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d4c-datapkg-statistical/census_of_population/process_2021.ipynb
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In [1]:
import gc
import glob
import os

import duckdb
from IPython.core.interactiveshell import InteractiveShell  
import numpy as np
import pandas as pd
from sqlalchemy import create_engine

# Enable multiple outputs per cell
InteractiveShell.ast_node_interactivity = "all"
# Show all columns
pd.set_option('display.max_columns', None)

data_dir = '/data/census_of_population/output/2021/tabular'

# PostgreSQL DB
DATABASE = os.environ.get("POSTGRES_DB")
USER = os.environ.get("POSTGRES_USER")
PASSWORD = os.environ.get("POSTGRES_PASSWORD")

engine = create_engine(f"postgresql://{USER}:{PASSWORD}@db:5432/{DATABASE}")

# DuckDB
con = duckdb.connect()
con.install_extension("spatial")
con.load_extension("spatial")

Datasets

  • 1.0 Canada, provinces, territories, census divisions (CDs), census subdivisions (CSDs) and dissemination areas (DAs)
  • 2.0 Census metropolitan areas (CMAs), tracted census agglomerations (CAs) and census tracts (CTs)
  • 3.0 Economic regions (ERs)
  • 4.0 Population centres (POPCTRs)
  • 5.0 Canada, provinces, territories and federal electoral districts (FEDs) (2013 Representation Order). Just process FEDs
  • 6.0 Canada, provinces, territories and federal electoral districts (FEDs) (2023 Representation Order). Just process FEDs
  • 7.0 Designated places (DPLs)
  • 8.0 Aggregate dissemination areas (ADAs)
  • 9.0 Forward sortation areas (FSAs)
  • 10.0 Health regions (HRs)
  • 11.0 Dissemination Blocks (DBs)
In [2]:
def process_cop_csv(csvs_to_process):
    """
    1. Reads subset of fields for Census of Population CSV files
    2. Pivots on characteristic_id
    3. Appends all of the processed CSVs as one dataframe
    """
    dataframes_to_concatenate = []
    for filename in csvs_to_process:
        print(f"Processing {filename}")
        params = {
            'filepath_or_buffer': filename,
            'encoding': 'latin-1',
            'usecols': ['DGUID', 
                        'CHARACTERISTIC_ID', 
                        'C1_COUNT_TOTAL',
                        'C2_COUNT_MEN+',
                        'C3_COUNT_WOMEN+'
                        ],
            'dtype': {
                'CHARACTERISTIC_ID': np.int16
            }
        }
        cop_df = pd.read_csv(**params)
        cop_df.rename(columns={
            'C1_COUNT_TOTAL': 'count_total',
            'C2_COUNT_MEN+': 'count_men', 
            'C3_COUNT_WOMEN+': 'count_women',
            'DGUID': 'dguid'
        }, inplace=True)

        cop_df = cop_df.pivot(index='dguid', columns='CHARACTERISTIC_ID')

        # Flatten the hierarchical index
        # https://stackoverflow.com/questions/14507794/how-to-flatten-a-hierarchical-index-in-columns/57630176#57630176
        level_one = cop_df.columns.get_level_values(0).astype(str)
        level_two = cop_df.columns.get_level_values(1).astype(str)
        column_separator = ['_' if x != '' else '' for x in level_two]
        cop_df.columns = level_one + column_separator + level_two
        dataframes_to_concatenate.append(cop_df)
        
    print("Concatenating all dataframes into one")
    cop_df = pd.concat(dataframes_to_concatenate)
    
    return cop_df
In [3]:
def drop_na_columns(dataframe):
    """
    Delete columns where there are no values.
    There are cases where there are values for the count_total
    columns, but no values for the count_men and count_women columns
    """
    columns_to_drop = []
    for field in dataframe.columns:
        minimum_value = dataframe[field].min()
        maximum_value = dataframe[field].max()
        if pd.isna(minimum_value) and pd.isna(maximum_value):
            columns_to_drop.append(field)

    if columns_to_drop:
        print("Dropping columns that don't have values")
        dataframe.drop(columns=columns_to_drop, inplace=True)
In [4]:
def convert_to_lowest_type(df):
    """
    Convert columns to the best possible dtypes
    For example, if the column is numerical and has a maximum value of 32,000 we can assign it a type of int16
    """
    params = {
        'convert_string': False,
        'convert_boolean': False
    }
    df = df.convert_dtypes(**params)

    dtypes = pd.DataFrame(df.dtypes)
    
