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 13:02:24.762563
Analysis finished2023-12-10 13:02:34.299472
Duration9.54 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
2023-12-10T22:02:34.397951image/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
2023-12-10T22:02:34.786482image/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

2023-12-10T22:02:34.923072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:35.004702image/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
2023-12-10T22:02:35.252591image/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[0134-0]
2nd row[0134-0]
3rd row[0141-1]
4th row[0141-1]
5th row[0142-0]
ValueCountFrequency (%)
0134-0 2
 
2.0%
4207-0 2
 
2.0%
4509-0 2
 
2.0%
3804-1 2
 
2.0%
3804-2 2
 
2.0%
3906-1 2
 
2.0%
3906-4 2
 
2.0%
3907-1 2
 
2.0%
3918-2 2
 
2.0%
4202-1 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:02:35.642003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 90
11.2%
1 68
8.5%
4 64
8.0%
2 54
6.8%
7 26
 
3.2%
8 22
 
2.8%
Other values (3) 56
7.0%

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 (%)
0 120
24.0%
3 90
18.0%
1 68
13.6%
4 64
12.8%
2 54
10.8%
7 26
 
5.2%
8 22
 
4.4%
5 22
 
4.4%
6 18
 
3.6%
9 16
 
3.2%
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 (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 90
11.2%
1 68
8.5%
4 64
8.0%
2 54
6.8%
7 26
 
3.2%
8 22
 
2.8%
Other values (3) 56
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 90
11.2%
1 68
8.5%
4 64
8.0%
2 54
6.8%
7 26
 
3.2%
8 22
 
2.8%
Other values (3) 56
7.0%

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

2023-12-10T22:02:35.778331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:35.887226image/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
2023-12-10T22:02:36.139578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.22
Min length4

Characters and Unicode

Total characters522
Distinct characters81
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 row송탄-오산
2nd row송탄-오산
3rd row고양-파주
4th row고양-파주
5th row당동-파평
ValueCountFrequency (%)
송탄-오산 2
 
2.0%
부발-여주 2
 
2.0%
광주-팔당 2
 
2.0%
죽산교-일죽 2
 
2.0%
안성-죽산 2
 
2.0%
아산만-덕목 2
 
2.0%
발안ic-청북ic 2
 
2.0%
팔탄-비봉 2
 
2.0%
일영-의정부 2
 
2.0%
시흥-목감 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:02:36.525575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.2%
천 24
 
4.6%
평 22
 
4.2%
안 14
 
2.7%
주 14
 
2.7%
부 14
 
2.7%
성 12
 
2.3%
양 12
 
2.3%
중 12
 
2.3%
동 10
 
1.9%
Other values (71) 288
55.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 414
79.3%
Dash Punctuation 100
 
19.2%
Uppercase Letter 8
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 24
 
5.8%
평 22
 
5.3%
안 14
 
3.4%
주 14
 
3.4%
부 14
 
3.4%
성 12
 
2.9%
양 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
영 10
 
2.4%
Other values (68) 270
65.2%
Uppercase Letter
ValueCountFrequency (%)
I 4
50.0%
C 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 414
79.3%
Common 100
 
19.2%
Latin 8
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 24
 
5.8%
평 22
 
5.3%
안 14
 
3.4%
주 14
 
3.4%
부 14
 
3.4%
성 12
 
2.9%
양 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
영 10
 
2.4%
Other values (68) 270
65.2%
Latin
ValueCountFrequency (%)
I 4
50.0%
C 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 414
79.3%
ASCII 108
 
20.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
I 4
 
3.7%
C 4
 
3.7%
Hangul
ValueCountFrequency (%)
천 24
 
5.8%
평 22
 
5.3%
안 14
 
3.4%
주 14
 
3.4%
부 14
 
3.4%
성 12
 
2.9%
양 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
영 10
 
2.4%
Other values (68) 270
65.2%

연장((km))
Real number (ℝ)

