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 측정구간High correlation
연장((km)) is highly overall correlated with 측정구간High correlation
좌표위치위도((°)) is highly overall correlated with 측정구간High correlation
좌표위치경도((°)) is highly overall correlated with 측정구간High 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
측정구간 is highly overall correlated with 기본키 and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
pm((g/km)) has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:05:06.242177
Analysis finished2023-12-10 11:05:22.247607
Duration16.01 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
2023-12-10T20:05:22.404772image/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
2023-12-10T20:05:22.664892image/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

2023-12-10T20:05:22.879724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:05:23.027269image/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
2023-12-10T20:05:23.345452image/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%
5915-0 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%
2023-12-10T20:05:23.906699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 62
7.8%
2 60
7.5%
4 54
6.8%
5 30
 
3.8%
6 28
 
3.5%
Other values (3) 58
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106
21.2%
1 102
20.4%
3 62
12.4%
2 60
12.0%
4 54
10.8%
5 30
 
6.0%
6 28
 
5.6%
7 26
 
5.2%
8 18
 
3.6%
9 14
 
2.8%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 62
7.8%
2 60
7.5%
4 54
6.8%
5 30
 
3.8%
6 28
 
3.5%
Other values (3) 58
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 62
7.8%
2 60
7.5%
4 54
6.8%
5 30
 
3.8%
6 28
 
3.5%
Other values (3) 58
7.2%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2023-12-10T20:05:24.165661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:05:24.330664image/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

2023-12-10T20:05:24.606925image/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%
양구-동 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 (39) 78
78.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.06
Minimum0.4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:05:24.901384image/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 deviation6.5420783
Coefficient of variation (CV)0.65030599
Kurtosis-0.019153746
Mean10.06
Median Absolute Deviation (MAD)4.45
Skewness0.80452001
Sum1006
Variance42.798788
MonotonicityNot monotonic
2023-12-10T20:05:25.178896image/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%
18.0 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%
22.9 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%

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

2023-12-10T20:05:25.414395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:05:25.578417image/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
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T20:05:25.743702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:05:25.907985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

좌표위치위도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.690692
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:05:26.101892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.35513988
Coefficient of variation (CV)0.0094224823
Kurtosis-1.2508873
Mean37.690692
Median Absolute Deviation (MAD)0.30844
Skewness0.16006151
Sum3769.0692
Variance0.12612434
MonotonicityNot monotonic
2023-12-10T20:05:26.386807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3551 2
 
2.0%
38.23094 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%
Mean128.27329
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:05:26.691378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.35058
5-th percentile127.47033
Q1127.91342
median128.20314
Q3128.63363
95-th percentile129.07044
Maximum129.20253
Range1.85195
Interquartile range (IQR)0.72021

Descriptive statistics

Standard deviation0.47058758
Coefficient of variation (CV)0.0036686325
Kurtosis-0.8491615
Mean128.27329
Median Absolute Deviation (MAD)0.352325
Skewness0.070766627
Sum12827.329
Variance0.22145267
MonotonicityNot monotonic
2023-12-10T20:05:26.988252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.99487 2
 
2.0%
127.35058 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%
127.83787 2
2.0%
127.83897 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 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.0284
Minimum0
Maximum82.44
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:05:27.269357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.65
Q12.78
median7.005
Q324.4175
95-th percentile54.281
Maximum82.44
Range82.44
Interquartile range (IQR)21.6375

Descriptive statistics

Standard deviation19.289728
Coefficient of variation (CV)1.2034718
Kurtosis1.7038366
Mean16.0284
Median Absolute Deviation (MAD)5.705
Skewness1.5563663
Sum1602.84
Variance372.09359
MonotonicityNot monotonic
2023-12-10T20:05:27.930311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.26 4
 
4.0%
1.3 4
 
4.0%
2.83 4
 
4.0%
2.78 3
 
3.0%
1.05 3
 
3.0%
0.65 3
 
3.0%
3.28 3
 
3.0%
0.52 3
 
3.0%
2.68 2
 
2.0%
3.98 2
 
2.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.52 3
3.0%
0.65 3
3.0%
1.05 3
3.0%
1.3 4
4.0%
1.78 1
 
1.0%
1.98 1
 
1.0%
2.26 4
4.0%
2.63 1
 
1.0%
2.68 2
2.0%
ValueCountFrequency (%)
82.44 1
1.0%
79.91 1
1.0%
63.08 1
1.0%
59.29 1
1.0%
54.3 1
1.0%
54.28 1
1.0%
53.35 1
1.0%
52.96 1
1.0%
52.92 1
1.0%
51.96 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.6613
Minimum0
Maximum93.83
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:05:28.194316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32
Q11.79
median4.775
Q321.4425
95-th percentile50.248
Maximum93.83
Range93.83
Interquartile range (IQR)19.6525

