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 13:01:45.169597
Analysis finished2023-12-10 13:01:56.523089
Duration11.35 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
2023-12-10T22:01:56.622785image/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
2023-12-10T22:01:56.805146image/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

2023-12-10T22:01:56.996146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:57.113717image/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
2023-12-10T22:01:57.380336image/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%
4301-1 2
 
2.0%
4512-1 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%
4205-1 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:01:57.887069image/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 86
10.8%
1 68
8.5%
4 62
7.8%
2 54
6.8%
7 26
 
3.2%
8 24
 
3.0%
Other values (3) 60
7.5%

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 86
17.2%
1 68
13.6%
4 62
12.4%
2 54
10.8%
7 26
 
5.2%
8 24
 
4.8%
6 22
 
4.4%
5 22
 
4.4%
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 86
10.8%
1 68
8.5%
4 62
7.8%
2 54
6.8%
7 26
 
3.2%
8 24
 
3.0%
Other values (3) 60
7.5%

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 86
10.8%
1 68
8.5%
4 62
7.8%
2 54
6.8%
7 26
 
3.2%
8 24
 
3.0%
Other values (3) 60
7.5%

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

2023-12-10T22:01:58.050199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:58.175010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%
Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:01:58.437623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.2
Min length4

Characters and Unicode

Total characters520
Distinct characters80
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%
팔탄-양감 2
 
2.0%
화도-청평 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%
보라-용인 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:01:58.906507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.2%
26
 
5.0%
24
 
4.6%
14
 
2.7%
14
 
2.7%
14
 
2.7%
14
 
2.7%
12
 
2.3%
12
 
2.3%
10
 
1.9%
Other values (70) 280
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 412
79.2%
Dash Punctuation 100
 
19.2%
Uppercase Letter 8
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
 
6.3%
24
 
5.8%
14
 
3.4%
14
 
3.4%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (67) 262
63.6%
Uppercase Letter
ValueCountFrequency (%)
I 4
50.0%
C 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 412
79.2%
Common 100
 
19.2%
Latin 8
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
 
6.3%
24
 
5.8%
14
 
3.4%
14
 
3.4%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (67) 262
63.6%
Latin
ValueCountFrequency (%)
I 4
50.0%
C 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 412
79.2%
ASCII 108
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
I 4
 
3.7%
C 4
 
3.7%
Hangul
ValueCountFrequency (%)
26
 
6.3%
24
 
5.8%
14
 
3.4%
14
 
3.4%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (67) 262
63.6%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.578
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:59.420375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median7.45
Q310.9
95-th percentile17.6
Maximum27.5
Range26
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.9511264
Coefficient of variation (CV)0.5771889
Kurtosis2.8423337
Mean8.578
Median Absolute Deviation (MAD)2.8
Skewness1.3468619
Sum857.8
Variance24.513653
MonotonicityNot monotonic
2023-12-10T22:01:59.582987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
6.0 4
 
4.0%
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%
5.0 2
 
2.0%
5.6 2
 
2.0%
2.7 2
 
2.0%
27.5 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
1.5 2
2.0%
2.0 2
2.0%
2.6 2
2.0%
2.7 2
2.0%
2.9 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%
ValueCountFrequency (%)
27.5 2
2.0%
19.0 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%
12.1 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210301
2nd row20210301
3rd row20210301
4th row20210301
5th row20210301

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2023-12-10T22:01:59.765338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:59.885764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 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

2023-12-10T22:01:59.995529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:00.125696image/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%
Mean37.436322
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:00.241887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.380095
Q337.72112
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.5561

Descriptive statistics

Standard deviation0.33552787
Coefficient of variation (CV)0.0089626291
Kurtosis-1.0721334
Mean37.436322
Median Absolute Deviation (MAD)0.266565
Skewness0.33734741
Sum3743.6322
Variance0.11257895
MonotonicityNot monotonic
2023-12-10T22:02:00.407687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.34137 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%
37.23653 2
 
