From a55e1d325d550bf190b1f42dca9e7f8fb57334a8 Mon Sep 17 00:00:00 2001 From: Diego Ripley Date: Thu, 26 Jun 2025 13:35:37 +0000 Subject: [PATCH] Made changes --- .../check_duplicate_column_names.ipynb | 1065 ++++++++++++++++- .../csv_file_size_inside_zip.ipynb | 14 +- .../statcan_products/date_parsing.ipynb | 8 +- .../statcan_products/duck_experiment.ipynb | 909 +++++++++++++- .../process_files_multiprocessing.py | 119 +- .../process_product_test.ipynb | 963 ++++++++++----- 6 files changed, 2676 insertions(+), 402 deletions(-) diff --git a/experiments/statcan_products/check_duplicate_column_names.ipynb b/experiments/statcan_products/check_duplicate_column_names.ipynb index 9754f9a..1bb6c7d 100644 --- a/experiments/statcan_products/check_duplicate_column_names.ipynb +++ b/experiments/statcan_products/check_duplicate_column_names.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 1, "id": "5909b14d-1f07-46bc-84bf-09f269e15e41", "metadata": {}, "outputs": [], @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 2, "id": "04e61bd3-ab4c-46aa-9c0b-de949699ca0a", "metadata": {}, "outputs": [ @@ -206,7 +206,7 @@ "757 None 1 " ] }, - "execution_count": 38, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 3, "id": "cbf8953d-8523-42e8-b28c-6b464869ce61", "metadata": {}, "outputs": [], @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 4, "id": "d52074f9-7746-4569-9aea-57c204eda2eb", "metadata": {}, "outputs": [ @@ -240,7 +240,6 @@ "output_type": "stream", "text": [ "{'10100164': ['VALUE'],\n", - " '13100902': ['STATUS'],\n", " '13100904': ['STATUS'],\n", " '23100049': ['VALUE'],\n", " '23100050': ['VALUE'],\n", @@ -271,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 5, "id": "85dc8ce4-3ba5-4db8-bc2c-9ee101296e84", "metadata": {}, "outputs": [ @@ -283,19 +282,1067 @@ "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'North American Product Classification System (NAPCS) Canada 2012 Version 1.1', 'UOM', 'UOM_ID', 'SCALAR_FACTOR', 'SCALAR_ID', 'VECTOR', 'COORDINATE', 'VALUE', 'STATUS', 'SYMBOL', 'TERMINATED', 'DECIMALS']\n", "/data/tables/output/en/june_20_2025/11100236.parquet\n", "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'North American Product Classification System (NAPCS) Canada 2012 Version 1.1', 'North American Industry Classification System (NAICS)', 'UOM', 'UOM_ID', 'SCALAR_FACTOR', 'SCALAR_ID', 'VECTOR', 'COORDINATE', 'VALUE', 'STATUS', 'SYMBOL', 'TERMINATED', 'DECIMALS']\n", + "/data/tables/output/en/june_20_2025/13100902.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Age group in 2019', 'Gender in 2019', 'Status.1', 'Domains', 'Characteristics', 'UOM', 'UOM_ID', 'SCALAR_FACTOR', 'SCALAR_ID', 'VECTOR', 'COORDINATE', 'VALUE', 'STATUS', 'SYMBOL', 'TERMINATED', 'DECIMALS']\n", "/data/tables/output/en/june_20_2025/20100014.parquet\n", "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'North American Product Classification System (NAPCS) Canada 2012 Version 1.2', 'Financial variables', 'UOM', 'UOM_ID', 'SCALAR_FACTOR', 'SCALAR_ID', 'VECTOR', 'COORDINATE', 'VALUE', 'STATUS', 'SYMBOL', 'TERMINATED', 'DECIMALS']\n", "/data/tables/output/en/june_20_2025/20100015.parquet\n", "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'North American Product Classification System (NAPCS) Canada 2022 Version 1.0', 'Financial variables', 'UOM', 'UOM_ID', 'SCALAR_FACTOR', 'SCALAR_ID', 'VECTOR', 'COORDINATE', 'VALUE', 'STATUS', 'SYMBOL', 'TERMINATED', 'DECIMALS']\n", "/data/tables/output/en/june_20_2025/23100313.parquet\n", - 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"/data/tables/output/en/june_20_2025/98100014.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Coordinate', 'Population and dwelling counts (13): Population, 2021 [1]', 'Symbols', 'Population and dwelling counts (13): Population, 2016 [2]', 'Symbols.1', 'Population and dwelling counts (13): Population percentage change, 2016 to 2021 [3]', 'Symbols.2', 'Population and dwelling counts (13): Total private dwellings, 2021 [4]', 'Symbols.3', 'Population and dwelling counts (13): Private dwellings occupied by usual residents, 2021 [5]', 'Symbols.4', 'Population and dwelling counts (13): Land area in square kilometres, 2021 [6]', 'Symbols.5', 'Population and dwelling counts (13): Population density per square kilometre, 2021 [7]', 'Symbols.6']\n", + "/data/tables/output/en/june_20_2025/98100015.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Coordinate', 'Population and dwelling counts (5): Population, 2021 [1]', 'Symbols', 'Population and dwelling counts (5): Total private dwellings, 2021 [2]', 'Symbols.1', 'Population and dwelling counts (5): Private dwellings occupied by usual residents, 2021 [3]', 'Symbols.2', 'Population and dwelling counts (5): Land area in square kilometres, 2021 [4]', 'Symbols.3', 'Population and dwelling counts (5): Population density per square kilometre, 2021 [5]', 'Symbols.4']\n", + "/data/tables/output/en/june_20_2025/98100016.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Statistical Area Classification (SAC)', 'Coordinate', 'Population and dwelling counts (3): Population, 2021 [1]', 'Symbols', 'Population and dwelling counts (3): Total private dwellings, 2021 [2]', 'Symbols.1', 'Population and dwelling counts (3): Private dwellings occupied by usual residents, 2021 [3]', 'Symbols.2']\n", + "/data/tables/output/en/june_20_2025/98100017.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Census year', 'Coordinate', 'Population and dwelling counts (4): Population, count [1]', 'Symbols', 'Population and dwelling counts (4): Population, intercensal growth (percent) [2]', 'Symbols.1', 'Population and dwelling counts (4): Private dwellings occupied by usual residents, count (9) [3]', 'Symbols.2', 'Population and dwelling counts (4): Private dwellings occupied by usual residents, intercensal growth (percent) [4]', 'Symbols.3']\n", + "/data/tables/output/en/june_20_2025/98100018.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Coordinate', 'Population and dwelling counts (11): Population, 2021 [1]', 'Symbols', 'Population and dwelling counts (11): Population, 2016 [2]', 'Symbols.1', 'Population and dwelling counts (11): Population percentage change, 2016 to 2021 [3]', 'Symbols.2', 'Population and dwelling counts (11): Total private dwellings, 2021 [4]', 'Symbols.3', 'Population and dwelling counts (11): Total private dwellings, 2016 [5]', 'Symbols.4', 'Population and dwelling counts (11): Total private dwellings percentage change, 2016 to 2021 [6]', 'Symbols.5', 'Population and dwelling counts (11): Private dwellings occupied by usual residents, 2021 [7]', 'Symbols.6', 'Population and dwelling counts (11): Private dwellings occupied by usual residents, 2016 [8]', 'Symbols.7', 'Population and dwelling counts (11): Private dwellings occupied by usual residents percentage change, 2016 to 2021 [9]', 'Symbols.8', 'Population and dwelling counts (11): Land area in square kilometres, 2021 [10]', 'Symbols.9', 'Population and dwelling counts (11): Population density per square kilometre, 2021 [11]', 'Symbols.10']\n", + "/data/tables/output/en/june_20_2025/98100019.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Coordinate', 'Population and dwelling counts (3):Population, 2021[1]', 'Symbol', 'Population and dwelling counts (3):Total private dwellings, 2021[2]', 'Symbol.1', 'Population and dwelling counts (3):Private dwellings occupied by usual residents, 2021[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100020.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Census year (2)', 'Age (in single years), average age and median age (128)', 'Coordinate', 'Gender (3a):Total - Gender[1]', 'Symbol', 'Gender (3a):Men+[2]', 'Symbol.1', 'Gender (3a):Women+[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100021.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Census year (2)', 'Age (in single years), average age and median age (128)', 'Coordinate', 'Gender (3a):Total - Gender[1]', 'Symbol', 'Gender (3a):Men+[2]', 'Symbol.1', 'Gender (3a):Women+[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100022.