Overview

Dataset statistics

Number of variables19
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory166.8 B

Variable types

Categorical6
Numeric12
DateTime1

Dataset

Description제주특별자치도 보건환경연구원에서 제공하는 대기보전에 따른 대기오염 측정망 운영과 관련한 구분, 지역, 월별 운영 결과 현황 정보 입니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15083388/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
행정시 is highly overall correlated with 지역명 and 1 other fieldsHigh correlation
지역명 is highly overall correlated with 행정시 and 1 other fieldsHigh correlation
단위 is highly overall correlated with 01월 and 12 other fieldsHigh correlation
구분 is highly overall correlated with 03월 and 4 other fieldsHigh correlation
01월 is highly overall correlated with 02월 and 11 other fieldsHigh correlation
02월 is highly overall correlated with 01월 and 11 other fieldsHigh correlation
03월 is highly overall correlated with 01월 and 12 other fieldsHigh correlation
04월 is highly overall correlated with 01월 and 11 other fieldsHigh correlation
05월 is highly overall correlated with 01월 and 11 other fieldsHigh correlation
06월 is highly overall correlated with 01월 and 11 other fieldsHigh correlation
07월 is highly overall correlated with 01월 and 11 other fieldsHigh correlation
08월 is highly overall correlated with 01월 and 11 other fieldsHigh correlation
09월 is highly overall correlated with 01월 and 11 other fieldsHigh correlation
10월 is highly overall correlated with 01월 and 12 other fieldsHigh correlation
11월 is highly overall correlated with 01월 and 12 other fieldsHigh correlation
12월 is highly overall correlated with 01월 and 12 other fieldsHigh correlation
해당연도 is highly overall correlated with 특이사항High correlation
특이사항 is highly overall correlated with 행정시 and 2 other fieldsHigh correlation
01월 has 12 (16.7%) zerosZeros
02월 has 12 (16.7%) zerosZeros
03월 has 12 (16.7%) zerosZeros
04월 has 12 (16.7%) zerosZeros
05월 has 12 (16.7%) zerosZeros
06월 has 12 (16.7%) zerosZeros
07월 has 12 (16.7%) zerosZeros
08월 has 13 (18.1%) zerosZeros
09월 has 12 (16.7%) zerosZeros
10월 has 6 (8.3%) zerosZeros
11월 has 6 (8.3%) zerosZeros

Reproduction

Analysis started2023-12-12 22:11:09.216608
Analysis finished2023-12-12 22:11:23.328104
Duration14.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size708.0 B
아황산가스(SO2) 오염도
12 
이산화질소(NO2) 오염도
12 
일산화탄소 (CO) 오염도
12 
오존(O3) 오염도
12 
미세먼지(PM-10) 오염도
12 

Length

Max length16
Median length15.5
Mean length13.833333
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아황산가스(SO2) 오염도
2nd row아황산가스(SO2) 오염도
3rd row아황산가스(SO2) 오염도
4th row아황산가스(SO2) 오염도
5th row아황산가스(SO2) 오염도

Common Values

ValueCountFrequency (%)
아황산가스(SO2) 오염도 12
16.7%
이산화질소(NO2) 오염도 12
16.7%
일산화탄소 (CO) 오염도 12
16.7%
오존(O3) 오염도 12
16.7%
미세먼지(PM-10) 오염도 12
16.7%
미세먼지(PM-2.5) 오염도 12
16.7%

Length

2023-12-13T07:11:23.404380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:11:23.539208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오염도 72
46.2%
아황산가스(so2 12
 
7.7%
이산화질소(no2 12
 
7.7%
일산화탄소 12
 
7.7%
co 12
 
7.7%
오존(o3 12
 
7.7%
미세먼지(pm-10 12
 
7.7%
미세먼지(pm-2.5 12
 
7.7%

행정시
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size708.0 B
제주시
36 
서귀포시
36 

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주시
2nd row제주시
3rd row제주시
4th row제주시
5th row제주시

Common Values

ValueCountFrequency (%)
제주시 36
50.0%
서귀포시 36
50.0%

Length

2023-12-13T07:11:23.673188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:11:23.770800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 36
50.0%
서귀포시 36
50.0%

지역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size708.0 B
이도동
18 
연동
18 
동홍동
18 
성산읍
12 
대정읍

Length

Max length3
Median length3
Mean length2.75
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row이도동
2nd row이도동
3rd row이도동
4th row연동
5th row연동

Common Values

ValueCountFrequency (%)
이도동 18
25.0%
연동 18
25.0%
동홍동 18
25.0%
성산읍 12
16.7%
대정읍 6
 
8.3%

Length

2023-12-13T07:11:23.879387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:11:23.979816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이도동 18
25.0%
연동 18
25.0%
동홍동 18
25.0%
성산읍 12
16.7%
대정읍 6
 
