Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Numeric9
Categorical7

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
지점 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
기본키 is highly overall correlated with 지점 and 3 other fieldsHigh correlation
연장 is highly overall correlated with 지점 and 2 other fieldsHigh correlation
좌표위치위도 is highly overall correlated with 지점 and 2 other fieldsHigh correlation
좌표위치경도 is highly overall correlated with 지점 and 2 other fieldsHigh correlation
co is highly overall correlated with nox and 6 other fieldsHigh correlation
nox is highly overall correlated with co and 3 other fieldsHigh correlation
hc is highly overall correlated with co and 3 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with co and 3 other fieldsHigh correlation
측정시분 is highly overall correlated with 기본키High correlation
측정시분 is highly imbalanced (67.3%)Imbalance
기본키 has unique valuesUnique
pm has 17 (17.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:39:19.796424
Analysis finished2023-12-10 10:39:35.567313
Duration15.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:39:35.737434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T19:39:36.039116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
건기연
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T19:39:36.305663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:39:36.573745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
[0324-1]
 
4
[0325-3]
 
4
[0325-4]
 
4
[2111-1]
 
2
[1721-0]
 
2
Other values (42)
84 

Length

Max length8
Median length8
Mean length7.86
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0324-1]
2nd row[0324-1]
3rd row[0325-3]
4th row[0325-3]
5th row[0325-4]

Common Values

ValueCountFrequency (%)
[0324-1] 4
 
4.0%
[0325-3] 4
 
4.0%
[0325-4] 4
 
4.0%
[2111-1] 2
 
2.0%
[1721-0] 2
 
2.0%
[2516-3] 2
 
2.0%
[0326-2] 2
 
2.0%
[2114-0] 2
 
2.0%
[0406-1] 2
 
2.0%
[0408-1] 2
 
2.0%
Other values (37) 74
74.0%

Length

2023-12-10T19:39:36.818641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0324-1 4
 
4.0%
0325-4 4
 
4.0%
0325-3 4
 
4.0%
04008 2
 
2.0%
3613-0 2
 
2.0%
04009 2
 
2.0%
04012 2
 
2.0%
3711-1 2
 
2.0%
3409-0 2
 
2.0%
3409-1 2
 
2.0%
Other values (37) 74
74.0%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2023-12-10T19:39:37.037163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:39:37.222067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
수안보-충주
 
4
주덕-충주
 
4
오생-장호원
 
4
신니-신양
 
4
황산-묵정
 
2
Other values (41)
82 

Length

Max length13
Median length5
Mean length6.12
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수안보-충주
2nd row수안보-충주
3rd row신니-신양
4th row신니-신양
5th row주덕-충주

Common Values

ValueCountFrequency (%)
수안보-충주 4
 
4.0%
주덕-충주 4
 
4.0%
오생-장호원 4
 
4.0%
신니-신양 4
 
4.0%
황산-묵정 2
 
2.0%
영동-영동IC 2
 
2.0%
대전-옥천 2
 
2.0%
약목-황간 2
 
2.0%
단양-하시 2
 
2.0%
제천-신림 2
 
2.0%
Other values (36) 72
72.0%

Length

2023-12-10T19:39:37.438577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수안보-충주 4
 
4.0%
신니-신양 4
 
4.0%
주덕-충주 4
 
4.0%
오생-장호원 4
 
4.0%
군서-안내 2
 
2.0%
보은-장갑 2
 
2.0%
괴산-음성 2
 
2.0%
증평-원남 2
 
2.0%
증평-대사 2
 
2.0%
초정-증평 2
 
2.0%
Other values (36) 72
72.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.774
Minimum1.5
Maximum22.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:39:37.675133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.1
Q14.3
median7.3
Q311
95-th percentile15.2
Maximum22.4
Range20.9
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation4.3231493
Coefficient of variation (CV)0.55610359
Kurtosis1.0051838
Mean7.774
Median Absolute Deviation (MAD)3.55
Skewness0.90822801
Sum777.4
Variance18.68962
MonotonicityNot monotonic
2023-12-10T19:39:37.913742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
7.3 10
 