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.88
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:36.691460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median8
Q311
95-th percentile18.8
Maximum27.5
Range26
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation5.0968003
Coefficient of variation (CV)0.573964
Kurtosis2.1797304
Mean8.88
Median Absolute Deviation (MAD)2.85
Skewness1.2445086
Sum888
Variance25.977374
MonotonicityNot monotonic
2023-12-10T22:02:36.903440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
4.8 4
 
4.0%
5.4 4
 
4.0%
10.6 4
 
4.0%
6.1 4
 
4.0%
6.2 2
 
2.0%
5.5 2
 
2.0%
5.0 2
 
2.0%
5.6 2
 
2.0%
2.7 2
 
2.0%
18.8 2
 
2.0%
Other values (36) 72
72.0%
ValueCountFrequency (%)
1.5 2
2.0%
2.0 2
2.0%
2.6 2
2.0%
2.7 2
2.0%
3.1 2
2.0%
3.4 2
2.0%
4.1 2
2.0%
4.3 2
2.0%
4.8 4
4.0%
5.0 2
2.0%
ValueCountFrequency (%)
27.5 2
2.0%
19.0 2
2.0%
18.8 2
2.0%
17.6 2
2.0%
15.5 2
2.0%
15.4 2
2.0%
14.7 2
2.0%
14.6 2
2.0%
12.9 2
2.0%
12.6 2
2.0%

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

2023-12-10T22:02:37.051438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:37.164963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 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

2023-12-10T22:02:37.263081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:37.360257image/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%
Mean37.445408
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:37.482811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.3875
Q337.72112
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.5561

Descriptive statistics

Standard deviation0.34012046
Coefficient of variation (CV)0.0090831018
Kurtosis-1.0735491
Mean37.445408
Median Absolute Deviation (MAD)0.269725
Skewness0.33028681
Sum3744.5408
Variance0.11568193
MonotonicityNot monotonic
2023-12-10T22:02:37.662968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.19991 2
 
2.0%
37.02727 2
 
2.0%
36.95866 2
 
2.0%
37.05786 2
 
2.0%
37.23589 2
 
2.0%
37.71834 2
 
2.0%
37.39258 2
 
2.0%
37.23653 2
 
2.0%
37.2375 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
36.95627 2
2.0%
36.95866 2
2.0%
36.96033 2
2.0%
36.98521 2
2.0%
37.00613 2
2.0%
37.01644 2
2.0%
37.02727 2
2.0%
37.05786 2
2.0%
37.08149 2
2.0%
37.09529 2
2.0%
ValueCountFrequency (%)
38.06264 2
2.0%
38.06053 2
2.0%
38.0169 2
2.0%
38.00853 2
2.0%
37.99285 2
2.0%
37.96019 2
2.0%
37.93198 2
2.0%
37.91419 2
2.0%
37.87708 2
2.0%
37.83225 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.21477
Minimum126.77946
Maximum127.74421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:37.831819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.83253
Q1127.0284
median127.2374
Q3127.37997
95-th percentile127.62684
Maximum127.74421
Range0.96475
Interquartile range (IQR)0.35157

Descriptive statistics

Standard deviation0.2472636
Coefficient of variation (CV)0.0019436706
Kurtosis-0.78626499
Mean127.21477
Median Absolute Deviation (MAD)0.172565
Skewness0.10643924
Sum12721.477
Variance0.061139287
MonotonicityNot monotonic
2023-12-10T22:02:37.962301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
126.9889 2
 
