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[ISS-723] Load Census Data for 4 Territories (#816)
* Adding census decennial data for island territories
This commit is contained in:
Vincent La
GitHub
parent
053dde0d40
commit
b0dbc90064
6 changed files with 318 additions and 10 deletions
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@ -74,6 +74,11 @@ DATASET_LIST = [
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"module_dir": "ejscreen_areas_of_concern",
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"class_name": "EJSCREENAreasOfConcernETL",
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},
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{
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"name": "census_decennial",
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"module_dir": "census_decennial",
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"class_name": "CensusDecennialETL",
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},
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]
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CENSUS_INFO = {
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"name": "census",
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@ -0,0 +1,10 @@
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# Decennial Census of Island Areas
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Adding a new ETL folder for Decennial Census of Island Areas since the Island areas aren't included in ACS.
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There's the American Samoa Summary File, the Guam Summary File, the Commonwealth of the Northern Mariana Islands Summary File, and the US Virgin Islands Summary File:
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1. https://api.census.gov/data/2010/dec/as.html
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1. https://api.census.gov/data/2010/dec/gu.html
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1. https://api.census.gov/data/2010/dec/mp.html
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1. https://api.census.gov/data/2010/dec/vi.html
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@ -0,0 +1,250 @@
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import json
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import requests
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import numpy as np
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import pandas as pd
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from data_pipeline.etl.base import ExtractTransformLoad
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from data_pipeline.utils import get_module_logger
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pd.options.mode.chained_assignment = "raise"
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logger = get_module_logger(__name__)
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class CensusDecennialETL(ExtractTransformLoad):
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def __init__(self):
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self.DECENNIAL_YEAR = 2010
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self.OUTPUT_PATH = (
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self.DATA_PATH / "dataset" / f"census_dec_{self.DECENNIAL_YEAR}"
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)
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# Income Fields
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# AS, GU, and MP all share the same variable names, but VI is different
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# https://api.census.gov/data/2010/dec/as.html
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# https://api.census.gov/data/2010/dec/gu/variables.html
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# https://api.census.gov/data/2010/dec/mp/variables.html
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# https://api.census.gov/data/2010/dec/vi/variables.html
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self.MEDIAN_INCOME_FIELD = "PBG049001"
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self.MEDIAN_INCOME_VI_FIELD = "PBG047001"
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self.MEDIAN_INCOME_FIELD_NAME = (
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"MEDIAN HOUSEHOLD INCOME IN 2009 (DOLLARS)"
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)
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self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD = "PBG083001"
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self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_VI_FIELD = (
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"PBG077001"
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)
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self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME = (
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"TOTAL; RATIO OF INCOME TO POVERTY LEVEL IN 2009"
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)
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self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD = "PBG083010"
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self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD = "PBG077010"
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self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME = (
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"Total!!2.00 and over; RATIO OF INCOME TO POVERTY LEVEL IN 2009"
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)
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self.PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL_FIELD_NAME = (
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"PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL"
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)
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# High School Education Fields
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self.TOTAL_POPULATION_FIELD = "PBG026001"
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self.TOTAL_POPULATION_VI_FIELD = "PCT032001"
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self.TOTAL_POPULATION_FIELD_NAME = "Total; SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER"
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self.MALE_HIGH_SCHOOL_ED_FIELD = "PBG026005"
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self.MALE_HIGH_SCHOOL_ED_VI_FIELD = "PCT032011"
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self.MALE_HIGH_SCHOOL_ED_FIELD_NAME = "Total!!Male!!High school graduate, GED, or alternative; "\
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"SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER"
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self.FEMALE_HIGH_SCHOOL_ED_FIELD = "PBG026012"
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self.FEMALE_HIGH_SCHOOL_ED_VI_FIELD = "PCT032028"
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self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME = "Total!!Female!!High school graduate, GED, or alternative; "\
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"SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER"
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self.PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME = (
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"PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME"
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)
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var_list = [
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self.MEDIAN_INCOME_FIELD,
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self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD,
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self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD,
