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AQI_and_Lat_Long_of_Countries

(First 100 rows)

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Country City AQI_Value AQI_Category CO_AQI_Value CO_AQI_Category Ozone_AQI_Value Ozone_AQI_Category NO2_AQI_Value NO2_AQI_Category PM2_5_AQI_Value PM2_5_AQI_Category lat lng
0 Russian Federation Praskoveya 51 Moderate 1 Good 36 Good 0 Good 51 Moderate 44.7444 44.2031
1 Brazil Presidente Dutra 41 Good 1 Good 5 Good 1 Good 41 Good -5.2900 -44.4900
2 Brazil Presidente Dutra 41 Good 1 Good 5 Good 1 Good 41 Good -11.2958 -41.9869
3 Italy Priolo Gargallo 66 Moderate 1 Good 39 Good 2 Good 66 Moderate 37.1667 15.1833
4 Poland Przasnysz 34 Good 1 Good 34 Good 0 Good 20 Good 53.0167 20.8833
5 United States of America Punta Gorda 54 Moderate 1 Good 14 Good 11 Good 54 Moderate 16.1005 -88.8074
6 United States of America Punta Gorda 54 Moderate 1 Good 14 Good 11 Good 54 Moderate 26.8941 -82.0513
7 Belgium Puurs 64 Moderate 1 Good 29 Good 7 Good 64 Moderate 51.0761 4.2803
8 Russian Federation Pyatigorsk 54 Moderate 1 Good 41 Good 1 Good 54 Moderate 44.0500 43.0667
9 China Qinzhou 68 Moderate 2 Good 68 Moderate 1 Good 58 Moderate 21.9500 108.6167
10 Netherlands Raalte 41 Good 1 Good 24 Good 6 Good 41 Good 52.3833 6.2667
11 France Raismes 59 Moderate 1 Good 30 Good 4 Good 59 Moderate 50.3892 3.4858
12 Italy Ramacca 55 Moderate 1 Good 47 Good 0 Good 55 Moderate 37.3833 14.7000
13 United States of America Phoenix 72 Moderate 1 Good 4 Good 23 Good 72 Moderate 33.5722 -112.0892
14 Poland Piaseczno 28 Good 1 Good 28 Good 2 Good 28 Good 52.0667 21.0167
15 Brazil Pinheiral 154 Unhealthy 5 Good 0 Good 13 Good 154 Unhealthy -22.5128 -44.0008
16 Colombia Plato 67 Moderate 1 Good 16 Good 2 Good 67 Moderate 9.7919 -74.7872
17 Romania Poiana Mare 62 Moderate 1 Good 37 Good 1 Good 62 Moderate 43.9333 23.0833
18 Russian Federation Polevskoy 31 Good 1 Good 31 Good 0 Good 17 Good 56.4500 60.1833
19 France Pontarlier 56 Moderate 1 Good 35 Good 0 Good 56 Moderate 46.9061 6.3547
20 United States of America Pontiac 77 Moderate 2 Good 22 Good 15 Good 77 Moderate 42.6493 -83.2878
21 United States of America Pontiac 77 Moderate 2 Good 22 Good 15 Good 77 Moderate 40.8894 -88.6409
22 Indonesia Pontianak 44 Good 1 Good 15 Good 0 Good 44 Good -0.0206 109.3414
23 Brazil Porecatu 30 Good 1 Good 9 Good 2 Good 30 Good -22.7558 -51.3789
24 Finland Pori 30 Good 1 Good 30 Good 1 Good 15 Good 61.4833 21.8000
25 South Africa Port Elizabeth 79 Moderate 3 Good 18 Good 5 Good 79 Moderate -33.9581 25.6000
26 United States of America Port Neches 34 Good 1 Good 19 Good 7 Good 34 Good 29.9765 -93.9459
27 United Kingdom of Great Britain and Northern Ireland Port Talbot 51 Moderate 1 Good 20 Good 5 Good 51 Moderate 51.5906 -3.7986
