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 13:01:05.720597
Analysis finished2023-12-10 13:01:15.485185
Duration9.76 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
2023-12-10T22:01:15.588102image/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
2023-12-10T22:01:15.767952image/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

2023-12-10T22:01:15.929831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:16.035081image/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
2023-12-10T22:01:16.279337image/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%
4302-3 2
 
2.0%
4606-2 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%
4206-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:01:16.717744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 122
15.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
1 66
8.2%
4 64
8.0%
2 54
6.8%
7 28
 
3.5%
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 (%)
0 122
24.4%
3 82
16.4%
1 66
13.2%
4 64
12.8%
2 54
10.8%
7 28
 
5.6%
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 (%)
0 122
15.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
1 66
8.2%
4 64
8.0%
2 54
6.8%
7 28
 
3.5%
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 (%)
0 122
15.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
1 66
8.2%
4 64
8.0%
2 54
6.8%
7 28
 
3.5%
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

2023-12-10T22:01:16.880880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:16.983027image/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
2023-12-10T22:01:17.240948image/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%
봉담-향남 2
 
2.0%
청평-가평 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%
용인-마장 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:01:17.675962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.2%
28
 
5.4%
24
 
4.6%
16
 
3.1%
14
 
2.7%
14
 
2.7%
14
 
2.7%
12
 
2.3%
12
 
2.3%
10
 
1.9%
Other values (71) 278
53.3%

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 (%)
28
 
6.8%
24
 
5.8%
16
 
3.9%
14
 
3.4%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (68) 260
62.8%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
I 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 (%)
28
 
6.8%
24
 
5.8%
16
 
3.9%
14
 
3.4%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (68) 260
62.8%
Latin
ValueCountFrequency (%)
C 4
50.0%
I 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%
C 4
 
3.7%
I 4
 
3.7%
Hangul
ValueCountFrequency (%)
28
 
6.8%
24
 
5.8%
16
 
3.9%
14
 
3.4%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (68) 260
62.8%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.394
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:18.153623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median7.05
Q310.6
95-th percentile17.6
Maximum27.5
Range26
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation4.9335139
Coefficient of variation (CV)0.5877429
Kurtosis3.1436001
Mean8.394
Median Absolute Deviation (MAD)2.65
Skewness1.4663356
Sum839.4
Variance24.33956
MonotonicityNot monotonic
2023-12-10T22:01:18.309024image/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%
5.6 2
 
2.0%
2.7 2
 
2.0%
27.5 2
 
2.0%
7.2 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
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

2023-12-10T22:01:18.455558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:18.572578image/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

2023-12-10T22:01:18.691420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:01:18.801285image/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%
Mean37.435444
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:18.920755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.380095
Q337.71834
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.55332

Descriptive statistics

Standard deviation0.33487706
Coefficient of variation (CV)0.0089454544
Kurtosis-1.0532316
Mean37.435444
Median Absolute Deviation (MAD)0.266565
Skewness0.34387861
Sum3743.5444
Variance0.11214264
MonotonicityNot monotonic
2023-12-10T22:01:19.092941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.43583 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%
37.14026 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%
37.94255 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%
Mean127.22314
Minimum126.77946
Maximum127.74421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:19.319147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.83253
Q1127.06364
median127.2374
Q3127.37997
95-th percentile127.62684
Maximum127.74421
Range0.96475
Interquartile range (IQR)0.31633

Descriptive statistics

Standard deviation0.24306995
Coefficient of variation (CV)0.0019105796
Kurtosis-0.73130917
Mean127.22314
Median Absolute Deviation (MAD)0.172565
Skewness0.080976282
Sum12722.314
Variance0.059083001
MonotonicityNot monotonic
2023-12-10T22:01:19.509984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
127.26262 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%
126.91531 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%
Mean10471.196
Minimum1307.07
Maximum36420.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:19.653996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1307.07
5-th percentile2688.6895
Q15399.7925
median9503.81
Q313542.912
95-th percentile20480.276
Maximum36420.12
Range35113.05
Interquartile range (IQR)8143.12

Descriptive statistics

Standard deviation6658.3002
Coefficient of variation (CV)0.63586814
Kurtosis3.6333289
Mean10471.196
Median Absolute Deviation (MAD)4121.685
Skewness1.5560812
Sum1047119.6
Variance44332961
MonotonicityNot monotonic
2023-12-10T22:01:19.801039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11396.33 1
 
