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BitcoinPaper

Text Generation
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Safetensors
mistral
text-generation-inference
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  1. excel_sheet/csv/Bitcoin price and mining costs.csv +155 -0
  2. excel_sheet/csv/Electricity consumption and carbon footprint.csv +14 -0
  3. excel_sheet/csv/Hardware count by year.csv +14 -0
  4. excel_sheet/csv/Hardware mentions.csv +0 -0
  5. excel_sheet/csv/Power efficiency.csv +53 -0
  6. excel_sheet/excel_generator.ipynb +83 -0
  7. excel_sheet/joules_per_coin.csv +155 -0
  8. excel_sheet/main.ipynb +831 -0
  9. excel_sheet/mega_sheet.xlsx +0 -0
excel_sheet/csv/Bitcoin price and mining costs.csv ADDED
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+ date,Bitcoin price,Mining costs with C = 0.03 USD/kWh; PUE = 1.0,Mining costs with C = 0.05 USD/kWh; PUE = 1.1,Mining costs with C = 0.07 USD/kWh; PUE = 1.2,Price-cost ratio with C = 0.03 USD/kWh; PUE = 1.0,Price-cost ratio with C = 0.05 USD/kWh; PUE = 1.1,Price-cost ratio with C = 0.07 USD/kWh; PUE = 1.2
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+ 2015-05-31,232.155,48.434941640909045,88.79739300833326,135.61783659454534,4.793130581660856,2.614434862724103,1.7118323505931627
105
+ 2015-04-30,247.289,55.88414490957089,102.45426566754666,156.47560574679852,4.425029682392949,2.4136525540325167,1.5803677437117671
106
+ 2015-03-31,260.371,53.88768772429695,98.79409416121109,150.88552562803147,4.831734501805383,2.6354915464392996,1.725619464930494
107
+ 2015-02-28,226.998,51.418433725898346,94.26712849748033,143.9716144325154,4.414720238467047,2.4080292209820247,1.5766857994525165
108
+ 2015-01-31,314.136,44.816118377626125,82.16288369231458,125.48513145735318,7.009442391977178,3.8233322138057324,2.5033722828489915
109
+ 2014-12-31,379.366,45.98400032113993,84.30400058875655,128.7552008991918,8.24995644899551,4.499976244906641,2.9464130174983967
110
+ 2014-11-30,324.768,53.59015647813684,98.24862020991755,150.05243813878315,6.060217423184713,3.305573139918934,2.1643633654231116
111
+ 2014-10-31,383.615,51.778443489527795,94.92714639746765,144.97964177067783,7.408778135201887,4.041151710110118,2.645992191143531
112
+ 2014-09-30,475.725,47.2755975044971,86.6719287582447,132.3716730125919,10.062802483982283,5.488801354899426,3.5938580299936715
113
+ 2014-08-31,594.908,33.36420491956989,61.16770901921148,93.41977377479571,17.830726116031453,9.725850608744429,6.368116470011232
114
+ 2014-07-31,640.806,30.45194575119578,55.828567210525605,85.26544810334819,21.043187362660955,11.478102197815065,7.515424058093198
115
+ 2014-06-30,630.229,30.025285590176164,55.04635691532298,84.07079965249326,20.989941897711766,11.449059216933689,7.496407820611345
116
+ 2014-05-31,457.76,20.42867401565559,37.45256902870192,57.20028724383567,22.40771964196961,12.222392531983422,8.002757014989145
117
+ 2014-04-30,478.375,15.823894561489087,29.01047336273,44.30690477216945,30.231179697331296,16.48973438036252,10.796849891904031
118
+ 2014-03-31,565.61,11.44054962111134,20.974340972037464,32.033538939111764,49.43905832603315,26.966759086927166,17.656806545011833
119
+ 2014-02-28,832.58,8.666222286875485,15.888074192605059,24.26542240325136,96.0718491217213,52.40282679366615,34.31137468632903
120
+ 2014-01-31,771.4,5.227492283075604,9.583735852305274,14.63697839261169,147.56597584992426,80.49053228177686,52.70213423211581
121
+ 2013-12-31,955.85,2.6329290382232236,4.827036570075911,7.372201307025026,363.03674961366795,198.02004524381886,129.65598200488142
122
+ 2013-11-30,206.18,1.3276719313406669,2.43406520745789,3.7174814077538683,155.29438796811948,84.70602980079242,55.462281417185515
123
+ 2013-10-31,132.18,0.907471847787866,1.663698387611088,2.540921173806025,145.65741110560478,79.4494969666935,52.020503966287414
124
+ 2013-09-30,138.34,0.29510825840839555,0.5410318070820586,0.8263031235435077,468.7771218132212,255.69661189812064,167.420400647579
125
+ 2013-08-31,104.0,0.15764135175352448,0.2890091448814616,0.4413957849098686,659.7253756273681,359.85020488765525,235.61620558120288
126
+ 2013-07-31,88.05,0.09582875711433862,0.17568605470962084,0.26832051992014816,918.8264843604582,501.1780823784316,328.15231584302074
127
+ 2013-06-30,129.3,0.0662617797063722,0.1214799294616824,0.1855329831778422,1951.3511495310108,1064.373354289642,696.9111248325038
128
+ 2013-05-31,116.99,0.13214146271715144,0.2422593483147777,0.3699960956080241,885.3390721912689,482.9122211952374,316.19252578259596
129
+ 2013-04-30,104.0,0.14064952739703973,0.2578574668945729,0.3938186767117113,739.4265869548091,403.3235928844412,264.0809239124318
130
+ 2013-03-31,34.5,0.3369640110595453,0.6177673536091666,0.943499230966727,102.38482113124972,55.84626607159074,36.56600754687489
131
+ 2013-02-28,20.499,0.1176949122424643,0.21577400577785125,0.32954575427890004,174.170655378627,95.00217566106926,62.20380549236679
132
+ 2013-01-31,13.3041,0.037298252466263344,0.0683801295214828,0.10443510690553738,356.6949956176552,194.5609067005392,127.39106986344825
133
+ 2012-12-31,12.562,1.6169694862209858,2.9644440580718077,4.52751456141876,7.768854086021505,4.237556774193548,2.7745907450076803
134
+ 2012-11-30,10.57,1.29364007747691,2.3716734753743354,3.6221922169353484,8.170742530345473,4.456768652915711,2.9181223322662397