Descriptive statistics

Standard deviation19.392278
Coefficient of variation (CV)1.3226847
Kurtosis2.9448748
Mean14.6613
Median Absolute Deviation (MAD)4.135
Skewness1.7559395
Sum1466.13
Variance376.06044
MonotonicityNot monotonic
2023-12-10T20:05:28.460797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.51 4
 
4.0%
1.88 4
 
4.0%
0.64 4
 
4.0%
1.96 3
 
3.0%
0.28 3
 
3.0%
0.32 3
 
3.0%
0.55 3
 
3.0%
1.79 3
 
3.0%
8.53 2
 
2.0%
1.73 2
 
2.0%
Other values (68) 69
69.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.28 3
3.0%
0.32 3
3.0%
0.55 3
3.0%
0.64 4
4.0%
1.32 1
 
1.0%
1.33 1
 
1.0%
1.51 4
4.0%
1.64 1
 
1.0%
1.73 2
2.0%
ValueCountFrequency (%)
93.83 1
1.0%
75.89 1
1.0%
70.74 1
1.0%
57.88 1
1.0%
51.54 1
1.0%
50.18 1
1.0%
49.03 1
1.0%
48.66 1
1.0%
48.1 1
1.0%
45.14 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0824
Minimum0
Maximum13.11
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:05:28.720545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.27
median0.715
Q33.1575
95-th percentile7.3415
Maximum13.11
Range13.11
Interquartile range (IQR)2.8875

Descriptive statistics

Standard deviation2.684165
Coefficient of variation (CV)1.2889767
Kurtosis2.7731129
Mean2.0824
Median Absolute Deviation (MAD)0.595
Skewness1.7117777
Sum208.24
Variance7.2047417
MonotonicityNot monotonic
2023-12-10T20:05:28.987543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27 6
 
6.0%
0.22 4
 
4.0%
0.26 4
 
4.0%
0.32 4
 
4.0%
0.12 4
 
4.0%
0.04 3
 
3.0%
0.09 3
 
3.0%
0.06 3
 
3.0%
0.6 2
 
2.0%
0.54 2
 
2.0%
Other values (63) 65
65.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.04 3
3.0%
0.06 3
3.0%
0.09 3
3.0%
0.12 4
4.0%
0.18 1
 
1.0%
0.2 1
 
1.0%
0.22 4
4.0%
0.26 4
4.0%
0.27 6
6.0%
ValueCountFrequency (%)
13.11 1
1.0%
10.36 1
1.0%
8.97 1
1.0%
8.2 1
1.0%
7.94 1
1.0%
7.31 1
1.0%
6.7 1
1.0%
6.4 1
1.0%
6.31 1
1.0%
6.26 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8697
Minimum0
Maximum5.89
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:05:29.231010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.4
Q31.26
95-th percentile3.022
Maximum5.89
Range5.89
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation1.1279027
Coefficient of variation (CV)1.2968871
Kurtosis4.0695555
Mean0.8697
Median Absolute Deviation (MAD)0.27
Skewness1.9008071
Sum86.97
Variance1.2721646
MonotonicityNot monotonic
2023-12-10T20:05:29.489518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 14
 
14.0%
0.13 14
 
14.0%
0.14 12
 
12.0%
0.28 7
 
7.0%
0.4 6
 
6.0%
0.42 3
 
3.0%
1.26 2
 
2.0%
0.27 2
 
2.0%
0.44 2
 
2.0%
0.57 2
 
2.0%
Other values (36) 36
36.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.13 14
14.0%
0.14 12
12.0%
0.27 2
 
2.0%
0.28 7
7.0%
0.4 6
6.0%
0.42 3
 
3.0%
0.44 2
 
2.0%
0.54 1
 
1.0%
0.55 1
 
1.0%
ValueCountFrequency (%)
5.89 1
1.0%
4.54 1
1.0%
3.95 1
1.0%
3.42 1
1.0%
3.06 1
1.0%
3.02 1
1.0%
2.77 1
1.0%
2.76 1
1.0%
2.58 1
1.0%
2.51 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3789.6386
Minimum0
Maximum19075.65
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:05:29.790671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile153.68
Q1734.66
median1674.59
Q35628.6725
95-th percentile13289.406
Maximum19075.65
Range19075.65
Interquartile range (IQR)4894.0125

