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SinopiaStatistics.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline\n",
"\n",
"import datetime\n",
"import json\n",
"import sqlite3\n",
"import sys\n",
"import pandas as pd\n",
"import requests"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"db = sqlite3.connect(\"/Users/jpnelson/2020/ld4p/sinopia_stats/statistics.sqlite\")\n",
"cur = db.cursor()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Total Resources and Triples by Environment\n",
"## 2019"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>Group</th>\n",
" <th>Total Resources</th>\n",
" <th>Total Triples</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Development</td>\n",
" <td>558</td>\n",
" <td>67790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Stage</td>\n",
" <td>1660</td>\n",
" <td>131888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Production</td>\n",
" <td>486</td>\n",
" <td>19514</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Group Total Resources Total Triples\n",
"0 Development 558 67790\n",
"1 Stage 1660 131888\n",
"2 Production 486 19514"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total2019 = pd.read_sql_query(\"\"\"SELECT Environments.name, count(Resources.id), sum(Resources.triples)\n",
"FROM Resources, Environments\n",
"WHERE Resources.environment_id = Environments.id\n",
"AND Resources.created < '2019-12-31T23:59:00'\n",
"GROUP BY Resources.environment_id\"\"\", db)\n",
"total2019.rename(columns={'count(Resources.id)': 'Total Resources',\n",
" 'sum(Resources.triples)': 'Total Triples',\n",
" 'name': 'Group'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2020"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"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>Group</th>\n",
" <th>Total Resources</th>\n",
" <th>Total Triples</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Development</td>\n",
" <td>267</td>\n",
" <td>16731</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Stage</td>\n",
" <td>4640</td>\n",
" <td>258946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Production</td>\n",
" <td>3139</td>\n",
" <td>175549</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Group Total Resources Total Triples\n",
"0 Development 267 16731\n",
"1 Stage 4640 258946\n",
"2 Production 3139 175549"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total2020 = pd.read_sql_query(\"\"\"SELECT Environments.name, count(Resources.id), sum(Resources.triples)\n",
"FROM Resources, Environments\n",
"WHERE Resources.environment_id = Environments.id\n",
"AND Resources.created > '2019-12-31T23:59:00'\n",
"GROUP BY Resources.environment_id\"\"\", db)\n",
"total2020.rename(columns={'count(Resources.id)': 'Total Resources',\n",
" 'sum(Resources.triples)': 'Total Triples',\n",
" 'name': 'Group'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Total Resources and Triples by Group\n",
"## 2019 Stage"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"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>Group</th>\n",
" <th>Total Resources</th>\n",
" <th>Total Triples</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alberta</td>\n",
" <td>38</td>\n",
" <td>1261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>boulder</td>\n",
" <td>20</td>\n",
" <td>1625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>chicago</td>\n",
" <td>9</td>\n",
" <td>276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>cornell</td>\n",
" <td>39</td>\n",
" <td>981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>dlc</td>\n",
" <td>4</td>\n",
" <td>87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>frick</td>\n",
" <td>257</td>\n",
" <td>19020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>harvard</td>\n",
" <td>58</td>\n",
" <td>3735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>hrc</td>\n",
" <td>4</td>\n",
" <td>325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>ld4p</td>\n",
" <td>663</td>\n",
" <td>84125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>michigan</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>minnesota</td>\n",
" <td>10</td>\n",
" <td>443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>nlm</td>\n",