8.3%

해당연도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size708.0 B
2018
30 
2017
24 
2016
18 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2017
3rd row2018
4th row2016
5th row2017

Common Values

ValueCountFrequency (%)
2018 30
41.7%
2017 24
33.3%
2016 18
25.0%

Length

2023-12-13T07:11:24.110738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:11:24.247648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 30
41.7%
2017 24
33.3%
2016 18
25.0%

단위
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size708.0 B
ppm
48 
㎍/㎥
24 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowppm
2nd rowppm
3rd rowppm
4th rowppm
5th rowppm

Common Values

ValueCountFrequency (%)
ppm 48
66.7%
㎍/㎥ 24
33.3%

Length

2023-12-13T07:11:24.358663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:11:24.468588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ppm 48
66.7%
㎍/㎥ 24
33.3%

01월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3705
Minimum0
Maximum45
Zeros12
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:24.584171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.003
median0.0305
Q319.25
95-th percentile39.15
Maximum45
Range45
Interquartile range (IQR)19.247

Descriptive statistics

Standard deviation14.330787
Coefficient of variation (CV)1.7120586
Kurtosis0.49936276
Mean8.3705
Median Absolute Deviation (MAD)0.0305
Skewness1.4042494
Sum602.676
Variance205.37145
MonotonicityNot monotonic
2023-12-13T07:11:24.733396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 12
 
16.7%
0.3 6
 
8.3%
20.0 4
 
5.6%
0.003 4
 
5.6%
45.0 3
 
4.2%
0.4 3
 
4.2%
32.0 3
 
4.2%
0.001 3
 
4.2%
0.029 2
 
2.8%
0.037 2
 
2.8%
Other values (23) 30
41.7%
ValueCountFrequency (%)
0.0 12
16.7%
0.001 3
 
4.2%
0.002 1
 
1.4%
0.003 4
 
5.6%
0.004 2
 
2.8%
0.005 1
 
1.4%
0.011 2
 
2.8%
0.012 1
 
1.4%
0.013 2
 
2.8%
0.014 1
 
1.4%
ValueCountFrequency (%)
45.0 3
4.2%
43.0 1
 
1.4%
36.0 1
 
1.4%
35.0 1
 
1.4%
32.0 3
4.2%
31.0 2
2.8%
28.0 1
 
1.4%
24.0 2
2.8%
20.0 4
5.6%
19.0 1
 
1.4%

02월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4824722
Minimum0
Maximum55
Zeros12
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:24.875259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.003
median0.0375
Q320.25
95-th percentile44
Maximum55
Range55
Interquartile range (IQR)20.247

Descriptive statistics

Standard deviation16.597146
Coefficient of variation (CV)1.7502973
Kurtosis1.0294558
Mean9.4824722
Median Absolute Deviation (MAD)0.0375
Skewness1.5313632
Sum682.738
Variance275.46525
MonotonicityNot monotonic
2023-12-13T07:11:25.010181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 12
 
16.7%
0.003 6
 
8.3%
0.3 6
 
8.3%
0.4 3
 
4.2%
55.0 3
 
4.2%
0.037 3
 
4.2%
0.002 2
 
2.8%
25.0 2
 
2.8%
44.0 2
 
2.8%
0.001 2
 
2.8%
Other values (29) 31
43.1%
ValueCountFrequency (%)
0.0 12
16.7%
0.001 2
 
2.8%
0.002 2
 
2.8%
0.003 6
8.3%
0.006 1
 
1.4%
0.01 1
 
1.4%
0.011 1
 
1.4%
0.012 1
 
1.4%
0.013 2
 
2.8%
0.014 1
 
1.4%
ValueCountFrequency (%)
55.0 3
4.2%
44.0 2
2.8%
43.0 1
 
1.4%
42.0 1
 
1.4%
40.0 1
 
1.4%
34.0 1
 
1.4%
33.0 1
 
1.4%
31.0 1
 
1.4%
27.0 1
 
1.4%
25.0 2
2.8%

03월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.097181
Minimum0
Maximum55
Zeros12
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:25.132828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00275
median0.0405
Q321.75
95-th percentile47.6
Maximum55
Range55
Interquartile range (IQR)21.74725

Descriptive statistics

Standard deviation17.372319
Coefficient of variation (CV)1.7205119
Kurtosis0.62014335
Mean10.097181
Median Absolute Deviation (MAD)0.0405
Skewness1.4326935
Sum726.997
Variance301.79748
MonotonicityNot monotonic
2023-12-13T07:11:25.249091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 12
 
