Overview

Dataset statistics

Number of variables20
Number of observations500
Missing cells51
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.6 KiB
Average record size in memory171.3 B

Variable types

Numeric10
Categorical7
Text3

Dataset

Description샘플 데이터
Author서울시
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=42

Alerts

이산화실소_지수(NITROGENINDEX) has 7 (1.4%) missing valuesMissing
오존_농도(OZONE) has 13 (2.6%) missing valuesMissing
오존_지수(OZONEINDEX) has 10 (2.0%) missing valuesMissing
일산화탄소_지수(CARBONINDEX) has 10 (2.0%) missing valuesMissing
아황산가스_지수(SULFUROUSINDEX) has 11 (2.2%) missing valuesMissing
미세먼지_지수(PM10INDEX) has 15 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:53:49.800917
Analysis finished2023-12-10 14:54:09.464476
Duration19.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정날짜(MSRDATE)
Real number (ℝ)

Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0142859 × 1011
Minimum2.0080107 × 1011
Maximum2.0201122 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:09.601101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0080107 × 1011
5-th percentile2.0081018 × 1011
Q12.0111119 × 1011
median2.0141101 × 1011
Q32.0171145 × 1011
95-th percentile2.0200228 × 1011
Maximum2.0201122 × 1011
Range1.2101484 × 109
Interquartile range (IQR)6.0025752 × 108

Descriptive statistics

Standard deviation3.6242858 × 108
Coefficient of variation (CV)0.0017992907
Kurtosis-1.1292779
Mean2.0142859 × 1011
Median Absolute Deviation (MAD)3.0023035 × 108
Skewness-0.096452181
Sum1.0071429 × 1014
Variance1.3135447 × 1017
MonotonicityNot monotonic
2023-12-10T23:54:09.835926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201409262000 2
 
0.4%
201503170600 1
 
0.2%
201403251100 1
 
0.2%
201603300600 1
 
0.2%
201307050800 1
 
0.2%
201805160900 1
 
0.2%
201812091600 1
 
0.2%
201505171900 1
 
0.2%
201501230300 1
 
0.2%
201409251600 1
 
0.2%
Other values (489) 489
97.8%
ValueCountFrequency (%)
200801072100 1
0.2%
200801191600 1
0.2%
200801221700 1
0.2%
200801300000 1
0.2%
200801311700 1
0.2%
200802061300 1
0.2%
200802191100 1
0.2%
200803011700 1
0.2%
200803101000 1
0.2%
200803271800 1
0.2%
ValueCountFrequency (%)
202011220500 1
0.2%
202010231100 1
0.2%
202010161800 1
0.2%
202010122100 1
0.2%
202010040400 1
0.2%
202009110800 1
0.2%
202009061100 1
0.2%
202008200500 1
0.2%
202008182000 1
0.2%
202008020400 1
0.2%
Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111214.02
Minimum111121
Maximum111311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:10.064108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111121
5-th percentile111123
Q1111152
median111212
Q3111262
95-th percentile111301.5
Maximum111311
Range190
Interquartile range (IQR)110

Descriptive statistics

Standard deviation60.147811
Coefficient of variation (CV)0.00054082939
Kurtosis-1.359696
Mean111214.02
Median Absolute Deviation (MAD)60
Skewness-0.0040111593
Sum55607010
Variance3617.7591
MonotonicityNot monotonic
2023-12-10T23:54:10.273391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111231 29
 
5.8%
111261 28
 
5.6%
111151 27
 
5.4%
111212 26
 
5.2%
111191 25
 
5.0%
111311 25
 
5.0%
111281 25
 
5.0%
111301 23
 
4.6%
111161 22
 
4.4%
111201 21
 
4.2%
Other values (15) 249
49.8%
ValueCountFrequency (%)
111121 20
4.0%
111123 14
2.8%
111131 19
3.8%
111141 19
3.8%
111142 19
3.8%
111151 27
5.4%
111152 15
3.0%
111161 22
4.4%
111171 14
2.8%
111181 17
3.4%
ValueCountFrequency (%)
111311 25
5.0%
111301 23
4.6%
111291 20
4.0%
111281 25
5.0%
111274 13
2.6%
111273 18
3.6%
111262 17
3.4%
111261 28
5.6%
111251 18
3.6%
111241 19
3.8%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
점검중
296 
보통
152 
좋음
30 
나쁨
 
18
매우나쁨
 
4

Length

Max length4
Median length3
Mean length2.608
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row점검중
2nd row점검중
3rd row좋음
4th row보통
5th row나쁨

Common Values

ValueCountFrequency (%)
점검중 296
59.2%
보통 152
30.4%
좋음 30
 
6.0%
나쁨 18
 
3.6%
매우나쁨 4
 
0.8%

Length

2023-12-10T23:54:10.497113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:10.678010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
점검중 296
59.2%
보통 152
30.4%
좋음 30
 
6.0%
나쁨 18
 
3.6%
매우나쁨 4
 
0.8%
Distinct87
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-12.498
Minimum-99
Maximum362
Zeros0
Zeros (%)0.0%
Negative267
Negative (%)53.4%
Memory size4.5 KiB
2023-12-10T23:54:10.872699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q367
95-th percentile147.65
Maximum362
Range461
Interquartile range (IQR)166