2.0%
127.3367 2
 
2.0%
126.92256 2
 
2.0%
126.92508 2
 
2.0%
126.88236 2
 
2.0%
126.92818 2
 
2.0%
126.85818 2
 
2.0%
127.166 2
 
2.0%
127.3107 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.77946 2
2.0%
126.82904 2
2.0%
126.83253 2
2.0%
126.85818 2
2.0%
126.85908 2
2.0%
126.88236 2
2.0%
126.88641 2
2.0%
126.91531 2
2.0%
126.92256 2
2.0%
126.92508 2
2.0%
ValueCountFrequency (%)
127.74421 2
2.0%
127.6367 2
2.0%
127.62684 2
2.0%
127.61201 2
2.0%
127.60231 2
2.0%
127.56566 2
2.0%
127.55994 2
2.0%
127.49045 2
2.0%
127.44132 2
2.0%
127.42763 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7069.87
Minimum788.84
Maximum29300.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:38.144234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum788.84
5-th percentile1419.898
Q13689.4825
median6651.235
Q38651.095
95-th percentile14298.239
Maximum29300.13
Range28511.29
Interquartile range (IQR)4961.6125

Descriptive statistics

Standard deviation4743.7398
Coefficient of variation (CV)0.67097978
Kurtosis5.6945437
Mean7069.87
Median Absolute Deviation (MAD)2537.195
Skewness1.8463773
Sum706987
Variance22503067
MonotonicityNot monotonic
2023-12-10T22:02:38.326572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8750.97 1
 
1.0%
3424.17 1
 
1.0%
3106.96 1
 
1.0%
8637.38 1
 
1.0%
7146.58 1
 
1.0%
8511.67 1
 
1.0%
7256.33 1
 
1.0%
9767.28 1
 
1.0%
7152.95 1
 
1.0%
4878.95 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
788.84 1
1.0%
882.46 1
1.0%
1073.54 1
1.0%
1342.1 1
1.0%
1364.38 1
1.0%
1422.82 1
1.0%
1597.15 1
1.0%
1749.72 1
1.0%
2018.36 1
1.0%
2043.3 1
1.0%
ValueCountFrequency (%)
29300.13 1
1.0%
24770.1 1
1.0%
20702.58 1
1.0%
16519.54 1
1.0%
15045.3 1
1.0%
14258.92 1
1.0%
13008.46 1
1.0%
12688.5 1
1.0%
12400.29 1
1.0%
12227.12 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6566.9047
Minimum726.43
Maximum20791.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:38.493107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum726.43
5-th percentile1312.2335
Q13183.5525
median5638.69
Q39073.1025
95-th percentile14535.354
Maximum20791.2
Range20064.77
Interquartile range (IQR)5889.55

Descriptive statistics

Standard deviation4246.3141
Coefficient of variation (CV)0.64662338
Kurtosis0.70938262
Mean6566.9047
Median Absolute Deviation (MAD)2894.265
Skewness0.92700064
Sum656690.47
Variance18031184
MonotonicityNot monotonic
2023-12-10T22:02:38.681209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8514.85 1
 
1.0%
2150.36 1
 
1.0%
2495.49 1
 
1.0%
10227.78 1
 
1.0%
8205.97 1
 
1.0%
11256.96 1
 
1.0%
9836.9 1
 
1.0%
6502.38 1
 
1.0%
4540.2 1
 
1.0%
4578.8 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
726.43 1
1.0%
781.91 1
1.0%
837.85 1
1.0%
948.83 1
1.0%
1018.94 1
1.0%
1327.67 1
1.0%
1367.91 1
1.0%
1407.26 1
1.0%
1512.4 1
1.0%
1538.96 1
1.0%
ValueCountFrequency (%)
20791.2 1
1.0%
17634.79 1
1.0%
16935.18 1
1.0%
16906.05 1
1.0%
16478.95 1
1.0%
14433.06 1
1.0%
13039.73 1
1.0%
12846.83 1
1.0%
12251.54 1
1.0%
12053.04 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean844.3904
Minimum88.17
Maximum2896.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:38.892036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88.17
5-th percentile163.638
Q1427.875
median710.875
Q31174.98
95-th percentile1998.3755
Maximum2896.57
Range2808.4
Interquartile range (IQR)747.105

Descriptive statistics

Standard deviation542.34878
Coefficient of variation (CV)0.64229624
Kurtosis1.571667
Mean844.3904
Median Absolute Deviation (MAD)374.89
Skewness1.0756226
Sum84439.04
Variance294142.2
MonotonicityNot monotonic
2023-12-10T22:02:39.054305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051.39 1
 
