Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Numeric9
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
co((g/km)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
co((g/km)) has unique valuesUnique
nox((g/km)) has unique valuesUnique
hc((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2024-04-16 09:20:38.672011
Analysis finished2024-04-16 09:20:46.222819
Duration7.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:46.284532image/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
2024-04-16T18:20:46.402189image/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

2024-04-16T18:20:46.525530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:46.611209image/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
2024-04-16T18:20:46.797038image/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%
4710-0 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%
2024-04-16T18:20:47.097797image/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 56
7.0%
6 32
 
4.0%
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 (%)
0 104
20.8%
1 104
20.8%
3 64
12.8%
2 60
12.0%
4 56
11.2%
6 32
 
6.4%
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 (%)
0 104
13.0%
1 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 60
7.5%
4 56
7.0%
6 32
 
4.0%
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 (%)
0 104
13.0%
1 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 60
7.5%
4 56
7.0%
6 32
 
4.0%
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

2024-04-16T18:20:47.232324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:47.318832image/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
2024-04-16T18:20:47.492892image/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 row원주-횡성
4th row원주-횡성
5th row공근-동산
ValueCountFrequency (%)
원주-소초 2
 
2.0%
임계-나안 2
 
2.0%
이동-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%
2024-04-16T18:20:47.797194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.3%
22
 
4.2%
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 (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 (%)
22
 
5.3%
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 (77) 276
66.7%
Uppercase Letter
ValueCountFrequency (%)
I 2
50.0%
C 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 (%)
22
 
5.3%
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 (77) 276
66.7%
Latin
ValueCountFrequency (%)
I 2
50.0%
C 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%
I 2
 
1.9%
C 2
 
1.9%
Hangul
ValueCountFrequency (%)
22
 
5.3%
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 (77) 276
66.7%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.964
Minimum0.4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:47.926128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.3
Q14.8
median8.85
Q313.2
95-th percentile23
Maximum27
Range26.6
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation6.2207785
Coefficient of variation (CV)0.62432542
Kurtosis0.29974282
Mean9.964
Median Absolute Deviation (MAD)4.2
Skewness0.82938696
Sum996.4
Variance38.698085
MonotonicityNot monotonic
2024-04-16T18:20:48.050648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
8.0 4
 
4.0%
12.0 4
 
4.0%
3.5 4
 
4.0%
8.8 4
 
4.0%
7.2 4
 
4.0%
18.0 2
 
2.0%
6.5 2
 
2.0%
14.4 2
 
2.0%
3.8 2
 
2.0%
5.9 2
 
2.0%
Other values (35) 70
70.0%
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%
18.0 2
2.0%
17.4 2
2.0%
16.1 2
2.0%
15.3 2
2.0%
14.4 2
2.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2024-04-16T18:20:48.147780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:48.224339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-04-16T18:20:48.312306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:48.381488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.689222
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:48.462986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.08588
5-th percentile37.18732
Q137.40743
median37.65705
Q338.02855
95-th percentile38.23094
Maximum38.38086
Range1.29498
Interquartile range (IQR)0.62112

Descriptive statistics

Standard deviation0.35341543
Coefficient of variation (CV)0.0093770954
Kurtosis-1.235264
Mean37.689222
Median Absolute Deviation (MAD)0.30844
Skewness0.15991261
Sum3768.9222
Variance0.12490246
MonotonicityNot monotonic
2024-04-16T18:20:48.579284image/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%
38.14989 2
2.0%
38.11527 2
2.0%
38.08778 2
2.0%
38.07373 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.26203
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:48.690655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.35058
5-th percentile127.47033
Q1127.89528
median128.19891
Q3128.64197
95-th percentile129.07044
Maximum129.20253
Range1.85195
Interquartile range (IQR)0.74669

