_gas_price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, [0, 0, 0], [0, 0, 0])\n",
      "(1, [86686120329018, 7365609573116, 22787346013580], [16013, 1168, 3874])\n",
      "(2, [190007669309487, 18460688549473, 48159860967864], [31937, 2648, 7979])\n",
      "(3, [255842465023861, 26199597338984, 69840521684376], [45311, 3739, 12064])\n",
      "(4, [347713030540631, 36389591398319, 101159900885668], [60036, 5242, 16428])\n",
      "(5, [487255272421393, 50642580237823, 141293194360693], [77581, 6906, 21281])\n",
      "(6, [632759280666441, 60190715897747, 195644346084235], [98427, 8153, 28219])\n",
      "(7, [788329026731102, 69780702645498, 230058780774865], [121990, 9447, 34132])\n",
      "(8, [889659483976395, 77988824612337, 269200839255059], [138595, 10541, 39187])\n",
      "(9, [1061719108202050, 94384849716210, 304478197277322], [157758, 12199, 44045])\n",
      "(10, [1275171802076991, 119085333521337, 347697536588427], [177654, 14364, 49789])\n",
      "(11, [1422355615862180, 130994377532027, 385951079724227], [195736, 15755, 55125])\n",
      "(12, [1517389590363436, 137856213138031, 420968136908102], [212467, 16872, 59445])\n",
      "(13, [1632898491833663, 151308829296657, 450436304470254], [229416, 18673, 63974])\n",
      "(14, [1732780122109290, 161362537193905, 483659470364149], [244107, 20055, 67581])\n",
      "(15, [1835161010822287, 172377929880850, 805368478561763], [258989, 21475, 71010])\n",
      "(16, [1912743313822307, 181549591070155, 822741112072880], [269485, 22687, 73807])\n",
      "(17, [1969595205724536, 189559131608366, 846328621188592], [280509, 24012, 77721])\n",
      "(18, [2039640214840850, 195126489953039, 864844912574674], [292327, 25031, 81040])\n",
      "(19, [2136319598808404, 201185742934228, 886413508791806], [306768, 25998, 84547])\n",
      "(20, [2242646524173930, 209687705177006, 905088225698784], [321511, 27132, 87604])\n",
      "(21, [2314514021713356, 216300546778208, 936790464050058], [336168, 28266, 91895])\n",
      "(22, [2380715586042178, 222987229199388, 953756924458996], [348346, 29257, 95190])\n",
      "(23, [2458533233822902, 228643310543917, 976320805873740], [362852, 30217, 98946])\n",
      "(24, [2542362883822902, 236406229037952, 996135138532803], [377834, 31385, 102580])\n",
      "(25, [2642903586853875, 245190738936034, 1019725537809075], [393848, 32745, 106396])\n",
