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 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
주소 is highly overall correlated with 기본키 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 started2024-04-16 09:21:05.333665
Analysis finished2024-04-16 09:21:12.384911
Duration7.05 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
2024-04-16T18:21:12.447053image/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
2024-04-16T18:21:12.560714image/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

2024-04-16T18:21:12.661852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:21:12.749653image/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
2024-04-16T18:21:12.924788image/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[0527-2]
4th row[0527-2]
5th row[0529-0]
ValueCountFrequency (%)
0526-3 2
 
2.0%
4217-1 2
 
2.0%
5601-2 2
 
2.0%
3811-0 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%
Other values (40) 80
80.0%
2024-04-16T18:21:13.218280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 64
8.0%
3 62
7.8%
4 52
6.5%
6 34
 
4.2%
5 28
 
3.5%
Other values (3) 52
6.5%

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 (%)
0 106
21.2%
1 102
20.4%
2 64
12.8%
3 62
12.4%
4 52
10.4%
6 34
 
6.8%
5 28
 
5.6%
7 24
 
4.8%
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%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 64
8.0%
3 62
7.8%
4 52
6.5%
6 34
 
4.2%
5 28
 
3.5%
Other values (3) 52
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 64
8.0%
3 62
7.8%
4 52
6.5%
6 34
 
4.2%
5 28
 
3.5%
Other values (3) 52
6.5%

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

2024-04-16T18:21:13.324095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:21:13.398479image/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
2024-04-16T18:21:13.578240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.18
Min length4

Characters and Unicode

Total characters518
Distinct characters89
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 row원주-횡성
4th row원주-횡성
5th row공근-동산
ValueCountFrequency (%)
원주-소초 2
 
2.0%
임계-나안 2
 
2.0%
김화-근남 2
 
2.0%
쌍용-남 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%
Other values (40) 80
80.0%
2024-04-16T18:21:13.892919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.3%
24
 
4.6%
18
 
3.5%
16
 
3.1%
14
 
2.7%
12
 
2.3%
12
 
2.3%
12
 
2.3%
12
 
2.3%
10
 
1.9%
Other values (79) 288
55.6%

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%
16
 
3.9%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (76) 274
66.2%
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%
16
 
3.9%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (76) 274
66.2%
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%
16
 
3.9%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (76) 274
66.2%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.68
Minimum0.4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:14.016583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.3
Q14.8
median8.8
Q312.7
95-th percentile23
Maximum27
Range26.6
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.1294784
Coefficient of variation (CV)0.63321058
Kurtosis0.60062265
Mean9.68
Median Absolute Deviation (MAD)4
Skewness0.93722113
Sum968
Variance37.570505
MonotonicityNot monotonic
2024-04-16T18:21:14.134459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4.8 4
 
4.0%
7.2 4
 
4.0%
3.5 4
 
4.0%
8.8 4
 
4.0%
8.0 4
 
4.0%
12.0 4
 
4.0%
9.0 2
 
2.0%
12.1 2
 
2.0%
6.5 2
 
2.0%
14.4 2
 
2.0%
Other values (34) 68
68.0%
ValueCountFrequency (%)
0.4 2
2.0%
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%
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%
16.4 2
2.0%
16.1 2
2.0%
15.3 2
2.0%
14.4 2
2.0%
14.0 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2024-04-16T18:21:14.234681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:21:14.320218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-04-16T18:21:14.397593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:21:14.466012image/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%
Mean37.692204
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:14.553899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.08588
5-th percentile37.18732
Q137.40743
median37.65705
Q338.04937
95-th percentile38.23094
Maximum38.38086
Range1.29498
Interquartile range (IQR)0.64194

Descriptive statistics

Standard deviation0.35485699
Coefficient of variation (CV)0.0094145992
Kurtosis-1.2772105
Mean37.692204
Median Absolute Deviation (MAD)0.32117
Skewness0.13517787
Sum3769.2204
Variance0.12592348
MonotonicityNot monotonic
2024-04-16T18:21:14.680805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3551 2
 