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Census year (2)', 'Age (in single years), average age and median age (128)', 'Coordinate', 'Gender (3a):Total - Gender[1]', 'Symbol', 'Gender (3a):Men+[2]', 'Symbol.1', 'Gender (3a):Women+[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100023.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Age (in single years), average age and median age (128)', 'Coordinate', 'Gender (3):Total - Gender[1]', 'Symbol', 'Gender (3):Men+[2]', 'Symbol.1', 'Gender (3):Women+[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100024.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Age (in single years), average age and median age (128)', 'Coordinate', 'Gender (3):Total - Gender[1]', 'Symbol', 'Gender (3):Men+[2]', 'Symbol.1', 'Gender (3):Women+[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100025.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Age (in single years), average age and median age (128)', 'Coordinate', 'Gender (3):Total - Gender[1]', 'Symbol', 'Gender (3):Men+[2]', 'Symbol.1', 'Gender (3):Women+[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100026.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Age (in single years), average age and median age (128)', 'Coordinate', 'Gender (3):Total - Gender[1]', 'Symbol', 'Gender (3):Men+[2]', 'Symbol.1', 'Gender (3):Women+[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100027.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Age (in single years), average age and median age (128)', 'Coordinate', 'Gender (3):Total - Gender[1]', 'Symbol', 'Gender (3):Men+[2]', 'Symbol.1', 'Gender (3):Women+[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100030.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Gender (3a)', 'Broad age groups (5)', 'Coordinate', 'View:2021 counts[1]', 'Symbol', 'View:% distribution 2021[2]', 'Symbol.1', 'View:2016 counts[3]', 'Symbol.2', 'View:% distribution 2016[4]', 'Symbol.3']\n", + "/data/tables/output/en/june_20_2025/98100031.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Gender (3a)', 'Broad age groups (5)', 'Coordinate', 'View:2021 counts[1]', 'Symbol', 'View:% distribution 2021[2]', 'Symbol.1', 'View:2016 counts[3]', 'Symbol.2', 'View:% distribution 2016[4]', 'Symbol.3']\n", + "/data/tables/output/en/june_20_2025/98100032.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Gender (3a)', 'Broad age groups (5)', 'Coordinate', 'View:2021 counts[1]', 'Symbol', 'View:% distribution 2021[2]', 'Symbol.1', 'View:2016 counts[3]', 'Symbol.2', 'View:% distribution 2016[4]', 'Symbol.3']\n", + "/data/tables/output/en/june_20_2025/98100033.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Gender (3a)', 'Census year (25)', 'Coordinate', 'Broad age groups (5):Total - Age[1]', 'Symbol', 'Broad age groups (5):0 to 14 years[2]', 'Symbol.1', 'Broad age groups (5):15 to 64 years[3]', 'Symbol.2', 'Broad age groups (5):65 years and over[4]', 'Symbol.3', 'Broad age groups (5):Average age[5]', 'Symbol.4']\n", + "/data/tables/output/en/june_20_2025/98100034.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Broad age groups (5)', 'Coordinate', 'Sex (3):Total - Sex[1]', 'Symbol', 'Sex (3):Male[2]', 'Symbol.1', 'Sex (3):Female[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100035.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Broad age groups (5)', 'Coordinate', 'Sex (3):Total - Sex[1]', 'Symbol', 'Sex (3):Male[2]', 'Symbol.1', 'Sex (3):Female[3]', 'Symbol.2']\n", + "/data/tables/output/en/june_20_2025/98100036.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Broad age groups (3)', 'Coordinate', 'Gender (8):Total - Gender[1]', 'Symbol', 'Gender (8):Cisgender persons[2]', 'Symbol.1', 'Gender (8):Cisgender men[3]', 'Symbol.2', 'Gender (8):Cisgender women[4]', 'Symbol.3', 'Gender (8):Transgender persons[5]', 'Symbol.4', 'Gender (8):Transgender men[6]', 'Symbol.5', 'Gender (8):Transgender women[7]', 'Symbol.6', 'Gender (8):Non-binary persons[8]', 'Symbol.7']\n", + "/data/tables/output/en/june_20_2025/98100037.parquet\n", + "['REF_DATE', 'REF_START_DATE', 'REF_END_DATE', 'GEO', 'DGUID', 'Broad age groups (3)', 'Coordinate', 'Gender (8):Total - Gender[1]', 'Symbol', 'Gender (8):Cisgender persons[2]', 'Symbol.1', 'Gender (8):Cisgender men[3]', 'Symbol.2', 'Gender (8):Cisgender women[4]', 'Symbol.3', 'Gender (8):Transgender persons[5]', 'Symbol.4', 'Gender (8):Transgender men[6]', 'Symbol.5', 'Gender (8):Transgender women[7]', 'Symbol.6', 'Gender (8):Non-binary persons[8]', 'Symbol.7']\n" ] } ], "source": [ "for file in files:\n", " # Open the Parquet file metadata\n", - " dataset = pq.ParquetFile(file)\n", + " dataset = pq.VALUEParquetFile(file)\n", " # Get the column names\n", " column_names = dataset.schema.names\n", " has_dot = ['.' in x for x in column_names if '.' in x]\n", diff --git a/experiments/statcan_products/csv_file_size_inside_zip.ipynb b/experiments/statcan_products/csv_file_size_inside_zip.ipynb index 5c2af30..b315e91 100644 --- a/experiments/statcan_products/csv_file_size_inside_zip.ipynb +++ b/experiments/statcan_products/csv_file_size_inside_zip.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "b410f6f2-2ce8-4eaa-9c25-03e076a9d996", "metadata": { "editable": true, @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 3, "id": "0c7532d5-c8dc-4b93-900b-0e43d0718afd", "metadata": {}, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 4, "id": "da44a381-759b-443b-9440-c81c6cae3108", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 5, "id": "16409ae7-e62e-4d32-a1f2-42300bbeb80f", "metadata": {}, "outputs": [], @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, "id": "f228535d-7645-447a-ad80-0367d72102a2", "metadata": {}, "outputs": [], @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 7, "id": "08d51b9e-08f6-4f4c-b0e9-22142987bdf3", "metadata": { "editable": true, @@ -103,7 +103,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e162d24346c48dcbfc550b76cdbdd93", + "model_id": "340c8299e87348a2998c9c62a071decf", "version_major": 2, "version_minor": 1 }, diff --git a/experiments/statcan_products/date_parsing.ipynb b/experiments/statcan_products/date_parsing.ipynb index 27214a1..919264b 100644 --- a/experiments/statcan_products/date_parsing.ipynb +++ b/experiments/statcan_products/date_parsing.ipynb @@ -3,7 +3,13 @@ { "cell_type": "markdown", "id": "e4d2c52d-38be-4f84-a0bf-6bd8cb577ad9", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "# Purpose\n", "I need to find out what all possible date formats are for the \"REF_DATE\" field so that when I write the parquet file people will be able to filter on it\n", diff --git a/experiments/statcan_products/duck_experiment.ipynb b/experiments/statcan_products/duck_experiment.ipynb index b26e36b..36f4a66 100644 --- a/experiments/statcan_products/duck_experiment.ipynb +++ b/experiments/statcan_products/duck_experiment.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "id": "5e04e469-d3eb-42ca-b548-5e6f1fa6af9d", "metadata": { "editable": true, @@ -11,16 +11,25 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Buckaroo has been enabled as the default DataFrame viewer. To return to default dataframe visualization use `from buckaroo import disable; disable()`\n" + ] + } + ], "source": [ "import buckaroo\n", "import duckdb\n", + "import pandas as pd\n", "import polars as pl" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "id": "97c8e92b-21e4-4cc5-8dbe-7b42361ce3f9", "metadata": { "editable": true, @@ -36,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "id": "e02f2416-fd16-444b-8fd4-eec2cecee5a7", "metadata": { "editable": true, @@ -49,10 +58,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -65,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "id": "fafa7ce7-8619-4951-8c73-7bfbc66dc92f", "metadata": {}, "outputs": [ @@ -94,7 +103,7 @@ " ('DECIMALS', 'TINYINT', 'YES', None, None, None)]" ] }, - "execution_count": 15, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -105,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "a4ed2881-91b7-4473-b246-a969ef59efba", "metadata": {}, "outputs": [], @@ -115,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "f400feee-efb6-421a-b518-1f9c0fc21bcb", "metadata": { "editable": true, @@ -134,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "id": "a187c850-981e-4348-a57d-2f25e57cf9db", "metadata": { "editable": true, @@ -147,7 +156,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea3f2ee32a4d4ba1970f9f373b86fc02", + "model_id": "b34ba390571b41a9b387a493af10cf30", "version_major": 2, "version_minor": 1 }, @@ -165,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "id": "ff3eb520-c78b-4976-b734-840dc0fa53ab", "metadata": { "editable": true, @@ -182,7 +191,7 @@ "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mIOException\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcon\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\"\"\u001b[39;49m\n\u001b[32m 2\u001b[39m \u001b[33;43mSELECT *\u001b[39;49m\n\u001b[32m 3\u001b[39m \u001b[33;43mFROM parquet_file_metadata(\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m/data/tables/output/en/43100011.parquet\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m);\u001b[39;49m\n\u001b[32m 4\u001b[39m \u001b[33;43m\"\"\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcon\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\"\"\u001b[39;49m\n\u001b[32m 2\u001b[39m \u001b[33;43mSELECT *\u001b[39;49m\n\u001b[32m 3\u001b[39m \u001b[33;43mFROM parquet_file_metadata(\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m/data/tables/output/en/43100011.parquet\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m);\u001b[39;49m\n\u001b[32m 4\u001b[39m \u001b[33;43m\"\"\"\u001b[39;49m\u001b[43m)\u001b[49m\n", "\u001b[31mIOException\u001b[39m: IO Error: No files found that match the pattern \"/data/tables/output/en/43100011.parquet\"" ] } @@ -196,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "id": "30c8fc7e-cafd-43ec-a58f-aed4128df594", "metadata": { "editable": true, @@ -213,7 +222,7 @@ "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mInvalidInputException\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcon\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfetchall\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcon\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfetchall\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[31mInvalidInputException\u001b[39m: Invalid Input Error: No open result set" ] } @@ -224,64 +233,49 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "id": "10f3238b-36a6-4033-a299-fc04190e9d63", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.execute(\"\"\"\n", - "SELECT DISTINCT DGUID, GEO\n", - "FROM read_parquet('/data/tables/output/en/*.parquet')\n", + "SELECT DISTINCT DGUID, GEO, filename\n", + "FROM read_parquet('/data/tables/output/en/*.parquet', filename=true)\n", "ORDER BY DGUID ASC\n", "\"\"\")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 14, "id": "dcf81727-340c-4bcc-aec3-7133e0010eda", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1298d824f74c4fc59151d5b746d8e864", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "distinct_dguid_geo = con.fetch_df()" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "0d2f2de4-e033-4181-aaf5-9e8c3fd335b1", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ff5226caa7f947c593bf41fc302a2e52", + "model_id": "a63c08e7e631482e855887bbec5c1c28", "version_major": 2, "version_minor": 1 }, @@ -299,32 +293,32 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 19, "id": "cfd1621f-9c4e-46ba-a64e-1c191ff27b35", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.execute(\"\"\"\n", - "SELECT DISTINCT REF_DATE, REF_START_DATE, REF_END_DATE\n", - "FROM read_parquet('/data/tables/output/en/*.parquet')\n", + "SELECT DISTINCT REF_DATE, REF_START_DATE, REF_END_DATE, filename\n", + "FROM read_parquet('/data/tables/output/en/*.parquet', filename=true)\n", "ORDER BY REF_START_DATE ASC\n", "\"\"\")" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 20, "id": "5b5fed59-b900-47a0-8ab5-15e557f02dbf", "metadata": {}, "outputs": [], @@ -334,14 +328,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 21, "id": "41fbfb30-a471-4304-a985-9367ab589107", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0eaef40bea7f4a29810b29c1648f0ec1", + "model_id": "2f1c49faa4374435bf1147c06696b65f", "version_major": 2, "version_minor": 1 }, @@ -427,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 94, "id": "e5edf922-f945-413e-8aca-2929eb6b8d2f", "metadata": {}, "outputs": [ @@ -435,15 +429,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "/data/tables/output/en/10100164.parquet\n", - "/data/tables/output/en/23100049.parquet\n", - "/data/tables/output/en/23100050.parquet\n", - "/data/tables/output/en/36100658.parquet\n", - "/data/tables/output/en/36100374.parquet\n", - "/data/tables/output/en/36100396.parquet\n", - "/data/tables/output/en/36100397.parquet\n", - "/data/tables/output/en/38100104.parquet\n", - "/data/tables/output/en/38100105.parquet\n" + " column_name column_type null key default extra\n", + "0 REF_DATE VARCHAR YES None None None\n", + "1 REF_START_DATE TIMESTAMP_NS YES None None None\n", + "2 REF_END_DATE TIMESTAMP_NS YES None None None\n", + "3 GEO VARCHAR YES None None None\n", + "4 DGUID VARCHAR YES None None None\n", + "5 Federal public sector employment VARCHAR YES None None None\n", + "6 UOM VARCHAR YES None None None\n", + "7 UOM_ID SMALLINT YES None None None\n", + "8 SCALAR_FACTOR VARCHAR YES None None None\n", + "9 SCALAR_ID TINYINT YES None None None\n", + "10 VECTOR VARCHAR YES None None None\n", + "11 COORDINATE VARCHAR YES None None None\n", + "12 VALUE BIGINT YES None None None\n", + "13 STATUS VARCHAR YES None None None\n", + "14 SYMBOL VARCHAR YES None None None\n", + "15 TERMINATED VARCHAR YES None None None\n", + "16 DECIMALS TINYINT YES None None None\n" ] } ], @@ -451,9 +454,14 @@ "for parquet_file in output_parquet_files:\n", " con.execute(f\"DESCRIBE '{parquet_file}'\")\n", " value = con.fetch_df()\n", + " print(value)\n", + " break\n", " length_value_column = len(value[value['column_name'] == 'VALUE'][['column_name', 'column_type']])\n", " if length_value_column == 0:\n", - " print(parquet_file) " + " print(value)\n", + " #print(value[value['column_name'] == 'VALUE'])\n", + " print(parquet_file) \n", + " break" ] }, { @@ -467,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 88, "id": "fc588dd7-a0d2-4b79-9c27-a6502ff99053", "metadata": {}, "outputs": [ @@ -494,7 +502,7 @@ " ('DECIMALS', 'TINYINT', 'YES', None, None, None)]" ] }, - "execution_count": 65, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -506,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 69, "id": "fef3d2e2-91c5-4fdf-bb00-3928c8f52d11", "metadata": {}, "outputs": [ @@ -534,7 +542,7 @@ " ('DECIMALS', 'TINYINT', 'YES', None, None, None)]" ] }, - "execution_count": 66, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -544,10 +552,783 @@ "con.fetchall()" ] }, + { + "cell_type": "code", + "execution_count": 93, + "id": "0b251432-48d9-4538-acb9-58df20187a1d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " 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sales',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',),\n", + " ('Value of sales per capita',)]" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "con.execute(\"SELECT Value FROM '/data/tables/output/en/10100164.parquet'\")\n", + "con.fetchall()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "5cd6de4c-248a-45bd-bb15-3ebae33c55f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "con.execute(\"\"\"\n", + "SELECT * FROM '/data/tables/output/en/23100049.parquet';\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "f7a371cb-d45b-4cc7-b18f-cf5ed1f3cca1", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f17b83dd00ce4af8acd6824b2171e2da", + "version_major": 2, + "version_minor": 1 + }, + "text/plain": [ + "BuckarooInfiniteWidget(buckaroo_options={'sampled': ['random'], 'auto_clean': ['aggressive', 'conservative'], …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "con.fetch_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "ecf6ef73-c115-46c4-9230-7fd79cc53f88", + "metadata": {}, + "outputs": [], + "source": [ + "d = {'Value': [col11, 2], 'VALUE': [3, 4]}" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "a706dfcf-c983-4983-9f41-3eb5aae1c2b5", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(data=d)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "576b48d1-7050-4b2c-a8f6-b3dfad9177e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3\n", + "1 4\n", + "Name: VALUE, dtype: int64" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['VALUE']" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "e7f996fa-1c89-4836-a2e5-58d65f62c676", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c26a3ce238d843518e858ab868302a67", + "version_major": 2, + "version_minor": 1 + }, + "text/plain": [ + "BuckarooInfiniteWidget(buckaroo_options={'sampled': ['random'], 'auto_clean': ['aggressive', 'conservative'], …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.read_parquet(\"/data/tables/output/en/10100164.parquet\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "0d04167d-2710-4082-afc5-d12a0da276ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "con.execute(\"\"\"\n", + "SELECT DGUID, 'Federal public sector employment' FROM '/data/tables/output/en/10100001.parquet';\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "09ff55f7-9c51-4864-9304-7cbcc5301aea", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f837800f39db40b3af067c94bbd19d9c", + "version_major": 2, + "version_minor": 1 + }, + "text/plain": [ + "BuckarooInfiniteWidget(buckaroo_options={'sampled': ['random'], 'auto_clean': ['aggressive', 'conservative'], …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "con.fetch_df()" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "b472b408-4633-429d-824b-0d846db6e9db", + "id": "90f6840a-7f12-4c18-ba98-4a353b7d9346", "metadata": {}, "outputs": [], "source": [] diff --git a/experiments/statcan_products/process_files_multiprocessing.py b/experiments/statcan_products/process_files_multiprocessing.py index b118e3d..3bc5311 100644 --- a/experiments/statcan_products/process_files_multiprocessing.py +++ b/experiments/statcan_products/process_files_multiprocessing.py @@ -2,12 +2,14 @@ from datetime import datetime import glob from multiprocessing import Pool import json +import logging import os import sqlite3 import zipfile from zoneinfo import ZoneInfo import pandas as pd +import polars as pl import requests from tqdm import tqdm @@ -16,8 +18,18 @@ input_folder = f"{data_folder}/input" scratch_folder = f"{data_folder}/scratch" output_folder = f"{data_folder}/output" +import logging +logging.basicConfig( + filename="processing.log", + encoding="utf-8", + filemode="a", + format="{asctime} - {levelname} - {message}", + style="{", + datefmt="%Y-%m-%d %H:%M", +) + if not os.path.exists(f"{data_folder}/processing.db"): - con = sqlite3.connect(f"{data_folder}/processing.db") + con = sqlite3.connect(f"{data_folder}/processing.db", timeout=60.0) cur = con.cursor() cur.executescript(""" CREATE TABLE IF NOT EXISTS downloaded ( @@ -33,7 +45,7 @@ if not os.path.exists(f"{data_folder}/processing.db"): """) con.commit() else: - con = sqlite3.connect(f"{data_folder}/processing.db") + con = sqlite3.connect(f"{data_folder}/processing.db", timeout=60.0) cur = con.cursor() def setup(): @@ -223,14 +235,27 @@ def download_cube(product_id, language="en"): progress_bar.update(len(chunk)) progress_bar.close() +def cleanup_product(product_id): + """ + Remove the scratch files for a given productId + """ + print(f"Removing scratch files for productId {product_id}") + os.remove(f"{scratch_folder}/{product_id}.csv") + os.remove(f"{scratch_folder}/{product_id}_MetaData.csv") + def process_cube(product_id, language="en"): """ Examples: - productId 43100011 has all with DECIMAL = 1 (float64) - productId 17100009 has DECIMAL = 0 (int64) - productId 35100076 has multiple DECIMAL precisions [0, 1, 2] (int64, float64, float64) - - productId 10100164 has two columns named the same "Value" and "VALUE". It is processed fine with the read_csv, and when it is exported as parquet. - DuckDB has an issue with it (as it is case insensitive), but Pandas and Polars are able to handle "Value" and "VALUE" + - The duplicate column issue just happens with two column names in all data products ("Value", "VALUE", and "Status", "STATUS") + - productId 10100164 has two columns named the same "Value" and "VALUE". DuckDB treats column names in a case insensitve manner, so + "Value" and "VALUE" are the same. So we will need to rename "Value" to "Value.1" + - productId 13100902 has two columns named the same "Status" and "STATUS". We will need to rename "Status" to "STATUS" + - productId 13100442 has 18 fields, but 19 fields were seen in line 162 + - There are cases where the "DECIMALS" column does not exist in the CSV. productId 98100001 is one example. + In this case, we do let the .read_csv method guess the data types """ cur.execute("SELECT product_id FROM downloaded WHERE product_id = ?", (product_id,)) result = cur.fetchone() @@ -253,13 +278,22 @@ def process_cube(product_id, language="en"): product_csv = f"{scratch_folder}/{product_id}.csv" print(f"Reading {product_csv}") parameters = { + "filepath_or_buffer": product_csv, "engine": "c", - "low_memory": True, - "nrows": 100000, + #"nrows": 100000, "dtype": {} } columns = pd.read_csv(product_csv, nrows=0).columns - + + columns_to_rename = ['Value', 'Status'] + for column in columns_to_rename: + if column in columns: + print(f"Renaming '{column}' to '{column}.1'") + columns = [f'{column}.1' if x == column else x for x in columns] + # Explicitly tell pandas to not read column names from CSV + parameters["header"] = 0 + parameters["names"] = columns + columns_always_int_8 = ["DECIMALS", "SCALAR_ID"] for column in columns_always_int_8: if column in columns: @@ -269,33 +303,56 @@ def process_cube(product_id, language="en"): for column in columns_always_int_16: if column in columns: parameters["dtype"][column] = 'int16' + + # REF_DATE, GEO, DGUID should always be string + columns_always_string = ["REF_DATE", "GEO", "DGUID"] + for column in columns_always_string: + if column in columns: + parameters["dtype"][column] = 'string' # The remaining columns should be string, with the exception of VALUE - for column in columns: - if column not in columns_always_int_8 and column not in columns_always_int_16 and column != "VALUE": - parameters["dtype"][column] = 'string' + # Added "DECIMAL" check as there can be numeric columns that are not the VALUE column + if "DECIMALS" in columns: + for column in columns: + if column not in columns_always_int_8 and column not in columns_always_int_16 and column != "VALUE": + parameters["dtype"][column] = 'string' if not parameters["dtype"]: del parameters["dtype"] print(f"Reading {product_csv} as a Pandas dataframe") - df = pd.read_csv(product_csv, **parameters) - unique_decimal_values = df["DECIMALS"].unique() - if any(unique_decimal_values): - """ - A table can have both float and integer in the VALUE field. - productId 11100025 is an example - So if we have unique values for DECIMALS to be [0,1], then we convert to float64 - """ - convert_dict = {"VALUE": "float64"} - print(convert_dict) - df = df.astype(convert_dict) - elif 0 in (unique_decimal_values): - if df["VALUE"].dtype != "Int64": - # If DECIMALS = [0] - convert_dict = {"VALUE": "Int64"} - print(convert_dict) + try: + df = pd.read_csv(**parameters) + except Exception: + logging.error(f"Failed to process productId: {product_id}") + cleanup_product(product_id) + return + + if "DECIMALS" in columns: + unique_decimal_values = df["DECIMALS"].unique() + #print(unique_decimal_values) + if any(unique_decimal_values): + """ + A table can have both float and integer in the VALUE field. + productId 11100025 is an example + So if we have unique values for DECIMALS to be [0,1], then we convert to float64 + """ + convert_dict = {"VALUE": "float64"} + #print(convert_dict) df = df.astype(convert_dict) + elif 0 in (unique_decimal_values): + if df["VALUE"].dtype != "Int64": + # If DECIMALS = [0] + convert_dict = {"VALUE": "Int64"} + #print(convert_dict) + df = df.astype(convert_dict) + else: + parameters = { + "convert_string": True, + "convert_boolean": False + } + #print("DECIMALS not in columns, using .convert_dtypes") + df = df.convert_dtypes(**parameters) df = convert_to_lowest_type(df) df = compute_ref_date_bounds(df) @@ -309,10 +366,8 @@ def process_cube(product_id, language="en"): "compression_level": 22 } df.to_parquet(**parameters) - # Remove the scratch files - print("Removing scratch files") - os.remove(f"{scratch_folder}/{product_id}.csv") - os.remove(f"{scratch_folder}/{product_id}_MetaData.csv") + # Remove scratch files + cleanup_product(product_id) update_last_downloaded(product_id) update_last_processed(product_id) @@ -326,5 +381,5 @@ if __name__ == '__main__': print(f"Processing {to_process}") #for product_id in files_to_process: # process_cube(product_id) - with Pool(processes=16) as p: - p.map(process_cube, files_to_process, chunksize=8) + with Pool(processes=2) as p: + p.map(process_cube, files_to_process, chunksize=1) diff --git a/experiments/statcan_products/process_product_test.ipynb b/experiments/statcan_products/process_product_test.ipynb index 886bab3..4c5b572 100644 --- a/experiments/statcan_products/process_product_test.ipynb +++ b/experiments/statcan_products/process_product_test.ipynb @@ -46,6 +46,7 @@ "from zoneinfo import ZoneInfo\n", "\n", "import pandas as pd\n", + "import polars as pl\n", "import requests\n", "from tqdm import tqdm\n", "\n", @@ -55,7 +56,7 @@ "output_folder = f\"{data_folder}/output\"\n", "\n", "if not os.path.exists(f\"{data_folder}/processing.db\"):\n", - " con = sqlite3.connect(f\"{data_folder}/processing.db\")\n", + " con = sqlite3.connect(f\"{data_folder}/processing.db\", timeout=60.0)\n", " cur = con.cursor()\n", " cur.executescript(\"\"\"\n", " CREATE TABLE IF NOT EXISTS downloaded (\n", @@ -202,12 +203,14 @@ " WHERE a.product_id = b.product_id\n", " AND b.last_updated > a.last_updated\n", " \"\"\")\n", + " \"\"\"\n", " results = cur.fetchall()\n", " for result in results:\n", " product_id = result[0]\n", " print(f\"Updating product_id: {product_id}\")\n", " download_cube(product_id)\n", - " process_cube(product_id)" + " process_cube(product_id)\n", + " \"\"\"" ] }, { @@ -217,7 +220,7 @@ "metadata": {}, "outputs": [], "source": [ - "#update_tables()" + "update_tables()" ] }, { @@ -280,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "id": "e67642d3-cc6c-4c5d-b5a3-2fe18364ad71", "metadata": {}, "outputs": [], @@ -352,7 +355,23 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 11, + "id": "aa16ea14-1661-4dc8-8e2f-a09175b1cd17", + "metadata": {}, + "outputs": [], + "source": [ + "def cleanup_product(product_id):\n", + " \"\"\"\n", + " Remove the scratch files for a given productId\n", + " \"\"\"\n", + " print(f\"Removing scratch files for productId {product_id}\")\n", + " os.remove(f\"{scratch_folder}/{product_id}.csv\")\n", + " os.remove(f\"{scratch_folder}/{product_id}_MetaData.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "858e405e-7c02-4193-8abe-f23951761b09", "metadata": { "editable": true, @@ -366,12 +385,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "Already processed 10100164\n", - "Extracting /data/tables/input/en/10100164.zip to /data/tables/scratch\n", - "Reading /data/tables/scratch/10100164.csv\n", - "Index(['REF_DATE', 'GEO', 'DGUID', 'Value', 'Type of cannabis', 'UOM',\n", - " 'UOM_ID', 'SCALAR_FACTOR', 'SCALAR_ID', 'VECTOR', 'COORDINATE', 'VALUE',\n", - " 'STATUS', 'SYMBOL', 'TERMINATED', 'DECIMALS'],\n", + "Extracting /data/tables/input/en/98100001.zip to /data/tables/scratch\n", + "Reading /data/tables/scratch/98100001.csv\n", + "Index(['REF_DATE', 'GEO', 'DGUID', 'Coordinate',\n", + " 'Population and dwelling counts (11): Population, 2021 [1]', 'Symbols',\n", + " 'Population and dwelling counts (11): Population, 2016 [2]',\n", + " 'Symbols.1',\n", + " 'Population and dwelling counts (11): Population percentage change, 2016 to 2021 [3]',\n", + " 'Symbols.2',\n", + " 'Population and dwelling counts (11): Total private dwellings, 2021 [4]',\n", + " 'Symbols.3',\n", + " 'Population and dwelling counts (11): Total private dwellings, 2016 [5]',\n", + " 'Symbols.4',\n", + " 'Population and dwelling counts (11): Total private dwellings percentage change, 2016 to 2021 [6]',\n", + " 'Symbols.5',\n", + " 'Population and dwelling counts (11): Private dwellings occupied by usual residents, 2021 [7]',\n", + " 'Symbols.6',\n", + " 'Population and dwelling counts (11): Private dwellings occupied by usual residents, 2016 [8]',\n", + " 'Symbols.7',\n", + " 'Population and dwelling counts (11): Private dwellings occupied by usual residents percentage change, 2016 to 2021 [9]',\n", + " 'Symbols.8',\n", + " 'Population and dwelling counts (11): Land area in square kilometres, 2021 [10]',\n", + " 'Symbols.9',\n", + " 'Population and dwelling counts (11): Population density per square kilometre, 2021 [11]',\n", + " 'Symbols.10'],\n", " dtype='object')\n" ] } @@ -382,11 +419,16 @@ "- productId 43100011 has all with DECIMAL = 1 (float64)\n", "- productId 17100009 has DECIMAL = 0 (int64)\n", "- productId 35100076 has multiple DECIMAL precisions [0, 1, 2] (int64, float64, float64)\n", - "- productId 10100164 has two columns named the same \"Value\" and \"VALUE\". It is processed fine with the read_csv, and when it is exported as parquet.\n", - "DuckDB has an issue with it, but Pandas and Polars are able to handle \"Value\" and \"VALUE\"\n", + "- The duplicate column issue just happens with two column names in all data products (\"Value\", \"VALUE\", and \"Status\", \"STATUS\")\n", + " - productId 10100164 has two columns named the same \"Value\" and \"VALUE\". DuckDB treats column names in a case insensitve manner, so \n", + " \"Value\" and \"VALUE\" are the same. So we will need to rename \"Value\" to \"Value.1\"\n", + " - productId 13100902 has two columns named the same \"Status\" and \"STATUS\". We will need to rename \"Status\" to \"STATUS\"\n", + "- productId 13100442 has 18 fields, but 19 fields were seen in line 162\n", + "- There are cases where the \"DECIMALS\" column does not exist in the CSV. productId 98100001 is one example.\n", + " In this case, we do let the .read_csv method guess the data types\n", "\"\"\"\n", "\n", - "product_id = \"10100164\"\n", + "product_id = \"98100001\"\n", "#def process_cube(product_id, language=\"en\"):\n", "language = \"en\"\n", "cur.execute(\"SELECT product_id FROM downloaded WHERE product_id = ?\", (product_id,))\n", @@ -410,9 +452,9 @@ "product_csv = f\"{scratch_folder}/{product_id}.csv\"\n", "print(f\"Reading {product_csv}\")\n", "parameters = {\n", + " \"filepath_or_buffer\": product_csv,\n", " \"engine\": \"c\",\n", - " \"low_memory\": True,\n", - " \"nrows\": 1000000,\n", + " \"nrows\": 100000,\n", " \"dtype\": {}\n", "}\n", "\n", @@ -422,7 +464,24 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, + "id": "59f77277-f8cd-4eee-93b2-c390ce136f84", + "metadata": {}, + "outputs": [], + "source": [ + "columns_to_rename = ['Value', 'Status']\n", + "for column in columns_to_rename:\n", + " if column in columns:\n", + " print(f\"Renaming '{column}' to '{column}.1'\")\n", + " columns = [f'{column}.1' if x == column else x for x in columns]\n", + " # Explicitly tell pandas to not read column names from CSV\n", + " parameters[\"header\"] = 0\n", + " parameters[\"names\"] = columns" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "id": "c1f89175-78e5-4e95-8a3e-3b65f0cb4b2d", "metadata": {}, "outputs": [ @@ -430,8 +489,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'engine': 'c', 'low_memory': True, 'nrows': 1000000, 'dtype': {'DECIMALS': 'int8', 'SCALAR_ID': 'int8', 'UOM_ID': 'int16', 'REF_DATE': 'string', 'GEO': 'string', 'DGUID': 'string', 'Value': 'string', 'Type of cannabis': 'string', 'UOM': 'string', 'SCALAR_FACTOR': 'string', 'VECTOR': 'string', 'COORDINATE': 'string', 'STATUS': 'string', 'SYMBOL': 'string', 'TERMINATED': 'string'}}\n", - "Reading /data/tables/scratch/10100164.csv as a Pandas dataframe\n" + "{'filepath_or_buffer': '/data/tables/scratch/98100001.csv', 