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
측정시분 has constant value ""Constant
측정구간 is highly overall correlated with 기본키 and 10 other fieldsHigh correlation
지점 is highly overall correlated with 기본키 and 10 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 10 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 nox and 4 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 좌표위치위도 and 7 other fieldsHigh correlation
hc is highly overall correlated with co and 6 other fieldsHigh correlation
pm is highly overall correlated with 좌표위치위도 and 7 other fieldsHigh correlation
co2 is highly overall correlated with co and 6 other fieldsHigh correlation
측정일 is highly overall correlated with 기본키High correlation
측정일 is highly imbalanced (53.1%)Imbalance
기본키 has unique valuesUnique
co has unique valuesUnique
nox has unique valuesUnique
hc has unique valuesUnique
pm has unique valuesUnique
co2 has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:35:33.632896
Analysis finished2023-12-10 12:35:48.023653
Duration14.39 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
2023-12-10T21:35:48.158559image/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
2023-12-10T21:35:48.413064image/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

2023-12-10T21:35:48.659904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:35:48.793472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
[0324-1]
 
4
[0326-2]
 
4
[0406-1]
 
4
[0325-3]
 
4
[0325-4]
 
4
Other values (40)
80 

Length

Max length9
Median length8
Mean length7.84
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%
[0326-2] 4
 
4.0%
[0406-1] 4
 
4.0%
[0325-3] 4
 
4.0%
[0325-4] 4
 
4.0%
[1919-1] 2
 
2.0%
[0408-1] 2
 
2.0%
[0521-1] 2
 
2.0%
[1721-0] 2
 
2.0%
[1724-4] 2
 
2.0%
Other values (35) 70
70.0%

Length

2023-12-10T21:35:48.953148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0324-1 4
 
4.0%
0406-1 4
 
4.0%
0325-3 4
 
4.0%
0325-4 4
 
4.0%
0326-2 4
 
4.0%
3810-1 2
 
2.0%
5913-1 2
 
2.0%
3609-1 2
 
2.0%
3613-0 2
 
2.0%
3709-2 2
 
2.0%
Other values (35) 70
70.0%

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

2023-12-10T21:35:49.155792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:35:49.297890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
수안보-충주
 
4
오생-장호원
 
4
대전-옥천
 
4
신니-신양
 
4
주덕-충주
 
4
Other values (40)
80 

Length

Max length13
Median length5
Mean length6.42
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%
오생-장호원 4
 
4.0%
대전-옥천 4
 
4.0%
신니-신양 4
 
4.0%
주덕-충주 4
 
4.0%
내북-구방 2
 
2.0%
약목-황간 2
 
2.0%
단양-하시 2
 
2.0%
청주-진천 2
 
2.0%
진천-광혜원 2
 
2.0%
Other values (35) 70
70.0%

Length

2023-12-10T21:35:49.484305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수안보-충주 4
 
4.0%
대전-옥천 4
 
4.0%
신니-신양 4
 
4.0%
주덕-충주 4
 
4.0%
오생-장호원 4
 
4.0%
장락-쌍용 2
 
2.0%
대대-향산 2
 
2.0%
노암-중흥 2
 
2.0%
내사-덕산 2
 
2.0%
군서-안내 2
 
2.0%
Other values (35) 70
70.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.068
Minimum1.5
Maximum22.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:35:49.762566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.1
Q14.8
median7.3
Q311.1
95-th percentile15.5
Maximum22.4
Range20.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.4029118
Coefficient of variation (CV)0.54572531
Kurtosis0.67361127
Mean8.068
Median Absolute Deviation (MAD)3.45
Skewness0.82795809
Sum806.8
Variance19.385632
MonotonicityNot monotonic
2023-12-10T21:35:49.985035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
7.3 10
 
