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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Canadian Nutrient File as a DuckDB Database\n", | |
"\n", | |
"This gist contains a compacted and ready-to-use DuckDB version of the \n", | |
"[Canadian Nutrient File](https://www.canada.ca/en/health-canada/services/food-nutrition/healthy-eating/nutrient-data/canadian-nutrient-file-compilation-canadian-food-composition-data-database-structure.html) from Health Canada, along\n", | |
"with a Jupyter notebook demonstrating my particular use case. You will likely need duckdb 0.10 or greater [to open it](https://duckdb.org/docs/internals/storage).\n", | |
"\n", | |
"The dataset contains a detailed nutritional composition of over 5000 foods sold in Canada across 150 dimensions - essentially anything you may see on a nutition label. The products are Canada-centric, naturally, but there are plenty of less-processed foods that should still make the dataset useful for people elsewhere.\n", | |
"\n", | |
"To use, all you should need to do open up the notebook in your favorite Jupyter environment and `pip install requirements.txt` to install the needed packages.\n", | |
"\n", | |
"_The original source data can be downloaded from the [Canada Open Data portal](https://open.canada.ca/data/en/dataset/089885f9-ed53-44e6-854a-14d21a1ec2e0), my apologies I lost the script/commands I used to create the DB._\n", | |
"\n", | |
"\n", | |
"### Examples \n", | |
"\n", | |
"My original use case for this dataset was to do some analysis of essential amino acid composition,\n", | |
"and there are some functions below showing how to compare different foods.\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---------- \n", | |
"#### Helpers and base functions" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 32, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import duckdb\n", | |
"import pandas as pd\n", | |
"import plotly.express as px" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 33, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"db = duckdb.connect('cnf.duckdb', read_only=True)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 79, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def essential_amino_for_food(foodId, with_prot=False):\n", | |
" \"\"\" Return a dataframe with the scaled ratio of essential amino acids in its protein \n", | |
" https://en.wikipedia.org/wiki/Essential_amino_acid\n", | |
" \"\"\"\n", | |
" # histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan and valine.\n", | |
" ess_amn = [512, 503, 504, 505, 506, 508, 502, 501] \n", | |
" if with_prot:\n", | |
" ess_amn.append(203)\n", | |
" sql = f\"\"\"\n", | |
" SELECT fn.FoodDescription, nn.NutrientID, nn.NutrientName, nn.NutrientUnit , na.NutrientValue, NutrientValue / sum(NutrientValue) OVER (PARTITION by FoodDescription) as EssentialRatio\n", | |
" FROM\n", | |
" main.nutrient_amount na\n", | |
" NATURAL JOIN main.nutrient_name nn \n", | |
" NATURAL JOIN main.food_name fn\n", | |
" WHERE fn.FoodID = {foodId}\n", | |
" AND nn.NutrientID IN ({\",\".join([str(e) for e in ess_amn])})\n", | |
" \"\"\"\n", | |
" return db.sql(sql).to_df()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 82, | |
"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>FoodID</th>\n", | |
" <th>FoodDescription</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>4487</td>\n", | |
" <td>Grains, buckwheat</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>4488</td>\n", | |
" <td>Grains, buckwheat flour, whole-groat</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>4413</td>\n", | |
" <td>Grains, buckwheat groats, roasted, cooked</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>4412</td>\n", | |
" <td>Grains, buckwheat groats, roasted, dry</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>501831</td>\n", | |
" <td>Pancake, buckwheat, dry mix, incomplete, prepa...</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>5</th>\n", | |
" <td>3937</td>\n", | |
" <td>Pancake, buckwheat, dry mix, incomplete, unpre...</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>6</th>\n", | |
" <td>4125</td>\n", | |
" <td>Snacks, rice cakes, brown rice, buckwheat</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>7</th>\n", | |
" <td>4388</td>\n", | |
" <td>Snacks, rice cakes, brown rice, buckwheat, uns...</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" FoodID FoodDescription\n", | |
"0 4487 Grains, buckwheat\n", | |
