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
co((g/km)) has 2 (2.0%) zerosZeros
nox((g/km)) has 2 (2.0%) zerosZeros
hc((g/km)) has 2 (2.0%) zerosZeros
pm((g/km)) has 34 (34.0%) zerosZeros
co2((g/km)) has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:42:43.491805
Analysis finished2023-12-10 11:42:58.650387
Duration15.16 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
2023-12-10T20:42:58.751409image/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
2023-12-10T20:42:58.929206image/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

2023-12-10T20:42:59.111480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:59.248142image/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
2023-12-10T20:42:59.513113image/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%
4617-0 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%
2023-12-10T20:43:00.061461image/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 58
7.2%
4 56
7.0%
5 30
 
3.8%
6 30
 
3.8%
Other values (3) 54
6.8%

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 58
11.6%
4 56
11.2%
5 30
 
6.0%
6 30
 
6.0%
7 24
 
4.8%
8 18
 
3.6%
9 12
 
2.4%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 104
13.0%
1 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 58
7.2%
4 56
7.0%
5 30
 
3.8%
6 30
 
3.8%
Other values (3) 54
6.8%

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 58
7.2%
4 56
7.0%
5 30
 
3.8%
6 30
 
3.8%
Other values (3) 54
6.8%

방향
Categorical

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T20:43:00.358040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:00.536409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

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

Length

Max length8
Median length5
Mean length5.18
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T20:43:00.688121image/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%
하장-송현 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 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.374
Minimum0.4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:00.869676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.3
Q15.7
median8.95
Q313.3
95-th percentile23
Maximum27
Range26.6
Interquartile range (IQR)7.6

Descriptive statistics

Standard deviation6.3838119
Coefficient of variation (CV)0.61536648
Kurtosis0.05181103
Mean10.374
Median Absolute Deviation (MAD)4.2
Skewness0.7763885
Sum1037.4
Variance40.753055
MonotonicityNot monotonic
2023-12-10T20:43:01.044363image/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%
2.0 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%
2.6 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%
4.4 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
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

2023-12-10T20:43:01.219205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:01.340483image/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

2023-12-10T20:43:01.482097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:01.622600image/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%
Mean37.67388
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:01.792214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.08588
5-th percentile37.18732
Q137.40743
median37.634805
Q338.02786
95-th percentile38.19136
Maximum38.38086
Range1.29498
Interquartile range (IQR)0.62043

Descriptive statistics

Standard deviation0.34510165
Coefficient of variation (CV)0.0091602365
Kurtosis-1.1723289
Mean37.67388
Median Absolute Deviation (MAD)0.293605
Skewness0.21405883
Sum3767.388
Variance0.11909515
MonotonicityNot monotonic
2023-12-10T20:43:02.043341image/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%
38.23094 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%
38.06709 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.28442
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:02.304414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.35058
5-th percentile127.62463
Q1127.91342
median128.20314
Q3128.64197
95-th percentile129.07044
Maximum129.20253
Range1.85195
Interquartile range (IQR)0.72855

Descriptive statistics

Standard deviation0.46533303
Coefficient of variation (CV)0.0036273541
Kurtosis-0.88995236
Mean128.28442
Median Absolute Deviation (MAD)0.352325
Skewness0.11492919
Sum12828.442
Variance0.21653483
MonotonicityNot monotonic
2023-12-10T20:43:02.872730image/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%
127.62463 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%
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%
128.84543 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  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.6052
Minimum0
Maximum76.48
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:03.234306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.65
Q12.1
median6.27
Q318.6925
95-th percentile55.9215
Maximum76.48
Range76.48
Interquartile range (IQR)16.5925

Descriptive statistics

Standard deviation18.281709
Coefficient of variation (CV)1.251726
Kurtosis2.6706619
Mean14.6052
Median Absolute Deviation (MAD)4.96
Skewness1.7935373
Sum1460.52
Variance334.22087
MonotonicityNot monotonic
2023-12-10T20:43:03.480433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.95 4
 
4.0%
0.65 4
 
4.0%
2.1 4
 
4.0%
3.83 3
 
3.0%
2.83 2
 
2.0%
1.05 2
 
2.0%
1.31 2
 
2.0%
1.57 2
 
2.0%
3.89 2
 
2.0%
0.0 2
 
2.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.44 1
 
1.0%
0.52 1
 
1.0%
0.65 4
4.0%
1.05 2
2.0%
1.3 1
 
1.0%
1.31 2
2.0%
1.57 2
2.0%
1.95 4
4.0%
1.98 1
 
1.0%
ValueCountFrequency (%)
76.48 1
1.0%
75.97 1
1.0%
72.34 1
1.0%
62.93 1
1.0%
56.14 1
1.0%
55.91 1
1.0%
55.42 1
1.0%
48.04 1
1.0%
47.23 1
1.0%
43.84 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.2333
Minimum0
Maximum65.02
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:03.727129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32
Q11.3725
median3.67
Q312.5675
95-th percentile42.6005
Maximum65.02
Range65.02
Interquartile range (IQR)11.195