16.7%
0.3 8
 
11.1%
0.002 5
 
6.9%
0.003 3
 
4.2%
55.0 3
 
4.2%
28.0 2
 
2.8%
43.0 2
 
2.8%
0.016 2
 
2.8%
0.013 2
 
2.8%
26.0 2
 
2.8%
Other values (29) 31
43.1%
ValueCountFrequency (%)
0.0 12
16.7%
0.001 1
 
1.4%
0.002 5
6.9%
0.003 3
 
4.2%
0.004 1
 
1.4%
0.007 1
 
1.4%
0.01 1
 
1.4%
0.011 1
 
1.4%
0.013 2
 
2.8%
0.015 1
 
1.4%
ValueCountFrequency (%)
55.0 3
4.2%
52.0 1
 
1.4%
44.0 1
 
1.4%
43.0 2
2.8%
42.0 1
 
1.4%
37.0 1
 
1.4%
35.0 1
 
1.4%
33.0 1
 
1.4%
29.0 1
 
1.4%
28.0 2
2.8%

04월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.701708
Minimum0
Maximum65
Zeros12
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:25.353830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.003
median0.0425
Q322
95-th percentile61.7
Maximum65
Range65
Interquartile range (IQR)21.997

Descriptive statistics

Standard deviation20.946254
Coefficient of variation (CV)1.7900168
Kurtosis1.1168531
Mean11.701708
Median Absolute Deviation (MAD)0.0425
Skewness1.594439
Sum842.523
Variance438.74557
MonotonicityNot monotonic
2023-12-13T07:11:25.468509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 12
 
16.7%
0.3 7
 
9.7%
0.003 6
 
8.3%
65.0 4
 
5.6%
25.0 3
 
4.2%
0.002 2
 
2.8%
0.016 2
 
2.8%
0.013 2
 
2.8%
22.0 2
 
2.8%
0.2 2
 
2.8%
Other values (30) 30
41.7%
ValueCountFrequency (%)
0.0 12
16.7%
0.002 2
 
2.8%
0.003 6
8.3%
0.004 1
 
1.4%
0.005 1
 
1.4%
0.007 1
 
1.4%
0.008 1
 
1.4%
0.009 1
 
1.4%
0.011 1
 
1.4%
0.012 1
 
1.4%
ValueCountFrequency (%)
65.0 4
5.6%
59.0 1
 
1.4%
58.0 1
 
1.4%
54.0 1
 
1.4%
53.0 1
 
1.4%
52.0 1
 
1.4%
44.0 1
 
1.4%
35.0 1
 
1.4%
31.0 1
 
1.4%
30.0 1
 
1.4%

05월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7239861
Minimum0
Maximum58
Zeros12
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:25.633467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.003
median0.046
Q320
95-th percentile49.3
Maximum58
Range58
Interquartile range (IQR)19.997

Descriptive statistics

Standard deviation17.339067
Coefficient of variation (CV)1.7831234
Kurtosis1.4836825
Mean9.7239861
Median Absolute Deviation (MAD)0.046
Skewness1.6359283
Sum700.127
Variance300.64324
MonotonicityNot monotonic
2023-12-13T07:11:25.763043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 12
 
16.7%
0.003 6
 
8.3%
0.2 6
 
8.3%
0.006 3
 
4.2%
20.0 3
 
4.2%
0.3 3
 
4.2%
58.0 3
 
4.2%
23.0 2
 
2.8%
38.0 2
 
2.8%
0.014 2
 
2.8%
Other values (26) 30
41.7%
ValueCountFrequency (%)
0.0 12
16.7%
0.002 2
 
2.8%
0.003 6
8.3%
0.004 1
 
1.4%
0.006 3
 
4.2%
0.007 1
 
1.4%
0.009 1
 
1.4%
0.011 1
 
1.4%
0.012 1
 
1.4%
0.014 2
 
2.8%
ValueCountFrequency (%)
58.0 3
4.2%
57.0 1
 
1.4%
43.0 1
 
1.4%
42.0 1
 
1.4%
41.0 1
 
1.4%
38.0 2
2.8%
35.0 1
 
1.4%
31.0 1
 
1.4%
30.0 1
 
1.4%
23.0 2
2.8%

06월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1267639
Minimum0
Maximum43
Zeros12
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:25.880458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00375
median0.04
Q318.75
95-th percentile38.45
Maximum43
Range43
Interquartile range (IQR)18.74625

Descriptive statistics

Standard deviation13.783838
Coefficient of variation (CV)1.6961042
Kurtosis0.15868799
Mean8.1267639
Median Absolute Deviation (MAD)0.04
Skewness1.3286575
Sum585.127
Variance189.9942
MonotonicityNot monotonic
2023-12-13T07:11:26.036080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 12
 