10.0%
5.0 4
 
4.0%
5.4 4
 
4.0%
3.6 4
 
4.0%
8.8 4
 
4.0%
12.3 4
 
4.0%
3.4 4
 
4.0%
11.1 4
 
4.0%
13.9 2
 
2.0%
2.7 2
 
2.0%
Other values (29) 58
58.0%
ValueCountFrequency (%)
1.5 2
2.0%
2.0 2
2.0%
2.1 2
2.0%
2.7 2
2.0%
2.8 2
2.0%
3.1 2
2.0%
3.3 2
2.0%
3.4 4
4.0%
3.6 4
4.0%
3.7 2
2.0%
ValueCountFrequency (%)
22.4 2
2.0%
15.5 2
2.0%
15.2 2
2.0%
14.4 2
2.0%
13.9 2
2.0%
13.6 2
2.0%
12.3 4
4.0%
12.1 2
2.0%
11.2 2
2.0%
11.1 4
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T19:39:38.145014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:39:38.321589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 100
100.0%

측정시분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
94 
15
 
6

Length

Max length2
Median length1
Mean length1.06
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 94
94.0%
15 6
 
6.0%

Length

2023-12-10T19:39:38.485237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:39:38.652670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 94
94.0%
15 6
 
6.0%

좌표위치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.814463
Minimum36.10527
Maximum37.18469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:39:38.871243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.10527
5-th percentile36.24756
Q136.64694
median36.89448
Q337.01554
95-th percentile37.11937
Maximum37.18469
Range1.07942
Interquartile range (IQR)0.3686

Descriptive statistics

Standard deviation0.26616602
Coefficient of variation (CV)0.0072299307
Kurtosis0.084717016
Mean36.814463
Median Absolute Deviation (MAD)0.13938
Skewness-0.97665141
Sum3681.4463
Variance0.070844349
MonotonicityNot monotonic
2023-12-10T19:39:39.152974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
36.88774 4
 
4.0%
36.96342 4
 
4.0%
36.9979 4
 
4.0%
37.05585 2
 
2.0%
37.03807 2
 
2.0%
36.78721 2
 
2.0%
36.7919 2
 
2.0%
36.80475 2
 
2.0%
36.7235 2
 
2.0%
36.83418 2
 
2.0%
Other values (37) 74
74.0%
ValueCountFrequency (%)
36.10527 2
2.0%
36.20366 2
2.0%
36.24756 2
2.0%
36.3326 2
2.0%
36.35443 2
2.0%
36.44592 2
2.0%
36.45034 2
2.0%
36.47999 2
2.0%
36.51373 2
2.0%
36.54466 2
2.0%
ValueCountFrequency (%)
37.18469 2
2.0%
37.15606 2
2.0%
37.11937 2
2.0%
37.1091 2
2.0%
37.07857 2
2.0%
37.06589 2
2.0%
37.05585 2
2.0%
37.04502 2
2.0%
37.03807 2
2.0%
37.03413 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.74132
Minimum127.37509
Maximum128.39227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:39:39.409520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.37509
5-th percentile127.43014
Q1127.57716
median127.7073
Q3127.86686
95-th percentile128.27212
Maximum128.39227
Range1.01718
Interquartile range (IQR)0.2897

Descriptive statistics

Standard deviation0.23345238
Coefficient of variation (CV)0.0018275401
Kurtosis0.59092181
Mean127.74132
Median Absolute Deviation (MAD)0.14776
Skewness0.86888311
Sum12774.132
Variance0.054500015
MonotonicityNot monotonic
2023-12-10T19:39:39.692925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
127.98484 4
 
4.0%
127.86686 4
 
4.0%
127.72238 4
 
4.0%
127.84924 2
 
2.0%
127.76549 2
 
2.0%
127.56961 2
 
2.0%
127.6034 2
 
2.0%
127.64739 2
 
2.0%
127.55372 2
 
2.0%
127.61577 2
 
2.0%
Other values (37) 74
74.0%
ValueCountFrequency (%)
127.37509 2
2.0%
127.38986 2
2.0%
127.43014 2
2.0%
127.44767 2
2.0%
127.46142 2
2.0%
127.47145 2
2.0%
127.48385 2
2.0%
127.52268 2
2.0%
127.53139 2
2.0%
127.53503 2
2.0%
ValueCountFrequency (%)
128.39227 2
2.0%
128.33167 2
2.0%
128.27212 2
2.0%
128.11702 2
2.0%
128.10947 2
2.0%
127.98484 4
4.0%
127.95441 2
2.0%
127.95118 2
2.0%
127.92804 2
2.0%
127.91705 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.5765
Minimum0
Maximum906.98
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:39:39.943525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.57
Q19.235
median32.355
Q383.615
95-th percentile160.5875
Maximum906.98
Range906.98
Interquartile range (IQR)74.38

