{"id":181,"date":"2021-05-20T18:05:12","date_gmt":"2021-05-20T18:05:12","guid":{"rendered":"http:\/\/www.plutohash.com\/?p=181"},"modified":"2021-05-21T11:15:19","modified_gmt":"2021-05-21T11:15:19","slug":"empty-blocks-in-the-bitcoin-blockchain","status":"publish","type":"post","link":"http:\/\/www.plutohash.com\/2021\/05\/20\/empty-blocks-in-the-bitcoin-blockchain\/","title":{"rendered":"Empty Blocks in the Bitcoin Blockchain?"},"content":{"rendered":"\n
Empty Blocks in the Bitcoin Blockchain?<\/p>\n\n\n\n
\nIn this notebook we'll have a brief look at a peculiar case: empty blocks in the Bitcoin Blockchain.\n\nPretty interesting there are \"empty\" blocks in the Bitcoin Blockchain. Well, they are not entirely empty. They just have only one transaction, which is the coinbase reward. \n\nNo other transactions. Miners earn fees from transactions, so why they should mine empty blocks? \n\nHere's a brief explanation:\nWhen a mining pool receives a new block, it needs to download the full block, validate its transactions and define a new block to mine on. To avoid wasting hashing power, they just start mining a new block with only the coinbase transaction. By mining an empty block, a miner removes the risk of duplicate transactions in their new block, which would instantly be rejected by the Bitcoin network. If the Bitcoin network rejects a block due to duplicate transactions, the miner that mined the block loses out on the coinbase reward, hence the incentive to mine empty blocks.\n\nBut let's start playing our favourite game.<\/code><\/pre>\n\n\n\n
In [1]:<\/p>\n\n\n\n
# First we import blocksci<\/em>\nimport<\/strong> blocksci\n\n# We import Counter from collections, which we will use to count items in the lists we'll create<\/em>\nfrom<\/strong> collections import<\/strong> Counter\n\n# we instantiate the chain object<\/em>\nchain =<\/strong> blocksci.<\/strong>Blockchain(\"\/BlockSci\/config_file\")\n\n# we also import <\/em>\nimport<\/strong> pandas as<\/strong> pd\nimport<\/strong> matplotlib.pyplot as<\/strong> plt\nimport<\/strong> seaborn as<\/strong> sns\n%<\/strong>matplotlib<\/strong> inline\nsns.<\/strong>set()\n\n%<\/strong>time<\/strong> \n<\/pre>\n\n\n\n
CPU times: user 2 \u00b5s, sys: 2 \u00b5s, total: 4 \u00b5s\nWall time: 7.63 \u00b5s\n<\/pre>\n\n\n\n
Let's start to collect all miners information from all the Blockchain. We will use the function miner() that returns the name of the Miner if he chose to write it in the Block.<\/code><\/pre>\n\n\n\n
In [2]:<\/p>\n\n\n\n
#instantiate the list of Miners of all Blocks in Bitcon Blockchain <\/em>\nlist_of_Miners =<\/strong> []\n\n#collect all Miners from Blockchain<\/em>\nfor<\/strong> x in<\/strong> chain :\n list_of_Miners.<\/strong>append(x.<\/strong>miner())\n<\/pre>\n\n\n\n
Let's see what we found:<\/code><\/pre>\n\n\n\n
In [3]:<\/p>\n\n\n\n
#print list of blocks of the Blockchain<\/em>\nprint(len(list_of_Miners))\n<\/pre>\n\n\n\n
684080\n<\/pre>\n\n\n\n
In [4]:<\/p>\n\n\n\n
#Enumerate Miners and Blocks mined each one<\/em>\nCounter(list_of_Miners)\n<\/pre>\n\n\n\n
Now let's show the data in a chart:<\/code><\/pre>\n\n\n\n
In [5]:<\/p>\n\n\n\n