1.0%
18181.31 1
 
1.0%
18237.99 1
 
1.0%
12640.08 1
 
1.0%
13296.62 1
 
1.0%
4721.04 1
 
1.0%
4495.61 1
 
1.0%
10983.92 1
 
1.0%
10589.13 1
 
1.0%
10186.66 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1307.07 1
1.0%
1446.95 1
1.0%
2135.2 1
1.0%
2228.34 1
1.0%
2416.22 1
1.0%
2703.03 1
1.0%
2714.87 1
1.0%
2848.22 1
1.0%
3117.99 1
1.0%
3131.79 1
1.0%
ValueCountFrequency (%)
36420.12 1
1.0%
35703.47 1
1.0%
29833.36 1
1.0%
28732.29 1
1.0%
21083.07 1
1.0%
20448.55 1
1.0%
18644.25 1
1.0%
18549.43 1
1.0%
18422.84 1
1.0%
18237.99 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9907.609
Minimum1439.03
Maximum35656.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:19.992147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1439.03
5-th percentile2165.444
Q14760.815
median9454.83
Q312318.462
95-th percentile21436.54
Maximum35656.76
Range34217.73
Interquartile range (IQR)7557.6475

Descriptive statistics

Standard deviation6758.2905
Coefficient of variation (CV)0.68213133
Kurtosis3.6271476
Mean9907.609
Median Absolute Deviation (MAD)4024.39
Skewness1.6221175
Sum990760.9
Variance45674491
MonotonicityNot monotonic
2023-12-10T22:01:20.175854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10914.56 1
 
1.0%
17774.57 1
 
1.0%
13186.03 1
 
1.0%
10730.83 1
 
1.0%
11999.12 1
 
1.0%
3681.48 1
 
1.0%
3747.03 1
 
1.0%
12355.61 1
 
1.0%
11116.5 1
 
1.0%
10242.61 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1439.03 1
1.0%
1541.77 1
1.0%
1721.68 1
1.0%
1780.29 1
1.0%
2162.86 1
1.0%
2165.58 1
1.0%
2311.23 1
1.0%
2602.59 1
1.0%
2638.48 1
1.0%
2772.09 1
1.0%
ValueCountFrequency (%)
35656.76 1
1.0%
33872.0 1
1.0%
32839.05 1
1.0%
27754.86 1
1.0%
24134.93 1
1.0%
21294.52 1
1.0%
20388.97 1
1.0%
18855.96 1
1.0%
18751.31 1
1.0%
17774.57 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1289.1817
Minimum202.91
Maximum4735.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:20.385201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202.91
5-th percentile310.9415
Q1642.575
median1245.88
Q31559.7075
95-th percentile2699.0805
Maximum4735.4
Range4532.49
Interquartile range (IQR)917.1325

Descriptive statistics

Standard deviation860.14731
Coefficient of variation (CV)0.6672041
Kurtosis3.7304522
Mean1289.1817
Median Absolute Deviation (MAD)511.3
Skewness1.6277185
Sum128918.17
Variance739853.4
MonotonicityNot monotonic
2023-12-10T22:01:20.572181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1322.95 1
 
1.0%
2345.14 1
 
1.0%
1854.84 1
 
1.0%
1497.04 1
 
1.0%
1637.09 1
 
1.0%
467.1 1
 
1.0%
446.47 1
 
1.0%
1548.74 1
 
1.0%
1437.68 1
 
1.0%
1356.11 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
202.91 1
1.0%
211.95 1
1.0%
219.47 1
1.0%
221.88 1
1.0%
284.75 1
1.0%
312.32 1
1.0%
313.18 1
1.0%
343.1 1
1.0%
365.67 1
1.0%
375.19 1
1.0%
ValueCountFrequency (%)
4735.4 1
1.0%
4125.51 1
1.0%
4104.7 1
1.0%
3817.69 1
1.0%
3010.5 1
1.0%
2682.69 1
1.0%
2541.57 1
1.0%
2351.7 1
1.0%
2345.14 1
1.0%
2283.54 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595.1917
Minimum90.85
Maximum2208.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:20.726965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90.85
5-th percentile113.4345
Q1292.6825
median528.8
Q3734.3675
95-th percentile1575.6585
Maximum2208.2
Range2117.35
Interquartile range (IQR)441.685

Descriptive statistics

Standard deviation437.71975
Coefficient of variation (CV)0.73542651
Kurtosis3.6041457
Mean595.1917
Median Absolute Deviation (MAD)232.58
Skewness1.7074037
Sum59519.17
Variance191598.58
MonotonicityNot monotonic
2023-12-10T22:01:20.879900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
605.98 1
 