16.7%
0.003 5
 
6.9%
0.2 4
 
5.6%
0.006 3
 
4.2%
0.004 3
 
4.2%
21.0 3
 
4.2%
0.3 3
 
4.2%
39.0 3
 
4.2%
0.4 2
 
2.8%
36.0 2
 
2.8%
Other values (27) 32
44.4%
ValueCountFrequency (%)
0.0 12
16.7%
0.001 1
 
1.4%
0.003 5
6.9%
0.004 3
 
4.2%
0.006 3
 
4.2%
0.007 1
 
1.4%
0.008 2
 
2.8%
0.011 1
 
1.4%
0.012 1
 
1.4%
0.013 1
 
1.4%
ValueCountFrequency (%)
43.0 1
 
1.4%
39.0 3
4.2%
38.0 1
 
1.4%
36.0 2
2.8%
33.0 1
 
1.4%
31.0 1
 
1.4%
28.0 2
2.8%
25.0 1
 
1.4%
24.0 2
2.8%
23.0 1
 
1.4%

07월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.207375
Minimum0
Maximum36
Zeros12
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:26.175681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.004
median0.022
Q311.25
95-th percentile31
Maximum36
Range36
Interquartile range (IQR)11.246

Descriptive statistics

Standard deviation10.794207
Coefficient of variation (CV)1.7389326
Kurtosis0.76751557
Mean6.207375
Median Absolute Deviation (MAD)0.022
Skewness1.4802785
Sum446.931
Variance116.5149
MonotonicityNot monotonic
2023-12-13T07:11:26.308493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.0 12
16.7%
0.004 6
 
8.3%
0.022 5
 
6.9%
0.005 4
 
5.6%
25.0 4
 
5.6%
0.3 3
 
4.2%
31.0 3
 
4.2%
0.2 3
 
4.2%
0.003 2
 
2.8%
0.02 2
 
2.8%
Other values (22) 28
38.9%
ValueCountFrequency (%)
0.0 12
16.7%
0.001 1
 
1.4%
0.002 2
 
2.8%
0.003 2
 
2.8%
0.004 6
8.3%
0.005 4
 
5.6%
0.006 1
 
1.4%
0.008 2
 
2.8%
0.01 1
 
1.4%
0.011 1
 
1.4%
ValueCountFrequency (%)
36.0 1
 
1.4%
35.0 1
 
1.4%
31.0 3
4.2%
26.0 1
 
1.4%
25.0 4
5.6%
19.0 2
2.8%
18.0 2
2.8%
17.0 1
 
1.4%
15.0 1
 
1.4%
14.0 1
 
1.4%

08월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9581944
Minimum0
Maximum33
Zeros13
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:26.449629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.003
median0.0275
Q311.5
95-th percentile29.7
Maximum33
Range33
Interquartile range (IQR)11.497

Descriptive statistics

Standard deviation10.444221
Coefficient of variation (CV)1.7529171
Kurtosis0.93277848
Mean5.9581944
Median Absolute Deviation (MAD)0.0275
Skewness1.5273852
Sum428.99
Variance109.08175
MonotonicityNot monotonic
2023-12-13T07:11:26.617373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 13
18.1%
0.003 5
 
6.9%
33.0 4
 
5.6%
0.3 4
 
5.6%
0.004 3
 
4.2%
15.0 3
 
4.2%
13.0 3
 
4.2%
26.0 3
 
4.2%
0.006 3
 
4.2%
0.2 3
 
4.2%
Other values (19) 28
38.9%
ValueCountFrequency (%)
0.0 13
18.1%
0.001 2
 
2.8%
0.002 2
 
2.8%
0.003 5
 
6.9%
0.004 3
 
4.2%
0.005 2
 
2.8%
0.006 3
 
4.2%
0.007 1
 
1.4%
0.009 1
 
1.4%
0.01 1
 
1.4%
ValueCountFrequency (%)
33.0 4
5.6%
27.0 1
 
1.4%
26.0 3
4.2%
25.0 1
 
1.4%
21.0 2
2.8%
16.0 1
 
1.4%
15.0 3
4.2%
13.0 3
4.2%
11.0 2
2.8%
0.4 2
2.8%

09월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8447778
Minimum0
Maximum41
Zeros12
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:26.764539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median0.0405
Q313.25
95-th percentile35.35
Maximum41
Range41
Interquartile range (IQR)13.248

Descriptive statistics

Standard deviation11.96789
Coefficient of variation (CV)1.7484702
Kurtosis0.93852028
Mean6.8447778
Median Absolute Deviation (MAD)0.0405
Skewness1.5143362
Sum492.824
Variance143.2304
MonotonicityNot monotonic
2023-12-13T07:11:26.883928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 12
 