2.0%
37.2375 2
 
2.0%
37.29776 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%
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%
37.76396 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.22819
Minimum126.77946
Maximum127.74421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:00.564531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.83253
Q1127.06364
median127.24601
Q3127.40224
95-th percentile127.62684
Maximum127.74421
Range0.96475
Interquartile range (IQR)0.3386

Descriptive statistics

Standard deviation0.24435108
Coefficient of variation (CV)0.0019205734
Kurtosis-0.77903447
Mean127.22819
Median Absolute Deviation (MAD)0.17778
Skewness0.025180817
Sum12722.819
Variance0.05970745
MonotonicityNot monotonic
2023-12-10T22:02:00.732824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
127.19674 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%
127.166 2
 
2.0%
127.3107 2
 
2.0%
127.60231 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%
126.85908 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%
126.92818 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%
127.44335 2
2.0%
127.44132 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5817.121
Minimum992.9
Maximum24912.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:00.900973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum992.9
5-th percentile1339.931
Q12921.8975
median5056.155
Q37387.88
95-th percentile11850.005
Maximum24912.28
Range23919.38
Interquartile range (IQR)4465.9825

Descriptive statistics

Standard deviation4062.8818
Coefficient of variation (CV)0.69843516
Kurtosis5.8096718
Mean5817.121
Median Absolute Deviation (MAD)2190.07
Skewness1.9358589
Sum581712.1
Variance16507009
MonotonicityNot monotonic
2023-12-10T22:02:01.042145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7412.75 1
 
1.0%
2808.41 1
 
1.0%
6387.31 1
 
1.0%
2901.04 1
 
1.0%
2789.16 1
 
1.0%
6104.88 1
 
1.0%
6619.21 1
 
1.0%
5071.2 1
 
1.0%
4369.19 1
 
1.0%
8912.45 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
992.9 1
1.0%
1034.17 1
1.0%
1136.24 1
1.0%
1163.82 1
1.0%
1164.39 1
1.0%
1349.17 1
1.0%
1398.6 1
1.0%
1446.22 1
1.0%
1513.54 1
1.0%
1519.58 1
1.0%
ValueCountFrequency (%)
24912.28 1
1.0%
20918.33 1
1.0%
17814.93 1
1.0%
14346.52 1
1.0%
12220.61 1
1.0%
11830.5 1
1.0%
11591.04 1
1.0%
11222.78 1
1.0%
11186.96 1
1.0%
10373.12 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4741.296
Minimum941.7
Maximum19060.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:01.170455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum941.7
5-th percentile1120.5315
Q12501.595
median4167.585
Q36301.13
95-th percentile8964.145
Maximum19060.44
Range18118.74
Interquartile range (IQR)3799.535

Descriptive statistics

Standard deviation3125.4851
Coefficient of variation (CV)0.65920481
Kurtosis5.1388907
Mean4741.296
Median Absolute Deviation (MAD)1844.54
Skewness1.776887
Sum474129.6
Variance9768657.2
MonotonicityNot monotonic
2023-12-10T22:02:01.315854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6392.97 1
 
1.0%
1851.32 1
 
1.0%
4175.76 1
 
1.0%
2413.02 1
 
1.0%
2727.92 1
 
1.0%
5008.27 1
 
1.0%
5846.69 1
 
1.0%
4132.5 1
 
1.0%
3807.68 1
 
1.0%
6718.58 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
941.7 1
1.0%
968.57 1
1.0%
974.22 1
1.0%
1055.5 1
1.0%
1080.47 1
1.0%
1122.64 1
1.0%
1146.69 1
1.0%
1155.02 1
1.0%
1209.87 1
1.0%
1264.78 1
1.0%
ValueCountFrequency (%)
19060.44 1
1.0%
15989.64 1
1.0%
14032.07 1
1.0%
11105.22 1
1.0%
10834.22 1
1.0%
8865.72 1
1.0%
8754.89 1
1.0%
8614.45 1
1.0%
8209.4 1
1.0%
8032.27 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean628.6498
Minimum120.87
Maximum2672.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:01.561621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120.87
5-th percentile143.2575
Q1317.805
median552.51
Q3804.745
95-th percentile1221.776
Maximum2672.14
Range2551.27
Interquartile range (IQR)486.94