1.0%
1048.6 1
 
1.0%
684.47 1
 
1.0%
620.78 1
 
1.0%
748.86 1
 
1.0%
183.55 1
 
1.0%
170.85 1
 
1.0%
857.78 1
 
1.0%
750.23 1
 
1.0%
618.7 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
90.85 1
1.0%
102.36 1
1.0%
102.98 1
1.0%
105.35 1
1.0%
107.06 1
1.0%
113.77 1
1.0%
116.94 1
1.0%
126.66 1
1.0%
137.78 1
1.0%
149.06 1
1.0%
ValueCountFrequency (%)
2208.2 1
1.0%
2179.67 1
1.0%
2026.23 1
1.0%
1801.44 1
1.0%
1622.56 1
1.0%
1573.19 1
1.0%
1253.12 1
1.0%
1165.83 1
1.0%
1115.86 1
1.0%
1048.6 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2664227.1
Minimum315553.05
Maximum9320379.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:01:21.034048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum315553.05
5-th percentile641759.09
Q11379177.9
median2409407.3
Q33446240.7
95-th percentile5005161.3
Maximum9320379.3
Range9004826.3
Interquartile range (IQR)2067062.7

Descriptive statistics

Standard deviation1702656.1
Coefficient of variation (CV)0.6390807
Kurtosis3.8855615
Mean2664227.1
Median Absolute Deviation (MAD)1036471.4
Skewness1.6082162
Sum2.6642271 × 108
Variance2.8990379 × 1012
MonotonicityNot monotonic
2023-12-10T22:01:21.183879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2891492.17 1
 
1.0%
4653185.96 1
 
1.0%
4793137.91 1
 
1.0%
3185596.12 1
 
1.0%
3301616.65 1
 
1.0%
1223487.48 1
 
1.0%
1160137.22 1
 
1.0%
2756477.67 1
 
1.0%
2622635.02 1
 
1.0%
2588268.13 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
315553.05 1
1.0%
329984.22 1
1.0%
561251.59 1
1.0%
583172.45 1
1.0%
613828.94 1
1.0%
643229.1 1
1.0%
686622.82 1
1.0%
736413.98 1
1.0%
780820.67 1
1.0%
806674.94 1
1.0%
ValueCountFrequency (%)
9320379.31 1
1.0%
9259518.55 1
1.0%
7654450.81 1
1.0%
7386878.16 1
1.0%
5207249.4 1
1.0%
4994525.12 1
1.0%
4793137.91 1
1.0%
4759725.17 1
1.0%
4680635.5 1
1.0%
4663877.03 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:01:21.467972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.74
Min length8

Characters and Unicode

Total characters1074
Distinct characters109
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%
광주 10
 
2.6%
포천 8
 
2.1%
가평 8
 
2.1%
여주 8
 
2.1%
안성 6
 
1.5%
양평 6
 
1.5%
화성 6
 
1.5%
Other values (94) 212
54.4%
2023-12-10T22:01:22.050973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
290
27.0%
102
 
9.5%
100
 
9.3%
36
 
3.4%
32
 
3.0%
24
 
2.2%
20
 
1.9%
18
 
1.7%
16
 
1.5%
16
 
1.5%
Other values (99) 420
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 784
73.0%
Space Separator 290
 
27.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
102
 
13.0%
100
 
12.8%
36
 
4.6%
32
 
4.1%
24
 
3.1%
20
 
2.6%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (98) 404
51.5%
Space Separator
ValueCountFrequency (%)
290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 784
73.0%
Common 290
 
27.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
102
 
13.0%
100
 
12.8%
36
 
4.6%
32
 
4.1%
24
 
3.1%
20
 
2.6%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (98) 404
51.5%
Common
ValueCountFrequency (%)
290
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 784
73.0%
ASCII 290
 
27.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
290
100.0%
Hangul
ValueCountFrequency (%)
102
 
