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config.py
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config.py
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import pandas as pd
from typing import NamedTuple, Callable, List
import operator
DB_PATH = 'OE.db'
DATA_FOLDER = 'data'
FULL_FOLDER = 'full'
NAT_FOLDER = 'nat'
STATE_FOLDER = 'state'
METRO_FOLDER = 'metro'
FINAL_OCCS = pd.read_csv("OE/occupation_codes_simple_2018.txt", sep="\t")
DATA_CODE_AGG_FUNCS = {'01':'sum','03':'mean','04':'mean','13':'mean'}
class Transformation(NamedTuple):
from_code: str
to_code: str
class TransformationGroup(NamedTuple):
year: int
operation: Callable
transformations: List[Transformation]
class Constants:
STATE_CODES_PATH = 'SM/state_codes.txt'
STATE_CODE_TABLE = 'state_code'
AREA_CODES_PATH = 'SM/area_codes.txt'
AREA_CODES_TABLE = 'area_code'
INDUSTRY_CODES_PATH = 'SM/industry_codes.txt'
INDUSTRY_CODES_TABLE = 'industry_code'
DATA_TYPES_PATH = 'SM/data_type_codes.txt'
DATA_TYPES_TABLE = 'data_type'
STATE_ABBREV_PATH = 'SM/state_abbrev.txt'
STATE_ABBREV_TABLE = 'state_abbrev'
STATE_AREA_CODE_TABLE = 'state_area_code'
SERIES_CODE_TABLE = 'series_code'
STATEWIDE_CODE = '00000'
SERIES_PREFIX = 'SMU'
DATA_TYPES = ['01', '03', '04', '13']
VALUE_TABLE = 'value'
class OE_Constants:
STATE_CODE_PATH = 'OE/state_codes.txt'
STATE_CODE_TABLE = 'state_code'
AREA_CODE_PATH = 'OE/area_codes.txt'
AREA_CODE_TABLE = 'area_code'
INDUSTRY_CODE_PATH = 'OE/industry_codes_simple.txt'
INDUSTRY_CODE_TABLE = 'industry_code'
DATA_TYPE_PATH = 'OE/data_types.txt'
DATA_TYPE_TABLE = 'data_type'
STATE_ABBREV_PATH = 'OE/state_abbrev.txt'
STATE_ABBREV_TABLE = 'state_abbrev'
OCCUPATION_CODE_TABLE='occupation_code'
OCCUPATION_CODE_PATH ='OE/occupation_codes_simple_2018.txt'
STATE_AREA_CODE_TABLE = 'state_area_code'
SERIES_CODE_TABLE = 'series_code'
STATEWIDE_CODE = '00000'
SERIES_PREFIX = 'OEU'
DATA_TYPES = ['01', '03', '04', '13']
VALUE_TABLE = 'value'
INDUSTRY_CODES = ['000000']
NATIONAL_AREA_CODE = 'N0000000'
ALL_INDUSTRY_CODE = '000000'
class XLS_Constants(object):
def __init__(self):
self.transformations = []
self.data_u_want = ['11','12','13','14','15','01','03','04']
self.column_heads = {
'data_codes':{
'01':'TOT_EMP',
'02':'EMP_PRSE',
'03':'H_MEAN',
'04':'A_MEAN',
'05':'MEAN_PRSE',
'06':'H_PCT10',
'07':'H_PCT25',
'08':'H_MEDIAN',
'09':'H_PCT75',
'10':'H_PCT90',
'11':'A_PCT10',
'12':'A_PCT25',
'13':'A_MEDIAN',
'14':'A_PCT75',
'15':'A_PCT90',
},
'other_codes':{
'area_type':'AREA_TYPE',
'area':'AREA',
'none':'AREA_TITLE',
'industry_code':'NAICS',
'occupation_code':'OCC_CODE',
}
}
self.start_row={
'2000':39,
'1999':40,
'1998':38,
'1997':38,
}
self.add_transformations()
def add_transformations(self):
self.transformation_groups = []
self.degrouping_transformation_groups = []
# 2010 transformations
transformations_2010 = self.get_transformations_2010()
transformation_group = TransformationGroup(transformations=transformations_2010, year=2010, operation=operator.le)
self.transformation_groups.append(transformation_group)
# 2018 transformations
transformations_2018 = self.get_transformations_2018()
transformation_group = TransformationGroup(transformations=transformations_2018, year=2018, operation=operator.le)
self.transformation_groups.append(transformation_group)
# 2010 transformation degrouping
group_2010_transformations = self.get_group_2010_transformations()
self.degrouping_transformation_groups.append(TransformationGroup(transformations=group_2010_transformations, year=2010, operation=operator.eq))
self.degrouping_transformation_groups.append(TransformationGroup(transformations=group_2010_transformations, year=2011, operation=operator.eq))
# 2018 transformations
transformations_2018 = self.get_group_transformations_2018()
self.degrouping_transformation_groups.append(TransformationGroup(transformations=transformations_2018, year=2019, operation=operator.eq))
