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j40-cejst-2/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py
Travis Newby 03a6d3c660
User Story 2152 – Clean up logging (#2155) 
Update logging messages and message consistency

This update includes changes to the level of many log messages. Rather than everything being logged at the info level, it differentiates between debug, info, warning, and error messages. It also changes the default log level to info to avoid much of the noise previously in the logs.

It also removes many extra log messages, and adds additional decorators at the beginning of each pipeline run.
2023-02-08 13:08:55 -06:00

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import os
from collections import namedtuple
import geopandas as gpd
import pandas as pd
from data_pipeline.config import settings
from data_pipeline.etl.base import ExtractTransformLoad
from data_pipeline.etl.sources.census_acs.etl_imputations import (
calculate_income_measures,
)
from data_pipeline.etl.sources.census_acs.etl_utils import (
retrieve_census_acs_data,
)
from data_pipeline.score import field_names
from data_pipeline.utils import get_module_logger
from data_pipeline.utils import unzip_file_from_url
logger = get_module_logger(__name__)
# because now there is a requirement for the us.json, this will port from
# AWS when a local copy does not exist.
CENSUS_DATA_S3_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/census.zip"
class CensusACSETL(ExtractTransformLoad):
NAME = "census_acs"
ACS_YEAR = 2019
MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION = 1
def __init__(self):
self.TOTAL_UNEMPLOYED_FIELD = "B23025_005E"
self.TOTAL_IN_LABOR_FORCE = "B23025_003E"
self.EMPLOYMENT_FIELDS = [
self.TOTAL_UNEMPLOYED_FIELD,
self.TOTAL_IN_LABOR_FORCE,
]
self.UNEMPLOYED_FIELD_NAME = "Unemployment (percent)"
self.LINGUISTIC_ISOLATION_FIELD_NAME = "Linguistic isolation (percent)"
self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME = (
"Linguistic isolation (total)"
)
self.LINGUISTIC_ISOLATION_FIELDS = [
"C16002_001E", # Estimate!!Total
"C16002_004E", # Estimate!!Total!!Spanish!!Limited English speaking household
"C16002_007E", # Estimate!!Total!!Other Indo-European languages!!Limited English speaking household
"C16002_010E", # Estimate!!Total!!Asian and Pacific Island languages!!Limited English speaking household
"C16002_013E", # Estimate!!Total!!Other languages!!Limited English speaking household
]
self.MEDIAN_INCOME_FIELD = "B19013_001E"
self.MEDIAN_INCOME_FIELD_NAME = (
"Median household income in the past 12 months"
)
self.POVERTY_FIELDS = [
"C17002_001E", # Estimate!!Total,
"C17002_002E", # Estimate!!Total!!Under .50
"C17002_003E", # Estimate!!Total!!.50 to .99
"C17002_004E", # Estimate!!Total!!1.00 to 1.24
"C17002_005E", # Estimate!!Total!!1.25 to 1.49
"C17002_006E", # Estimate!!Total!!1.50 to 1.84
"C17002_007E", # Estimate!!Total!!1.85 to 1.99
]
self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME = (
"Percent of individuals < 100% Federal Poverty Line"
)
self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME = (
"Percent of individuals < 150% Federal Poverty Line"
)
self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
"Percent of individuals < 200% Federal Poverty Line"
)
self.IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
"Percent of individuals < 200% Federal Poverty Line, imputed"
)
self.ADJUSTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
"Adjusted percent of individuals < 200% Federal Poverty Line"
)
self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME_PRELIMINARY = (
"Preliminary adjusted percent of individuals < 200% Federal Poverty Line,"
+ " imputed"
)
self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
"Adjusted percent of individuals < 200% Federal Poverty Line,"
+ " imputed"
)
self.MEDIAN_HOUSE_VALUE_FIELD = "B25077_001E"
self.MEDIAN_HOUSE_VALUE_FIELD_NAME = (
"Median value ($) of owner-occupied housing units"
)