135
+ 2012-10-31,12.4,0.6012881790871224,1.1023616616597247,1.683606901443943,20.622391111739,11.248576970039451,7.365139682763927
136
+ 2012-09-30,9.9654,1.2482682801066916,2.2884918468622684,3.4951511842987366,7.9833799823450144,4.354570899460916,2.851207136551791
137
+ 2012-08-31,9.5503,0.665392297446586,1.2198858786520745,1.8630984328504407,14.352886314808362,7.82884708080456,5.1260308267172725
138
+ 2012-07-31,6.629,0.5872533378774004,1.076631119441901,1.6443093460567215,11.288143587161562,6.157169229360851,4.0314798525577
139
+ 2012-06-30,5.2748,0.5566023881687416,1.0204377116426933,1.5584866868724767,9.476782910246644,5.169154314679987,3.384565325088087
140
+ 2012-05-31,5.0,0.6896171215066342,1.2642980560954964,1.9309279402185762,7.250400032232812,3.9547636539451694,2.5894285829402897
141
+ 2012-04-30,4.827,0.4907926096332722,0.8997864509943325,1.3742193069731623,9.835111420293815,5.364606229251171,3.5125397929620767
142
+ 2012-03-31,4.9213,0.3900528987443227,0.7150969810312584,1.0921481164841038,12.617006605624233,6.882003603067762,4.506073787722939
143
+ 2012-02-29,6.0756,0.9132002016877178,1.6742003697608163,2.55696056472561,6.65308657266114,3.6289563123606206,2.3761023473789784
144
+ 2012-01-31,5.2677,0.45832520644041735,0.8402628784740986,1.2833105780331688,11.49336742989021,6.269109507212841,4.104774082103646
145
+ 2011-12-31,3.06,0.25595413499382097,0.46924924748867186,0.7166715779826988,11.955266907775771,6.521054676968601,4.269738181348489
146
+ 2011-11-30,3.15,0.4796976274685408,0.8794456503589916,1.3431533569119145,6.5666366052781475,3.5818017846971704,2.345227359027909
147
+ 2011-10-31,5.0324,0.5773874194570489,1.058543602337923,1.616684774479737,8.715811655079461,4.754079084588796,3.1127898768140927
148
+ 2011-09-30,8.21,0.7198344734148694,1.3196965345939273,2.015536525561634,11.405399856792146,6.221127194613897,4.073357091711481
149
+ 2011-08-31,13.0946,0.6759100916685336,1.2391685013923117,1.892548256671894,19.3732867158042,10.567247299529562,6.919030969930071
150
+ 2011-07-31,15.397,0.7322261361647864,1.3424145829687755,2.050233181261402,21.02765694850167,11.469631062819088,7.509877481607739
151
+ 2011-06-30,9.57,0.46577451854356544,0.8539199506632035,1.3041686519219833,20.546422397525138,11.207139489559163,7.33800799911612
152
+ 2011-05-31,3.0331,0.13897595847279975,0.2547892572001329,0.3891326837238393,21.824638112451915,11.904348061337407,7.79451361158997
153
+ 2011-04-30,0.7741,0.04249535451983146,0.07790814995302436,0.11898699265552809,18.216108766400524,9.936059327127555,6.50575313085733
154
+ 2011-03-31,0.9202,0.03742689171249762,0.06861596813957899,0.10479529679499335,24.586599578418266,13.41087249731905,8.780928420863665
155
+ 2011-02-28,0.7,0.0165068047920325,0.030262475452059588,0.046219053417691,42.40675338560227,23.130956392146686,15.145269066286524
excel_sheet/csv/Electricity consumption and carbon footprint.csv ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ,Daily TWH,Yearly TWH,Cumulative TWH,,Daily Carbon (TCO2),Yearly Carbon (TCO2),Cumulative Carbon (TCO2),Intensity (gCO2/Wh)
2
+ 2011,6.757189163836875e-05,0.03240658251152295,0.032406582511522966,,34.94838267786807,16760.780546974078,16760.780546974085,0.5172029645833334
3
+ 2012,0.00021343997218117018,0.037793312648847194,0.0701998951603701,,110.14467624489268,19503.05813568936,36226.319961760695,0.5160452145833334
4
+ 2013,0.00034754663304546545,0.023301122714039342,0.09350101787440948,,179.46561122423435,12032.198941023584,48281.91594284212,0.51637850625
5
+ 2014,0.006069888042390471,1.3428070164045518,1.4363080342789618,,3122.902907919117,690862.1554496551,738967.5897796098,0.5144910229166667
6
+ 2015,0.008299383930427468,2.4899098963740185,3.926217930652977,,4233.62050534279,1270134.4680655166,2002813.3267478375,0.5101126229166667
7
+ 2016,0.01385951426454198,4.177671049789533,8.103888980442509,,7029.458175665885,2118888.3936082306,4110241.351964354,0.5071936895833333
8
+ 2017,0.0435178283238431,9.28867289305591,17.392561873498426,,22013.188016858487,4698609.988079465,8797905.34966553,0.50584298125
9
+ 2018,0.11469454013852293,38.36003309393567,55.75259496743408,,57879.00709942638,19357858.055811778,28134772.90230424,0.5046361145833334
10
+ 2019,0.19851745866452566,57.28957629155716,113.04217125899123,,102274.90533306959,29515217.609612234,58238592.42372995,0.5151935049999999
11
+ 2020,0.25920274750872835,82.3883224548953,195.43049371388673,,133539.57199465178,42445928.61660771,100684521.04033776,0.5151935049999999
12
+ 2021,0.2584307768296501,89.76538833454197,285.19588204842836,,133141.8577147402,46246545.04375879,146931066.08409637,0.5151935049999999
13
+ 2022,0.47220481104563167,122.19658032761679,407.39246237604493,,243276.85168046167,62954884.517998934,209885950.6020952,0.5151935049999999
14
+ 2023,0.38424348116239887,172.41936406589906,579.8118264419443,,197959.7458334577,88829336.50298157,298715287.1050769,0.5151935049999999
excel_sheet/csv/Hardware count by year.csv ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Year,Count
2
+ 2011,1147
3
+ 2012,803
4
+ 2013,2001
5
+ 2014,2379
6
+ 2015,1899
7
+ 2016,1320
8
+ 2017,1569
9
+ 2018,1160
10
+ 2019,575
11
+ 2020,363
12
+ 2021,644
13
+ 2022,422
14
+ 2023,246
excel_sheet/csv/Hardware mentions.csv ADDED
