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
Categorical6
Text1

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
주소 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
기본키 is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
연장((km)) is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
좌표위치위도((°)) is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
좌표위치경도((°)) is highly overall correlated with 측정구간 and 1 other fieldsHigh 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
기본키 has unique valuesUnique
co((g/km)) has 4 (4.0%) zerosZeros
nox((g/km)) has 4 (4.0%) zerosZeros
hc((g/km)) has 4 (4.0%) zerosZeros
pm((g/km)) has 23 (23.0%) zerosZeros
co2((g/km)) has 4 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:43:09.347912
Analysis finished2023-12-10 11:43:24.032039
Duration14.68 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
2023-12-10T20:43:24.159128image/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
2023-12-10T20:43:24.382740image/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

2023-12-10T20:43:24.578825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:24.751571image/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
2023-12-10T20:43:25.071741image/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%
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%
2023-12-10T20:43:25.586759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 104
13.0%
1 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 60
7.5%
4 50
6.2%
6 34
 
4.2%
5 32
 
4.0%
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 (%)
0 104
20.8%
1 104
20.8%
3 64
12.8%
2 60
12.0%
4 50
10.0%
6 34
 
6.8%
5 32
 
6.4%
7 22
 
4.4%
8 18
 
3.6%
9 12
 
2.4%
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 (%)
0 104
13.0%
1 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 60
7.5%
4 50
6.2%
6 34
 
4.2%
5 32
 
4.0%
Other values (3) 52
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 104
13.0%
1 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 60
7.5%
4 50
6.2%
6 34
 
4.2%
5 32
 
4.0%
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

2023-12-10T20:43:25.776522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:25.914665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정선-임계
 
4
원주-횡성
 
2
공근-동산
 
2
횡성-춘천
 
2
동내-천전
 
2
Other values (44)
88 

Length

Max length8
Median length5
Mean length5.18
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row원주-소초
2nd row원주-소초
3rd row원주-횡성
4th row원주-횡성
5th row공근-동산

Common Values

ValueCountFrequency (%)
정선-임계 4
 
4.0%
원주-횡성 2
 
2.0%
공근-동산 2
 
2.0%
횡성-춘천 2
 
2.0%
동내-천전 2
 
2.0%
춘천-화천 2
 
2.0%
사북-화천 2
 
2.0%
갈운-신촌 2
 
2.0%
횡성-정금 2
 
2.0%
봉평-용평 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T20:43:26.159457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정선-임계 4
 
4.0%
원주-소초 2
 
2.0%
송현-성산 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 (39) 78
78.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.23
Minimum0.4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:26.375500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.3
Q14.8
median8.95
Q313.3
95-th percentile23
Maximum27
Range26.6
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation6.4390617
Coefficient of variation (CV)0.62942929
Kurtosis0.038066216
Mean10.23
Median Absolute Deviation (MAD)4.3
Skewness0.76958047
Sum1023
Variance41.461515
MonotonicityNot monotonic
2023-12-10T20:43:26.597070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
7.2 4
 
4.0%
12.0 4
 
4.0%
3.5 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%
10.3 2
 
2.0%
Other values (36) 72
72.0%
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%
22.9 2
2.0%
22.7 2
2.0%
18.1 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
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T20:43:26.770565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:26.914509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 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

2023-12-10T20:43:27.068776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:27.181577image/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%
Mean37.688967
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:27.310268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.08588
5-th percentile37.18732
Q137.40743
median37.634805
Q338.04937
95-th percentile38.23094
Maximum38.38086
Range1.29498
Interquartile range (IQR)0.64194

Descriptive statistics

Standard deviation0.35770529
Coefficient of variation (CV)0.0094909816
Kurtosis-1.2982125
Mean37.688967
Median Absolute Deviation (MAD)0.309315
Skewness0.17252856
Sum3768.8967
Variance0.12795307
MonotonicityNot monotonic
2023-12-10T20:43:27.562305image/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%
37.25108 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%
37.48537 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%
Mean128.24599
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:27.790019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.35058
5-th percentile127.47033
Q1127.86266
median128.19891
Q3128.62245
95-th percentile129.07044
Maximum129.20253
Range1.85195
Interquartile range (IQR)0.75979

Descriptive statistics

Standard deviation0.47581494
Coefficient of variation (CV)0.0037101741
Kurtosis-0.90457621
Mean128.24599
Median Absolute Deviation (MAD)0.36049
Skewness0.13020971
Sum12824.599
Variance0.22639985
MonotonicityNot monotonic
2023-12-10T20:43:28.032587image/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%
128.7796 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%
128.59008 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%
128.84543 2
2.0%
128.84271 2
2.0%
128.83913 2
2.0%
128.81034 2
2.0%
128.79017 2
2.0%
128.7796 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.9292
Minimum0
Maximum177.6
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:28.255985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.516
Q14.1625
median10.11
Q334.0225
95-th percentile123.0295
Maximum177.6
Range177.6
Interquartile range (IQR)29.86