" <td>17</td>\n",
" <td>554</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>northwestern</td>\n",
" <td>6</td>\n",
" <td>315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>pcc</td>\n",
" <td>10</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>penn</td>\n",
" <td>37</td>\n",
" <td>1344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>princeton</td>\n",
" <td>12</td>\n",
" <td>482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>stanford</td>\n",
" <td>172</td>\n",
" <td>7510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>tamu</td>\n",
" <td>26</td>\n",
" <td>1197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>ucdavis</td>\n",
" <td>56</td>\n",
" <td>2363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>ucsd</td>\n",
" <td>53</td>\n",
" <td>1557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>washington</td>\n",
" <td>6</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>yale</td>\n",
" <td>161</td>\n",
" <td>4572</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Group Total Resources Total Triples\n",
"0 alberta 38 1261\n",
"1 boulder 20 1625\n",
"2 chicago 9 276\n",
"3 cornell 39 981\n",
"4 dlc 4 87\n",
"5 frick 257 19020\n",
"6 harvard 58 3735\n",
"7 hrc 4 325\n",
"8 ld4p 663 84125\n",
"9 michigan 2 14\n",
"10 minnesota 10 443\n",
"11 nlm 17 554\n",
"12 northwestern 6 315\n",
"13 pcc 10 70\n",
"14 penn 37 1344\n",
"15 princeton 12 482\n",
"16 stanford 172 7510\n",
"17 tamu 26 1197\n",
"18 ucdavis 56 2363\n",
"19 ucsd 53 1557\n",
"20 washington 6 32\n",
"21 yale 161 4572"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stage2019 = pd.read_sql_query(\"\"\"SELECT Groups.name, count(Resources.id), sum(Resources.triples)\n",
"FROM Resources, Groups\n",
"WHERE Resources.group_id = Groups.id AND Resources.environment_id=2\n",
"AND Resources.created < '2019-12-31T23:59:00'\n",
"GROUP BY Groups.id\n",
"ORDER BY Groups.name\"\"\", db)\n",
"stage2019.rename(columns={'count(Resources.id)': 'Total Resources',\n",
" 'sum(Resources.triples)': 'Total Triples',\n",
" 'name': 'Group'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2020 Stage"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"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>Group</th>\n",
" <th>Total Resources</th>\n",
" <th>Total Triples</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Unknown</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>alberta</td>\n",
" <td>133</td>\n",
" <td>3012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>boulder</td>\n",
" <td>18</td>\n",
" <td>715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>chicago</td>\n",
" <td>121</td>\n",
" <td>4482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>cornell</td>\n",
" <td>18</td>\n",
" <td>566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>duke</td>\n",
" <td>8</td>\n",
" <td>643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>frick</td>\n",
" <td>509</td>\n",
" <td>39966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>harvard</td>\n",
" <td>42</td>\n",
" <td>2698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>hrc</td>\n",
" <td>201</td>\n",
" <td>15932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ld4p</td>\n",
" <td>630</td>\n",
" <td>49971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>michigan</td>\n",
" <td>3</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>minnesota</td>\n",
" <td>112</td>\n",
" <td>5433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>nlm</td>\n",
" <td>221</td>\n",
" <td>8152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>pcc</td>\n",
" <td>56</td>\n",
" <td>2642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>penn</td>\n",
" <td>139</td>\n",
" <td>4804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>princeton</td>\n",
" <td>2</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>stanford</td>\n",
" <td>335</td>\n",
" <td>12930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>tamu</td>\n",
" <td>1419</td>\n",
" <td>76138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>ucdavis</td>\n",
" <td>4</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>ucsd</td>\n",
" <td>100</td>\n",
" <td>3988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>washington</td>\n",