Descriptive statistics

Standard deviation100.13315
Coefficient of variation (CV)-8.0119337
Kurtosis-0.10759701
Mean-12.498
Median Absolute Deviation (MAD)0
Skewness0.74565649
Sum-6249
Variance10026.647
MonotonicityNot monotonic
2023-12-10T23:54:11.084391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99 267
53.4%
61 12
 
2.4%
55 9
 
1.8%
65 8
 
1.6%
58 7
 
1.4%
75 7
 
1.4%
51 7
 
1.4%
52 6
 
1.2%
76 6
 
1.2%
62 5
 
1.0%
Other values (77) 166
33.2%
ValueCountFrequency (%)
-99 267
53.4%
31 3
 
0.6%
32 1
 
0.2%
34 1
 
0.2%
36 1
 
0.2%
39 4
 
0.8%
42 1
 
0.2%
43 1
 
0.2%
44 2
 
0.4%
46 1
 
0.2%
ValueCountFrequency (%)
362 1
0.2%
336 1
0.2%
312 2
0.4%
294 1
0.2%
290 1
0.2%
282 1
0.2%
236 1
0.2%
233 1
0.2%
221 1
0.2%
214 1
0.2%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
점검중
293 
PM25
65 
NO2
50 
O3
46 
PM10
46 

Length

Max length4
Median length3
Mean length3.13
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row점검중
2nd rowO3
3rd rowO3
4th row점검중
5th row점검중

Common Values

ValueCountFrequency (%)
점검중 293
58.6%
PM25 65
 
13.0%
NO2 50
 
10.0%
O3 46
 
9.2%
PM10 46
 
9.2%

Length

2023-12-10T23:54:11.296055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:11.489286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
점검중 293
58.6%
pm25 65
 
13.0%
no2 50
 
10.0%
o3 46
 
9.2%
pm10 46
 
9.2%
Distinct76
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:54:11.825198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.964
Min length3

Characters and Unicode

Total characters2482
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)2.4%

Sample

1st row0.027
2nd row점검중
3rd row0.053
4th row0.060
5th row0.011
ValueCountFrequency (%)
0.017 20
 
4.0%
0.034 20
 
4.0%
0.014 16
 
3.2%
0.026 14
 
2.8%
0.021 14
 
2.8%
0.016 13
 
2.6%
0.023 13
 
2.6%
0.032 13
 
2.6%
0.018 13
 
2.6%
0.030 12
 
2.4%
Other values (66) 352
70.4%
2023-12-10T23:54:12.279053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1056
42.5%
. 491
19.8%
1 160
 
6.4%
2 153
 
6.2%
4 144
 
5.8%
3 128
 
5.2%
5 83
 
3.3%
6 73
 
2.9%
7 62
 
2.5%
8 58
 
2.3%
Other values (4) 74
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1964
79.1%
Other Punctuation 491
 
19.8%
Other Letter 27
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1056
53.8%
1 160
 
8.1%
2 153
 
7.8%
4 144
 
7.3%
3 128
 
6.5%
5 83
 
4.2%
6 73
 
3.7%
7 62
 
3.2%
8 58
 
3.0%
9 47
 
2.4%
Other Letter
ValueCountFrequency (%)
9
33.3%
9
33.3%
9
33.3%
Other Punctuation
ValueCountFrequency (%)
. 491
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2455
98.9%
Hangul 27
 
1.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1056
43.0%
. 491
20.0%
1 160
 
6.5%
2 153
 
6.2%
4 144
 
5.9%
3 128
 
5.2%
5 83
 
3.4%
6 73
 
3.0%
7 62
 
2.5%
8 58
 
2.4%
Hangul
ValueCountFrequency (%)
9
33.3%
9
33.3%
9
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2455
98.9%
Hangul 27
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1056
43.0%
. 491
20.0%
1 160
 
6.5%
2 153
 
6.2%
4 144
 
5.9%
3 128
 
5.2%
5 83
 
3.4%
6 73
 
3.0%
7 62
 
2.5%
8 58
 
2.4%
Hangul
ValueCountFrequency (%)
9
33.3%
9
33.3%
9
33.3%

이산화실소_지수(NITROGENINDEX)
Real number (ℝ)

MISSING 

Distinct73
Distinct (%)14.8%
Missing7
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean52.486815
Minimum5
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:12.518705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile17.6
Q130
median48
Q373
95-th percentile103
Maximum122
Range117
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.813461
Coefficient of variation (CV)0.51086089
Kurtosis-0.64731113
Mean52.486815
Median Absolute Deviation (MAD)20
Skewness0.52481158
Sum25876
Variance718.96172
MonotonicityNot monotonic
2023-12-10T23:54:12.774540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 18
 
3.6%
47 18
 
3.6%
32 15
 
3.0%
22 14
 
2.8%
25 14
 
2.8%
27 13
 
2.6%
35 13
 
2.6%
30 13
 
2.6%
38 12
 
2.4%
45 12
 
2.4%
Other values (63) 351
70.2%
ValueCountFrequency (%)
5 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
10 3
 
