force print with B code not C code

airflow/dags/acs_condition.py

@@ -19,7 +19,6 @@
 
 # CACHE ACS SOURCE INTO TMP JSON IN BUCKETS
 
-
 # acs_condition_gcs_payload_2012 = util.generate_gcs_payload(_ACS_WORKFLOW_ID, year='2012')
 # acs_condition_gcs_operator_2012 = util.create_gcs_ingest_operator(
 #     'acs_condition_to_gcs_2012', acs_condition_gcs_payload_2012, data_ingestion_dag
@@ -132,7 +131,7 @@
 #     "acs_condition_to_bq_2022", acs_condition_bq_payload_2022, data_ingestion_dag
 # )
 
-# EXPORT FROM BQ TO BUCKETS
+# # EXPORT FROM BQ TO BUCKETS
 # acs_condition_exporter_payload_race = {
 #     'dataset_name': _ACS_DATASET_NAME,
 #     'demographic': "by_race",
@@ -172,7 +171,7 @@
     # >> acs_condition_gcs_operator_2019
     # >> acs_condition_gcs_operator_2020
     # >> acs_condition_gcs_operator_2021
-    # >>
+    # >> acs_condition_gcs_operator_2022
     acs_condition_gcs_operator_2022
     # PROCESSING STEP
     # >> acs_condition_bq_operator_2012

python/datasources/acs_condition.py

@@ -173,14 +173,14 @@
 }
 
 EDUCATION_BY_RACE_SEX_AGE_GROUP_PREFIXES = {
-    'C15002A': Race.WHITE.value,
-    'C15002B': Race.BLACK.value,
-    'C15002C': Race.AIAN.value,
-    'C15002D': Race.ASIAN.value,
-    'C15002E': Race.NHPI.value,
-    'C15002F': Race.OTHER_STANDARD.value,
-    'C15002G': Race.MULTI.value,
-    'C15002I': Race.HISP.value,
+    'B15002A': Race.WHITE.value,
+    'B15002B': Race.BLACK.value,
+    'B15002C': Race.AIAN.value,
+    'B15002D': Race.ASIAN.value,
+    'B15002E': Race.NHPI.value,
+    'B15002F': Race.OTHER_STANDARD.value,
+    'B15002G': Race.MULTI.value,
+    'B15002I': Race.HISP.value,
 }
 
 
@@ -202,6 +202,7 @@ class AcsItem:
     prefix_map: A dictionary mapping the acs prefix to its corresponding race.
     concept_map: A dictionary mapping to its corresponding census concept.
     sex_age_prefix: The acs prefix representing the sex and age data.
+    sex_age_concept: The census concept representing the sex and age data.
     has_condition_key_list: List[str] Keys in acs metadata representing the tracker's "yes"
                         state for this condition. For example, it would be the
                         key represting that someone has poverty, or does not
@@ -249,7 +250,7 @@ def __init__(
 POVERTY_BY_SEX_AGE_CONCEPT_CAPS = 'POVERTY STATUS IN THE PAST 12 MONTHS BY SEX BY AGE'
 POVERTY_BY_SEX_AGE_CONCEPT_TITLE = 'Poverty Status in the Past 12 Months by Sex by Age'
 
-EDUCATION_BY_SEX_AGE_GROUPS_PREFIX = 'C15002'
+EDUCATION_BY_SEX_AGE_GROUPS_PREFIX = 'B15002'
 EDUCATION_BY_SEX_AGE_CONCEPT_CAPS = 'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER'
 EDUCATION_BY_SEX_AGE_CONCEPT_TITLE = 'Sex by Educational Attainment for the Population 25 Years and Over'
 
@@ -268,12 +269,33 @@ def __init__(
 NOT_IN_POVERTY_KEY = 'Income in the past 12 months at or above poverty level'
 POVERTY_KEY = 'Income in the past 12 months below poverty level'
 
+# HAS_HIGH_SCHOOL_KEY_LIST = [
+#     'High school graduate (includes equivalency)',
+#     "Some college or associate's degree",
+#     "Bachelor's degree or higher",
+# ]
+# NOT_HIGH_SCHOOL_KEY_LIST = ['Less than high school diploma']
+
 HAS_HIGH_SCHOOL_KEY_LIST = [
-    'High school graduate (includes equivalency)',
-    "Some college or associate's degree",
-    "Bachelor's degree or higher",
+    "Doctorate degree",
+    "Master's degree",
+    "Professional school degree",
+    "Bachelor's degree",
+    "Associate's degree",
+    "Some college, 1 or more years, no degree",
+    "High school graduate (includes equivalency)",
+]
+
+NOT_HIGH_SCHOOL_KEY_LIST = [
+    "12th grade, no diploma",
+    "11th grade",
+    "10th grade",
+    "9th grade",
+    "7th and 8th grade",
+    "5th and 6th grade",
+    "Nursery to 4th grade",
+    "No schooling completed",
 ]
-NOT_HIGH_SCHOOL_KEY_LIST = ['Less than high school diploma']
 
 HEALTH_INSURANCE_MEASURE = 'health_insurance'
 POVERTY_MEASURE = 'poverty'
@@ -647,13 +669,15 @@ def generate_df_for_concept(self, measure, acs_item, df, demo, geo, concept, var
         elif measure == EDUCATION_MEASURE:
             group_cols = [std_col.SEX_COL, tmp_amount_key]
 
+        print("concept", concept)
+
         group_vars = get_vars_for_group(concept, var_map, len(group_cols))
 
-        # print("---group_cols")
-        # print(group_cols)
+        print("---group_cols")
+        print(group_cols)
 
-        # print("---group_vars")
-        # print(group_vars)
+        print("---group_vars")
+        print(group_vars)
 
         # Creates a df with different rows for the amount of people
         # in a demographic group with and without the condition

python/ingestion/census.py

@@ -104,9 +104,18 @@ def parse_acs_metadata(acs_metadata, groups):
     groups: The list of group ids to include."""
     output_vars = {}
     for variable_id, metadata in acs_metadata["variables"].items():
+
         group = metadata.get("group")
+
+        # print("group", group)
+
         if group in groups and metadata["label"].startswith("Estimate!!Total"):
+            print("variable_id", variable_id)
+            print("metadata", metadata)
             output_vars[variable_id] = metadata
+
+    # print("output_vars", output_vars)
+
     return output_vars
 
 
@@ -136,13 +145,18 @@ def get_vars_for_group(group_concept, var_map, num_breakdowns):
         # print("group:", group)
         # print("metadata:", metadata)
         # print("<><><>")
+        # if group_concept == "Sex by Educational Attainment for the Population 25 Years and Over":
+        # print("metadata.get('concept')", metadata.get("concept"))
+        # print("group_concept", group_concept)
+        # print("+++")
 
         if metadata.get("concept") == group_concept:
-            # print("group", group)
-            # print("metadata.get('concept')", metadata.get("concept"))
+
+            print("MATCH" + group_concept)
 
             # TODO switch to use explicit prefix to handle median, etc
             parts = metadata["label"].split("!!")
+
             # If length is greater than (2 + num_breakdowns), it means it's a
             # sub-category, which we don't need to include. If the length is
             # less than (2 + num_breakdowns), it means it's a combination of