" <td>20</td>\n",
" <td>855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>yale</td>\n",
" <td>543</td>\n",
" <td>25752</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Group Total Resources Total Triples\n",
"0 Unknown 6 0\n",
"1 alberta 133 3012\n",
"2 boulder 18 715\n",
"3 chicago 121 4482\n",
"4 cornell 18 566\n",
"5 duke 8 643\n",
"6 frick 509 39966\n",
"7 harvard 42 2698\n",
"8 hrc 201 15932\n",
"9 ld4p 630 49971\n",
"10 michigan 3 29\n",
"11 minnesota 112 5433\n",
"12 nlm 221 8152\n",
"13 pcc 56 2642\n",
"14 penn 139 4804\n",
"15 princeton 2 58\n",
"16 stanford 335 12930\n",
"17 tamu 1419 76138\n",
"18 ucdavis 4 180\n",
"19 ucsd 100 3988\n",
"20 washington 20 855\n",
"21 yale 543 25752"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stage2020 = pd.read_sql_query(\"\"\"SELECT Groups.name, count(Resources.id), sum(Resources.triples)\n",
"FROM Resources, Groups\n",
"WHERE Resources.group_id = Groups.id AND Resources.environment_id=2\n",
"AND Resources.created > '2019-12-31T23:59:00'\n",
"GROUP BY Groups.id\n",
"ORDER BY Groups.name\"\"\", db)\n",
"stage2020.rename(columns={'count(Resources.id)': 'Total Resources',\n",
" 'sum(Resources.triples)': 'Total Triples',\n",
" 'name': 'Group'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2019 Production"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"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>Group</th>\n",
" <th>Total Resources</th>\n",
" <th>Total Triples</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alberta</td>\n",
" <td>3</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>chicago</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>cornell</td>\n",
" <td>8</td>\n",
" <td>614</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>dlc</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>duke</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>harvard</td>\n",
" <td>4</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ld4p</td>\n",
" <td>295</td>\n",
" <td>12435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>minnesota</td>\n",
" <td>8</td>\n",
" <td>461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>pcc</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>penn</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>princeton</td>\n",
" <td>3</td>\n",
" <td>125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>stanford</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>ucdavis</td>\n",
" <td>12</td>\n",
" <td>798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>washington</td>\n",
" <td>145</td>\n",
" <td>4813</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Group Total Resources Total Triples\n",
"0 alberta 3 70\n",
"1 chicago 1 15\n",
"2 cornell 8 614\n",
"3 dlc 2 11\n",
"4 duke 1 15\n",
"5 harvard 4 119\n",
"6 ld4p 295 12435\n",
"7 minnesota 8 461\n",
"8 pcc 1 9\n",
"9 penn 1 15\n",
"10 princeton 3 125\n",
"11 stanford 2 14\n",
"12 ucdavis 12 798\n",
"13 washington 145 4813"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prod2019 = pd.read_sql_query(\"\"\"SELECT Groups.name, count(Resources.id), sum(Resources.triples)\n",
"FROM Resources, Groups\n",
"WHERE Resources.group_id = Groups.id AND Resources.environment_id=3\n",
"AND created < '2019-12-31T23:59:00'\n",
"GROUP BY Groups.id\n",
"ORDER BY Groups.name\"\"\", db)\n",
"prod2019.rename(columns={'count(Resources.id)': 'Total Resources',\n",
" 'sum(Resources.triples)': 'Total Triples',\n",
" 'name': 'Group'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2020 Production"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"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>Group</th>\n",
" <th>Total Resources</th>\n",
" <th>Total Triples</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alberta</td>\n",
" <td>331</td>\n",
" <td>10086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>boulder</td>\n",
" <td>2</td>\n",
" <td>134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>chicago</td>\n",
" <td>78</td>\n",
" <td>3908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>cornell</td>\n",
" <td>46</td>\n",
" <td>3157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>harvard</td>\n",
" <td>15</td>\n",