    # Downcast to the smallest numerical dtype
    for row in dtypes.itertuples():
        column = row[0]
        the_type = str(row[1])
        
        # Skipping downcasting Float64 as there were issues with decimal places
        # For example, instead of a value being 65.4, it turned into 65.4000015258789
        if the_type == 'Float64':
            continue          
        elif the_type == 'Int64':
            df[column] = pd.to_numeric(df[column], downcast='integer')

    return df

Start processing

1.0 Process Canada, provinces, territories (PRs), census divisions (CDs), census subdivisions (CSDs) and dissemination areas (DAs)

In [5]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021006_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)
Processing /data/census_of_population/extracted/2021/98-401-X2021006_eng_CSV/98-401-X2021006_English_CSV_data_Atlantic.csv
Processing /data/census_of_population/extracted/2021/98-401-X2021006_eng_CSV/98-401-X2021006_English_CSV_data_BritishColumbia.csv
Processing /data/census_of_population/extracted/2021/98-401-X2021006_eng_CSV/98-401-X2021006_English_CSV_data_Ontario.csv
Processing /data/census_of_population/extracted/2021/98-401-X2021006_eng_CSV/98-401-X2021006_English_CSV_data_Prairies.csv
Processing /data/census_of_population/extracted/2021/98-401-X2021006_eng_CSV/98-401-X2021006_English_CSV_data_Quebec.csv
Processing /data/census_of_population/extracted/2021/98-401-X2021006_eng_CSV/98-401-X2021006_English_CSV_data_Territories.csv
Concatenating all dataframes into one

Remove duplicates

  • For example, for some reason, they included Canada (dguid 2021A000011124) 6 times (once per CSV), so we need to get unique values
In [ ]:
print(f"Number of records before {len(cop_df)}")
print("Before:")
cop_df[cop_df.index == '2021A000011124']

# Get unique records
cop_df = cop_df.groupby(cop_df.index).last()
print(f"Number of records after {len(cop_df)}")
cop_df[cop_df.index == '2021A000011124']

Get unique records

Split the Census of Population dataframe by geographic level

In [ ]:
# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS country_2021;
CREATE TABLE country_2021 AS SELECT country_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/country_2021.parquet';

DROP TABLE IF EXISTS pr_2021;
CREATE TABLE pr_2021 AS SELECT pr_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/pr_2021.parquet';

DROP TABLE IF EXISTS cd_2021;
CREATE TABLE cd_2021 AS SELECT cd_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/cd_2021.parquet';

DROP TABLE IF EXISTS csd_2021;
CREATE TABLE csd_2021 AS SELECT csd_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/csd_2021.parquet';

DROP TABLE IF EXISTS da_2021;
CREATE TABLE da_2021 AS SELECT da_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/da_2021.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
country_dguid = con.sql("SELECT * FROM country_2021").to_df()
pr_dguid = con.sql("SELECT * FROM pr_2021").to_df()
cd_dguid = con.sql("SELECT * FROM cd_2021").to_df()
csd_dguid = con.sql("SELECT * FROM csd_2021").to_df()
da_dguid = con.sql("SELECT * FROM da_2021").to_df()

# Join the Census of Population dataframe to each geographic level
cop_country = cop_df.join(country_dguid.set_index('dguid'), on='dguid', how='inner')
cop_pr = cop_df.join(pr_dguid.set_index('dguid'), on='dguid', how='inner')
cop_cd = cop_df.join(cd_dguid.set_index('dguid'), on='dguid', how='inner')
cop_csd = cop_df.join(csd_dguid.set_index('dguid'), on='dguid', how='inner')
cop_da = cop_df.join(da_dguid.set_index('dguid'), on='dguid', how='inner')

del(cop_df)
del(country_dguid)
del(pr_dguid)
del(cd_dguid)
del(csd_dguid)
del(da_dguid)
gc.collect()
<duckdb.duckdb.DuckDBPyConnection at 0x7f2d643eedf0>
0

Convert dataframe columns to lowest dtype

In [9]:
cop_country = convert_to_lowest_type(cop_country)
cop_pr = convert_to_lowest_type(cop_pr)
cop_cd = convert_to_lowest_type(cop_cd)
cop_csd = convert_to_lowest_type(cop_csd)
cop_da = convert_to_lowest_type(cop_da)