10.0%
5.4 6
 
6.0%
3.4 4
 
4.0%
8.8 4
 
4.0%
11.1 4
 
4.0%
12.3 4
 
4.0%
15.5 4
 
4.0%
5.0 4
 
4.0%
12.1 2
 
2.0%
7.7 2
 
2.0%
Other values (28) 56
56.0%
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%
3.4 4
4.0%
3.6 2
2.0%
3.7 2
2.0%
4.3 2
2.0%
ValueCountFrequency (%)
22.4 2
2.0%
15.5 4
4.0%
15.2 2
2.0%
14.5 2
2.0%
14.4 2
2.0%
13.9 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

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210601
90 
20210602
10 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 90
90.0%
20210602 10
 
10.0%

Length

2023-12-10T21:35:50.197464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:35:50.336537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 90
90.0%
20210602 10
 
10.0%

측정시분
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T21:35:50.577844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:35:50.826602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.803827
Minimum36.10527
Maximum37.19998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:35:51.011886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.10527
5-th percentile36.24756
Q136.62606
median36.89448
Q337.03359
95-th percentile37.11937
Maximum37.19998
Range1.09471
Interquartile range (IQR)0.40753

Descriptive statistics

Standard deviation0.28415477
Coefficient of variation (CV)0.007720794
Kurtosis-0.38757344
Mean36.803827
Median Absolute Deviation (MAD)0.154975
Skewness-0.84677035
Sum3680.3827
Variance0.080743931
MonotonicityNot monotonic
2023-12-10T21:35:51.254529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
36.88774 4
 
4.0%
36.96342 4
 
4.0%
37.06589 4
 
4.0%
36.3326 4
 
4.0%
36.9979 4
 
4.0%
36.32083 2
 
2.0%
36.83418 2
 
2.0%
36.91571 2
 
2.0%
36.35443 2
 
2.0%
36.51373 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
36.10527 2
2.0%
36.20366 2
2.0%
36.24756 2
2.0%
36.32083 2
2.0%
36.3326 4
4.0%
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%
ValueCountFrequency (%)
37.19998 2
2.0%
37.18469 2
2.0%
37.11937 2
2.0%
37.1091 2
2.0%
37.07857 2
2.0%
37.06589 4
4.0%
37.05585 2
2.0%
37.05389 2
2.0%
37.04502 2
2.0%
37.03807 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.73051
Minimum127.37509
Maximum128.39227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:35:51.666396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.37509
5-th percentile127.44767
Q1127.56961
median127.692
Q3127.86686
95-th percentile128.27212
Maximum128.39227
Range1.01718
Interquartile range (IQR)0.29725

Descriptive statistics

Standard deviation0.22676324
Coefficient of variation (CV)0.0017753256
Kurtosis1.0240897
Mean127.73051
Median Absolute Deviation (MAD)0.139235
Skewness1.0220576
Sum12773.051
Variance0.051421567
MonotonicityNot monotonic
2023-12-10T21:35:52.071086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
127.98484 4
 
4.0%
127.86686 4
 
4.0%
127.60416 4
 
4.0%
127.53139 4
 
4.0%
127.72238 4
 
4.0%
127.5518 2
 
2.0%
127.61577 2
 
2.0%
128.10947 2
 
2.0%
127.59919 2
 
2.0%
127.79015 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
127.37509 2
2.0%
127.38986 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 4
4.0%
127.53503 2
2.0%
127.5518 2
2.0%
ValueCountFrequency (%)
128.39227 2
2.0%
128.33167 2
2.0%
128.27212 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%
127.8901 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8055.1603
Minimum628.29
Maximum40325.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:35:52.334952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum628.29
5-th percentile971.162
Q12910.62
median5807.45
Q311660.275
95-th percentile22060.51
Maximum40325.61
Range39697.32
Interquartile range (IQR)8749.655

Descriptive statistics

Standard deviation7346.7917
Coefficient of variation (CV)0.91206027
Kurtosis5.7416913
Mean8055.1603
Median Absolute Deviation (MAD)4042.49
Skewness2.0204023
Sum805516.03
Variance53975348
MonotonicityNot monotonic
2023-12-10T21:35:52.620136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3660.7 1
 