'engine': 'c', 'nrows': 100000, 'dtype': {'REF_DATE': 'string', 'GEO': 'string', 'DGUID': 'string'}}\n", + "Reading /data/tables/scratch/98100001.csv as a Pandas dataframe\n" ] } ], @@ -446,22 +505,30 @@ " if column in columns:\n", " parameters[\"dtype\"][column] = 'int16'\n", "\n", - "# The remaining columns should be string, with the exception of VALUE\n", - "for column in columns:\n", - " if column not in columns_always_int_8 and column not in columns_always_int_16 and column != \"VALUE\":\n", + "# GEO, DGUID should always be string\n", + "columns_always_string = [\"REF_DATE\", \"GEO\", \"DGUID\"]\n", + "for column in columns_always_string:\n", + " if column in columns:\n", " parameters[\"dtype\"][column] = 'string'\n", "\n", + "# The remaining columns should be string, with the exception of VALUE\n", + "# Added \"DECIMAL\" check as there can be numeric columns that are not the VALUE column\n", + "if \"DECIMALS\" in columns:\n", + " for column in columns:\n", + " if column not in columns_always_int_8 and column not in columns_always_int_16 and column != \"VALUE\":\n", + " parameters[\"dtype\"][column] = 'string'\n", + "\n", "print(parameters)\n", "if not parameters[\"dtype\"]:\n", " del parameters[\"dtype\"]\n", "\n", "print(f\"Reading {product_csv} as a Pandas dataframe\")\n", - "df = pd.read_csv(product_csv, **parameters)" + "df = pd.read_csv(**parameters)" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 15, "id": "87ff5f69-ca1f-40e0-ac73-c73dd8c1bd4d", "metadata": {}, "outputs": [ @@ -489,145 +556,262 @@ " REF_DATE\n", " GEO\n", " DGUID\n", - " Value\n", - " Type of cannabis\n", - " UOM\n", - " UOM_ID\n", - " SCALAR_FACTOR\n", - " SCALAR_ID\n", - " VECTOR\n", - " COORDINATE\n", - " VALUE\n", - " STATUS\n", - " SYMBOL\n", - " TERMINATED\n", - " DECIMALS\n", + " Coordinate\n", + " Population and dwelling counts (11): Population, 2021 [1]\n", + " Symbols\n", + " Population and dwelling counts (11): Population, 2016 [2]\n", + " Symbols.1\n", + " Population and dwelling counts (11): Population percentage change, 2016 to 2021 [3]\n", + " Symbols.2\n", + " ...\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents, 2021 [7]\n", + " Symbols.6\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents, 2016 [8]\n", + " Symbols.7\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents percentage change, 2016 to 2021 [9]\n", + " Symbols.8\n", + " Population and dwelling counts (11): Land area in square kilometres, 2021 [10]\n", + " Symbols.9\n", + " Population and dwelling counts (11): Population density per square kilometre, 2021 [11]\n", + " Symbols.10\n", " \n", " \n", " \n", " \n", " 0\n", - " 2021/2022\n", + " 2021\n", " Canada\n", " 2021A000011124\n", - " Value of sales\n", - " Total cannabis products\n", - " Dollars\n", - " 81\n", - " thousands\n", - " 3\n", - " v1490436660\n", - " 1.1.1\n", - " 4027928.0\n", - " <NA>\n", - " <NA>\n", - " <NA>\n", - " 0\n", + " 1\n", + " 36991981\n", + " NaN\n", + " 35151728\n", + " NaN\n", + " 5.2\n", + " NaN\n", + " ...\n", + " 14978941\n", + " NaN\n", + " 14072079\n", + " NaN\n", + " 6.4\n", + " NaN\n", + " 8788702.80\n", + " NaN\n", + " 4.2\n", + " NaN\n", " \n", " \n", " 1\n", - " 2021/2022\n", - " Canada\n", - " 2021A000011124\n", - " Value of sales\n", - " Dried cannabis\n", - " Dollars\n", - " 81\n", - " thousands\n", - " 3\n", - " v1490436647\n", - " 1.1.2\n", - " 2861838.0\n", - " <NA>\n", - " <NA>\n", - " <NA>\n", - " 0\n", + " 2021\n", + " Newfoundland and Labrador\n", + " 2021A000210\n", + " 2\n", + " 510550\n", + " NaN\n", + " 519716\n", + " NaN\n", + " -1.8\n", + " NaN\n", + " ...\n", + " 223253\n", + " NaN\n", + " 218673\n", + " NaN\n", + " 2.1\n", + " NaN\n", + " 358170.37\n", + " NaN\n", + " 1.4\n", + " NaN\n", " \n", " \n", " 2\n", - " 2021/2022\n", - " Canada\n", - " 2021A000011124\n", - " Value of sales\n", - " Inhaled cannabis extracts\n", - " Dollars\n", - " 81\n", - " thousands\n", + " 2021\n", + " Prince Edward Island\n", + " 2021A000211\n", " 3\n", - " v1490436648\n", - " 1.1.3\n", - " 729178.0\n", - " <NA>\n", - " <NA>\n", - " <NA>\n", - " 0\n", + " 154331\n", + " NaN\n", + " 142907\n", + " NaN\n", + " 8.0\n", + " NaN\n", + " ...\n", + " 64570\n", + " NaN\n", + " 59472\n", + " NaN\n", + " 8.6\n", + " NaN\n", + " 5681.18\n", + " NaN\n", + " 27.2\n", + " NaN\n", " \n", " \n", " 3\n", - " 2021/2022\n", - " Canada\n", - " 2021A000011124\n", - " Value of sales\n", - " Ingested cannabis extracts\n", - " Dollars\n", - " 81\n", - " thousands\n", - " 3\n", - " v1490436649\n", - " 1.1.4\n", - " 158283.0\n", - " <NA>\n", - " <NA>\n", - " <NA>\n", - " 0\n", + " 2021\n", + " Nova Scotia\n", + " 2021A000212\n", + " 4\n", + " 969383\n", + " NaN\n", + " 923598\n", + " NaN\n", + " 5.0\n", + " NaN\n", + " ...\n", + " 428228\n", + " NaN\n", + " 401990\n", + " NaN\n", + " 6.5\n", + " NaN\n", + " 52824.71\n", + " NaN\n", + " 18.4\n", + " NaN\n", " \n", " \n", " 4\n", - " 2021/2022\n", - " Canada\n", - " 2021A000011124\n", - " Value of sales\n", - " Solid cannabis edibles\n", - " Dollars\n", - " 81\n", - " thousands\n", - " 3\n", - " v1490436650\n", - " 1.1.5\n", - " 166336.0\n", - " <NA>\n", - " <NA>\n", - " <NA>\n", - " 0\n", + " 2021\n", + " New Brunswick\n", + " 2021A000213\n", + " 5\n", + " 775610\n", + " NaN\n", + " 747101\n", + " NaN\n", + " 3.8\n", + " NaN\n", + " ...\n", + " 337651\n", + " NaN\n", + " 319773\n", + " NaN\n", + " 5.6\n", + " NaN\n", + " 71248.50\n", + " NaN\n", + " 10.9\n", + " NaN\n", " \n", " \n", "\n", + "

5 rows × 26 columns

\n", "" ], "text/plain": [ - " REF_DATE GEO DGUID Value \\\n", - "0 2021/2022 Canada 2021A000011124 Value of sales \n", - "1 2021/2022 Canada 2021A000011124 Value of sales \n", - "2 2021/2022 Canada 2021A000011124 Value of sales \n", - "3 2021/2022 Canada 2021A000011124 Value of sales \n", - "4 2021/2022 Canada 2021A000011124 Value of sales \n", + " REF_DATE GEO DGUID Coordinate \\\n", + "0 2021 Canada 2021A000011124 1 \n", + "1 2021 Newfoundland and Labrador 2021A000210 2 \n", + "2 2021 Prince Edward Island 2021A000211 3 \n", + "3 2021 Nova Scotia 2021A000212 4 \n", + "4 2021 New Brunswick 2021A000213 5 \n", "\n", - " Type of cannabis UOM UOM_ID SCALAR_FACTOR SCALAR_ID \\\n", - "0 Total cannabis products Dollars 81 thousands 3 \n", - "1 Dried cannabis Dollars 81 thousands 3 \n", - "2 Inhaled cannabis extracts Dollars 81 thousands 3 \n", - "3 Ingested cannabis extracts Dollars 81 thousands 3 \n", - "4 Solid cannabis edibles Dollars 81 thousands 3 \n", + " Population and dwelling counts (11): Population, 2021 [1] Symbols \\\n", + "0 36991981 NaN \n", + "1 510550 NaN \n", + "2 154331 NaN \n", + "3 969383 NaN \n", + "4 775610 NaN \n", "\n", - " VECTOR COORDINATE VALUE STATUS SYMBOL TERMINATED DECIMALS \n", - "0 v1490436660 1.1.1 4027928.0 0 \n", - "1 v1490436647 1.1.2 2861838.0 0 \n", - "2 v1490436648 1.1.3 729178.0 0 \n", - "3 v1490436649 1.1.4 158283.0 0 \n", - "4 v1490436650 1.1.5 166336.0 0 " + " Population and dwelling counts (11): Population, 2016 [2] Symbols.1 \\\n", + "0 35151728 NaN \n", + "1 519716 NaN \n", + "2 142907 NaN \n", + "3 923598 NaN \n", + "4 747101 NaN \n", + "\n", + " Population and dwelling counts (11): Population percentage change, 2016 to 2021 [3] \\\n", + "0 5.2 \n", + "1 -1.8 \n", + "2 8.0 \n", + "3 5.0 \n", + "4 3.8 \n", + "\n", + " Symbols.2 ... \\\n", + "0 NaN ... \n", + "1 NaN ... \n", + "2 NaN ... \n", + "3 NaN ... \n", + "4 NaN ... \n", + "\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents, 2021 [7] \\\n", + "0 14978941 \n", + "1 223253 \n", + "2 64570 \n", + "3 428228 \n", + "4 337651 \n", + "\n", + " Symbols.6 \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents, 2016 [8] \\\n", + "0 14072079 \n", + "1 218673 \n", + "2 59472 \n", + "3 401990 \n", + "4 319773 \n", + "\n", + " Symbols.7 \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents percentage change, 2016 to 2021 [9] \\\n", + "0 6.4 \n", + "1 2.1 \n", + "2 8.6 \n", + "3 6.5 \n", + "4 5.6 \n", + "\n", + " Symbols.8 \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Population and dwelling counts (11): Land area in square