Delete columns where there are no values

Number of columns before

In [11]:
print(f"Country- length: {len(cop_country.columns)}")
print(f"PR- length: {len(cop_pr.columns)}")
print(f"CD- length: {len(cop_cd.columns)}")
print(f"CSD- length: {len(cop_csd.columns)}")
print(f"DA- length: {len(cop_da.columns)}")
Country- length: 7893
PR- length: 7893
CD- length: 7893
CSD- length: 7893
DA- length: 7893
In [12]:
drop_na_columns(cop_country)
drop_na_columns(cop_pr)
drop_na_columns(cop_cd)
drop_na_columns(cop_csd)
drop_na_columns(cop_da)
Dropping columns that don't have values
Dropping columns that don't have values
Dropping columns that don't have values
Dropping columns that don't have values
Dropping columns that don't have values

Number of columns after

In [13]:
print(f"Country- length: {len(cop_country.columns)}")
print(f"PR- length: {len(cop_pr.columns)}")
print(f"CD- length: {len(cop_cd.columns)}")
print(f"CSD- length: {len(cop_csd.columns)}")
print(f"DA- length: {len(cop_da.columns)}")
Country- length: 7433
PR- length: 7433
CD- length: 7433
CSD- length: 7433
DA- length: 7431
In [27]:
# Country
cop_country = cop_country.reset_index()
cop_country.rename(columns={'dguid': 'country_dguid'}, inplace=True)
cop_country.to_parquet(path=f'{data_dir}/country_2021.parquet', index=False, compression='zstd')

# Provinces and Territories
cop_pr = cop_pr.reset_index()
cop_pr.rename(columns={'dguid': 'pr_dguid'}, inplace=True)
cop_pr.to_parquet(path=f'{data_dir}/pr_2021.parquet', index=False, compression='zstd')

# Census Divisions
cop_cd = cop_cd.reset_index()
cop_cd.rename(columns={'dguid': 'cd_dguid'}, inplace=True)
cop_cd.to_parquet(path=f'{data_dir}/cd_2021.parquet', index=False, compression='zstd')

# Census Subdivisions
cop_csd = cop_csd.reset_index()
cop_csd.rename(columns={'dguid': 'csd_dguid'}, inplace=True)
cop_csd.to_parquet(path=f'{data_dir}/csd_2021.parquet', index=False, compression='zstd')

# Dissemination Areas
cop_da = cop_da.reset_index()
cop_da.rename(columns={'dguid': 'da_dguid'}, inplace=True)
cop_da.to_parquet(path=f'{data_dir}/da_2021.parquet', index=False, compression='zstd')
In [28]:
del(cop_country)
del(cop_pr)
del(cop_cd)
del(cop_csd)
del(cop_da)
gc.collect()
Out [28]:
0

2.0 Process Census metropolitan areas (CMAs), tracted census agglomerations (CAs) and census tracts (CTs)

They don't seem to be using CMA_P_DGUID, but CMA_DGUID

In [5]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021007_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)
Processing /data/census_of_population/extracted/2021/98-401-X2021007_eng_CSV/98-401-X2021007_English_CSV_data.csv
Concatenating all dataframes into one
In [8]:
# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS cma_2021;
CREATE TABLE cma_2021 AS SELECT cma_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/cma_2021.parquet';

DROP TABLE IF EXISTS ct_2021;
CREATE TABLE ct_2021 AS SELECT ct_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/ct_2021.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
cma_dguid = con.sql("SELECT * FROM cma_2021").to_df()
ct_dguid = con.sql("SELECT * FROM ct_2021").to_df()

