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 5 (5.0%) zerosZeros
nox((g/km)) has 5 (5.0%) zerosZeros
hc((g/km)) has 5 (5.0%) zerosZeros
pm((g/km)) has 35 (35.0%) zerosZeros
co2((g/km)) has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:43:33.080214
Analysis finished2023-12-10 11:43:47.419050
Duration14.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:47.555575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:43:47.808863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T20:43:48.033308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:48.186872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:43:48.504326image/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%
4215-1 2
 
2.0%
4617-0 2
 
2.0%
3517-1 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%
Other values (40) 80
80.0%
2023-12-10T20:43:49.072714image/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 68
8.5%
2 60
7.5%
4 52
6.5%
6 30
 
3.8%
5 28
 
3.5%
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 68
13.6%
2 60
12.0%
4 52
10.4%
6 30
 
6.0%
5 28
 
5.6%
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 68
8.5%
2 60
7.5%
4 52
6.5%
6 30
 
3.8%
5 28
 
3.5%
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 68
8.5%
2 60
7.5%
4 52
6.5%
6 30
 
3.8%
5 28
 
3.5%
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

2023-12-10T20:43:49.311414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:49.837440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
인제-가오작
 
4
정선-임계
 
4
동막-삼척
 
2
공근-동산
 
2
횡성-춘천
 
2
Other values (43)
86 

Length

Max length8
Median length5
Mean length5.2
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 (%)
인제-가오작 4
 
4.0%
정선-임계 4
 
4.0%
동막-삼척 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%
Other values (38) 76
76.0%

Length

2023-12-10T20:43:50.043370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인제-가오작 4
 
4.0%
정선-임계 4
 
4.0%
노론-용탄 2
 
2.0%
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%
Other values (38) 76
76.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.11
Minimum0.4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:50.275634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation6.3352549
Coefficient of variation (CV)0.62663253
Kurtosis0.25741412
Mean10.11
Median Absolute Deviation (MAD)4.05
Skewness0.87202239
Sum1011
Variance40.135455
MonotonicityNot monotonic
2023-12-10T20:43:50.522120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4.8 4
 
4.0%
7.2 4
 
4.0%
3.5 4
 
4.0%
8.8 4
 
4.0%
8.0 4
 
4.0%
12.0 4
 
4.0%
11.0 2
 
2.0%
4.0 2
 
2.0%
14.0 2
 
2.0%
6.5 2
 
2.0%
Other values (34) 68
68.0%
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%
17.4 2
2.0%
16.1 2
2.0%
15.3 2
2.0%
14.4 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T20:43:50.738037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:50.892839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 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

2023-12-10T20:43:51.081713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:51.239208image/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%
Mean37.678848
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:51.421652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.08588
5-th percentile37.18732
Q137.40743
median37.634805
Q338.02855
95-th percentile38.19136
Maximum38.38086
Range1.29498
Interquartile range (IQR)0.62112

Descriptive statistics

Standard deviation0.34914669
Coefficient of variation (CV)0.0092663843
Kurtosis-1.2340506
Mean37.678848
Median Absolute Deviation (MAD)0.302455
Skewness0.19375611
Sum3767.8848
Variance0.12190341
MonotonicityNot monotonic
2023-12-10T20:43:51.671343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3551 2
 
2.0%
37.48273 2
 
2.0%
37.18732 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%
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%
38.08036 2
2.0%
38.07373 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.28786
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:51.903594image/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 deviation0.46393124
Coefficient of variation (CV)0.0036163301
Kurtosis-0.86903722
Mean128.28786
Median Absolute Deviation (MAD)0.352325
Skewness0.097252473
Sum12828.786
Variance0.2152322
MonotonicityNot monotonic
2023-12-10T20:43:52.140100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.99487 2
 