"1 4488 Grains, buckwheat flour, whole-groat\n", | |
"2 4413 Grains, buckwheat groats, roasted, cooked\n", | |
"3 4412 Grains, buckwheat groats, roasted, dry\n", | |
"4 501831 Pancake, buckwheat, dry mix, incomplete, prepa...\n", | |
"5 3937 Pancake, buckwheat, dry mix, incomplete, unpre...\n", | |
"6 4125 Snacks, rice cakes, brown rice, buckwheat\n", | |
"7 4388 Snacks, rice cakes, brown rice, buckwheat, uns..." | |
] | |
}, | |
"execution_count": 82, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"def food_search(food): \n", | |
" \"\"\" Helper to return FoodIDs to use in other functions \"\"\"\n", | |
" return db.sql(f\"\"\" \n", | |
" SELECT FoodID, FoodDescription\n", | |
" FROM main.food_name\n", | |
" WHERE FoodDescription ilike '%{food}%'\n", | |
" ORDER BY 2 ASC\n", | |
" LIMIT 20\n", | |
" \"\"\").to_df()\n", | |
"food_search('buckwheat')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 80, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"quinoa = essential_amino_for_food(4495)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 81, | |
"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>FoodDescription</th>\n", | |
" <th>NutrientID</th>\n", | |
" <th>NutrientName</th>\n", | |
" <th>NutrientUnit</th>\n", | |
" <th>NutrientValue</th>\n", | |
" <th>EssentialRatio</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>Grains, quinoa, dry</td>\n", | |
" <td>501</td>\n", | |
" <td>TRYPTOPHAN</td>\n", | |
" <td>g</td>\n", | |
" <td>0.160</td>\n", | |
" <td>0.04000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>Grains, quinoa, dry</td>\n", | |
" <td>502</td>\n", | |
" <td>THREONINE</td>\n", | |
" <td>g</td>\n", | |
" <td>0.421</td>\n", | |
" <td>0.10525</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>Grains, quinoa, dry</td>\n", | |
" <td>503</td>\n", | |
" <td>ISOLEUCINE</td>\n", | |
" <td>g</td>\n", | |
" <td>0.504</td>\n", | |
" <td>0.12600</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>Grains, quinoa, dry</td>\n", | |
" <td>504</td>\n", | |
" <td>LEUCINE</td>\n", | |
" <td>g</td>\n", | |
" <td>0.840</td>\n", | |
" <td>0.21000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>Grains, quinoa, dry</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>g</td>\n", | |
" <td>0.766</td>\n", | |
" <td>0.19150</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>5</th>\n", | |
" <td>Grains, quinoa, dry</td>\n", | |
" <td>506</td>\n", | |
" <td>METHIONINE</td>\n", | |
" <td>g</td>\n", | |
" <td>0.309</td>\n", | |
" <td>0.07725</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>6</th>\n", | |
" <td>Grains, quinoa, dry</td>\n", | |
" <td>508</td>\n", | |
" <td>PHENYLALANINE</td>\n", | |
" <td>g</td>\n", | |
" <td>0.593</td>\n", | |
" <td>0.14825</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>7</th>\n", | |
" <td>Grains, quinoa, dry</td>\n", | |
" <td>512</td>\n", | |
" <td>HISTIDINE</td>\n", | |
" <td>g</td>\n", | |
" <td>0.407</td>\n", | |
" <td>0.10175</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" FoodDescription NutrientID NutrientName NutrientUnit NutrientValue \\\n", | |
"0 Grains, quinoa, dry 501 TRYPTOPHAN g 0.160 \n", | |
"1 Grains, quinoa, dry 502 THREONINE g 0.421 \n", | |
"2 Grains, quinoa, dry 503 ISOLEUCINE g 0.504 \n", | |
"3 Grains, quinoa, dry 504 LEUCINE g 0.840 \n", | |
"4 Grains, quinoa, dry 505 LYSINE g 0.766 \n", | |
"5 Grains, quinoa, dry 506 METHIONINE g 0.309 \n", | |
"6 Grains, quinoa, dry 508 PHENYLALANINE g 0.593 \n", | |
"7 Grains, quinoa, dry 512 HISTIDINE g 0.407 \n", | |
"\n", | |
" EssentialRatio \n", | |
"0 0.04000 \n", | |
"1 0.10525 \n", | |
"2 0.12600 \n", | |
"3 0.21000 \n", | |
"4 0.19150 \n", | |
"5 0.07725 \n", | |
"6 0.14825 \n", | |
"7 0.10175 " | |
] | |
}, | |
"execution_count": 81, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"quinoa" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 24, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def compare_proteins(*args):\n", | |
" \"\"\" Render a bar/histogram with the essential amino acid content of a\n", | |
" variable number of foods \"\"\"\n", | |
" all_df = []\n", | |
" for f in args:\n", | |
" all_df.append(essential_amino_for_food(f))\n", | |
" df = pd.concat(all_df)\n", | |
" return px.histogram(\n", | |
" df, x='NutrientName', y='EssentialRatio', color='FoodDescription', \n", | |
" title='Essential Ratio Comparison', barmode='group'\n", | |
" )" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"-------------------\n", | |