Descriptive statistics

Standard deviation13.986124
Coefficient of variation (CV)1.3667267
Kurtosis3.1816585
Mean10.2333
Median Absolute Deviation (MAD)2.98
Skewness1.9202587
Sum1023.33
Variance195.61167
MonotonicityNot monotonic
2023-12-10T20:43:03.907393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.11 4
 
4.0%
0.96 4
 
4.0%
0.32 4
 
4.0%
2.34 3
 
3.0%
1.88 2
 
2.0%
0.74 2
 
2.0%
1.41 2
 
2.0%
1.93 2
 
2.0%
2.06 2
 
2.0%
0.55 2
 
2.0%
Other values (71) 73
73.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.24 1
 
1.0%
0.28 1
 
1.0%
0.32 4
4.0%
0.55 2
2.0%
0.64 1
 
1.0%
0.74 2
2.0%
0.83 2
2.0%
0.96 4
4.0%
1.11 4
4.0%
ValueCountFrequency (%)
65.02 1
1.0%
51.48 1
1.0%
50.34 1
1.0%
47.51 1
1.0%
45.46 1
1.0%
42.45 1
1.0%
41.39 1
1.0%
39.74 1
1.0%
35.59 1
1.0%
33.84 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4535
Minimum0
Maximum8.08
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:04.161459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.2075
median0.575
Q31.74
95-th percentile5.6655
Maximum8.08
Range8.08
Interquartile range (IQR)1.5325

Descriptive statistics

Standard deviation1.9104949
Coefficient of variation (CV)1.31441
Kurtosis2.7976193
Mean1.4535
Median Absolute Deviation (MAD)0.46
Skewness1.8591979
Sum145.35
Variance3.6499907
MonotonicityNot monotonic
2023-12-10T20:43:04.434018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35 5
 
5.0%
0.18 4
 
4.0%
0.17 4
 
4.0%
0.06 4
 
4.0%
0.0 2
 
2.0%
0.31 2
 
2.0%
0.09 2
 
2.0%
0.13 2
 
2.0%
0.1 2
 
2.0%
0.75 2
 
2.0%
Other values (67) 71
71.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.03 1
 
1.0%
0.04 1
 
1.0%
0.06 4
4.0%
0.09 2
2.0%
0.1 2
2.0%
0.12 1
 
1.0%
0.13 2
2.0%
0.17 4
4.0%
0.18 4
4.0%
ValueCountFrequency (%)
8.08 1
1.0%
7.68 1
1.0%
7.09 1
1.0%
7.03 1
1.0%
5.96 1
1.0%
5.65 1
1.0%
5.63 1
1.0%
5.42 1
1.0%
5.11 1
1.0%
4.86 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4075
Minimum0
Maximum3
Zeros34
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:04.676006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.135
Q30.465
95-th percentile1.6805
Maximum3
Range3
Interquartile range (IQR)0.465

Descriptive statistics

Standard deviation0.61760923
Coefficient of variation (CV)1.5156055
Kurtosis5.6875988
Mean0.4075
Median Absolute Deviation (MAD)0.135
Skewness2.3153474
Sum40.75
Variance0.38144116
MonotonicityNot monotonic
2023-12-10T20:43:04.916235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.0 34
34.0%
0.13 16
16.0%
0.27 7
 
7.0%
0.14 5
 
5.0%
0.28 4
 
4.0%
0.4 3
 
3.0%
0.94 2
 
2.0%
0.58 2
 
2.0%
0.83 2
 
2.0%
0.87 1
 
1.0%
Other values (24) 24
24.0%
ValueCountFrequency (%)
0.0 34
34.0%
0.13 16
16.0%
0.14 5
 
5.0%
0.15 1
 
1.0%
0.26 1
 
1.0%
0.27 7
 
7.0%
0.28 4
 
4.0%
0.39 1
 
1.0%
0.4 3
 
3.0%
0.41 1
 
1.0%
ValueCountFrequency (%)
3.0 1
1.0%
2.82 1
1.0%
2.6 1
1.0%
1.83 1
1.0%
1.69 1
1.0%
1.68 1
1.0%
1.61 1
1.0%
1.47 1
1.0%
1.4 1
1.0%
1.34 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3598.6922
Minimum0
Maximum17787.52
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:05.164008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile153.68
Q1559.7125
median1641.755
Q34946.7475
95-th percentile13848.818
Maximum17787.52
Range17787.52
Interquartile range (IQR)4387.035