Descriptive statistics

Standard deviation4498.0332
Coefficient of variation (CV)1.1869293
Kurtosis1.5354975
Mean3789.6386
Median Absolute Deviation (MAD)1354.1
Skewness1.5330473
Sum378963.86
Variance20232302
MonotonicityNot monotonic
2023-12-10T20:05:30.063982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
595.97 4
 
4.0%
307.36 4
 
4.0%
740.29 4
 
4.0%
734.66 3
 
3.0%
277.37 3
 
3.0%
153.68 3
 
3.0%
794.65 3
 
3.0%
138.68 3
 
3.0%
646.6 2
 
2.0%
953.96 2
 
2.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
138.68 3
3.0%
153.68 3
3.0%
277.37 3
3.0%
307.36 4
4.0%
462.92 1
 
1.0%
487.28 1
 
1.0%
595.97 4
4.0%
640.96 1
 
1.0%
646.6 2
2.0%
ValueCountFrequency (%)
19075.65 1
1.0%
18117.59 1
1.0%
14210.89 1
1.0%
13540.26 1
1.0%
13397.06 1
1.0%
13283.74 1
1.0%
13152.36 1
1.0%
13027.08 1
1.0%
12445.67 1
1.0%
12384.31 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:05:30.507234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

Total characters1070
Distinct characters105
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%
평창 8
 
2.0%
영월 8
 
2.0%
Other values (83) 204
51.8%
2023-12-10T20:05:31.192939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
294
27.5%
126
 
11.8%
110
 
10.3%
24
 
2.2%
20
 
1.9%
20
 
1.9%
18
 
1.7%
16
 
1.5%
16
 
1.5%
14
 
1.3%
Other values (95) 412
38.5%

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%
24
 
3.1%
20
 
2.6%
20
 
2.6%
18
 
2.3%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
Other values (94) 398
51.3%
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%
24
 
3.1%
20
 
2.6%
20
 
2.6%
18
 
2.3%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
Other values (94) 398
51.3%
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%
24
 
3.1%
20
 
2.6%
20
 
2.6%
18
 
2.3%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
Other values (94) 398
51.3%

Interactions

2023-12-10T20:05:19.942732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:07.679071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:09.176181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:10.687329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:12.254211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:13.741899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:15.144368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:16.698605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:18.565964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:20.118763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:07.825111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:09.322737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:10.856693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:12.417714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:13.884013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:15.285619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:16.862018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:18.708286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:20.295204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:07.970879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:09.512055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:11.037850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:12.565889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:14.052557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:15.420663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:17.031600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:18.852134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:20.501947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:08.141663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:09.705152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:11.208896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:12.730283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:14.228533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:15.585857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:17.192504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:18.989381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:20.708970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:08.306893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:09.877679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:11.402493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:12.915013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:14.389022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:15.779700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:17.376188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:19.137568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:20.907304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:08.464056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:10.026951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:11.544000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:13.083745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:14.527931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:16.010116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:17.521499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:19.263722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:21.069452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:08.673166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:10.166920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:11.701675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:13.240418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:14.678899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:16.150589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:18.021496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:19.406242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:21.240603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:08.849784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:10.322133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:11.864031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:13.396019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:14.819958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:16.325319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:18.198305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:19.573142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:21.405774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:08.988180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:10.493476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:12.068102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:13.555999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:14.962267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:16.499003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:18.388647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:05:19.755205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:05:31.388153image/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
기본키1.0001.0000.0001.0000.6190.8530.8340.3420.4130.2680.2280.4271.000
지점1.0001.0000.0001.0001.0001.0001.0000.8320.8220.7510.6650.8721.000
방향0.0000.0001.0000.0000.0000.0000.0000.0650.0000.1860.0660.0000.000
측정구간1.0001.0000.0001.0000.9970.9990.9980.8420.8170.7710.6980.8791.000
연장((km))0.6191.0000.0000.9971.0000.6240.6240.2820.0000.2250.2170.5251.000
좌표위치위도((°))0.8531.0000.0000.9990.6241.0000.8060.0000.4500.1530.2090.2781.000
좌표위치경도((°))0.8341.0000.0000.9980.6240.8061.0000.0000.4040.0000.0000.1691.000
co((g/km))0.3420.8320.0650.8420.2820.0000.0001.0000.8820.9540.9580.9950.832
nox((g/km))0.4130.8220.0000.8170.0000.4500.4040.8821.0000.9580.9590.8680.822
hc((g/km))0.2680.7510.1860.7710.2250.1530.0000.9540.9581.0000.9830.9350.751
pm((g/km))0.2280.6650.0660.6980.2170.2090.0000.9580.9590.9831.0000.9530.665
co2((g/km))0.4270.8720.0000.8790.5250.2780.1690.9950.8680.9350.9531.0000.872
주소1.0001.0000.0001.0001.0001.0001.0000.8320.8220.7510.6650.8721.000
2023-12-10T20:05:31.631804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향측정구간
방향1.0000.000
측정구간0.0001.000
2023-12-10T20:05:31.813043image/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))방향측정구간
기본키1.0000.2140.292-0.007-0.158-0.155-0.164-0.162-0.1630.0000.753
연장((km))0.2141.0000.0760.185-0.312-0.288-0.293-0.319-0.3170.0000.727
좌표위치위도((°))0.2920.0761.000-0.349-0.291-0.303-0.317-0.313-0.2840.0000.738
좌표위치경도((°))-0.0070.185-0.3491.000-0.017-0.0010.009-0.012-0.0260.0000.737
co((g/km))-0.158-0.312-0.291-0.0171.0000.9940.9940.9740.9990.0560.365
nox((g/km))-0.155-0.288-0.303-0.0010.9941.0000.9990.9820.9910.0000.325
hc((g/km))-0.164-0.293-0.3170.0090.9940.9991.0000.9810.9900.1770.295
pm((g/km))-0.162-0.319-0.313-0.0120.9740.9820.9811.0000.9710.0570.241
co2((g/km))-0.163-0.317-0.284-0.0260.9990.9910.9900.9711.0000.0000.413
방향0.0000.0000.0000.0000.0560.0000.1770.0570.0001.0000.000
측정구간0.7530.7270.7380.7370.3650.3250.2950.2410.4130.0001.000