# Educational attainment figures
self.EDUCATION_POPULATION_OVER_25 = "B15003_001E" # Estimate!!Total
self.EDUCATION_NO_SCHOOLING = (
"B15003_002E" # Estimate!!Total!!No schooling completed
)
self.EDUCATION_NURSERY = (
"B15003_003E" # Estimate!!Total!!Nursery school
)
self.EDUCATION_KINDERGARTEN = (
"B15003_004E" # Estimate!!Total!!Kindergarten
)
self.EDUCATION_FIRST = "B15003_005E" # Estimate!!Total!!1st grade
self.EDUCATION_SECOND = "B15003_006E" # Estimate!!Total!!2nd grade
self.EDUCATION_THIRD = "B15003_007E" # Estimate!!Total!!3rd grade
self.EDUCATION_FOURTH = "B15003_008E" # Estimate!!Total!!4th grade
self.EDUCATION_FIFTH = "B15003_009E" # Estimate!!Total!!5th grade
self.EDUCATION_SIXTH = "B15003_010E" # Estimate!!Total!!6th grade
self.EDUCATION_SEVENTH = "B15003_011E" # Estimate!!Total!!7th grade
self.EDUCATION_EIGHTH = "B15003_012E" # Estimate!!Total!!8th grade
self.EDUCATION_NINTH = "B15003_013E" # Estimate!!Total!!9th grade
self.EDUCATION_TENTH = "B15003_014E" # Estimate!!Total!!10th grade
self.EDUCATION_ELEVENTH = "B15003_015E" # Estimate!!Total!!11th grade
self.EDUCATION_TWELFTH_NO_DIPLOMA = (
"B15003_016E" # Estimate!!Total!!12th grade, no diploma
)
self.EDUCATIONAL_FIELDS = [
self.EDUCATION_POPULATION_OVER_25,
self.EDUCATION_NO_SCHOOLING,
self.EDUCATION_NURSERY,
self.EDUCATION_KINDERGARTEN,
self.EDUCATION_FIRST,
self.EDUCATION_SECOND,
self.EDUCATION_THIRD,
self.EDUCATION_FOURTH,
self.EDUCATION_FIFTH,
self.EDUCATION_SIXTH,
self.EDUCATION_SEVENTH,
self.EDUCATION_EIGHTH,
self.EDUCATION_NINTH,
self.EDUCATION_TENTH,
self.EDUCATION_ELEVENTH,
self.EDUCATION_TWELFTH_NO_DIPLOMA,
]
self.HIGH_SCHOOL_ED_RAW_COUNT_FIELD = (
"Individuals age 25 or over with less than high school degree"
)
self.HIGH_SCHOOL_ED_FIELD = "Percent individuals age 25 or over with less than high school degree"
# College attendance fields
self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED = (
"B14004_001E" # Estimate!!Total
)
self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC = "B14004_003E" # Estimate!!Total!!Male!!Enrolled in public college or graduate school
self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE = "B14004_008E" # Estimate!!Total!!Male!!Enrolled in private college or graduate school
self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC = "B14004_019E" # Estimate!!Total!!Female!!Enrolled in public college or graduate school
self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE = "B14004_024E" # Estimate!!Total!!Female!!Enrolled in private college or graduate school
self.COLLEGE_ATTENDANCE_FIELDS = [
self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED,
self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC,
self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE,
self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC,
self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE,
]
self.COLLEGE_ATTENDANCE_FIELD = (
"Percent enrollment in college or graduate school"
)
self.IMPUTED_COLLEGE_ATTENDANCE_FIELD = (
"Percent enrollment in college or graduate school, imputed"
)
self.COLLEGE_NON_ATTENDANCE_FIELD = "Percent of population not currently enrolled in college or graduate school"
self.RE_FIELDS = [
"B02001_001E",
"B02001_002E",
"B02001_003E",
"B02001_004E",
"B02001_005E",
"B02001_006E",
"B02001_007E",
"B02001_008E",
"B02001_009E",
"B02001_010E",
"B03002_001E",
"B03002_003E",
"B03003_001E",
"B03003_003E",
"B02001_007E", # "Some other race alone"
]
self.BLACK_FIELD_NAME = "Black or African American"
self.AMERICAN_INDIAN_FIELD_NAME = "American Indian / Alaska Native"
self.ASIAN_FIELD_NAME = "Asian"
self.HAWAIIAN_FIELD_NAME = "Native Hawaiian or Pacific"
self.TWO_OR_MORE_RACES_FIELD_NAME = "two or more races"
self.NON_HISPANIC_WHITE_FIELD_NAME = "White"
self.HISPANIC_FIELD_NAME = "Hispanic or Latino"
# Note that `other` is lowercase because the whole field will show up in the download
# file as "Percent other races"
self.OTHER_RACE_FIELD_NAME = "other races"
self.TOTAL_RACE_POPULATION_FIELD_NAME = (
"Total population surveyed on racial data"
)
