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m-kus/originations_by_language.ipynb

Created July 10, 2019 21:29
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Smart contract originations by source language
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originations_by_language.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
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" });\n",
" }\n",
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},
"metadata": {},
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],
"source": [
"from conseil import conseil\n",
"import pandas as pd\n",
"import cufflinks as cf\n",
"import numpy as np\n",
"import requests\n",
"import dateutil"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
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"data": {
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"source": [
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},
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"source": [
"Operation = conseil.tezos.alphanet.operations"
]
},
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"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"non_liq_ts = Operation.query(Operation.timestamp.max().label('ts')) \\\n",
" .filter(Operation.kind == Operation.kind.origination,\n",
" Operation.script.notlike('_slash_'),\n",
" Operation.script.notlike('_prim_'),\n",
" Operation.script.notlike('_sharp_'),\n",
" Operation.script.notlike('_Liq_')) \\\n",
" .group_by(Operation.script) \\\n",
" .order_by(Operation.timestamp.max()) \\\n",
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},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"all_ts = Operation.query(Operation.timestamp.max().label('ts')) \\\n",
" .filter(Operation.kind == Operation.kind.origination) \\\n",
" .group_by(Operation.script) \\\n",
" .order_by(Operation.timestamp.max()) \\\n",
" .all()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df_oth = pd.DataFrame(non_liq_ts)\n",
"df_oth.index = df_oth['ts'].astype('M8[ms]')\n",
"df_oth['non_liquidity'] = 1\n",
"\n",
"df_all = pd.DataFrame(all_ts)\n",
"df_all.index = df_all['ts'].astype('M8[ms]')\n",
"df_all['all'] = 1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df = pd.merge(df_oth, df_all, left_index=True, right_index=True, how='outer').fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df['liquidity'] = df['all'] - df['non_liquidity']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df_liq = df[df['liquidity'] > 0][['liquidity']].copy()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"df = pd.merge_asof(df_oth.cumsum(), df_liq.cumsum(), left_index=True, right_index=True)"
]
},
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"execution_count": 11,
"metadata": {},
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