1.0%
6364.74 1
 
1.0%
14186.95 1
 
1.0%
39353.0 1
 
1.0%
40325.61 1
 
1.0%
20447.96 1
 
1.0%
20563.55 1
 
1.0%
1405.53 1
 
1.0%
1228.2 1
 
1.0%
12558.32 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
628.29 1
1.0%
656.42 1
1.0%
860.42 1
1.0%
890.14 1
1.0%
963.98 1
1.0%
971.54 1
1.0%
987.65 1
1.0%
1099.5 1
1.0%
1144.3 1
1.0%
1222.01 1
1.0%
ValueCountFrequency (%)
40325.61 1
1.0%
39353.0 1
1.0%
25140.32 1
1.0%
23015.08 1
1.0%
22945.73 1
1.0%
22013.92 1
1.0%
20563.55 1
1.0%
20447.96 1
1.0%
17887.7 1
1.0%
17048.7 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13797.064
Minimum500.02
Maximum82634.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:35:52.897073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500.02
5-th percentile945.0755
Q13326.165
median7590.665
Q316387.467
95-th percentile47228.52
Maximum82634.76
Range82134.74
Interquartile range (IQR)13061.302

Descriptive statistics

Standard deviation16478.985
Coefficient of variation (CV)1.1943834
Kurtosis4.8711662
Mean13797.064
Median Absolute Deviation (MAD)5850.035
Skewness2.1033758
Sum1379706.4
Variance2.7155694 × 108
MonotonicityNot monotonic
2023-12-10T21:35:53.195423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4572.39 1
 
1.0%
11009.62 1
 
1.0%
27456.05 1
 
1.0%
80780.2 1
 
1.0%
82634.76 1
 
1.0%
51150.44 1
 
1.0%
49816.71 1
 
1.0%
1694.73 1
 
1.0%
1316.06 1
 
1.0%
31970.77 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
500.02 1
1.0%
641.88 1
1.0%
728.31 1
1.0%
728.38 1
1.0%
903.95 1
1.0%
947.24 1
1.0%
953.85 1
1.0%
1037.0 1
1.0%
1316.06 1
1.0%
1317.48 1
1.0%
ValueCountFrequency (%)
82634.76 1
1.0%
80780.2 1
1.0%
52947.85 1
1.0%
51150.44 1
1.0%
49816.71 1
1.0%
47092.3 1
1.0%
46120.25 1
1.0%
44653.4 1
1.0%
41821.17 1
1.0%
40645.11 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1506.8376
Minimum67.28
Maximum8361.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:35:53.464277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67.28
5-th percentile126.1025
Q1425.6025
median929.835
Q31978.5675
95-th percentile4818.033
Maximum8361.34
Range8294.06
Interquartile range (IQR)1552.965

Descriptive statistics

Standard deviation1628.0404
Coefficient of variation (CV)1.0804352
Kurtosis5.1518788
Mean1506.8376
Median Absolute Deviation (MAD)685.725
Skewness2.0812512
Sum150683.76
Variance2650515.6
MonotonicityNot monotonic
2023-12-10T21:35:53.767492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
623.13 1
 
1.0%
1309.89 1
 
1.0%
3007.41 1
 
1.0%
8301.91 1
 
1.0%
8361.34 1
 
1.0%
4873.79 1
 
1.0%
4817.21 1
 
1.0%
234.6 1
 
1.0%
173.68 1
 
1.0%
2747.08 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
67.28 1
1.0%
87.89 1
1.0%
94.71 1
1.0%
95.06 1
1.0%
120.26 1
1.0%
126.41 1
1.0%
136.01 1
1.0%
143.53 1
1.0%
152.76 1
1.0%
173.68 1
1.0%
ValueCountFrequency (%)
8361.34 1
1.0%
8301.91 1
1.0%
5388.87 1
1.0%
4873.79 1
1.0%
4833.67 1
1.0%
4817.21 1
1.0%
4782.91 1
1.0%
4467.93 1
1.0%
4371.97 1
1.0%
4078.71 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean864.3032
Minimum42.53
Maximum4980.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:35:54.091648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.53
5-th percentile62.0795
Q1225.0375
median450.59
Q31071.72
95-th percentile2872.5955
Maximum4980.98
Range4938.45
Interquartile range (IQR)846.6825