# Name output demographics fields.
self.RE_OUTPUT_FIELDS = [
self.BLACK_FIELD_NAME,
self.AMERICAN_INDIAN_FIELD_NAME,
self.ASIAN_FIELD_NAME,
self.HAWAIIAN_FIELD_NAME,
self.TWO_OR_MORE_RACES_FIELD_NAME,
self.NON_HISPANIC_WHITE_FIELD_NAME,
self.HISPANIC_FIELD_NAME,
self.OTHER_RACE_FIELD_NAME,
]
# Note: this field does double-duty here. It's used as the total population
# within the age questions.
# It's also what EJScreen used as their variable for total population in the
# census tract, so we use it similarly.
# See p. 83 of https://www.epa.gov/sites/default/files/2021-04/documents/ejscreen_technical_document.pdf
self.TOTAL_POPULATION_FROM_AGE_TABLE = "B01001_001E" # Estimate!!Total:
self.AGE_INPUT_FIELDS = [
self.TOTAL_POPULATION_FROM_AGE_TABLE,
"B01001_003E", # Estimate!!Total:!!Male:!!Under 5 years
"B01001_004E", # Estimate!!Total:!!Male:!!5 to 9 years
"B01001_005E", # Estimate!!Total:!!Male:!!10 to 14 years
"B01001_006E", # Estimate!!Total:!!Male:!!15 to 17 years
"B01001_007E", # Estimate!!Total:!!Male:!!18 and 19 years
"B01001_008E", # Estimate!!Total:!!Male:!!20 years
"B01001_009E", # Estimate!!Total:!!Male:!!21 years
"B01001_010E", # Estimate!!Total:!!Male:!!22 to 24 years
"B01001_011E", # Estimate!!Total:!!Male:!!25 to 29 years
"B01001_012E", # Estimate!!Total:!!Male:!!30 to 34 years
"B01001_013E", # Estimate!!Total:!!Male:!!35 to 39 years
"B01001_014E", # Estimate!!Total:!!Male:!!40 to 44 years
"B01001_015E", # Estimate!!Total:!!Male:!!45 to 49 years
"B01001_016E", # Estimate!!Total:!!Male:!!50 to 54 years
"B01001_017E", # Estimate!!Total:!!Male:!!55 to 59 years
"B01001_018E", # Estimate!!Total:!!Male:!!60 and 61 years
"B01001_019E", # Estimate!!Total:!!Male:!!62 to 64 years
"B01001_020E", # Estimate!!Total:!!Male:!!65 and 66 years
"B01001_021E", # Estimate!!Total:!!Male:!!67 to 69 years
"B01001_022E", # Estimate!!Total:!!Male:!!70 to 74 years
"B01001_023E", # Estimate!!Total:!!Male:!!75 to 79 years
"B01001_024E", # Estimate!!Total:!!Male:!!80 to 84 years
"B01001_025E", # Estimate!!Total:!!Male:!!85 years and over
"B01001_027E", # Estimate!!Total:!!Female:!!Under 5 years
"B01001_028E", # Estimate!!Total:!!Female:!!5 to 9 years
"B01001_029E", # Estimate!!Total:!!Female:!!10 to 14 years
"B01001_030E", # Estimate!!Total:!!Female:!!15 to 17 years
"B01001_031E", # Estimate!!Total:!!Female:!!18 and 19 years
"B01001_032E", # Estimate!!Total:!!Female:!!20 years
"B01001_033E", # Estimate!!Total:!!Female:!!21 years
"B01001_034E", # Estimate!!Total:!!Female:!!22 to 24 years
"B01001_035E", # Estimate!!Total:!!Female:!!25 to 29 years
"B01001_036E", # Estimate!!Total:!!Female:!!30 to 34 years
"B01001_037E", # Estimate!!Total:!!Female:!!35 to 39 years
"B01001_038E", # Estimate!!Total:!!Female:!!40 to 44 years
"B01001_039E", # Estimate!!Total:!!Female:!!45 to 49 years
"B01001_040E", # Estimate!!Total:!!Female:!!50 to 54 years
"B01001_041E", # Estimate!!Total:!!Female:!!55 to 59 years
"B01001_042E", # Estimate!!Total:!!Female:!!60 and 61 years
"B01001_043E", # Estimate!!Total:!!Female:!!62 to 64 years