16.7%
0.002 5
 
6.9%
0.2 4
 
5.6%
37.0 3
 
4.2%
0.3 3
 
4.2%
0.001 3
 
4.2%
0.006 3
 
4.2%
26.0 2
 
2.8%
27.0 2
 
2.8%
0.003 2
 
2.8%
Other values (30) 33
45.8%
ValueCountFrequency (%)
0.0 12
16.7%
0.001 3
 
4.2%
0.002 5
6.9%
0.003 2
 
2.8%
0.004 1
 
1.4%
0.006 3
 
4.2%
0.008 1
 
1.4%
0.009 1
 
1.4%
0.01 2
 
2.8%
0.012 2
 
2.8%
ValueCountFrequency (%)
41.0 1
 
1.4%
37.0 3
4.2%
34.0 1
 
1.4%
27.0 2
2.8%
26.0 2
2.8%
25.0 1
 
1.4%
24.0 1
 
1.4%
23.0 1
 
1.4%
21.0 1
 
1.4%
19.0 1
 
1.4%

10월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.721625
Minimum0
Maximum48
Zeros6
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:27.000256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00475
median0.0435
Q316.25
95-th percentile31.35
Maximum48
Range48
Interquartile range (IQR)16.24525

Descriptive statistics

Standard deviation13.099128
Coefficient of variation (CV)1.6964211
Kurtosis1.9508244
Mean7.721625
Median Absolute Deviation (MAD)0.0435
Skewness1.6529491
Sum555.957
Variance171.58715
MonotonicityNot monotonic
2023-12-13T07:11:27.122133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.2 7
 
9.7%
0.0 6
 
8.3%
0.002 5
 
6.9%
0.003 3
 
4.2%
17.0 3
 
4.2%
28.0 3
 
4.2%
0.3 3
 
4.2%
48.0 3
 
4.2%
0.009 3
 
4.2%
0.001 3
 
4.2%
Other values (26) 33
45.8%
ValueCountFrequency (%)
0.0 6
8.3%
0.001 3
4.2%
0.002 5
6.9%
0.003 3
4.2%
0.004 1
 
1.4%
0.005 1
 
1.4%
0.006 1
 
1.4%
0.008 1
 
1.4%
0.009 3
4.2%
0.01 1
 
1.4%
ValueCountFrequency (%)
48.0 3
4.2%
33.0 1
 
1.4%
30.0 1
 
1.4%
29.0 2
2.8%
28.0 3
4.2%
20.0 2
2.8%
19.0 2
2.8%
18.0 1
 
1.4%
17.0 3
4.2%
16.0 1
 
1.4%

11월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6331528
Minimum0
Maximum45
Zeros6
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:27.261071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0065
median0.0415
Q320
95-th percentile42
Maximum45
Range45
Interquartile range (IQR)19.9935

Descriptive statistics

Standard deviation15.489865
Coefficient of variation (CV)1.6079746
Kurtosis-0.068807595
Mean9.6331528
Median Absolute Deviation (MAD)0.0415
Skewness1.2380963
Sum693.587
Variance239.93591
MonotonicityNot monotonic
2023-12-13T07:11:27.398925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.3 6
 
8.3%
0.0 6
 
8.3%
0.003 4
 
5.6%
0.002 4
 
5.6%
0.2 4
 
5.6%
25.0 4
 
5.6%
42.0 4
 
5.6%
0.001 3
 
4.2%
0.014 3
 
4.2%
0.039 2
 
2.8%
Other values (27) 32
44.4%
ValueCountFrequency (%)
0.0 6
8.3%
0.001 3
4.2%
0.002 4
5.6%
0.003 4
5.6%
0.005 1
 
1.4%
0.007 1
 
1.4%
0.01 1
 
1.4%
0.012 2
 
2.8%
0.014 3
4.2%
0.015 1
 
1.4%
ValueCountFrequency (%)
45.0 1
 
1.4%
44.0 1
 
1.4%
43.0 1
 
1.4%
42.0 4
5.6%
40.0 1
 
1.4%
36.0 2
2.8%
32.0 1
 
1.4%
29.0 1
 
1.4%
25.0 4
5.6%
22.0 1
 
1.4%

12월
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0106667
Minimum0.001
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T07:11:27.890884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.00155
Q10.012
median0.1205
Q319.25
95-th percentile36.9
Maximum43
Range42.999
Interquartile range (IQR)19.238

Descriptive statistics

Standard deviation13.791263
Coefficient of variation (CV)1.5305485
Kurtosis-0.001074619
Mean9.0106667
Median Absolute Deviation (MAD)0.1185
Skewness1.196776
Sum648.768
Variance190.19893
MonotonicityNot monotonic
2023-12-13T07:11:28.070408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.2 7
 
