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larsyencken/2021-10-26 Daniel & Lars food exploring.ipynb

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OWID: Notebook exploring potential sharding options for a food explorer
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2021-10-26 Daniel & Lars food exploring.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "776d27f9-587c-45d4-b710-84656c084e36",
"metadata": {},
"source": [
"# 2021-10-26 Daniel & Lars food exploring"
]
},
{
"cell_type": "markdown",
"id": "f3eec933-cb42-41b4-bbfb-1391e3a3770b",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"- How big is the current data in CSV form?\n",
" - 272 MB, but it needs auditing against Hannah's design again\n",
"- Is there a shard key we could use to shard the data into reasonable CSV files?\n",
" - Measures might be a reasonable shard key, but we'd need to try and see the maximum file size\n",
" - Any shard key we use will prevent us from doing charts which cover multiple shards"
]
},
{
"cell_type": "markdown",
"id": "6ff7e167-2c96-47ee-9d92-6576c0541aac",
"metadata": {},
"source": [
"## Loading the data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1cff3f82-401a-4d95-b2ea-2f9cbacf4b8d",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_feather('hannah-2021-09-08-raw.feather')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7b8c3837-5506-4b51-8788-bebb1eae742c",
"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>dataset_id</th>\n",
" <th>variable</th>\n",
" <th>year</th>\n",
" <th>entity</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5010</td>\n",
" <td>New Food Balances - Alcohol, Non-Food - 2659 -...</td>\n",
" <td>2014</td>\n",
" <td>United Kingdom</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5010</td>\n",
" <td>New Food Balances - Alcohol, Non-Food - 2659 -...</td>\n",
" <td>2015</td>\n",
" <td>United Kingdom</td>\n",
" <td>319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5010</td>\n",
" <td>New Food Balances - Alcohol, Non-Food - 2659 -...</td>\n",
" <td>2016</td>\n",
" <td>United Kingdom</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5010</td>\n",
" <td>New Food Balances - Alcohol, Non-Food - 2659 -...</td>\n",
" <td>2017</td>\n",
" <td>United Kingdom</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5010</td>\n",
" <td>New Food Balances - Alcohol, Non-Food - 2659 -...</td>\n",
" <td>2014</td>\n",
" <td>Ireland</td>\n",
" <td>41</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_id variable year entity value\n",
"0 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2014 United Kingdom 132\n",
"1 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2015 United Kingdom 319\n",
"2 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2016 United Kingdom 200\n",
"3 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2017 United Kingdom 51\n",
"4 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2014 Ireland 41"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "de0aa525-5adc-442c-b7a1-6fdc42d1778f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['New Food Balances - Alcohol, Non-Food - 2659 - Domestic supply quantity - 5301 - 1000 tonnes',\n",
" 'New Food Balances - Alcohol, Non-Food - 2659 - Domestic supply quantity - 5301 - 1000 tonnes',\n",
" 'New Food Balances - Alcohol, Non-Food - 2659 - Domestic supply quantity - 5301 - 1000 tonnes'],\n",
" dtype=object)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"examples = df.variable.values[:3]\n",
"examples"
]
},
{
"cell_type": "markdown",
"id": "1c6faaaa-1a1d-4cd5-bbbc-1e7306fcff4a",
"metadata": {},
"source": [
"## Reconstruct the dimensions"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9403c498-97fe-4b16-92e5-2f0a0288917d",
"metadata": {},
"outputs": [],
"source": [
"import parse"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "5ad992c4-7198-4119-aee3-99b70095ba4f",
"metadata": {},
"outputs": [],
"source": [
"pattern = '{dataset} - {food_type} - {food_type_id} - {measure} - {measure_id} - {unit}'\n",
"\n",
"parser = parse.compile(pattern)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "38993e1b-b9c3-4bf4-91ea-2bbb8fbd7771",
"metadata": {},
"outputs": [],
"source": [
"rows = df.variable.apply(lambda v: parser.parse(v).named)\n",
"unpacked_vars = pd.DataFrame.from_records(rows)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "ba7ae2c8-a4c9-47fb-b856-a6c2aec8b3d3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\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>dataset</th>\n",