0.6%
12 4
 
0.8%
13 6
1.2%
15 5
1.0%
17 4
 
0.8%
18 7
1.4%
20 12
2.4%
ValueCountFrequency (%)
122 1
 
0.2%
121 1
 
0.2%
115 2
0.4%
114 1
 
0.2%
111 2
0.4%
110 2
0.4%
109 3
0.6%
108 2
0.4%
107 3
0.6%
106 1
 
0.2%

오존_농도(OZONE)
Real number (ℝ)

MISSING 

Distinct76
Distinct (%)15.6%
Missing13
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean0.023293634
Minimum0
Maximum0.112
Zeros5
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:12.970674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.007
median0.02
Q30.034
95-th percentile0.058
Maximum0.112
Range0.112
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.019342219
Coefficient of variation (CV)0.83036499
Kurtosis1.7224466
Mean0.023293634
Median Absolute Deviation (MAD)0.013
Skewness1.1956376
Sum11.344
Variance0.00037412142
MonotonicityNot monotonic
2023-12-10T23:54:13.135205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 31
 
6.2%
0.003 30
 
6.0%
0.009 15
 
3.0%
0.016 15
 
3.0%
0.007 15
 
3.0%
0.02 13
 
2.6%
0.004 13
 
2.6%
0.026 12
 
2.4%
0.006 12
 
2.4%
0.012 12
 
2.4%
Other values (66) 319
63.8%
(Missing) 13
 
2.6%
ValueCountFrequency (%)
0.0 5
 
1.0%
0.001 9
 
1.8%
0.002 31
6.2%
0.003 30
6.0%
0.004 13
2.6%
0.005 11
 
2.2%
0.006 12
 
2.4%
0.007 15
3.0%
0.008 9
 
1.8%
0.009 15
3.0%
ValueCountFrequency (%)
0.112 1
0.2%
0.103 1
0.2%
0.092 1
0.2%
0.091 1
0.2%
0.09 1
0.2%
0.086 1
0.2%
0.083 1
0.2%
0.079 1
0.2%
0.075 1
0.2%
0.072 1
0.2%

오존_지수(OZONEINDEX)
Real number (ℝ)

MISSING 

Distinct85
Distinct (%)17.3%
Missing10
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean28.955102
Minimum0
Maximum115
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:13.306200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median24.5
Q342.75
95-th percentile74.1
Maximum115
Range115
Interquartile range (IQR)33.75

Descriptive statistics

Standard deviation22.935448
Coefficient of variation (CV)0.79210387
Kurtosis0.30829214
Mean28.955102
Median Absolute Deviation (MAD)16.5
Skewness0.87281315
Sum14188
Variance526.03479
MonotonicityNot monotonic
2023-12-10T23:54:13.478664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 43
 
8.6%
5 26
 
5.2%
13 20
 
4.0%
8 18
 
3.6%
33 16
 
3.2%
38 14
 
2.8%
4 12
 
2.4%
25 12
 
2.4%
23 12
 
2.4%
15 11
 
2.2%
Other values (75) 306
61.2%
ValueCountFrequency (%)
0 1
 
0.2%
1 11
 
2.2%
2 2
 
0.4%
3 43
8.6%
4 12
 
2.4%
5 26
5.2%
6 6
 
1.2%
7 2
 
0.4%
8 18
3.6%
9 5
 
1.0%
ValueCountFrequency (%)
115 2
0.4%
97 1
 
0.2%
92 1
 
0.2%
91 2
0.4%
88 1
 
0.2%
85 1
 
0.2%
84 2
0.4%
83 2
0.4%
82 3
0.6%
80 3
0.6%
Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0.4
118 
0.3
84 
0.5
73 
0.6
47 
0.7
43 
Other values (16)
135 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st row0.4
2nd row0.3
3rd row0.4
4th row0.4
5th row0.3

Common Values

ValueCountFrequency (%)
0.4 118
23.6%
0.3 84
16.8%
0.5 73
14.6%
0.6 47
 
9.4%
0.7 43
 
8.6%
0.2 41
 
8.2%
0.8 22
 
4.4%
1.0 14
 
2.8%
0.9 12
 
2.4%
1.1 9
 
1.8%
Other values (11) 37
 
7.4%

Length

2023-12-10T23:54:13.643949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.4 118
23.6%
0.3 84
16.8%
0.5 73
14.6%
0.6 47
 
9.4%
0.7 43
 
8.6%
0.2 41
 
8.2%
0.8 22
 
4.4%
1.0 14
 
2.8%
0.9 12
 
2.4%
1.2 9
 
1.8%
Other values (11) 37
 
7.4%

일산화탄소_지수(CARBONINDEX)
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)3.3%
Missing10
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean13.042857
Minimum3
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:13.780531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q18
median10
Q315
95-th percentile28
Maximum50
Range47
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.6501995
Coefficient of variation (CV)0.50987291
Kurtosis3.0396711
Mean13.042857
Median Absolute Deviation (MAD)3
Skewness1.4755092
Sum6391
Variance44.225153
MonotonicityNot monotonic
2023-12-10T23:54:13.929011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
10 108
21.6%
8 95
19.0%
13 76
15.2%
15 59
11.8%
5 37
 
