Overview

Dataset statistics

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

Variable types

Numeric9
Categorical5
Text2

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
기본키 is highly overall correlated with 주소High correlation
연장((km)) is highly overall correlated with 주소High correlation
좌표위치위도((°)) is highly overall correlated with 주소High correlation
좌표위치경도((°)) is highly overall correlated with 주소High correlation
co((g/km)) is highly overall correlated with nox((g/km)) and 4 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with co((g/km)) and 4 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with co((g/km)) and 4 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with co((g/km)) and 4 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 4 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 8 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 09:19:57.372936
Analysis finished2024-04-16 09:20:04.374696
Duration7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:04.439767image/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
2024-04-16T18:20:04.560600image/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

2024-04-16T18:20:04.671508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:04.755246image/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
2024-04-16T18:20:04.928872image/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%
2024-04-16T18:20:05.226157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 108
13.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 100
12.5%
2 62
7.8%
3 62
7.8%
4 54
6.8%
6 30
 
3.8%
5 28
 
3.5%
Other values (3) 56
7.0%

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 (%)
0 108
21.6%
1 100
20.0%
2 62
12.4%
3 62
12.4%
4 54
10.8%
6 30
 
6.0%
5 28
 
5.6%
7 26
 
5.2%
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 (%)
0 108
13.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 100
12.5%
2 62
7.8%
3 62
7.8%
4 54
6.8%
6 30
 
3.8%
5 28
 
3.5%
Other values (3) 56
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 108
13.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 100
12.5%
2 62
7.8%
3 62
7.8%
4 54
6.8%
6 30
 
3.8%
5 28
 
3.5%
Other values (3) 56
7.0%

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

2024-04-16T18:20:05.333098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:05.414118image/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
2024-04-16T18:20:05.587761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.18
Min length4

Characters and Unicode

Total characters518
Distinct characters88
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%
2024-04-16T18:20:05.893287image/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 (78) 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 (75) 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 (75) 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 (75) 276
66.7%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.93
Minimum0.4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:06.040784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.3
Q14.4
median8.85
Q313.3
95-th percentile23
Maximum27
Range26.6
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation6.3453236
Coefficient of variation (CV)0.6390054
Kurtosis0.10681371
Mean9.93
Median Absolute Deviation (MAD)4.45
Skewness0.79418635
Sum993
Variance40.263131
MonotonicityNot monotonic
2024-04-16T18:20:06.159050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
8.0 4
 
4.0%
12.0 4
 
4.0%
3.5 4
 
4.0%
7.2 4
 
4.0%
18.1 2
 
2.0%
6.5 2
 
2.0%
14.4 2
 
2.0%
3.8 2
 
2.0%
5.9 2
 
2.0%
16.1 2
 
2.0%
Other values (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%
3.3 2
2.0%
3.5 4
4.0%
3.6 2
2.0%
3.8 2
2.0%
ValueCountFrequency (%)
27.0 2
2.0%
24.7 2
2.0%
23.0 2
2.0%
22.7 2
2.0%
18.1 2
2.0%
18.0 2
2.0%
17.4 2
2.0%
16.4 2
2.0%
16.1 2
2.0%
15.3 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2024-04-16T18:20:06.258621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:06.347555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 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

2024-04-16T18:20:06.462106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:06.558978image/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%
Mean37.701996
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:06.673541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.08588
5-th percentile37.18732
Q137.40743
median37.67506
Q338.05058
95-th percentile38.23094
Maximum38.38086
Range1.29498
Interquartile range (IQR)0.64315

Descriptive statistics

Standard deviation0.35743552
Coefficient of variation (CV)0.0094805462
Kurtosis-1.295624
Mean37.701996
Median Absolute Deviation (MAD)0.33918
Skewness0.085099739
Sum3770.1996
Variance0.12776015
MonotonicityNot monotonic
2024-04-16T18:20:07.115405image/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%
38.10576 2
2.0%
38.08778 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.25358
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:07.236278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.35058
5-th percentile127.47033
Q1127.89528
median128.19891
Q3128.63363
95-th percentile129.07044
Maximum129.20253
Range1.85195
Interquartile range (IQR)0.73835

Descriptive statistics

Standard deviation0.47757418
Coefficient of variation (CV)0.0037236714
Kurtosis-0.88733221
Mean128.25358
Median Absolute Deviation (MAD)0.34817
Skewness0.13145921
Sum12825.358
Variance0.2280771
MonotonicityNot monotonic
2024-04-16T18:20:07.361451image/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%
127.83787 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%
128.84271 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  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3118.6219
Minimum391.69
Maximum10235.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:07.494047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum391.69
5-th percentile487.8835
Q1908.755
median1937.835
Q34383.3375
95-th percentile9063.415
Maximum10235.73
Range9844.04
Interquartile range (IQR)3474.5825