Descriptive statistics

Standard deviation0.47700078
Coefficient of variation (CV)0.0037189555
Kurtosis-0.90540608
Mean128.26203
Median Absolute Deviation (MAD)0.34817
Skewness0.14262684
Sum12826.203
Variance0.22752975
MonotonicityNot monotonic
2024-04-16T18:20:48.804909image/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%
127.62463 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%
127.83787 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%
Mean1631.7402
Minimum181.28
Maximum5281.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:48.918763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum181.28
5-th percentile274.6975
Q1512.99
median977.385
Q32360.4
95-th percentile4697.8595
Maximum5281.42
Range5100.14
Interquartile range (IQR)1847.41

Descriptive statistics

Standard deviation1419.9089
Coefficient of variation (CV)0.87018076
Kurtosis0.12882129
Mean1631.7402
Median Absolute Deviation (MAD)622.35
Skewness1.1035598
Sum163174.02
Variance2016141.4
MonotonicityNot monotonic
2024-04-16T18:20:49.041194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2252.92 1
 
1.0%
4601.66 1
 
1.0%
717.31 1
 
1.0%
725.69 1
 
1.0%
601.34 1
 
1.0%
907.68 1
 
1.0%
897.13 1
 
1.0%
482.91 1
 
1.0%
443.46 1
 
1.0%
1033.73 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
181.28 1
1.0%
194.41 1
1.0%
236.72 1
1.0%
269.06 1
1.0%
273.7 1
1.0%
274.75 1
1.0%
311.38 1
1.0%
314.59 1
1.0%
330.8 1
1.0%
379.27 1
1.0%
ValueCountFrequency (%)
5281.42 1
1.0%
5178.33 1
1.0%
4967.3 1
1.0%
4863.46 1
1.0%
4805.2 1
1.0%
4692.21 1
1.0%
4601.66 1
1.0%
4498.56 1
1.0%
4380.86 1
1.0%
4332.19 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1378.3906
Minimum172.93
Maximum6712.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:49.194634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum172.93
5-th percentile263.3245
Q1484.9025
median977.76
Q31855.74
95-th percentile3890.805
Maximum6712.31
Range6539.38
Interquartile range (IQR)1370.8375

Descriptive statistics

Standard deviation1248.4971
Coefficient of variation (CV)0.90576439
Kurtosis2.747745
Mean1378.3906
Median Absolute Deviation (MAD)577.61
Skewness1.5883888
Sum137839.06
Variance1558745.1
MonotonicityNot monotonic
2024-04-16T18:20:49.548151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1578.8 1
 
1.0%
4433.02 1
 
1.0%
522.48 1
 
1.0%
1059.53 1
 
1.0%
626.89 1
 
1.0%
629.45 1
 
1.0%
643.08 1
 
1.0%
432.53 1
 
1.0%
477.86 1
 
1.0%
877.91 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
172.93 1
1.0%
190.54 1
1.0%
192.13 1
1.0%
223.11 1
1.0%
247.45 1
1.0%
264.16 1
1.0%
265.71 1
1.0%
279.52 1
1.0%
280.07 1
1.0%
287.36 1
1.0%
ValueCountFrequency (%)
6712.31 1
1.0%
4433.02 1
1.0%
4322.72 1
1.0%
4226.55 1
1.0%
4016.11 1
1.0%
3884.21 1
1.0%
3859.97 1
1.0%
3796.17 1
1.0%
3532.39 1
1.0%
3432.38 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.8147
Minimum21.76
Maximum773.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:49.658367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.76
5-th percentile33.623
Q164.0425
median120.17
Q3239.775
95-th percentile513.495
Maximum773.85
Range752.09
Interquartile range (IQR)175.7325

Descriptive statistics

Standard deviation156.77144
Coefficient of variation (CV)0.8767257
Kurtosis1.5493053
Mean178.8147
Median Absolute Deviation (MAD)74.765
Skewness1.3869068
Sum17881.47
Variance24577.285
MonotonicityNot monotonic
2024-04-16T18:20:49.776397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226.6 1
 