Descriptive statistics

Standard deviation121.89082
Coefficient of variation (CV)1.8587576
Kurtosis33.151637
Mean65.5765
Median Absolute Deviation (MAD)26.795
Skewness5.3668341
Sum6557.65
Variance14857.372
MonotonicityNot monotonic
2023-12-10T19:39:40.179126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.57 2
 
2.0%
1.57 2
 
2.0%
4.19 2
 
2.0%
0.52 2
 
2.0%
21.47 1
 
1.0%
38.91 1
 
1.0%
26.27 1
 
1.0%
9.92 1
 
1.0%
31.32 1
 
1.0%
13.1 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.52 2
2.0%
1.05 1
1.0%
1.57 2
2.0%
2.05 1
1.0%
2.6 1
1.0%
2.62 1
1.0%
3.57 1
1.0%
4.19 2
2.0%
4.23 1
1.0%
ValueCountFrequency (%)
906.98 1
1.0%
767.55 1
1.0%
194.07 1
1.0%
182.45 1
1.0%
167.19 1
1.0%
160.24 1
1.0%
157.58 1
1.0%
151.89 1
1.0%
146.14 1
1.0%
141.64 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.9737
Minimum0
Maximum1790.07
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:39:40.418571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.83
Q15.2
median22.795
Q360.54
95-th percentile262.2035
Maximum1790.07
Range1790.07
Interquartile range (IQR)55.34

Descriptive statistics

Standard deviation231.91195
Coefficient of variation (CV)2.8291019
Kurtosis40.465013
Mean81.9737
Median Absolute Deviation (MAD)19.83
Skewness6.1243642
Sum8197.37
Variance53783.154
MonotonicityNot monotonic
2023-12-10T19:39:40.687505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.83 2
 
2.0%
2.22 2
 
2.0%
0.28 2
 
2.0%
14.75 1
 
1.0%
28.69 1
 
1.0%
20.03 1
 
1.0%
16.88 1
 
1.0%
6.44 1
 
1.0%
45.14 1
 
1.0%
12.72 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.28 2
2.0%
0.55 1
1.0%
0.83 2
2.0%
1.28 1
1.0%
1.39 1
1.0%
1.51 1
1.0%
2.22 2
2.0%
2.25 1
1.0%
2.49 1
1.0%
ValueCountFrequency (%)
1790.07 1
1.0%
1421.28 1
1.0%
289.77 1
1.0%
277.62 1
1.0%
270.63 1
1.0%
261.76 1
1.0%
243.1 1
1.0%
239.58 1
1.0%
237.58 1
1.0%
230.85 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5718
Minimum0
Maximum179.5
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:39:40.942976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.8625
median3.31
Q38.3825
95-th percentile30.5145
Maximum179.5
Range179.5
Interquartile range (IQR)7.52

Descriptive statistics

Standard deviation23.200927
Coefficient of variation (CV)2.4238834
Kurtosis37.707556
Mean9.5718
Median Absolute Deviation (MAD)2.8
Skewness5.8064404
Sum957.18
Variance538.28303
MonotonicityNot monotonic
2023-12-10T19:39:41.198356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.53 2
 
2.0%
0.13 2
 
2.0%
0.37 2
 
2.0%
2.42 2
 
2.0%
0.04 2
 
2.0%
0.48 2
 
2.0%
0.35 2
 
2.0%
5.27 1
 
1.0%
3.0 1
 
1.0%
2.48 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.04 2
2.0%
0.09 1
1.0%
0.13 2
2.0%
0.2 1
1.0%
0.22 1
1.0%
0.23 1
1.0%
0.35 2
2.0%
0.37 2
2.0%
0.39 1
1.0%
ValueCountFrequency (%)
179.5 1
1.0%
137.26 1
1.0%
32.89 1
1.0%
30.79 1
1.0%
30.6 1
1.0%
30.51 1
1.0%
30.42 1
1.0%
29.59 1
1.0%
29.18 1
1.0%
28.24 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7662
Minimum0
Maximum104.97
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:39:41.808943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2675
median1.055
Q32.8475
95-th percentile18.863
Maximum104.97
Range104.97
Interquartile range (IQR)2.58