" <td>1503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ld4p</td>\n",
" <td>495</td>\n",
" <td>71538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>minnesota</td>\n",
" <td>148</td>\n",
" <td>6080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>nlm</td>\n",
" <td>496</td>\n",
" <td>13609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>northwestern</td>\n",
" <td>31</td>\n",
" <td>1390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>pcc</td>\n",
" <td>10</td>\n",
" <td>215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>penn</td>\n",
" <td>4</td>\n",
" <td>228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>princeton</td>\n",
" <td>33</td>\n",
" <td>1121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>stanford</td>\n",
" <td>102</td>\n",
" <td>3873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>ucsd</td>\n",
" <td>34</td>\n",
" <td>1764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>washington</td>\n",
" <td>405</td>\n",
" <td>13641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>yale</td>\n",
" <td>909</td>\n",
" <td>43302</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Group Total Resources Total Triples\n",
"0 alberta 331 10086\n",
"1 boulder 2 134\n",
"2 chicago 78 3908\n",
"3 cornell 46 3157\n",
"4 harvard 15 1503\n",
"5 ld4p 495 71538\n",
"6 minnesota 148 6080\n",
"7 nlm 496 13609\n",
"8 northwestern 31 1390\n",
"9 pcc 10 215\n",
"10 penn 4 228\n",
"11 princeton 33 1121\n",
"12 stanford 102 3873\n",
"13 ucsd 34 1764\n",
"14 washington 405 13641\n",
"15 yale 909 43302"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prod2020 = pd.read_sql_query(\"\"\"SELECT Groups.name, count(Resources.id), sum(Resources.triples)\n",
"FROM Resources, Groups\n",
"WHERE Resources.group_id = Groups.id AND Resources.environment_id=3\n",
"AND created > '2019-12-31T23:59:00'\n",
"GROUP BY Groups.id\n",
"ORDER BY Groups.name\"\"\", db)\n",
"prod2020.rename(columns={'count(Resources.id)': 'Total Resources',\n",
" 'sum(Resources.triples)': 'Total Triples',\n",
" 'name': 'Group'})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"stage_users = pd.read_csv(\"../cognito/stage-users.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"392"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(stage_users)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"def no_resources_users(users, env):\n",
" no_resources = 0\n",
" for row in stage_users.iterrows():\n",
" username = row[1]['Username']\n",
" query = cur.execute(\"\"\"SELECT count(Resources.id) FROM Resources, Users WHERE\n",
" Resources.environment_id = ? AND\n",
" Resources.user_id = Users.id AND\n",
" Users.username=?\"\"\", (env,username)).fetchone()\n",
" if query[0] < 1:\n",
" no_resources += 1\n",
" print(f\"Percentage of users that did not add a single resource {no_resources / len(stage_users) * 100}%\")\n",
" return no_resources\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In Sinopia Stage Environment\n",
"Percentage of users that did not add a single resource 45.40816326530612%\n"
]
},
{
"data": {
"text/plain": [
"178"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"In Sinopia Stage Environment\")\n",
"no_resources_users(stage_users, 2)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"prod_users = pd.read_csv(\"../cognito/prod-users.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In Sinopia Production Environment\n",
"Percentage of users that did not add a single resource 80.10204081632652%\n"
]
},
{
"data": {
"text/plain": [
"314"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"In Sinopia Production Environment\")\n",
"no_resources_users(prod_users, 3)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"stage_df = pd.read_sql_query(\"\"\"SELECT Resources.created as Created, Resources.triples as Triples, \n",
"Users.username as User, Groups.name as Institution\n",
"FROM Resources, Users, Groups\n",
"WHERE Resources.environment_id = 2 AND\n",
"Resources.user_id = Users.id AND\n",
"Resources.group_id = Groups.id\n",
"\"\"\", db, parse_dates=['Created'])\n",
"prod_df = pd.read_sql_query(\"\"\"SELECT Resources.created as Created, Resources.triples as Triples, \n",
"Users.username as User, Groups.name as Institution\n",