kilometres, 2021 [10] \\\n", + "0 8788702.80 \n", + "1 358170.37 \n", + "2 5681.18 \n", + "3 52824.71 \n", + "4 71248.50 \n", + "\n", + " Symbols.9 \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " Population and dwelling counts (11): Population density per square kilometre, 2021 [11] \\\n", + "0 4.2 \n", + "1 1.4 \n", + "2 27.2 \n", + "3 18.4 \n", + "4 10.9 \n", + "\n", + " Symbols.10 \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 26 columns]" ] }, - "execution_count": 31, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -638,33 +822,43 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 16, "id": "fde0e149-d146-4516-8966-0989e4ccf290", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "REF_DATE string[python]\n", - "GEO string[python]\n", - "DGUID string[python]\n", - "Value string[python]\n", - "Type of cannabis string[python]\n", - "UOM string[python]\n", - "UOM_ID int16\n", - "SCALAR_FACTOR string[python]\n", - "SCALAR_ID int8\n", - "VECTOR string[python]\n", - "COORDINATE string[python]\n", - "VALUE float64\n", - "STATUS string[python]\n", - "SYMBOL string[python]\n", - "TERMINATED string[python]\n", - "DECIMALS int8\n", + "REF_DATE string[python]\n", + "GEO string[python]\n", + "DGUID string[python]\n", + "Coordinate int64\n", + "Population and dwelling counts (11): Population, 2021 [1] int64\n", + "Symbols float64\n", + "Population and dwelling counts (11): Population, 2016 [2] int64\n", + "Symbols.1 float64\n", + "Population and dwelling counts (11): Population percentage change, 2016 to 2021 [3] float64\n", + "Symbols.2 float64\n", + "Population and dwelling counts (11): Total private dwellings, 2021 [4] int64\n", + "Symbols.3 float64\n", + "Population and dwelling counts (11): Total private dwellings, 2016 [5] int64\n", + "Symbols.4 float64\n", + "Population and dwelling counts (11): Total private dwellings percentage change, 2016 to 2021 [6] float64\n", + "Symbols.5 float64\n", + "Population and dwelling counts (11): Private dwellings occupied by usual residents, 2021 [7] int64\n", + "Symbols.6 float64\n", + "Population and dwelling counts (11): Private dwellings occupied by usual residents, 2016 [8] int64\n", + "Symbols.7 float64\n", + "Population and dwelling counts (11): Private dwellings occupied by usual residents percentage change, 2016 to 2021 [9] float64\n", + "Symbols.8 float64\n", + "Population and dwelling counts (11): Land area in square kilometres, 2021 [10] float64\n", + "Symbols.9 float64\n", + "Population and dwelling counts (11): Population density per square kilometre, 2021 [11] float64\n", + "Symbols.10 float64\n", "dtype: object" ] }, - "execution_count": 32, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -675,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 17, "id": "7579a135-1dfe-4fc0-991b-4b261d6577e0", "metadata": {}, "outputs": [ @@ -683,34 +877,88 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0]\n", - "{'VALUE': 'Int64'}\n" + "DECIMALS not in columns, using .convert_dtypes\n" ] } ], "source": [ - "unique_decimal_values = df[\"DECIMALS\"].unique()\n", - "print(unique_decimal_values)\n", - "if any(unique_decimal_values):\n", - " \"\"\"\n", - " A table can have both float and integer in the VALUE field. \n", - " productId 11100025 is an example\n", - " So if we have unique values for DECIMALS to be [0,1], then we convert to float64\n", - " \"\"\"\n", - " convert_dict = {\"VALUE\": \"float64\"}\n", - " print(convert_dict)\n", - " df = df.astype(convert_dict)\n", - "elif 0 in (unique_decimal_values):\n", - " if df[\"VALUE\"].dtype != \"Int64\":\n", - " # If DECIMALS = [0]\n", - " convert_dict = {\"VALUE\": \"Int64\"}\n", + "if \"DECIMALS\" in columns:\n", + " unique_decimal_values = df[\"DECIMALS\"].unique()\n", + " print(unique_decimal_values)\n", + " if any(unique_decimal_values):\n", + " \"\"\"\n", + " A table can have both float and integer in the VALUE field. \n", + " productId 11100025 is an example\n", + " So if we have unique values for DECIMALS to be [0,1], then we convert to float64\n", + " \"\"\"\n", + " convert_dict = {\"VALUE\": \"float64\"}\n", " print(convert_dict)\n", - " df = df.astype(convert_dict)" + " df = df.astype(convert_dict)\n", + " elif 0 in (unique_decimal_values):\n", + " if df[\"VALUE\"].dtype != \"Int64\":\n", + " # If DECIMALS = [0]\n", + " convert_dict = {\"VALUE\": \"Int64\"}\n", + " print(convert_dict)\n", + " df = df.astype(convert_dict)\n", + "else:\n", + " parameters = {\n", + " \"convert_string\": True,\n", + " \"convert_boolean\": False\n", + " }\n", + " print(\"DECIMALS not in columns, using .convert_dtypes\")\n", + " df = df.convert_dtypes(**parameters)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 18, + "id": "07b6c4e9-75bd-485e-92b8-154c4f55ce51", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "REF_DATE string[python]\n", + "GEO string[python]\n", + "DGUID string[python]\n", + "Coordinate Int64\n", + "Population and dwelling counts (11): Population, 2021 [1] Int64\n", + "Symbols Int64\n", + "Population and dwelling counts (11): Population, 2016 [2] Int64\n", + "Symbols.1 Int64\n", + "Population and dwelling counts (11): Population percentage change, 2016 to 2021 [3] Float64\n", + "Symbols.2 Int64\n", + "Population and dwelling counts (11): Total private dwellings, 2021 [4] Int64\n", + "Symbols.3 Int64\n", + "Population and dwelling counts (11): Total private dwellings, 2016 [5] Int64\n", + "Symbols.4 Int64\n", + "Population and dwelling counts (11): Total private dwellings percentage change, 2016 to 2021 [6] Float64\n", + "Symbols.5 Int64\n", + "Population and dwelling counts (11): Private dwellings occupied by usual residents, 2021 [7] Int64\n", + "Symbols.6 Int64\n", + "Population and dwelling counts (11): Private dwellings occupied by usual residents, 2016 [8] Int64\n", + "Symbols.7 Int64\n", + "Population and dwelling counts (11): Private dwellings occupied by usual residents percentage change, 2016 to 2021 [9] Float64\n", + "Symbols.8 Int64\n", + "Population and dwelling counts (11): Land area in square kilometres, 2021 [10] Float64\n", + "Symbols.9 Int64\n", + "Population and dwelling counts (11): Population density per square kilometre, 2021 [11] Float64\n", + "Symbols.10 Int64\n", + "dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "id": "fe87b3b0-4e04-41a8-a704-75cb9829b0a5", "metadata": {}, "outputs": [], @@ -720,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 20, "id": "74507546-4080-4962-88fc-58c1e3943d17", "metadata": {}, "outputs": [], @@ -730,7 +978,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 21, "id": "6c2781b3-8eea-4317-a8c0-083d97ee04fc", "metadata": {}, "outputs": [ @@ -760,162 +1008,253 @@ " REF_END_DATE\n", " GEO\n", " DGUID\n", - " Value\n", - " Type of cannabis\n", - " UOM\n", - " UOM_ID\n", - " SCALAR_FACTOR\n", - " SCALAR_ID\n", - " VECTOR\n", - " COORDINATE\n", - " VALUE\n", - " STATUS\n", - " SYMBOL\n", - " TERMINATED\n", - " DECIMALS\n", + " Coordinate\n", + " Population and dwelling counts (11): Population, 2021 [1]\n", + " Symbols\n", + " Population and dwelling counts (11): Population, 2016 [2]\n", + " Symbols.1\n", + " ...\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents, 2021 [7]\n", + " Symbols.6\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents, 2016 [8]\n", + " Symbols.7\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents percentage change, 2016 to 2021 [9]\n", + " Symbols.8\n", + " Population and dwelling counts (11): Land area in square kilometres, 2021 [10]\n", + " Symbols.9\n", + " Population and dwelling counts (11): Population density per square kilometre, 2021 [11]\n", + " Symbols.10\n", " \n", " \n", " \n", " \n", " 0\n", - " 2021/2022\n", - " NaT\n", - " NaT\n", + " 2021\n", + " 2021-01-01\n", + " 2021-12-31\n", " Canada\n", " 2021A000011124\n", - " Value of sales\n", - " Total cannabis products\n", - " Dollars\n", - " 81\n", - " thousands\n", - " 3\n", - " v1490436660\n", - " 1.1.1\n", - " 4027928\n", + " 1\n", + " 36991981\n", " <NA>\n", + " 35151728\n", " <NA>\n", + " ...