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
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
co((g/km)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
co((g/km)) has unique valuesUnique
nox((g/km)) has unique valuesUnique
hc((g/km)) has unique valuesUnique
pm((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:42:13.313704
Analysis finished2023-12-10 12:42:21.047477
Duration7.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

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
2023-12-10T21:42:21.122155image/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
2023-12-10T21:42:21.272087image/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

2023-12-10T21:42:21.388899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:21.462838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:21.627510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0216-2]
2nd row[0216-2]
3rd row[0218-1]
4th row[0218-1]
5th row[0220-2]
ValueCountFrequency (%)
0216-2 2
 
2.0%
3305-0 2
 
2.0%
7901-0 2
 
2.0%
2419-1 2
 
2.0%
2421-0 2
 
2.0%
2422-0 2
 
2.0%
2423-1 2
 
2.0%
2502-0 2
 
2.0%
3101-6 2
 
2.0%
3301-4 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:42:21.920143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 90
11.2%
2 78
9.8%
3 54
 
6.8%
4 40
 
5.0%
7 26
 
3.2%
5 24
 
3.0%
Other values (3) 38
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 150
30.0%
1 90
18.0%
2 78
15.6%
3 54
 
10.8%
4 40
 
8.0%
7 26
 
5.2%
5 24
 
4.8%
9 18
 
3.6%
6 10
 
2.0%
8 10
 
2.0%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 90
11.2%
2 78
9.8%
3 54
 
6.8%
4 40
 
5.0%
7 26
 
3.2%
5 24
 
3.0%
Other values (3) 38
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 90
11.2%
2 78
9.8%
3 54
 
6.8%
4 40
 
5.0%
7 26
 
3.2%
5 24
 
3.0%
Other values (3) 38
 
4.8%

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

2023-12-10T21:42:22.035022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:22.116983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:22.341494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.08
Min length5

Characters and Unicode

Total characters508
Distinct characters84
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row북천-완사
2nd row북천-완사
3rd row진주-사봉
4th row진주-사봉
5th row일반성-진북
ValueCountFrequency (%)
북천-완사 2
 
2.0%
합천-쌍림 2
 
2.0%
정곡-의령 2
 
2.0%
초계-적교 2
 
2.0%
창녕-청도 2
 
2.0%
가산-남기 2
 
2.0%
부북-금곡 2
 
2.0%
창원-대산 2
 
2.0%
서생-온산 2
 
2.0%
상리-고성 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:42:22.705741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.7%
산 36
 
7.1%
남 16
 
3.1%
양 14
 
2.8%
천 14
 
2.8%
창 12
 
2.4%
진 12
 
2.4%
성 12
 
2.4%
부 10
 
2.0%
정 8
 
1.6%
Other values (74) 274
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 404
79.5%
Dash Punctuation 100
 
19.7%
Uppercase Letter 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 36
 
8.9%
남 16
 
4.0%
양 14
 
3.5%
천 14
 
3.5%
창 12
 
3.0%
진 12
 
3.0%
성 12
 
3.0%
부 10
 
2.5%
정 8
 
2.0%
안 8
 
2.0%
Other values (71) 262
64.9%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 404
79.5%
Common 100
 
19.7%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 36
 
8.9%
남 16
 
4.0%
양 14
 
3.5%
천 14
 
3.5%
창 12
 
3.0%
진 12
 
3.0%
성 12
 
3.0%
부 10
 
2.5%
정 8
 
2.0%
안 8
 
2.0%
Other values (71) 262
64.9%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 404
79.5%
ASCII 104
 
20.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
96.2%
C 2
 
1.9%
I 2
 
1.9%
Hangul
ValueCountFrequency (%)
산 36
 
8.9%
남 16
 
4.0%
양 14
 
3.5%
천 14
 
3.5%
창 12
 
3.0%
진 12
 
3.0%
성 12
 
3.0%
부 10
 
2.5%
정 8
 
2.0%
안 8
 
2.0%
Other values (71) 262
64.9%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.598
Minimum2.7
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:22.835932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile3.3
Q15.2
median7.75
Q311
95-th percentile17.6
Maximum20.1
Range17.4
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.2070883
Coefficient of variation (CV)0.48931011
Kurtosis0.43648909
Mean8.598
Median Absolute Deviation (MAD)2.8
Skewness0.87516361
Sum859.8
Variance17.699592
MonotonicityNot monotonic
2023-12-10T21:42:22.973490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
11.2 4
 