1.0%
313.96 1
 
1.0%
306.09 1
 
1.0%
1235.35 1
 
1.0%
998.04 1
 
1.0%
1382.25 1
 
1.0%
1223.49 1
 
1.0%
941.27 1
 
1.0%
652.29 1
 
1.0%
565.14 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
88.17 1
1.0%
105.01 1
1.0%
105.49 1
1.0%
135.77 1
1.0%
156.57 1
1.0%
164.01 1
1.0%
168.19 1
1.0%
168.98 1
1.0%
196.0 1
1.0%
199.75 1
1.0%
ValueCountFrequency (%)
2896.57 1
1.0%
2399.47 1
1.0%
2121.12 1
1.0%
2074.94 1
1.0%
2011.78 1
1.0%
1997.67 1
1.0%
1729.1 1
1.0%
1575.88 1
1.0%
1470.14 1
1.0%
1407.79 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean378.6155
Minimum29.92
Maximum1286.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:39.251056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.92
5-th percentile46.3595
Q1167.29
median283.49
Q3562.1225
95-th percentile837.261
Maximum1286.57
Range1256.65
Interquartile range (IQR)394.8325

Descriptive statistics

Standard deviation280.1771
Coefficient of variation (CV)0.74000431
Kurtosis0.60544874
Mean378.6155
Median Absolute Deviation (MAD)177.58
Skewness0.99978185
Sum37861.55
Variance78499.208
MonotonicityNot monotonic
2023-12-10T22:02:39.450708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
522.0 1
 
1.0%
82.58 1
 
1.0%
90.31 1
 
1.0%
726.36 1
 
1.0%
549.93 1
 
1.0%
800.28 1
 
1.0%
718.09 1
 
1.0%
244.89 1
 
1.0%
146.43 1
 
1.0%
339.81 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
29.92 1
1.0%
37.46 1
1.0%
38.4 1
1.0%
44.86 1
1.0%
45.02 1
1.0%
46.43 1
1.0%
48.65 1
1.0%
50.62 1
1.0%
70.35 1
1.0%
77.05 1
1.0%
ValueCountFrequency (%)
1286.57 1
1.0%
1191.5 1
1.0%
1143.63 1
1.0%
967.65 1
1.0%
852.48 1
1.0%
836.46 1
1.0%
809.88 1
1.0%
800.28 1
1.0%
791.68 1
1.0%
753.51 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1799040.6
Minimum199200.65
Maximum7707401.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:39.637385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum199200.65
5-th percentile369570.79
Q1928715.27
median1628533
Q32230476.6
95-th percentile3406196.3
Maximum7707401.6
Range7508200.9
Interquartile range (IQR)1301761.3

Descriptive statistics

Standard deviation1231746.9
Coefficient of variation (CV)0.68466879
Kurtosis6.3911028
Mean1799040.6
Median Absolute Deviation (MAD)643836.61
Skewness1.9749941
Sum1.7990406 × 108
Variance1.5172005 × 1012
MonotonicityNot monotonic
2023-12-10T22:02:39.844724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2202853.5 1
 
1.0%
899791.33 1
 
1.0%
800993.57 1
 
1.0%
2172189.56 1
 
1.0%
1761555.57 1
 
1.0%
2154613.67 1
 
1.0%
1810586.03 1
 
1.0%
2543102.2 1
 
1.0%
1877263.52 1
 
1.0%
1255670.55 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
199200.65 1
1.0%
221176.79 1
1.0%
278892.51 1
1.0%
311448.93 1
1.0%
339230.65 1
1.0%
371167.64 1
1.0%
376694.16 1
1.0%
453181.47 1
1.0%
479083.18 1
1.0%
500755.34 1
1.0%
ValueCountFrequency (%)
7707401.59 1
1.0%
6506910.13 1
1.0%
5374711.48 1
1.0%
4261380.93 1
1.0%
3847606.07 1
1.0%
3382964.24 1
1.0%
3372059.53 1
1.0%
3312462.6 1
1.0%
3226349.73 1
1.0%
3188992.13 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:02:40.183189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.64
Min length8