Descriptive statistics

Standard deviation997.35206
Coefficient of variation (CV)1.1539377
Kurtosis4.5711418
Mean864.3032
Median Absolute Deviation (MAD)348.1
Skewness2.032595
Sum86430.32
Variance994711.14
MonotonicityNot monotonic
2023-12-10T21:35:54.350997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285.63 1
 
1.0%
720.61 1
 
1.0%
1744.79 1
 
1.0%
4874.18 1
 
1.0%
4980.98 1
 
1.0%
3139.38 1
 
1.0%
3014.25 1
 
1.0%
116.89 1
 
1.0%
90.02 1
 
1.0%
1942.43 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
42.53 1
1.0%
47.1 1
1.0%
48.35 1
1.0%
49.84 1
1.0%
52.0 1
1.0%
62.61 1
1.0%
70.0 1
1.0%
81.33 1
1.0%
87.88 1
1.0%
90.02 1
1.0%
ValueCountFrequency (%)
4980.98 1
1.0%
4874.18 1
1.0%
3239.8 1
1.0%
3139.38 1
1.0%
3014.25 1
1.0%
2865.14 1
1.0%
2798.12 1
1.0%
2718.54 1
1.0%
2494.66 1
1.0%
2492.59 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2131731.6
Minimum161587.28
Maximum10974454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:35:54.625613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum161587.28
5-th percentile243169.09
Q1732564.48
median1451041.6
Q32855329.7
95-th percentile6112249.7
Maximum10974454
Range10812866
Interquartile range (IQR)2122765.3

Descriptive statistics

Standard deviation2043125.3
Coefficient of variation (CV)0.95843459
Kurtosis5.4443915
Mean2131731.6
Median Absolute Deviation (MAD)1078561.4
Skewness2.0293224
Sum2.1317316 × 108
Variance4.1743608 × 1012
MonotonicityNot monotonic
2023-12-10T21:35:55.337665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
846266.15 1
 
1.0%
1558515.49 1
 
1.0%
3746674.72 1
 
1.0%
10699787.59 1
 
1.0%
10974453.58 1
 
1.0%
6112155.35 1
 
1.0%
6067144.45 1
 
1.0%
322848.02 1
 
1.0%
295942.9 1
 
1.0%
4012292.39 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
161587.28 1
1.0%
162695.06 1
1.0%
219750.37 1
1.0%
228854.99 1
1.0%
239809.85 1
1.0%
243345.89 1
1.0%
246428.69 1
1.0%
249102.42 1
1.0%
285882.11 1
1.0%
295942.9 1
1.0%
ValueCountFrequency (%)
10974453.58 1
1.0%
10699787.59 1
1.0%
7025844.28 1
1.0%
6374486.08 1
1.0%
6114043.2 1
1.0%
6112155.35 1
1.0%
6067144.45 1
1.0%
5811066.77 1
1.0%
5179485.54 1
1.0%
4888496.11 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 충주 수안보 중산
 
4
충북 음성 생극 송곡
 
4
충북 옥천 군북 이백
 
4
충북 충주 신니 원평
 
4
충북 충주 대소원 만정
 
4
Other values (40)
80 

Length

Max length12
Median length11
Mean length11.1
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 음성 생극 송곡 4
 
4.0%
충북 옥천 군북 이백 4
 
4.0%
충북 충주 신니 원평 4
 
4.0%
충북 충주 대소원 만정 4
 
4.0%
충북 청주 미원 미원 2
 
2.0%
충북 영동 심천 약목 2
 
2.0%
충북 단양 매포 매포 2
 
2.0%
충북 청주 오창 가곡 2
 
2.0%
충북 진천 이월 중산 2
 
2.0%
Other values (35) 70
70.0%

Length

2023-12-10T21:35:55.554577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 100
25.0%
충주 30
 