Descriptive statistics

Standard deviation13.785737
Coefficient of variation (CV)2.8923959
Kurtosis36.751999
Mean4.7662
Median Absolute Deviation (MAD)0.925
Skewness5.7469869
Sum476.62
Variance190.04656
MonotonicityNot monotonic
2023-12-10T19:39:42.076260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
17.0%
0.28 7
 
7.0%
0.13 4
 
4.0%
0.4 3
 
3.0%
0.27 3
 
3.0%
0.14 3
 
3.0%
0.38 2
 
2.0%
1.16 2
 
2.0%
2.81 1
 
1.0%
1.11 1
 
1.0%
Other values (57) 57
57.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.13 4
 
4.0%
0.14 3
 
3.0%
0.26 1
 
1.0%
0.27 3
 
3.0%
0.28 7
7.0%
0.38 2
 
2.0%
0.4 3
 
3.0%
0.42 1
 
1.0%
0.44 1
 
1.0%
ValueCountFrequency (%)
104.97 1
1.0%
80.47 1
1.0%
24.45 1
1.0%
21.02 1
1.0%
19.11 1
1.0%
18.85 1
1.0%
18.82 1
1.0%
17.67 1
1.0%
16.8 1
1.0%
15.41 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16904.192
Minimum0
Maximum247123.41
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:39:42.337152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile416.06
Q12212.465
median8067.525
Q321749.103
95-th percentile42473.121
Maximum247123.41
Range247123.41
Interquartile range (IQR)19536.638

Descriptive statistics

Standard deviation32730.594
Coefficient of variation (CV)1.9362413
Kurtosis34.79393
Mean16904.192
Median Absolute Deviation (MAD)6382.665
Skewness5.5314696
Sum1690419.2
Variance1.0712918 × 109
MonotonicityNot monotonic
2023-12-10T19:39:42.578122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
416.06 2
 
2.0%
1109.48 2
 
2.0%
138.68 2
 
2.0%
5061.67 1
 
1.0%
10963.4 1
 
1.0%
6548.31 1
 
1.0%
6887.48 1
 
1.0%
2828.49 1
 
1.0%
8216.17 1
 
1.0%
3362.45 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.0 1
1.0%
138.68 2
2.0%
277.37 1
1.0%
416.06 2
2.0%
567.01 1
1.0%
614.73 1
1.0%
693.42 1
1.0%
925.84 1
1.0%
1075.77 1
1.0%
1109.48 2
2.0%
ValueCountFrequency (%)
247123.41 1
1.0%
204399.06 1
1.0%
50781.64 1
1.0%
47394.18 1
1.0%
42697.34 1
1.0%
42461.32 1
1.0%
41026.28 1
1.0%
40621.2 1
1.0%
35608.79 1
1.0%
35562.01 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 충주 수안보 중산
 
4
충북 충주 신니 원평
 
4
충북 충주 대소원 만정
 
4
충북 진천 덕산 옥동
 
2
충북 청주 오창 가곡
 
2
Other values (42)
84 

Length

Max length13
Median length11
Mean length11.14
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충북 충주 수안보 중산
2nd row충북 충주 수안보 중산
3rd row충북 충주 신니 원평
4th row충북 충주 신니 원평
5th row충북 충주 대소원 만정

Common Values

ValueCountFrequency (%)
충북 충주 수안보 중산 4
 
4.0%
충북 충주 신니 원평 4
 
4.0%
충북 충주 대소원 만정 4
 
4.0%
충북 진천 덕산 옥동 2
 
2.0%
충북 청주 오창 가곡 2
 
2.0%
충북 보은 탄부 상장 2
 
2.0%
충북 음성 생극 송곡 2
 
2.0%
충북 음성 생극 병암 2
 
2.0%
충북 옥천 군북 이백 2
 
2.0%
충북 영동 심천 약목 2
 
2.0%
Other values (37) 74
74.0%

Length

2023-12-10T19:39:42.807254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 100
25.0%
충주 28
 