2.0%
129.09293 2
 
2.0%
128.38975 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%
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 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.8382
Minimum0
Maximum203.71
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:52.392264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.494
Q13.6025
median9.8
Q329.05
95-th percentile102.541
Maximum203.71
Range203.71
Interquartile range (IQR)25.4475

Descriptive statistics

Standard deviation36.657849
Coefficient of variation (CV)1.4187463
Kurtosis6.2166644
Mean25.8382
Median Absolute Deviation (MAD)7.2
Skewness2.3270971
Sum2583.82
Variance1343.7979
MonotonicityNot monotonic
2023-12-10T20:43:52.609123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
5.93 3
 
3.0%
2.62 3
 
3.0%
0.52 3
 
3.0%
1.57 2
 
2.0%
3.67 2
 
2.0%
5.24 2
 
2.0%
3.14 2
 
2.0%
2.6 2
 
2.0%
3.24 2
 
2.0%
Other values (74) 74
74.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.52 3
3.0%
0.65 1
 
1.0%
1.57 2
 
2.0%
2.1 1
 
1.0%
2.6 2
 
2.0%
2.62 3
3.0%
2.83 1
 
1.0%
2.87 1
 
1.0%
3.14 2
 
2.0%
ValueCountFrequency (%)
203.71 1
1.0%
140.81 1
1.0%
127.0 1
1.0%
126.65 1
1.0%
114.34 1
1.0%
101.92 1
1.0%
100.8 1
1.0%
84.26 1
1.0%
82.91 1
1.0%
70.66 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.2265
Minimum0
Maximum205.28
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:52.864280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.266
Q12.03
median5.37
Q319.185
95-th percentile97.2905
Maximum205.28
Range205.28
Interquartile range (IQR)17.155

Descriptive statistics

Standard deviation35.063258
Coefficient of variation (CV)1.7335306
Kurtosis10.012569
Mean20.2265
Median Absolute Deviation (MAD)4.4
Skewness2.9788861
Sum2022.65
Variance1229.432
MonotonicityNot monotonic
2023-12-10T20:43:53.088084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
3.45 3
 
3.0%
1.39 3
 
3.0%
0.28 3
 
3.0%
0.83 2
 
2.0%
1.94 2
 
2.0%
1.66 2
 
2.0%
1.28 2
 
2.0%
1.6 2
 
2.0%
14.77 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.28 3
3.0%
0.32 1
 
1.0%
0.83 2
 
2.0%
1.11 1
 
1.0%
1.28 2
 
2.0%
1.39 3
3.0%
1.6 2
 
2.0%
1.66 2
 
2.0%
1.88 1
 
1.0%
ValueCountFrequency (%)
205.28 1
1.0%
144.67 1
1.0%
129.82 1
1.0%
123.84 1
1.0%
121.24 1
1.0%
96.03 1
1.0%
82.4 1
1.0%
73.13 1
1.0%
62.51 1
1.0%
57.02 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7449
Minimum0
Maximum26.52
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:53.328348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.038
Q10.31
median0.885
Q32.79
95-th percentile12.449
Maximum26.52
Range26.52
Interquartile range (IQR)2.48

Descriptive statistics

Standard deviation4.4479101
Coefficient of variation (CV)1.620427
Kurtosis9.7161133
Mean2.7449
Median Absolute Deviation (MAD)0.73
Skewness2.8649326
Sum274.49
Variance19.783904
MonotonicityNot monotonic
2023-12-10T20:43:53.573760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.53 4
 
4.0%
0.04 3
 
3.0%
0.26 3
 
3.0%
0.22 3
 
3.0%
0.31 3
 
3.0%
0.93 2
 
2.0%
0.13 2
 
2.0%
0.84 2
 
2.0%
0.57 2
 
2.0%
Other values (69) 71
71.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.04 3
3.0%
0.06 1
 