2.0%
37.68024 2
 
2.0%
37.21543 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%
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%
38.11527 2
2.0%
38.08778 2
2.0%
38.08036 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%
Mean128.25537
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:14.796877image/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 deviation0.48437521
Coefficient of variation (CV)0.0037766467
Kurtosis-0.95806144
Mean128.25537
Median Absolute Deviation (MAD)0.36049
Skewness0.1300637
Sum12825.537
Variance0.23461935
MonotonicityNot monotonic
2024-04-16T18:21:15.180512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.99487 2
 
2.0%
127.86266 2
 
2.0%
128.64197 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%
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%
Mean3267.8693
Minimum182.11
Maximum11896.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:15.293429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182.11
5-th percentile254.329
Q1814.5325
median1901.365
Q35116.4975
95-th percentile9938.771
Maximum11896.86
Range11714.75
Interquartile range (IQR)4301.965

Descriptive statistics

Standard deviation3139.0992
Coefficient of variation (CV)0.9605951
Kurtosis0.50236122
Mean3267.8693
Median Absolute Deviation (MAD)1520.75
Skewness1.1388717
Sum326786.93
Variance9853944
MonotonicityNot monotonic
2024-04-16T18:21:15.404472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2714.79 1
 
1.0%
5713.68 1
 
1.0%
876.74 1
 
1.0%
882.62 1
 
1.0%
572.19 1
 
1.0%
1703.61 1
 
1.0%
1032.98 1
 
1.0%
1119.74 1
 
1.0%
503.27 1
 
1.0%
2630.23 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
182.11 1
1.0%
203.6 1
1.0%
246.98 1
1.0%
249.4 1
1.0%
251.08 1
1.0%
254.5 1
1.0%
315.78 1
1.0%
330.48 1
1.0%
393.98 1
1.0%
397.07 1
1.0%
ValueCountFrequency (%)
11896.86 1
1.0%
11851.27 1
1.0%
11488.73 1
1.0%
11376.14 1
1.0%
10451.6 1
1.0%
9911.78 1
1.0%
9718.42 1
1.0%
9484.3 1
1.0%
7972.34 1
1.0%
7809.95 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2744.6772
Minimum171.18
Maximum12817.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:15.532063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum171.18
5-th percentile263.686
Q1653.795
median1441.5
Q33881.5575
95-th percentile8563.5415
Maximum12817.85
Range12646.67
Interquartile range (IQR)3227.7625

Descriptive statistics

Standard deviation2806.5944
Coefficient of variation (CV)1.022559
Kurtosis1.3647666
Mean2744.6772
Median Absolute Deviation (MAD)1094.13
Skewness1.3742345
Sum274467.72
Variance7876972.3
MonotonicityNot monotonic
2024-04-16T18:21:15.668106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1737.61 1
 
1.0%
5137.97 1
 
1.0%
657.38 1
 
1.0%
1451.08 1
 
1.0%
475.57 1
 
1.0%
1263.7 1
 
1.0%
673.88 1
 
1.0%
979.66 1
 
1.0%
557.86 1
 
1.0%
2104.26 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
171.18 1
1.0%
191.73 1
1.0%
202.45 1
1.0%
204.53 1
1.0%
229.79 1
1.0%
265.47 1
1.0%
269.45 1
1.0%
286.44 1
1.0%
329.35 1
1.0%
338.89 1
1.0%
ValueCountFrequency (%)
12817.85 1
1.0%
10825.82 1
1.0%
9390.09 1
1.0%
9040.64 1
1.0%
9034.58 1
1.0%
8538.75 1
1.0%
8358.87 1
1.0%
8187.82 1
1.0%
7281.67 1
1.0%
7121.54 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean357.3906
Minimum24.71
Maximum1488.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:15.793684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.71
5-th percentile30.1265
Q182.9475
median184.095
Q3532.745
95-th percentile1089.7615
Maximum1488.74
Range1464.03
Interquartile range (IQR)449.7975