4.0%
10.2 4
 
4.0%
6.8 4
 
4.0%
5.2 4
 
4.0%
10.5 4
 
4.0%
4.2 2
 
2.0%
9.9 2
 
2.0%
7.6 2
 
2.0%
6.1 2
 
2.0%
3.3 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
2.7 2
2.0%
3.0 2
2.0%
3.3 2
2.0%
3.5 2
2.0%
3.8 2
2.0%
3.9 2
2.0%
4.1 2
2.0%
4.2 2
2.0%
4.7 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
20.1 2
2.0%
19.3 2
2.0%
17.6 2
2.0%
17.2 2
2.0%
13.5 2
2.0%
13.3 2
2.0%
13.2 2
2.0%
12.2 2
2.0%
11.9 2
2.0%
11.2 4
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210501
2nd row20210501
3rd row20210501
4th row20210501
5th row20210501

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2023-12-10T21:42:23.103305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:23.208246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 100
100.0%

측정시간
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

2023-12-10T21:42:23.287886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:23.374616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.283464
Minimum34.86496
Maximum35.72809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:23.468725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.86496
5-th percentile34.89878
Q135.10632
median35.301425
Q335.51362
95-th percentile35.62274
Maximum35.72809
Range0.86313
Interquartile range (IQR)0.4073

Descriptive statistics

Standard deviation0.24456267
Coefficient of variation (CV)0.0069313679
Kurtosis-1.0775585
Mean35.283464
Median Absolute Deviation (MAD)0.208965
Skewness-0.15959767
Sum3528.3464
Variance0.059810901
MonotonicityNot monotonic
2023-12-10T21:42:24.142554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.12031 2
 
2.0%
35.52321 2
 
2.0%
35.57918 2
 
2.0%
35.51777 2
 
2.0%
35.50716 2
 
2.0%
35.32823 2
 
2.0%
35.39903 2
 
2.0%
34.97974 2
 
2.0%
35.28102 2
 
2.0%
35.51362 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.86496 2
2.0%
34.87506 2
2.0%
34.89878 2
2.0%
34.90043 2
2.0%
34.90831 2
2.0%
34.92571 2
2.0%
34.93132 2
2.0%
34.95217 2
2.0%
34.97974 2
2.0%
34.98771 2
2.0%
ValueCountFrequency (%)
35.72809 2
2.0%
35.65095 2
2.0%
35.62274 2
2.0%
35.61557 2
2.0%
35.61463 2
2.0%
35.58349 2
2.0%
35.57918 2
2.0%
35.57341 2
2.0%
35.55894 2
2.0%
35.53342 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.38208
Minimum127.78878
Maximum129.33586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:24.281896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.78878
5-th percentile127.80269
Q1127.99393
median128.30922
Q3128.71274
95-th percentile129.24056
Maximum129.33586
Range1.54708
Interquartile range (IQR)0.71881

Descriptive statistics

Standard deviation0.45496977
Coefficient of variation (CV)0.0035438729
Kurtosis-0.72837029
Mean128.38208
Median Absolute Deviation (MAD)0.324595
Skewness0.62484388
Sum12838.208
Variance0.20699749
MonotonicityNot monotonic
2023-12-10T21:42:24.440566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.97011 2
 