The diff for this file is too large to render. See raw diff
 
excel_sheet/csv/Power efficiency.csv ADDED
@@ -0,0 +1,53 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Quarter,Average power efficiency (TH/J),Max attainable power efficiency (TH/J)
2
+ 2011Q1,1676752.7777777778,3780000.0
3
+ 2011Q2,1662900.8656853999,3780000.0
4
+ 2011Q3,1777678.0986995462,23300000.0
5
+ 2011Q4,2189561.391953277,23300000.0
6
+ 2012Q1,3438894.7593923067,23300000.0
7
+ 2012Q2,4121574.227676913,23300000.0
8
+ 2012Q3,4435689.641889276,23300000.0
9
+ 2012Q4,4657043.739837398,250000000.0
10
+ 2013Q1,63705216.33670033,1250000000.0
11
+ 2013Q2,226422589.96336854,1250000000.0
12
+ 2013Q3,563914181.5308821,1250000000.0
13
+ 2013Q4,629530314.7448593,1430000000.0
14
+ 2014Q1,596719438.2844583,2000000000.0
15
+ 2014Q2,699611418.530675,2000000000.0
16
+ 2014Q3,960426734.5761482,5350000000.0
17
+ 2014Q4,1111303271.7713087,5350000000.0
18
+ 2015Q1,1324885385.435234,5350000000.0
19
+ 2015Q2,1367142416.7579641,5350000000.0
20
+ 2015Q3,1527599750.5560048,7140000000.0
21
+ 2015Q4,1948571985.1762028,7140000000.0
22
+ 2016Q1,2310961075.983057,7140000000.0
23
+ 2016Q2,2646975162.211039,10200000000.0
24
+ 2016Q3,4916343555.816055,10200000000.0
25
+ 2016Q4,4993386626.659032,10200000000.0
26
+ 2017Q1,5718996362.770835,10300000000.0
27
+ 2017Q2,5655282841.335991,10300000000.0
28
+ 2017Q3,5762293216.092274,10300000000.0
29
+ 2017Q4,7328707049.923111,10300000000.0
30
+ 2018Q1,8445557235.836413,11100000000.0
31
+ 2018Q2,8957676404.005846,11100000000.0
32
+ 2018Q3,8790391289.5254,15699999999.999998
33
+ 2018Q4,9208949069.734346,20000000000.0
34
+ 2019Q1,9228423944.634146,20000000000.0
35
+ 2019Q2,9527655114.651136,25300000000.0
36
+ 2019Q3,11508378100.678732,25300000000.0
37
+ 2019Q4,12860226375.38655,25300000000.0
38
+ 2020Q1,13362058195.446428,33900000000.0
39
+ 2020Q2,14323278707.146463,33900000000.0
40
+ 2020Q3,16858898759.614313,33900000000.0
41
+ 2020Q4,12325806483.739317,33900000000.0
42
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43
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44
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45
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46
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48
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49
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50
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51
+ 2023Q2,18992003845.910255,46500000000.0
52
+ 2023Q3,19578041991.652554,46500000000.0
53
+ 2023Q4,29146312722.727272,54100000000.0
excel_sheet/excel_generator.ipynb ADDED
@@ -0,0 +1,83 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 5,
6
+ "metadata": {},
7
+ "outputs": [
8
+ {
9
+ "name": "stderr",
10
+ "output_type": "stream",
11
+ "text": [
12
+ "c:\\ProgramData\\Anaconda3\\envs\\py310\\lib\\site-packages\\openpyxl\\workbook\\child.py:99: UserWarning: Title is more than 31 characters. Some applications may not be able to read the file\n",
13
+ " warnings.warn(\"Title is more than 31 characters. Some applications may not be able to read the file\")\n"
14
+ ]
15
+ },
16
+ {
17
+ "name": "stdout",
18
+ "output_type": "stream",
19
+ "text": [
20
+ "Excel file 'mega_sheet.xlsx' has been created with 5 sheets.\n"
21
+ ]
22
+ }
23
+ ],
24
+ "source": [
25
+ "import os\n",
26
+ "import pandas as pd\n",
27
+ "from openpyxl import Workbook\n",
28
+ "from openpyxl.utils.dataframe import dataframe_to_rows\n",
29
+ "\n",
30
+ "# Set the path to the \"csv\" folder\n",
31
+ "csv_folder = \"csv\"\n",
32
+ "\n",
33
+ "# Create a new Excel workbook\n",
34
+ "workbook = Workbook()\n",
35
+ "\n",
36
+ "# Remove the default sheet created by openpyxl\n",
37
+ "workbook.remove(workbook.active)\n",
38
+ "\n",
39
+ "# Iterate through all CSV files in the folder\n",
40
+ "for filename in os.listdir(csv_folder):\n",
41
+ " if filename.endswith(\".csv\"):\n",
42
+ " # Read the CSV file\n",
43
+ " csv_path = os.path.join(csv_folder, filename)\n",
44
+ " df = pd.read_csv(csv_path)\n",
45
+ " \n",
46
+ " # Create a new sheet with the CSV filename (without extension)\n",
47
+ " sheet_name = os.path.splitext(filename)[0]\n",
48
+ " sheet = workbook.create_sheet(title=sheet_name)\n",
49
+ " \n",
50
+ " # Write the DataFrame to the sheet, including column names\n",
51
+ " for row in dataframe_to_rows(df, index=False, header=True):\n",
52
+ " sheet.append(row)\n",
53
+ "\n",
54
+ "# Save the Excel workbook\n",
55
+ "output_filename = \"mega_sheet.xlsx\"\n",
56
+ "workbook.save(output_filename)\n",
57
+ "\n",
58
+ "print(f\"Excel file '{output_filename}' has been created with {len(workbook.sheetnames)} sheets.\")"
59
+ ]
60
+ }
61
+ ],
62
+ "metadata": {
63
+ "kernelspec": {
64
+ "display_name": "py310",
65
+ "language": "python",
66
+ "name": "python3"
67
+ },
68
+ "language_info": {
69
+ "codemirror_mode": {
70
+ "name": "ipython",
71
+ "version": 3
72
+ },
73
+ "file_extension": ".py",
74
+ "mimetype": "text/x-python",
75
+ "name": "python",
76
+ "nbconvert_exporter": "python",
77
+ "pygments_lexer": "ipython3",
78
+ "version": "3.10.13"
79
+ }
80
+ },
81
+ "nbformat": 4,
82
+ "nbformat_minor": 2
83
+ }
excel_sheet/joules_per_coin.csv ADDED
@@ -0,0 +1,155 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ date,joules_per_coin
2
+ 2023-12-31,1397249022408.723