Descriptive statistics

Standard deviation41.308009
Coefficient of variation (CV)1.3801909
Kurtosis2.2215926
Mean29.9292
Median Absolute Deviation (MAD)7.145
Skewness1.7772315
Sum2992.92
Variance1706.3516
MonotonicityNot monotonic
2023-12-10T20:43:28.475332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.0%
1.57 2
 
2.0%
3.67 2
 
2.0%
3.89 2
 
2.0%
4.19 2
 
2.0%
5.93 2
 
2.0%
31.67 1
 
1.0%
13.2 1
 
1.0%
5.65 1
 
1.0%
6.81 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.44 1
 
1.0%
0.52 1
 
1.0%
0.87 1
 
1.0%
1.05 1
 
1.0%
1.57 2
2.0%
2.1 1
 
1.0%
2.68 1
 
1.0%
2.87 1
 
1.0%
3.14 1
 
1.0%
ValueCountFrequency (%)
177.6 1
1.0%
150.66 1
1.0%
137.04 1
1.0%
136.56 1
1.0%
129.67 1
1.0%
122.68 1
1.0%
116.8 1
1.0%
115.52 1
1.0%
114.39 1
1.0%
114.19 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.8802
Minimum0
Maximum169.33
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:28.671216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.278
Q12.475
median7.305
Q326.1
95-th percentile127.6595
Maximum169.33
Range169.33
Interquartile range (IQR)23.625

Descriptive statistics

Standard deviation39.290993
Coefficient of variation (CV)1.5792073
Kurtosis4.1704792
Mean24.8802
Median Absolute Deviation (MAD)5.815
Skewness2.1860395
Sum2488.02
Variance1543.7821
MonotonicityNot monotonic
2023-12-10T20:43:28.896680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.0%
0.83 2
 
2.0%
13.24 2
 
2.0%
1.94 2
 
2.0%
1.93 2
 
2.0%
2.22 2
 
2.0%
18.11 1
 
1.0%
6.64 1
 
1.0%
3.6 1
 
1.0%
7.46 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.24 1
 
1.0%
0.28 1
 
1.0%
0.49 1
 
1.0%
0.55 1
 
1.0%
0.83 2
2.0%
1.11 1
 
1.0%
1.54 1
 
1.0%
1.6 1
 
1.0%
1.66 1
 
1.0%
ValueCountFrequency (%)
169.33 1
1.0%
165.1 1
1.0%
150.05 1
1.0%
137.45 1
1.0%
130.5 1
1.0%
127.51 1
1.0%
109.22 1
1.0%
103.97 1
1.0%
79.59 1
1.0%
79.09 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4102
Minimum0
Maximum21.65
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:29.159435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0395
Q10.3825
median1.02
Q33.475
95-th percentile15.6855
Maximum21.65
Range21.65
Interquartile range (IQR)3.0925

Descriptive statistics

Standard deviation5.0738633
Coefficient of variation (CV)1.4878492
Kurtosis3.2874472
Mean3.4102
Median Absolute Deviation (MAD)0.8
Skewness2.0074393
Sum341.02
Variance25.744089
MonotonicityNot monotonic
2023-12-10T20:43:29.717359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.0%
0.35 4
 
4.0%
0.31 3
 
3.0%
0.57 3
 
3.0%
0.13 2
 
2.0%
0.89 2
 
2.0%
1.02 2
 
2.0%
0.4 2
 
2.0%
0.26 2
 
2.0%
3.02 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.03 1
 
1.0%
0.04 1
 
1.0%
0.07 1
 
1.0%
0.09 1
 
1.0%
0.13 2
2.0%
0.18 1
 
1.0%
0.26 2
2.0%
0.27 1
 
1.0%
0.29 1
 
1.0%
ValueCountFrequency (%)
21.65 1
1.0%
21.07 1
1.0%
17.62 1
1.0%
17.1 1
1.0%
16.17 1
1.0%
15.66 1
1.0%
15.51 1
1.0%
13.54 1
1.0%
13.34 1
1.0%
12.0 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2069
Minimum0
Maximum9.65
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:29.940241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.275
Q31.125
95-th percentile6.1985
Maximum9.65
Range9.65
Interquartile range (IQR)0.995

Descriptive statistics

Standard deviation2.0572192
Coefficient of variation (CV)1.7045482
Kurtosis5.6811255
Mean1.2069
Median Absolute Deviation (MAD)0.275
Skewness2.4465628
Sum120.69
Variance4.2321509
MonotonicityNot monotonic
2023-12-10T20:43:30.174327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 23
23.0%
0.14 10
 
10.0%
0.27 7
 
7.0%
0.13 7
 
7.0%
0.26 3
 
3.0%
0.44 3
 
3.0%
0.39 3
 
3.0%
0.81 2
 
2.0%
2.94 2
 
2.0%
4.0 1
 
1.0%
Other values (39) 39
39.0%
ValueCountFrequency (%)
0.0 23
23.0%
0.13 7
 
7.0%
0.14 10
10.0%
0.26 3
 
3.0%
0.27 7
 
7.0%
0.28 1
 
1.0%
0.3 1
 
1.0%
0.39 3
 
3.0%
0.42 1
 
1.0%
0.44 3
 
3.0%
ValueCountFrequency (%)
9.65 1
1.0%
8.71 1
1.0%
7.92 1
1.0%
7.83 1
1.0%
6.74 1
1.0%
6.17 1
1.0%
5.39 1
1.0%
5.38 1
1.0%
4.0 1
1.0%
3.69 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7311.048
Minimum0
Maximum43807.49
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:30.376581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138.1035
Q11086.525
median2545.09
Q38282.91
95-th percentile28923.853
Maximum43807.49
Range43807.49
Interquartile range (IQR)7196.385

Descriptive statistics

Standard deviation9991.1903
Coefficient of variation (CV)1.366588
Kurtosis2.2989115
Mean7311.048
Median Absolute Deviation (MAD)1829.26
Skewness1.7741116
Sum731104.8
Variance99823883
MonotonicityNot monotonic
2023-12-10T20:43:30.566730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.0%
416.06 2
 
2.0%
970.8 2
 
2.0%
922.09 2
 
2.0%
1109.48 2
 
2.0%
7567.94 1
 
1.0%
2986.35 1
 
1.0%
1243.92 1
 
1.0%
1802.9 1
 
1.0%
3279.79 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 4
4.0%
127.15 1
 
1.0%
138.68 1
 
1.0%
254.3 1
 
1.0%
277.37 1
 
1.0%
416.06 2
2.0%
554.74 1
 
1.0%
646.6 1
 
1.0%
648.41 1
 
1.0%
697.05 1
 
1.0%
ValueCountFrequency (%)
43807.49 1
1.0%
37511.25 1
1.0%
34741.1 1
1.0%
31207.67 1
1.0%
29091.68 1
1.0%
28915.02 1
1.0%
28887.91 1
1.0%
27649.55 1
1.0%
27257.69 1
1.0%
25703.68 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 정선 임계 봉산
 
2
강원 강릉 연곡 동덕
 
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 정선 임계 봉산 2
 
2.0%
강원 강릉 연곡 동덕 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

2023-12-10T20:43:30.752074image/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%
정선 12
 