2.0%
129.09913 2
 
2.0%
128.51771 2
 
2.0%
128.72421 2
 
2.0%
128.83128 2
 
2.0%
128.71274 2
 
2.0%
129.33586 2
 
2.0%
128.2752 2
 
2.0%
128.08474 2
 
2.0%
128.17297 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
127.78878 2
2.0%
127.78997 2
2.0%
127.80269 2
2.0%
127.83555 2
2.0%
127.86341 2
2.0%
127.86709 2
2.0%
127.89437 2
2.0%
127.90225 2
2.0%
127.93378 2
2.0%
127.95798 2
2.0%
ValueCountFrequency (%)
129.33586 2
2.0%
129.28105 2
2.0%
129.24056 2
2.0%
129.2158 2
2.0%
129.17553 2
2.0%
129.12842 2
2.0%
129.09913 2
2.0%
129.02747 2
2.0%
128.87085 2
2.0%
128.83128 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5242.7685
Minimum366.34
Maximum22779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:24.608045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum366.34
5-th percentile743.1445
Q11720.0625
median4344.82
Q36377.3825
95-th percentile13410.964
Maximum22779
Range22412.66
Interquartile range (IQR)4657.32

Descriptive statistics

Standard deviation4439.2429
Coefficient of variation (CV)0.8467364
Kurtosis3.3054455
Mean5242.7685
Median Absolute Deviation (MAD)2448.06
Skewness1.6609724
Sum524276.85
Variance19706878
MonotonicityNot monotonic
2023-12-10T21:42:24.741946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3247.5 1
 
1.0%
4340.85 1
 
1.0%
8952.19 1
 
1.0%
4966.89 1
 
1.0%
5024.85 1
 
1.0%
4348.79 1
 
1.0%
4390.16 1
 
1.0%
4364.91 1
 
1.0%
4453.52 1
 
1.0%
4725.25 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
366.34 1
1.0%
389.12 1
1.0%
562.57 1
1.0%
621.0 1
1.0%
703.14 1
1.0%
745.25 1
1.0%
817.72 1
1.0%
871.01 1
1.0%
957.51 1
1.0%
963.17 1
1.0%
ValueCountFrequency (%)
22779.0 1
1.0%
21065.25 1
1.0%
18707.86 1
1.0%
14283.86 1
1.0%
13847.48 1
1.0%
13387.99 1
1.0%
12767.04 1
1.0%
12279.09 1
1.0%
12266.25 1
1.0%
11888.24 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4509.1884
Minimum268.5
Maximum22999.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:24.874067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum268.5
5-th percentile678.0645
Q11341.4375
median3592.21
Q36031.9
95-th percentile13096.181
Maximum22999.36
Range22730.86
Interquartile range (IQR)4690.4625

Descriptive statistics

Standard deviation4025.9435
Coefficient of variation (CV)0.89283107
Kurtosis5.1485633
Mean4509.1884
Median Absolute Deviation (MAD)2284.23
Skewness1.9379962
Sum450918.84
Variance16208221
MonotonicityNot monotonic
2023-12-10T21:42:25.016531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2431.82 1
 
1.0%
4884.79 1
 
1.0%
7319.32 1
 
1.0%
8644.93 1
 
1.0%
9263.26 1
 
1.0%
4470.71 1
 
1.0%
5206.01 1
 
1.0%
4327.78 1
 
1.0%
5021.34 1
 
1.0%
3262.1 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
268.5 1
1.0%
292.87 1
1.0%
363.3 1
1.0%
387.57 1
1.0%
491.19 1
1.0%
687.9 1
1.0%
690.43 1
1.0%
706.47 1
1.0%
716.36 1
1.0%
738.98 1
1.0%
ValueCountFrequency (%)
22999.36 1
1.0%
18871.66 1
1.0%
14998.53 1
1.0%
13899.25 1
1.0%
13517.62 1
1.0%
13074.0 1
1.0%
10383.83 1
1.0%
10223.25 1
1.0%
9814.67 1
1.0%
9263.26 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595.8689
Minimum37.04
Maximum2784.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:25.141386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.04
5-th percentile90.9035
Q1191.285
median488.255
Q3765.2375
95-th percentile1676.2035
Maximum2784.12
Range2747.08
Interquartile range (IQR)573.9525