3
+ 2023-10-31,1773133520194.1943
4
+ 2023-09-30,1738278366342.6611
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+ 2022-12-31,1717108403802.2969
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excel_sheet/main.ipynb ADDED
@@ -0,0 +1,831 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 47,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "import pandas as pd\n",
10
+ "import numpy as np\n",
11
+ "import matplotlib.pyplot as plt\n",
12
+ "from datetime import timedelta"
13
+ ]
14
+ },
15
+ {
16
+ "cell_type": "code",
17
+ "execution_count": 48,
18
+ "metadata": {},
19
+ "outputs": [
20
+ {
21
+ "data": {
22
+ "text/plain": [
23
+ "'\\nCould you make a Google drive with a giant excel sheet with many tabs. Here are tabs needed (I added columns name in \" \", some are quite long):\\n\\n•\\t\"Hardware mentions\" (which should have 14,530 rows right?): \\no\\tColumn 1: \"Date mentioned\"\\no\\tColumn 2: \"Link to the thread\" (if you have, otherwise no need of this column\\no\\tColumn 3: \"Hardware name\" (put the after-mapping name for consistency)\\no\\tColumn 3: \"Power efficiency (TH/J)\"\\no\\tColumn 4: \"Release date\" (so that\\'s not the date it was mentioned, but the date the hardware company released it)\\n'"
24
+ ]
25
+ },
26
+ "execution_count": 48,
27
+ "metadata": {},
28
+ "output_type": "execute_result"
29
+ }
30
+ ],
31
+ "source": [
32
+ "\"\"\"\n",
33
+ "Could you make a Google drive with a giant excel sheet with many tabs. Here are tabs needed (I added columns name in \" \", some are quite long):\n",
34
+ "\n",
35
+ "•\t\"Hardware mentions\" (which should have 14,530 rows right?): \n",
36
+ "o\tColumn 1: \"Date mentioned\"\n",
37
+ "o\tColumn 2: \"Link to the thread\" (if you have, otherwise no need of this column\n",
38
+ "o\tColumn 3: \"Hardware name\" (put the after-mapping name for consistency)\n",
39
+ "o\tColumn 3: \"Power efficiency (TH/J)\"\n",
40
+ "o\tColumn 4: \"Release date\" (so that's not the date it was mentioned, but the date the hardware company released it)\n",
41
+ "\"\"\""
42
+ ]
43
+ },
44
+ {
45
+ "cell_type": "code",
46
+ "execution_count": 49,
47
+ "metadata": {},
48
+ "outputs": [
49
+ {
50
+ "data": {
51
+ "text/html": [
52
+ "<div>\n",
53
+ "<style scoped>\n",
54
+ " .dataframe tbody tr th:only-of-type {\n",
55
+ " vertical-align: middle;\n",
56
+ " }\n",
57
+ "\n",
58
+ " .dataframe tbody tr th {\n",
59
+ " vertical-align: top;\n",
60
+ " }\n",
61
+ "\n",
62
+ " .dataframe thead th {\n",
63
+ " text-align: right;\n",
64
+ " }\n",
65
+ "</style>\n",
66
+ "<table border=\"1\" class=\"dataframe\">\n",
67
+ " <thead>\n",
68
+ " <tr style=\"text-align: right;\">\n",
69
+ " <th></th>\n",
70
+ " <th>Date mentioned</th>\n",
71
+ " <th>Hardware name</th>\n",
72
+ " <th>Power efficiency (TH/J)</th>\n",
73
+ " <th>Release date</th>\n",
74
+ " </tr>\n",
75
+ " </thead>\n",
76
+ " <tbody>\n",
77
+ " <tr>\n",
78
+ " <th>0</th>\n",
79
+ " <td>2010-09-09 12:59:39</td>\n",
80
+ " <td>gtx460</td>\n",
81
+ " <td>4.270000e-07</td>\n",
82
+ " <td>Jul 2010</td>\n",
83
+ " </tr>\n",
84
+ " <tr>\n",
85
+ " <th>1</th>\n",
86
+ " <td>2010-10-06 20:25:17</td>\n",
87
+ " <td>4350</td>\n",
88
+ " <td>3.460000e-07</td>\n",
89
+ " <td>Jan 2009</td>\n",
90
+ " </tr>\n",
91
+ " <tr>\n",
92
+ " <th>2</th>\n",
93
+ " <td>2010-10-06 20:25:17</td>\n",
94
+ " <td>5770</td>\n",
95
+ " <td>1.940100e-06</td>\n",
96
+ " <td>Oct 2009</td>\n",
97
+ " </tr>\n",
98
+ " <tr>\n",
99
+ " <th>3</th>\n",
100
+ " <td>2010-10-06 20:25:17</td>\n",
101
+ " <td>5870</td>\n",
102
+ " <td>1.906000e-06</td>\n",
103
+ " <td>Sep 2009</td>\n",
104
+ " </tr>\n",
105
+ " <tr>\n",
106
+ " <th>4</th>\n",
107
+ " <td>2010-10-06 20:25:17</td>\n",
108
+ " <td>gtx260</td>\n",
109
+ " <td>2.100000e-07</td>\n",
110
+ " <td>Dec 2009</td>\n",
111
+ " </tr>\n",
112
+ " <tr>\n",
113
+ " <th>...</th>\n",
114
+ " <td>...</td>\n",
115
+ " <td>...</td>\n",
116
+ " <td>...</td>\n",
117
+ " <td>...</td>\n",
118
+ " </tr>\n",
119
+ " <tr>\n",
120
+ " <th>14530</th>\n",
121
+ " <td>2023-12-17 03:45:25</td>\n",
122
+ " <td>microbt whatsminer m30s</td>\n",
123
+ " <td>2.631579e-02</td>\n",
124
+ " <td>Apr 2020</td>\n",
125
+ " </tr>\n",
126
+ " <tr>\n",
127
+ " <th>14531</th>\n",
128
+ " <td>2023-12-17 03:45:25</td>\n",
129
+ " <td>microbt whatsminer m50s</td>\n",
130
+ " <td>3.846100e-02</td>\n",
131
+ " <td>Jul 2022</td>\n",
132
+ " </tr>\n",
133
+ " <tr>\n",
134
+ " <th>14532</th>\n",
135
+ " <td>2023-12-17 03:45:25</td>\n",
136
+ " <td>microbt whatsminer m50s</td>\n",
137
+ " <td>3.846100e-02</td>\n",
138
+ " <td>Jul 2022</td>\n",
139
+ " </tr>\n",
140
+ " <tr>\n",
141
+ " <th>14533</th>\n",
142
+ " <td>2023-12-17 03:45:25</td>\n",
143
+ " <td>microbt whatsminer m50s</td>\n",
144
+ " <td>3.846100e-02</td>\n",
145
+ " <td>Jul 2022</td>\n",
146
+ " </tr>\n",
147
+ " <tr>\n",
148
+ " <th>14534</th>\n",
149
+ " <td>2023-12-17 03:45:25</td>\n",
150
+ " <td>microbt whatsminer m50s</td>\n",
151
+ " <td>3.846100e-02</td>\n",
152
+ " <td>Jul 2022</td>\n",
153
+ " </tr>\n",
154
+ " </tbody>\n",
155
+ "</table>\n",
156
+ "<p>14535 rows × 4 columns</p>\n",
157
+ "</div>"
158
+ ],
159
+ "text/plain": [
160
+ " Date mentioned Hardware name Power efficiency (TH/J) \\\n",
161
+ "0 2010-09-09 12:59:39 gtx460 4.270000e-07 \n",
162