7.4%
18 34
 
6.8%
20 20
 
4.0%
25 15
 
3.0%
30 10
 
2.0%
23 10
 
2.0%
Other values (6) 26
 
5.2%
(Missing) 10
 
2.0%
ValueCountFrequency (%)
3 9
 
1.8%
5 37
 
7.4%
8 95
19.0%
10 108
21.6%
13 76
15.2%
15 59
11.8%
18 34
 
6.8%
20 20
 
4.0%
23 10
 
2.0%
25 15
 
3.0%
ValueCountFrequency (%)
50 1
 
0.2%
38 2
 
0.4%
35 3
 
0.6%
33 2
 
0.4%
30 10
 
2.0%
28 9
 
1.8%
25 15
3.0%
23 10
 
2.0%
20 20
4.0%
18 34
6.8%
Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0.004
103 
0.003
83 
0.005
77 
0.006
65 
0.002
50 
Other values (13)
122 

Length

Max length5
Median length5
Mean length4.96
Min length3

Unique

Unique6 ?
Unique (%)1.2%

Sample

1st row0.003
2nd row0.004
3rd row0.005
4th row0.007
5th row0.003

Common Values

ValueCountFrequency (%)
0.004 103
20.6%
0.003 83
16.6%
0.005 77
15.4%
0.006 65
13.0%
0.002 50
10.0%
0.007 43
8.6%
0.008 25
 
5.0%
0.010 12
 
2.4%
0.009 11
 
2.2%
0.011 11
 
2.2%
Other values (8) 20
 
4.0%

Length

2023-12-10T23:54:14.086435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.004 103
20.6%
0.003 83
16.6%
0.005 77
15.4%
0.006 65
13.0%
0.002 50
10.0%
0.007 43
8.6%
0.008 25
 
5.0%
0.010 12
 
2.4%
0.011 11
 
2.2%
0.009 11
 
2.2%
Other values (8) 20
 
4.0%

아황산가스_지수(SULFUROUSINDEX)
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)3.5%
Missing11
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean12.488753
Minimum3
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:14.235515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q18
median10
Q315
95-th percentile24.2
Maximum51
Range48
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0071999
Coefficient of variation (CV)0.4810088
Kurtosis5.1287442
Mean12.488753
Median Absolute Deviation (MAD)3
Skewness1.6194241
Sum6107
Variance36.086451
MonotonicityNot monotonic
2023-12-10T23:54:14.374007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 118
23.6%
8 86
17.2%
13 72
14.4%
15 61
12.2%
5 47
 
9.4%
18 42
 
8.4%
20 23
 
4.6%
25 12
 
2.4%
23 8
 
1.6%
3 7
 
1.4%
Other values (7) 13
 
2.6%
(Missing) 11
 
2.2%
ValueCountFrequency (%)
3 7
 
1.4%
5 47
 
9.4%
8 86
17.2%
10 118
23.6%
13 72
14.4%
15 61
12.2%
18 42
 
8.4%
20 23
 
4.6%
23 8
 
1.6%
25 12
 
2.4%
ValueCountFrequency (%)
51 1
 
0.2%
40 1
 
0.2%
38 1
 
0.2%
35 2
 
0.4%
33 1
 
0.2%
30 2
 
0.4%
28 5
 
1.0%
25 12
2.4%
23 8
 
1.6%
20 23
4.6%
Distinct126
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:54:14.721732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.054
Min length1

Characters and Unicode

Total characters1027
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)8.4%

Sample

1st row25
2nd row43
3rd row178
4th row52
5th row48
ValueCountFrequency (%)
38 17
 
3.4%
30 15
 
3.0%
점검중 14
 
2.8%
28 14
 
2.8%
22 14
 
2.8%
35 13
 
2.6%
43 11
 
2.2%
24 10
 
2.0%
27 10
 
2.0%
31 10
 
2.0%
Other values (116) 372
74.4%
2023-12-10T23:54:15.296579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 156
15.2%
3 145
14.1%
1 120
11.7%
5 108
10.5%
4 105
10.2%
8 78
7.6%
7 78
7.6%
6 74
7.2%
0 61
 
5.9%
9 60
 
5.8%
Other values (3) 42
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 985
95.9%
Other Letter 42
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 156
15.8%
3 145
14.7%
1 120
12.2%
5 108
11.0%
4 105
10.7%
8 78
7.9%
7 78
7.9%
6 74
7.5%
0 61
 
6.2%
9 60
 
6.1%
Other Letter
ValueCountFrequency (%)
14
33.3%
14
33.3%
14
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 985
95.9%
Hangul 42
 
4.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 156
15.8%
3 145
14.7%
1 120
12.2%
5 108
11.0%
4 105
10.7%
8 78
7.9%
7 78
7.9%
6 74
7.5%
0 61
 
6.2%
9 60
 
6.1%
Hangul
ValueCountFrequency (%)
14
33.3%
14
33.3%
14
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 985
95.9%
Hangul 42
 
4.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 156
15.8%
3 145
14.7%
1 120
12.2%
5 108
11.0%
4 105
10.7%
8 78
7.9%
7 78
7.9%
6 74
7.5%
0 61
 
6.2%
9 60
 
6.1%
Hangul
ValueCountFrequency (%)
14
33.3%
14
33.3%
14
33.3%

미세먼지_지수(PM10INDEX)
Real number (ℝ)

