| # 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 | |