Characters and Unicode

Total characters1064
Distinct characters107
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 row경기 평택 진위 신
2nd row경기 평택 진위 신
3rd row경기 파주 조리 장곡
4th row경기 파주 조리 장곡
5th row경기 파주 문산 당동
ValueCountFrequency (%)
경기 100
25.8%
평택 14
 
3.6%
포천 10
 
2.6%
용인 10
 
2.6%
광주 10
 
2.6%
가평 8
 
2.1%
여주 8
 
2.1%
파주 6
 
1.5%
화성 6
 
1.5%
안성 6
 
1.5%
Other values (95) 210
54.1%
2023-12-10T22:02:40.667643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
288
27.1%
기 102
 
9.6%
경 100
 
9.4%
평 32
 
3.0%
주 30
 
2.8%
성 24
 
2.3%
양 20
 
1.9%
천 18
 
1.7%
포 18
 
1.7%
가 16
 
1.5%
Other values (97) 416
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 776
72.9%
Space Separator 288
 
27.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.9%
평 32
 
4.1%
주 30
 
3.9%
성 24
 
3.1%
양 20
 
2.6%
천 18
 
2.3%
포 18
 
2.3%
가 16
 
2.1%
용 14
 
1.8%
Other values (96) 402
51.8%
Space Separator
ValueCountFrequency (%)
288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 776
72.9%
Common 288
 
27.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.9%
평 32
 
4.1%
주 30
 
3.9%
성 24
 
3.1%
양 20
 
2.6%
천 18
 
2.3%
포 18
 
2.3%
가 16
 
2.1%
용 14
 
1.8%
Other values (96) 402
51.8%
Common
ValueCountFrequency (%)
288
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 776
72.9%
ASCII 288
 
27.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
288
100.0%
Hangul
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.9%
평 32
 