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self.TOTAL_POPULATION_FIELD,
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self.MALE_HIGH_SCHOOL_ED_FIELD,
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self.FEMALE_HIGH_SCHOOL_ED_FIELD,
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]
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var_list = ",".join(var_list)
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var_list_vi = [
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self.MEDIAN_INCOME_VI_FIELD,
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self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_VI_FIELD,
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self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD,
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self.TOTAL_POPULATION_VI_FIELD,
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self.MALE_HIGH_SCHOOL_ED_VI_FIELD,
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self.FEMALE_HIGH_SCHOOL_ED_VI_FIELD,
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]
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var_list_vi = ",".join(var_list_vi)
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self.FIELD_NAME_XWALK = {
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self.MEDIAN_INCOME_FIELD: self.MEDIAN_INCOME_FIELD_NAME,
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self.MEDIAN_INCOME_VI_FIELD: self.MEDIAN_INCOME_FIELD_NAME,
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self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD: self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME,
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self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_VI_FIELD: self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME,
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self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD: self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME,
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self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD: self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME,
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self.TOTAL_POPULATION_FIELD: self.TOTAL_POPULATION_FIELD_NAME,
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self.TOTAL_POPULATION_VI_FIELD: self.TOTAL_POPULATION_FIELD_NAME,
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self.MALE_HIGH_SCHOOL_ED_FIELD: self.MALE_HIGH_SCHOOL_ED_FIELD_NAME,
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self.MALE_HIGH_SCHOOL_ED_VI_FIELD: self.MALE_HIGH_SCHOOL_ED_FIELD_NAME,
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self.FEMALE_HIGH_SCHOOL_ED_FIELD: self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME,
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self.FEMALE_HIGH_SCHOOL_ED_VI_FIELD: self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME,
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}
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# To do: Ask Census Slack Group about whether you need to hardcode the county fips
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# https://uscensusbureau.slack.com/archives/C6DGLC05B/p1635218909012600
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self.ISLAND_TERRITORIES = [
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{
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"state_abbreviation": "as",
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"fips": "60",
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"county_fips": ["010", "020", "030", "040", "050"],
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"var_list": var_list,
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},
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{
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"state_abbreviation": "gu",
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"fips": "66",
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"county_fips": ["010"],
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"var_list": var_list,
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},
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{
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"state_abbreviation": "mp",
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"fips": "69",
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"county_fips": ["085", "100", "110", "120"],
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"var_list": var_list,
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},
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{
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"state_abbreviation": "vi",
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"fips": "78",
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"county_fips": ["010", "020", "030"],
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"var_list": var_list_vi,
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},
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]
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self.API_URL = (
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"https://api.census.gov/data/{}/dec/{}?get=NAME,{}"
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+ "&for=block%20group:*&in=state:{}%20county:{}"
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)
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self.df: pd.DataFrame
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self.df_vi: pd.DataFrame
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self.df_all: pd.DataFrame
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def extract(self) -> None:
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dfs = []
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dfs_vi = []
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for island in self.ISLAND_TERRITORIES:
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logger.info(
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f"Downloading data for state/territory {island['state_abbreviation']}"
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)
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for county in island["county_fips"]:
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download = requests.get(
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self.API_URL.format(
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self.DECENNIAL_YEAR,
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island["state_abbreviation"],
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island["var_list"],
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island["fips"],
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county,
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)
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)
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df = json.loads(download.content)
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# First row is the header
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df = pd.DataFrame(df[1:], columns=df[0])
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for col in island["var_list"].split(","):
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# Converting appropriate variables to numeric.
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# Also replacing 0s with NaNs
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df[col] = pd.to_numeric(df[col])
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# TO-DO: CHECK THIS. I think it makes sense to replace 0 with NaN
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# because for our variables of interest (e.g. Median Household Income,
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# it doesn't make sense for that to be 0.)