1.0%
0.13 2
 
2.0%
0.18 1
 
1.0%
0.22 3
3.0%
0.23 2
 
2.0%
0.26 3
3.0%
0.27 1
 
1.0%
0.29 2
 
2.0%
ValueCountFrequency (%)
26.52 1
1.0%
18.41 1
1.0%
16.2 1
1.0%
14.61 1
1.0%
13.38 1
1.0%
12.4 1
1.0%
11.21 1
1.0%
10.09 1
1.0%
9.04 1
1.0%
8.01 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8846
Minimum0
Maximum11.42
Zeros35
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:53.794324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.14
Q30.8175
95-th percentile4.8415
Maximum11.42
Range11.42
Interquartile range (IQR)0.8175

Descriptive statistics

Standard deviation1.8701426
Coefficient of variation (CV)2.114111
Kurtosis13.171295
Mean0.8846
Median Absolute Deviation (MAD)0.14
Skewness3.4508223
Sum88.46
Variance3.4974332
MonotonicityNot monotonic
2023-12-10T20:43:54.055112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 35
35.0%
0.13 12
 
12.0%
0.14 7
 
7.0%
1.55 3
 
3.0%
0.4 3
 
3.0%
0.27 3
 
3.0%
1.56 2
 
2.0%
0.69 2
 
2.0%
0.67 2
 
2.0%
0.81 2
 
2.0%
Other values (29) 29
29.0%
ValueCountFrequency (%)
0.0 35
35.0%
0.13 12
 
12.0%
0.14 7
 
7.0%
0.15 1
 
1.0%
0.26 1
 
1.0%
0.27 3
 
3.0%
0.28 1
 
1.0%
0.3 1
 
1.0%
0.39 1
 
1.0%
0.4 3
 
3.0%
ValueCountFrequency (%)
11.42 1
1.0%
7.82 1
1.0%
7.08 1
1.0%
6.49 1
1.0%
6.39 1
1.0%
4.76 1
1.0%
4.72 1
1.0%
3.39 1
1.0%
2.61 1
1.0%
2.27 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6415.9465
Minimum0
Maximum47546.99
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:54.338126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile131.746
Q1929.5825
median2472.03
Q37459.5775
95-th percentile25785.666
Maximum47546.99
Range47546.99
Interquartile range (IQR)6529.995

Descriptive statistics

Standard deviation8890.8312
Coefficient of variation (CV)1.3857396
Kurtosis5.2553304
Mean6415.9465
Median Absolute Deviation (MAD)1887.295
Skewness2.1933468
Sum641594.65
Variance79046879
MonotonicityNot monotonic
2023-12-10T20:43:54.604482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
1566.77 3
 
3.0%
693.42 3
 
3.0%
138.68 3
 
3.0%
416.06 2
 
2.0%
970.8 2
 
2.0%
832.11 2
 
2.0%
614.73 2
 
2.0%
768.41 2
 
2.0%
6694.25 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 5
5.0%
138.68 3
3.0%
153.68 1
 
1.0%
416.06 2
 
2.0%
554.74 1
 
1.0%
614.73 2
 
2.0%
693.42 3
3.0%
740.29 1
 
1.0%
768.41 2
 
2.0%
815.15 1
 
1.0%
ValueCountFrequency (%)
47546.99 1
1.0%
32819.4 1
1.0%
32489.75 1
1.0%
31687.12 1
1.0%
27582.61 1
1.0%
25691.09 1
1.0%
24084.57 1
1.0%
19772.78 1
1.0%
19570.74 1
1.0%
18329.35 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:43:55.065573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

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

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원 원주 봉산
2nd row강원 원주 봉산
3rd row강원 원주 소초 장양
4th row강원 원주 소초 장양
5th row강원 홍천 홍천 삼마치
ValueCountFrequency (%)
강원 100
25.4%
인제 18
 
4.6%
정선 12
 
3.0%
횡성 12
 
3.0%
원주 10
 
2.5%
홍천 10
 
2.5%
춘천 8
 
2.0%
남 8
 
2.0%
영월 8
 
2.0%
강릉 8
 
2.0%
Other values (83) 200
50.8%
2023-12-10T20:43:55.721915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
294
27.5%
원 124
 