"FROM Resources, Users, Groups\n",
"WHERE Resources.environment_id = 3 AND\n",
"Resources.user_id = Users.id AND\n",
"Resources.group_id = Groups.id\n",
"\"\"\", db, parse_dates=['Created'])"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"def display_by_month_year(df):\n",
" df['YearMonth'] = pd.to_datetime(df['Created']).apply(lambda x: f\"{x.year}-{x.month:02}\")\n",
" date_groups = [group for group in df.groupby('YearMonth')]\n",
" for row in date_groups:\n",
" total_triples = row[1]['Triples'].sum()\n",
" print(f\"{row[0]} Total Resources {len(row[1]):,} and Triples {total_triples:,}\")\n",
" groups = [group for group in row[1].groupby('Institution')]\n",
" for group in groups:\n",
" group_triples = group[1]['Triples'].sum()\n",
" print(f\"\\t{group[0]} Resources: {len(group[1])} Triples: {group_triples:,}\")"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stage By Year-Month\n",
"2019-05 Total Resources 211 and Triples 13,887\n",
"\tld4p Resources: 211 Triples: 13,887\n",
"2019-06 Total Resources 34 and Triples 1,715\n",
"\tld4p Resources: 34 Triples: 1,715\n",
"2019-07 Total Resources 11 and Triples 1,221\n",
"\tld4p Resources: 11 Triples: 1,221\n",
"2019-08 Total Resources 91 and Triples 4,211\n",
"\talberta Resources: 2 Triples: 70\n",
"\tboulder Resources: 1 Triples: 96\n",
"\tcornell Resources: 11 Triples: 201\n",
"\tdlc Resources: 3 Triples: 65\n",
"\tfrick Resources: 1 Triples: 12\n",
"\tharvard Resources: 15 Triples: 1,709\n",
"\thrc Resources: 1 Triples: 7\n",
"\tld4p Resources: 6 Triples: 382\n",
"\tmichigan Resources: 2 Triples: 14\n",
"\tnlm Resources: 1 Triples: 53\n",
"\tnorthwestern Resources: 2 Triples: 173\n",
"\tpcc Resources: 2 Triples: 7\n",
"\tpenn Resources: 1 Triples: 7\n",
"\tprinceton Resources: 7 Triples: 302\n",
"\tstanford Resources: 8 Triples: 336\n",
"\ttamu Resources: 2 Triples: 7\n",
"\tucdavis Resources: 4 Triples: 154\n",
"\tucsd Resources: 2 Triples: 6\n",
"\twashington Resources: 3 Triples: 10\n",
"\tyale Resources: 17 Triples: 600\n",
"2019-09 Total Resources 191 and Triples 12,078\n",
"\talberta Resources: 1 Triples: 58\n",
"\tboulder Resources: 10 Triples: 908\n",
"\tchicago Resources: 1 Triples: 78\n",
"\tharvard Resources: 27 Triples: 1,263\n",
"\tld4p Resources: 141 Triples: 9,639\n",
"\tpenn Resources: 1 Triples: 3\n",
"\tstanford Resources: 7 Triples: 52\n",
"\tucdavis Resources: 1 Triples: 59\n",
"\twashington Resources: 2 Triples: 18\n",
"2019-10 Total Resources 270 and Triples 11,707\n",
"\talberta Resources: 12 Triples: 459\n",
"\tboulder Resources: 4 Triples: 397\n",
"\tchicago Resources: 6 Triples: 129\n",
"\tcornell Resources: 16 Triples: 282\n",
"\tfrick Resources: 7 Triples: 376\n",
"\tharvard Resources: 11 Triples: 529\n",
"\thrc Resources: 1 Triples: 291\n",
"\tld4p Resources: 97 Triples: 5,167\n",
"\tminnesota Resources: 3 Triples: 288\n",
"\tnlm Resources: 2 Triples: 41\n",
"\tnorthwestern Resources: 1 Triples: 28\n",
"\tpcc Resources: 1 Triples: 3\n",
"\tpenn Resources: 17 Triples: 722\n",
"\tprinceton Resources: 2 Triples: 36\n",
"\tstanford Resources: 57 Triples: 2,225\n",
"\ttamu Resources: 1 Triples: 33\n",
"\tucdavis Resources: 1 Triples: 73\n",
"\tucsd Resources: 13 Triples: 286\n",
"\tyale Resources: 18 Triples: 342\n",
"2019-11 Total Resources 490 and Triples 69,122\n",
"\talberta Resources: 22 Triples: 608\n",
"\tboulder Resources: 3 Triples: 121\n",
"\tcornell Resources: 9 Triples: 346\n",
"\tdlc Resources: 1 Triples: 22\n",
"\tfrick Resources: 133 Triples: 10,165\n",
"\tharvard Resources: 5 Triples: 234\n",
"\thrc Resources: 2 Triples: 27\n",
"\tld4p Resources: 134 Triples: 50,396\n",
"\tminnesota Resources: 6 Triples: 138\n",
"\tnlm Resources: 2 Triples: 87\n",
"\tnorthwestern Resources: 3 Triples: 114\n",
"\tpcc Resources: 4 Triples: 18\n",
"\tpenn Resources: 12 Triples: 368\n",
"\tprinceton Resources: 2 Triples: 97\n",
"\tstanford Resources: 69 Triples: 3,296\n",
"\ttamu Resources: 6 Triples: 346\n",
"\tucdavis Resources: 4 Triples: 178\n",
"\tucsd Resources: 36 Triples: 1,162\n",
"\twashington Resources: 1 Triples: 4\n",
"\tyale Resources: 36 Triples: 1,395\n",
"2019-12 Total Resources 362 and Triples 17,947\n",
"\talberta Resources: 1 Triples: 66\n",
