DATA_DICTIONARY.md

CPCB Clean Dataset -- Final Data Dictionary

Generated from: merged_cpcb_clean.csv
File size on disk: 151.6 MB

Dataset Overview

• Rows: 972,562
• Columns: 20
• Date range: 2009-01-01 to 2025-12-31 (0 rows with unparseable date)

Column-by-Column Dictionary

Every dtype below is read directly from df.dtypes on the actual loaded file -- not assumed. Every unit is sourced as noted; where no unit applies (categorical/text/boolean fields) that's stated explicitly instead of left blank.

date

• dtype (as loaded by pandas): object
• unit: calendar date (IST local, no time-of-day) (source: clean_cpcb.py docstring: 'CPCB timestamps are IST local')
• non-null: 972,562 / 972,562 (100.0%)
• unique values: 6,209
• top 5 values: {'2025-10-21': 514, '2025-10-24': 514, '2025-10-22': 513, '2025-10-20': 513, '2025-10-23': 512}
• sample values: ['2009-01-01', '2009-01-01', '2009-01-01']

station

• dtype (as loaded by pandas): object
• unit: N/A -- free-text station name, parsed from source filename
• non-null: 972,562 / 972,562 (100.0%)
• unique values: 532
• top 5 values: {'Nehru Nagar': 8005, 'Shastri Nagar': 7302, 'Collectorate': 6577, 'Anand Vihar': 6327, 'Shadipur': 5941}
• sample values: ['DTU', 'ITO', 'NSIT Dwarka']

city

• dtype (as loaded by pandas): object
• unit: N/A -- free-text city name, parsed from source filename
• non-null: 972,562 / 972,562 (100.0%)
• unique values: 289
• top 5 values: {'Delhi': 116163, 'Mumbai': 46430, 'Bengaluru': 39281, 'Hyderabad': 30930, 'Chennai': 25919}
• sample values: ['Delhi', 'Delhi', 'Delhi']

state

• dtype (as loaded by pandas): object
• unit: N/A -- categorical -- from State_Geocoded (Nominatim's returned state), PRIMARY state field
• non-null: 850,652 / 972,562 (87.5%)
• unique values: 30
• top 5 values: {'Maharashtra': 127095, nan: 121910, 'Uttar Pradesh': 120153, 'Karnataka': 86218, 'Haryana': 68098}
• sample values: ['Karnataka', 'Karnataka', 'Karnataka']

state_hint

• dtype (as loaded by pandas): object
• unit: N/A -- categorical -- from State_Hint, canonical state mapped from the source folder name
• non-null: 972,562 / 972,562 (100.0%)
• unique values: 31
• top 5 values: {'Maharashtra': 127095, 'Delhi': 121910, 'Uttar Pradesh': 120153, 'Karnataka': 86218, 'Bihar': 72673}
• sample values: ['Delhi', 'Delhi', 'Delhi']

state_mismatch_flag

• dtype (as loaded by pandas): object
• unit: N/A -- boolean -- True if state_hint disagrees with the geocoded state
• non-null: 850,652 / 972,562 (87.5%)
• unique values: 2
• top 5 values: {False: 841064, nan: 121910, True: 9588}
• sample values: [False, False, False]

geocode_confidence

• dtype (as loaded by pandas): object
• unit: N/A -- categorical enum: high / medium / medium_state_mismatch / low_ambiguous / not_found
• non-null: 972,562 / 972,562 (100.0%)
• unique values: 3
• top 5 values: {'high': 841064, 'medium_state_mismatch': 126158, 'low_ambiguous': 5340}
• sample values: ['medium_state_mismatch', 'medium_state_mismatch', 'medium_state_mismatch']

agency

• dtype (as loaded by pandas): object
• unit: N/A -- free-text monitoring agency code (e.g. CPCB, UPPCB, MPCB), parsed from source filename
• non-null: 972,562 / 972,562 (100.0%)
• unique values: 49
• top 5 values: {'UPPCB': 99695, 'MPCB': 98974, 'CPCB': 80590, 'DPCC': 73243, 'KSPCB': 70341}
• sample values: ['CPCB', 'CPCB', 'CPCB']