11.6%
강 112
 
10.5%
동 22
 
2.1%
천 20
 
1.9%
평 18
 
1.7%
제 18
 
1.7%
인 18
 
1.7%
성 16
 
1.5%
북 16
 
1.5%
Other values (96) 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 (%)
원 124
 
16.0%
강 112
 
14.4%
동 22
 
2.8%
천 20
 
2.6%
평 18
 
2.3%
제 18
 
2.3%
인 18
 
2.3%
성 16
 
2.1%
북 16
 
2.1%
양 16
 
2.1%
Other values (95) 396
51.0%
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 (%)
원 124
 
16.0%
강 112
 
14.4%
동 22
 
2.8%
천 20
 
2.6%
평 18
 
2.3%
제 18
 
2.3%
인 18
 
2.3%
성 16
 
2.1%
북 16
 
2.1%
양 16
 
2.1%
Other values (95) 396
51.0%
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 (%)
원 124
 
16.0%
강 112
 
14.4%
동 22
 
2.8%
천 20
 
2.6%
평 18
 
2.3%
제 18
 
2.3%
인 18
 
2.3%
성 16
 
2.1%
북 16
 
2.1%
양 16
 
2.1%
Other values (95) 396
51.0%

Interactions

2023-12-10T20:43:44.999889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:33.990028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.252841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.462351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.722328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.020749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.809359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.923688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.424797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:45.197408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.113116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.397017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.596655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.855596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.558551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.925540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:42.075778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.693555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:45.388958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.255703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.518260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.766703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.992076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.719187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.058326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:42.247937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.857107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:45.569436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.405311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.656144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.922477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.143600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.888303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.201573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:42.394968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.998064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:45.783636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.567242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.812697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.084644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.311834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.053675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.350416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:42.536383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.166177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:46.013544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.713155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.967954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.224541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.444551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.211480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.480997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:42.692762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.335968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:46.179624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.876493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.096475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.331799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.607649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.358431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.580233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.003308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.482796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:46.393243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.018198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.216455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.459471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.747252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.507187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.693182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.131782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.643696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:46.594011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.138371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.335850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.593601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.892028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.661159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.802479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.278622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.802495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:43:55.929234image/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
기본키1.0001.0000.0001.0000.6060.8460.8380.3020.2950.1560.3960.3491.000
지점1.0001.0000.0001.0001.0001.0001.0000.8420.8140.7350.8200.8741.000
방향0.0000.0001.0000.0000.0000.0000.0000.1590.2290.0000.1350.0000.000
측정구간1.0001.0000.0001.0000.9930.9970.9980.8630.8330.7860.8320.8911.000
연장((km))0.6061.0000.0000.9931.0000.6010.6290.0000.1320.0000.1130.0611.000
좌표위치위도((°))0.8461.0000.0000.9970.6011.0000.8190.5500.3970.4780.2590.5171.000
좌표위치경도((°))0.8381.0000.0000.9980.6290.8191.0000.2430.3040.2690.3250.3051.000
co((g/km))0.3020.8420.1590.8630.0000.5500.2431.0000.9200.9670.8680.9960.842
nox((g/km))0.2950.8140.2290.8330.1320.3970.3040.9201.0000.9530.9830.9130.814
hc((g/km))0.1560.7350.0000.7860.0000.4780.2690.9670.9531.0000.9230.9710.735
pm((g/km))0.3960.8200.1350.8320.1130.2590.3250.8680.9830.9231.0000.8850.820
co2((g/km))0.3490.8740.0000.8910.0610.5170.3050.9960.9130.9710.8851.0000.874
주소1.0001.0000.0001.0001.0001.0001.0000.8420.8140.7350.8200.8741.000
2023-12-10T20:43:56.203254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향측정구간
방향1.0000.000
측정구간0.0001.000
2023-12-10T20:43:56.379265image/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
기본키1.0000.2330.2740.046-0.012-0.004-0.0050.027-0.0130.0000.760
연장((km))0.2331.0000.1350.205-0.301-0.303-0.289-0.332-0.3060.0000.706
좌표위치위도((°))0.2740.1351.000-0.364-0.232-0.210-0.227-0.144-0.2260.0000.733
좌표위치경도((°))0.0460.205-0.3641.000-0.072-0.086-0.072-0.117-0.0810.0000.744
co((g/km))-0.012-0.301-0.232-0.0721.0000.9950.9990.8650.9980.1120.383
nox((g/km))-0.004-0.303-0.210-0.0860.9951.0000.9960.8960.9940.2190.358
hc((g/km))-0.005-0.289-0.227-0.0720.9990.9961.0000.8770.9950.0000.303
pm((g/km))0.027-0.332-0.144-0.1170.8650.8960.8771.0000.8660.1390.381
co2((g/km))-0.013-0.306-0.226-0.0810.9980.9940.9950.8661.0000.0000.420
방향0.0000.0000.0000.0000.1120.2190.0000.1390.0001.0000.000
측정구간0.7600.7060.7330.7440.3830.3580.3030.3810.4200.0001.000