"B01001_044E", # Estimate!!Total:!!Female:!!65 and 66 years
"B01001_045E", # Estimate!!Total:!!Female:!!67 to 69 years
"B01001_046E", # Estimate!!Total:!!Female:!!70 to 74 years
"B01001_047E", # Estimate!!Total:!!Female:!!75 to 79 years
"B01001_048E", # Estimate!!Total:!!Female:!!80 to 84 years
"B01001_049E", # Estimate!!Total:!!Female:!!85 years and over
]
self.AGE_OUTPUT_FIELDS = [
field_names.PERCENT_AGE_UNDER_10,
field_names.PERCENT_AGE_10_TO_64,
field_names.PERCENT_AGE_OVER_64,
]
self.STATE_GEOID_FIELD_NAME = "GEOID2"
self.COLUMNS_TO_KEEP = (
[
self.GEOID_TRACT_FIELD_NAME,
field_names.TOTAL_POP_FIELD,
self.UNEMPLOYED_FIELD_NAME,
self.LINGUISTIC_ISOLATION_FIELD_NAME,
self.MEDIAN_INCOME_FIELD_NAME,
self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME,
self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME,
self.IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
self.HIGH_SCHOOL_ED_FIELD,
self.COLLEGE_ATTENDANCE_FIELD,
self.COLLEGE_NON_ATTENDANCE_FIELD,
self.IMPUTED_COLLEGE_ATTENDANCE_FIELD,
field_names.IMPUTED_INCOME_FLAG_FIELD_NAME,
]
+ self.RE_OUTPUT_FIELDS
+ [
field_names.PERCENT_PREFIX + field
for field in self.RE_OUTPUT_FIELDS
]
+ self.AGE_OUTPUT_FIELDS
+ [
field_names.POVERTY_LESS_THAN_200_FPL_FIELD,
field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
]
)
self.df: pd.DataFrame
# pylint: disable=too-many-arguments
def _merge_geojson(
self,
df: pd.DataFrame,
usa_geo_df: gpd.GeoDataFrame,
geoid_field: str = "GEOID10",
geometry_field: str = "geometry",
state_code_field: str = "STATEFP10",
county_code_field: str = "COUNTYFP10",
) -> gpd.GeoDataFrame:
usa_geo_df[geoid_field] = (
usa_geo_df[geoid_field].astype(str).str.zfill(11)
)
return gpd.GeoDataFrame(
df.merge(
usa_geo_df[
[
geoid_field,
geometry_field,
state_code_field,
county_code_field,
]
],
left_on=[self.GEOID_TRACT_FIELD_NAME],
right_on=[geoid_field],
)
)
def extract(self) -> None:
# Define the variables to retrieve
variables = (
[
self.MEDIAN_INCOME_FIELD,
self.MEDIAN_HOUSE_VALUE_FIELD,
]
+ self.EMPLOYMENT_FIELDS
+ self.LINGUISTIC_ISOLATION_FIELDS
+ self.POVERTY_FIELDS
+ self.EDUCATIONAL_FIELDS
+ self.RE_FIELDS
+ self.COLLEGE_ATTENDANCE_FIELDS
+ self.AGE_INPUT_FIELDS
)
self.df = retrieve_census_acs_data(
acs_year=self.ACS_YEAR,
variables=variables,
tract_output_field_name=self.GEOID_TRACT_FIELD_NAME,
data_path_for_fips_codes=self.DATA_PATH,
)
def transform(self) -> None:
df = self.df
# Here we join the geometry of the US to the dataframe so that we can impute
# The income of neighbors. first this looks locally; if there's no local
# geojson file for all of the US, this will read it off of S3
logger.debug("Reading in geojson for the country")
if not os.path.exists(
self.DATA_PATH / "census" / "geojson" / "us.json"
):
logger.debug("Fetching Census data from AWS S3")
unzip_file_from_url(
CENSUS_DATA_S3_URL,
self.DATA_PATH / "tmp",
self.DATA_PATH,
)
geo_df = gpd.read_file(
self.DATA_PATH / "census" / "geojson" / "us.json",
)
df = self._merge_geojson(
df=df,
usa_geo_df=geo_df,
)