Missing values

2023-12-10T20:05:21.669169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:05:22.063134image/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-소초4.820210601137.3551127.9948711.146.531.080.42691.3강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210601137.3551127.9948719.3112.11.80.85101.37강원 원주 봉산
23건기연[0527-2]1원주-횡성0.420210601137.42063127.9631937.8231.414.271.919604.81강원 원주 소초 장양
34건기연[0527-2]2원주-횡성0.420210601137.42063127.9631935.2429.924.01.788884.66강원 원주 소초 장양
45건기연[0529-0]1공근-동산12.020210601137.62539127.8952824.6521.63.461.115468.4강원 홍천 홍천 삼마치
56건기연[0529-0]2공근-동산12.020210601137.62539127.895287.34.80.70.41926.6강원 홍천 홍천 삼마치
67건기연[0530-0]1횡성-춘천13.320210601137.73176127.837875.557.311.110.441182.64강원 홍천 북방 부사원
78건기연[0530-0]2횡성-춘천13.320210601137.73176127.837872.831.880.270.14740.29강원 홍천 북방 부사원
89건기연[0531-2]1동내-천전7.520210601137.86064127.7766359.2950.187.943.0213397.06강원 춘천 동내 거두
910건기연[0531-2]2동내-천전7.520210601137.86064127.7766326.124.123.61.536313.56강원 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-신남4.020210601138.08778128.045112.631.640.260.13640.96강원 양구 남 청
9192건기연[4613-0]2양구-신남4.020210601138.08778128.045110.520.280.040.0138.68강원 양구 남 청
9293건기연[4616-0]1진부령-거진11.020210601138.38086128.44450.650.320.060.0153.68강원 고성 간성 교동
9394건기연[4616-0]2진부령-거진11.020210601138.38086128.44452.781.790.260.13734.66강원 고성 간성 교동
9495건기연[4617-0]1북-외가평12.120210601138.19136128.317858.815.420.810.282312.69강원 인제 북 용대
9596건기연[4617-0]2북-외가평12.120210601138.19136128.317853.312.150.290.13942.83강원 인제 북 용대
9697건기연[4710-0]1이동-근남9.020210601138.18502127.418942.781.790.260.13734.66강원 철원 서 자등
9798건기연[4710-0]2이동-근남9.020210601138.18502127.418944.42.710.40.141156.34강원 철원 서 자등
9899건기연[5601-2]1김화-근남2.420210601138.25247127.470331.981.320.20.13487.28강원 철원 근남 사곡
99100건기연[5601-2]2김화-근남2.420210601138.25247127.470330.00.00.00.00.0강원 철원 근남 사곡