9.7%
0.002 6
 
8.3%
0.001 4
 
5.6%
0.012 3
 
4.2%
43.0 3
 
4.2%
0.4 3
 
4.2%
0.003 2
 
2.8%
0.032 2
 
2.8%
20.0 2
 
2.8%
19.0 2
 
2.8%
Other values (30) 38
52.8%
ValueCountFrequency (%)
0.001 4
5.6%
0.002 6
8.3%
0.003 2
 
2.8%
0.004 1
 
1.4%
0.005 1
 
1.4%
0.006 1
 
1.4%
0.01 1
 
1.4%
0.012 3
4.2%
0.013 1
 
1.4%
0.014 2
 
2.8%
ValueCountFrequency (%)
43.0 3
4.2%
38.0 1
 
1.4%
36.0 1
 
1.4%
35.0 1
 
1.4%
33.0 1
 
1.4%
31.0 1
 
1.4%
30.0 1
 
1.4%
29.0 2
2.8%
27.0 1
 
1.4%
25.0 1
 
1.4%

특이사항
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size708.0 B
없음
48 
10월부터 신규 측정
12월부터 신규 측정
8월은 유효 측정 처리 비율 75%·9월은 50% 미만인 값.
8월은 50% 미만인 값.
 
3
Other values (2)
 
4

Length

Max length34
Median length2
Mean length7.9861111
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row없음
2nd row8월은 유효 측정 처리 비율 75%·9월은 50% 미만인 값
3rd row없음
4th row없음
5th row8월은 50% 미만인 값.

Common Values

ValueCountFrequency (%)
없음 48
66.7%
10월부터 신규 측정 6
 
8.3%
12월부터 신규 측정 6
 
8.3%
8월은 유효 측정 처리 비율 75%·9월은 50% 미만인 값. 5
 
6.9%
8월은 50% 미만인 값. 3
 
4.2%
7월은 유효 측정 처리 비율 75%·8월은 50% 미만인 값. 3
 
4.2%
8월은 유효 측정 처리 비율 75%·9월은 50% 미만인 값 1
 
1.4%

Length

2023-12-13T07:11:28.228860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:11:28.389127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 48
27.1%
측정 21
11.9%
신규 12
 