Descriptive statistics

Standard deviation516.30249
Coefficient of variation (CV)0.86646995
Kurtosis4.4343002
Mean595.8689
Median Absolute Deviation (MAD)285.69
Skewness1.8571602
Sum59586.89
Variance266568.26
MonotonicityNot monotonic
2023-12-10T21:42:25.287707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350.85 1
 
1.0%
645.4 1
 
1.0%
949.64 1
 
1.0%
840.25 1
 
1.0%
944.47 1
 
1.0%
552.77 1
 
1.0%
592.51 1
 
1.0%
524.43 1
 
1.0%
558.54 1
 
1.0%
466.69 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
37.04 1
1.0%
39.43 1
1.0%
53.59 1
1.0%
57.86 1
1.0%
67.98 1
1.0%
92.11 1
1.0%
95.58 1
1.0%
96.88 1
1.0%
100.06 1
1.0%
102.82 1
1.0%
ValueCountFrequency (%)
2784.12 1
1.0%
2447.31 1
1.0%
2211.71 1
1.0%
1861.5 1
1.0%
1694.7 1
1.0%
1675.23 1
1.0%
1367.5 1
1.0%
1321.94 1
1.0%
1247.56 1
1.0%
1202.86 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.0259
Minimum25.19
Maximum1462.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:25.446109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.19
5-th percentile42.8
Q186.66
median194.165
Q3344.3625
95-th percentile690.1995
Maximum1462.54
Range1437.35
Interquartile range (IQR)257.7025

Descriptive statistics

Standard deviation243.72398
Coefficient of variation (CV)0.95568324
Kurtosis6.9171702
Mean255.0259
Median Absolute Deviation (MAD)117.83
Skewness2.2548505
Sum25502.59
Variance59401.378
MonotonicityNot monotonic
2023-12-10T21:42:25.574649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151.35 1
 
1.0%
269.1 1
 
1.0%
482.01 1
 
1.0%
566.66 1
 
1.0%
609.02 1
 
1.0%
254.32 1
 
1.0%
283.23 1
 
1.0%
201.99 1
 
1.0%
283.82 1
 
1.0%
171.86 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
25.19 1
1.0%
25.73 1
1.0%
26.19 1
1.0%
28.01 1
1.0%
31.02 1
1.0%
43.42 1
1.0%
45.1 1
1.0%
46.1 1
1.0%
49.65 1
1.0%
50.03 1
1.0%
ValueCountFrequency (%)
1462.54 1
1.0%
1134.06 1
1.0%
902.84 1
1.0%
888.98 1
1.0%
757.07 1
1.0%
686.68 1
1.0%
686.23 1
1.0%
609.02 1
1.0%
566.66 1
1.0%
502.88 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1349774.9
Minimum96235.94
Maximum6118361.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:25.709753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum96235.94
5-th percentile186899.01
Q1445288.02
median1120470.4
Q31649026.7
95-th percentile3357285.2
Maximum6118361.9
Range6022126
Interquartile range (IQR)1203738.7

Descriptive statistics

Standard deviation1134167.5
Coefficient of variation (CV)0.8402642
Kurtosis3.3356739
Mean1349774.9
Median Absolute Deviation (MAD)635302.85
Skewness1.6369157
Sum1.3497749 × 108
Variance1.286336 × 1012
MonotonicityNot monotonic
2023-12-10T21:42:25.845753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
838083.3 1
 
1.0%
1087519.17 1
 
1.0%
2308307.32 1
 
1.0%
1379158.95 1
 
1.0%
1346603.46 1
 
1.0%
1153421.6 1
 
1.0%
1190599.4 1
 
1.0%
1170950.31 1
 
1.0%
1230290.01 1
 
1.0%
1230130.34 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
96235.94 1
1.0%
101876.65 1
1.0%
148550.86 1
1.0%
164049.8 1
1.0%
184176.11 1
1.0%
187042.32 1
1.0%
207621.97 1
1.0%
222116.34 1
1.0%
247308.15 1
1.0%
248186.55 1
1.0%
ValueCountFrequency (%)
6118361.93 1
1.0%
4960084.0 1
1.0%
4640326.13 1
1.0%
3814802.87 1
1.0%
3621710.52 1
1.0%
3343368.06 1
1.0%
3196950.17 1
1.0%
3139714.32 1
1.0%
3123319.24 1
1.0%
3099027.2 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:26.162865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.86
Min length8