data1 =<\/strong> dict(Counter(list_of_Miners))\n\n# Data to plot<\/em>\nlabels =<\/strong> []\nsizes =<\/strong> []\n\nfor<\/strong> x, y in<\/strong> data1.<\/strong>items():\n labels.<\/strong>append(x)\n sizes.<\/strong>append(y)\n\n# Plot<\/em>\n\nplt.<\/strong>figure(figsize=<\/strong>(30,30))\nplt.<\/strong>pie(sizes, labels=<\/strong>labels)\n\nplt.<\/strong>axis('equal')\nplt.<\/strong>show()\n\n#Bars<\/em>\n\nnames =<\/strong> list(data1.<\/strong>keys())\nvalues =<\/strong> list(data1.<\/strong>values())\n\n#tick_label does the some work as plt.xticks()<\/em>\nplt.<\/strong>bar(range(len(data1)),values,tick_label=<\/strong>names)\nplt.<\/strong>savefig('bar.png')\n\n# plotting a line plot after changing it's width and height<\/em>\nplt.<\/strong>xticks(rotation=<\/strong>'vertical')\nfig =<\/strong> plt.<\/strong>gcf()\nfig.<\/strong>set_size_inches(20,10) \n\nplt.<\/strong>show()\n<\/pre>\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n\n\n\n
So 329360 blocks on a total of 684080 blocks shows 'Unknown' as Miner. That's 48.14% of the total.<\/code><\/pre>\n\n\n\n
In [6]:<\/p>\n\n\n\n
#Now We exclude 'Unknown', in order to make the chart more readable<\/em>\ndata1.<\/strong>pop('Unknown', None<\/strong>)\n\n# Data to plot<\/em>\nlabels =<\/strong> []\nsizes =<\/strong> []\n\nfor<\/strong> x, y in<\/strong> data1.<\/strong>items():\n labels.<\/strong>append(x)\n sizes.<\/strong>append(y)\n\n# Plot<\/em>\n\nplt.<\/strong>figure(figsize=<\/strong>(30,30))\nplt.<\/strong>pie(sizes, labels=<\/strong>labels)\n\nplt.<\/strong>axis('equal')\nplt.<\/strong>show()\n\n#Bars<\/em>\n\nnames =<\/strong> list(data1.<\/strong>keys())\nvalues =<\/strong> list(data1.<\/strong>values())\n\n#tick_label does the some work as plt.xticks()<\/em>\nplt.<\/strong>bar(range(len(data1)),values,tick_label=<\/strong>names)\nplt.<\/strong>savefig('bar.png')\n\n# plotting a line plot after changing it's width and height<\/em>\nplt.<\/strong>xticks(rotation=<\/strong>'vertical')\nfig =<\/strong> plt.<\/strong>gcf()\nfig.<\/strong>set_size_inches(20,10) \n\nplt.<\/strong>show()\n<\/pre>\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n\n\n\n
Now we'll check the number of empty blocks and see its distribution between the miners.<\/code><\/pre>\n\n\n\n
In [7]:<\/p>\n\n\n\n
# Let's collect all blocks with zero fees (except coinbase reward)<\/em>\n# We instantiate a list variable to collect blocks with zero fees<\/em>\nzero_fees_blocks =<\/strong> []\n \n# collect blocks with zero fees <\/em>\nfor<\/strong> x in<\/strong> chain :\n if<\/strong> x.<\/strong>fee ==<\/strong> 0 :\n \n zero_fees_blocks.<\/strong>append(x)\n<\/pre>\n\n\n\n
In [8]:<\/p>\n\n\n\n
# We print the lenght of the list of blocks with zero fees and the lenght of the used blockchain data<\/em>\nprint(\"Number of blocks without fees:\", len(zero_fees_blocks))\nprint(\"On a total number of blocks: \", len(chain))\n<\/pre>\n\n\n\n
Number of blocks without fees: 125451\nOn a total number of blocks: 684080\n<\/pre>\n\n\n\n
So we found 125.451 blocks with zero fees. On a total of 684080 blocks analyzed, it’s about 18,33% of the blocks.In [9]:<\/p>\n\n\n\n
# So we instantiate a list variable to collect miners name from the list of the blocks with zero fees<\/em>\nlist_zero_fee_block_miners =<\/strong> []\n\n# Collect names of miners that have mined zero fee blocks<\/em>\nfor<\/strong> x in<\/strong> zero_fees_blocks :\n list_zero_fee_block_miners.<\/strong>append(x.<\/strong>miner())\n<\/pre>\n\n\n\n
In [10]:<\/p>\n\n\n\n
# Eumerate list of Miners of zero fees Blocks and put in a dict call data<\/em>\ndata =<\/strong> dict(Counter(list_zero_fee_block_miners))\ndata\n<\/pre>\n\n\n\n
On a total of 125451 blocks without fees, 120078 are labeled as ‘Unknown’. It makes the 95.71%. So most of the empty blocks are mined by Unknown entities. It is quite a different percentage between the distribution between known and unknown miners for all the Blocks. Let’s see the data in charts.In [11]:<\/p>\n\n\n\n
# Data to plot<\/em>\nlabels =<\/strong> []\nsizes =<\/strong> []\n\nfor<\/strong> x, y in<\/strong> data.<\/strong>items():\n labels.<\/strong>append(x)\n sizes.<\/strong>append(y)\n\n# Plot<\/em>\n\nplt.<\/strong>figure(figsize=<\/strong>(20,20))\nplt.<\/strong>pie(sizes, labels=<\/strong>labels)\n\nplt.<\/strong>axis('equal')\nplt.<\/strong>show()\n\n#Bars<\/em>\n\nnames =<\/strong> list(data.<\/strong>keys())\nvalues =<\/strong> list(data.<\/strong>values())\n\n#tick_label does the some work as plt.xticks()<\/em>\nplt.<\/strong>bar(range(len(data)),values,tick_label=<\/strong>names)\nplt.<\/strong>savefig('bar.png')\n\n# plotting a line plot after changing it's width and height<\/em>\nplt.<\/strong>xticks(rotation=<\/strong>'vertical')\nfig =<\/strong> plt.<\/strong>gcf()\nfig.<\/strong>set_size_inches(20,10) \n\nplt.<\/strong>show()\n<\/pre>\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n\n\n\n
In [ ]:<\/p>\n\n\n\n
\n<\/pre>\n\n\n\n
In [12]:<\/p>\n\n\n\n
#Now We exclude 'Unknown', in order to make the chart more readable<\/em>\ndata.<\/strong>pop('Unknown', None<\/strong>)\n\n# Data to plot<\/em>\nlabels =<\/strong> []\nsizes =<\/strong> []\n\nfor<\/strong> x, y in<\/strong> data.<\/strong>items():\n labels.<\/strong>append(x)\n sizes.<\/strong>append(y)\n\n# Plot<\/em>\n\nplt.<\/strong>figure(figsize=<\/strong>(20,20))\nplt.<\/strong>pie(sizes, labels=<\/strong>labels)\n\nplt.<\/strong>axis('equal')\nplt.<\/strong>show()\n\n#Bars<\/em>\n\nnames =<\/strong> list(data.<\/strong>keys())\nvalues =<\/strong> list(data.<\/strong>values())\n\n#tick_label does the some work as plt.xticks()<\/em>\nplt.<\/strong>bar(range(len(data)),values,tick_label=<\/strong>names)\nplt.<\/strong>savefig('bar.png')\n\n# plotting a line plot after changing it's width and height<\/em>\nplt.<\/strong>xticks(rotation=<\/strong>'vertical')\nfig =<\/strong> plt.<\/strong>gcf()\nfig.<\/strong>set_size_inches(20,10) \n\nplt.<\/strong>show()\n<\/pre>\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n\n\n\n
For this tutorial we finish here. There are many things that can be discovered and analyzed in the Bitcoin Blockchain. If you are curious, join our beta tester program, and join the fray! :) And don't forget to smile!<\/code><\/pre>\n\n\n\n
Empty Blocks in the Bitcoin Blockchain? In [1]: # First we import blocksci import blocksci # We import Counter from collections, which we will use to count items in the lists we’ll create from collections import Counter # we instantiate the chain object chain = blocksci.Blockchain(“\/BlockSci\/config_file”) # we also import import pandas as pd import matplotlib.pyplot …<\/p>\n
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