3.0%
원주 10
 
2.5%
춘천 10
 
2.5%
홍천 10
 
2.5%
평창 8
 
2.0%
영월 8
 
2.0%
남 8
 
2.0%
Other values (82) 202
51.3%

Interactions

2023-12-10T20:43:22.231676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:10.562675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:12.050236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.862606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.229194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.573099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.857249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.144472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.554857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.366056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:10.749485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:12.240647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.018031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.383890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.686621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.993828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.333202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.687401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.490797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:10.928765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:12.386212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.181279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.524300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.813271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.191524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.591894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.820728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.614427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.088308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:12.556973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.335801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.697553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.968789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.336307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.749996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.970694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.771948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.262757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.133114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.493094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.864031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.148914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.448920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.899430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.125554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.926199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.448008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.291672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.641042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.022384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.307578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.597355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.045385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.334418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:23.097835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.617586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.436195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.796698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.188256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.459685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.754350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.187492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.484194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:23.204301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.750784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.577716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.934349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.330150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.597995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.886996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.297784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.967647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:23.330268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.901042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.724375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.087632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.451254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.723638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.026669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.432758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.105237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:43:30.901417image/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
기본키1.0001.0000.0000.9980.5420.8940.7770.6420.5620.6260.4730.6520.998
지점1.0001.0000.0001.0001.0001.0001.0000.9090.8570.8210.8390.8861.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.2090.0000.0000.000
측정구간0.9981.0000.0001.0000.9970.9990.9980.8990.8640.8150.8490.8771.000
연장((km))0.5421.0000.0000.9971.0000.6360.6520.2840.0000.0000.3230.2470.997
좌표위치위도((°))0.8941.0000.0000.9990.6361.0000.7950.5440.4820.5440.4600.6141.000
좌표위치경도((°))0.7771.0000.0000.9980.6520.7951.0000.5220.3160.3880.2700.4901.000
co((g/km))0.6420.9090.0000.8990.2840.5440.5221.0000.8850.9590.8890.9980.899
nox((g/km))0.5620.8570.0000.8640.0000.4820.3160.8851.0000.9390.9810.8850.864
hc((g/km))0.6260.8210.2090.8150.0000.5440.3880.9590.9391.0000.8860.9610.815
pm((g/km))0.4730.8390.0000.8490.3230.4600.2700.8890.9810.8861.0000.8790.849
co2((g/km))0.6520.8860.0000.8770.2470.6140.4900.9980.8850.9610.8791.0000.877
주소0.9981.0000.0001.0000.9971.0001.0000.8990.8640.8150.8490.8771.000
2023-12-10T20:43:31.097023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향측정구간
주소1.0000.0000.980
방향0.0001.0000.000
측정구간0.9800.0001.000
2023-12-10T20:43:31.248570image/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))방향측정구간주소
기본키1.0000.1940.360-0.058-0.083-0.092-0.095-0.109-0.0780.0000.7360.736
연장((km))0.1941.0000.1310.208-0.319-0.318-0.319-0.374-0.3220.0000.7280.726
좌표위치위도((°))0.3600.1311.000-0.431-0.380-0.430-0.428-0.421-0.3750.0000.7390.753
좌표위치경도((°))-0.0580.208-0.4311.0000.0680.1180.1220.0700.0600.0000.7370.753
co((g/km))-0.083-0.319-0.3800.0681.0000.9820.9840.9340.9970.0000.4310.431
nox((g/km))-0.092-0.318-0.4300.1180.9821.0000.9970.9710.9740.0000.3930.393
hc((g/km))-0.095-0.319-0.4280.1220.9840.9971.0000.9630.9730.1510.3230.323
pm((g/km))-0.109-0.374-0.4210.0700.9340.9710.9631.0000.9260.0000.3730.373
co2((g/km))-0.078-0.322-0.3750.0600.9970.9740.9730.9261.0000.0000.3970.397
방향0.0000.0000.0000.0000.0000.0000.1510.0000.0001.0000.0000.000
측정구간0.7360.7280.7390.7370.4310.3930.3230.3730.3970.0001.0000.980
주소0.7360.7260.7530.7530.4310.3930.3230.3730.3970.0000.9801.000

Missing values

2023-12-10T20:43:23.533288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:43:23.892718image/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-소초4.820210201037.3551127.9948731.6718.113.020.847567.94강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210201037.3551127.9948734.6919.873.311.078355.03강원 원주 봉산
23건기연[0527-2]1원주-횡성0.420210201037.42063127.9631980.9764.459.132.9418785.16강원 원주 소초 장양
34건기연[0527-2]2원주-횡성0.420210201037.42063127.9631956.6837.175.61.613412.39강원 원주 소초 장양
45건기연[0529-0]1공근-동산12.020210201037.62539127.895288.029.080.890.31983.16강원 홍천 홍천 삼마치
56건기연[0529-0]2공근-동산12.020210201037.62539127.8952816.713.212.160.583754.04강원 홍천 홍천 삼마치
67건기연[0530-0]1횡성-춘천13.320210201037.73176127.837877.233.980.680.141722.37강원 홍천 북방 부사원
78건기연[0530-0]2횡성-춘천13.320210201037.73176127.837877.864.570.760.271896.66강원 홍천 북방 부사원
89건기연[0531-2]1동내-천전7.520210201037.86064127.7766361.1543.336.161.8615809.45강원 춘천 동내 거두
910건기연[0531-2]2동내-천전7.520210201037.86064127.7766345.8835.864.951.8211692.51강원 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-거진11.020210201038.38086128.44454.192.220.350.01109.48강원 고성 간성 교동
9192건기연[4616-0]2진부령-거진11.020210201038.38086128.44456.453.720.570.131705.45강원 고성 간성 교동
9293건기연[4617-0]1북-외가평12.120210201038.19136128.3178515.0513.171.510.443832.93강원 인제 북 용대
9394건기연[4617-0]2북-외가평12.120210201038.19136128.3178515.629.021.380.284115.6강원 인제 북 용대
9495건기연[4710-0]1이동-근남9.020210201038.18502127.418945.283.020.50.141261.32강원 철원 서 자등
9596건기연[4710-0]2이동-근남9.020210201038.18502127.418944.442.910.430.261199.16강원 철원 서 자등
9697건기연[5601-2]1김화-근남2.420210201038.25247127.470332.871.90.260.13815.68강원 철원 근남 사곡
9798건기연[5601-2]2김화-근남2.420210201038.25247127.470332.681.730.270.14646.6강원 철원 근남 사곡
9899건기연[5602-0]1사내-화천16.420210201038.05058127.604194.192.220.350.01109.48강원 춘천 사북 오탄
99100건기연[5602-0]2사내-화천16.420210201038.05058127.604194.722.490.40.01248.16강원 춘천 사북 오탄