+ "1 2010-10-06 20:25:17 4350 3.460000e-07 \n",
163
+ "2 2010-10-06 20:25:17 5770 1.940100e-06 \n",
164
+ "3 2010-10-06 20:25:17 5870 1.906000e-06 \n",
165
+ "4 2010-10-06 20:25:17 gtx260 2.100000e-07 \n",
166
+ "... ... ... ... \n",
167
+ "14530 2023-12-17 03:45:25 microbt whatsminer m30s 2.631579e-02 \n",
168
+ "14531 2023-12-17 03:45:25 microbt whatsminer m50s 3.846100e-02 \n",
169
+ "14532 2023-12-17 03:45:25 microbt whatsminer m50s 3.846100e-02 \n",
170
+ "14533 2023-12-17 03:45:25 microbt whatsminer m50s 3.846100e-02 \n",
171
+ "14534 2023-12-17 03:45:25 microbt whatsminer m50s 3.846100e-02 \n",
172
+ "\n",
173
+ " Release date \n",
174
+ "0 Jul 2010 \n",
175
+ "1 Jan 2009 \n",
176
+ "2 Oct 2009 \n",
177
+ "3 Sep 2009 \n",
178
+ "4 Dec 2009 \n",
179
+ "... ... \n",
180
+ "14530 Apr 2020 \n",
181
+ "14531 Jul 2022 \n",
182
+ "14532 Jul 2022 \n",
183
+ "14533 Jul 2022 \n",
184
+ "14534 Jul 2022 \n",
185
+ "\n",
186
+ "[14535 rows x 4 columns]"
187
+ ]
188
+ },
189
+ "execution_count": 49,
190
+ "metadata": {},
191
+ "output_type": "execute_result"
192
+ }
193
+ ],
194
+ "source": [
195
+ "# date,hardware_name,TH/J\n",
196
+ "# 2010-09-09 12:59:39,gtx460,0.0000004270\n",
197
+ "hardware = pd.read_csv(\"../bitcoinforum/5_processing_extracted_data/hardware_instances_with_efficiency.csv\")\n",
198
+ "hardware.rename(columns={\"date\": \"Date mentioned\", \"hardware_name\": \"Hardware name\", \"TH/J\": \"Power efficiency (TH/J)\"}, inplace=True)\n",
199
+ "\n",
200
+ "# \\#,Type,Hardware name,Release date,First date used,Eff. (TH/J)\n",
201
+ "# 1,GPU,8800gts,Feb 2007,2011-03-08,1.09E-07\n",
202
+ "paper_list = pd.read_csv(\"../hardwarelist/paper_list.csv\")\n",
203
+ "paper_list[\"Hardware name\"] = paper_list[\"Hardware name\"].str.lower()\n",
204
+ "\n",
205
+ "# Merge on hardware name to get the release date\n",
206
+ "df1 = hardware.merge(paper_list[[\"Hardware name\", \"Release date\"]], on=\"Hardware name\", how=\"left\")\n",
207
+ "df1.to_csv(\"csv/Hardware mentions.csv\", index=False)\n",
208
+ "df1"
209
+ ]
210
+ },
211
+ {
212
+ "cell_type": "code",
213
+ "execution_count": 50,
214
+ "metadata": {},
215
+ "outputs": [
216
+ {
217
+ "data": {
218
+ "text/plain": [
219
+ "'\\n•\\t\"Hardware count by year\" \\no\\tHere you just remake tab S20\\n'"
220
+ ]
221
+ },
222
+ "execution_count": 50,
223
+ "metadata": {},
224
+ "output_type": "execute_result"
225
+ }
226
+ ],
227
+ "source": [
228
+ "\"\"\"\n",
229
+ "•\t\"Hardware count by year\" \n",
230
+ "o\tHere you just remake tab S20\n",
231
+ "\"\"\""
232
+ ]
233
+ },
234
+ {
235
+ "cell_type": "code",
236
+ "execution_count": 51,
237
+ "metadata": {},
238
+ "outputs": [
239
+ {
240
+ "data": {
241
+ "text/html": [
242
+ "<div>\n",
243
+ "<style scoped>\n",
244
+ " .dataframe tbody tr th:only-of-type {\n",
245
+ " vertical-align: middle;\n",
246
+ " }\n",
247
+ "\n",
248
+ " .dataframe tbody tr th {\n",
249
+ " vertical-align: top;\n",
250
+ " }\n",
251
+ "\n",
252
+ " .dataframe thead th {\n",
253
+ " text-align: right;\n",
254
+ " }\n",
255
+ "</style>\n",
256
+ "<table border=\"1\" class=\"dataframe\">\n",
257
+ " <thead>\n",
258
+ " <tr style=\"text-align: right;\">\n",
259
+ " <th></th>\n",
260
+ " <th>Year</th>\n",
261
+ " <th>Count</th>\n",
262
+ " </tr>\n",
263
+ " </thead>\n",
264
+ " <tbody>\n",
265
+ " <tr>\n",
266
+ " <th>1</th>\n",
267
+ " <td>2011</td>\n",
268
+ " <td>1147</td>\n",
269
+ " </tr>\n",
270
+ " <tr>\n",
271
+ " <th>2</th>\n",
272
+ " <td>2012</td>\n",
273
+ " <td>803</td>\n",
274
+ " </tr>\n",
275
+ " <tr>\n",
276
+ " <th>3</th>\n",
277
+ " <td>2013</td>\n",
278
+ " <td>2001</td>\n",
279
+ " </tr>\n",
280
+ " <tr>\n",
281
+ " <th>4</th>\n",
282
+ " <td>2014</td>\n",
283
+ " <td>2379</td>\n",
284
+ " </tr>\n",
285
+ " <tr>\n",
286
+ " <th>5</th>\n",
287
+ " <td>2015</td>\n",
288
+ " <td>1899</td>\n",
289
+ " </tr>\n",
290
+ " <tr>\n",
291
+ " <th>6</th>\n",
292
+ " <td>2016</td>\n",
293
+ " <td>1320</td>\n",
294
+ " </tr>\n",
295
+ " <tr>\n",
296
+ " <th>7</th>\n",
297
+ " <td>2017</td>\n",
298
+ " <td>1569</td>\n",
299
+ " </tr>\n",
300
+ " <tr>\n",
301
+ " <th>8</th>\n",
302
+ " <td>2018</td>\n",
303
+ " <td>1160</td>\n",
304
+ " </tr>\n",
305
+ " <tr>\n",
306
+ " <th>9</th>\n",
307
+ " <td>2019</td>\n",
308
+ " <td>575</td>\n",
309
+ " </tr>\n",
310
+ " <tr>\n",
311
+ " <th>10</th>\n",
312
+ " <td>2020</td>\n",
313
+ " <td>363</td>\n",
314
+ " </tr>\n",
315
+ " <tr>\n",
316
+ " <th>11</th>\n",
317
+ " <td>2021</td>\n",
318
+ " <td>644</td>\n",
319
+ " </tr>\n",
320
+ " <tr>\n",
321
+ " <th>12</th>\n",
322
+ " <td>2022</td>\n",
323
+ " <td>422</td>\n",
324
+ " </tr>\n",
325
+ " <tr>\n",
326
+ " <th>13</th>\n",
327
+ " <td>2023</td>\n",
328
+ " <td>246</td>\n",
329
+ " </tr>\n",
330
+ " </tbody>\n",
331
+ "</table>\n",
332
+ "</div>"
333
+ ],
334
+ "text/plain": [
335
+ " Year Count\n",
336
+ "1 2011 1147\n",
337
+ "2 2012 803\n",
338
+ "3 2013 2001\n",
339
+ "4 2014 2379\n",
340
+ "5 2015 1899\n",
341
+ "6 2016 1320\n",
342
+ "7 2017 1569\n",
343
+ "8 2018 1160\n",
344
+ "9 2019 575\n",
345
+ "10 2020 363\n",
346
+ "11 2021 644\n",
347
+ "12 2022 422\n",
348
+ "13 2023 246"
349
+ ]
350
+ },
351
+ "execution_count": 51,
352
+ "metadata": {},
353
+ "output_type": "execute_result"
354
+ }
355
+ ],
356
+ "source": [
357