" <th>food_type</th>\n",
" <th>food_type_id</th>\n",
" <th>measure</th>\n",
" <th>measure_id</th>\n",
" <th>unit</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset food_type food_type_id measure measure_id unit\n",
"0 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes\n",
"1 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes\n",
"2 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes\n",
"3 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes\n",
"4 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unpacked_vars.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "d7153859-8082-4639-8c7b-534453c11f38",
"metadata": {},
"outputs": [],
"source": [
"for col in ['dataset', 'food_type', 'measure', 'unit']:\n",
" unpacked_vars[col] = unpacked_vars[col].astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "884e01ab-f6a9-47da-83b4-29fa20655609",
"metadata": {},
"outputs": [],
"source": [
"# the concat doesn't work right if the index isn't reset\n",
"df.reset_index(drop=True, inplace=True)\n",
"comb = pd.concat([df, unpacked_vars], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "3d2f7a37-294c-4353-b4c3-0ac38b879152",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
" .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>dataset_id</th>\n",
" <th>variable</th>\n",
" <th>year</th>\n",
" <th>entity</th>\n",
" <th>value</th>\n",
" <th>dataset</th>\n",
" <th>food_type</th>\n",
" <th>food_type_id</th>\n",
" <th>measure</th>\n",
" <th>measure_id</th>\n",
" <th>unit</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5010</td>\n",
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" <td>2014</td>\n",
" <td>United Kingdom</td>\n",
" <td>132</td>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5010</td>\n",
" <td>New Food Balances - Alcohol, Non-Food - 2659 -...</td>\n",
" <td>2015</td>\n",
" <td>United Kingdom</td>\n",
" <td>319</td>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5010</td>\n",
" <td>New Food Balances - Alcohol, Non-Food - 2659 -...</td>\n",
" <td>2016</td>\n",
" <td>United Kingdom</td>\n",
" <td>200</td>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5010</td>\n",
" <td>New Food Balances - Alcohol, Non-Food - 2659 -...</td>\n",
" <td>2017</td>\n",
" <td>United Kingdom</td>\n",
" <td>51</td>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5010</td>\n",
" <td>New Food Balances - Alcohol, Non-Food - 2659 -...</td>\n",
" <td>2014</td>\n",
" <td>Ireland</td>\n",
" <td>41</td>\n",
" <td>New Food Balances</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>2659</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>5301</td>\n",
" <td>1000 tonnes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_id variable year entity value dataset food_type food_type_id measure measure_id unit\n",
"0 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2014 United Kingdom 132 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes\n",
"1 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2015 United Kingdom 319 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes\n",
"2 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2016 United Kingdom 200 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes\n",
"3 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2017 United Kingdom 51 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes\n",
"4 5010 New Food Balances - Alcohol, Non-Food - 2659 -... 2014 Ireland 41 New Food Balances Alcohol, Non-Food 2659 Domestic supply quantity 5301 1000 tonnes"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comb.head()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "6544e56a-91a9-49e5-a7ee-5d62460be6ff",
"metadata": {},
"outputs": [],
"source": [
"comb.drop(columns=['dataset_id', 'variable', 'food_type_id', 'measure_id'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "f51eb269-66b4-4164-96da-237f142bad75",
"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>entity</th>\n",
" <th>year</th>\n",
" <th>food_type</th>\n",
" <th>measure</th>\n",
" <th>unit</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>United Kingdom</td>\n",
" <td>2014</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>1000 tonnes</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>United Kingdom</td>\n",
" <td>2015</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>1000 tonnes</td>\n",
" <td>319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>United Kingdom</td>\n",