# Join the Census of Population dataframe to each geographic level
# There's going to be missing links
cop_cma = cop_df.join(cma_dguid.set_index('dguid'), on='dguid', how='inner')
cop_ct = cop_df.join(ct_dguid.set_index('dguid'), on='dguid', how='inner')
Out [8]:
<duckdb.duckdb.DuckDBPyConnection at 0x7391c2b89330>
In [11]:
len(cop_cma)
Out [11]:
51
In [12]:
cop_cma.head()
Out [12]:
count_total_1 count_total_2 count_total_3 count_total_4 count_total_5 count_total_6 count_total_7 count_total_8 count_total_9 count_total_10 count_total_11 count_total_12 count_total_13 count_total_14 count_total_15 count_total_16 count_total_17 count_total_18 count_total_19 count_total_20 count_total_21 count_total_22 count_total_23 count_total_24 count_total_25 count_total_26 count_total_27 count_total_28 count_total_29 count_total_30 count_total_31 count_total_32 count_total_33 count_total_34 count_total_35 count_total_36 count_total_37 count_total_38 count_total_39 count_total_40 count_total_41 count_total_42 count_total_43 count_total_44 count_total_45 count_total_46 count_total_47 count_total_48 count_total_49 count_total_50 count_total_51 count_total_52 count_total_53 count_total_54 count_total_55 count_total_56 count_total_57 count_total_58 count_total_59 count_total_60 count_total_61 count_total_62 count_total_63 count_total_64 count_total_65 count_total_66 count_total_67 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In [27]:
# For whatever reason, there's duplicates for Ottawa - Gatineau CMA
cop_cma[cop_cma.index == '2021S0503505']
Out [27]:
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1000.0 13350.0 30335.0 6480.0 7090.0 7360.0 685.0 1345.0 985.0 3540.0 2860.0 86135.0 1425.0 10070.0 13360.0 13050.0 2725.0 15380.0 30120.0 94240.0 135.0 11705.0 82395.0 19295.0 10890.0 3280.0 820.0 3625.0 680.0 16530.0 10900.0 2785.0 2350.0 490.0 17005.0 2675.0 10160.0 3085.0 50.0 400.0 315.0 315.0 6885.0 4375.0 2510.0 71600.0 0.0 4175.0 65700.0 155.0 1575.0 17250.0 13245.0 115.0 65.0 3400.0 425.0 125.0 409005.0 94200.0 22895.0 22895.0 11025.0 765.0 10255.0 21465.0 4845.0 5080.0 4910.0 480.0 1005.0 625.0 2700.0 1820.0 69345.0 1175.0 8385.0 10310.0 11220.0 2040.0 11970.0 24235.0 75285.0 90.0 9465.0 65725.0 14495.0 8665.0 2290.0 345.0 2735.0 465.0 13155.0 9200.0 1720.0 1800.0 440.0 14280.0 2165.0 8730.0 2460.0 45.0 350.0 270.0 260.0 5635.0 3445.0 2190.0 53840.0 0.0 3330.0 49100.0 135.0 1280.0 13285.0 10085.0 105.0 40.0 2725.0 335.0 95.0 409005.0 94200.0 314795.0 263765.0 208285.0 55480.0 51035.0 9890.0 5280.0 670.0 245.0 580.0 145.0 925.0 715.0 175.0 255.0 895.0 11810.0 235.0 1865.0 385.0 225.0 180.0 110.0 150.0 525.0 655.0 865.0 645.0 310.0 3665.0 300.0 135.0 205.0 1350.0 7300.0 855.0 705.0 770.0 295.0 1085.0 295.0 3300.0 21140.0 995.0 445.0 230.0 1320.0 665.0 385.0 3740.0 160.0 145.0 390.0 665.0 3600.0 290.0 565.0 225.0 4270.0 920.0 2135.0 890.0 740.0 160.0 0.0 623830.0 390545.0 350240.0 40305.0 233295.0 62.6 56.1 10.3 623835.0 232220.0 391615.0 217520.0 174090.0 41.7 390540.0 9640.0 380900.0 344220.0 280870.0 63350.0 23675.0 39675.0 36675.0 390540.0 9640.0 380900.0 4270.0 107210.0 26035.0 44300.0 85695.0 15795.0 84495.0 8495.0 1950.0 2660.0 390540.0 9640.0 380900.0 1770.0 150.0 780.0 6845.0 7015.0 3885.0 37980.0 5945.0 6160.0 12530.0 5245.0 30280.0 505.0 12250.0 41595.0 72685.0 6845.0 20415.0 17025.0 91010.0 416910.0 243215.0 49895.0 1320.0 0.0 1320.0 113350.0 6335.0 280.0 2470.0 35.0 416910.0 395855.0 393060.0 314210.0 78855.0 2790.0 15.0 10.0 0.0 10.0 0.0 10.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2770.0 425.0 0.0 0.0 0.0 0.0 0.0 0.0 30.0 0.0 0.0 30.0 0.0 395.0 10.0 385.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 95.0 10.0 90.0 0.0 30.0 0.0 0.0 0.0 0.0 0.0 15.0 0.0 0.0 0.0 0.0 0.0 15.0 0.0 25.0 25.0 0.0 0.0 0.0 0.0 25.0 0.0 10.0 10.0 0.0 0.0 0.0 980.0 0.0 0.0 210.0 0.0 0.0 0.0 210.0 0.0 10.0 0.0 25.0 145.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 110.0 0.0 80.0 70.0 0.0 0.0 0.0 20.0 0.0 15.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 0.0 10.0 165.0 100.0 0.0 0.0 15.0 0.0 0.0 0.0 40.0 0.0 0.0 30.0 0.0 65.0 0.0 0.0 60.0 25.0 35.0 0.0 0.0 0.0 480.0 0.0 10.0 120.0 0.0 345.0 0.0 0.0 30.0 105.0 0.0 15.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.0 10.0 0.0 0.0 900.0 900.0 0.0 680.0 0.0 0.0 0.0 210.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 25.0 0.0 55.0 0.0 55.0 0.0 0.0 0.0 25.0 0.0 10.0 0.0 0.0 21060.0 19460.0 1280.0 95.0 210.0 0.0 416910.0 314780.0 29730.0 66025.0 4140.0 115.0 4025.0 90.0 390.0 1265.0 0.0 490.0 350235.0 151485.0 735.0 16105.0 181915.0 181915.0 148120.0 3210.0 16565.0 14020.0 198015.0 152035.0 133070.0 18970.0 23040.0 13850.0 1845.0 7245.0 198015.0 59195.0 77685.0 38475.0 12685.0 9985.0 198015.0 6950.0 32195.0 58890.0 45740.0 26035.0 28210.0 150960.0 50020.0 100940.0 111600.0 37795.0 28135.0 9660.0 73805.0
In [29]:
cop_cma = cop_cma.groupby(cop_cma.index).last()
In [31]:
del(cma_dguid)
del(cop_df)
gc.collect()