7.5%
음성 12
 
3.0%
청주 10
 
2.5%
옥천 10
 
2.5%
증평 10
 
2.5%
보은 10
 
2.5%
진천 8
 
2.0%
신니 6
 
1.5%
노은 6
 
1.5%
Other values (77) 198
49.5%

Interactions

2023-12-10T21:35:45.791399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:35.040602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:36.412744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:38.185759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:39.369462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:40.681778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:42.032342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:43.148837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:44.416440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:45.982709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:35.273438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:36.613518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:38.315517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:39.487446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:40.850716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:42.148061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:43.289262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:44.543663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:46.107191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:35.398351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:37.196727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:38.436515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:39.621831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:40.985474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:42.253585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:43.404604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:44.665663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:46.265481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:35.520039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:37.322934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:38.570827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:39.825991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:41.131879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:42.375434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:43.559592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:44.791380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:46.398558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:35.647990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:37.506239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:38.709411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:39.970113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:41.270157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:42.498044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:43.688687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:44.944602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:46.926413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:35.788155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:37.709067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:38.858671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:40.114720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:41.451468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:42.641406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:43.822605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:45.115411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:47.043373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:35.996425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:37.835248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:39.000103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:40.232579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:41.605166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:42.757878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:44.004868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:45.234880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:47.169611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:36.123918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:37.963142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:39.125843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:40.364943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:41.739282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:42.876077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:44.135028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:45.373569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:47.306899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:36.275366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:38.071209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:39.229537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:40.508319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:41.884022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:43.016098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:44.276444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:35:45.555853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:35:55.730579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장측정일좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0000.9900.0000.9900.5921.0000.8060.6290.6250.6390.6000.5740.6250.990
지점0.9901.0000.0001.0001.0000.1071.0001.0000.9700.9560.9680.9460.9701.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간0.9901.0000.0001.0001.0000.1071.0001.0000.9700.9560.9680.9460.9701.000
연장0.5921.0000.0001.0001.0000.1790.7040.5360.6200.5300.6250.4580.6241.000
측정일1.0000.1070.0000.1070.1791.0000.1700.0000.0000.0000.0000.0000.0000.107
좌표위치위도0.8061.0000.0001.0000.7040.1701.0000.6460.6320.5740.5650.5500.6131.000
좌표위치경도0.6291.0000.0001.0000.5360.0000.6461.0000.4640.3130.4460.3030.5061.000
co0.6250.9700.0000.9700.6200.0000.6320.4641.0000.9500.9800.9450.9950.970
nox0.6390.9560.0000.9560.5300.0000.5740.3130.9501.0000.9860.9970.9620.956
hc0.6000.9680.0000.9680.6250.0000.5650.4460.9800.9861.0000.9850.9890.968
pm0.5740.9460.0000.9460.4580.0000.5500.3030.9450.9970.9851.0000.9580.946
co20.6250.9700.0000.9700.6240.0000.6130.5060.9950.9620.9890.9581.0000.970
주소0.9901.0000.0001.0001.0000.1071.0001.0000.9700.9560.9680.9460.9701.000
2023-12-10T21:35:55.970865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일측정구간지점주소방향
측정일1.0000.0000.0000.0000.000
측정구간0.0001.0001.0001.0000.000
지점0.0001.0001.0001.0000.000
주소0.0001.0001.0001.0000.000
방향0.0000.0000.0000.0001.000
2023-12-10T21:35:56.148101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2지점방향측정구간측정일주소
기본키1.0000.1450.294-0.0860.3350.3860.3810.3890.3550.6990.0000.6990.9580.699
연장0.1451.0000.1660.037-0.054-0.036-0.034-0.052-0.0530.7730.0000.7730.1280.773
좌표위치위도0.2940.1661.0000.3420.4010.5060.5000.5070.4010.7820.0000.7820.1210.782
좌표위치경도-0.0860.0370.3421.000-0.405-0.319-0.331-0.318-0.3960.7770.0000.7770.0000.777
co0.335-0.0540.401-0.4051.0000.9690.9750.9670.9950.6380.0000.6380.0000.638
nox0.386-0.0360.506-0.3190.9691.0000.9980.9970.9770.5970.0000.5970.0000.597
hc0.381-0.0340.500-0.3310.9750.9981.0000.9950.9810.6290.0000.6290.0000.629
pm0.389-0.0520.507-0.3180.9670.9970.9951.0000.9750.5710.0000.5710.0000.571
co20.355-0.0530.401-0.3960.9950.9770.9810.9751.0000.6380.0000.6380.0000.638
지점0.6990.7730.7820.7770.6380.5970.6290.5710.6381.0000.0001.0000.0001.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
측정구간0.6990.7730.7820.7770.6380.5970.6290.5710.6381.0000.0001.0000.0001.000
측정일0.9580.1280.1210.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
주소0.6990.7730.7820.7770.6380.5970.6290.5710.6381.0000.0001.0000.0001.000