Descriptive statistics

Standard deviation434.31009
Coefficient of variation (CV)0.69086174
Kurtosis5.9510156
Mean628.6498
Median Absolute Deviation (MAD)236.975
Skewness1.9528837
Sum62864.98
Variance188625.26
MonotonicityNot monotonic
2023-12-10T22:02:01.791830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
788.8 1
 
1.0%
267.73 1
 
1.0%
610.52 1
 
1.0%
288.15 1
 
1.0%
294.59 1
 
1.0%
637.91 1
 
1.0%
737.5 1
 
1.0%
566.66 1
 
1.0%
488.75 1
 
1.0%
951.16 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
120.87 1
1.0%
122.36 1
1.0%
122.96 1
1.0%
131.42 1
1.0%
141.88 1
1.0%
143.33 1
1.0%
150.83 1
1.0%
152.03 1
1.0%
154.22 1
1.0%
159.23 1
1.0%
ValueCountFrequency (%)
2672.14 1
1.0%
2219.39 1
1.0%
1986.07 1
1.0%
1571.89 1
1.0%
1429.75 1
1.0%
1210.83 1
1.0%
1201.92 1
1.0%
1152.89 1
1.0%
1127.59 1
1.0%
1101.02 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.7821
Minimum40.29
Maximum945.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:02.005538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40.29
5-th percentile53.1655
Q1121.7925
median223.23
Q3295.99
95-th percentile554.905
Maximum945.35
Range905.06
Interquartile range (IQR)174.1975

Descriptive statistics

Standard deviation162.52862
Coefficient of variation (CV)0.67500293
Kurtosis4.0724558
Mean240.7821
Median Absolute Deviation (MAD)84.78
Skewness1.6634976
Sum24078.21
Variance26415.553
MonotonicityNot monotonic
2023-12-10T22:02:02.214464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
295.77 1
 
1.0%
81.21 1
 
1.0%
157.5 1
 
1.0%
101.26 1
 
1.0%
115.53 1
 
1.0%
260.37 1
 
1.0%
324.76 1
 
1.0%
202.28 1
 
1.0%
203.59 1
 
1.0%
276.23 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
40.29 1
1.0%
45.78 1
1.0%
47.71 1
1.0%
49.38 1
1.0%
50.8 1
1.0%
53.29 1
1.0%
53.93 1
1.0%
56.03 1
1.0%
56.55 1
1.0%
69.21 1
1.0%
ValueCountFrequency (%)
945.35 1
1.0%
764.61 1
1.0%
717.25 1
1.0%
676.05 1
1.0%
558.99 1
1.0%
554.69 1
1.0%
509.89 1
1.0%
469.89 1
1.0%
464.72 1
1.0%
448.48 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1491592.6
Minimum246195.01
Maximum6445724.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:02.412648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum246195.01
5-th percentile338895.46
Q1762209.13
median1285439.4
Q31846101.7
95-th percentile3100088.5
Maximum6445724.1
Range6199529.1
Interquartile range (IQR)1083892.5

Descriptive statistics

Standard deviation1049528.8
Coefficient of variation (CV)0.70362962
Kurtosis5.9718509
Mean1491592.6
Median Absolute Deviation (MAD)541995.12
Skewness1.97137
Sum1.4915926 × 108
Variance1.1015106 × 1012
MonotonicityNot monotonic
2023-12-10T22:02:02.627192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1889514.67 1
 