# Rename some fields.
df = df.rename(
columns={
self.MEDIAN_HOUSE_VALUE_FIELD: self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
self.MEDIAN_INCOME_FIELD: self.MEDIAN_INCOME_FIELD_NAME,
self.TOTAL_POPULATION_FROM_AGE_TABLE: field_names.TOTAL_POP_FIELD,
},
errors="raise",
)
# Handle null values for various fields, which are `-666666666`.
for field in [
self.MEDIAN_INCOME_FIELD_NAME,
self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
]:
missing_value_count = sum(df[field] == -666666666)
logger.debug(
f"There are {missing_value_count} ({int(100*missing_value_count/df[field].count())}%) values of "
+ f"`{field}` being marked as null values."
)
df[field] = df[field].replace(to_replace=-666666666, value=None)
# Calculate percent unemployment.
# TODO: remove small-sample data that should be `None` instead of a high-variance fraction.
df[self.UNEMPLOYED_FIELD_NAME] = (
df[self.TOTAL_UNEMPLOYED_FIELD] / df[self.TOTAL_IN_LABOR_FORCE]
)
# Calculate linguistic isolation.
individual_limited_english_fields = [
"C16002_004E",
"C16002_007E",
"C16002_010E",
"C16002_013E",
]
df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME] = df[
individual_limited_english_fields
].sum(axis=1, skipna=True)
df[self.LINGUISTIC_ISOLATION_FIELD_NAME] = (
df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME].astype(float)
/ df["C16002_001E"]
)
# Calculate percent at different poverty thresholds
df[self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME] = (
df["C17002_002E"] + df["C17002_003E"]
) / df["C17002_001E"]
df[self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME] = (
df["C17002_002E"]
+ df["C17002_003E"]
+ df["C17002_004E"]
+ df["C17002_005E"]
) / df["C17002_001E"]
df[self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME] = (
df["C17002_002E"]
+ df["C17002_003E"]
+ df["C17002_004E"]
+ df["C17002_005E"]
+ df["C17002_006E"]
+ df["C17002_007E"]
) / df["C17002_001E"]
# Calculate educational attainment
educational_numerator_fields = [
self.EDUCATION_NO_SCHOOLING,
self.EDUCATION_NURSERY,
self.EDUCATION_KINDERGARTEN,
self.EDUCATION_FIRST,
self.EDUCATION_SECOND,
self.EDUCATION_THIRD,
self.EDUCATION_FOURTH,
self.EDUCATION_FIFTH,
self.EDUCATION_SIXTH,
self.EDUCATION_SEVENTH,
self.EDUCATION_EIGHTH,
self.EDUCATION_NINTH,
self.EDUCATION_TENTH,
self.EDUCATION_ELEVENTH,
self.EDUCATION_TWELFTH_NO_DIPLOMA,
]
df[self.HIGH_SCHOOL_ED_RAW_COUNT_FIELD] = df[
educational_numerator_fields
].sum(axis=1)
df[self.HIGH_SCHOOL_ED_FIELD] = (
df[self.HIGH_SCHOOL_ED_RAW_COUNT_FIELD]
/ df[self.EDUCATION_POPULATION_OVER_25]
)