+ "df2 = df1[[\"Date mentioned\"]].copy()\n",
358
+ "df2[\"Date mentioned\"] = df2[\"Date mentioned\"].apply(lambda x: x[:4])\n",
359
+ "df2 = df2.groupby(\"Date mentioned\").size().reset_index(name=\"Count\")\n",
360
+ "df2.rename(columns={\"Date mentioned\": \"Year\"}, inplace=True)\n",
361
+ "# remove 2010\n",
362
+ "df2 = df2[df2[\"Year\"] != \"2010\"]\n",
363
+ "df2.to_csv(\"csv/Hardware count by year.csv\", index=False)\n",
364
+ "df2"
365
+ ]
366
+ },
367
+ {
368
+ "cell_type": "code",
369
+ "execution_count": 52,
370
+ "metadata": {},
371
+ "outputs": [
372
+ {
373
+ "data": {
374
+ "text/plain": [
375
+ "'\\n•\\t\"Power efficiency\" \\no\\tColumn 1: \"Quarter\" (I guess you did quarterly, so you can write like Q1 2011, Q2 2011....)\\no\\tColumn 2: \"Average power efficiency (TH/J)\" (here you put the average we calculated for each quarter)\\no\\tColumn 3: \"Max attainable power efficiency (TH/J)\" (here you put Pmax value for each quarter, you can put the Pmax value you have for each quarter beginning)\\n'"
376
+ ]
377
+ },
378
+ "execution_count": 52,
379
+ "metadata": {},
380
+ "output_type": "execute_result"
381
+ }
382
+ ],
383
+ "source": [
384
+ "\"\"\"\n",
385
+ "•\t\"Power efficiency\" \n",
386
+ "o\tColumn 1: \"Quarter\" (I guess you did quarterly, so you can write like Q1 2011, Q2 2011....)\n",
387
+ "o\tColumn 2: \"Average power efficiency (TH/J)\" (here you put the average we calculated for each quarter)\n",
388
+ "o\tColumn 3: \"Max attainable power efficiency (TH/J)\" (here you put Pmax value for each quarter, you can put the Pmax value you have for each quarter beginning)\n",
389
+ "\"\"\""
390
+ ]
391
+ },
392
+ {
393
+ "cell_type": "code",
394
+ "execution_count": 53,
395
+ "metadata": {},
396
+ "outputs": [
397
+ {
398
+ "data": {
399
+ "text/html": [
400
+ "<div>\n",
401
+ "<style scoped>\n",
402
+ " .dataframe tbody tr th:only-of-type {\n",
403
+ " vertical-align: middle;\n",
404
+ " }\n",
405
+ "\n",
406
+ " .dataframe tbody tr th {\n",
407
+ " vertical-align: top;\n",
408
+ " }\n",
409
+ "\n",
410
+ " .dataframe thead th {\n",
411
+ " text-align: right;\n",
412
+ " }\n",
413
+ "</style>\n",
414
+ "<table border=\"1\" class=\"dataframe\">\n",
415
+ " <thead>\n",
416
+ " <tr style=\"text-align: right;\">\n",
417
+ " <th></th>\n",
418
+ " <th>Quarter</th>\n",
419
+ " <th>Average power efficiency (TH/J)</th>\n",
420
+ " <th>Max attainable power efficiency (TH/J)</th>\n",
421
+ " </tr>\n",
422
+ " </thead>\n",
423
+ " <tbody>\n",
424
+ " <tr>\n",
425
+ " <th>0</th>\n",
426
+ " <td>2011Q1</td>\n",
427
+ " <td>1.676753e+06</td>\n",
428
+ " <td>3780000.0</td>\n",
429
+ " </tr>\n",
430
+ " <tr>\n",
431
+ " <th>1</th>\n",
432
+ " <td>2011Q2</td>\n",
433
+ " <td>1.662901e+06</td>\n",
434
+ " <td>3780000.0</td>\n",
435
+ " </tr>\n",
436
+ " <tr>\n",
437
+ " <th>2</th>\n",
438
+ " <td>2011Q3</td>\n",
439
+ " <td>1.777678e+06</td>\n",
440
+ " <td>23300000.0</td>\n",
441
+ " </tr>\n",
442
+ " </tbody>\n",
443
+ "</table>\n",
444
+ "</div>"
445
+ ],
446
+ "text/plain": [
447
+ " Quarter Average power efficiency (TH/J) \\\n",
448
+ "0 2011Q1 1.676753e+06 \n",
449
+ "1 2011Q2 1.662901e+06 \n",
450
+ "2 2011Q3 1.777678e+06 \n",
451
+ "\n",
452
+ " Max attainable power efficiency (TH/J) \n",
453
+ "0 3780000.0 \n",
454
+ "1 3780000.0 \n",
455
+ "2 23300000.0 "
456
+ ]
457
+ },
458
+ "execution_count": 53,
459
+ "metadata": {},
460
+ "output_type": "execute_result"
461
+ }
462
+ ],
463
+ "source": [
464
+ "# date,price,hashrate,coins_per_block,efficiency,max_efficiency\n",
465
+ "# 2023-12-31,38658.06,5.008642376892796e+20,6.25,34412596500.0,54100000000.0\n",
466
+ "monthly_stuff = pd.read_csv(\"../bitcoinforum/6_merging/monthly_stuff.csv\")\n",
467
+ "monthly_stuff[\"date\"] = pd.to_datetime(monthly_stuff[\"date\"])\n",
468
+ "monthly_stuff[\"Quarter\"] = monthly_stuff[\"date\"].dt.to_period(\"Q\")\n",
469
+ "monthly_stuff[\"Quarter\"] = monthly_stuff[\"Quarter\"].astype(str)\n",
470
+ "monthly_stuff = monthly_stuff[[\"Quarter\", \"efficiency\", \"max_efficiency\"]]\n",
471
+ "df3 = monthly_stuff.groupby(\"Quarter\").agg({\"efficiency\": \"mean\", \"max_efficiency\": \"first\"}).reset_index()\n",
472
+ "df3.rename(columns={\"efficiency\": \"Average power efficiency (TH/J)\", \"max_efficiency\": \"Max attainable power efficiency (TH/J)\"}, inplace=True)\n",
473
+ "df3.to_csv(\"csv/Power efficiency.csv\", index=False)\n",
474
+ "df3.head(3)"
475
+ ]
476
+ },
477
+ {
478
+ "cell_type": "code",
479
+ "execution_count": 54,
480
+ "metadata": {},
481
+ "outputs": [
482
+ {
483
+ "data": {
484
+ "text/plain": [
485
+ "'\\n•\\t\"Electricity consumption and carbon footprint\"\\no\\tHere you just remake Tab S18\\n'"
486
+ ]
487
+ },
488
+ "execution_count": 54,
489
+ "metadata": {},
490
+ "output_type": "execute_result"
491
+ }
492
+ ],
493
+ "source": [
494
+ "\"\"\"\n",
495
+ "•\t\"Electricity consumption and carbon footprint\"\n",
496
+ "o\tHere you just remake Tab S18\n",
497
+ "\"\"\""
498
+ ]
499
+ },
500
+ {
501
+ "cell_type": "code",
502
+ "execution_count": 55,
503
+ "metadata": {},
504
+ "outputs": [],
505
+ "source": [
506
+ "# the file is exported by plots/main.ipynb"
507
+ ]
508
+ },
509
+ {
510
+ "cell_type": "code",
511
+ "execution_count": 56,
512
+ "metadata": {},
513
+ "outputs": [
514
+ {
515
+ "data": {