lat

• dtype (as loaded by pandas): float64
• unit: decimal degrees (WGS84) (source: Nominatim/OpenStreetMap geocoder output format)
• non-null: 972,562 / 972,562 (100.0%)
• unique values: 289
• min: 8.4882 max: 34.0747 mean: 22.9735 median: 24.7964

lon

• dtype (as loaded by pandas): float64
• unit: decimal degrees (WGS84) (source: Nominatim/OpenStreetMap geocoder output format)
• non-null: 972,562 / 972,562 (100.0%)
• unique values: 289
• min: 70.7775 max: 94.6394 mean: 78.5007 median: 77.3091

coverage_area_km2

• dtype (as loaded by pandas): float64
• unit: km² (source: computed from Nominatim bounding box by cpcb_pipeline.py)
• non-null: 972,562 / 972,562 (100.0%)
• unique values: 266
• min: 0 max: 63,238.2000 mean: 2,805.6955 median: 1,141.7000

pm25

• dtype (as loaded by pandas): float64
• unit: µg/m³ (source: from raw column 'PM2.5 (µg/m³)')
• non-null: 906,236 / 972,562 (93.2%)
• unique values: 156,644
• min: 0.0100 max: 1,000 mean: 57.8282 median: 41.0100

pm10

• dtype (as loaded by pandas): float64
• unit: µg/m³ (source: from raw column 'PM10 (µg/m³)')
• non-null: 861,062 / 972,562 (88.5%)
• unique values: 179,248
• min: 0.0300 max: 1,000 mean: 118.8644 median: 92.7900

no2

• dtype (as loaded by pandas): float64
• unit: µg/m³ (source: from raw column 'NO2 (µg/m³)')
• non-null: 939,539 / 972,562 (96.6%)
• unique values: 153,689
• min: 0.0100 max: 500 mean: 25.9318 median: 18.8000

so2

• dtype (as loaded by pandas): float64
• unit: µg/m³ (source: from raw column 'SO2 (µg/m³)')
• non-null: 915,167 / 972,562 (94.1%)
• unique values: 138,954
• min: 0.0100 max: 199.8300 mean: 12.6577 median: 9.0200

co

• dtype (as loaded by pandas): float64
• unit: mg/m³ (source: from raw column 'CO (mg/m³)' -- NOTE different unit family than other pollutants)
• non-null: 920,104 / 972,562 (94.6%)
• unique values: 79,333
• min: 0 max: 50 mean: 1.1384 median: 0.7300

o3

• dtype (as loaded by pandas): float64
• unit: µg/m³ (source: from raw column 'Ozone (µg/m³)')
• non-null: 892,019 / 972,562 (91.7%)
• unique values: 151,934
• min: 0.0100 max: 499.6300 mean: 30.9691 median: 25.8400

nh3

• dtype (as loaded by pandas): float64
• unit: µg/m³ (source: from raw column 'NH3 (µg/m³)')
• non-null: 779,161 / 972,562 (80.1%)
• unique values: 137,757
• min: 0.0100 max: 498.4000 mean: 24.3772 median: 17.8800

no

• dtype (as loaded by pandas): float64
• unit: µg/m³ (source: from raw column 'NO (µg/m³)')
• non-null: 923,787 / 972,562 (95.0%)
• unique values: 141,244
• min: 0.0100 max: 500 mean: 14.9131 median: 7.1064

nox_ppb

• dtype (as loaded by pandas): float64
• unit: ppb (source: from raw column 'NOx (ppb)' -- NOTE ppb, not µg/m³ like the others)
• non-null: 933,951 / 972,562 (96.0%)
• unique values: 157,146
• min: 0 max: 499.4400 mean: 29.7808 median: 19.9900