Lebovits/refactor etl pipeline

data/src/main.py

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+import sys
+
+from new_etl.data_utils.access_process import access_process
+from new_etl.data_utils.contig_neighbors import contig_neighbors
+from new_etl.data_utils.dev_probability import dev_probability
+from new_etl.data_utils.negligent_devs import negligent_devs
+from new_etl.data_utils.opa_properties import opa_properties
+from new_etl.data_utils.priority_level import priority_level
+from new_etl.data_utils.vacant_properties import vacant_properties
+from new_etl.data_utils.pwd_parcels import pwd_parcels
+from new_etl.data_utils.city_owned_properties import city_owned_properties
+from new_etl.data_utils.phs_properties import phs_properties
+from new_etl.data_utils.li_violations import li_violations
+from new_etl.data_utils.li_complaints import li_complaints
+from new_etl.data_utils.rco_geoms import rco_geoms
+from new_etl.data_utils.council_dists import council_dists
+from new_etl.data_utils.tree_canopy import tree_canopy
+from new_etl.data_utils.nbhoods import nbhoods
+from new_etl.data_utils.gun_crimes import gun_crimes
+from new_etl.data_utils.drug_crimes import drug_crimes
+from new_etl.data_utils.delinquencies import delinquencies
+from new_etl.data_utils.unsafe_buildings import unsafe_buildings
+from new_etl.data_utils.imm_dang_buildings import imm_dang_buildings
+from new_etl.data_utils.tactical_urbanism import tactical_urbanism
+from new_etl.data_utils.conservatorship import conservatorship
+from new_etl.data_utils.owner_type import owner_type
+from new_etl.data_utils.community_gardens import community_gardens
+from new_etl.data_utils.park_priority import park_priority
+from new_etl.data_utils.ppr_properties import ppr_properties
+
+import pandas as pd
+
+
+# Ensure the directory containing awkde is in the Python path
+awkde_path = "/usr/src/app"
+if awkde_path not in sys.path:
+    sys.path.append(awkde_path)
+
+services = [
+    # vacant designation
+    vacant_properties,  # needs to run early so that other utils can make use of the `vacant` designation
+    # geometries/areas
+    pwd_parcels,
+    council_dists,
+    nbhoods,
+    rco_geoms,
+    # ownership
+    city_owned_properties,
+    phs_properties,
+    community_gardens,
+    ppr_properties,
+    owner_type,
+    # quality of life
+    li_violations,
+    li_complaints,
+    tree_canopy,
+    gun_crimes,
+    drug_crimes,
+    delinquencies,
+    unsafe_buildings,
+    imm_dang_buildings,
+    # development
+    contig_neighbors,
+    dev_probability,
+    negligent_devs,
+    # access/interventions
+    tactical_urbanism,
+    conservatorship,
+    park_priority,
+]
+
+dataset = opa_properties()
+
+print("Initial Dataset:")
+print("Shape:", dataset.gdf.shape)
+print("Head:\n", dataset.gdf.head())
+print("NA Counts:\n", dataset.gdf.isna().sum())
+
+for service in services:
+    dataset = service(dataset)
+    print(f"After {service.__name__}:")
+    print("Dataset type:", type(dataset.gdf).__name__)
+    print("Shape:", dataset.gdf.shape)
+    print("Head:\n", dataset.gdf.head())
+    print("NA Counts:\n", dataset.gdf.isna().sum())
+
+before_drop = dataset.gdf.shape[0]
+dataset.gdf = dataset.gdf.drop_duplicates(subset="opa_id")
+after_drop = dataset.gdf.shape[0]
+print(
+    f"Duplicate dataset rows dropped after initial services: {before_drop - after_drop}"
+)
+
+# Add Priority Level
+dataset = priority_level(dataset)
+
+# Print the distribution of "priority_level"
+distribution = dataset.gdf["priority_level"].value_counts()
+print("Distribution of priority level:")
+print(distribution)
+
+# Add Access Process
+dataset = access_process(dataset)
+
+# Print the distribution of "access_process"
+distribution = dataset.gdf["access_process"].value_counts()
+print("Distribution of access process:")
+print(distribution)
+
+before_drop = dataset.gdf.shape[0]
+dataset.gdf = dataset.gdf.drop_duplicates(subset="opa_id")
+after_drop = dataset.gdf.shape[0]
+print(f"Duplicate final dataset rows droppeds: {before_drop - after_drop}")
+
+# Convert problematic columns to numeric
+numeric_columns = [
+    "market_value",
+    "sale_price",
+    "total_assessment",
+    "total_due",
+    "num_years_owed",
+    "permit_count",
+]
+for col in numeric_columns:
+    dataset.gdf[col] = pd.to_numeric(dataset.gdf[col], errors="coerce")
+
+dataset.gdf["most_recent_year_owed"] = dataset.gdf["most_recent_year_owed"].astype(str)
+
+print("Column data types before exporting to Parquet:")
+print(dataset.gdf.dtypes)
+
+# Quick dataset profiling
+print("\nQuick dataset profile:")
+
+# 1) Number of NA values per column
+print("\nNumber of NA values per column:")
+print(dataset.gdf.isna().sum())
+
+# 2) Mean, median, and std of numeric columns
+print("\nMean, Median, and Standard Deviation of numeric columns:")
+numeric_columns = dataset.gdf.select_dtypes(include=["float", "int"]).columns
+
+for column in numeric_columns:
+    mean = dataset.gdf[column].mean()
+    median = dataset.gdf[column].median()
+    std = dataset.gdf[column].std()
+    print(f"{column}:\n  Mean: {mean:.2f}\n  Median: {median:.2f}\n  Std: {std:.2f}")
+
+# 3) Number of unique values in string columns
+print("\nNumber of unique values in string columns:")
+string_columns = dataset.gdf.select_dtypes(include=["object", "string"]).columns
+unique_values = dataset.gdf[string_columns].nunique()
+print(unique_values)
+
+dataset.gdf.to_parquet("tmp/test_output.parquet")