"\tboulder Resources: 2 Triples: 103\n",
"\tchicago Resources: 2 Triples: 69\n",
"\tcornell Resources: 3 Triples: 152\n",
"\tfrick Resources: 116 Triples: 8,467\n",
"\tld4p Resources: 29 Triples: 1,718\n",
"\tminnesota Resources: 1 Triples: 17\n",
"\tnlm Resources: 12 Triples: 373\n",
"\tpcc Resources: 3 Triples: 42\n",
"\tpenn Resources: 6 Triples: 244\n",
"\tprinceton Resources: 1 Triples: 47\n",
"\tstanford Resources: 31 Triples: 1,601\n",
"\ttamu Resources: 17 Triples: 811\n",
"\tucdavis Resources: 46 Triples: 1,899\n",
"\tucsd Resources: 2 Triples: 103\n",
"\tyale Resources: 90 Triples: 2,235\n",
"2020-01 Total Resources 348 and Triples 15,571\n",
"\talberta Resources: 5 Triples: 119\n",
"\tchicago Resources: 18 Triples: 710\n",
"\tfrick Resources: 71 Triples: 5,127\n",
"\tharvard Resources: 1 Triples: 6\n",
"\tld4p Resources: 18 Triples: 1,119\n",
"\tmichigan Resources: 1 Triples: 4\n",
"\tminnesota Resources: 24 Triples: 895\n",
"\tnlm Resources: 6 Triples: 157\n",
"\tpcc Resources: 3 Triples: 21\n",
"\tstanford Resources: 41 Triples: 1,930\n",
"\tucdavis Resources: 2 Triples: 64\n",
"\tyale Resources: 158 Triples: 5,419\n",
"2020-02 Total Resources 354 and Triples 19,443\n",
"\talberta Resources: 22 Triples: 691\n",
"\tboulder Resources: 2 Triples: 126\n",
"\tchicago Resources: 15 Triples: 655\n",
"\tcornell Resources: 2 Triples: 6\n",
"\tduke Resources: 1 Triples: 134\n",
"\tfrick Resources: 80 Triples: 5,974\n",
"\tharvard Resources: 2 Triples: 58\n",
"\thrc Resources: 7 Triples: 108\n",
"\tld4p Resources: 82 Triples: 5,946\n",
"\tminnesota Resources: 27 Triples: 1,122\n",
"\tnlm Resources: 23 Triples: 699\n",
"\tpcc Resources: 3 Triples: 25\n",
"\tpenn Resources: 2 Triples: 177\n",
"\tstanford Resources: 19 Triples: 1,008\n",
"\ttamu Resources: 11 Triples: 349\n",
"\tucdavis Resources: 1 Triples: 79\n",
"\tucsd Resources: 36 Triples: 1,486\n",
"\tyale Resources: 19 Triples: 800\n",
"2020-03 Total Resources 571 and Triples 31,942\n",
"\talberta Resources: 3 Triples: 34\n",
"\tboulder Resources: 3 Triples: 102\n",
"\tchicago Resources: 16 Triples: 627\n",
"\tduke Resources: 2 Triples: 125\n",
"\tfrick Resources: 141 Triples: 10,206\n",
"\tharvard Resources: 2 Triples: 42\n",
"\thrc Resources: 3 Triples: 186\n",
"\tld4p Resources: 41 Triples: 3,629\n",
"\tminnesota Resources: 38 Triples: 2,210\n",
"\tnlm Resources: 58 Triples: 2,484\n",
"\tpcc Resources: 1 Triples: 5\n",
"\tpenn Resources: 19 Triples: 696\n",
"\tprinceton Resources: 2 Triples: 58\n",
"\tstanford Resources: 75 Triples: 3,599\n",
"\ttamu Resources: 9 Triples: 411\n",
"\tucdavis Resources: 1 Triples: 37\n",
"\tucsd Resources: 41 Triples: 1,435\n",
"\tyale Resources: 116 Triples: 6,056\n",
"2020-04 Total Resources 390 and Triples 24,118\n",
"\talberta Resources: 12 Triples: 355\n",
"\tboulder Resources: 4 Triples: 136\n",
"\tchicago Resources: 17 Triples: 786\n",
"\tduke Resources: 2 Triples: 135\n",
"\tfrick Resources: 40 Triples: 2,961\n",
"\tharvard Resources: 1 Triples: 189\n",
"\thrc Resources: 16 Triples: 902\n",
"\tld4p Resources: 48 Triples: 6,289\n",
"\tminnesota Resources: 13 Triples: 898\n",
"\tnlm Resources: 68 Triples: 2,620\n",
"\tpcc Resources: 1 Triples: 19\n",
"\tpenn Resources: 15 Triples: 645\n",
"\tstanford Resources: 18 Triples: 1,113\n",
"\ttamu Resources: 17 Triples: 771\n",
"\tucsd Resources: 4 Triples: 102\n",
"\tyale Resources: 114 Triples: 6,197\n",
"2020-05 Total Resources 1,182 and Triples 65,918\n",
"\talberta Resources: 13 Triples: 411\n",
"\tboulder Resources: 7 Triples: 235\n",
"\tchicago Resources: 21 Triples: 615\n",
"\tcornell Resources: 1 Triples: 41\n",
"\tduke Resources: 2 Triples: 229\n",
"\tfrick Resources: 89 Triples: 7,892\n",
"\tharvard Resources: 2 Triples: 297\n",
"\thrc Resources: 30 Triples: 2,120\n",
"\tld4p Resources: 216 Triples: 15,999\n",
"\tnlm Resources: 12 Triples: 393\n",
"\tpcc Resources: 5 Triples: 24\n",
"\tpenn Resources: 14 Triples: 525\n",
"\tstanford Resources: 14 Triples: 387\n",
"\ttamu Resources: 605 Triples: 28,715\n",
"\tucsd Resources: 19 Triples: 965\n",
"\tyale Resources: 132 Triples: 7,070\n",
"2020-06 Total Resources 862 and Triples 52,954\n",
"\talberta Resources: 78 Triples: 1,402\n",
"\tboulder Resources: 1 Triples: 24\n",
"\tchicago Resources: 23 Triples: 739\n",
"\tcornell Resources: 5 Triples: 380\n",
"\tfrick Resources: 88 Triples: 7,806\n",
"\tharvard Resources: 13 Triples: 1,026\n",
"\thrc Resources: 1 Triples: 28\n",
"\tld4p Resources: 40 Triples: 4,928\n",
"\tpcc Resources: 3 Triples: 13\n",
"\tpenn Resources: 2 Triples: 49\n",
"\tstanford Resources: 11 Triples: 495\n",
"\ttamu Resources: 597 Triples: 36,064\n",
"2020-07 Total Resources 223 and Triples 11,416\n",
"\tchicago Resources: 1 Triples: 25\n",
"\tcornell Resources: 1 Triples: 14\n",
"\tduke Resources: 1 Triples: 20\n",
"\tharvard Resources: 9 Triples: 674\n",
"\tld4p Resources: 43 Triples: 2,108\n",
"\tpenn Resources: 4 Triples: 49\n",
"\tstanford Resources: 8 Triples: 286\n",
"\ttamu Resources: 156 Triples: 8,240\n",
"2020-08 Total Resources 87 and Triples 4,450\n",
"\tchicago Resources: 2 Triples: 44\n",
"\tharvard Resources: 1 Triples: 26\n",
"\thrc Resources: 8 Triples: 592\n",
"\tld4p Resources: 11 Triples: 1,259\n",
"\tpcc Resources: 1 Triples: 22\n",
"\tpenn Resources: 45 Triples: 1,187\n",
"\ttamu Resources: 19 Triples: 1,320\n",
"2020-09 Total Resources 188 and Triples 6,444\n",
"\tboulder Resources: 1 Triples: 92\n",
"\tchicago Resources: 3 Triples: 115\n",
"\tcornell Resources: 1 Triples: 8\n",
"\thrc Resources: 20 Triples: 1,582\n",
"\tld4p Resources: 58 Triples: 1,607\n",
"\tminnesota Resources: 4 Triples: 107\n",
"\tnlm Resources: 7 Triples: 196\n",
"\tpenn Resources: 18 Triples: 743\n",
"\tstanford Resources: 71 Triples: 1,731\n",
"\ttamu Resources: 3 Triples: 188\n",
"\twashington Resources: 2 Triples: 75\n",
"2020-10 Total Resources 267 and Triples 11,625\n",
"\tUnknown Resources: 2 Triples: 0\n",
"\tchicago Resources: 5 Triples: 166\n",
"\tcornell Resources: 2 Triples: 11\n",
"\tharvard Resources: 5 Triples: 148\n",
"\thrc Resources: 17 Triples: 900\n",
"\tld4p Resources: 52 Triples: 3,516\n",
"\tmichigan Resources: 2 Triples: 25\n",
"\tminnesota Resources: 4 Triples: 119\n",
"\tnlm Resources: 44 Triples: 1,500\n",
"\tpcc Resources: 31 Triples: 1,708\n",
"\tpenn Resources: 7 Triples: 240\n",
"\tstanford Resources: 75 Triples: 2,276\n",
"\ttamu Resources: 2 Triples: 80\n",
"\twashington Resources: 15 Triples: 726\n",
"\tyale Resources: 4 Triples: 210\n",
"2020-11 Total Resources 168 and Triples 15,065\n",
"\tUnknown Resources: 4 Triples: 0\n",
"\tcornell Resources: 6 Triples: 106\n",
"\tharvard Resources: 6 Triples: 232\n",
"\thrc Resources: 99 Triples: 9,514\n",
"\tld4p Resources: 21 Triples: 3,571\n",
"\tminnesota Resources: 2 Triples: 82\n",
"\tnlm Resources: 3 Triples: 103\n",
"\tpcc Resources: 8 Triples: 805\n",
"\tpenn Resources: 13 Triples: 493\n",
"\tstanford Resources: 3 Triples: 105\n",
"\twashington Resources: 3 Triples: 54\n"
]
}
],
"source": [
"print(\"Stage By Year-Month\")\n",
"display_by_month_year(stage_df)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Production By Year-Month\n",
"2019-05 Total Resources 1 and Triples 23\n",
"\tld4p Resources: 1 Triples: 23\n",
"2019-06 Total Resources 41 and Triples 1,269\n",
"\tld4p Resources: 41 Triples: 1,269\n",
"2019-07 Total Resources 78 and Triples 3,356\n",
"\tld4p Resources: 78 Triples: 3,356\n",
"2019-08 Total Resources 2 and Triples 5\n",
"\tdlc Resources: 1 Triples: 3\n",
"\tstanford Resources: 1 Triples: 2\n",
"2019-09 Total Resources 22 and Triples 1,445\n",
"\tld4p Resources: 21 Triples: 1,428\n",
"\twashington Resources: 1 Triples: 17\n",
"2019-10 Total Resources 149 and Triples 6,085\n",
"\tcornell Resources: 3 Triples: 24\n",
"\tduke Resources: 1 Triples: 15\n",
"\tharvard Resources: 1 Triples: 3\n",
"\tld4p Resources: 129 Triples: 5,157\n",
"\tminnesota Resources: 1 Triples: 64\n",
"\tpcc Resources: 1 Triples: 9\n",
"\tpenn Resources: 1 Triples: 15\n",
"\tucdavis Resources: 12 Triples: 798\n",
"2019-11 Total Resources 89 and Triples 2,711\n",
"\talberta Resources: 2 Triples: 31\n",
"\tcornell Resources: 1 Triples: 4\n",
"\tdlc Resources: 1 Triples: 8\n",
"\tld4p Resources: 15 Triples: 452\n",
"\tminnesota Resources: 4 Triples: 188\n",
"\twashington Resources: 66 Triples: 2,028\n",
"2019-12 Total Resources 104 and Triples 4,620\n",
"\talberta Resources: 1 Triples: 39\n",