ZEROS 

Distinct117
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.374
Minimum0
Maximum284
Zeros15
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:15.460587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q147
median71
Q390
95-th percentile155.55
Maximum284
Range284
Interquartile range (IQR)43

Descriptive statistics

Standard deviation44.439305
Coefficient of variation (CV)0.59751129
Kurtosis4.8496984
Mean74.374
Median Absolute Deviation (MAD)20
Skewness1.6783689
Sum37187
Variance1974.8518
MonotonicityNot monotonic
2023-12-10T23:54:15.626602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
3.0%
76 12
 
2.4%
71 12
 
2.4%
47 11
 
2.2%
57 10
 
2.0%
42 10
 
2.0%
80 10
 
2.0%
81 9
 
1.8%
51 9
 
1.8%
31 9
 
1.8%
Other values (107) 393
78.6%
ValueCountFrequency (%)
0 15
3.0%
9 2
 
0.4%
12 1
 
0.2%
14 4
 
0.8%
16 1
 
0.2%
17 5
 
1.0%
21 4
 
0.8%
22 4
 
0.8%
24 1
 
0.2%
26 8
1.6%
ValueCountFrequency (%)
284 1
0.2%
280 1
0.2%
270 1
0.2%
265 1
0.2%
260 1
0.2%
255 1
0.2%
251 1
0.2%
235 1
0.2%
216 1
0.2%
209 2
0.4%
Distinct99
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:54:15.991494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.022
Min length1

Characters and Unicode

Total characters1011
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)4.2%

Sample

1st row37
2nd row43
3rd row11
4th row69
5th row65
ValueCountFrequency (%)
46 12
 
2.4%
29 12
 
2.4%
26 12
 
2.4%
40 11
 
2.2%
61 11
 
2.2%
25 11
 
2.2%
21 10
 
2.0%
33 10
 
2.0%
35 10
 
2.0%
41 10
 
2.0%
Other values (89) 391
78.2%
2023-12-10T23:54:16.524012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 135
13.4%
2 131
13.0%
1 124
12.3%
3 123
12.2%
6 122
12.1%
5 109
10.8%
7 75
7.4%
8 64
6.3%
0 54
 
5.3%
9 53
 
5.2%
Other values (3) 21
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 990
97.9%
Other Letter 21
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 135
13.6%
2 131
13.2%
1 124
12.5%
3 123
12.4%
6 122
12.3%
5 109
11.0%
7 75
7.6%
8 64
6.5%
0 54
 
5.5%
9 53
 
5.4%
Other Letter
ValueCountFrequency (%)
7
33.3%
7
33.3%
7
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 990
97.9%
Hangul 21
 
2.1%

Most frequent character per script

Common
ValueCountFrequency (%)
4 135
13.6%
2 131
13.2%
1 124
12.5%
3 123
12.4%
6 122
12.3%
5 109
11.0%
7 75
7.6%
8 64
6.5%
0 54
 
5.5%
9 53
 
5.4%
Hangul
ValueCountFrequency (%)
7
33.3%
7
33.3%
7
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 990
97.9%
Hangul 21
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 135
13.6%
2 131
13.2%
1 124
12.5%
3 123
12.4%
6 122
12.3%
5 109
11.0%
7 75
7.6%
8 64
6.5%
0 54
 
5.5%
9 53
 
5.4%
Hangul
ValueCountFrequency (%)
7
33.3%
7
33.3%
7
33.3%
Distinct104
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.5
Minimum1
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:17.026864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.95
Q146
median61
Q377.25
95-th percentile93
Maximum104
Range103
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation22.798718
Coefficient of variation (CV)0.38317174
Kurtosis-0.06749404
Mean59.5
Median Absolute Deviation (MAD)16
Skewness-0.51057384
Sum29750
Variance519.78156
MonotonicityNot monotonic
2023-12-10T23:54:17.203079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 13
 
2.6%
62 13
 
2.6%
83 13
 
2.6%
38 12
 
2.4%
49 12
 
2.4%
79 12
 
2.4%
73 11
 
2.2%
63 11
 
2.2%
72 11
 
2.2%
60 11
 
2.2%
Other values (94) 381
76.2%
ValueCountFrequency (%)
1 8
1.6%
2 1
 
0.2%
3 2
 
0.4%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 3
 
0.6%
ValueCountFrequency (%)
104 1
 
0.2%
103 1
 
0.2%
102 1
 
0.2%
101 1
 
0.2%
100 3
0.6%
99 2
0.4%
98 3
0.6%
97 2
0.4%
96 4
0.8%
95 4
0.8%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
102
176 
103
134 
104
74 
101
60 
100
56 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row101
3rd row101
4th row104
5th row103

Common Values

ValueCountFrequency (%)
102 176
35.2%
103 134
26.8%
104 74
14.8%
101 60
 
12.0%
100 56
 
11.2%

Length

2023-12-10T23:54:17.404731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:17.539848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
102 176
35.2%
103 134
26.8%
104 74
14.8%
101 60
 
12.0%
100 56
 
11.2%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
동북권
164 
서남권
134 
동남권
71 
도심권
70 
서북권
61 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서남권
2nd row서남권
3rd row서남권
4th row도심권
5th row동북권