6.8%
50 12
 
6.8%
미만인 12
 
6.8%
12
 
6.8%
8월은 9
 
5.1%
유효 9
 
5.1%
처리 9
 
5.1%
비율 9
 
5.1%
Other values (5) 24
13.6%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size708.0 B
Minimum2020-02-10 00:00:00
Maximum2020-02-10 00:00:00
2023-12-13T07:11:28.517793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:28.623856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T07:11:21.609020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:10.250323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.398066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.311309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.386332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.482052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.640732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:16.940881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.936011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.877290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.748873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.677265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.676574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:10.588543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.462672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.387077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.474942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.559063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.724272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.027237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.011176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.937899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.820899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.743286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.748389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:10.652608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.528359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.509277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.564877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.645520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.814571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.115580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.098922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.004554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.900246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.819769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.820663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:10.720226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.596613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.604668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.659000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.739221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.911955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.207537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.190629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.063647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.972970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.906071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.896856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:10.795340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.666157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.682203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.757958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.816965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:16.011022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.313837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.270702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.128150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.047376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.989849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:22.306175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:10.872003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.745731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.755345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.854406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.919148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:16.094652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.397487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.356708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.202300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.131811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.074437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:22.386765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:10.943534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.821238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.825569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.941432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.012162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:16.180974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.471922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.427455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.268727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.211423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.154378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:22.485976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.030279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.911582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.915475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.049896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.133338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:16.270785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.553953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.512273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.359931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.303847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.234072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:22.580945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.106057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.004101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.039769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.134856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.229880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:16.345773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.627942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.588456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.450558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.401444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.312275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:22.681078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.172711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.084297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.128428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.232387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.311903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:16.408744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.693514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.656972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.521569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.471774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.393949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:22.792336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.250943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.152285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.203358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.324162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.423271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:16.802753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.765828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.730578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.595658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.543106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.468204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:22.856348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:11.331743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:12.223647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:13.287477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:14.403112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:15.541163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:16.871507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:17.851611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:18.805891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:19.672433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:20.608961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:11:21.535527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:11:28.732404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분행정시지역명해당연도단위01월02월03월04월05월06월07월08월09월10월11월12월특이사항
구분1.0000.0000.0000.0001.0000.6440.6940.6970.7110.8550.6940.7110.6800.6560.7060.7320.7760.000
행정시0.0001.0001.0000.0790.0000.0000.0000.0000.0000.0000.1320.0000.0000.0000.0000.2390.0720.487
지역명0.0001.0001.0000.2880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.787
해당연도0.0000.0790.2881.0000.0000.0000.2970.1560.0000.2110.1170.1250.0000.1390.2280.0000.4090.662
단위1.0000.0000.0000.0001.0000.7970.9760.7970.9760.9780.9760.9760.7970.9760.8620.8621.0000.000
01월0.6440.0000.0000.0000.7971.0000.9010.9650.8770.9040.8970.8540.9590.8670.9320.9240.7890.000
02월0.6940.0000.0000.2970.9760.9011.0000.8930.9630.8740.9630.9580.8300.9600.8530.8280.8380.156
03월0.6970.0000.0000.1560.7970.9650.8931.0000.9290.8880.9100.8740.9680.8800.9380.9510.8400.000
04월0.7110.0000.0000.0000.9760.8770.9630.9291.0000.9390.9710.9780.9110.9640.8150.8410.8530.000
05월0.8550.0000.0000.2110.9780.9040.8740.8880.9391.0000.9100.9020.9070.8810.8200.8010.8510.000
06월0.6940.1320.0000.1170.9760.8970.9630.9100.9710.9101.0000.9600.9030.9590.8150.8220.8690.000
07월0.7110.0000.0000.1250.9760.8540.9580.8740.9780.9020.9601.0000.9370.9560.8520.8620.8960.000
08월0.6800.0000.0000.0000.7970.9590.8300.9680.9110.9070.9030.9371.0000.8620.9350.9360.8520.000
09월0.6560.0000.0000.1390.9760.8670.9600.8800.9640.8810.9590.9560.8621.0000.8340.8280.8930.000
10월0.7060.0000.0000.2280.8620.9320.8530.9380.8150.8200.8150.8520.9350.8341.0000.9230.8650.000
11월0.7320.2390.0000.0000.8620.9240.8280.9510.8410.8010.8220.8620.9360.8280.9231.0000.8510.261
12월0.7760.0720.0000.4091.0000.7890.8380.8400.8530.8510.8690.8960.8520.8930.8650.8511.0000.000
특이사항0.0000.4870.7870.6620.0000.0000.1560.0000.0000.0000.0000.0000.0000.0000.0000.2610.0001.000
2023-12-13T07:11:28.940463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정시해당연도지역명단위구분특이사항
행정시1.0000.1280.9780.0000.0000.502
해당연도0.1281.0000.2220.0000.0000.550
지역명0.9780.2221.0000.0000.0000.651
단위0.0000.0000.0001.0000.9710.000
구분0.0000.0000.0000.9711.0000.000
특이사항0.5020.5500.6510.0000.0001.000
2023-12-13T07:11:29.073647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
01월02월03월04월05월06월07월08월09월10월11월12월구분행정시지역명해당연도단위특이사항
01월1.0000.9930.9930.9910.9920.9920.9880.9640.9900.8440.8430.7090.4490.0000.0000.0000.8330.000
02월0.9931.0000.9950.9930.9900.9900.9890.9590.9870.8430.8430.7100.4700.0000.0000.2100.8250.000
03월0.9930.9951.0000.9960.9960.9940.9900.9640.9910.8440.8460.7120.5040.0000.0000.0960.8330.000
04월0.9910.9930.9961.0000.9980.9960.9920.9630.9910.8420.8470.7130.4890.0000.0000.0000.8250.000
05월0.9920.9900.9960.9981.0000.9950.9900.9650.9900.8400.8450.7110.4770.0000.0000.0810.8420.000
06월0.9920.9900.9940.9960.9951.0000.9930.9660.9940.8450.8490.7140.4700.0870.0000.0600.8250.000
07월0.9880.9890.9900.9920.9900.9931.0000.9720.9900.8410.8460.7110.4890.0000.0000.1560.8250.000
08월0.9640.9590.9640.9630.9650.9660.9721.0000.9700.8170.8170.6840.4860.0000.0000.0000.8330.000
09월0.9900.9870.9910.9910.9900.9940.9900.9701.0000.8450.8480.7150.4310.0000.0000.0760.8250.000
10월0.8440.8430.8440.8420.8400.8450.8410.8170.8451.0000.9930.8560.5140.0000.0000.1490.8980.000
11월0.8430.8430.8460.8470.8450.8490.8460.8170.8480.9931.0000.8570.5300.2240.0000.0000.8900.000
12월0.7090.7100.7120.7130.7110.7140.7110.6840.7150.8560.8571.0000.5080.0560.0000.1850.9490.000
구분0.4490.4700.5040.4890.4770.4700.4890.4860.4310.5140.5300.5081.0000.0000.0000.0000.9710.000
행정시0.0000.0000.0000.0000.0000.0870.0000.0000.0000.0000.2240.0560.0001.0000.9780.1280.0000.502
지역명0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9781.0000.2220.0000.651
해당연도0.0000.2100.0960.0000.0810.0600.1560.0000.0760.1490.0000.1850.0000.1280.2221.0000.0000.550
단위0.8330.8250.8330.8250.8420.8250.8250.8330.8250.8980.8900.9490.9710.0000.0000.0001.0000.000
특이사항0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5020.6510.5500.0001.000

Missing values

2023-12-13T07:11:22.985254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:11:23.230010image/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.