"\tchicago Resources: 1 Triples: 15\n",
"\tcornell Resources: 4 Triples: 586\n",
"\tharvard Resources: 3 Triples: 116\n",
"\tld4p Resources: 10 Triples: 750\n",
"\tminnesota Resources: 3 Triples: 209\n",
"\tprinceton Resources: 3 Triples: 125\n",
"\tstanford Resources: 1 Triples: 12\n",
"\twashington Resources: 78 Triples: 2,768\n",
"2020-01 Total Resources 100 and Triples 4,158\n",
"\talberta Resources: 48 Triples: 1,754\n",
"\tchicago Resources: 5 Triples: 159\n",
"\tcornell Resources: 15 Triples: 1,017\n",
"\tld4p Resources: 6 Triples: 359\n",
"\tpcc Resources: 5 Triples: 107\n",
"\tprinceton Resources: 4 Triples: 179\n",
"\twashington Resources: 17 Triples: 583\n",
"2020-02 Total Resources 702 and Triples 29,721\n",
"\talberta Resources: 81 Triples: 2,564\n",
"\tboulder Resources: 2 Triples: 134\n",
"\tchicago Resources: 7 Triples: 350\n",
"\tcornell Resources: 22 Triples: 1,502\n",
"\tld4p Resources: 33 Triples: 2,188\n",
"\tminnesota Resources: 58 Triples: 2,127\n",
"\tnorthwestern Resources: 8 Triples: 543\n",
"\tprinceton Resources: 10 Triples: 258\n",
"\twashington Resources: 69 Triples: 2,119\n",
"\tyale Resources: 412 Triples: 17,936\n",
"2020-03 Total Resources 732 and Triples 36,702\n",
"\talberta Resources: 73 Triples: 2,058\n",
"\tchicago Resources: 6 Triples: 380\n",
"\tcornell Resources: 5 Triples: 366\n",
"\tld4p Resources: 56 Triples: 4,949\n",
"\tminnesota Resources: 38 Triples: 1,710\n",
"\tnorthwestern Resources: 3 Triples: 157\n",
"\tprinceton Resources: 12 Triples: 378\n",
"\tstanford Resources: 16 Triples: 900\n",
"\twashington Resources: 45 Triples: 1,477\n",
"\tyale Resources: 478 Triples: 24,327\n",
"2020-04 Total Resources 447 and Triples 23,748\n",
"\talberta Resources: 75 Triples: 2,235\n",
"\tchicago Resources: 30 Triples: 1,538\n",
"\tcornell Resources: 2 Triples: 148\n",
"\tld4p Resources: 88 Triples: 10,368\n",
"\tminnesota Resources: 35 Triples: 1,457\n",
"\tnlm Resources: 31 Triples: 1,056\n",
"\tpcc Resources: 1 Triples: 7\n",
"\tprinceton Resources: 7 Triples: 306\n",
"\tstanford Resources: 17 Triples: 729\n",
"\tucsd Resources: 3 Triples: 274\n",
"\twashington Resources: 145 Triples: 5,021\n",
"\tyale Resources: 13 Triples: 609\n",
"2020-05 Total Resources 428 and Triples 15,896\n",
"\talberta Resources: 54 Triples: 1,475\n",
"\tchicago Resources: 28 Triples: 1,348\n",
"\tcornell Resources: 1 Triples: 92\n",
"\tld4p Resources: 23 Triples: 1,895\n",
"\tminnesota Resources: 14 Triples: 636\n",
"\tnlm Resources: 124 Triples: 3,785\n",
"\tstanford Resources: 50 Triples: 1,644\n",
"\tucsd Resources: 31 Triples: 1,490\n",
"\twashington Resources: 103 Triples: 3,531\n",
"2020-06 Total Resources 355 and Triples 43,481\n",
"\tchicago Resources: 2 Triples: 133\n",
"\tld4p Resources: 41 Triples: 35,027\n",
"\tnlm Resources: 273 Triples: 7,040\n",
"\tnorthwestern Resources: 20 Triples: 690\n",
"\tpcc Resources: 3 Triples: 63\n",
"\twashington Resources: 16 Triples: 528\n",
"2020-07 Total Resources 262 and Triples 14,369\n",
"\tld4p Resources: 173 Triples: 11,590\n",
"\tminnesota Resources: 3 Triples: 150\n",
"\tnlm Resources: 60 Triples: 1,547\n",
"\tpcc Resources: 1 Triples: 38\n",
"\tstanford Resources: 18 Triples: 597\n",
"\twashington Resources: 1 Triples: 17\n",
"\tyale Resources: 6 Triples: 430\n",
"2020-08 Total Resources 82 and Triples 4,465\n",
"\tcornell Resources: 1 Triples: 32\n",
"\tharvard Resources: 5 Triples: 403\n",
"\tld4p Resources: 62 Triples: 3,523\n",
"\tnlm Resources: 5 Triples: 142\n",
"\twashington Resources: 9 Triples: 365\n",
"2020-09 Total Resources 10 and Triples 665\n",
"\tharvard Resources: 4 Triples: 371\n",
"\tld4p Resources: 2 Triples: 66\n",
"\tpenn Resources: 4 Triples: 228\n",
"2020-10 Total Resources 17 and Triples 1,130\n",
"\tharvard Resources: 5 Triples: 526\n",
"\tld4p Resources: 8 Triples: 562\n",
"\tnlm Resources: 3 Triples: 39\n",
"\tstanford Resources: 1 Triples: 3\n",
"2020-11 Total Resources 4 and Triples 1,214\n",
"\tharvard Resources: 1 Triples: 203\n",
"\tld4p Resources: 3 Triples: 1,011\n"
]
}
],
"source": [
"print(\"Production By Year-Month\")\n",
"display_by_month_year(prod_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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