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# Likely, it's actually missing but can't find a cite for that in the docs
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df[col] = df[col].replace(0, np.nan)
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if island["state_abbreviation"] == "vi":
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dfs_vi.append(df)
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else:
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dfs.append(df)
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self.df = pd.concat(dfs)
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self.df_vi = pd.concat(dfs_vi)
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def transform(self) -> None:
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logger.info("Starting Census Decennial Transform")
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# Rename All Fields
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self.df.rename(columns=self.FIELD_NAME_XWALK, inplace=True)
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self.df_vi.rename(columns=self.FIELD_NAME_XWALK, inplace=True)
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# Combine the dfs after renaming
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self.df_all = pd.concat([self.df, self.df_vi])
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# Percentage of households below 200% which is
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# [PBG083001 (total) - PBG083010 (num households over 200%)] / PBG083001 (total)
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self.df_all[
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self.PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL_FIELD_NAME
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] = (
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self.df_all[
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self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME
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]
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- self.df_all[self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME]
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) / self.df_all[
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self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME
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]
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# Percentage High School Achievement is
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# Percentage = (Male + Female) / (Total)
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self.df_all[self.PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME] = (
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self.df_all[self.MALE_HIGH_SCHOOL_ED_FIELD_NAME]
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+ self.df_all[self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME]
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) / self.df_all[self.TOTAL_POPULATION_FIELD_NAME]
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# Creating Geo ID (Census Block Group) Field Name
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self.df_all[self.GEOID_FIELD_NAME] = (
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self.df_all["state"]
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+ self.df_all["county"]
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+ self.df_all["tract"]
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+ self.df_all["block group"]
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)
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# Reporting Missing Values
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for col in self.df_all.columns:
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missing_value_count = self.df_all[col].isnull().sum()
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logger.info(
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f"There are {missing_value_count} missing values in the field {col} out of a total of {self.df_all.shape[0]} rows"
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)
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def load(self) -> None:
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logger.info("Saving Census Decennial Data")
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# mkdir census
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self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
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columns_to_include = [
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self.GEOID_FIELD_NAME,
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self.MEDIAN_INCOME_FIELD_NAME,
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self.PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL_FIELD_NAME,
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self.PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME,
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]
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self.df_all[columns_to_include].to_csv(
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path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
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)
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def validate(self) -> None:
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logger.info("Validating Census Decennial Data")
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pass
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61
data/data-pipeline/poetry.lock
generated
61
data/data-pipeline/poetry.lock
generated
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@ -40,7 +40,7 @@ tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
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name = "astroid"
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version = "2.8.3"
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description = "An abstract syntax tree for Python with inference support."
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category = "dev"
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category = "main"
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optional = false
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python-versions = "~=3.6"
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@ -426,6 +426,19 @@ category = "dev"
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optional = false
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python-versions = "*"
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[[package]]
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name = "ipdb"
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version = "0.13.9"
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description = "IPython-enabled pdb"
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category = "main"
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optional = false
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python-versions = ">=2.7"
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[package.dependencies]
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decorator = {version = "*", markers = "python_version > \"3.6\""}
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ipython = {version = ">=7.17.0", markers = "python_version > \"3.6\""}
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toml = {version = ">=0.10.2", markers = "python_version > \"3.6\""}
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[[package]]
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name = "ipykernel"
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version = "6.4.2"
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@ -513,7 +526,7 @@ test = ["pytest (>=3.6.0)", "pytest-cov", "mock"]
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name = "isort"
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version = "5.9.3"
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description = "A Python utility / library to sort Python imports."
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=3.6.1,<4.0"
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@ -759,7 +772,7 @@ python-versions = ">=3.7"
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name = "lazy-object-proxy"
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version = "1.6.0"
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description = "A fast and thorough lazy object proxy."