self.degrouping_transformation_groups.append(TransformationGroup(transformations=transformations_2018, year=2018, operation=operator.eq))
self.degrouping_transformation_groups.append(TransformationGroup(transformations=transformations_2018, year=2017, operation=operator.eq))
# 2019 transformations
transformations_2019 = self.get_group_transformations_2019()
self.degrouping_transformation_groups.append(TransformationGroup(transformations=transformations_2019, year=2019, operation=operator.eq))
def get_transformations_2010(self):
crosswalk_2010_df = pd.read_excel('soc_2000_to_2010_crosswalk.xls',skiprows=6)
transformations = [
Transformation(from_code=row["2000 SOC code"], to_code=row["2010 SOC code"])
for _,row in crosswalk_2010_df.iterrows()
]
##
transformations.append(Transformation(from_code='29-1111', to_code='29-1141'))
transformations.append(Transformation(from_code='13-1079', to_code='13-1071'))
transformations.append(Transformation(from_code='13-1078', to_code='13-1071'))
transformations.append(Transformation(from_code='33-9032', to_code='33-9032'))
transformations.append(Transformation(from_code='27-3031', to_code='27-3031'))
transformations.append(Transformation(from_code='47-2111', to_code='47-2111'))
transformations.append(Transformation(from_code='13-1121', to_code='13-1121'))
transformations.append(Transformation(from_code='13-1071', to_code='13-1071'))
transformations.append(Transformation(from_code='21-1091', to_code='21-1091'))
transformations.append(Transformation(from_code='21-1099', to_code='21-1099'))
transformations.append(Transformation(from_code='15-1081', to_code='15-1152'))
transformations.append(Transformation(from_code='29-2099', to_code='29-2099'))
transformations.append(Transformation(from_code='13-1199', to_code='13-1199'))
transformations.append(Transformation(from_code='49-9099', to_code='49-9099'))
transformations.append(Transformation(from_code='31-9099', to_code='31-9099'))
transformations.append(Transformation(from_code='33-9099', to_code='33-9099'))
transformations.append(Transformation(from_code='47-4099', to_code='47-4099'))
transformations.append(Transformation(from_code='13-1041', to_code='13-1041'))
transformations.append(Transformation(from_code='47-2181', to_code='47-2181'))
transformations.append(Transformation(from_code='41-9099', to_code='41-9099'))
transformations.append(Transformation(from_code='25-3099', to_code='25-3099'))
transformations.append(Transformation(from_code='31-1012', to_code='31-1015'))
transformations.append(Transformation(from_code='51-9199', to_code='51-9199'))
transformations.append(Transformation(from_code='15-1051', to_code='15-1051'))
transformations.append(Transformation(from_code='29-9099', to_code='29-9099'))
transformations.append(Transformation(from_code='29-2034', to_code='29-2034'))
transformations.append(Transformation(from_code='51-5021', to_code='51-5113'))
transformations.append(Transformation(from_code='29-1129', to_code='29-1129'))
transformations.append(Transformation(from_code='23-2092', to_code='23-2011'))
transformations.append(Transformation(from_code='43-9199', to_code='43-9199'))
transformations.append(Transformation(from_code='11-9061', to_code='11-9061'))
transformations.append(Transformation(from_code='49-9021', to_code='49-9021'))
transformations.append(Transformation(from_code='25-2041', to_code='25-2052'))
return transformations
# def get_reverse_transformations_2018
def get_transformations_2018(self):
crosswalk_df = pd.read_excel('soc_2010_to_2018_crosswalk.xlsx',skiprows=8)
transformations = [
Transformation(from_code=row["2010 SOC Code"], to_code=row["2018 SOC Code"])
for _,row in crosswalk_df.iterrows()
]
transformations.append(Transformation(from_code='29-1069', to_code='29-1229'))
transformations.append(Transformation(from_code='15-1199', to_code='15-1299'))
transformations.append(Transformation(from_code='11-9199', to_code='11-9199'))