4.1%
주 30
 
3.9%
성 24
 
3.1%
양 20
 
2.6%
천 18
 
2.3%
포 18
 
2.3%
가 16
 
2.1%
용 14
 
1.8%
Other values (96) 402
51.8%

Interactions

2023-12-10T22:02:33.027734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:25.395350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:26.416904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.384639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:28.240159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:29.467705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.374575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.288013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.197690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:33.122399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:25.477059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:26.515438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.468174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:28.327328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:29.569112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.455089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.362564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.285387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:33.236336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:25.567432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:26.615589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.566188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:28.407346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:29.681435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.552454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.438594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.360746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:33.344543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:25.702607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:26.717746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.659499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:28.491278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:29.784722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.646442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.523892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.443913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:33.431216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:25.820432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:26.820030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.741684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:28.605505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:29.888447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.747456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.603364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.531092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:33.526127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:25.921708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:26.924461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.839169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:28.737967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:29.979924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.834893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.715023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.639973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:33.620080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:26.056451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.048193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.938189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:28.864438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.076122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.930752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.831256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.746669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:33.713974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:26.170252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.148072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:28.034324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:29.280183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.175887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.020420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.975092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.849701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:33.812918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:26.299491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:27.273955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:28.142435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:29.374924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:30.269901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:31.125172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.089480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:32.931541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:02:40.802321image/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
기본키1.0001.0000.0001.0000.5980.8320.8420.3740.4930.5550.6180.4551.000
지점1.0001.0000.0001.0001.0001.0001.0000.8970.8380.8940.8650.9061.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.8970.8380.8940.8650.9061.000
연장((km))0.5981.0000.0001.0001.0000.5640.6180.2660.1410.2480.3850.3751.000
좌표위치위도((°))0.8321.0000.0001.0000.5641.0000.8190.4320.5310.5890.5300.4481.000
좌표위치경도((°))0.8421.0000.0001.0000.6180.8191.0000.2810.0000.0000.0000.2581.000
co((g/km))0.3740.8970.0000.8970.2660.4320.2811.0000.8440.9190.6750.9980.897
nox((g/km))0.4930.8380.0000.8380.1410.5310.0000.8441.0000.9680.9480.8220.838
hc((g/km))0.5550.8940.0000.8940.2480.5890.0000.9190.9681.0000.9050.9120.894
pm((g/km))0.6180.8650.0000.8650.3850.5300.0000.6750.9480.9051.0000.6050.865
co2((g/km))0.4550.9060.0000.9060.3750.4480.2580.9980.8220.9120.6051.0000.906
주소1.0001.0000.0001.0001.0001.0001.0000.8970.8380.8940.8650.9061.000
2023-12-10T22:02:41.005336image/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
기본키1.000-0.189-0.177-0.0190.0180.0820.0630.1340.0240.000
연장((km))-0.1891.0000.2300.013-0.025-0.118-0.089-0.117-0.0180.000
좌표위치위도((°))-0.1770.2301.0000.108-0.238-0.367-0.335-0.341-0.2370.000
좌표위치경도((°))-0.0190.0130.1081.000-0.030-0.0040.0080.020-0.0420.000
co((g/km))0.018-0.025-0.238-0.0301.0000.8980.9380.7590.9970.000
nox((g/km))0.082-0.118-0.367-0.0040.8981.0000.9880.9460.8900.000
hc((g/km))0.063-0.089-0.3350.0080.9380.9881.0000.9120.9280.000
pm((g/km))0.134-0.117-0.3410.0200.7590.9460.9121.0000.7450.000
co2((g/km))0.024-0.018-0.237-0.0420.9970.8900.9280.7451.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T22:02:33.983013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:02:34.198526image/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[0134-0]1송탄-오산6.220210101037.09529127.063648750.978514.851051.39522.02202853.5경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210101037.09529127.063649068.08936.541087.09511.512290119.24경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210101037.73328126.832538395.256261.84878.14300.141974234.37경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210101037.73328126.832535850.934937.03611.4322.951504804.95경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210101037.87708126.779461749.721327.67168.9846.43453181.47경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210101037.87708126.779462018.361512.4196.050.62522232.9경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210101037.19066127.559944665.284532.47573.24285.631181375.72경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210101037.19066127.559943417.13416.92439.18223.06842773.48경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210101037.31793127.427638628.586240.8907.01298.32227688.06경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210101037.31793127.427636644.934696.98682.16252.721718805.41경기 이천 신둔 수하
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4504-0]1안중-안성6.020210101036.96033127.066036985.014439.95669.83140.01818966.46경기 평택 팽성 남산
9192건기연[4504-0]2안중-안성6.020210101036.96033127.066036657.544478.12658.97239.471732386.26경기 평택 팽성 남산
9293건기연[4506-2]1장서-천5.420210101037.16502127.2056712107.3717634.792121.121191.53026998.71경기 용인 이동 덕성
9394건기연[4506-2]2장서-천5.420210101037.16502127.2056712227.1216478.952011.781143.633074661.4경기 용인 이동 덕성
9495건기연[4508-0]1포곡-광주3.120210101037.34342127.250535187.95903.68705.44465.511285193.52경기 용인 모현 왕산
9596건기연[4508-0]2포곡-광주3.120210101037.34342127.250535984.646795.54814.91550.661489439.92경기 용인 모현 왕산
9697건기연[4509-0]1광주-팔당19.020210101037.4815127.281014174.742736.42397.69155.911095604.26경기 광주 남종 삼성
9798건기연[4509-0]2광주-팔당19.020210101037.4815127.281013219.262147.84310.85121.62839022.36경기 광주 남종 삼성
9899건기연[4512-1]1화도-청평8.320210101037.68735127.379978296.729567.41160.33642.642077799.42경기 가평 청평 대성
99100건기연[4512-1]2화도-청평8.320210101037.68735127.3799711087.3513039.731575.88852.482738341.46경기 가평 청평 대성