516
+ "text/plain": [
517
+ "'\\n•\\t\"Bitcoin price and mining costs\" \\no\\tColumn 1: \"Bitcoin price\" each row is a day since Jan 2011, and you put the price everyday until end of December 2023\\no\\tColumn 2: \"Mining costs with C = 0.03 USD/kWh; PUE = 1.0\"\\no\\tColumn 3: \"Mining costs with C = 0.05 USD/kWh; PUE = 1.1\"\\no\\tColumn 4: \"Mining costs with C = 0.07 USD/kWh; PUE = 1.2\"\\no\\tColumn 5: \"Price-cost price with C = 0.03 USD/kWh; PUE = 1.0\"\\no\\tColumn 6: \"Price-cost price with C = 0.05 USD/kWh; PUE = 1.1\"\\no\\tColumn 7: \"Price-cost price with C = 0.07 USD/kWh; PUE = 1.2\"\\n'"
518
+ ]
519
+ },
520
+ "execution_count": 56,
521
+ "metadata": {},
522
+ "output_type": "execute_result"
523
+ }
524
+ ],
525
+ "source": [
526
+ "\"\"\"\n",
527
+ "•\t\"Bitcoin price and mining costs\" \n",
528
+ "o\tColumn 1: \"Bitcoin price\" each row is a day since Jan 2011, and you put the price everyday until end of December 2023\n",
529
+ "o\tColumn 2: \"Mining costs with C = 0.03 USD/kWh; PUE = 1.0\"\n",
530
+ "o\tColumn 3: \"Mining costs with C = 0.05 USD/kWh; PUE = 1.1\"\n",
531
+ "o\tColumn 4: \"Mining costs with C = 0.07 USD/kWh; PUE = 1.2\"\n",
532
+ "o\tColumn 5: \"Price-cost ratio with C = 0.03 USD/kWh; PUE = 1.0\"\n",
533
+ "o\tColumn 6: \"Price-cost ratio with C = 0.05 USD/kWh; PUE = 1.1\"\n",
534
+ "o\tColumn 7: \"Price-cost ratio with C = 0.07 USD/kWh; PUE = 1.2\"\n",
535
+ "\"\"\""
536
+ ]
537
+ },
538
+ {
539
+ "cell_type": "code",
540
+ "execution_count": 66,
541
+ "metadata": {},
542
+ "outputs": [
543
+ {
544
+ "data": {
545
+ "text/html": [
546
+ "<div>\n",
547
+ "<style scoped>\n",
548
+ " .dataframe tbody tr th:only-of-type {\n",
549
+ " vertical-align: middle;\n",
550
+ " }\n",
551
+ "\n",
552
+ " .dataframe tbody tr th {\n",
553
+ " vertical-align: top;\n",
554
+ " }\n",
555
+ "\n",
556
+ " .dataframe thead th {\n",
557
+ " text-align: right;\n",
558
+ " }\n",
559
+ "</style>\n",
560
+ "<table border=\"1\" class=\"dataframe\">\n",
561
+ " <thead>\n",
562
+ " <tr style=\"text-align: right;\">\n",
563
+ " <th></th>\n",
564
+ " <th>date</th>\n",
565
+ " <th>Bitcoin price</th>\n",
566
+ " <th>Mining costs with C = 0.03 USD/kWh; PUE = 1.0</th>\n",
567
+ " <th>Mining costs with C = 0.05 USD/kWh; PUE = 1.1</th>\n",
568
+ " <th>Mining costs with C = 0.07 USD/kWh; PUE = 1.2</th>\n",
569
+ " <th>Price-cost ratio with C = 0.03 USD/kWh; PUE = 1.0</th>\n",
570
+ " <th>Price-cost ratio with C = 0.05 USD/kWh; PUE = 1.1</th>\n",
571
+ " <th>Price-cost ratio with C = 0.07 USD/kWh; PUE = 1.2</th>\n",
572
+ " </tr>\n",
573
+ " </thead>\n",
574
+ " <tbody>\n",
575
+ " <tr>\n",
576
+ " <th>0</th>\n",
577
+ " <td>2023-12-31</td>\n",
578
+ " <td>38658.0600</td>\n",
579
+ " <td>11643.741853</td>\n",
580
+ " <td>21346.860065</td>\n",
581
+ " <td>32602.477190</td>\n",
582
+ " <td>3.320072</td>\n",
583
+ " <td>1.810948</td>\n",
584
+ " <td>1.185740</td>\n",
585
+ " </tr>\n",
586
+ " <tr>\n",
587
+ " <th>1</th>\n",
588
+ " <td>2023-10-31</td>\n",
589
+ " <td>27978.1000</td>\n",
590
+ " <td>14776.112668</td>\n",
591
+ " <td>27089.539892</td>\n",
592
+ " <td>41373.115471</td>\n",
593
+ " <td>1.893468</td>\n",
594
+ " <td>1.032801</td>\n",
595
+ " <td>0.676239</td>\n",
596
+ " </tr>\n",
597
+ " <tr>\n",
598
+ " <th>2</th>\n",
599
+ " <td>2023-09-30</td>\n",
600
+ " <td>25816.5700</td>\n",
601
+ " <td>14485.653053</td>\n",
602
+ " <td>26557.030597</td>\n",
603
+ " <td>40559.828548</td>\n",
604
+ " <td>1.782217</td>\n",
605
+ " <td>0.972118</td>\n",
606
+ " <td>0.636506</td>\n",
607
+ " </tr>\n",
608
+ " <tr>\n",
609
+ " <th>3</th>\n",
610
+ " <td>2023-08-31</td>\n",
611
+ " <td>29629.2400</td>\n",
612
+ " <td>16929.701012</td>\n",
613
+ " <td>31037.785189</td>\n",
614
+ " <td>47403.162835</td>\n",
615
+ " <td>1.750134</td>\n",
616
+ " <td>0.954618</td>\n",
617
+ " <td>0.625048</td>\n",
618
+ " </tr>\n",
619
+ " <tr>\n",
620
+ " <th>4</th>\n",
621
+ " <td>2023-07-31</td>\n",
622
+ " <td>30589.0500</td>\n",
623
+ " <td>16393.181084</td>\n",
624
+ " <td>30054.165320</td>\n",
625
+ " <td>45900.907034</td>\n",
626
+ " <td>1.865962</td>\n",
627
+ " <td>1.017797</td>\n",
628
+ " <td>0.666415</td>\n",
629
+ " </tr>\n",
630
+ " <tr>\n",
631
+ " <th>...</th>\n",
632
+ " <td>...</td>\n",
633
+ " <td>...</td>\n",
634
+ " <td>...</td>\n",
635
+ " <td>...</td>\n",
636
+ " <td>...</td>\n",
637
+ " <td>...</td>\n",
638
+ " <td>...</td>\n",
639
+ " <td>...</td>\n",
640
+ " </tr>\n",
641
+ " <tr>\n",
642
+ " <th>149</th>\n",
643
+ " <td>2011-06-30</td>\n",
644
+ " <td>9.5700</td>\n",
645
+ " <td>0.465775</td>\n",
646
+ " <td>0.853920</td>\n",
647
+ " <td>1.304169</td>\n",
648
+ " <td>20.546422</td>\n",
649
+ " <td>11.207139</td>\n",
650
+ " <td>7.338008</td>\n",
651
+ " </tr>\n",
652
+ " <tr>\n",
653
+ " <th>150</th>\n",
654
+ " <td>2011-05-31</td>\n",
655
+ " <td>3.0331</td>\n",
656
+ " <td>0.138976</td>\n",
657
+ " <td>0.254789</td>\n",
658
+ " <td>0.389133</td>\n",
659
+ " <td>21.824638</td>\n",
660
+ " <td>11.904348</td>\n",
661
+ " <td>7.794514</td>\n",
662
+ " </tr>\n",
663
+ " <tr>\n",
664
+ " <th>151</th>\n",
665
+ " <td>2011-04-30</td>\n",
666
+ " <td>0.7741</td>\n",
667
+ " <td>0.042495</td>\n",
668
+ " <td>0.077908</td>\n",
669
+ " <td>0.118987</td>\n",
670
+ " <td>18.216109</td>\n",
671
+ " <td>9.936059</td>\n",