Descriptive statistics

Standard deviation352.60524
Coefficient of variation (CV)0.98661027
Kurtosis0.6589965
Mean357.3906
Median Absolute Deviation (MAD)143.595
Skewness1.2019983
Sum35739.06
Variance124330.45
MonotonicityNot monotonic
2024-04-16T18:21:15.914538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255.28 1
 
1.0%
607.83 1
 
1.0%
91.42 1
 
1.0%
122.97 1
 
1.0%
61.51 1
 
1.0%
168.33 1
 
1.0%
95.33 1
 
1.0%
120.57 1
 
1.0%
69.06 1
 
1.0%
283.89 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
24.71 1
1.0%
25.79 1
1.0%
28.72 1
1.0%
29.39 1
1.0%
29.49 1
1.0%
30.16 1
1.0%
33.14 1
1.0%
37.36 1
1.0%
43.14 1
1.0%
44.99 1
1.0%
ValueCountFrequency (%)
1488.74 1
1.0%
1339.41 1
1.0%
1242.03 1
1.0%
1108.4 1
1.0%
1104.99 1
1.0%
1088.96 1
1.0%
1044.76 1
1.0%
959.75 1
1.0%
955.97 1
1.0%
918.84 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.7332
Minimum7.24
Maximum719.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:16.037769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.24
5-th percentile16.2675
Q133.8025
median64.36
Q3182.36
95-th percentile456.992
Maximum719.77
Range712.53
Interquartile range (IQR)148.5575

Descriptive statistics

Standard deviation147.93472
Coefficient of variation (CV)1.1402996
Kurtosis3.2541975
Mean129.7332
Median Absolute Deviation (MAD)44.57
Skewness1.8639431
Sum12973.32
Variance21884.681
MonotonicityNot monotonic
2024-04-16T18:21:16.155208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.92 1
 
1.0%
213.21 1
 
1.0%
35.81 1
 
1.0%
81.54 1
 
1.0%
24.28 1
 
1.0%
47.23 1
 
1.0%
30.02 1
 
1.0%
57.89 1
 
1.0%
38.14 1
 
1.0%
106.21 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
7.24 1
1.0%
10.99 1
1.0%
14.2 1
1.0%
15.01 1
1.0%
15.84 1
1.0%
16.29 1
1.0%
16.43 1
1.0%
16.55 1
1.0%
18.16 1
1.0%
18.36 1
1.0%
ValueCountFrequency (%)
719.77 1
1.0%
597.8 1
1.0%
543.38 1
1.0%
518.54 1
1.0%
476.41 1
1.0%
455.97 1
1.0%
451.32 1
1.0%
432.46 1
1.0%
398.57 1
1.0%
337.49 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean829322.28
Minimum42151.99
Maximum3047158.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:16.277477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42151.99
5-th percentile65635.834
Q1194879.95
median495036.03
Q31279002.2
95-th percentile2573326.7
Maximum3047158.4
Range3005006.4
Interquartile range (IQR)1084122.2

Descriptive statistics

Standard deviation801457.92
Coefficient of variation (CV)0.96640106
Kurtosis0.64275872
Mean829322.28
Median Absolute Deviation (MAD)396117.79
Skewness1.1808285
Sum82932228
Variance6.4233481 × 1011
MonotonicityNot monotonic
2024-04-16T18:21:16.387748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
709001.9 1
 
1.0%
1462978.08 1
 
1.0%
225658.34 1
 
1.0%
266710.69 1
 
1.0%
146026.42 1
 
1.0%
440364.12 1
 
1.0%
271396.94 1
 
1.0%
286029.98 1
 
1.0%
123257.58 1
 
1.0%
671493.1 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
42151.99 1
1.0%
48213.54 1
1.0%
55056.47 1
1.0%
59673.65 1
1.0%
64245.87 1
1.0%
65708.99 1
1.0%
81206.32 1
1.0%
87379.17 1
1.0%
99579.34 1
1.0%
100025.02 1
1.0%
ValueCountFrequency (%)
3047158.43 1
1.0%
3029930.08 1
1.0%
2956414.52 1
1.0%
2920806.43 1
1.0%
2685204.94 1
1.0%
2567438.42 1
1.0%
2536861.72 1
1.0%
2474773.18 1
1.0%
2035972.31 1
1.0%
2020602.68 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 양구 양구 도사
 