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
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@ -829,7 +842,7 @@ traitlets = "*"
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name = "mccabe"
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version = "0.6.1"
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||||
description = "McCabe checker, plugin for flake8"
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||||
category = "dev"
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category = "main"
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optional = false
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python-versions = "*"
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@ -1096,7 +1109,7 @@ python-versions = ">=3.6"
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name = "platformdirs"
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version = "2.4.0"
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description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
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category = "dev"
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category = "main"
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optional = false
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python-versions = ">=3.6"
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@ -1193,7 +1206,7 @@ python-versions = ">=3.5"
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name = "pylint"
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version = "2.11.1"
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||||
description = "python code static checker"
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||||
category = "dev"
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||||
category = "main"
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||||
optional = false
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||||
python-versions = "~=3.6"
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@ -1475,7 +1488,7 @@ test = ["pytest", "pathlib2"]
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name = "toml"
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version = "0.10.2"
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||||
description = "Python Library for Tom's Obvious, Minimal Language"
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category = "dev"
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||||
category = "main"
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optional = false
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||||
python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
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@ -1549,7 +1562,7 @@ test = ["pytest"]
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name = "typed-ast"
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||||
version = "1.4.3"
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||||
description = "a fork of Python 2 and 3 ast modules with type comment support"
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||||
category = "dev"
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||||
category = "main"
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||||
optional = false
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||||
python-versions = "*"
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||||
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@ -1644,7 +1657,7 @@ notebook = ">=4.4.1"
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name = "wrapt"
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||||
version = "1.13.2"
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||||
description = "Module for decorators, wrappers and monkey patching."
|
||||
category = "dev"
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||||
category = "main"
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||||
optional = false
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||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
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||||
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||||
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@ -1671,7 +1684,7 @@ testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytes
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[metadata]
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||||
lock-version = "1.1"
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||||
python-versions = "^3.7.1"
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||||
content-hash = "11cb464e2b6d817b910efe796b87c479e7e4954c7d618bbcd715250b80817a56"
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||||
content-hash = "1823efb9fae610a00eb714ec61fd165ec5cda3d38fa83c12b00e9d38f4358fa3"
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[metadata.files]
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appnope = [
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|
|
@ -1896,6 +1909,9 @@ iniconfig = [
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|||
{file = "iniconfig-1.1.1-py2.py3-none-any.whl", hash = "sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3"},
|
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@ -2104,6 +2120,9 @@ markupsafe = [
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@ -2115,6 +2134,9 @@ markupsafe = [
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@ -2126,6 +2148,9 @@ markupsafe = [
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@ -2138,6 +2163,9 @@ markupsafe = [
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@ -2150,6 +2178,9 @@ markupsafe = [
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@ -2539,24 +2570,32 @@ pyzmq = [
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@ -2564,6 +2603,8 @@ pyzmq = [
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|
||||
{file = "pyzmq-22.3.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d1b5d457acbadcf8b27561deeaa386b0217f47626b29672fa7bd31deb6e91e1b"},
|
||||
{file = "pyzmq-22.3.0-cp39-cp39-win32.whl", hash = "sha256:e6a02cf7271ee94674a44f4e62aa061d2d049001c844657740e156596298b70b"},
|
||||
{file = "pyzmq-22.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:d3dcb5548ead4f1123851a5ced467791f6986d68c656bc63bfff1bf9e36671e2"},
|
||||
{file = "pyzmq-22.3.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3a4c9886d61d386b2b493377d980f502186cd71d501fffdba52bd2a0880cef4f"},
|
||||
|
|
|
|||
|
|
@ -22,6 +22,8 @@ tqdm = "4.62.0"
|
|||
types-requests = "^2.25.0"
|
||||
us = "^2.0.2"
|
||||
xlsxwriter = "^2.0.0"
|
||||
ipdb = "^0.13.9"
|
||||
pylint = "^2.11.1"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
black = {version = "^21.6b0", allow-prereleases = true}
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue