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
Categorical5
Text2

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
기본키 is highly overall correlated with 주소High correlation
연장((km)) is highly overall correlated with 주소High correlation
좌표위치위도((°)) is highly overall correlated with 주소High correlation
좌표위치경도((°)) is highly overall correlated with 주소High correlation
co((g/km)) is highly overall correlated with nox((g/km)) and 4 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 4 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
기본키 has unique valuesUnique
co((g/km)) has unique valuesUnique
nox((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2024-04-16 09:20:24.184602
Analysis finished2024-04-16 09:20:32.328089
Duration8.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:32.401715image/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
2024-04-16T18:20:32.562256image/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

2024-04-16T18:20:32.675634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:32.750831image/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
2024-04-16T18:20:32.966044image/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[0526-3]
2nd row[0526-3]
3rd row[0529-0]
4th row[0529-0]
5th row[0530-0]
ValueCountFrequency (%)
0526-3 2
 
2.0%
4313-0 2
 
2.0%
5601-2 2
 
2.0%
3813-1 2
 
2.0%
3814-0 2
 
2.0%
3818-0 2
 
2.0%
4209-1 2
 
2.0%
4209-2 2
 
2.0%
4212-1 2
 
2.0%
4213-1 2
 
2.0%
Other values (40) 80
80.0%
2024-04-16T18:20:33.251914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 106
13.2%
1 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 58
7.2%
4 56
7.0%
6 34
 
4.2%
5 28
 
3.5%
Other values (3) 50
6.2%

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 (%)
0 106
21.2%
1 104
20.8%
3 64
12.8%
2 58
11.6%
4 56
11.2%
6 34
 
6.8%
5 28
 
5.6%
7 22
 
4.4%
8 18
 
3.6%
9 10
 
2.0%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106
13.2%
1 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 58
7.2%
4 56
7.0%
6 34
 
4.2%
5 28
 
3.5%
Other values (3) 50
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
13.2%
1 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 58
7.2%
4 56
7.0%
6 34
 
4.2%
5 28
 
3.5%
Other values (3) 50
6.2%

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

2024-04-16T18:20:33.361151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:33.434240image/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
2024-04-16T18:20:33.619212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.18
Min length4

Characters and Unicode

Total characters518
Distinct characters90
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 row원주-소초
2nd row원주-소초
3rd row공근-동산
4th row공근-동산
5th row횡성-춘천
ValueCountFrequency (%)
원주-소초 2
 
2.0%
문혜-김화 2
 
2.0%
김화-근남 2
 
2.0%
신동-사북 2
 
2.0%
남-사북 2
 
2.0%
도계-고천 2
 
2.0%
원주-새말 2
 
2.0%
문막-원주 2
 
2.0%
안흥-운교 2
 
2.0%
노론-용탄 2
 
2.0%
Other values (40) 80
80.0%
2024-04-16T18:20:33.969642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.3%
24
 
4.6%
18
 
3.5%
14
 
2.7%
14
 
2.7%
12
 
2.3%
12
 
2.3%
12
 
2.3%
12
 
2.3%
10
 
1.9%
Other values (80) 290
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 414
79.9%
Dash Punctuation 100
 
19.3%
Uppercase Letter 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
24
 
5.8%
18
 
4.3%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (77) 276
66.7%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 414
79.9%
Common 100
 
19.3%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
24
 
5.8%
18
 
4.3%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (77) 276
66.7%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 414
79.9%
ASCII 104
 
20.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
96.2%
C 2
 
1.9%
I 2
 
1.9%
Hangul
ValueCountFrequency (%)
24
 
5.8%
18
 
4.3%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (77) 276
66.7%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.284
Minimum2
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:34.130352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.4
Q15.7
median8.95
Q313.3
95-th percentile23
Maximum27
Range25
Interquartile range (IQR)7.6

Descriptive statistics

Standard deviation6.1305448
Coefficient of variation (CV)0.59612454
Kurtosis0.20968578
Mean10.284
Median Absolute Deviation (MAD)4.2
Skewness0.80873663
Sum1028.4
Variance37.58358
MonotonicityNot monotonic
2024-04-16T18:20:34.235106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
7.2 4
 
4.0%
12.0 4
 
4.0%
3.5 4
 
4.0%
8.0 4
 
4.0%
8.8 4
 
4.0%
4.8 2
 
2.0%
14.4 2
 
2.0%
3.8 2
 
2.0%
5.9 2
 
2.0%
16.1 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
2.0 2
2.0%
2.3 2
2.0%
2.4 2
2.0%
2.6 2
2.0%
2.9 2
2.0%
3.5 4
4.0%
3.6 2
2.0%
3.8 2
2.0%
4.0 2
2.0%
4.4 2
2.0%
ValueCountFrequency (%)
27.0 2
2.0%
24.7 2
2.0%
23.0 2
2.0%
22.7 2
2.0%
18.1 2
2.0%
18.0 2
2.0%
17.4 2
2.0%
16.4 2
2.0%
16.1 2
2.0%
15.3 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210401
2nd row20210401
3rd row20210401
4th row20210401
5th row20210401

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2024-04-16T18:20:34.329999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:34.400658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2024-04-16T18:20:34.472050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:34.545725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.701821
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:34.888312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.08588
5-th percentile37.18732
Q137.40743
median37.67506
Q338.04937
95-th percentile38.23094
Maximum38.38086
Range1.29498
Interquartile range (IQR)0.64194

Descriptive statistics

Standard deviation0.35485589
Coefficient of variation (CV)0.0094121684
Kurtosis-1.2690701
Mean37.701821
Median Absolute Deviation (MAD)0.33918
Skewness0.079947021
Sum3770.1821
Variance0.1259227
MonotonicityNot monotonic
2024-04-16T18:20:35.012917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3551 2
 
2.0%
37.83194 2
 
2.0%
37.25108 2
 
2.0%
37.30412 2
 
2.0%
37.40802 2
 
2.0%
37.32395 2
 
2.0%
37.4163 2
 
2.0%
37.32703 2
 
2.0%
37.4489 2
 
2.0%
37.48273 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
37.08588 2
2.0%
37.18474 2
2.0%
37.18732 2
2.0%
37.19159 2
2.0%
37.21543 2
2.0%
37.25108 2
2.0%
37.28643 2
2.0%
37.30412 2
2.0%
37.32395 2
2.0%
37.32703 2
2.0%
ValueCountFrequency (%)
38.38086 2
2.0%
38.25247 2
2.0%
38.23094 2
2.0%
38.19136 2
2.0%
38.18502 2
2.0%
38.17869 2
2.0%
38.14989 2
2.0%
38.11527 2
2.0%
38.08778 2
2.0%
38.07373 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.25485
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:35.132752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.35058
5-th percentile127.47033
Q1127.86266
median128.19891
Q3128.64197
95-th percentile129.07044
Maximum129.20253
Range1.85195
Interquartile range (IQR)0.77931

Descriptive statistics

Standard deviation0.48416528
Coefficient of variation (CV)0.0037750251
Kurtosis-0.95508247
Mean128.25485
Median Absolute Deviation (MAD)0.36049
Skewness0.13492916
Sum12825.485
Variance0.23441602
MonotonicityNot monotonic
2024-04-16T18:20:35.254404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.99487 2
 
2.0%
128.01254 2
 
2.0%
128.7796 2
 
2.0%
129.07044 2
 
2.0%
127.99641 2
 
2.0%
127.83897 2
 
2.0%
128.2034 2
 
2.0%
128.51597 2
 
2.0%
128.66017 2
 
2.0%
129.09293 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
127.35058 2
2.0%
127.41894 2
2.0%
127.47033 2
2.0%
127.60419 2
2.0%
127.62463 2
2.0%
127.63815 2
2.0%
127.67987 2
2.0%
127.77663 2
2.0%
127.81252 2
2.0%
127.81502 2
2.0%
ValueCountFrequency (%)
129.20253 2
2.0%
129.09293 2
2.0%
129.07044 2
2.0%
129.02671 2
2.0%
128.98396 2
2.0%
128.84543 2
2.0%
128.84271 2
2.0%
128.83913 2
2.0%
128.81034 2
2.0%
128.79017 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2420.4145
Minimum363.68
Maximum9595.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:35.398088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum363.68
5-th percentile417.1115
Q1775.6425
median1506.35
Q33363.415
95-th percentile7159.519
Maximum9595.97
Range9232.29
Interquartile range (IQR)2587.7725

Descriptive statistics

Standard deviation2143.8448
Coefficient of variation (CV)0.88573456
Kurtosis1.4595773
Mean2420.4145
Median Absolute Deviation (MAD)1003.675
Skewness1.39705
Sum242041.45
Variance4596070.4
MonotonicityNot monotonic
2024-04-16T18:20:35.514081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4760.27 1
 
1.0%
1167.16 1
 
1.0%
7158.4 1
 
1.0%
1590.06 1
 
1.0%
1640.8 1
 
1.0%
962.03 1
 
1.0%
776.91 1
 
1.0%
1401.26 1
 
1.0%
1422.64 1
 
1.0%
662.18 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
363.68 1
1.0%
377.73 1
1.0%
386.74 1
1.0%
401.86 1
1.0%
406.88 1
1.0%
417.65 1
1.0%
418.77 1
1.0%
501.11 1
1.0%
504.24 1
1.0%
517.22 1
1.0%
ValueCountFrequency (%)
9595.97 1
1.0%
8959.09 1
1.0%
7747.54 1
1.0%
7572.7 1
1.0%
7180.78 1
1.0%
7158.4 1
1.0%
7088.87 1
1.0%
6909.22 1
1.0%
6205.35 1
1.0%
6003.61 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2662.8698
Minimum297.59
Maximum10289.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:35.627429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum297.59
5-th percentile527.815
Q1810.785
median1612.98
Q33899.27
95-th percentile7526.2225
Maximum10289.1
Range9991.51
Interquartile range (IQR)3088.485

Descriptive statistics

Standard deviation2344.6279
Coefficient of variation (CV)0.88048911
Kurtosis1.5332133
Mean2662.8698
Median Absolute Deviation (MAD)1011.475
Skewness1.3853116
Sum266286.98
Variance5497279.8
MonotonicityNot monotonic
2024-04-16T18:20:35.736803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3891.43 1
 
1.0%
1020.53 1
 
1.0%
7907.6 1
 
1.0%
1621.24 1
 
1.0%
1768.34 1
 
1.0%
1604.72 1
 
1.0%
1197.5 1
 
1.0%
1478.48 1
 
1.0%
1428.05 1
 
1.0%
887.39 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
297.59 1
1.0%
339.62 1
1.0%
371.34 1
1.0%
390.23 1
1.0%
449.25 1
1.0%
531.95 1
1.0%
535.62 1
1.0%
556.2 1
1.0%
586.35 1
1.0%
600.92 1
1.0%
ValueCountFrequency (%)
10289.1 1
1.0%
9887.01 1
1.0%
9207.06 1
1.0%
9165.63 1
1.0%
7907.6 1
1.0%
7506.15 1
1.0%
6931.89 1
1.0%
6835.79 1
1.0%
6543.5 1
1.0%
6229.55 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.9219
Minimum42.34
Maximum1332.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:35.858531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.34
5-th percentile71.035
Q1111.885
median228.595
Q3506.9225
95-th percentile955.557
Maximum1332.25
Range1289.91
Interquartile range (IQR)395.0375

Descriptive statistics

Standard deviation294.36182
Coefficient of variation (CV)0.84849592
Kurtosis1.0298708
Mean346.9219
Median Absolute Deviation (MAD)144.8
Skewness1.2497918
Sum34692.19
Variance86648.88
MonotonicityNot monotonic
2024-04-16T18:20:35.977070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.56 2
 
2.0%
552.86 1
 
1.0%
132.34 1
 
1.0%
955.29 1
 
1.0%
228.91 1
 
1.0%
243.54 1
 
1.0%
187.14 1
 
1.0%
150.6 1
 
1.0%
209.46 1
 
1.0%
201.09 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
42.34 1
1.0%
46.57 1
1.0%
47.55 1
1.0%
47.89 1
1.0%
63.15 1
1.0%
71.45 1
1.0%
76.33 1
1.0%
77.53 1
1.0%
80.06 1
1.0%
80.33 1
1.0%
ValueCountFrequency (%)
1332.25 1
1.0%
1184.46 1
1.0%
1092.93 1
1.0%
1013.78 1
1.0%
960.63 1
1.0%
955.29 1
1.0%
940.95 1
1.0%
923.78 1
1.0%
901.6 1
1.0%
861.18 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.5474
Minimum17.51
Maximum648.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:36.086687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.51
5-th percentile38.247
Q166.435
median113.52
Q3238.14
95-th percentile469.3725
Maximum648.39
Range630.88
Interquartile range (IQR)171.705

Descriptive statistics

Standard deviation142.98217
Coefficient of variation (CV)0.82865444
Kurtosis1.5348312
Mean172.5474
Median Absolute Deviation (MAD)69.11
Skewness1.3739413
Sum17254.74
Variance20443.9
MonotonicityNot monotonic
2024-04-16T18:20:36.194527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.29 2
 
2.0%
104.37 2
 
2.0%
99.61 2
 
2.0%
261.13 1
 
1.0%
648.39 1
 
1.0%
121.42 1
 
1.0%
130.68 1
 
1.0%
74.57 1
 
1.0%
70.1 1
 
1.0%
53.41 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
17.51 1
1.0%
21.8 1
1.0%
23.54 1
1.0%
23.58 1
1.0%
29.45 1
1.0%
38.71 1
1.0%
41.7 1
1.0%
42.14 1
1.0%
42.3 1
1.0%
43.45 1
1.0%
ValueCountFrequency (%)
648.39 1
1.0%
612.6 1
1.0%
571.72 1
1.0%
531.85 1
1.0%
514.83 1
1.0%
466.98 1
1.0%
461.71 1
1.0%
402.83 1
1.0%
388.44 1
1.0%
378.31 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595020.06
Minimum83440.16
Maximum2361100.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:36.315666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83440.16
5-th percentile104589.86
Q1185872.68
median364128.1
Q3837240.62
95-th percentile1791667.3
Maximum2361100.1
Range2277659.9
Interquartile range (IQR)651367.93

Descriptive statistics

Standard deviation537885.04
Coefficient of variation (CV)0.90397799
Kurtosis1.4332261
Mean595020.06
Median Absolute Deviation (MAD)241888.73
Skewness1.4179372
Sum59502006
Variance2.8932032 × 1011
MonotonicityNot monotonic
2024-04-16T18:20:36.432496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1208808.83 1
 
1.0%
295451.23 1
 
1.0%
1791364.81 1
 
1.0%
383634.59 1
 
1.0%
394370.11 1
 
1.0%
241073.43 1
 
1.0%
182026.42 1
 
1.0%
333542.67 1
 
1.0%
344621.62 1
 
1.0%
163940.75 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
83440.16 1
1.0%
89359.6 1
1.0%
89814.51 1
1.0%
91705.46 1
1.0%
94400.94 1
1.0%
105126.12 1
1.0%
105878.0 1
1.0%
106380.32 1
1.0%
121261.93 1
1.0%
123216.82 1
1.0%
ValueCountFrequency (%)
2361100.09 1
1.0%
2230379.12 1
1.0%
1922391.51 1
1.0%
1903897.72 1
1.0%
1797414.23 1
1.0%
1791364.81 1
1.0%
1759865.9 1
1.0%
1717102.99 1
1.0%
1619397.93 1
1.0%
1578310.87 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원 춘천 사북 오탄
 