Common Values

ValueCountFrequency (%)
동북권 164
32.8%
서남권 134
26.8%
동남권 71
14.2%
도심권 70
14.0%
서북권 61
 
12.2%

Length

2023-12-10T23:54:17.711319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:17.874569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 164
32.8%
서남권 134
26.8%
동남권 71
14.2%
도심권 70
14.0%
서북권 61
 
12.2%
Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
은평구
 
27
성북구
 
25
금천구
 
25
광진구
 
25
구로구
 
24
Other values (20)
374 

Length

Max length4
Median length3
Mean length3.074
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강동구
2nd row노원구
3rd row동대문구
4th row은평구
5th row도봉구

Common Values

ValueCountFrequency (%)
은평구 27
 
5.4%
성북구 25
 
5.0%
금천구 25
 
5.0%
광진구 25
 
5.0%
구로구 24
 
4.8%
양천구 24
 
4.8%
관악구 24
 
4.8%
영등포구 24
 
4.8%
중랑구 23
 
4.6%
중구 22
 
4.4%
Other values (15) 257
51.4%

Length

2023-12-10T23:54:18.066734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 27
 
5.4%
성북구 25
 
5.0%
금천구 25
 
5.0%
광진구 25
 
5.0%
구로구 24
 
4.8%
양천구 24
 
4.8%
관악구 24
 
4.8%
영등포구 24
 
4.8%
중랑구 23
 
4.6%
중구 22
 
4.4%
Other values (15) 257
51.4%

Interactions

2023-12-10T23:54:06.729557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:53.912696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:55.101523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:56.321874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:57.499593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:58.591725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:00.129789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:02.399244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:03.759059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:05.359587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:06.898312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:54.076114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:55.211668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:56.437962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:57.601633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:58.707916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:00.560350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:02.547345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:03.936139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:05.516466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:07.067184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:54.200696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:55.376352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:56.544258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:57.706818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:58.826729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:01.151659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:02.672762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:04.119867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:05.658119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:07.208117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:54.334650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:55.498309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:56.720161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:57.834204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:58.945956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:01.438092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:02.789460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:04.286309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:05.773972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:07.360536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:54.442075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:55.608655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:56.840809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:57.936938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:59.065720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:01.635283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:02.913865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:04.462752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:05.890182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:07.505987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:54.571640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:55.709942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:56.958090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:58.058615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:59.486919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:01.746411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:03.039004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:04.626890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:06.007978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:07.642668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:54.688379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:55.823229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:57.076830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:58.182055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:59.611858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:01.888000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:03.181268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:04.783458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:06.127675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:07.808211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:54.790343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:55.993121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:57.184056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:58.277737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:59.698718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:02.028789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:03.321601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:04.919178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:06.264824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:07.939721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:54.901047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:56.100299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:57.300310