1.0%
527.95 1
 
1.0%
72.38 1
 
1.0%
103.16 1
 
1.0%
85.12 1
 
1.0%
86.6 1
 
1.0%
88.55 1
 
1.0%
50.72 1
 
1.0%
62.88 1
 
1.0%
119.65 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
21.76 1
1.0%
26.57 1
1.0%
26.95 1
1.0%
29.88 1
1.0%
33.11 1
1.0%
33.65 1
1.0%
34.6 1
1.0%
34.97 1
1.0%
37.84 1
1.0%
39.77 1
1.0%
ValueCountFrequency (%)
773.85 1
1.0%
574.1 1
1.0%
527.95 1
1.0%
519.35 1
1.0%
514.54 1
1.0%
513.44 1
1.0%
509.79 1
1.0%
480.25 1
1.0%
456.23 1
1.0%
444.6 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.4839
Minimum3.5
Maximum396.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:49.898145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile15.0425
Q129.6475
median46.155
Q389.7
95-th percentile169.6215
Maximum396.45
Range392.95
Interquartile range (IQR)60.0525

Descriptive statistics

Standard deviation60.222752
Coefficient of variation (CV)0.87937095
Kurtosis8.4496302
Mean68.4839
Median Absolute Deviation (MAD)24.21
Skewness2.3295919
Sum6848.39
Variance3626.7799
MonotonicityNot monotonic
2024-04-16T18:20:50.015104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.38 2
 
2.0%
17.69 2
 
2.0%
142.33 1
 
1.0%
32.08 1
 
1.0%
64.22 1
 
1.0%
37.4 1
 
1.0%
32.14 1
 
1.0%
33.82 1
 
1.0%
29.78 1
 
1.0%
30.92 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
3.5 1
1.0%
4.5 1
1.0%
12.46 1
1.0%
12.87 1
1.0%
13.0 1
1.0%
15.15 1
1.0%
17.05 1
1.0%
17.17 1
1.0%
17.28 1
1.0%
17.55 1
1.0%
ValueCountFrequency (%)
396.45 1
1.0%
247.8 1
1.0%
212.72 1
1.0%
204.35 1
1.0%
182.76 1
1.0%
168.93 1
1.0%
153.92 1
1.0%
145.55 1
1.0%
143.3 1
1.0%
142.33 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416153.18
Minimum43347.86
Maximum1366146.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:50.133211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43347.86
5-th percentile68984.043
Q1129985.11
median243376.7
Q3609184.04
95-th percentile1210467.9
Maximum1366146.1
Range1322798.2
Interquartile range (IQR)479198.93

Descriptive statistics

Standard deviation362684.07
Coefficient of variation (CV)0.87151579
Kurtosis0.13480366
Mean416153.18
Median Absolute Deviation (MAD)157275.01
Skewness1.09958
Sum41615318
Variance1.3153973 × 1011
MonotonicityNot monotonic
2024-04-16T18:20:50.259433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
583347.19 1
 
1.0%
1153375.98 1
 
1.0%
186383.77 1
 
1.0%
207276.64 1
 
1.0%
144190.5 1
 
1.0%
237402.66 1
 
1.0%
233344.56 1
 
1.0%
131013.7 1
 
1.0%
106432.14 1
 
1.0%
260970.92 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
43347.86 1
1.0%
49913.53 1
1.0%
56696.02 1
1.0%
62527.24 1
1.0%
67710.15 1
1.0%
69051.09 1
1.0%
81960.38 1
1.0%
82352.73 1
1.0%
85341.02 1
1.0%
97333.76 1
1.0%
ValueCountFrequency (%)
1366146.08 1
1.0%
1313944.25 1
1.0%
1277088.44 1
1.0%
1243466.75 1
1.0%
1224941.41 1
1.0%
1209706.18 1
1.0%
1168307.61 1
1.0%
1153375.98 1
1.0%
1125607.4 1
1.0%
1041666.88 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-16T18:20:50.490953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