# Convert columns to lowest dtypes
cop_cma = convert_to_lowest_type(cop_cma)

# Drop NA columns
print(f"CMA - Number of Columns BEFORE: {len(cop_cma.columns)}")
drop_na_columns(cop_ct)
print(f"CMA - Number of Columns AFTER: {len(cop_cma.columns)}")

# Export
# Census Metropolitan Areas. They seem to be using cma_dguid so that's what we'll release it as
cop_cma = cop_cma.reset_index()
cop_cma.rename(columns={'dguid': 'cma_dguid'}, inplace=True)
cop_cma.to_parquet(path=f'{data_dir}/cma_2021.parquet', index=False, compression='zstd')

del(cop_cma)
gc.collect()
Out [31]:
770
CMA - Number of Columns BEFORE: 7893
Dropping columns that don't have values
CMA - Number of Columns AFTER: 7893

0
In [ ]:
del(ct_dguid)
del(cop_df)
gc.collect()

# Convert columns to lowest dtypes
cop_ct = convert_to_lowest_type(cop_ct)

# Drop NA columns
print(f"CT - Number of Columns BEFORE: {len(cop_ct.columns)}")
drop_na_columns(cop_ct)
print(f"CT - Number of Columns AFTER: {len(cop_ct.columns)}")

# Export
# Census Tracts
cop_ct = cop_ct.reset_index()
cop_ct.rename(columns={'dguid': 'ct_dguid'}, inplace=True)
cop_ct.to_parquet(path=f'{data_dir}/ct_2021.parquet', index=False, compression='zstd')

del(cop_ct)
gc.collect()

3.0 Process Economic regions (ERs) (98-401-X2021008_eng_CSV)

This file also includes Provinces and Territories and Country

In [ ]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021008_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)

# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS cma_2021;
DROP TABLE IF EXISTS ct_2021;

DROP TABLE IF EXISTS er_2021;
CREATE TABLE er_2021 AS SELECT er_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/er_2021.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
er_dguid = con.sql("SELECT * FROM er_2021").to_df()

# Join the Census of Population dataframe to each geographic level
cop_er = cop_df.join(er_dguid.set_index('dguid'), on='dguid', how='inner')

del(er_dguid)
del(cop_df)
gc.collect()