Missing values

2023-12-10T21:35:47.531676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:35:47.879998image/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-충주7.320210601036.88774127.984843660.74572.39623.13285.63846266.15충북 충주 수안보 중산
12건기연[0324-1]2수안보-충주7.320210601036.88774127.984843701.485156.58656.02336.34875041.71충북 충주 수안보 중산
23건기연[0325-3]1신니-신양8.820210601036.9979127.722386889.839137.351152.4564.061704832.17충북 충주 신니 원평
34건기연[0325-3]2신니-신양8.820210601036.9979127.722387322.3411746.311430.46766.581788680.55충북 충주 신니 원평
45건기연[0325-4]1주덕-충주5.020210601036.96342127.8668612600.7114461.741820.57967.923087028.24충북 충주 대소원 만정
56건기연[0325-4]2주덕-충주5.020210601036.96342127.8668612638.3814353.01823.17891.823095463.75충북 충주 대소원 만정
67건기연[0326-2]1오생-장호원5.420210601037.06589127.6041610675.7115694.281900.951063.662588396.28충북 음성 생극 송곡
78건기연[0326-2]2오생-장호원5.420210601037.06589127.6041611963.8717575.312178.171213.292842057.58충북 음성 생극 송곡
89건기연[0406-1]1대전-옥천15.520210601036.3326127.531395192.654619.62632.06273.61298456.3충북 옥천 군북 이백
910건기연[0406-1]2대전-옥천15.520210601036.3326127.531395171.654471.77617.83254.931295999.58충북 옥천 군북 이백
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[0324-1]1수안보-충주7.320210602036.88774127.984843314.913979.92539.1252.41770680.76충북 충주 수안보 중산
9192건기연[0324-1]2수안보-충주7.320210602036.88774127.984843428.034627.21581.99304.69821886.84충북 충주 수안보 중산
9293건기연[0325-3]1신니-신양8.820210602036.9979127.722386288.918380.381079.54522.131529492.31충북 충주 신니 원평
9394건기연[0325-3]2신니-신양8.820210602036.9979127.722387555.8812709.911541.49831.381826717.47충북 충주 신니 원평
9495건기연[0325-4]1주덕-충주5.020210602036.96342127.8668612278.514297.741786.71978.873005606.87충북 충주 대소원 만정
9596건기연[0325-4]2주덕-충주5.020210602036.96342127.8668612418.4214979.71857.32937.933065941.99충북 충주 대소원 만정
9697건기연[0326-2]1오생-장호원5.420210602037.06589127.6041610627.3416524.022009.641147.02547534.22충북 음성 생극 송곡
9798건기연[0326-2]2오생-장호원5.420210602037.06589127.6041612043.2218269.372263.871282.392849023.55충북 음성 생극 송곡
9899건기연[0406-1]1대전-옥천15.520210602036.3326127.531394794.444117.57550.78248.991216583.51충북 옥천 군북 이백
99100건기연[0406-1]2대전-옥천15.520210602036.3326127.531395009.874225.0569.33248.531271183.67충북 옥천 군북 이백