Total characters1070
Distinct characters106
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원 원주 봉산
2nd row강원 원주 봉산
3rd row강원 원주 소초 장양
4th row강원 원주 소초 장양
5th row강원 홍천 홍천 삼마치
ValueCountFrequency (%)
강원 100
25.4%
인제 14
 
3.6%
홍천 12
 
3.0%
횡성 12
 
3.0%
원주 10
 
2.5%
정선 10
 
2.5%
춘천 8
 
2.0%
8
 
2.0%
영월 8
 
2.0%
강릉 8
 
2.0%
Other values (85) 204
51.8%
2024-04-16T18:20:50.824570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
294
27.5%
126
 
11.8%
110
 
10.3%
22
 
2.1%
22
 
2.1%
16
 
1.5%
16
 
1.5%
16
 
1.5%
14
 
1.3%
14
 
1.3%
Other values (96) 420
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 776
72.5%
Space Separator 294
 
27.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
126
 
16.2%
110
 
14.2%
22
 
2.8%
22
 
2.8%
16
 
2.1%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (95) 406
52.3%
Space Separator
ValueCountFrequency (%)
294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 776
72.5%
Common 294
 
27.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
126
 
16.2%
110
 
14.2%
22
 
2.8%
22
 
2.8%
16
 
2.1%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (95) 406
52.3%
Common
ValueCountFrequency (%)
294
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 776
72.5%
ASCII 294
 
27.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
294
100.0%
Hangul
ValueCountFrequency (%)
126
 
16.2%
110
 
14.2%
22
 
2.8%
22
 
2.8%
16
 
2.1%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (95) 406
52.3%

Interactions

2024-04-16T18:20:45.263489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.117870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.769321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:40.781694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.511688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.278389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.903347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.622860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.328475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.347229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.175208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.860341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:40.879962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.587417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.341533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.975538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.690753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.397467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.416213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.237623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.960323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:40.973612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.656383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.406868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.043259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.768344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.468180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.490805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.303861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:40.346213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.053549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.732760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.475076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.128234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.850858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.551886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.566779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.369495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:40.423644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.138800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.810264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.546585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.206640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.932066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.630549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.642941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.429458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:40.487031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.205168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.898287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.610249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.275306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.004599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.938868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.723289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.502687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:40.556504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.284418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.994358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.679734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.357495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.092417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.017609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.807557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.592156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:40.629142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.360012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.097042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.751226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.454837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.169627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.097734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.882053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:39.680213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:40.700957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:41.436736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.194238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:42.825030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:43.547351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:44.248179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:45.182299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T18:20:50.916082image/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
기본키1.0001.0000.0001.0000.5830.8800.8340.5990.4620.4320.4450.5661.000
지점1.0001.0000.0001.0001.0001.0001.0000.9080.8680.8830.8240.9081.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0930.0920.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9080.8680.8830.8240.9081.000
연장((km))0.5831.0000.0001.0001.0000.6210.6120.3010.1780.3820.2960.2921.000
좌표위치위도((°))0.8801.0000.0001.0000.6211.0000.8000.4190.3860.4240.3440.3881.000
좌표위치경도((°))0.8341.0000.0001.0000.6120.8001.0000.2470.2360.4210.1540.4561.000
co((g/km))0.5990.9080.0000.9080.3010.4190.2471.0000.8790.8770.8090.9980.908
nox((g/km))0.4620.8680.0000.8680.1780.3860.2360.8791.0000.9360.9720.8870.868
hc((g/km))0.4320.8830.0000.8830.3820.4240.4210.8770.9361.0000.8770.8880.883
pm((g/km))0.4450.8240.0930.8240.2960.3440.1540.8090.9720.8771.0000.8040.824
co2((g/km))0.5660.9080.0920.9080.2920.3880.4560.9980.8870.8880.8041.0000.908
주소1.0001.0000.0001.0001.0001.0001.0000.9080.8680.8830.8240.9081.000
2024-04-16T18:20:51.034391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))방향
기본키1.0000.1730.339-0.032-0.023-0.034-0.062-0.024-0.0170.000
연장((km))0.1731.0000.1240.209-0.209-0.238-0.227-0.310-0.2090.000
좌표위치위도((°))0.3390.1241.000-0.390-0.253-0.305-0.317-0.283-0.2440.000
좌표위치경도((°))-0.0320.209-0.3901.000-0.061-0.033-0.016-0.078-0.0680.000
co((g/km))-0.023-0.209-0.253-0.0611.0000.9730.9790.9310.9980.000
nox((g/km))-0.034-0.238-0.305-0.0330.9731.0000.9930.9730.9640.000
hc((g/km))-0.062-0.227-0.317-0.0160.9790.9931.0000.9580.9680.000
pm((g/km))-0.024-0.310-0.283-0.0780.9310.9730.9581.0000.9210.062
co2((g/km))-0.017-0.209-0.244-0.0680.9980.9640.9680.9211.0000.071
방향0.0000.0000.0000.0000.0000.0000.0000.0620.0711.000

