File size: 7,149 Bytes
60b91f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | # 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
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