Descriptive statistics

Standard deviation2683.6126
Coefficient of variation (CV)0.86051234
Kurtosis0.24301191
Mean3118.6219
Median Absolute Deviation (MAD)1237.835
Skewness1.1082615
Sum311862.19
Variance7201776.8
MonotonicityNot monotonic
2024-04-16T18:20:07.613588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5579.43 1
 
1.0%
10052.95 1
 
1.0%
1839.76 1
 
1.0%
1173.04 1
 
1.0%
1014.56 1
 
1.0%
1598.78 1
 
1.0%
1733.41 1
 
1.0%
711.12 1
 
1.0%
781.33 1
 
1.0%
1472.36 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
391.69 1
1.0%
414.42 1
1.0%
418.12 1
1.0%
452.98 1
1.0%
454.7 1
1.0%
489.63 1
1.0%
651.02 1
1.0%
672.72 1
1.0%
677.61 1
1.0%
688.88 1
1.0%
ValueCountFrequency (%)
10235.73 1
1.0%
10052.95 1
1.0%
9539.14 1
1.0%
9530.58 1
1.0%
9116.9 1
1.0%
9060.6 1
1.0%
8745.88 1
1.0%
8176.4 1
1.0%
7859.09 1
1.0%
7625.86 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3243.0367
Minimum322.1
Maximum14408.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:07.720222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum322.1
5-th percentile510.2765
Q11011.055
median1765.085
Q34784.5
95-th percentile8412.1565
Maximum14408.5
Range14086.4
Interquartile range (IQR)3773.445

Descriptive statistics

Standard deviation2846.0717
Coefficient of variation (CV)0.87759466
Kurtosis2.336202
Mean3243.0367
Median Absolute Deviation (MAD)1228.285
Skewness1.4133495
Sum324303.67
Variance8100124.1
MonotonicityNot monotonic
2024-04-16T18:20:07.830240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4469.95 1
 
1.0%
10106.65 1
 
1.0%
1685.06 1
 
1.0%
1767.22 1
 
1.0%
1338.04 1
 
1.0%
1311.52 1
 
1.0%
1286.91 1
 
1.0%
672.66 1
 
1.0%
951.79 1
 
1.0%
1272.39 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
322.1 1
1.0%
400.48 1
1.0%
407.75 1
1.0%
445.59 1
1.0%
493.87 1
1.0%
511.14 1
1.0%
521.21 1
1.0%
552.39 1
1.0%
672.66 1
1.0%
691.55 1
1.0%
ValueCountFrequency (%)
14408.5 1
1.0%
13072.46 1
1.0%
10106.65 1
1.0%
8706.81 1
1.0%
8489.04 1
1.0%
8408.11 1
1.0%
8104.88 1
1.0%
7636.48 1
1.0%
7181.25 1
1.0%
7078.56 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean431.7552
Minimum47.65
Maximum1678.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:07.943494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.65
5-th percentile74.368
Q1129.8825
median241.145
Q3640.3625
95-th percentile1096.5005
Maximum1678.3
Range1630.65
Interquartile range (IQR)510.48

Descriptive statistics

Standard deviation368.30177
Coefficient of variation (CV)0.85303377
Kurtosis1.2406954
Mean431.7552
Median Absolute Deviation (MAD)164.47
Skewness1.2154267
Sum43175.52
Variance135646.19
MonotonicityNot monotonic
2024-04-16T18:20:08.059155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
634.32 1
 
1.0%
1309.29 1
 
1.0%
236.48 1
 
1.0%
217.33 1
 
1.0%
190.92 1
 
1.0%
185.01 1
 
1.0%
194.03 1
 
1.0%
92.38 1
 
1.0%
107.49 1
 
1.0%
172.49 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
47.65 1
1.0%
50.5 1
1.0%
56.32 1
1.0%
58.34 1
1.0%
72.24 1
1.0%
74.48 1
1.0%
76.35 1
1.0%
77.05 1
1.0%
92.38 1
1.0%
92.98 1
1.0%
ValueCountFrequency (%)
1678.3 1
1.0%
1673.16 1
1.0%
1309.29 1
1.0%
1221.67 1
1.0%
1151.04 1
1.0%
1093.63 1
1.0%
1058.22 1
1.0%
1032.91 1
1.0%
1023.82 1
1.0%
943.09 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.1515
Minimum17.36
Maximum858.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:08.202681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.36
5-th percentile36.8835
Q170.535
median123.275
Q3286.7625
95-th percentile474.6655
Maximum858.17
Range840.81
Interquartile range (IQR)216.2275