28 United States of America Portales 77 Moderate 1 Good 34 Good 0 Good 77 Moderate 34.1754 -103.3565
29 United States of America Post Falls 61 Moderate 1 Good 32 Good 3 Good 61 Moderate 47.7213 -116.9384
30 Brazil Pouso Alegre 32 Good 1 Good 7 Good 2 Good 32 Good -22.2281 -45.9336
31 Russian Federation Dalnegorsk 29 Good 0 Good 29 Good 0 Good 25 Good 44.5500 135.5833
32 India Darbhanga 247 Very Unhealthy 3 Good 162 Unhealthy 1 Good 247 Very Unhealthy 26.1700 85.9000
33 United States of America Dayton 45 Good 1 Good 32 Good 7 Good 45 Good 39.7805 -84.2003
34 United States of America Dayton 45 Good 1 Good 32 Good 7 Good 45 Good 39.2592 -119.5653
35 United States of America Dayton 45 Good 1 Good 32 Good 7 Good 45 Good 30.0315 -94.9158
36 Belgium Deinze 36 Good 1 Good 25 Good 3 Good 36 Good 50.9833 3.5333
37 Haiti Delmas 124 Unhealthy for Sensitive Groups 2 Good 15 Good 5 Good 124 Unhealthy for Sensitive Groups 18.5500 -72.3000
38 United States of America Deming 72 Moderate 1 Good 26 Good 2 Good 72 Moderate 32.2631 -107.7525
39 United Kingdom of Great Britain and Northern Ireland Denton 55 Moderate 0 Good 32 Good 1 Good 55 Moderate 33.2175 -97.1418
40 United Kingdom of Great Britain and Northern Ireland Denton 55 Moderate 0 Good 32 Good 1 Good 55 Moderate 53.4554 -2.1122
41 United States of America Destin 31 Good 0 Good 31 Good 0 Good 25 Good 30.3950 -86.4701
42 India Dharmapuri 60 Moderate 1 Good 31 Good 1 Good 60 Moderate 12.1270 78.1580
43 Philippines Dipolog 30 Good 1 Good 17 Good 0 Good 30 Good 8.5872 123.3408
44 Latvia Dobele 44 Good 1 Good 34 Good 0 Good 44 Good 56.6167 23.2667
45 United States of America Grandville 47 Good 1 Good 37 Good 4 Good 47 Good 42.9004 -85.7564
46 Netherlands Grave 37 Good 0 Good 32 Good 2 Good 37 Good 51.7667 5.7333
47 United States of America Green Valley 44 Good 1 Good 14 Good 8 Good 44 Good 31.8393 -111.0009
48 United States of America Green Valley 44 Good 1 Good 14 Good 8 Good 44 Good 39.3414 -77.2400
49 United States of America Greendale 58 Moderate 2 Good 14 Good 24 Good 58 Moderate 42.9371 -88.0018
50 Colombia Guamo 89 Moderate 3 Good 3 Good 6 Good 89 Moderate 4.0833 -74.9167
51 Italy Guardiagrele 52 Moderate 2 Good 38 Good 7 Good 52 Moderate 42.2000 14.2167
52 United Kingdom of Great Britain and Northern Ireland Guildford 51 Moderate 1 Good 25 Good 6 Good 51 Moderate 51.2365 -0.5703
53 Russian Federation Gukovo 38 Good 1 Good 38 Good 0 Good 13 Good 48.0500 39.9167
54 United States of America Hazelwood 88 Moderate 2 Good 11 Good 20 Good 88 Moderate 38.7931 -90.3899
55 Germany Heddesheim 54 Moderate 1 Good 31 Good 3 Good 54 Moderate 49.5053 8.6033
56 Germany Heiligenhaus 44 Good 1 Good 28 Good 3 Good 44 Good 51.3167 6.9667
57 United Kingdom of Great Britain and Northern Ireland Hemel Hempstead 51 Moderate 1 Good 26 Good 6 Good 51 Moderate 51.7526 -0.4692