2
강원 강릉 강동 모전
 
2
강원 홍천 홍천 삼마치
 
2
강원 홍천 북방 부사원
 
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 원주 소초 장양
4th row강원 원주 소초 장양
5th row강원 홍천 홍천 삼마치

Common Values

ValueCountFrequency (%)
강원 춘천 사북 오탄 4
 
4.0%
강원 양구 양구 도사 2
 
2.0%
강원 강릉 강동 모전 2
 
2.0%
강원 홍천 홍천 삼마치 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%
Other values (39) 78
78.0%

Length

2024-04-16T18:21:16.511305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원 100
25.4%
인제 14
 
3.6%
횡성 12
 
3.0%
춘천 10
 
2.5%
원주 10
 
2.5%
홍천 10
 
2.5%
정선 10
 
2.5%
8
 
2.0%
평창 8
 
2.0%
강릉 8
 
2.0%
Other values (83) 204
51.8%

Interactions

2024-04-16T18:21:11.434578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:05.792068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.432078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.137276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.832318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.519711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.170623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.090402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.747263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.499992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:05.870014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.501469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.220724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.914517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.582737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.237445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.158266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.816075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.567849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:05.942744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.568583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.296736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.989094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.653177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.320545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.227908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.885407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.637686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.012490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.634534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.377443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.062327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.722219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.405938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.300736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.967542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.714980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.089303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.725337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.455577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.149308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.802794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.495536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.380485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.059319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.784898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.154006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.812964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.525013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.222799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.886605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.562881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.449964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.140094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.862988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.221291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.885831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.597254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.294456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.956824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.626186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.521625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.215105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.938257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.290798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.972328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.676033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.370014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.031077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.932100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.603800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.289789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:12.016112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:06.362589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.063398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:07.761986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:08.444902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:09.101414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.014278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:10.676744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:21:11.362359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T18:21:16.594098image/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
기본키1.0001.0000.0001.0000.5800.8740.8210.0820.3920.3920.4390.0000.998
지점1.0001.0000.0001.0001.0001.0001.0000.7130.7910.6440.7270.8041.000
방향0.0000.0001.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.7130.7910.6440.7270.8041.000
연장((km))0.5801.0000.0001.0001.0000.6280.5860.2460.0000.0630.0000.3400.996
좌표위치위도((°))0.8741.0000.0001.0000.6281.0000.8290.4480.3790.4230.2900.3631.000
좌표위치경도((°))0.8211.0000.0001.0000.5860.8291.0000.3530.2680.2860.3880.3521.000
co((g/km))0.0820.7130.0490.7130.2460.4480.3531.0000.9400.9520.8620.9800.722
nox((g/km))0.3920.7910.0000.7910.0000.3790.2680.9401.0000.9800.9720.8650.789
hc((g/km))0.3920.6440.0000.6440.0630.4230.2860.9520.9801.0000.9510.8540.667
pm((g/km))0.4390.7270.0000.7270.0000.2900.3880.8620.9720.9511.0000.7700.733
co2((g/km))0.0000.8040.0000.8040.3400.3630.3520.9800.8650.8540.7701.0000.817
주소0.9981.0000.0001.0000.9961.0001.0000.7220.7890.6670.7330.8171.000
2024-04-16T18:21:16.704628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향
주소1.0000.000
방향0.0001.000
2024-04-16T18:21:16.778772image/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
기본키1.0000.1960.357-0.078-0.031-0.019-0.041-0.024-0.0250.0000.736
연장((km))0.1961.0000.0840.184-0.180-0.189-0.180-0.237-0.1860.0000.718
좌표위치위도((°))0.3570.0841.000-0.413-0.217-0.274-0.270-0.295-0.2090.0000.753
좌표위치경도((°))-0.0780.184-0.4131.0000.1640.2020.2140.1890.1510.0000.753
co((g/km))-0.031-0.180-0.2170.1641.0000.9790.9850.9210.9980.0530.256
nox((g/km))-0.019-0.189-0.2740.2020.9791.0000.9950.9710.9740.0000.306
hc((g/km))-0.041-0.180-0.2700.2140.9850.9951.0000.9580.9800.0000.210
pm((g/km))-0.024-0.237-0.2950.1890.9210.9710.9581.0000.9140.0000.253
co2((g/km))-0.025-0.186-0.2090.1510.9980.9740.9800.9141.0000.0000.345
방향0.0000.0000.0000.0000.0530.0000.0000.0000.0001.0000.000
주소0.7360.7180.7530.7530.2560.3060.2100.2530.3450.0001.000