      "(26, [2703997083584270, 249322488676039, 1036278875947211], [406660, 33473, 109658])\n",
      "(27, [2767462398584268, 253330939696420, 1052771220937930], [420386, 34275, 112911])\n",
      "(28, [2833888077688524, 257954620290678, 1070315320248076], [432425, 35043, 116076])\n",
      "(29, [2918632421657210, 262803944179205, 1094061565460788], [447589, 35752, 119902])\n",
      "(30, [2998078254875341, 268432135156706, 1109414426261788], [462695, 36670, 122937])\n",
      "('iteration', 31)\n",
      "(0, 6461226272)\n",
      "(1, 7307490442)\n",
      "(2, 9000617501)\n"
     ]
    }
   ],
   "source": [
    "IDEX_address = ['0x2a0c0DBEcC7E4D658f48E01e3fA353F44050c208'.lower()]\n",
    "Kyber_address = [\n",
    "'0x964F35fAe36d75B1e72770e244F6595B68508CF5'.lower(),\n",
    "'0x63825c174ab367968EC60f061753D3bbD36A0D8F'.lower(),\n",
    "'0x818E6FECD516Ecc3849DAf6845e3EC868087B755'.lower()\n",
    "]\n",
    "Forkdelta_address = ['0x8d12a197cb00d4747a1fe03395095ce2a5cc6819'.lower()]\n",
    "\n",
    "contract_address = [IDEX_address, Kyber_address, Forkdelta_address]\n",
    "contract_number = 3\n",
    "\n",
    "chunkSize = 1000000\n",
    "chunkNum = 40\n",
    "\n",
    "transactions_chunk = pd.read_csv('../../transactions.csv',sep='\\s+|\"',engine='python',chunksize=chunkSize,skiprows=1,names=['first_Na','id','hash','nonce','block_hash','block_number','transaction_index','from_address','to_address','value','gas','gas_price','input','block_timestamp','Last_Na'],usecols=range(1,14))\n",
    "\n",
    "gas_price_sum = [0]*contract_number\n",
    "length = [0]*contract_number\n",
    "\n",
    "i=0\n",
    "for chunk in transactions_chunk:\n",
    "    \n",
    "    print(i, gas_price_sum, length)\n",
    "    \n",
    "    if(i>=chunkNum):\n",
    "        break\n",
    "    i+=1\n",
    "    \n",
    "    for num in range(0, contract_number):\n",
    "        for contract in contract_address[num]:\n",
    "            gas_price_sum[num] = gas_price_sum[num] + numpy.sum((chunk.loc[chunk['to_address'] == contract])['gas_price'].astype(int))\n",
    "            length[num] = length[num] + len(chunk.loc[chunk['to_address'] == contract].index)\n",
    "            gas_price_sum[num] = gas_price_sum[num] + numpy.sum((chunk.loc[chunk['from_address'] == contract])['gas_price'].astype(int))\n",
    "            length[num] = length[num] + len(chunk.loc[chunk['from_address'] == contract].index)\n",