58 United States of America Hicksville 67 Moderate 2 Good 15 Good 23 Good 67 Moderate 40.7637 -73.5245
59 Germany Haiger 49 Good 1 Good 25 Good 3 Good 49 Good 50.7422 8.2039
60 China Hangzhou 203 Very Unhealthy 5 Good 203 Very Unhealthy 5 Good 151 Unhealthy 30.2500 120.1675
61 Germany Harrislee 35 Good 0 Good 32 Good 0 Good 35 Good 54.7972 9.3764
62 United States of America Harrison 90 Moderate 1 Good 25 Good 2 Good 90 Moderate 40.7431 -74.1531
63 United States of America Harrison 90 Moderate 1 Good 25 Good 2 Good 90 Moderate 41.0236 -73.7193
64 United States of America Harrison 90 Moderate 1 Good 25 Good 2 Good 90 Moderate 36.2438 -93.1198
65 United States of America Harrison 90 Moderate 1 Good 25 Good 2 Good 90 Moderate 39.2584 -84.7868
66 United States of America Harrison 90 Moderate 1 Good 25 Good 2 Good 90 Moderate 44.1935 -88.2941
67 United States of America Harrison 90 Moderate 1 Good 25 Good 2 Good 90 Moderate 40.6374 -79.7173
68 Germany Hasbergen 36 Good 0 Good 34 Good 1 Good 36 Good 52.2167 7.9167
69 France Haubourdin 48 Good 1 Good 28 Good 4 Good 48 Good 50.6092 2.9869
70 United States of America Taunton 41 Good 1 Good 28 Good 10 Good 41 Good 51.0190 -3.1000
71 United States of America Taunton 41 Good 1 Good 28 Good 10 Good 41 Good 41.9036 -71.0943
72 New Zealand Tauranga 19 Good 0 Good 19 Good 1 Good 17 Good -37.6833 176.1667
73 Italy Teano 47 Good 1 Good 47 Good 1 Good 36 Good 41.2500 14.0667
74 India Tekkali 155 Unhealthy 3 Good 82 Moderate 1 Good 155 Unhealthy 18.6057 84.2355
75 Brazil Teodoro Sampaio 46 Good 1 Good 9 Good 1 Good 46 Good -22.5328 -52.1678
76 Mexico Tequila 64 Moderate 1 Good 15 Good 2 Good 64 Moderate 20.8794 -103.8356
77 Italy Teramo 44 Good 1 Good 44 Good 1 Good 38 Good 42.6589 13.7039
78 China Tianjin 142 Unhealthy for Sensitive Groups 4 Good 113 Unhealthy for Sensitive Groups 9 Good 142 Unhealthy for Sensitive Groups 39.1467 117.2056
79 Philippines Toboso 54 Moderate 1 Good 20 Good 0 Good 54 Moderate 10.7167 123.5167
80 Japan Tokorozawa 60 Moderate 1 Good 44 Good 3 Good 60 Moderate 35.7996 139.4686
81 Mexico Toluca 166 Unhealthy 4 Good 8 Good 19 Good 166 Unhealthy 19.2925 -99.6569
82 Brazil Itapissuma 27 Good 0 Good 23 Good 1 Good 27 Good -7.7764 -34.8919
83 Brazil Itarantim 23 Good 1 Good 7 Good 1 Good 23 Good -15.6600 -40.0658
84 Brazil Itumbiara 31 Good 1 Good 13 Good 0 Good 31 Good -18.4167 -49.2167
85 El Salvador Izalco 90 Moderate 2 Good 22 Good 9 Good 90 Moderate 13.7333 -89.6667
86 Russian Federation Izberbash 51 Moderate 1 Good 38 Good 0 Good 51 Moderate 42.5633 47.8636
87 India Jabalpur 170 Unhealthy 1 Good 38 Good 0 Good 170 Unhealthy 23.1667 79.9333
88 Brazil Jaguaquara 22 Good 0 Good 9 Good 1 Good 22 Good -13.5308 -39.9708