1.0%
733953.2 1
 
1.0%
1661789.87 1
 
1.0%
747640.42 1
 
1.0%
707337.08 1
 
1.0%
1576595.34 1
 
1.0%
1562724.03 1
 
1.0%
1296031.52 1
 
1.0%
1141055.25 1
 
1.0%
2294281.68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
246195.01 1
1.0%
270652.31 1
1.0%
286188.45 1
1.0%
289508.63 1
1.0%
298032.95 1
1.0%
341046.12 1
1.0%
342611.04 1
1.0%
359710.4 1
1.0%
385286.25 1
1.0%
390518.32 1
1.0%
ValueCountFrequency (%)
6445724.13 1
1.0%
5444685.14 1
1.0%
4556265.34 1
1.0%
3673906.21 1
1.0%
3191417.04 1
1.0%
3095281.73 1
1.0%
3004856.81 1
1.0%
2905553.3 1
1.0%
2709413.21 1
1.0%
2696569.19 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:02:02.992938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

Total characters1070
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 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
25.6%
평택 14
 
3.6%
용인 12
 
3.1%
가평 10
 
2.6%
광주 10
 
2.6%
포천 8
 
2.1%
여주 8
 
2.1%
파주 6
 
1.5%
화성 6
 
1.5%
안성 6
 
1.5%
Other values (94) 210
53.8%
2023-12-10T22:02:03.491214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
290
27.1%
102
 
9.5%
100
 
9.3%
38
 
3.6%
30
 
2.8%
24
 
2.2%
18
 
1.7%
18
 
1.7%
18
 
1.7%
16
 
1.5%
Other values (95) 416
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 780
72.9%
Space Separator 290
 
27.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
102
 
13.1%
100
 
12.8%
38
 
4.9%
30
 
3.8%
24
 
3.1%
18
 
2.3%
18
 
2.3%
18
 
2.3%
16
 
2.1%
16
 
2.1%
Other values (94) 400
51.3%
Space Separator
ValueCountFrequency (%)
290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 780
72.9%
Common 290
 
27.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
102
 
13.1%
100
 
12.8%
38
 
4.9%
30
 
3.8%
24
 
3.1%
18
 
2.3%
18
 
2.3%
18
 
2.3%
16
 
2.1%
16
 
2.1%
Other values (94) 400
51.3%
Common
ValueCountFrequency (%)
290
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 780
72.9%
ASCII 290
 
27.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
290
100.0%
Hangul
ValueCountFrequency (%)
102
 