# Calculate some demographic information.
df = df.rename(
columns={
"B02001_003E": self.BLACK_FIELD_NAME,
"B02001_004E": self.AMERICAN_INDIAN_FIELD_NAME,
"B02001_005E": self.ASIAN_FIELD_NAME,
"B02001_006E": self.HAWAIIAN_FIELD_NAME,
"B02001_008E": self.TWO_OR_MORE_RACES_FIELD_NAME,
"B03002_003E": self.NON_HISPANIC_WHITE_FIELD_NAME,
"B03003_003E": self.HISPANIC_FIELD_NAME,
"B02001_007E": self.OTHER_RACE_FIELD_NAME,
"B02001_001E": self.TOTAL_RACE_POPULATION_FIELD_NAME,
},
errors="raise",
)
for race_field_name in self.RE_OUTPUT_FIELDS:
df[field_names.PERCENT_PREFIX + race_field_name] = (
df[race_field_name] / df[self.TOTAL_RACE_POPULATION_FIELD_NAME]
)
# First value is the `age bucket`, and the second value is a list of all fields
# that will be summed in the calculations of the total population in that age
# bucket.
age_bucket_and_its_sum_columns = [
(
field_names.PERCENT_AGE_UNDER_10,
[
"B01001_003E", # Estimate!!Total:!!Male:!!Under 5 years
"B01001_004E", # Estimate!!Total:!!Male:!!5 to 9 years
"B01001_027E", # Estimate!!Total:!!Female:!!Under 5 years
"B01001_028E", # Estimate!!Total:!!Female:!!5 to 9 years
],
),
(
field_names.PERCENT_AGE_10_TO_64,
[
"B01001_005E", # Estimate!!Total:!!Male:!!10 to 14 years
"B01001_006E", # Estimate!!Total:!!Male:!!15 to 17 years
"B01001_007E", # Estimate!!Total:!!Male:!!18 and 19 years
"B01001_008E", # Estimate!!Total:!!Male:!!20 years
"B01001_009E", # Estimate!!Total:!!Male:!!21 years
"B01001_010E", # Estimate!!Total:!!Male:!!22 to 24 years
"B01001_011E", # Estimate!!Total:!!Male:!!25 to 29 years
"B01001_012E", # Estimate!!Total:!!Male:!!30 to 34 years
"B01001_013E", # Estimate!!Total:!!Male:!!35 to 39 years
"B01001_014E", # Estimate!!Total:!!Male:!!40 to 44 years
"B01001_015E", # Estimate!!Total:!!Male:!!45 to 49 years
"B01001_016E", # Estimate!!Total:!!Male:!!50 to 54 years
"B01001_017E", # Estimate!!Total:!!Male:!!55 to 59 years
"B01001_018E", # Estimate!!Total:!!Male:!!60 and 61 years
"B01001_019E", # Estimate!!Total:!!Male:!!62 to 64 years
"B01001_029E", # Estimate!!Total:!!Female:!!10 to 14 years
"B01001_030E", # Estimate!!Total:!!Female:!!15 to 17 years
"B01001_031E", # Estimate!!Total:!!Female:!!18 and 19 years
"B01001_032E", # Estimate!!Total:!!Female:!!20 years
"B01001_033E", # Estimate!!Total:!!Female:!!21 years
"B01001_034E", # Estimate!!Total:!!Female:!!22 to 24 years
"B01001_035E", # Estimate!!Total:!!Female:!!25 to 29 years
"B01001_036E", # Estimate!!Total:!!Female:!!30 to 34 years
"B01001_037E", # Estimate!!Total:!!Female:!!35 to 39 years
"B01001_038E", # Estimate!!Total:!!Female:!!40 to 44 years
"B01001_039E", # Estimate!!Total:!!Female:!!45 to 49 years
"B01001_040E", # Estimate!!Total:!!Female:!!50 to 54 years
"B01001_041E", # Estimate!!Total:!!Female:!!55 to 59 years
"B01001_042E", # Estimate!!Total:!!Female:!!60 and 61 years
"B01001_043E", # Estimate!!Total:!!Female:!!62 to 64 years
],
),
(
field_names.PERCENT_AGE_OVER_64,
[
"B01001_020E", # Estimate!!Total:!!Male:!!65 and 66 years
"B01001_021E", # Estimate!!Total:!!Male:!!67 to 69 years
"B01001_022E", # Estimate!!Total:!!Male:!!70 to 74 years
"B01001_023E", # Estimate!!Total:!!Male:!!75 to 79 years
"B01001_024E", # Estimate!!Total:!!Male:!!80 to 84 years
"B01001_025E", # Estimate!!Total:!!Male:!!85 years and over
"B01001_044E", # Estimate!!Total:!!Female:!!65 and 66 years