Characters and Unicode

Total characters1086
Distinct characters105
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경남 사천 곤명 작팔
2nd row경남 사천 곤명 작팔
3rd row경남 진주 문산 상문
4th row경남 진주 문산 상문
5th row경남 창원 진전 근곡
ValueCountFrequency (%)
경남 100
25.1%
고성 10
 
2.5%
합천 10
 
2.5%
창원 10
 
2.5%
남해 8
 
2.0%
산청 8
 
2.0%
울주 8
 
2.0%
진주 8
 
2.0%
밀양 6
 
1.5%
고현 4
 
1.0%
Other values (103) 226
56.8%
2023-12-10T21:42:26.570430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
남 112
 
10.3%
경 100
 
9.2%
산 32
 
2.9%
천 24
 
2.2%
고 20
 
1.8%
창 20
 
1.8%
주 16
 
1.5%
양 16
 
1.5%
대 14
 
1.3%
Other values (95) 434
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 788
72.6%
Space Separator 298
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 112
 
14.2%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.0%
고 20
 
2.5%
창 20
 
2.5%
주 16
 
2.0%
양 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (94) 420
53.3%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 788
72.6%
Common 298
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 112
 
14.2%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.0%
고 20
 
2.5%
창 20
 
2.5%
주 16
 
2.0%
양 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (94) 420
53.3%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 788
72.6%
ASCII 298
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
남 112
 
14.2%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.0%
고 20
 
2.5%
창 20
 
2.5%
주 16
 
2.0%
양 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (94) 420
53.3%

Interactions

2023-12-10T21:42:19.914437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:13.810474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.576176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.214955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.953033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.798323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.502510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.398269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.153422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.987700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:13.890810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.645059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.284292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.028994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.885932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.580409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.474703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.228258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:20.072140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:13.977500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.714297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.378216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.114443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.981961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.657960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.568823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.317154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:20.141779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.056456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.776590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.476423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.206555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.055568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.723368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.650841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.407586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:20.229826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.182861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.855542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.552106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.336577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.156199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.033900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.741591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.513638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:20.325917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.250916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.921432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.616026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.417446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.221193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.097606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.817272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.586029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:20.395877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.326445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.990262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.687650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.523880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.287416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.163306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.899875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.666739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:20.491500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.401583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.059470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.755913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.621257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.356003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.233327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.987475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.743923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:20.577807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:14.489916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.135958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:15.839703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:16.709222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:17.430548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:18.313817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.077021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:19.835264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:42:26.668973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
기본키1.0001.0000.0001.0000.5660.8040.6210.5170.5240.6130.4100.6531.000
지점1.0001.0000.0001.0001.0001.0001.0000.9370.9340.9090.8880.9511.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9370.9340.9090.8880.9511.000
연장((km))0.5661.0000.0001.0001.0000.5600.6860.5620.4440.2260.0000.4231.000
좌표위치위도((°))0.8041.0000.0001.0000.5601.0000.5910.5300.5930.6440.5410.7431.000
좌표위치경도((°))0.6211.0000.0001.0000.6860.5911.0000.7230.7110.6230.6190.6491.000
co((g/km))0.5170.9370.0000.9370.5620.5300.7231.0000.9650.9150.9550.9610.937
nox((g/km))0.5240.9340.0000.9340.4440.5930.7110.9651.0000.9670.9790.9440.934
hc((g/km))0.6130.9090.0000.9090.2260.6440.6230.9150.9671.0000.9190.9880.909
pm((g/km))0.4100.8880.0000.8880.0000.5410.6190.9550.9790.9191.0000.9200.888
co2((g/km))0.6530.9510.0000.9510.4230.7430.6490.9610.9440.9880.9201.0000.951
주소1.0001.0000.0001.0001.0001.0001.0000.9370.9340.9090.8880.9511.000
2023-12-10T21:42:26.809762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))방향
기본키1.000-0.0500.0790.116-0.335-0.311-0.320-0.324-0.3310.000
연장((km))-0.0501.0000.2070.020-0.145-0.152-0.172-0.172-0.1400.000
좌표위치위도((°))0.0790.2071.0000.315-0.281-0.185-0.202-0.122-0.2790.000
좌표위치경도((°))0.1160.0200.3151.0000.3140.3040.3170.2940.3150.000
co((g/km))-0.335-0.145-0.2810.3141.0000.9540.9750.9050.9980.000
nox((g/km))-0.311-0.152-0.1850.3040.9541.0000.9920.9780.9580.000
hc((g/km))-0.320-0.172-0.2020.3170.9750.9921.0000.9650.9760.000
pm((g/km))-0.324-0.172-0.1220.2940.9050.9780.9651.0000.9090.000
co2((g/km))-0.331-0.140-0.2790.3150.9980.9580.9760.9091.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T21:42:20.701051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:42:20.962028image/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