13.0%
100
 
12.8%
36
 
4.6%
32
 
4.1%
24
 
3.1%
20
 
2.6%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (98) 404
51.5%

Interactions

2023-12-10T22:01:14.218555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:06.374637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.267720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.207250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:09.208689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:10.191646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:11.462178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:12.364913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.246675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:14.314024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:06.460826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.358742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.320258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:09.309022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:10.286088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:11.573870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:12.450254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.341911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:14.412929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:06.593590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.473436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.404934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:09.416319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:10.374147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:11.674149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:12.542686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.431898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:14.500298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:06.689858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.580072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.534264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:09.550408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:10.478377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:11.765836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:12.647218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.584465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:14.599146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:06.785588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.683275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.659237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:09.651791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:10.874343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:11.856958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:12.739990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.665628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:14.700512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:06.885949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.785661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.782156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:09.775360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:10.991374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:11.966393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:12.859005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.767309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:14.809151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.005010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.902442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.894212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:09.898335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:11.113674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:12.074379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.001085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.890671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:14.898991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.095273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.001942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.984537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:10.001534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:11.229907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:12.183805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.074408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.999385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:14.995665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:07.178397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:08.099988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:09.101443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:10.102131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:11.348862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:12.277503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:13.168077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:01:14.111441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:01:22.200460image/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
기본키1.0001.0000.0001.0000.5690.8460.8220.3960.4630.5180.4620.4091.000
지점1.0001.0000.0001.0001.0001.0001.0000.9390.9190.9260.8470.9351.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9390.9190.9260.8470.9351.000
연장((km))0.5691.0000.0001.0001.0000.5780.6170.4220.3890.3090.0000.4461.000
좌표위치위도((°))0.8461.0000.0001.0000.5781.0000.8180.3900.3940.3620.3930.3541.000
좌표위치경도((°))0.8221.0000.0001.0000.6170.8181.0000.3730.0000.0000.0000.4341.000
co((g/km))0.3960.9390.0000.9390.4220.3900.3731.0000.9380.8970.9011.0000.939
nox((g/km))0.4630.9190.0000.9190.3890.3940.0000.9381.0000.9590.9800.9310.919
hc((g/km))0.5180.9260.0000.9260.3090.3620.0000.8970.9591.0000.9000.8940.926
pm((g/km))0.4620.8470.0000.8470.0000.3930.0000.9010.9800.9001.0000.8940.847
co2((g/km))0.4090.9350.0000.9350.4460.3540.4341.0000.9310.8940.8941.0000.935
주소1.0001.0000.0001.0001.0001.0001.0000.9390.9190.9260.8470.9351.000
2023-12-10T22:01:22.414051image/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
기본키1.000-0.236-0.0770.0320.0870.0990.1160.1060.0870.000
연장((km))-0.2361.0000.1910.0200.0360.0170.0250.0110.0350.000
좌표위치위도((°))-0.0770.1911.0000.099-0.205-0.324-0.286-0.334-0.2120.000
좌표위치경도((°))0.0320.0200.0991.000-0.051-0.081-0.051-0.048-0.0610.000
co((g/km))0.0870.036-0.205-0.0511.0000.9410.9710.8780.9980.000
nox((g/km))0.0990.017-0.324-0.0810.9411.0000.9870.9700.9400.000
hc((g/km))0.1160.025-0.286-0.0510.9710.9871.0000.9480.9680.000
pm((g/km))0.1060.011-0.334-0.0480.8780.9700.9481.0000.8750.000
co2((g/km))0.0870.035-0.212-0.0610.9980.9400.9680.8751.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T22:01:15.144377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:01:15.372797image/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-오산6.220210501037.09529127.0636411396.3310914.561322.95605.982891492.17경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210501037.09529127.0636411061.5810238.391254.82525.02814752.52경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210501037.73328126.832538626.499162.351143.13671.02136957.03경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210501037.73328126.832538288.328349.821048.36529.72073539.62경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210501037.87708126.779462848.222162.86284.75102.98736413.98경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210501037.87708126.779463117.992311.23312.32107.06806674.94경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210501037.19066127.559945081.844378.29601.39344.241302260.31경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210501037.19066127.559945615.634446.91621.54320.11442034.47경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210501037.31793127.4276312108.229191.941298.72517.033119489.76경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210501037.31793127.4276313353.6210387.171472.64603.063429029.63경기 이천 신둔 수하
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4508-0]1포곡-광주3.120210501037.34342127.250537733.258418.331098.42540.661879604.0경기 용인 모현 왕산
9192건기연[4508-0]2포곡-광주3.120210501037.34342127.250538907.3411132.511295.07709.842148194.64경기 용인 모현 왕산
9293건기연[4509-0]1광주-팔당19.020210501037.4815127.281015346.794358.67608.58298.341360451.93경기 광주 남종 삼성
9394건기연[4509-0]2광주-팔당19.020210501037.4815127.281014858.644155.59588.73286.741221105.42경기 광주 남종 삼성
9495건기연[4512-1]1화도-청평8.320210501037.68735127.3799713962.4912306.081581.49691.343609071.1경기 가평 청평 대성
9596건기연[4512-1]2화도-청평8.320210501037.68735127.3799714041.9712614.561623.55731.773604289.27경기 가평 청평 대성
9697건기연[4606-2]1청평-가평2.920210501037.76396127.4433512610.89744.031350.11520.53227919.11경기 가평 청평 상천
9798건기연[4606-2]2청평-가평2.920210501037.76396127.4433511642.559151.261265.93492.333010704.98경기 가평 청평 상천
9899건기연[4707-0]1내각-부평6.820210501037.70419127.1710310875.8710500.191371.75590.082756185.24경기 남양주 진접 내각
99100건기연[4707-0]2내각-부평6.820210501037.70419127.171039775.859167.141200.53541.92478916.53경기 남양주 진접 내각