\n", + " 14978941\n", + " <NA>\n", + " 14072079\n", + " <NA>\n", + " 6.4\n", + " <NA>\n", + " 8788702.8\n", + " <NA>\n", + " 4.2\n", " <NA>\n", - " 0\n", " \n", " \n", " 1\n", - " 2021/2022\n", - " NaT\n", - " NaT\n", - " Canada\n", - " 2021A000011124\n", - " Value of sales\n", - " Dried cannabis\n", - " Dollars\n", - " 81\n", - " thousands\n", - " 3\n", - " v1490436647\n", - " 1.1.2\n", - " 2861838\n", + " 2021\n", + " 2021-01-01\n", + " 2021-12-31\n", + " Newfoundland and Labrador\n", + " 2021A000210\n", + " 2\n", + " 510550\n", " <NA>\n", + " 519716\n", " <NA>\n", + " ...\n", + " 223253\n", + " <NA>\n", + " 218673\n", + " <NA>\n", + " 2.1\n", + " <NA>\n", + " 358170.37\n", + " <NA>\n", + " 1.4\n", " <NA>\n", - " 0\n", " \n", " \n", " 2\n", - " 2021/2022\n", - " NaT\n", - " NaT\n", - " Canada\n", - " 2021A000011124\n", - " Value of sales\n", - " Inhaled cannabis extracts\n", - " Dollars\n", - " 81\n", - " thousands\n", + " 2021\n", + " 2021-01-01\n", + " 2021-12-31\n", + " Prince Edward Island\n", + " 2021A000211\n", " 3\n", - " v1490436648\n", - " 1.1.3\n", - " 729178\n", + " 154331\n", " <NA>\n", + " 142907\n", " <NA>\n", + " ...\n", + " 64570\n", + " <NA>\n", + " 59472\n", + " <NA>\n", + " 8.6\n", + " <NA>\n", + " 5681.18\n", + " <NA>\n", + " 27.2\n", " <NA>\n", - " 0\n", " \n", " \n", " 3\n", - " 2021/2022\n", - " NaT\n", - " NaT\n", - " Canada\n", - " 2021A000011124\n", - " Value of sales\n", - " Ingested cannabis extracts\n", - " Dollars\n", - " 81\n", - " thousands\n", - " 3\n", - " v1490436649\n", - " 1.1.4\n", - " 158283\n", + " 2021\n", + " 2021-01-01\n", + " 2021-12-31\n", + " Nova Scotia\n", + " 2021A000212\n", + " 4\n", + " 969383\n", " <NA>\n", + " 923598\n", " <NA>\n", + " ...\n", + " 428228\n", + " <NA>\n", + " 401990\n", + " <NA>\n", + " 6.5\n", + " <NA>\n", + " 52824.71\n", + " <NA>\n", + " 18.4\n", " <NA>\n", - " 0\n", " \n", " \n", " 4\n", - " 2021/2022\n", - " NaT\n", - " NaT\n", - " Canada\n", - " 2021A000011124\n", - " Value of sales\n", - " Solid cannabis edibles\n", - " Dollars\n", - " 81\n", - " thousands\n", - " 3\n", - " v1490436650\n", - " 1.1.5\n", - " 166336\n", + " 2021\n", + " 2021-01-01\n", + " 2021-12-31\n", + " New Brunswick\n", + " 2021A000213\n", + " 5\n", + " 775610\n", " <NA>\n", + " 747101\n", " <NA>\n", + " ...\n", + " 337651\n", + " <NA>\n", + " 319773\n", + " <NA>\n", + " 5.6\n", + " <NA>\n", + " 71248.5\n", + " <NA>\n", + " 10.9\n", " <NA>\n", - " 0\n", " \n", " \n", "\n", + "

5 rows × 28 columns

\n", "" ], "text/plain": [ - " REF_DATE REF_START_DATE REF_END_DATE GEO DGUID \\\n", - "0 2021/2022 NaT NaT Canada 2021A000011124 \n", - "1 2021/2022 NaT NaT Canada 2021A000011124 \n", - "2 2021/2022 NaT NaT Canada 2021A000011124 \n", - "3 2021/2022 NaT NaT Canada 2021A000011124 \n", - "4 2021/2022 NaT NaT Canada 2021A000011124 \n", + " REF_DATE REF_START_DATE REF_END_DATE GEO \\\n", + "0 2021 2021-01-01 2021-12-31 Canada \n", + "1 2021 2021-01-01 2021-12-31 Newfoundland and Labrador \n", + "2 2021 2021-01-01 2021-12-31 Prince Edward Island \n", + "3 2021 2021-01-01 2021-12-31 Nova Scotia \n", + "4 2021 2021-01-01 2021-12-31 New Brunswick \n", "\n", - " Value Type of cannabis UOM UOM_ID SCALAR_FACTOR \\\n", - "0 Value of sales Total cannabis products Dollars 81 thousands \n", - "1 Value of sales Dried cannabis Dollars 81 thousands \n", - "2 Value of sales Inhaled cannabis extracts Dollars 81 thousands \n", - "3 Value of sales Ingested cannabis extracts Dollars 81 thousands \n", - "4 Value of sales Solid cannabis edibles Dollars 81 thousands \n", + " DGUID Coordinate \\\n", + "0 2021A000011124 1 \n", + "1 2021A000210 2 \n", + "2 2021A000211 3 \n", + "3 2021A000212 4 \n", + "4 2021A000213 5 \n", "\n", - " SCALAR_ID VECTOR COORDINATE VALUE STATUS SYMBOL TERMINATED \\\n", - "0 3 v1490436660 1.1.1 4027928 \n", - "1 3 v1490436647 1.1.2 2861838 \n", - "2 3 v1490436648 1.1.3 729178 \n", - "3 3 v1490436649 1.1.4 158283 \n", - "4 3 v1490436650 1.1.5 166336 \n", + " Population and dwelling counts (11): Population, 2021 [1] Symbols \\\n", + "0 36991981 \n", + "1 510550 \n", + "2 154331 \n", + "3 969383 \n", + "4 775610 \n", "\n", - " DECIMALS \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 " + " Population and dwelling counts (11): Population, 2016 [2] Symbols.1 ... \\\n", + "0 35151728 ... \n", + "1 519716 ... \n", + "2 142907 ... \n", + "3 923598 ... \n", + "4 747101 ... \n", + "\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents, 2021 [7] \\\n", + "0 14978941 \n", + "1 223253 \n", + "2 64570 \n", + "3 428228 \n", + "4 337651 \n", + "\n", + " Symbols.6 \\\n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents, 2016 [8] \\\n", + "0 14072079 \n", + "1 218673 \n", + "2 59472 \n", + "3 401990 \n", + "4 319773 \n", + "\n", + " Symbols.7 \\\n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "\n", + " Population and dwelling counts (11): Private dwellings occupied by usual residents percentage change, 2016 to 2021 [9] \\\n", + "0 6.4 \n", + "1 2.1 \n", + "2 8.6 \n", + "3 6.5 \n", + "4 5.6 \n", + "\n", + " Symbols.8 \\\n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "\n", + " Population and dwelling counts (11): Land area in square kilometres, 2021 [10] \\\n", + "0 8788702.8 \n", + "1 358170.37 \n", + "2 5681.18 \n", + "3 52824.71 \n", + "4 71248.5 \n", + "\n", + " Symbols.9 \\\n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "\n", + " Population and dwelling counts (11): Population density per square kilometre, 2021 [11] \\\n", + "0 4.2 \n", + "1 1.4 \n", + "2 27.2 \n", + "3 18.4 \n", + "4 10.9 \n", + "\n", + " Symbols.10 \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "\n", + "[5 rows x 28 columns]" ] }, - "execution_count": 39, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -926,7 +1265,56 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, + "id": "1043bfbc-201d-4ef0-9ecb-f8902ceb8106", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "REF_DATE string[python]\n", + "REF_START_DATE datetime64[ns]\n", + "REF_END_DATE datetime64[ns]\n", + "GEO string[python]\n", + "DGUID string[python]\n", + "Coordinate Int8\n", + "Population and dwelling counts (11): Population, 2021 [1] Int32\n", + "Symbols Int8\n", + "Population and dwelling counts (11): Population, 2016 [2] Int32\n", + "Symbols.1 Int8\n", + "Population and dwelling counts (11): Population percentage change, 2016 to 2021 [3] Float64\n", + "Symbols.2 Int8\n", + "Population and dwelling counts (11): Total private dwellings, 2021 [4] Int32\n", + "Symbols.3 Int8\n", + "Population and dwelling counts (11): Total private dwellings, 2016 [5] Int32\n", + "Symbols.4 Int8\n", + "Population and dwelling counts (11): Total private dwellings percentage change, 2016 to 2021 [6] Float64\n", + "Symbols.5 Int8\n", + "Population and dwelling counts (11): Private dwellings occupied by usual residents, 2021 [7] Int32\n", + "Symbols.6 Int8\n", + "Population and dwelling counts (11): Private dwellings occupied by usual residents, 2016 [8] Int32\n", + "Symbols.7 Int8\n", + "Population and dwelling counts (11): Private dwellings occupied by usual residents percentage change, 2016 to 2021 [9] Float64\n", + "Symbols.8 Int8\n", + "Population and dwelling counts (11): Land area in square kilometres, 2021 [10] Float64\n", + "Symbols.9 Int8\n", + "Population and dwelling counts (11): Population density per square kilometre, 2021 [11] Float64\n", + "Symbols.10 Int8\n", + "dtype: object" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 48, "id": "49cc1fa3-1ac8-4510-b4fd-7827a041e4a9", "metadata": { "editable": true, @@ -940,7 +1328,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Exporting dataframe as parquet to /data/tables/output/en/10100164_test.parquet\n" + "Exporting dataframe as parquet to /data/tables/output/en/12100013.parquet\n" ] } ], @@ -970,10 +1358,7 @@ }, "outputs": [], "source": [ - "# Remove the scratch files\n", - "print(\"Removing scratch files\")\n", - "os.remove(f\"{scratch_folder}/{product_id}.csv\")\n", - "os.remove(f\"{scratch_folder}/{product_id}_MetaData.csv\")\n", + "cleanup_product(product_id)\n", "update_last_downloaded(product_id)\n", "update_last_processed(product_id)" ]