89 Poland Jarocin 47 Good 1 Good 27 Good 2 Good 47 Good 51.9667 17.5000
90 Philippines Jasaan 59 Moderate 1 Good 32 Good 0 Good 59 Moderate 8.6500 124.7500
91 Nigeria Ilobu 133 Unhealthy for Sensitive Groups 4 Good 23 Good 3 Good 133 Unhealthy for Sensitive Groups 7.8400 4.4860
92 Finland Imatra 29 Good 1 Good 29 Good 1 Good 7 Good 61.1833 28.7667
93 Brazil Indaial 73 Moderate 2 Good 2 Good 8 Good 73 Moderate -26.8978 -49.2319
94 Philippines Indang 50 Good 0 Good 24 Good 0 Good 50 Good 14.2000 120.8833
95 Brazil Itagi 25 Good 0 Good 9 Good 1 Good 25 Good -14.1628 -40.0058
96 Nigeria Iseyin 90 Moderate 3 Good 18 Good 2 Good 90 Moderate 7.9667 3.6000
97 Mexico Isla 81 Moderate 2 Good 26 Good 3 Good 81 Moderate 18.0292 -95.5264
98 Brazil Itaitinga 29 Good 0 Good 20 Good 1 Good 29 Good -3.9689 -38.5278
99 Brazil Itapemirim 44 Good 1 Good 16 Good 1 Good 44 Good -21.0108 -40.8339
Group: Location
1 ▼
Group: Country
1 ▼
Column: Country
1 ▼
Country Nation where the city is located
✓ Country Data Type: VARCHAR
! Country has 1.81% Missing Values
Group: City
0 ▼
Column: City
0 ▼
City Name of the city
✓ City Data Type: VARCHAR
Group: Coordinates
0 ▼
Group: Latitude
0 ▼
Column: lat
0 ▼
lat Latitude of the city
✓ lat Data Type: DECIMAL
Group: Longitude
0 ▼
Column: lng
0 ▼
lng Longitude of the city
✓ lng Data Type: DECIMAL
Group: AirQuality
0 ▼
Group: Overall
0 ▼
Group: Value
0 ▼
Column: AQI_Value
0 ▼
AQI_Value Overall Air Quality Index value
✓ AQI_Value Data Type: INT
Group: Category
0 ▼
Column: AQI_Category
0 ▼
AQI_Category General air quality classification
✓ AQI_Category Data Type: VARCHAR
Group: SpecificPollutants
0 ▼
Group: CarbonMonoxide
0 ▼
Group: Value
0 ▼
Column: CO_AQI_Value
0 ▼
CO_AQI_Value Carbon monoxide AQI value
✓ CO_AQI_Value Data Type: INT
Group: Category
0 ▼
Column: CO_AQI_Category
0 ▼
CO_AQI_Category Carbon monoxide air quality classification
✓ CO_AQI_Category Data Type: VARCHAR
Group: Ozone
0 ▼
Group: Value
0 ▼
Column: Ozone_AQI_Value
0 ▼
Ozone_AQI_Value Ozone AQI value
✓ Ozone_AQI_Value Data Type: INT
Group: Category
0 ▼
Column: Ozone_AQI_Category
0 ▼
Ozone_AQI_Category Ozone air quality classification
✓ Ozone_AQI_Category Data Type: VARCHAR
Group: NitrogenDioxide
0 ▼
Group: Value
0 ▼
Column: NO2_AQI_Value
0 ▼
NO2_AQI_Value Nitrogen dioxide AQI value
✓ NO2_AQI_Value Data Type: INT
Group: Category
0 ▼
Column: NO2_AQI_Category
0 ▼
NO2_AQI_Category Nitrogen dioxide air quality classification
✓ NO2_AQI_Category Data Type: VARCHAR
Group: FineParticulateMatter
0 ▼
Group: Value
0 ▼
Column: PM2_5_AQI_Value
0 ▼
PM2_5_AQI_Value Fine particulate matter AQI value
✓ PM2_5_AQI_Value Data Type: INT
Group: Category
0 ▼
Column: PM2_5_AQI_Category
0 ▼
PM2_5_AQI_Category Fine particulate matter air quality classification
✓ PM2_5_AQI_Category Data Type: VARCHAR