4
강원 정선 임계 봉산
 
2
강원 강릉 연곡 동덕
 
2
강원 홍천 북방 부사원
 
2
강원 춘천 동내 거두
 
2
Other values (44)
88 

Length

Max length12
Median length11
Mean length10.7
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원 원주 봉산
2nd row강원 원주 봉산
3rd row강원 홍천 홍천 삼마치
4th row강원 홍천 홍천 삼마치
5th row강원 홍천 북방 부사원

Common Values

ValueCountFrequency (%)
강원 춘천 사북 오탄 4
 
4.0%
강원 정선 임계 봉산 2
 
2.0%
강원 강릉 연곡 동덕 2
 
2.0%
강원 홍천 북방 부사원 2
 
2.0%
강원 춘천 동내 거두 2
 
2.0%
강원 춘천 신북 용산 2
 
2.0%
강원 횡성 서원 유현 2
 
2.0%
강원 횡성 횡성 조곡 2
 
2.0%
강원 정선 용평 용전 2
 
2.0%
강원 강릉 연곡 송림 2
 
2.0%
Other values (39) 78
78.0%

Length

2024-04-16T18:20:36.560963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원 100
25.4%
인제 14
 
3.6%
홍천 12
 
3.0%
횡성 12
 
3.0%
춘천 10
 
2.5%
정선 10
 
2.5%
원주 8
 
2.0%
강릉 8
 
2.0%
8
 
2.0%
평창 8
 
2.0%
Other values (84) 204
51.8%

Interactions

2024-04-16T18:20:31.277696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:24.732041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.389213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.074412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.807248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.517073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.528555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.342489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:30.204875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:31.388293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:24.801544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.469844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.145852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.892951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.588593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.624103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.434352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:30.317366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:31.497132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:24.867698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.539976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.229499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.966354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.655875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.708367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.532574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:30.421894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:31.620402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:24.942942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.617657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.319382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.047418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.727586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.786766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.648063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:30.519306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:31.733081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.017506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.702185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.409570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.127979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.107706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.884134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.772167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:30.617114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:31.797892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.085488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.776038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.487548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.203174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.175334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.964893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.854148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:30.748999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:31.863125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.153410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.857294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.563511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.281729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.244716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.042361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.957897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:30.883075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:31.931184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.237896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.933447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.639917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.355130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.331594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.130304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:30.053796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:31.023909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:32.002416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:25.314860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.007292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:26.725846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:27.431974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:28.450789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:29.248886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:30.129821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:31.160913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T18:20:36.646006image/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
기본키1.0001.0000.0001.0000.6140.8790.7460.5930.5790.5220.5970.7120.998
지점1.0001.0000.0001.0001.0001.0001.0000.9970.9410.9110.9110.9941.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9970.9410.9110.9110.9941.000
연장((km))0.6141.0000.0001.0001.0000.5770.5550.6400.3920.4410.4110.4300.997
좌표위치위도((°))0.8791.0000.0001.0000.5771.0000.7930.5960.5500.4500.3620.7051.000
좌표위치경도((°))0.7461.0000.0001.0000.5550.7931.0000.4940.5130.4060.5010.6941.000
co((g/km))0.5930.9970.0000.9970.6400.5960.4941.0000.8700.8830.8420.9850.997
nox((g/km))0.5790.9410.0000.9410.3920.5500.5130.8701.0000.9730.9840.9400.931
hc((g/km))0.5220.9110.0000.9110.4410.4500.4060.8830.9731.0000.9670.9550.901
pm((g/km))0.5970.9110.0000.9110.4110.3620.5010.8420.9840.9671.0000.9400.894
co2((g/km))0.7120.9940.0000.9940.4300.7050.6940.9850.9400.9550.9401.0000.987
주소0.9981.0000.0001.0000.9971.0001.0000.9970.9310.9010.8940.9871.000
2024-04-16T18:20:36.768054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향
주소1.0000.000
방향0.0001.000
2024-04-16T18:20:36.840659image/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))방향주소
기본키1.0000.1610.359-0.116-0.074-0.090-0.104-0.080-0.0640.0000.736
연장((km))0.1611.0000.1290.146-0.255-0.240-0.236-0.257-0.2530.0000.729
좌표위치위도((°))0.3590.1291.000-0.425-0.246-0.326-0.321-0.274-0.2330.0000.753
좌표위치경도((°))-0.1160.146-0.4251.000-0.0730.0110.002-0.042-0.0860.0000.753
co((g/km))-0.074-0.255-0.246-0.0731.0000.9720.9770.9760.9970.0000.729
nox((g/km))-0.090-0.240-0.3260.0110.9721.0000.9950.9900.9660.0000.495
hc((g/km))-0.104-0.236-0.3210.0020.9770.9951.0000.9880.9680.0000.434
pm((g/km))-0.080-0.257-0.274-0.0420.9760.9900.9881.0000.9700.0000.423
co2((g/km))-0.064-0.253-0.233-0.0860.9970.9660.9680.9701.0000.0000.666
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
주소0.7360.7290.7530.7530.7290.4950.4340.4230.6660.0001.000