# Convert columns to lowest dtypes
cop_er = convert_to_lowest_type(cop_er)

# Drop NA columns
print(f"ER - Number of Columns BEFORE: {len(cop_er.columns)}")
drop_na_columns(cop_er)
print(f"CT - Number of Columns AFTER: {len(cop_er.columns)}")

# Export
# Economic Regions
cop_er = cop_er.reset_index()
cop_er.rename(columns={'dguid': 'er_dguid'}, inplace=True)
cop_er.to_parquet(path=f'{data_dir}/er_2021.parquet', index=False, compression='zstd')

del(cop_er)
gc.collect()
Processing /data/census_of_population/extracted/2021/98-401-X2021008_eng_CSV/98-401-X2021008_English_CSV_data.csv
Concatenating all dataframes into one

<duckdb.duckdb.DuckDBPyConnection at 0x7f2d643eedf0>

4.0 Process Population centres (POPCTRs)

There are 1026 DGUIDs in the Census of Population data, but there should be 1030

  • They also use the pop_ctr_dguid and not the pop_ctr_p_dguid. So, there's no way to differentiate between Ottawa, and Gatineau for pop_ctr_dguid 2021S05100616
In [ ]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021009_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)

# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS er_2021;

DROP TABLE IF EXISTS pop_ctr_2021;
CREATE TABLE pop_ctr_2021 AS SELECT DISTINCT pop_ctr_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/pop_ctr_2021.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
pop_ctr_dguid = con.sql("SELECT * FROM pop_ctr_2021").to_df()

# Join the Census of Population dataframe to each geographic level
cop_pop_ctr = cop_df.join(pop_ctr_dguid.set_index('dguid'), on='dguid', how='inner')

del(pop_ctr_dguid)
del(cop_df)
gc.collect()

# Convert columns to lowest dtypes
cop_pop_ctr = convert_to_lowest_type(cop_pop_ctr)

# Drop NA columns
print(f"POP CTR - Number of Columns BEFORE: {len(cop_pop_ctr.columns)}")
drop_na_columns(cop_pop_ctr)
print(f"POP CTR- Number of Columns AFTER: {len(cop_pop_ctr.columns)}")

# Export
# Population Centers
cop_pop_ctr = cop_pop_ctr.reset_index()
cop_pop_ctr.rename(columns={'dguid': 'pop_ctr_dguid'}, inplace=True)
cop_pop_ctr.to_parquet(path=f'{data_dir}/pop_ctr_2021.parquet', index=False, compression='zstd')

del(cop_pop_ctr)
gc.collect()
Processing /data/census_of_population/extracted/2021/98-401-X2021009_eng_CSV/98-401-X2021009_English_CSV_data.csv
Concatenating all dataframes into one

<duckdb.duckdb.DuckDBPyConnection at 0x7f2d643eedf0>

5.0 Process Federal electoral districts (FEDs) (2013 Representation Order)

In [ ]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021010_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)

# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS pop_ctr_2021;

DROP TABLE IF EXISTS fed_2013;
CREATE TABLE fed_2013 AS SELECT fed_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/fed_2021_2013.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
fed_dguid = con.sql("SELECT * FROM fed_2013").to_df()

# Join the Census of Population dataframe to each geographic level
cop_fed = cop_df.join(fed_dguid.set_index('dguid'), on='dguid', how='inner')

del(fed_dguid)
del(cop_df)
gc.collect()

# Convert columns to lowest dtypes
cop_fed = convert_to_lowest_type(cop_fed)

# Drop NA columns
print(f"FED - Number of Columns BEFORE: {len(cop_fed.columns)}")
drop_na_columns(cop_fed)
print(f"FED - Number of Columns AFTER: {len(cop_fed.columns)}")

# Export
cop_fed = cop_fed.reset_index()
cop_fed.rename(columns={'dguid': 'fed_dguid'}, inplace=True)
cop_fed.to_parquet(path=f'{data_dir}/fed_2013.parquet', index=False, compression='zstd')

del(cop_fed)
gc.collect()
Processing /data/census_of_population/extracted/2021/98-401-X2021010_eng_CSV/98-401-X2021010_English_CSV_data.csv
Concatenating all dataframes into one

<duckdb.duckdb.DuckDBPyConnection at 0x7f2d643eedf0>
0
FED - Number of Columns BEFORE: 7893
Dropping columns that don't have values
FED - Number of Columns AFTER: 7433

0

6.0 Process Federal electoral districts (FEDs) (2023 Representation Order)

There should be 343 2023 FEDs

In [5]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021029_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)

# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS fed_2013;

/*
DROP TABLE IF EXISTS fed_2023;
CREATE TABLE fed_2023 AS SELECT fed_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/fed_2023.parquet';
*/
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
#fed_dguid = con.sql("SELECT * FROM fed_2023").to_df()

# Join the Census of Population dataframe to each geographic level
#cop_fed = cop_df.join(fed_dguid.set_index('dguid'), on='dguid', how='inner')
cop_df = cop_df.reset_index()
cop_df = cop_df[cop_df['dguid'].str.contains("2023")]

# Convert columns to lowest dtypes
cop_fed = convert_to_lowest_type(cop_df)

del(cop_df)
gc.collect()

# Drop NA columns
print(f"FED - Number of Columns BEFORE: {len(cop_fed.columns)}")
drop_na_columns(cop_fed)
print(f"FED - Number of Columns AFTER: {len(cop_fed.columns)}")

# Export
cop_fed.rename(columns={'dguid': 'fed_dguid'}, inplace=True)
cop_fed.to_parquet(path=f'{data_dir}/fed_2023.parquet', index=False, compression='zstd')

del(cop_fed)
gc.collect()
Out [5]:
Processing /data/census_of_population/extracted/2021/98-401-X2021029_eng_CSV/98-401-X2021029_English_CSV_data.csv
Concatenating all dataframes into one

<duckdb.duckdb.DuckDBPyConnection at 0x7fe655324f30>
In [ ]:
# Convert columns to lowest dtypes
cop_fed = convert_to_lowest_type(cop_df)

del(cop_df)
gc.collect()

# Drop NA columns
print(f"FED - Number of Columns BEFORE: {len(cop_fed.columns)}")
drop_na_columns(cop_fed)
print(f"FED - Number of Columns AFTER: {len(cop_fed.columns)}")

# Export
cop_fed.rename(columns={'dguid': 'fed_dguid'}, inplace=True)
cop_fed.to_parquet(path=f'{data_dir}/fed_2023.parquet', index=False, compression='zstd')

del(cop_fed)
gc.collect()

7.0 Process Designated places (DPLs)

There should be 1685 DPLs

In [ ]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021011_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)

# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS dpl_2021;
CREATE TABLE dpl_2021 AS SELECT dpl_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/dpl_2021.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
dpl_dguid = con.sql("SELECT * FROM dpl_2021").to_df()

# Join the Census of Population dataframe to each geographic level
cop_dpl = cop_df.join(dpl_dguid.set_index('dguid'), on='dguid', how='inner')

del(dpl_dguid)
del(cop_df)
gc.collect()

# Convert columns to lowest dtypes
cop_dpl = convert_to_lowest_type(cop_dpl)

# Drop NA columns
print(f"DPL - Number of Columns BEFORE: {len(cop_dpl.columns)}")
drop_na_columns(cop_dpl)
print(f"DPL - Number of Columns AFTER: {len(cop_dpl.columns)}")

# Export
cop_dpl = cop_dpl.reset_index()
cop_dpl.rename(columns={'dguid': 'dpl_dguid'}, inplace=True)
cop_dpl.to_parquet(path=f'{data_dir}/dpl_2021.parquet', index=False, compression='zstd')

del(cop_dpl)
gc.collect()
Processing /data/census_of_population/extracted/2021/98-401-X2021011_eng_CSV/98-401-X2021011_English_CSV_data.csv
Concatenating all dataframes into one

<duckdb.duckdb.DuckDBPyConnection at 0x7f2d643eedf0>
0
DPL - Number of Columns BEFORE: 7893
Dropping columns that don't have values
DPL - Number of Columns AFTER: 7433

8.0 Process Aggregate dissemination areas (ADAs)

There should be 5433 ADAs

In [ ]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021012_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)

# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS dpl_2021;
DROP TABLE IF EXISTS ada_2021;
CREATE TABLE ada_2021 AS SELECT ada_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/ada_2021.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
ada_dguid = con.sql("SELECT * FROM ada_2021").to_df()

# Join the Census of Population dataframe to each geographic level
cop_ada = cop_df.join(ada_dguid.set_index('dguid'), on='dguid', how='inner')

del(ada_dguid)
del(cop_df)
gc.collect()

