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00_cnf-nutrition.ipynb
{
"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": [
{
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" .dataframe thead th {\n",
" text-align: right;\n",
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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",
" </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": [
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"</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>"
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" 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 "
]
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"execution_count": 81,
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"output_type": "execute_result"
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],
"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": [
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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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" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Legumes - lentils are particularly high in lysine for a plant-based source\n",
"compare_proteins(3288, 3400, 3362, 3392)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
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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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" <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",
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" <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",
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" <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",
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],
"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 "
]
},
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}
],
"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": [
{
"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",
" <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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