    "\n",
    "print('iteration', i)\n",
    "for num in range(0, contract_number):\n",
    "    print(num, gas_price_sum[num]/length[num])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "objects = ('IDEX', 'Kyber', 'ForkDelta')\n",
    "y_pos = numpy.arange(len(objects))\n",
    "performance = [gas_price_sum[0]/length[0], gas_price_sum[1]/length[1], gas_price_sum[2]/length[2]]\n",
    "\n",
    "plt.bar(y_pos, performance, align='center', alpha=0.9)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Avg Gas Price')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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obApcPzC/tG2byMHA98dbkGRekoVJFi5btmwaS5QkDeozFDJOW43bMXkdMAf4+HjLq2p+Vc2pqjmzZ8+exhIlSYP6vCDeUmDzgfnNgBvGdkqyB/B+4EVV9V891iNJmkKfI4WLgK2TbJXkUcABwILBDkl2AD4P7FNVN/ZYiyRpCL2FQlXdCxwKnAlcDXyjqhYlOSbJPm23jwMbAKcluSzJgglWJ0maAb1+n0JVnQGcMabtqIHp/i8OLkkamp9oliR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1eg2FJHsluSbJ4iRHjrN8vSRfb5f/NMmWfdYjSZpcb6GQZG3gOGBvYFtgbpJtx3Q7GLilqv4U+BTwv/uqR5I0tT5HCjsBi6tqSVXdDZwK7Dumz77Al9rpbwK7J0mPNUmSJrFOj+veFLh+YH4psPNEfarq3iS3AZsAvxvslGQeMK+dvT3JNb1U3L9ZjHlsMykfGNU9Tyu34apx+62a1Xn7PXWYTn2Gwnh7/PUQ+lBV84H501HUKCVZWFVzRl3H6sxtuGrcfqvmkbD9+jx8tBTYfGB+M+CGifokWQfYGLi5x5okSZPoMxQuArZOslWSRwEHAAvG9FkAvKGd3g/4l6paYaQgSZoZvR0+as8RHAqcCawNnFRVi5IcAyysqgXAPwGnJFlMM0I4oK96HiZW+0NgDwNuw1Xj9ls1a/z2izvmkqTl/ESzJKljKEiSOobCNEhye/t7yyR3Jbk0ydVJLkzyhoF+ByVZluSygZ9tk8xJcmV7Qp4kT0+yJMlGo3pMKytJJTllYH6d9rF+b4rbHZTks6twv7smua3d5tckOT/Jy8bpd3mSr41pS5IPJPllkl8kOTfJnz/UWoao9b72b76oreeIJDPyP9g+N1/7EG97+zTWcOU0rOdPkpya5FdJrkpyRpJtpmG97xuy3+0D0y9pnz9bJDkkyevb9oOSPGWg3zuSPGZg/owkj1vVmntRVf6s4g9we/t7S+DKgfanAZcBb2znDwI+O8E6jgfe107/AJg76se1stsAuBRYv53fu33s35vidhNukyHucx1g18H7ALYHrgN2H2j7M+BnwG+Bxw60HwqcATymnd8T+BXw6D6fJ+30E4EfAh+aob/Pg7bT2O04bN2rWMOD/j8e4joCXAAcMuZvvsvA/Nqr+vcZph+we/t8efo4fc4D5gzMXwfMmom/9ar+OFLoUVUtAY4ADhui+/uANyd5N7BuVX1tqhs8DH0feGk7PRfoHkOSnZL8uN2j/3GSZ4y9cZKXJrkgyawks5N8K8lF7c8L2j5HJ5mf5Czgy2PXUVWXAcfQvOAv91rgFOAsYJ+B9vcAb6+qO9vbngX8GDjwoW+C4VTVjTSf0j+0HbGsneTj7WO9Islbl/dN8u4kP2tHFx9t256e5AdJLk7yoyTPbNtPTvLpdhsvSbJfu5qPAru0I5V3tnuypyX5LnBWkg2SnJPkkva+xl6Shon6tCOAq5N8oR0FnZVk/XbZs9u6LwDeNg2bbjfgnqo6YWBbXgas3Y70vgr8LMmHkxw+UPuxSQ5rR5bnJzm9HWWckGStdruu326fr7S3OSLNCP7KJO8Ysy12Ab4AvLSqftW2HZ3kXe02nwN8pV3f4cBTgHOTnNv2vS7JrHb6dWmOKlyW5PPtc2Ht9m95Zbut3zkN2244o06lNeGHCUYKbdvjgLva6YOAZTR70Mt/1h/o+1bgPuAZo