Descriptive statistics

Standard deviation4368.9231
Coefficient of variation (CV)1.2140308
Kurtosis2.0805465
Mean3598.6922
Median Absolute Deviation (MAD)1266.42
Skewness1.6695129
Sum359869.22
Variance19087489
MonotonicityNot monotonic
2023-12-10T20:43:05.417773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
461.05 4
 
4.0%
153.68 4
 
4.0%
554.74 4
 
4.0%
1012.03 3
 
3.0%
740.29 2
 
2.0%
277.37 2
 
2.0%
381.46 2
 
2.0%
416.06 2
 
2.0%
922.09 2
 
2.0%
0.0 2
 
2.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 2
2.0%
127.15 1
 
1.0%
138.68 1
 
1.0%
153.68 4
4.0%
277.37 2
2.0%
307.36 1
 
1.0%
381.46 2
2.0%
416.06 2
2.0%
461.05 4
4.0%
487.28 1
 
1.0%
ValueCountFrequency (%)
17787.52 1
1.0%
17109.26 1
1.0%
16361.78 1
1.0%
15856.12 1
1.0%
14410.23 1
1.0%
13819.27 1
1.0%
13152.41 1
1.0%
11247.6 1
1.0%
11064.21 1
1.0%
10546.21 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:43:05.830064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

Total characters1070
Distinct characters107
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%
횡성 12
 
3.0%
홍천 12
 
3.0%
정선 12
 
3.0%
원주 10
 
2.5%
평창 8
 
2.0%
영월 8
 
2.0%
8
 
2.0%
춘천 8
 
2.0%
Other values (84) 202
51.3%
2023-12-10T20:43:06.410690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
294
27.5%
124
 
11.6%
110
 
10.3%
22
 
2.1%
22
 
2.1%
18
 
1.7%
16
 
1.5%
16
 
1.5%
16
 
1.5%
14
 
1.3%
Other values (97) 418
39.1%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
124
 
16.0%
110
 
14.2%
22
 
2.8%
22
 
2.8%
18
 
2.3%
16
 
2.1%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
Other values (96) 404
52.1%
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 (%)
124
 
16.0%
110
 
14.2%
22
 
2.8%
22
 
2.8%
18
 
2.3%
16
 
2.1%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
Other values (96) 404
52.1%
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 (%)
124
 
16.0%
110
 
14.2%
22
 
2.8%
22
 
2.8%
18
 
2.3%
16
 
2.1%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
Other values (96) 404
52.1%