Descriptive statistics

Standard deviation167.58386
Coefficient of variation (CV)0.84148932
Kurtosis2.699066
Mean199.1515
Median Absolute Deviation (MAD)79.475
Skewness1.4753819
Sum19915.15
Variance28084.35
MonotonicityNot monotonic
2024-04-16T18:20:08.330322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285.92 1
 
1.0%
595.81 1
 
1.0%
122.43 1
 
1.0%
109.64 1
 
1.0%
84.37 1
 
1.0%
92.19 1
 
1.0%
92.09 1
 
1.0%
53.84 1
 
1.0%
62.99 1
 
1.0%
98.75 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
17.36 1
1.0%
27.1 1
1.0%
35.47 1
1.0%
35.7 1
1.0%
35.81 1
1.0%
36.94 1
1.0%
38.95 1
1.0%
43.54 1
1.0%
44.06 1
1.0%
46.32 1
1.0%
ValueCountFrequency (%)
858.17 1
1.0%
812.79 1
1.0%
595.81 1
1.0%
535.91 1
1.0%
506.88 1
1.0%
472.97 1
1.0%
472.52 1
1.0%
456.67 1
1.0%
445.6 1
1.0%
425.89 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean764997.08
Minimum92066.14
Maximum2512157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:08.442357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92066.14
5-th percentile123809.83
Q1221882.61
median482211.78
Q31062447.2
95-th percentile2288245.1
Maximum2512157
Range2420090.9
Interquartile range (IQR)840564.6

Descriptive statistics

Standard deviation671314.82
Coefficient of variation (CV)0.87753906
Kurtosis0.34280002
Mean764997.08
Median Absolute Deviation (MAD)305383.72
Skewness1.1560907
Sum76499708
Variance4.5066359 × 1011
MonotonicityNot monotonic
2024-04-16T18:20:08.584440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1421038.01 1
 
1.0%
2512157.0 1
 
1.0%
456431.86 1
 
1.0%
279812.42 1
 
1.0%
222354.03 1
 
1.0%
405806.93 1
 
1.0%
411501.19 1
 
1.0%
176420.15 1
 
1.0%
207752.42 1
 
1.0%
373411.51 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
92066.14 1
1.0%
97740.69 1
1.0%
102173.41 1
1.0%
104576.23 1
1.0%
116659.47 1
1.0%
124186.16 1
1.0%
158485.83 1
1.0%
165605.39 1
1.0%
172822.5 1
1.0%
173593.58 1
1.0%
ValueCountFrequency (%)
2512157.0 1
1.0%
2503467.34 1
1.0%
2435028.97 1
1.0%
2403130.97 1
1.0%
2292470.47 1
1.0%
2288022.67 1
1.0%
2135731.46 1
1.0%
2041464.18 1
1.0%
1987837.32 1
1.0%
1942082.33 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 인제 인제 고사
 
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 인제 인제 고사 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

2024-04-16T18:20:08.703712image/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%
홍천 12
 