Missing values

2024-04-16T18:21:12.150451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T18:21:12.314034image/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-소초4.820210101037.3551127.994872714.791737.61255.2854.92709001.9강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210101037.3551127.994873556.582257.83333.3464.59928718.38강원 원주 봉산
23건기연[0527-2]1원주-횡성0.420210101037.42063127.963194225.243248.25414.96129.311092870.58강원 원주 소초 장양
34건기연[0527-2]2원주-횡성0.420210101037.42063127.963194801.123746.79477.89136.011234728.74강원 원주 소초 장양
45건기연[0529-0]1공근-동산12.020210101037.62539127.895281296.161296.91155.8969.29322264.68강원 홍천 홍천 삼마치
56건기연[0529-0]2공근-동산12.020210101037.62539127.895281373.171299.73150.4555.88346565.16강원 홍천 홍천 삼마치
67건기연[0530-0]1횡성-춘천13.320210101037.73176127.837871363.131102.19140.052.9350523.44강원 홍천 북방 부사원
78건기연[0530-0]2횡성-춘천13.320210101037.73176127.83787879.44738.6891.5136.34225483.0강원 홍천 북방 부사원
89건기연[0531-2]1동내-천전7.520210101037.86064127.776635108.133805.61561.54131.091295465.96강원 춘천 동내 거두
910건기연[0531-2]2동내-천전7.520210101037.86064127.776636177.614366.09619.46211.891601284.02강원 춘천 동내 거두
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4616-0]1진부령-거진11.020210101038.38086128.44451358.48937.49126.1332.34354208.41강원 고성 간성 교동
9192건기연[4616-0]2진부령-거진11.020210101038.38086128.44452493.911570.75243.1448.34589247.94강원 고성 간성 교동
9293건기연[4617-0]1북-외가평12.120210101038.19136128.317854325.743839.46454.26172.21100122.41강원 인제 북 용대
9394건기연[4617-0]2북-외가평12.120210101038.19136128.317859911.787121.54959.75223.912567438.42강원 인제 북 용대
9495건기연[4710-0]1이동-근남9.020210101038.18502127.41894721.21680.1876.140.47184577.33강원 철원 서 자등
9596건기연[4710-0]2이동-근남9.020210101038.18502127.41894720.84699.0177.6240.59182884.34강원 철원 서 자등
9697건기연[5601-2]1김화-근남2.420210101038.25247127.47033246.98202.4525.7918.1664245.87강원 철원 근남 사곡
9798건기연[5601-2]2김화-근남2.420210101038.25247127.47033249.4171.1824.7116.4365708.99강원 철원 근남 사곡
9899건기연[5602-0]1사내-화천16.420210101038.05058127.60419749.25536.1574.8823.05177456.2강원 춘천 사북 오탄
99100건기연[5602-0]2사내-화천16.420210101038.05058127.60419883.34676.4288.1235.45228950.69강원 춘천 사북 오탄