# Convert columns to lowest dtypes
cop_ada = convert_to_lowest_type(cop_ada)

# Drop NA columns
print(f"ADA - Number of Columns BEFORE: {len(cop_ada.columns)}")
drop_na_columns(cop_ada)
print(f"ADA - Number of Columns AFTER: {len(cop_ada.columns)}")

# Export
cop_ada = cop_ada.reset_index()
cop_ada.rename(columns={'dguid': 'ada_dguid'}, inplace=True)
cop_ada.to_parquet(path=f'{data_dir}/ada_2021.parquet', index=False, compression='zstd')

del(cop_ada)
gc.collect()
Processing /data/census_of_population/extracted/2021/98-401-X2021012_eng_CSV/98-401-X2021012_English_CSV_data.csv
Concatenating all dataframes into one

<duckdb.duckdb.DuckDBPyConnection at 0x7f2d643eedf0>

9.0 Process Forward sortation areas (FSAs)

There should be 1643 FSAs

In [ ]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021013_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)

# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS ada_2021;
DROP TABLE IF EXISTS fsa_2021;
CREATE TABLE fsa_2021 AS SELECT fsa_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/fsa_2021.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
fsa_dguid = con.sql("SELECT * FROM fsa_2021").to_df()

# Join the Census of Population dataframe to each geographic level
cop_fsa = cop_df.join(fsa_dguid.set_index('dguid'), on='dguid', how='inner')

del(fsa_dguid)
del(cop_df)
gc.collect()

# Convert columns to lowest dtypes
cop_fsa = convert_to_lowest_type(cop_fsa)

# Drop NA columns
print(f"FSA - Number of Columns BEFORE: {len(cop_fsa.columns)}")
drop_na_columns(cop_fsa)
print(f"FSA - Number of Columns AFTER: {len(cop_fsa.columns)}")

# Export
cop_fsa = cop_fsa.reset_index()
cop_fsa.rename(columns={'dguid': 'fsa_dguid'}, inplace=True)
cop_fsa.to_parquet(path=f'{data_dir}/fsa_2021.parquet', index=False, compression='zstd')

del(cop_fsa)
gc.collect()
Processing /data/census_of_population/extracted/2021/98-401-X2021013_eng_CSV/98-401-X2021013_English_CSV_data.csv
Concatenating all dataframes into one

<duckdb.duckdb.DuckDBPyConnection at 0x7f2d643eedf0>
684
FSA - Number of Columns BEFORE: 7893
Dropping columns that don't have values
FSA - Number of Columns AFTER: 7429

0

10.0 Process Health regions (HRs) and Local health integration networks

Start looking here https://www150.statcan.gc.ca/n1/en/catalogue/82-402-X

In [ ]:
csvs_to_process = glob.glob("/data/census_of_population/extracted/2021/98-401-X2021015_eng_CSV/*English_CSV_data*")
cop_df = process_cop_csv(csvs_to_process)

# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS fsa_2021;
DROP TABLE IF EXISTS hr_2022;
CREATE TABLE hr_2022 AS SELECT hr_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/hr_2022.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
hr_dguid = con.sql("SELECT * FROM hr_2022").to_df()

11.0 Dissemination Blocks (DBs)

In [25]:
sql = """
SELECT db_dguid, 
db_pop_2021 AS count_total_1, 
db_total_private_dwell_2021 AS count_total_4,
db_usual_residents_dwellings_2021 AS count_total_5
FROM silver.gaf_2021;
"""

cop_df = pd.read_sql_query(sql=sql, con=engine, index_col='db_dguid')

# Get the dguid per level of geography
con.sql("""
DROP TABLE IF EXISTS db_2021;
CREATE TABLE db_2021 AS SELECT db_dguid AS dguid FROM 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/db_2021.parquet';
""")
con.commit()

# Convert the duckdb tables to pandas dataframe
db_dguid = con.sql("SELECT * FROM db_2021").to_df()

# Join the Census of Population dataframe to each geographic level
cop_db = cop_df.join(db_dguid.set_index('dguid'), on='db_dguid', how='inner')

# Convert columns to lowest dtypes
cop_db = convert_to_lowest_type(cop_db)

# Export
cop_db = cop_db.reset_index()
cop_db.to_parquet(path=f'{data_dir}/db_2021.parquet', index=False, compression='zstd')
Out [25]:
<duckdb.duckdb.DuckDBPyConnection at 0x7f6df0455b30>
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