35MD2UbANvRXMPq0e1j25V27xTYiHaPFNgD+NbANvks8ErgR8Dj2/avAi9sp7cArm6njwYu5oERSXcfA7Vsv7x/O/8Lmo/47wksGKjn5nEex+HAJ/t8noxpuwV4Ek1AfKBtWw9YCGxFM+L6MQ+MZp7Q/j4H2Lqd3pnmMz4AJwOn0Rwa3pbm+mMrbKd2uy8dWN86wEbt9CxgMQ+8O/H2yfq0z/t7ge3bZd8AXtdOXwG8qJ3+OKs+UjgM+NQ47bsCdwBb1QP/i5e002vR7NFv0vb7I80ofm3gbGC/sX8f4Nk0o8vHAhsAi4Ad2mX30LyFfrsxNRwNvKudPo9JRgrL52lGsd+l2RGE5ojB69v7P3ug/+Nm6n+5z8tcqDH2Uh5fr6pDx+3ZvAD8J80/82p3faequiLN5c/n0hyWGbQx8KUkW9NcymTdgWW70exZ7VlVv2/b9gC2zQPXR9woyYbt9IKqumuSUrobJXkOsKyqfp1kKXBSksfThO9Et53J92kvr3VPYLuBPfuNga1ptsMX64HRzM1JNgCeD5w2sH3WG1jnd6rqfuCqJE+a5L7PrqrlVxAI8JEkfwncT3NdsicB/zGm1vH6AFxbzR47NKG9ZZKNaV7M/rVtP4XmOd6XC6vqWoCqui7JTUl2aGu8tKpuarfXhdWM4klznumFNDszg14InF5Vd7T9vg3sQnOI9B6aoD6YZidiVexOEwAXtbWtD9xIExRPS/IZ4P/SjHJnhKHQvx2Aq6fqlObk6MbAXwGnJzlz+QvBamYB8A80e2SbDLR/GDi3ql7ZBsd5A8uW0Oy5bUOzhwzN3t3zxr74t/84d0xRw+A2nws8M8l17fxGwKuq6sQkdyR52vIXiNaOwL8yA5I8jSacbqR5wX17VZ05ps9erBhSawG3VtX2E6z6vwZXMUkJg9vxQGA28OyquqfdXo8e03+yPoP3eR/Ni1sfAbuI5uoH4xn7vDiRZkT0J8BJA+1jaxqvxsm22/3Aq4EfJnlfVX1kkr5TCfClqnrvCguSv6B5PXhbe39vWoX7GZrnFHrUvvj9A/CZKfqtD3wCeFtV/Qz4Z+D9fdfXk5OAY9rHMWhjmhO90PyjDvo18D+AL+eBd/+cxcB5gSQTvQA+SJLtgL8Djkvzzp79aYb5W1bVljSXa5/bdv848OmB49970OwhfnWY+1oVSWYDJ9CcZC+aT/7/dZJ12+XbJHkszXZ4U9p3riR5QjuaujbJ/m1b2heQyfwB2HCS5RsDN7Yv9rsx/hU1h+nTqapbgduSvLBtmo5zNf8CrJfkLcsb2tHgi8bpezqwF/Acmu273E5pLr+zFvAa4N/a9nuWb3/gfOAVSR7T/h2WH94EoN1hexlwYJKDx7nvsdt7ou1/DrBfkie2j+UJSZ7anm9Yq6q+RfN83nGc2/bCkcL0e3qSS2n2oP4AfKaqvjiw/DUD/yQA/4vmyfWdqrqqbTsauCzJyVX1y5koerpU1VLgH8dZ9DGaw0dH0Pxjj73dNUkOpDkk8nKaY8fHJbmC5nl6PnDIBHe7S7vNH0Oz131YVZ2TZFfgt1X124G+59MclnoyTVg/nubE5H00h0r2neLQ1KpYP8llNIfO7qU5nPLJdtmJtMfB0wyHlgGvqKoftIG4MMndNIfl3kfzAvu5JB9o13cqcPkk930FcG+Sy2nOO9wyZvlXgO8mWUhzPujn46xjmD5jvZHmkN2dPPiF+SGpqkrySuD/pPk2xz/SHJ//zjh9725P7N5aVYOHCy+gOfH+32ieD6e37fOBK5JcUlUHJjkZuLBddmJVXTpm/Te3I7nzk4y9nPbJwAlJ7gKe1677+0n+vap2G1jHVe3f8Kw2pO6hGRncBXwxD7xleYWRRF+8zIWkNVL7gnoJsP/ynat2R+FdVbXCZ1nU8PCRpDVOmq/+XQycs7qNtkfNkYIkqeNIQZLUMRQkSR1DQZLUMRQkSR1DQZLU+f8GO4ZNPUcRmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "objects = ('IDEX', 'MakerDAO', 'Decentraland', 'CryptoKitties')\n",
    "y_pos = numpy.arange(len(objects))\n",
    "performance = [5413484064, 6532569929, 11469721042, 2605700931]\n",
    "\n",
    "plt.bar(y_pos, performance, align='center', alpha=0.9)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Avg Gas Price')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}