3.0%
춘천 10
 
2.5%
원주 10
 
2.5%
8
 
2.0%
강릉 8
 
2.0%
평창 8
 
2.0%
영월 8
 
2.0%
Other values (82) 204
51.8%

Interactions

2024-04-16T18:20:03.395602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:57.843776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.515312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.162449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.807520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.500216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.116515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.016047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.726980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.460958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:57.902689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.588913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.235207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.875522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.569592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.191623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.098375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.789991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.530383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:57.965657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.653326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.312725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.945052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.636531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.274230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.168810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.857870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.621662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.032162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.720797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.385847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.022309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.705863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.357235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.242753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.933462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.699986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.102974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.793911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.462668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.109209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.788027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.432863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.323977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.007924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.765102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.168043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.858772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.528126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.187257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.851150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.509066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.400292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.069277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.837090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.248931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.946718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.602303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.263933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.920626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.807055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.504288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.150156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.912920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.364516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.021379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.675713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.343132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.992647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.880501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.580045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.219386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.984933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:58.437658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.085808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:19:59.740145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:00.413975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.053484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:01.943619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:02.648911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:03.298083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T18:20:08.784830image/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
기본키1.0001.0000.0001.0000.5830.8520.8310.7720.5140.5120.4690.7880.998
지점1.0001.0000.0001.0001.0001.0001.0000.9910.9520.9580.9630.9981.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9910.9520.9580.9630.9981.000
연장((km))0.5831.0000.0001.0001.0000.6390.6060.4950.3570.5970.4550.5530.998
좌표위치위도((°))0.8521.0000.0001.0000.6391.0000.8120.6510.3500.4370.5160.6851.000
좌표위치경도((°))0.8311.0000.0001.0000.6060.8121.0000.6530.4440.5010.4960.6051.000
co((g/km))0.7720.9910.0000.9910.4950.6510.6531.0000.8690.8610.8360.9930.983
nox((g/km))0.5140.9520.0000.9520.3570.3500.4440.8691.0000.9370.9730.8420.955
hc((g/km))0.5120.9580.0000.9580.5970.4370.5010.8610.9371.0000.9490.8620.959
pm((g/km))0.4690.9630.0000.9630.4550.5160.4960.8360.9730.9491.0000.8210.964
co2((g/km))0.7880.9980.0000.9980.5530.6850.6050.9930.8420.8620.8211.0000.994
주소0.9981.0000.0001.0000.9981.0001.0000.9830.9550.9590.9640.9941.000
2024-04-16T18:20:08.900163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향
주소1.0000.000
방향0.0001.000
2024-04-16T18:20:08.970824image/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
기본키1.0000.2350.326-0.065-0.137-0.139-0.167-0.136-0.1330.0000.736
연장((km))0.2351.0000.1130.129-0.280-0.297-0.291-0.318-0.2850.0000.729
좌표위치위도((°))0.3260.1131.000-0.361-0.208-0.252-0.252-0.219-0.2030.0000.753
좌표위치경도((°))-0.0650.129-0.3611.000-0.046-0.021-0.006-0.062-0.0490.0000.753
co((g/km))-0.137-0.280-0.208-0.0461.0000.9750.9800.9650.9970.0000.646
nox((g/km))-0.139-0.297-0.252-0.0210.9751.0000.9930.9840.9720.0000.565
hc((g/km))-0.167-0.291-0.252-0.0060.9800.9931.0000.9790.9710.0000.571
pm((g/km))-0.136-0.318-0.219-0.0620.9650.9840.9791.0000.9600.0000.587
co2((g/km))-0.133-0.285-0.203-0.0490.9970.9720.9710.9601.0000.0000.703
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
주소0.7360.7290.7530.7530.6460.5650.5710.5870.7030.0001.000

Missing values

2024-04-16T18:20:04.106914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T18:20:04.310372image/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-소초4.820210601037.3551127.994875579.434469.95634.32285.921421038.01강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210601037.3551127.994875585.324487.79640.34285.811417338.29강원 원주 봉산
23건기연[0527-2]1원주-횡성0.420210601037.42063127.963197625.866969.96890.94409.461942082.33강원 원주 소초 장양
34건기연[0527-2]2원주-횡성0.420210601037.42063127.963197437.976548.99875.38386.061869824.63강원 원주 소초 장양
45건기연[0529-0]1공근-동산12.020210601037.62539127.895282624.452923.57400.99153.93629906.83강원 홍천 홍천 삼마치
56건기연[0529-0]2공근-동산12.020210601037.62539127.895282476.532712.66369.58167.1586598.33강원 홍천 홍천 삼마치
67건기연[0530-0]1횡성-춘천13.320210601037.73176127.837872849.993933.34553.04251.25622142.58강원 홍천 북방 부사원
78건기연[0530-0]2횡성-춘천13.320210601037.73176127.837872854.33964.13557.58252.97612459.2강원 홍천 북방 부사원
89건기연[0531-2]1동내-천전7.520210601037.86064127.776638745.888489.041221.67535.912135731.46강원 춘천 동내 거두
910건기연[0531-2]2동내-천전7.520210601037.86064127.776638176.47078.561023.82456.672041464.18강원 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-거진11.020210601038.38086128.44451605.381636.14229.96105.68385819.45강원 고성 간성 교동
9192건기연[4616-0]2진부령-거진11.020210601038.38086128.44451619.261615.5224.8108.67392892.04강원 고성 간성 교동
9293건기연[4617-0]1북-외가평12.120210601038.19136128.317852013.861760.12232.0495.19507296.37강원 인제 북 용대
9394건기연[4617-0]2북-외가평12.120210601038.19136128.317852468.82219.47281.05126.43640042.38강원 인제 북 용대
9495건기연[4710-0]1이동-근남9.020210601038.18502127.418941309.181392.73172.3992.27319811.51강원 철원 서 자등
9596건기연[4710-0]2이동-근남9.020210601038.18502127.418941202.531226.55153.8875.68296154.8강원 철원 서 자등
9697건기연[5601-2]1김화-근남2.420210601038.25247127.47033739.99847.0112.3978.19177235.95강원 철원 근남 사곡
9798건기연[5601-2]2김화-근남2.420210601038.25247127.47033651.02691.5592.9864.35158485.83강원 철원 근남 사곡
9899건기연[5602-0]1사내-화천16.420210601038.05058127.604191273.621419.99177.5587.22305723.4강원 춘천 사북 오탄
99100건기연[5602-0]2사내-화천16.420210601038.05058127.604191384.921517.88197.74102.17332613.88강원 춘천 사북 오탄