Missing values

2024-04-16T18:20:45.992129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T18:20:46.158329image/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-소초4.820210301037.3551127.994872252.921578.8226.672.6583347.19강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210301037.3551127.994872357.011633.93234.174.79611663.16강원 원주 봉산
23건기연[0527-2]1원주-횡성0.420210301037.42063127.963193080.942623.23328.34130.89786612.33강원 원주 소초 장양
34건기연[0527-2]2원주-횡성0.420210301037.42063127.963193140.122919.84345.23141.5813680.38강원 원주 소초 장양
45건기연[0529-0]1공근-동산12.020210301037.62539127.895281257.891229.56151.4457.97312045.88강원 홍천 홍천 삼마치
56건기연[0529-0]2공근-동산12.020210301037.62539127.895281107.411072.0128.9756.02277239.67강원 홍천 홍천 삼마치
67건기연[0530-0]1횡성-춘천13.320210301037.73176127.837871008.62992.92127.260.26249350.74강원 홍천 북방 부사원
78건기연[0530-0]2횡성-춘천13.320210301037.73176127.83787946.15962.6120.6959.22233170.42강원 홍천 북방 부사원
89건기연[0531-2]1동내-천전7.520210301037.86064127.776634054.223075.1417.32130.371041666.88강원 춘천 동내 거두
910건기연[0531-2]2동내-천전7.520210301037.86064127.776634498.563122.51444.43143.31168307.61강원 춘천 동내 거두
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4613-0]1양구-신남4.020210301038.08778128.04511181.28223.1126.5717.2843347.86강원 양구 남 청
9192건기연[4613-0]2양구-신남4.020210301038.08778128.04511194.41190.5421.7617.5549913.53강원 양구 남 청
9293건기연[4616-0]1진부령-거진11.020210301038.38086128.4445704.95545.9171.1234.8183059.51강원 고성 간성 교동
9394건기연[4616-0]2진부령-거진11.020210301038.38086128.4445854.34641.2586.2235.28221345.29강원 고성 간성 교동
9495건기연[4617-0]1북-외가평12.120210301038.19136128.317851323.01139.02138.0753.04338418.08강원 인제 북 용대
9596건기연[4617-0]2북-외가평12.120210301038.19136128.317852651.261915.32282.9176.9619807.75강원 인제 북 용대
9697건기연[4710-0]1이동-근남9.020210301038.18502127.41894666.04647.0271.5441.81170161.94강원 철원 서 자등
9798건기연[4710-0]2이동-근남9.020210301038.18502127.41894660.54633.8270.5640.47168929.07강원 철원 서 자등
9899건기연[5601-2]1김화-근남2.420210301038.25247127.47033269.06264.1633.1123.2267710.15강원 철원 근남 사곡
99100건기연[5601-2]2김화-근남2.420210301038.25247127.47033273.7265.7133.6524.7669051.09강원 철원 근남 사곡