Table Summary

The table lists air quality information for different cities in various countries. Each row shows data for a city, including the Country it's in and the city's name under City. The overall air quality is given by AQI_Value and described by AQI_Category. Specific pollutants like carbon monoxide and ozone have their own values, CO_AQI_Value and Ozone_AQI_Value, and categories, CO_AQI_Category and Ozone_AQI_Category. Nitrogen dioxide and fine particulate matter are also measured, with NO2_AQI_Value, NO2_AQI_Category, PM2_5_AQI_Value, and PM2_5_AQI_Category showing their levels and health implications. The table also includes geographical coordinates with lat for latitude and lng for longitude, pinpointing each city's location.


Column Grouping

Column Summary

Column Summary
0 Country Nation where the city is located
1 City Name of the city
2 AQI_Value Overall Air Quality Index value
3 AQI_Category General air quality classification
4 CO_AQI_Value Carbon monoxide AQI value
5 CO_AQI_Category Carbon monoxide air quality classification
6 Ozone_AQI_Value Ozone AQI value
7 Ozone_AQI_Category Ozone air quality classification
8 NO2_AQI_Value Nitrogen dioxide AQI value
9 NO2_AQI_Category Nitrogen dioxide air quality classification
10 PM2_5_AQI_Value Fine particulate matter AQI value
11 PM2_5_AQI_Category Fine particulate matter air quality classification
12 lat Latitude of the city
13 lng Longitude of the city

Data Type

Column Current Type Target Type Matched?
0 Country VARCHAR VARCHAR ✔️ Yes
1 City VARCHAR VARCHAR ✔️ Yes
2 AQI_Value INT INT ✔️ Yes
3 AQI_Category VARCHAR VARCHAR ✔️ Yes
4 CO_AQI_Value INT INT ✔️ Yes
5 CO_AQI_Category VARCHAR VARCHAR ✔️ Yes
6 Ozone_AQI_Value INT INT ✔️ Yes
7 Ozone_AQI_Category VARCHAR VARCHAR ✔️ Yes
8 NO2_AQI_Value INT INT ✔️ Yes
9 NO2_AQI_Category VARCHAR VARCHAR ✔️ Yes
10 PM2_5_AQI_Value INT INT ✔️ Yes
11 PM2_5_AQI_Category VARCHAR VARCHAR ✔️ Yes
12 lat DECIMAL DECIMAL ✔️ Yes
13 lng DECIMAL DECIMAL ✔️ Yes

Missing Value

Column NULL (%) Is NULL Acceptable? Explanation
0 Country 1.81 False

Column Uniqueness

Column Is Unique Should be Unique? Explanation

Column Range

Column True Range Expected Range Explanation Within Range?
0 AQI_Value [7, 500] [0, 500] AQI scale range ✔️ Yes
1 CO_AQI_Value [0, 133] [0, 50] CO AQI scale range ❌ No
2 Ozone_AQI_Value [0, 222] [0, 300] Ozone AQI scale range ✔️ Yes
3 NO2_AQI_Value [0, 91] [0, 200] NO2 AQI scale range ✔️ Yes
4 PM2_5_AQI_Value [0, 500] [0, 500] PM2.5 AQI scale range ✔️ Yes
5 lat [-54.8019, 70.767] [-90, 90] latitude range ✔️ Yes
6 lng [-171.75, 178.0178] [-180, 180] longitude range ✔️ Yes