Interactions

2023-12-10T20:42:56.922453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:44.770346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.506579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.813377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.235324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.661897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:52.080336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:53.541104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:55.434199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:57.066332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:44.913916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.644103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.948098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.386090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.777809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:52.230105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:53.716191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:55.569476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:57.192081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.027149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.787897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.073399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.546285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.905878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:52.368332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:53.870512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:55.720484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:57.347072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.210331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.932239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.206879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.715640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:51.059966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:52.528425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:54.042013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:55.872524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:57.498406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.376424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.097816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.360815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.887028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:51.216855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:52.684394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:54.226453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:56.041657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:57.632086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.531742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.275253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.508610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.067573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:51.375785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:52.842988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:54.771991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:56.277764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:57.778831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.676530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.423428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.725422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.202525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:51.560420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:53.008870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:54.945351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:56.443183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:57.929913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.188380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.570748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.904038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.351787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:51.713540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:53.188106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:55.118665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:56.611381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:58.076469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.344231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.685783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.073457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.512498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:51.905845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:53.362855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:55.275795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:56.781389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:43:06.555725image/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
기본키1.0001.0000.0001.0000.6430.8640.8150.4490.3290.3010.4430.4181.000
지점1.0001.0000.0001.0001.0001.0001.0000.8440.8230.8310.8790.8261.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9970.9990.9980.8370.8330.8250.8850.8201.000
연장((km))0.6431.0000.0000.9971.0000.6040.6230.2950.0000.2120.3930.1821.000
좌표위치위도((°))0.8641.0000.0000.9990.6041.0000.8020.0000.2900.2710.3630.4201.000
좌표위치경도((°))0.8151.0000.0000.9980.6230.8021.0000.0000.0000.0850.2180.0001.000
co((g/km))0.4490.8440.0000.8370.2950.0000.0001.0000.8780.9840.8430.9870.844
nox((g/km))0.3290.8230.0000.8330.0000.2900.0000.8781.0000.9270.9690.8830.823
hc((g/km))0.3010.8310.0000.8250.2120.2710.0850.9840.9271.0000.8580.9570.831
pm((g/km))0.4430.8790.0000.8850.3930.3630.2180.8430.9690.8581.0000.8120.879
co2((g/km))0.4180.8260.0000.8200.1820.4200.0000.9870.8830.9570.8121.0000.826
주소1.0001.0000.0001.0001.0001.0001.0000.8440.8230.8310.8790.8261.000
2023-12-10T20:43:06.775757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향측정구간
방향1.0000.000
측정구간0.0001.000
2023-12-10T20:43:06.901991image/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
기본키1.0000.2490.2910.027-0.034-0.027-0.037-0.050-0.0370.0000.753
연장((km))0.2491.0000.1680.186-0.320-0.324-0.317-0.339-0.3300.0000.728
좌표위치위도((°))0.2910.1681.000-0.362-0.264-0.268-0.269-0.299-0.2660.0000.739
좌표위치경도((°))0.0270.186-0.3621.000-0.089-0.089-0.084-0.058-0.0970.0000.737
co((g/km))-0.034-0.320-0.264-0.0891.0000.9940.9980.8900.9980.0000.346
nox((g/km))-0.027-0.324-0.268-0.0890.9941.0000.9960.9170.9950.0000.356
hc((g/km))-0.037-0.317-0.269-0.0840.9980.9961.0000.9030.9950.0000.334
pm((g/km))-0.050-0.339-0.299-0.0580.8900.9170.9031.0000.8950.0000.423
co2((g/km))-0.037-0.330-0.266-0.0970.9980.9950.9950.8951.0000.0000.328
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
측정구간0.7530.7280.7390.7370.3460.3560.3340.4230.3280.0001.000

Missing values

2023-12-10T20:42:58.280038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:42:58.543140image/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-소초4.820210301037.3551127.9948716.739.791.50.44422.93강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210301037.3551127.9948711.616.80.940.133358.72강원 원주 봉산
23건기연[0527-2]1원주-횡성0.420210301037.42063127.9631929.7821.862.850.977730.65강원 원주 소초 장양
34건기연[0527-2]2원주-횡성0.420210301037.42063127.9631920.315.881.970.675262.38강원 원주 소초 장양
45건기연[0529-0]1공근-동산12.020210301037.62539127.895281.950.960.170.0461.05강원 홍천 홍천 삼마치
56건기연[0529-0]2공근-동산12.020210301037.62539127.895283.832.340.350.131012.03강원 홍천 홍천 삼마치
67건기연[0530-0]1횡성-춘천13.320210301037.73176127.837874.582.60.440.131102.01강원 홍천 북방 부사원
78건기연[0530-0]2횡성-춘천13.320210301037.73176127.837875.232.920.490.131255.69강원 홍천 북방 부사원
89건기연[0531-2]1동내-천전7.520210301037.86064127.7766375.9741.397.681.115856.12강원 춘천 동내 거두
910건기연[0531-2]2동내-천전7.520210301037.86064127.7766341.926.674.040.949944.17강원 춘천 동내 거두
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4610-0]1천전-양구8.820210301038.02786127.8150218.5912.251.660.815281.16강원 화천 간동 간척
9192건기연[4610-0]2천전-양구8.820210301038.02786127.8150217.79.571.640.284213.15강원 화천 간동 간척
9293건기연[4613-0]1양구-신남4.020210301038.08778128.045110.00.00.00.00.0강원 양구 남 청
9394건기연[4613-0]2양구-신남4.020210301038.08778128.045111.981.320.20.13487.28강원 양구 남 청
9495건기연[4616-0]1진부령-거진11.020210301038.38086128.44457.183.890.670.131716.74강원 고성 간성 교동
9596건기연[4616-0]2진부령-거진11.020210301038.38086128.44453.932.20.310.01144.37강원 고성 간성 교동
9697건기연[4617-0]1북-외가평12.120210301038.19136128.3178513.97.831.210.153667.55강원 인제 북 용대
9798건기연[4617-0]2북-외가평12.120210301038.19136128.317858.914.710.750.02357.64강원 인제 북 용대
9899건기연[4710-0]1이동-근남9.020210301038.18502127.418944.882.890.440.131289.4강원 철원 서 자등
99100건기연[4710-0]2이동-근남9.020210301038.18502127.418941.310.740.10.0381.46강원 철원 서 자등