13.1%
100
 
12.8%
38
 
4.9%
30
 
3.8%
24
 
3.1%
18
 
2.3%
18
 
2.3%
18
 
2.3%
16
 
2.1%
16
 
2.1%
Other values (94) 400
51.3%

Interactions

2023-12-10T22:01:54.969203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:45.855892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:47.052019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:48.065588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:49.171060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:50.367949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:51.759362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:52.898764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.897753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:55.083630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:45.965829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:47.156065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:48.206134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:49.289115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:50.488721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:51.867430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.004688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.992667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:55.204099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:46.077245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:47.255541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:48.310300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:49.422240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:50.912841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:51.997376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.115692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:54.094199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:55.323506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:46.223193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:47.356729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:48.411875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:49.551166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:51.024858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:52.123424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.224247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:54.200779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:55.431785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:46.434931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:47.452992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:48.524624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:49.689145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:51.140296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:52.263467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.325027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:54.320298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:55.552000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:46.582417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:47.555603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:48.640277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:49.814466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:51.253200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:52.397743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.428225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:54.474165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:55.665531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:46.722601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:47.731075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:48.765983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:49.934482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:51.384698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:52.542725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.556015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:54.636866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:55.777442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:46.840687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:47.837630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:48.891968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:50.077996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:51.503788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:52.654621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.668829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:54.745594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:55.886383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:46.949391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:47.940096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:49.026402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:50.227211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:51.623641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:52.772219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:53.779580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:54.824432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:02:03.646656image/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
기본키1.0001.0000.0001.0000.5980.8380.8340.4320.3820.2840.4050.4011.000
지점1.0001.0000.0001.0001.0001.0001.0000.9250.8650.8590.8440.9281.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9250.8650.8590.8440.9281.000
연장((km))0.5981.0000.0001.0001.0000.5700.6250.3460.2750.2780.4210.3871.000
좌표위치위도((°))0.8381.0000.0001.0000.5701.0000.8320.2820.3260.2400.3390.3111.000
좌표위치경도((°))0.8341.0000.0001.0000.6250.8321.0000.4170.1160.1160.1110.4551.000
co((g/km))0.4320.9250.0000.9250.3460.2820.4171.0000.9910.9920.9641.0000.925
nox((g/km))0.3820.8650.0210.8650.2750.3260.1160.9911.0000.9980.9770.9890.865
hc((g/km))0.2840.8590.0000.8590.2780.2400.1160.9920.9981.0000.9760.9900.859
pm((g/km))0.4050.8440.0000.8440.4210.3390.1110.9640.9770.9761.0000.9620.844
co2((g/km))0.4010.9280.0000.9280.3870.3110.4551.0000.9890.9900.9621.0000.928
주소1.0001.0000.0001.0001.0001.0001.0000.9250.8650.8590.8440.9281.000
2023-12-10T22:02:03.846792image/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
기본키1.000-0.259-0.1180.0210.025-0.015-0.003-0.0350.0240.000
연장((km))-0.2591.0000.2460.0270.0370.0690.0660.0790.0400.000
좌표위치위도((°))-0.1180.2461.0000.123-0.139-0.235-0.210-0.230-0.1420.000
좌표위치경도((°))0.0210.0270.1231.000-0.083-0.094-0.070-0.067-0.0990.000
co((g/km))0.0250.037-0.139-0.0831.0000.9600.9790.8870.9990.000
nox((g/km))-0.0150.069-0.235-0.0940.9601.0000.9880.9590.9560.000
hc((g/km))-0.0030.066-0.210-0.0700.9790.9881.0000.9350.9750.000
pm((g/km))-0.0350.079-0.230-0.0670.8870.9590.9351.0000.8810.000
co2((g/km))0.0240.040-0.142-0.0990.9990.9560.9750.8811.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T22:01:56.094081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:01:56.403933image/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-오산6.220210301037.09529127.063647412.756392.97788.8295.771889514.67경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210301037.09529127.063647519.496350.21771.98255.721921672.43경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210301037.73328126.832535629.834740.93599.19267.871444663.3경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210301037.73328126.832535607.175191.2631.01327.391418237.84경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210301037.87708126.779461398.61146.69141.8850.8359710.4경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210301037.87708126.779461970.881408.57198.4553.93464624.02경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210301037.19066127.559942780.42531.12321.82194.56719501.31경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210301037.19066127.559942681.22253.44288.87176.02691056.14경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210301037.31793127.427637379.596284.77925.19353.61831630.66경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210301037.31793127.427636840.125798.51852.58326.911700103.95경기 이천 신둔 수하
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4506-2]1장서-천5.420210301037.16502127.205679231.168865.721152.89558.992376040.28경기 용인 이동 덕성
9192건기연[4506-2]2장서-천5.420210301037.16502127.2056711222.7810834.221429.75717.252696569.19경기 용인 이동 덕성
9293건기연[4508-0]1포곡-광주3.120210301037.34342127.250534094.253688.25442.1223.571045569.03경기 용인 모현 왕산
9394건기연[4508-0]2포곡-광주3.120210301037.34342127.250535534.534572.51608.15261.871302920.99경기 용인 모현 왕산
9495건기연[4509-0]1광주-팔당19.020210301037.4815127.281014805.943968.6582.01225.581202696.69경기 광주 남종 삼성
9596건기연[4509-0]2광주-팔당19.020210301037.4815127.281013941.813471.08506.2195.54968153.25경기 광주 남종 삼성
9697건기연[4512-1]1화도-청평8.320210301037.68735127.379976918.385282.38694.66273.921793974.7경기 가평 청평 대성
9798건기연[4512-1]2화도-청평8.320210301037.68735127.379978918.396649.17899.38366.642311297.43경기 가평 청평 대성
9899건기연[4606-2]1청평-가평2.920210301037.76396127.443355313.484152.04540.96231.121381048.67경기 가평 청평 상천
99100건기연[4606-2]2청평-가평2.920210301037.76396127.443356668.844977.51673.59277.741728224.28경기 가평 청평 상천