Missing values

2024-04-16T18:20:32.101676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T18:20:32.258926image/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[0526-3]1원주-소초4.820210401037.3551127.994874760.273891.43552.86261.131208808.83강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210401037.3551127.994874939.24112.07581.84276.161253023.19강원 원주 봉산
23건기연[0529-0]1공근-동산12.020210401037.62539127.895282586.842958.37424.1194.77596998.76강원 홍천 홍천 삼마치
34건기연[0529-0]2공근-동산12.020210401037.62539127.895282291.142463.24332.35161.97548226.4강원 홍천 홍천 삼마치
45건기연[0530-0]1횡성-춘천13.320210401037.73176127.837872111.132548.18332.72181.06504807.77강원 홍천 북방 부사원
56건기연[0530-0]2횡성-춘천13.320210401037.73176127.837872175.052687.85364.22190.18499200.9강원 홍천 북방 부사원
67건기연[0531-2]1동내-천전7.520210401037.86064127.776636909.226110.48861.18388.441717102.99강원 춘천 동내 거두
78건기연[0531-2]2동내-천전7.520210401037.86064127.776637088.876835.79940.95514.831759865.9강원 춘천 동내 거두
89건기연[0532-0]1춘천-화천3.620210401037.96161127.679871245.061318.35172.14105.62303724.55강원 춘천 신북 용산
910건기연[0532-0]2춘천-화천3.620210401037.96161127.67987980.371012.65128.8580.57240253.91강원 춘천 신북 용산
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4616-0]1진부령-거진11.020210401038.38086128.44451245.081265.12170.9390.56307399.26강원 고성 간성 교동
9192건기연[4616-0]2진부령-거진11.020210401038.38086128.44451192.61164.33158.8886.3292929.29강원 고성 간성 교동
9293건기연[4617-0]1북-외가평12.120210401038.19136128.317851838.272033.22249.22134.88458257.88강원 인제 북 용대
9394건기연[4617-0]2북-외가평12.120210401038.19136128.317851835.741946.32249.93129.1455955.19강원 인제 북 용대
9495건기연[4710-0]1이동-근남9.020210401038.18502127.418941020.821017.32129.673.29253269.56강원 철원 서 자등
9596건기연[4710-0]2이동-근남9.020210401038.18502127.41894995.33993.12125.4172.4247427.89강원 철원 서 자등
9697건기연[5601-2]1김화-근남2.420210401038.25247127.47033564.4602.0980.3357.42137610.07강원 철원 근남 사곡
9798건기연[5601-2]2김화-근남2.420210401038.25247127.47033504.24531.9571.4550.57123216.82강원 철원 근남 사곡
9899건기연[5602-0]1사내-화천16.420210401038.05058127.604191102.131228.98163.0187.17262341.29강원 춘천 사북 오탄
99100건기연[5602-0]2사내-화천16.420210401038.05058127.604191095.881235.03169.5191.82258678.98강원 춘천 사북 오탄