" <td>2016</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>1000 tonnes</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>United Kingdom</td>\n",
" <td>2017</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>1000 tonnes</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Ireland</td>\n",
" <td>2014</td>\n",
" <td>Alcohol, Non-Food</td>\n",
" <td>Domestic supply quantity</td>\n",
" <td>1000 tonnes</td>\n",
" <td>41</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" entity year food_type measure unit value\n",
"0 United Kingdom 2014 Alcohol, Non-Food Domestic supply quantity 1000 tonnes 132\n",
"1 United Kingdom 2015 Alcohol, Non-Food Domestic supply quantity 1000 tonnes 319\n",
"2 United Kingdom 2016 Alcohol, Non-Food Domestic supply quantity 1000 tonnes 200\n",
"3 United Kingdom 2017 Alcohol, Non-Food Domestic supply quantity 1000 tonnes 51\n",
"4 Ireland 2014 Alcohol, Non-Food Domestic supply quantity 1000 tonnes 41"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comb = comb[['entity', 'year', 'food_type', 'measure', 'unit', 'value']]\n",
"comb.head()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "e9fb417c-7a5f-44a8-b24e-c427bc714727",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4544207"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(comb)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "39f0ef00-f9e0-4587-aa0d-7db3f6f67903",
"metadata": {},
"outputs": [],
"source": [
"comb.to_csv('2021-10-26-food-explorer-full.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "54e55301-48d4-4f14-be18-1b68338f1370",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"272M\t2021-10-26-food-explorer-full.csv\n"
]
}
],
"source": [
"!du -hs 2021-10-26-food-explorer-full.csv"
]
},
{
"cell_type": "markdown",
"id": "68c40ebe-b98b-45d0-a7e7-1a042f31d58a",
"metadata": {},
"source": [
"## Cardinality"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "c0e5fd1c-ea73-49dc-9f28-3743e2064d89",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"entity 260\n",
"year 58\n",
"food_type 363\n",
"measure 30\n",
"unit 33\n",
"value 713379\n",
"dtype: int64"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series({col: len(comb[col].unique()) for col in comb.columns})"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "c267341d-ec34-47fe-b388-7fb15c97e37d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tonnes 1274140\n",
"ha 753579\n",
"hg/ha 749532\n",
"1000 tonnes 643157\n",
"Head 294089\n",
"hg/An 226901\n",
"g/capita/day 141216\n",
"1000 Head 118803\n",
"0.1g/An 76910\n",
"kcal/capita/day 70360\n",
"kg 69872\n",
"100mg/An 28902\n",
"1000 No 16822\n",
"No 16388\n",
"hg 12479\n",
"5301 - 1000 tonnes 3440\n",
"5142 - 1000 tonnes 3438\n",
"645 - kg 3438\n",
"664 - kcal/capita/day 3435\n",
"684 - g/capita/day 3435\n",
"674 - g/capita/day 3435\n",
"5611 - 1000 tonnes 3430\n",
"5170 - 1000 tonnes 3404\n",
"5072 - 1000 tonnes 3276\n",
"5911 - 1000 tonnes 3160\n",
"5511 - 1000 tonnes 3107\n",
"5123 - 1000 tonnes 2945\n",
"5521 - 1000 tonnes 2768\n",
"5131 - 1000 tonnes 2551\n",
"5154 - 1000 tonnes 2217\n",
"5171 - 1000 tonnes 1667\n",
"5527 - 1000 tonnes 1223\n",
"511 - 1000 persons 688\n",
"Name: unit, dtype: int64"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comb.unit.value_counts()"
]
},
{
"cell_type": "markdown",
"id": "ecaa11aa-4a78-4109-aab0-c21cfd6f3f27",
"metadata": {},
"source": [
"^ not all units got parsed correctly"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "2588fde3-1e51-4dd2-934f-c5543e53d7ef",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Production 1445546\n",
"Yield 872292\n",
"Area harvested 753579\n",
"Producing Animals/Slaughtered 242946\n",
"Yield/Carcass Weight 222432\n",
"Domestic supply quantity 76013\n",
"Import Quantity 72754\n",
"Fat supply quantity (g/capita/day) 70619\n",
"Protein supply quantity (g/capita/day) 70597\n",
"Food supply (kcal/capita/day) 70360\n",
"Food 69980\n",
"Food supply quantity (kg/capita/yr) 69872\n",
"Residuals 64819\n",
"Export Quantity 61236\n",
"Stock Variation 60970\n",
"Milk Animals 41695\n",
"Other uses (non-food) 36875\n",
"Losses 36338\n",
"Feed 30676\n",
"Tourist consumption 30186\n",
"Laying 28904\n",
"Processing 26427\n",
"Seed 21646\n",
"Prod Popultn 16388\n",
"2905 11200\n",
"2848 10163\n",
"2948 10163\n",
"2919 9925\n",
"2560 8918\n",
"Total Population 688\n",
"Name: measure, dtype: int64"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comb.measure.value_counts()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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