transformations.append(Transformation(from_code='39-1021', to_code='39-1022'))
transformations.append(Transformation(from_code='51-9199', to_code='51-9199'))
transformations.append(Transformation(from_code='53-1031', to_code='53-1043'))
transformations.append(Transformation(from_code='29-1067', to_code='29-1242'))
transformations.append(Transformation(from_code='25-9041', to_code='25-9042'))
transformations.append(Transformation(from_code='25-3099', to_code='25-3099'))
transformations.append(Transformation(from_code='29-9099', to_code='29-9099'))
return transformations
def get_group_transformations_2018(self):
transformations = [
Transformation(from_code='13-1020', to_code='13-1023'),
Transformation(from_code='47-4090', to_code='47-4091'),
Transformation(from_code='29-2010', to_code='29-2011'),
Transformation(from_code='39-7010', to_code='39-7011'),
Transformation(from_code='15-2090', to_code='15-2099'),
Transformation(from_code='39-1010', to_code='39-1011'),
]
return transformations
def get_group_transformations_2019(self):
transformations = [
Transformation(from_code='51-2090', to_code='51-2099'),
Transformation(from_code='31-1120', to_code='31-1122'),
Transformation(from_code='11-2030', to_code='11-2031'),
Transformation(from_code='29-2040', to_code='29-2042'),
Transformation(from_code='19-4010', to_code='19-4012'),
Transformation(from_code='27-2090', to_code='27-2099'),
Transformation(from_code='13-2020', to_code='13-2022'),
Transformation(from_code='33-1090', to_code='33-1091'),
Transformation(from_code='11-3010', to_code='11-3011'),
]
return transformations
def get_group_2010_transformations(self):
transformations = [
Transformation(from_code='15-1150', to_code='15-1151'),
]
return transformations
CONSTANTS = XLS_Constants()
def sum_groups_df(df,data_type_code):
# Aggregate by group. We calculate group totals from constituents, results in more consistent group totals, as totals change per year.
# WE ALSO SUBSET to only the columns with aggregation functions: DATA_CODE_AGG_FUNCS
group_values = {}
INDEX_COLUMNS = ["SERIES_CODE"]
data_col = CONSTANTS.column_heads["data_codes"][data_type_code]
agg_func = DATA_CODE_AGG_FUNCS[data_type_code]
for num_zeros in (1,2,3,4):
print(num_zeros)
df_copy = df.copy()
df_copy = df_copy[~df_copy["OCC_CODE"].str.endswith("0")].reset_index(drop=True)
# df_copy["OCC_CODE"] = df_copy["OCC_CODE"].str.slice(stop=7-num_zeros) + "0"*num_zeros
df_copy["SERIES_CODE"] = (
df_copy["SERIES_CODE"].str.slice(stop=25-(2+num_zeros)) +
"0"*num_zeros +
df_copy["SERIES_CODE"].str.slice(start=23)
)
agg_df = df_copy[INDEX_COLUMNS + [data_col]].groupby(INDEX_COLUMNS).agg(agg_func).reset_index()
agg_dict = agg_df.set_index(["SERIES_CODE"])[data_col].to_dict()
group_values = {**group_values, **agg_dict}
df[data_col] = [group_values.get(series_code, value)
for series_code, value
in zip(df["SERIES_CODE"], df[data_col]) ]
return df
def apply_transformation_group(
transformation_group: TransformationGroup,
df: pd.DataFrame) -> pd.DataFrame:
transformation_dict = {
trans.from_code: trans.to_code for trans in transformation_group.transformations
}
df["OCC_CODE"] = df["OCC_CODE"].apply(lambda x: transformation_dict.get(x, x))
return df
def apply_occ_transformations(df: pd.DataFrame, year: str) -> pd.DataFrame:
year = int(year)
for transformation_group in CONSTANTS.transformation_groups:
year_condition = transformation_group.operation(year, transformation_group.year)
if year_condition:
df = apply_transformation_group(transformation_group, df)
return df
def apply_degrouping_transformations(df: pd.DataFrame, year: str) -> pd.DataFrame:
year = int(year)
for transformation_group in CONSTANTS.degrouping_transformation_groups:
year_condition = transformation_group.operation(year, transformation_group.year)
if year_condition:
df = apply_transformation_group(transformation_group, df)
return df