672
+ " <td>6.505753</td>\n",
673
+ " </tr>\n",
674
+ " <tr>\n",
675
+ " <th>152</th>\n",
676
+ " <td>2011-03-31</td>\n",
677
+ " <td>0.9202</td>\n",
678
+ " <td>0.037427</td>\n",
679
+ " <td>0.068616</td>\n",
680
+ " <td>0.104795</td>\n",
681
+ " <td>24.586600</td>\n",
682
+ " <td>13.410872</td>\n",
683
+ " <td>8.780928</td>\n",
684
+ " </tr>\n",
685
+ " <tr>\n",
686
+ " <th>153</th>\n",
687
+ " <td>2011-02-28</td>\n",
688
+ " <td>0.7000</td>\n",
689
+ " <td>0.016507</td>\n",
690
+ " <td>0.030262</td>\n",
691
+ " <td>0.046219</td>\n",
692
+ " <td>42.406753</td>\n",
693
+ " <td>23.130956</td>\n",
694
+ " <td>15.145269</td>\n",
695
+ " </tr>\n",
696
+ " </tbody>\n",
697
+ "</table>\n",
698
+ "<p>154 rows × 8 columns</p>\n",
699
+ "</div>"
700
+ ],
701
+ "text/plain": [
702
+ " date Bitcoin price Mining costs with C = 0.03 USD/kWh; PUE = 1.0 \\\n",
703
+ "0 2023-12-31 38658.0600 11643.741853 \n",
704
+ "1 2023-10-31 27978.1000 14776.112668 \n",
705
+ "2 2023-09-30 25816.5700 14485.653053 \n",
706
+ "3 2023-08-31 29629.2400 16929.701012 \n",
707
+ "4 2023-07-31 30589.0500 16393.181084 \n",
708
+ ".. ... ... ... \n",
709
+ "149 2011-06-30 9.5700 0.465775 \n",
710
+ "150 2011-05-31 3.0331 0.138976 \n",
711
+ "151 2011-04-30 0.7741 0.042495 \n",
712
+ "152 2011-03-31 0.9202 0.037427 \n",
713
+ "153 2011-02-28 0.7000 0.016507 \n",
714
+ "\n",
715
+ " Mining costs with C = 0.05 USD/kWh; PUE = 1.1 \\\n",
716
+ "0 21346.860065 \n",
717
+ "1 27089.539892 \n",
718
+ "2 26557.030597 \n",
719
+ "3 31037.785189 \n",
720
+ "4 30054.165320 \n",
721
+ ".. ... \n",
722
+ "149 0.853920 \n",
723
+ "150 0.254789 \n",
724
+ "151 0.077908 \n",
725
+ "152 0.068616 \n",
726
+ "153 0.030262 \n",
727
+ "\n",
728
+ " Mining costs with C = 0.07 USD/kWh; PUE = 1.2 \\\n",
729
+ "0 32602.477190 \n",
730
+ "1 41373.115471 \n",
731
+ "2 40559.828548 \n",
732
+ "3 47403.162835 \n",
733
+ "4 45900.907034 \n",
734
+ ".. ... \n",
735
+ "149 1.304169 \n",
736
+ "150 0.389133 \n",
737
+ "151 0.118987 \n",
738
+ "152 0.104795 \n",
739
+ "153 0.046219 \n",
740
+ "\n",
741
+ " Price-cost ratio with C = 0.03 USD/kWh; PUE = 1.0 \\\n",
742
+ "0 3.320072 \n",
743
+ "1 1.893468 \n",
744
+ "2 1.782217 \n",
745
+ "3 1.750134 \n",
746
+ "4 1.865962 \n",
747
+ ".. ... \n",
748
+ "149 20.546422 \n",
749
+ "150 21.824638 \n",
750
+ "151 18.216109 \n",
751
+ "152 24.586600 \n",
752
+ "153 42.406753 \n",
753
+ "\n",
754
+ " Price-cost ratio with C = 0.05 USD/kWh; PUE = 1.1 \\\n",
755
+ "0 1.810948 \n",
756
+ "1 1.032801 \n",
757
+ "2 0.972118 \n",
758
+ "3 0.954618 \n",
759
+ "4 1.017797 \n",
760
+ ".. ... \n",
761
+ "149 11.207139 \n",
762
+ "150 11.904348 \n",
763
+ "151 9.936059 \n",
764
+ "152 13.410872 \n",
765
+ "153 23.130956 \n",
766
+ "\n",
767
+ " Price-cost ratio with C = 0.07 USD/kWh; PUE = 1.2 \n",
768
+ "0 1.185740 \n",
769
+ "1 0.676239 \n",
770
+ "2 0.636506 \n",
771
+ "3 0.625048 \n",
772
+ "4 0.666415 \n",
773
+ ".. ... \n",
774
+ "149 7.338008 \n",
775
+ "150 7.794514 \n",
776
+ "151 6.505753 \n",
777
+ "152 8.780928 \n",
778
+ "153 15.145269 \n",
779
+ "\n",
780
+ "[154 rows x 8 columns]"
781
+ ]
782
+ },
783
+ "execution_count": 66,
784
+ "metadata": {},
785
+ "output_type": "execute_result"
786
+ }
787
+ ],
788
+ "source": [
789
+ "# date,joules_per_coin\n",
790
+ "# 2023-12-31,1397249022408.723\n",
791
+ "joules_per_coin = pd.read_csv(\"joules_per_coin.csv\") # exported by plots/main.ipynb\n",
792
+ "monthly_stuff = pd.read_csv(\"../bitcoinforum/6_merging/monthly_stuff.csv\")\n",
793
+ "price = monthly_stuff[[\"date\", \"price\"]].copy()\n",
794
+ "\n",
795
+ "df4 = price.merge(joules_per_coin, on=\"date\", how=\"left\")\n",
796
+ "df4[\"kwh_per_coin\"] = df4[\"joules_per_coin\"] / 3600000\n",
797
+ "df4[\"Mining costs with C = 0.03 USD/kWh; PUE = 1.0\"] = df4[\"kwh_per_coin\"] * 0.03\n",
798
+ "df4[\"Mining costs with C = 0.05 USD/kWh; PUE = 1.1\"] = df4[\"kwh_per_coin\"] * 0.05 * 1.1\n",
799
+ "df4[\"Mining costs with C = 0.07 USD/kWh; PUE = 1.2\"] = df4[\"kwh_per_coin\"] * 0.07 * 1.2\n",
800
+ "df4[\"Price-cost ratio with C = 0.03 USD/kWh; PUE = 1.0\"] = df4[\"price\"] / df4[\"Mining costs with C = 0.03 USD/kWh; PUE = 1.0\"]\n",
801
+ "df4[\"Price-cost ratio with C = 0.05 USD/kWh; PUE = 1.1\"] = df4[\"price\"] / df4[\"Mining costs with C = 0.05 USD/kWh; PUE = 1.1\"]\n",
802
+ "df4[\"Price-cost ratio with C = 0.07 USD/kWh; PUE = 1.2\"] = df4[\"price\"] / df4[\"Mining costs with C = 0.07 USD/kWh; PUE = 1.2\"]\n",
803
+ "df4.rename(columns={\"price\": \"Bitcoin price\"}, inplace=True)\n",
804
+ "df4.drop(columns=[\"joules_per_coin\", \"kwh_per_coin\"], inplace=True)\n",
805
+ "df4.to_csv(\"csv/Bitcoin price and mining costs.csv\", index=False)\n",
806
+ "df4"
807
+ ]
808
+ }
809
+ ],
810
+ "metadata": {
811
+ "kernelspec": {
812
+ "display_name": "py310",
813
+ "language": "python",
814
+ "name": "python3"
815
+ },
816
+ "language_info": {
817
+ "codemirror_mode": {
818
+ "name": "ipython",
819
+ "version": 3
820
+ },
821
+ "file_extension": ".py",
822
+ "mimetype": "text/x-python",
823
+ "name": "python",
824
+ "nbconvert_exporter": "python",
825
+ "pygments_lexer": "ipython3",
826
+ "version": "3.10.13"
827
+ }
828
+ },
829
+ "nbformat": 4,
830
+ "nbformat_minor": 2
831
+ }
excel_sheet/mega_sheet.xlsx ADDED
Binary file (440 kB). View file