기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
01건기연[0216-2]1북천-완사4.220210501035.12031127.970113247.52431.82350.85151.35838083.3경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210501035.12031127.970113461.853153.58424.61212.34890953.84경남 사천 곤명 작팔
23건기연[0218-1]1진주-사봉3.520210501035.16901128.18736221.95998.3766.1401.891611939.42경남 진주 문산 상문
34건기연[0218-1]2진주-사봉3.520210501035.16901128.18737185.836855.19881.06438.721871828.68경남 진주 문산 상문
45건기연[0220-2]1일반성-진북4.820210501035.10632128.4421814283.8613899.251694.7757.073814802.87경남 창원 진전 근곡
56건기연[0220-2]2일반성-진북4.820210501035.10632128.4421822779.022999.362784.121462.546118361.93경남 창원 진전 근곡
67건기연[0222-1]1마산-부산9.420210501035.1839128.639510262.899814.671164.91488.942691553.13경남 창원 양곡
78건기연[0222-1]2마산-부산9.420210501035.1839128.639510484.6810383.831247.56501.22708600.95경남 창원 양곡
89건기연[0302-4]1상죽-사천10.220210501034.87506128.009585016.263302.48487.37141.871306848.11경남 남해 창선 동대
910건기연[0302-4]2상죽-사천10.220210501034.87506128.009584784.213874.93486.25228.381237288.7경남 남해 창선 동대
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[7702-0]1통영-고성6.420210501034.89878128.355381087.52958.45135.6755.51269867.3경남 통영 도산 오륜
9192건기연[7702-0]2통영-고성6.420210501034.89878128.355381014.9903.5130.3253.84250306.96경남 통영 도산 오륜
9293건기연[7702-2]1삼산-하이11.120210501034.92571128.130591900.911277.96180.9863.15497485.53경남 고성 하이 덕호
9394건기연[7702-2]2삼산-하이11.120210501034.92571128.130591892.611289.7180.5266.53495338.2경남 고성 하이 덕호
9495건기연[7703-0]1유포-설천11.220210501034.90043127.86341621.0387.5757.8625.19164049.8경남 남해 고현 포상
9596건기연[7703-0]2유포-설천11.220210501034.90043127.86341562.57363.353.5928.01148550.86경남 남해 고현 포상
9697건기연[7901-0]1정곡-의령2.720210501035.29567128.2985329.874225.18589.3253.131387518.49경남 함안 군북 월촌
9798건기연[7901-0]2정곡-의령2.720210501035.29567128.2985713.164693.57642.0253.251486775.91경남 함안 군북 월촌
9899건기연[7904-0]1마산-진영5.220210501035.28732128.6117213847.488942.81321.94404.473621710.52경남 창원 북 외감
99100건기연[7904-0]2마산-진영5.220210501035.28732128.6117212767.048168.91202.86343.083343368.06경남 창원 북 외감