7.0%
음성 14
 
3.5%
청주 12
 
3.0%
증평 10
 
2.5%
보은 10
 
2.5%
진천 8
 
2.0%
중산 6
 
1.5%
신니 6
 
1.5%
괴산 6
 
1.5%
Other values (82) 200
50.0%

Interactions

2023-12-10T19:39:33.296617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:21.260415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:22.927476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:24.248986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:25.757936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:27.205828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:28.452981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:29.969612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:31.482902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:33.441767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:21.394032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:23.071458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:24.403000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:25.931675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:27.334158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:28.612154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:30.152637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:31.639467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:33.575816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:21.519874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:23.183433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:24.625549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:26.113131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:27.442235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:28.737633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:30.294227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:31.793189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:33.732043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:21.670242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:23.315894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:24.800326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:26.292630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:27.577577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:28.904833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:30.464548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:31.945691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:33.956953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:21.818488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:23.459144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:24.951483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:26.457405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:27.697977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:29.085221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:30.635245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:32.166969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:34.108691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:21.955483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:23.593314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:25.100452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:26.597913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:27.837273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:29.236174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:30.787738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:32.315033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:34.279903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:22.456258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:23.767971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:25.276792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:26.748574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:27.989816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:29.433190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:30.968997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:32.475154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:34.466059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:22.620342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:23.967818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:25.448158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:26.911692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:28.149707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:29.637798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:31.138411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:33.011593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:34.713644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:22.780503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:24.118454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:25.611500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:27.053286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:28.297947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:29.812098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:31.311587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:39:33.155263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:39:42.981189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0000.9930.0000.9940.6010.8930.8170.6360.6060.6650.6650.6800.5770.993
지점0.9931.0000.0001.0001.0000.1471.0001.0000.9010.8550.8550.7410.7671.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0000.000
측정구간0.9941.0000.0001.0001.0000.2281.0001.0000.9070.8610.8610.7580.7811.000
연장0.6011.0000.0001.0001.0000.0960.6120.6010.0000.1140.1140.0000.0001.000
측정시분0.8930.1470.0000.2280.0961.0000.0000.0760.0000.0000.0000.0000.0000.147
좌표위치위도0.8171.0000.0001.0000.6120.0001.0000.6530.4700.4000.4000.3460.4271.000
좌표위치경도0.6361.0000.0001.0000.6010.0760.6531.0000.4270.1500.1500.0000.3571.000
co0.6060.9010.0000.9070.0000.0000.4700.4271.0000.9030.9030.9790.9950.901
nox0.6650.8550.0000.8610.1140.0000.4000.1500.9031.0001.0000.9560.9120.855
hc0.6650.8550.0000.8610.1140.0000.4000.1500.9031.0001.0000.9560.9120.855
pm0.6800.7410.0950.7580.0000.0000.3460.0000.9790.9560.9561.0000.9890.741
co20.5770.7670.0000.7810.0000.0000.4270.3570.9950.9120.9120.9891.0000.767
주소0.9931.0000.0001.0001.0000.1471.0001.0000.9010.8550.8550.7410.7671.000
2023-12-10T19:39:43.228172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점측정구간방향주소측정시분
지점1.0000.9910.0001.0000.059
측정구간0.9911.0000.0000.9910.117
방향0.0000.0001.0000.0000.000
주소1.0000.9910.0001.0000.059
측정시분0.0590.1170.0000.0591.000
2023-12-10T19:39:43.425137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2지점방향측정구간측정시분주소
기본키1.0000.0880.2220.0110.2620.2850.2680.3210.2700.7180.0000.7260.6990.718
연장0.0881.0000.2130.181-0.148-0.167-0.165-0.173-0.1450.7590.0000.7660.0640.759
좌표위치위도0.2220.2131.0000.3970.2960.3170.3150.3400.3040.7670.0000.7750.0000.767
좌표위치경도0.0110.1810.3971.000-0.380-0.365-0.367-0.376-0.3760.7630.0000.7700.0680.763
co0.262-0.1480.296-0.3801.0000.9900.9950.9450.9980.5050.0000.5150.0000.505
nox0.285-0.1670.317-0.3650.9901.0000.9960.9700.9910.4560.0000.4680.0000.456
hc0.268-0.1650.315-0.3670.9950.9961.0000.9580.9930.4560.0000.4680.0000.456
pm0.321-0.1730.340-0.3760.9450.9700.9581.0000.9470.3310.1130.3470.0000.331
co20.270-0.1450.304-0.3760.9980.9910.9930.9471.0000.3530.0000.3680.0000.353
지점0.7180.7590.7670.7630.5050.4560.4560.3310.3531.0000.0000.9910.0591.000
방향0.0000.0000.0000.0000.0000.0000.0000.1130.0000.0001.0000.0000.0000.000
측정구간0.7260.7660.7750.7700.5150.4680.4680.3470.3680.9910.0001.0000.1170.991
측정시분0.6990.0640.0000.0680.0000.0000.0000.0000.0000.0590.0000.1171.0000.059
주소0.7180.7590.7670.7630.5050.4560.4560.3310.3531.0000.0000.9910.0591.000