"B01001_045E", # Estimate!!Total:!!Female:!!67 to 69 years
"B01001_046E", # Estimate!!Total:!!Female:!!70 to 74 years
"B01001_047E", # Estimate!!Total:!!Female:!!75 to 79 years
"B01001_048E", # Estimate!!Total:!!Female:!!80 to 84 years
"B01001_049E", # Estimate!!Total:!!Female:!!85 years and over
],
),
]
# For each age bucket, sum the relevant columns and calculate the total
# percentage.
for age_bucket, sum_columns in age_bucket_and_its_sum_columns:
df[age_bucket] = (
df[sum_columns].sum(axis=1) / df[field_names.TOTAL_POP_FIELD]
)
# Calculate college attendance and adjust low income
df[self.COLLEGE_ATTENDANCE_FIELD] = (
df[self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC]
+ df[self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE]
+ df[self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC]
+ df[self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE]
) / df[self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED]
df[self.COLLEGE_NON_ATTENDANCE_FIELD] = (
1 - df[self.COLLEGE_ATTENDANCE_FIELD]
)
# we impute income for both income measures
## TODO: Convert to pydantic for clarity
logger.debug("Imputing income information")
ImputeVariables = namedtuple(
"ImputeVariables", ["raw_field_name", "imputed_field_name"]
)
df = calculate_income_measures(
impute_var_named_tup_list=[
ImputeVariables(
raw_field_name=self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
imputed_field_name=self.IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
),
ImputeVariables(
raw_field_name=self.COLLEGE_ATTENDANCE_FIELD,
imputed_field_name=self.IMPUTED_COLLEGE_ATTENDANCE_FIELD,
),
],
geo_df=df,
geoid_field=self.GEOID_TRACT_FIELD_NAME,
minimum_population_required_for_imputation=self.MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION,
)
logger.debug("Calculating with imputed values")
df[
self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME
] = (
df[self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME].fillna(
df[self.IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME]
)
- df[self.COLLEGE_ATTENDANCE_FIELD].fillna(
df[self.IMPUTED_COLLEGE_ATTENDANCE_FIELD]
)
# Use clip to ensure that the values are not negative if college attendance
# is very high
).clip(
lower=0
)
# All values should have a value at this point
assert (
# For tracts with >0 population
df[
df[field_names.TOTAL_POP_FIELD]
>= self.MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION
][
# Then the imputed field should have no nulls
self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME
]
.isna()
.sum()
== 0
), "Error: not all values were filled..."
logger.debug("Renaming columns...")
df = df.rename(
columns={
self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME: field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME: field_names.POVERTY_LESS_THAN_200_FPL_FIELD,
}
)
# We generate a boolean that is TRUE when there is an imputed income but not a baseline income, and FALSE otherwise.
# This allows us to see which tracts have an imputed income.
df[field_names.IMPUTED_INCOME_FLAG_FIELD_NAME] = (
df[field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD].notna()
& df[field_names.POVERTY_LESS_THAN_200_FPL_FIELD].isna()
)
# Save results to self.
self.output_df = df
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