"## Comparing Protein Sources" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 66, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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"config": { | |
"plotlyServerURL": "https://plot.ly" | |
}, | |
"data": [ | |
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"alignmentgroup": "True", | |
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"hovertemplate": "FoodDescription=Grains, rice, brown, long-grain, dry<br>NutrientName=%{x}<br>sum of EssentialRatio=%{y}<extra></extra>", | |
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"marker": { | |
"color": "#636efa", | |
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"histfunc": "sum", | |
"hovertemplate": "FoodDescription=Grains, quinoa, dry<br>NutrientName=%{x}<br>sum of EssentialRatio=%{y}<extra></extra>", | |
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"source": [ | |
"# Various grains; as expected most have little lysine. Buckwheat is surprisingly close to quinoa in composition.\n", | |
"compare_proteins(4496, 1464, 4436, 4485,4412, 4495)" | |
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} | |
], | |
"source": [ | |
"# Legumes - lentils are particularly high in lysine for a plant-based source\n", | |
"compare_proteins(3288, 3400, 3362, 3392)" | |
] | |
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of EssentialRatio\"}},\"legend\":{\"title\":{\"text\":\"FoodDescription\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Essential Ratio Comparison\"},\"barmode\":\"group\"}, {\"responsive\": true} ).then(function(){\n", | |
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" Plotly.purge(gd);\n", | |
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" }}\n", | |
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] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Animal-sourced proteins for reference\n", | |
"compare_proteins(6119, 125, 3221, 838, 25, 61)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Miscellaneous \n", | |
"\n", | |
"What are good sources of lysine?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 69, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>FoodID</th>\n", | |
" <th>FoodDescription</th>\n", | |
" <th>NutrientID</th>\n", | |
" <th>NutrientName</th>\n", | |
" <th>NutrientValue</th>\n", | |
" <th>EssentialRatio</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>1689</td>\n", | |
" <td>Tamarind, raw</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>0.139</td>\n", | |
" <td>0.812865</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>2298</td>\n", | |
" <td>Parsley, freeze-dried</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>3.115</td>\n", | |
" <td>0.810987</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>1597</td>\n", | |
" <td>Lychee, dried</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>0.187</td>\n", | |
" <td>0.713740</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>2239</td>\n", | |
" <td>Sweet potato leaves, steamed</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>0.132</td>\n", | |
" <td>0.653465</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>2238</td>\n", | |
" <td>Sweet potato leaves, raw</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>0.228</td>\n", | |
" <td>0.653295</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>5</th>\n", | |
" <td>501788</td>\n", | |
" <td>Sweet potato leaves, steamed, with salt</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>0.130</td>\n", | |
" <td>0.650000</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>6</th>\n", | |
" <td>2385</td>\n", | |
" <td>Cauliflower, raw</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>0.217</td>\n", | |
" <td>0.336434</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>7</th>\n", | |
" <td>194</td>\n", | |
" <td>Spices, onion powder</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>0.490</td>\n", | |
" <td>0.310127</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8</th>\n", | |
" <td>4998</td>\n", | |
" <td>Beef, ground, extra lean, crumbled, pan-fried</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>2.355</td>\n", | |
" <td>0.309055</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>9</th>\n", | |
" <td>5009</td>\n", | |
" <td>Beef, ground, lean, crumbled, pan-fried</td>\n", | |
" <td>505</td>\n", | |
" <td>LYSINE</td>\n", | |
" <td>2.295</td>\n", | |
" <td>0.308966</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" FoodID FoodDescription NutrientID \\\n", | |
"0 1689 Tamarind, raw 505 \n", | |