Missing values

2023-12-10T19:39:34.998635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:39:35.428256image/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

기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
01건기연[0324-1]1수안보-충주7.320210101036.88774127.9848421.4714.752.080.385061.67충북 충주 수안보 중산
12건기연[0324-1]2수안보-충주7.320210101036.88774127.9848417.7210.131.560.284670.34충북 충주 수안보 중산
23건기연[0325-3]1신니-신양8.820210101036.9979127.7223848.4440.225.611.7812132.46충북 충주 신니 원평
34건기연[0325-3]2신니-신양8.820210101036.9979127.7223853.8438.165.851.5613788.24충북 충주 신니 원평
45건기연[0325-4]1주덕-충주5.020210101036.96342127.86686101.0885.4110.574.7925969.73충북 충주 대소원 만정
56건기연[0325-4]2주덕-충주5.020210101036.96342127.86686128.52108.613.34.6932788.54충북 충주 대소원 만정
67건기연[0326-2]1오생-장호원5.420210101037.06589127.6041624.916.022.180.947108.42충북 음성 생극 송곡
78건기연[0326-2]2오생-장호원5.420210101037.06589127.6041632.7832.084.341.87985.56충북 음성 생극 송곡
89건기연[2114-0]1오생-장호원3.620210101037.02646127.6046219.9617.422.421.465325.49충북 음성 생극 병암
910건기연[2114-0]2오생-장호원3.620210101037.02646127.6046234.2828.084.011.68046.65충북 음성 생극 병암
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[04012]1충주JCT-노은JCT5.420210101037.03807127.7654993.384.3512.447.222306.7충북 충주 노은 문성
9192건기연[04012]2충주JCT-노은JCT5.420210101037.03807127.7654976.2363.759.246.5520018.54충북 충주 노은 문성
9293건기연[04013]1노은JCT-동충주IC12.320210101037.05585127.84924138.99270.6330.4218.8535562.01충북 충주 노은 신효
9394건기연[04013]2노은JCT-동충주IC12.320210101037.05585127.84924160.24289.7732.8921.0240621.2충북 충주 노은 신효
9495건기연[0324-1]1수안보-충주7.3202101011536.88774127.9848418.7613.991.750.384889.13충북 충주 수안보 중산
9596건기연[0324-1]2수안보-충주7.3202101011536.88774127.9848418.2410.411.60.284809.02충북 충주 수안보 중산
9697건기연[0325-3]1신니-신양8.8202101011536.9979127.7223845.3138.655.351.6711289.64충북 충주 신니 원평
9798건기연[0325-3]2신니-신양8.8202101011536.9979127.7223852.0932.995.11.0413567.73충북 충주 신니 원평
9899건기연[0325-4]1주덕-충주5.0202101011536.96342127.8668693.5778.079.824.3823923.55충북 충주 대소원 만정
99100건기연[0325-4]2주덕-충주5.0202101011536.96342127.86686107.43100.6911.094.4729255.84충북 충주 대소원 만정