Sample

구분행정시지역명해당연도단위01월02월03월04월05월06월07월08월09월10월11월12월특이사항데이터기준일자
0아황산가스(SO2) 오염도제주시이도동2016ppm0.0040.0020.0040.0050.0060.0060.0030.0030.0030.0030.0030.003없음2020-02-10
1아황산가스(SO2) 오염도제주시이도동2017ppm0.0030.0030.0020.0030.0030.0040.0020.0030.0020.0020.0020.0018월은 유효 측정 처리 비율 75%·9월은 50% 미만인 값2020-02-10
2아황산가스(SO2) 오염도제주시이도동2018ppm0.0030.0030.0030.0030.0030.0030.0040.0030.0020.0030.0030.002없음2020-02-10
3아황산가스(SO2) 오염도제주시연동2016ppm0.0020.0020.0020.0030.0030.0040.0040.0020.0030.0020.0030.003없음2020-02-10
4아황산가스(SO2) 오염도제주시연동2017ppm0.0030.0030.0030.0040.0040.0040.0040.0030.0020.0020.0020.0028월은 50% 미만인 값.2020-02-10
5아황산가스(SO2) 오염도제주시연동2018ppm0.0030.0030.0030.0030.0030.0030.0040.0040.0020.0030.0030.002없음2020-02-10
6아황산가스(SO2) 오염도서귀포시동홍동2016ppm0.0050.0030.0020.0030.0030.0030.0010.0010.0010.0020.0020.001없음2020-02-10
7아황산가스(SO2) 오염도서귀포시동홍동2017ppm0.0010.0010.0010.0020.0020.0010.0020.0010.0010.0010.0010.001없음2020-02-10
8아황산가스(SO2) 오염도서귀포시동홍동2018ppm0.0010.0010.0020.0030.0030.0030.0040.0020.0010.0010.0010.002없음2020-02-10
9아황산가스(SO2) 오염도서귀포시성산읍2017ppm0.00.00.00.00.00.00.00.00.00.0010.0010.00110월부터 신규 측정2020-02-10
구분행정시지역명해당연도단위01월02월03월04월05월06월07월08월09월10월11월12월특이사항데이터기준일자
62미세먼지(PM-2.5) 오염도제주시이도동2018㎍/㎥20.025.026.025.020.023.018.015.014.017.025.019.0없음2020-02-10
63미세먼지(PM-2.5) 오염도제주시연동2016㎍/㎥32.031.033.035.035.028.019.015.019.016.018.018.0없음2020-02-10
64미세먼지(PM-2.5) 오염도제주시연동2017㎍/㎥20.020.027.025.021.025.017.016.024.019.029.029.07월은 유효 측정 처리 비율 75%·8월은 50% 미만인 값.2020-02-10
65미세먼지(PM-2.5) 오염도제주시연동2018㎍/㎥17.016.020.022.020.021.014.011.013.017.025.020.0없음2020-02-10
66미세먼지(PM-2.5) 오염도서귀포시동홍동2016㎍/㎥24.021.024.022.023.015.011.013.016.015.019.019.0없음2020-02-10
67미세먼지(PM-2.5) 오염도서귀포시동홍동2017㎍/㎥19.022.026.023.020.021.012.013.017.011.020.020.0없음2020-02-10
68미세먼지(PM-2.5) 오염도서귀포시동홍동2018㎍/㎥20.025.021.021.019.018.018.015.015.017.018.016.0없음2020-02-10
69미세먼지(PM-2.5) 오염도서귀포시성산읍2017㎍/㎥0.00.00.00.00.00.00.00.00.020.025.022.010월부터 신규 측정2020-02-10
70미세먼지(PM-2.5) 오염도서귀포시성산읍2018㎍/㎥20.023.028.030.023.021.015.011.011.015.022.012.0없음2020-02-10
71미세먼지(PM-2.5) 오염도서귀포시대정읍2018㎍/㎥0.00.00.00.00.00.00.00.00.00.00.014.012월부터 신규 측정2020-02-10