"1 2298 Parsley, freeze-dried 505 \n", | |
"2 1597 Lychee, dried 505 \n", | |
"3 2239 Sweet potato leaves, steamed 505 \n", | |
"4 2238 Sweet potato leaves, raw 505 \n", | |
"5 501788 Sweet potato leaves, steamed, with salt 505 \n", | |
"6 2385 Cauliflower, raw 505 \n", | |
"7 194 Spices, onion powder 505 \n", | |
"8 4998 Beef, ground, extra lean, crumbled, pan-fried 505 \n", | |
"9 5009 Beef, ground, lean, crumbled, pan-fried 505 \n", | |
"\n", | |
" NutrientName NutrientValue EssentialRatio \n", | |
"0 LYSINE 0.139 0.812865 \n", | |
"1 LYSINE 3.115 0.810987 \n", | |
"2 LYSINE 0.187 0.713740 \n", | |
"3 LYSINE 0.132 0.653465 \n", | |
"4 LYSINE 0.228 0.653295 \n", | |
"5 LYSINE 0.130 0.650000 \n", | |
"6 LYSINE 0.217 0.336434 \n", | |
"7 LYSINE 0.490 0.310127 \n", | |
"8 LYSINE 2.355 0.309055 \n", | |
"9 LYSINE 2.295 0.308966 " | |
] | |
}, | |
"execution_count": 69, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"\n", | |
"db.sql(\"\"\"\n", | |
"SELECT * FROM (\n", | |
"\tSELECT fn.FoodId, fn.FoodDescription, nn.NutrientID, nn.NutrientName, na.NutrientValue, \n", | |
" NutrientValue / sum(NutrientValue) OVER (PARTITION by FoodDescription) as EssentialRatio\n", | |
" FROM\n", | |
" main.nutrient_amount na\n", | |
" NATURAL JOIN main.nutrient_name nn \n", | |
" NATURAL JOIN main.food_name fn\n", | |
" WHERE \n", | |
" nn.NutrientID IN (512, 503, 504, 505, 506, 508, 502, 501)\n", | |
") ess\n", | |
"WHERE NutrientId = 505\n", | |
"-- Filter out foods with neglible overall protein amounts\n", | |
"AND NutrientValue >= 0.1\n", | |
"ORDER BY EssentialRatio DESC \n", | |
"LIMIT 10\"\"\").to_df()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Dried parsley seems surprisingly high in protein as a leafy vegetable, are there others?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 56, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
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"</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>FoodID</th>\n", | |
" <th>FoodDescription</th>\n", | |
" <th>NutrientID</th>\n", | |
" <th>NutrientName</th>\n", | |
" <th>NutrientValue</th>\n", | |
" <th>MacroSum</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>2320</td>\n", | |
" <td>Seaweed, spirulina, dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>57.47</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>2499</td>\n", | |
" <td>Seaweed, dulse (laver, nori), dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>36.22</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>2298</td>\n", | |
" <td>Parsley, freeze-dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>31.30</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>5605</td>\n", | |
" <td>Yeast extract spread</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>23.88</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>2289</td>\n", | |
" <td>Chives, freeze-dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>21.20</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>5</th>\n", | |
" <td>501703</td>\n", | |
" <td>Pepper, sweet, red, freeze-dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>17.90</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>6</th>\n", | |
" <td>2300</td>\n", | |
" <td>Pepper, sweet, green, freeze-dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>17.90</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>7</th>\n", | |
" <td>502350</td>\n", | |
" <td>Seaweed, Canadian cultivated EMI-TSUNOMATA, dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>15.34</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8</th>\n", | |
" <td>2297</td>\n", | |
" <td>Leeks (bulb and lower-leaf portion), freeze-dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>15.20</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>9</th>\n", | |
" <td>6263</td>\n", | |
" <td>Corn, dried, yellow</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>14.48</td>\n", | |
" <td>99.99</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>10</th>\n", | |
" <td>2348</td>\n", | |
" <td>Tomato, sun-dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>14.11</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>11</th>\n", | |
" <td>501765</td>\n", | |
" <td>Soybeans, mature seeds, sprouted, bean sprouts...</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>13.10</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>12</th>\n", | |
" <td>2212</td>\n", | |