Missing values

2023-12-10T20:43:46.847518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:43:47.274232image/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-소초4.820210101037.3551127.9948727.9414.772.570.396694.25강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210101037.3551127.9948723.7912.962.030.146287.69강원 원주 봉산
23건기연[0527-2]1원주-횡성0.420210101037.42063127.9631953.3935.654.951.3813968.2강원 원주 소초 장양
34건기연[0527-2]2원주-횡성0.420210101037.42063127.9631937.6630.843.711.159678.65강원 원주 소초 장양
45건기연[0529-0]1공근-동산12.020210101037.62539127.895285.763.050.480.01525.54강원 홍천 홍천 삼마치
56건기연[0529-0]2공근-동산12.020210101037.62539127.895289.965.260.840.02635.02강원 홍천 홍천 삼마치
67건기연[0530-0]1횡성-춘천13.320210101037.73176127.837875.933.450.530.131566.77강원 홍천 북방 부사원
78건기연[0530-0]2횡성-춘천13.320210101037.73176127.837875.933.450.530.131566.77강원 홍천 북방 부사원
89건기연[0531-2]1동내-천전7.520210101037.86064127.7766361.8845.525.881.5517331.3강원 춘천 동내 거두
910건기연[0531-2]2동내-천전7.520210101037.86064127.7766345.5632.364.361.5611835.53강원 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-양구8.820210101038.02786127.8150228.7817.62.410.678274.41강원 화천 간동 간척
9192건기연[4610-0]2천전-양구8.820210101038.02786127.8150227.0318.622.660.686399.48강원 화천 간동 간척
9293건기연[4613-0]1양구-신남4.020210101038.08778128.045110.00.00.00.00.0강원 양구 남 청
9394건기연[4613-0]2양구-신남4.020210101038.08778128.045110.00.00.00.00.0강원 양구 남 청
9495건기연[4616-0]1진부령-거진11.020210101038.38086128.44455.283.020.50.141261.32강원 고성 간성 교동
9596건기연[4616-0]2진부령-거진11.020210101038.38086128.44455.933.450.530.131566.77강원 고성 간성 교동
9697건기연[4617-0]1북-외가평12.120210101038.19136128.317859.645.480.840.142543.2강원 인제 북 용대
9798건기연[4617-0]2북-외가평12.120210101038.19136128.3178511.536.090.970.03051.07강원 인제 북 용대
9899건기연[4710-0]1이동-근남9.020210101038.18502127.418947.183.890.670.131716.74강원 철원 서 자등
99100건기연[4710-0]2이동-근남9.020210101038.18502127.418948.189.30.930.552042.66강원 철원 서 자등