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:58.377439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:59.789989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:02.133039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:03.461263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:05.069704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:06.388503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:08.082096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:55.002952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:56.208150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:57.393085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:58.486902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:59.889551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:02.253384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:03.602359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:05.225491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:06.553733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:54:18.217335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정날짜(MSRDATE)측정소_행정동코드(MSRADMCODE)대기환경등급(GRADE)통합대기환경지수(MAXINDEX)지수결정물질(POLLUTANT)이산화질소_농도(NITROGEN)이산화실소_지수(NITROGENINDEX)오존_농도(OZONE)오존_지수(OZONEINDEX)일산화탄소_농도(CARBON)일산화탄소_지수(CARBONINDEX)아황산가스_농도(SULFUROUS)아황산가스_지수(SULFUROUSINDEX)미세먼지_지수(PM10INDEX)미세먼지(24시)농도(PM24)미세먼지(24시)지수결정물질(PM24INDEX)권역코드(MSRRGNCODE)권역명(MSRRGNNAME)측정소명(MSRSTENAME)
측정날짜(MSRDATE)1.0000.1440.0000.0000.0000.0000.0000.0000.0000.0000.0500.2050.0640.0000.1380.1860.2160.0000.253
측정소_행정동코드(MSRADMCODE)0.1441.0000.0000.0000.0000.2740.0000.0000.0000.0000.1700.0000.0000.0000.0000.0000.0000.1300.000
대기환경등급(GRADE)0.0000.0001.0000.0430.1170.0000.1170.0000.2290.0000.0000.0000.0000.2180.0000.2710.0000.2210.260
통합대기환경지수(MAXINDEX)0.0000.0000.0431.0000.0790.1890.0560.2230.0000.3880.0000.2940.2560.1600.0000.0360.0590.0000.072
지수결정물질(POLLUTANT)0.0000.0000.1170.0791.0000.1720.0000.0000.1630.3200.0000.0820.0240.1230.3600.1540.0000.0000.000
이산화질소_농도(NITROGEN)0.0000.2740.0000.1890.1721.0000.1950.4660.0000.0000.2900.0000.5690.0000.5990.0000.3680.0000.368
이산화실소_지수(NITROGENINDEX)0.0000.0000.1170.0560.0000.1951.0000.0000.0000.2180.0000.0000.0000.0000.0640.0000.0000.0000.000
오존_농도(OZONE)0.0000.0000.0000.2230.0000.4660.0001.0000.0000.1950.0000.0000.0000.0000.0000.0000.1670.1110.034
오존_지수(OZONEINDEX)0.0000.0000.2290.0000.1630.0000.0000.0001.0000.0000.0820.0000.0000.0000.0000.2090.0000.0000.000
일산화탄소_농도(CARBON)0.0000.0000.0000.3880.3200.0000.2180.1950.0001.0000.0000.0000.0000.0000.0000.1580.0000.1910.202
일산화탄소_지수(CARBONINDEX)0.0500.1700.0000.0000.0000.2900.0000.0000.0820.0001.0000.0000.0000.2180.0000.1140.0000.0640.000
아황산가스_농도(SULFUROUS)0.2050.0000.0000.2940.0820.0000.0000.0000.0000.0000.0001.0000.1700.2270.5770.0000.0000.0000.280
아황산가스_지수(SULFUROUSINDEX)0.0640.0000.0000.2560.0240.5690.0000.0000.0000.0000.0000.1701.0000.0000.5650.0000.0000.0480.164
미세먼지_지수(PM10INDEX)0.0000.0000.2180.1600.1230.0000.0000.0000.0000.0000.2180.2270.0001.0000.0000.1730.1930.0000.000
미세먼지(24시)농도(PM24)0.1380.0000.0000.0000.3600.5990.0640.0000.0000.0000.0000.5770.5650.0001.0000.0260.1860.0000.000
미세먼지(24시)지수결정물질(PM24INDEX)0.1860.0000.2710.0360.1540.0000.0000.0000.2090.1580.1140.0000.0000.1730.0261.0000.0000.0000.186
권역코드(MSRRGNCODE)0.2160.0000.0000.0590.0000.3680.0000.1670.0000.0000.0000.0000.0000.1930.1860.0001.0000.1460.073
권역명(MSRRGNNAME)0.0000.1300.2210.0000.0000.0000.0000.1110.0000.1910.0640.0000.0480.0000.0000.0000.1461.0000.108
측정소명(MSRSTENAME)0.2530.0000.2600.0720.0000.3680.0000.0340.0000.2020.0000.2800.1640.0000.0000.1860.0730.1081.000
2023-12-10T23:54:18.468038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역코드(MSRRGNCODE)대기환경등급(GRADE)지수결정물질(POLLUTANT)권역명(MSRRGNNAME)일산화탄소_농도(CARBON)아황산가스_농도(SULFUROUS)측정소명(MSRSTENAME)
권역코드(MSRRGNCODE)1.0000.0000.0000.0550.0000.0000.029
대기환경등급(GRADE)0.0001.0000.0440.0840.0000.0000.112
지수결정물질(POLLUTANT)0.0000.0441.0000.0000.1600.0400.000
권역명(MSRRGNNAME)0.0550.0840.0001.0000.0930.0000.045
일산화탄소_농도(CARBON)0.0000.0000.1600.0931.0000.0000.055
아황산가스_농도(SULFUROUS)0.0000.0000.0400.0000.0001.0000.082
측정소명(MSRSTENAME)0.0290.1120.0000.0450.0550.0821.000
2023-12-10T23:54:18.638915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정날짜(MSRDATE)측정소_행정동코드(MSRADMCODE)통합대기환경지수(MAXINDEX)이산화실소_지수(NITROGENINDEX)오존_농도(OZONE)오존_지수(OZONEINDEX)일산화탄소_지수(CARBONINDEX)아황산가스_지수(SULFUROUSINDEX)미세먼지_지수(PM10INDEX)미세먼지(24시)지수결정물질(PM24INDEX)대기환경등급(GRADE)지수결정물질(POLLUTANT)일산화탄소_농도(CARBON)아황산가스_농도(SULFUROUS)권역코드(MSRRGNCODE)권역명(MSRRGNNAME)측정소명(MSRSTENAME)
측정날짜(MSRDATE)1.0000.1140.038-0.0550.0550.0040.001-0.040-0.027-0.0790.0000.0000.0000.0770.0930.0000.089
측정소_행정동코드(MSRADMCODE)0.1141.000-0.031-0.0840.0020.0620.0060.002-0.0340.0260.0400.0000.0000.0000.0000.0580.000
통합대기환경지수(MAXINDEX)0.038-0.0311.0000.080-0.0100.041-0.0920.044-0.0430.0070.0240.0450.1580.1000.0330.0000.025
이산화실소_지수(NITROGENINDEX)-0.055-0.0840.0801.000-0.014-0.116-0.008-0.013-0.0600.0200.0480.0000.0800.0000.0000.0000.000
오존_농도(OZONE)0.0550.002-0.010-0.0141.000-0.072-0.033-0.022-0.0050.0130.0000.0000.0690.0000.0640.0390.022
오존_지수(OZONEINDEX)0.0040.0620.041-0.116-0.0721.0000.0440.0210.012-0.0610.0940.0660.0000.0000.0000.0000.000
일산화탄소_지수(CARBONINDEX)0.0010.006-0.092-0.008-0.0330.0441.000-0.0220.021-0.1050.0000.0000.0000.0000.0000.0360.000
아황산가스_지수(SULFUROUSINDEX)-0.0400.0020.044-0.013-0.0220.021-0.0221.0000.0810.0250.0000.0120.0000.0560.0000.0270.061
미세먼지_지수(PM10INDEX)-0.027-0.034-0.043-0.060-0.0050.0120.0210.0811.000-0.0950.0920.0510.0000.0880.0810.0000.000
미세먼지(24시)지수결정물질(PM24INDEX)-0.0790.0260.0070.0200.013-0.061-0.1050.025-0.0951.0000.1150.0640.0570.0000.0000.0000.065
대기환경등급(GRADE)0.0000.0400.0240.0480.0000.0940.0000.0000.0920.1151.0000.0440.0000.0000.0000.0840.112
지수결정물질(POLLUTANT)0.0000.0000.0450.0000.0000.0660.0000.0120.0510.0640.0441.0000.1600.0400.0000.0000.000
일산화탄소_농도(CARBON)0.0000.0000.1580.0800.0690.0000.0000.0000.0000.0570.0000.1601.0000.0000.0000.0930.055
아황산가스_농도(SULFUROUS)0.0770.0000.1000.0000.0000.0000.0000.0560.0880.0000.0000.0400.0001.0000.0000.0000.082
권역코드(MSRRGNCODE)0.0930.0000.0330.0000.0640.0000.0000.0000.0810.0000.0000.0000.0000.0001.0000.0550.029
권역명(MSRRGNNAME)0.0000.0580.0000.0000.0390.0000.0360.0270.0000.0000.0840.0000.0930.0000.0551.0000.045
측정소명(MSRSTENAME)0.0890.0000.0250.0000.0220.0000.0000.0610.0000.0650.1120.0000.0550.0820.0290.0451.000