" <td>Soybeans, mature seeds, sprouted, bean sprouts...</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>13.10</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>13</th>\n", | |
" <td>2210</td>\n", | |
" <td>Soybeans, mature seeds, sprouted, bean sprouts...</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>13.09</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>14</th>\n", | |
" <td>2208</td>\n", | |
" <td>Soybeans, green (edamame), raw</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>12.95</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>15</th>\n", | |
" <td>2260</td>\n", | |
" <td>Tomato powder</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>12.91</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>16</th>\n", | |
" <td>2209</td>\n", | |
" <td>Soybeans, green (edamame), boiled, drained</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>12.35</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>17</th>\n", | |
" <td>501764</td>\n", | |
" <td>Soybeans, green (edamame), boiled, drained, wi...</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>12.35</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>18</th>\n", | |
" <td>4863</td>\n", | |
" <td>Pepper, pasilla, dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>12.35</td>\n", | |
" <td>99.99</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>19</th>\n", | |
" <td>2302</td>\n", | |
" <td>Shallot, freeze-dried</td>\n", | |
" <td>203</td>\n", | |
" <td>PROTEIN</td>\n", | |
" <td>12.30</td>\n", | |
" <td>100.00</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" FoodID FoodDescription NutrientID \\\n", | |
"0 2320 Seaweed, spirulina, dried 203 \n", | |
"1 2499 Seaweed, dulse (laver, nori), dried 203 \n", | |
"2 2298 Parsley, freeze-dried 203 \n", | |
"3 5605 Yeast extract spread 203 \n", | |
"4 2289 Chives, freeze-dried 203 \n", | |
"5 501703 Pepper, sweet, red, freeze-dried 203 \n", | |
"6 2300 Pepper, sweet, green, freeze-dried 203 \n", | |
"7 502350 Seaweed, Canadian cultivated EMI-TSUNOMATA, dried 203 \n", | |
"8 2297 Leeks (bulb and lower-leaf portion), freeze-dried 203 \n", | |
"9 6263 Corn, dried, yellow 203 \n", | |
"10 2348 Tomato, sun-dried 203 \n", | |
"11 501765 Soybeans, mature seeds, sprouted, bean sprouts... 203 \n", | |
"12 2212 Soybeans, mature seeds, sprouted, bean sprouts... 203 \n", | |
"13 2210 Soybeans, mature seeds, sprouted, bean sprouts... 203 \n", | |
"14 2208 Soybeans, green (edamame), raw 203 \n", | |
"15 2260 Tomato powder 203 \n", | |
"16 2209 Soybeans, green (edamame), boiled, drained 203 \n", | |
"17 501764 Soybeans, green (edamame), boiled, drained, wi... 203 \n", | |
"18 4863 Pepper, pasilla, dried 203 \n", | |
"19 2302 Shallot, freeze-dried 203 \n", | |
"\n", | |
" NutrientName NutrientValue MacroSum \n", | |
"0 PROTEIN 57.47 100.00 \n", | |
"1 PROTEIN 36.22 100.00 \n", | |
"2 PROTEIN 31.30 100.00 \n", | |
"3 PROTEIN 23.88 100.00 \n", | |
"4 PROTEIN 21.20 100.00 \n", | |
"5 PROTEIN 17.90 100.00 \n", | |
"6 PROTEIN 17.90 100.00 \n", | |
"7 PROTEIN 15.34 100.00 \n", | |
"8 PROTEIN 15.20 100.00 \n", | |
"9 PROTEIN 14.48 99.99 \n", | |
"10 PROTEIN 14.11 100.00 \n", | |
"11 PROTEIN 13.10 100.00 \n", | |
"12 PROTEIN 13.10 100.00 \n", | |
"13 PROTEIN 13.09 100.00 \n", | |
"14 PROTEIN 12.95 100.00 \n", | |
"15 PROTEIN 12.91 100.00 \n", | |
"16 PROTEIN 12.35 100.00 \n", | |
"17 PROTEIN 12.35 100.00 \n", | |
"18 PROTEIN 12.35 99.99 \n", | |
"19 PROTEIN 12.30 100.00 " | |
] | |
}, | |
"execution_count": 56, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"#\n", | |
"db.sql(\"\"\"\n", | |
"SELECT * FROM (\n", | |
"\tSELECT fn.FoodId, fn.FoodDescription, nn.NutrientID, nn.NutrientName, na.NutrientValue, \n", | |
" sum(NutrientValue) OVER (PARTITION by FoodDescription) as MacroSum\n", | |
" FROM\n", | |
" main.nutrient_amount na\n", | |
" NATURAL JOIN main.nutrient_name nn \n", | |
" NATURAL JOIN main.food_name fn\n", | |
" NATURAL JOIN main.food_group fg\n", | |
" WHERE fg.FoodGroupID = 11\n", | |
" -- Major macronutrients protein, fat, carbs, water, ash\n", | |
" AND NutrientId IN (203, 204, 205, 207, 255) \n", | |
") ess\n", | |
"WHERE NutrientId = 203\n", | |
"-- Filter out foods with neglible overall protein amounts\n", | |
"AND NutrientValue >= 0.1\n", | |
"ORDER BY NutrientValue DESC \n", | |
"LIMIT 20\"\"\").to_df()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "ddbtest", | |
"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": "3.9.5" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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