Missing values

2023-12-10T23:54:08.296510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:54:09.047778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T23:54:09.334075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

측정날짜(MSRDATE)측정소_행정동코드(MSRADMCODE)대기환경등급(GRADE)통합대기환경지수(MAXINDEX)지수결정물질(POLLUTANT)이산화질소_농도(NITROGEN)이산화실소_지수(NITROGENINDEX)오존_농도(OZONE)오존_지수(OZONEINDEX)일산화탄소_농도(CARBON)일산화탄소_지수(CARBONINDEX)아황산가스_농도(SULFUROUS)아황산가스_지수(SULFUROUSINDEX)미세먼지_농도(PM10)미세먼지_지수(PM10INDEX)미세먼지(24시)농도(PM24)미세먼지(24시)지수결정물질(PM24INDEX)권역코드(MSRRGNCODE)권역명(MSRRGNNAME)측정소명(MSRSTENAME)
0201503170600111152점검중87점검중0.027450.032110.4150.0038251013773100서남권강동구
1200912271400111151점검중-99O3점검중900.00550.330.0041043174372101서남권노원구
2201408191700111201좋음51O30.053610.002400.4100.00515178731151101서남권동대문구
3201606210800111262보통131점검중0.060420.024340.4150.0073552626966104도심권은평구
4201904170300111301나쁨67점검중0.011610.02430.3200.0038481066557103동북권도봉구
5201010232000111212보통-99점검중0.026530.0271150.2300.00653106115101동북권강서구
6201905312100111273보통-99점검중0.022220.012360.5180.0041088814155101도심권은평구
7200905130900111151점검중-99PM100.0121090.006270.280.0052545575496103서북권은평구
8201302201000111142보통68O30.051500.017530.4130.0041364806467102동북권동작구
9201006121700111191점검중201NO20.027650.006111.2130.00585836863102서남권중구
측정날짜(MSRDATE)측정소_행정동코드(MSRADMCODE)대기환경등급(GRADE)통합대기환경지수(MAXINDEX)지수결정물질(POLLUTANT)이산화질소_농도(NITROGEN)이산화실소_지수(NITROGENINDEX)오존_농도(OZONE)오존_지수(OZONEINDEX)일산화탄소_농도(CARBON)일산화탄소_지수(CARBONINDEX)아황산가스_농도(SULFUROUS)아황산가스_지수(SULFUROUSINDEX)미세먼지_농도(PM10)미세먼지_지수(PM10INDEX)미세먼지(24시)농도(PM24)미세먼지(24시)지수결정물질(PM24INDEX)권역코드(MSRRGNCODE)권역명(MSRRGNNAME)측정소명(MSRSTENAME)
490201907232100111181보통-99점검중0.036590.046300.3180.00215점검중682276104동남권영등포구
491201402172300111152점검중-99점검중0.018750.0430.3100.0061522814653103서북권서대문구
492202010161800111291보통-99점검중0.062510.027430.6230.00425471196472102동북권중랑구
493201212011300111121점검중69점검중0.018650.019280.5130.0021552725781102서남권은평구
494200906230000111231점검중52PM250.016450.029220.4180.00410104871777103동북권서초구
495202004181700111212점검중61점검중0.0431100.01350.7150.006201681193944102동북권광진구
496200908051800111152점검중-99PM250.054470.018340.4280.004845175155104서남권금천구
497201907301700111301점검중-99점검중0.049760.041482.0100.0112528574087100도심권송파구
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