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 is highly overall correlated with 측정구간High correlation
좌표위치위도 is highly overall correlated with 측정구간High correlation
좌표위치경도 is highly overall correlated with 측정구간High correlation
co is highly overall correlated with nox and 3 other fieldsHigh correlation
nox is highly overall correlated with co and 4 other fieldsHigh correlation
hc is highly overall correlated with co and 4 other fieldsHigh correlation
pm is highly overall correlated with co and 4 other fieldsHigh correlation
co2 is highly overall correlated with co and 3 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
기본키 has unique valuesUnique
co has 6 (6.0%) zerosZeros
nox has 6 (6.0%) zerosZeros
hc has 6 (6.0%) zerosZeros
pm has 14 (14.0%) zerosZeros
co2 has 6 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:28:37.375148
Analysis finished2023-12-10 13:28:53.137229
Duration15.76 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
2023-12-10T22:28:53.273697image/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
2023-12-10T22:28:53.516815image/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

2023-12-10T22:28:53.741459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:53.950074image/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
2023-12-10T22:28:54.275805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.02
Min length8

Characters and Unicode

Total characters802
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[0101-0]
2nd row[0101-0]
3rd row[0101-1]
4th row[0101-1]
5th row[0201-8]
ValueCountFrequency (%)
0101-0 2
 
2.0%
2209-1 2
 
2.0%
2408-2 2
 
2.0%
1707-1 2
 
2.0%
1801-4 2
 
2.0%
1806-2 2
 
2.0%
1809-2 2
 
2.0%
1810-1 2
 
2.0%
1812-1 2
 
2.0%
1815-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:28:54.948217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 148
18.5%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 90
11.2%
3 36
 
4.5%
5 22
 
2.7%
4 20
 
2.5%
7 16
 
2.0%
Other values (3) 40
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 502
62.6%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 148
29.5%
0 130
25.9%
2 90
17.9%
3 36
 
7.2%
5 22
 
4.4%
4 20
 
4.0%
7 16
 
3.2%
8 16
 
3.2%
6 16
 
3.2%
9 8
 
1.6%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 802
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 148
18.5%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 90
11.2%
3 36
 
4.5%
5 22
 
2.7%
4 20
 
2.5%
7 16
 
2.0%
Other values (3) 40
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 148
18.5%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 90
11.2%
3 36
 
4.5%
5 22
 
2.7%
4 20
 
2.5%
7 16
 
2.0%
Other values (3) 40
 
5.0%

방향
Categorical

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T22:28:55.167253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:55.376963image/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
광양-하동
 
4
순천-황전
 
2
학산-학송
 
2
목포-학산
 
2
암태-신안
 
2
Other values (44)
88 

Length

Max length6
Median length5
Mean length4.98
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row목포-무안
2nd row목포-무안
3rd row진도-무안
4th row진도-무안
5th row목포-학산

Common Values

ValueCountFrequency (%)
광양-하동 4
 
4.0%
순천-황전 2
 
2.0%
학산-학송 2
 
2.0%
목포-학산 2
 
2.0%
암태-신안 2
 
2.0%
성전-강진 2
 
2.0%
강진-장흥 2
 
2.0%
장흥-보성 2
 
2.0%
조성-별량 2
 
2.0%
벌교-순천 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T22:28:55.586575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광양-하동 4
 
4.0%
목포-무안 2
 
2.0%
주암-승주 2
 
2.0%
백동-죽림 2
 
2.0%
옥천-강진 2
 
2.0%
보성-복내 2
 
2.0%
미력-문덕 2
 
2.0%
송광-목사동 2
 
2.0%
마산-구례읍 2
 
2.0%
공음-영광 2
 
2.0%
Other values (39) 78
78.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.748
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:55.853257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.2
Q15.4
median9.65
Q314.1
95-th percentile21.8
Maximum33.8
Range31.2
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation6.3137253
Coefficient of variation (CV)0.58743257
Kurtosis2.1421516
Mean10.748
Median Absolute Deviation (MAD)4.25
Skewness1.2617156
Sum1074.8
Variance39.863127
MonotonicityNot monotonic
2023-12-10T22:28:56.145340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5.4 6
 
6.0%
4.4 6
 
6.0%
11.4 4
 
4.0%
7.9 4
 
4.0%
10.3 4
 
4.0%
19.0 2
 
2.0%
8.0 2
 
2.0%
4.5 2
 
2.0%
15.8 2
 
2.0%
11.7 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
2.6 2
 
2.0%
2.7 2
 
2.0%
3.2 2
 
2.0%
4.2 2
 
2.0%
4.3 2
 
2.0%
4.4 6
6.0%
4.5 2
 
2.0%
4.9 2
 
2.0%
5.4 6
6.0%
5.6 2
 
2.0%
ValueCountFrequency (%)
33.8 2
2.0%
24.3 2
2.0%
21.8 2
2.0%
21.3 2
2.0%
19.5 2
2.0%
19.0 2
2.0%
18.0 2
2.0%
17.0 2
2.0%
16.0 2
2.0%
15.8 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210401
2nd row20210401
3rd row20210401
4th row20210401
5th row20210401

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2023-12-10T22:28:56.386984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:56.544953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2023-12-10T22:28:56.712157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:56.896210image/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%
Mean34.94772
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:57.142009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38107
5-th percentile34.55042
Q134.80189
median34.951145
Q335.16902
95-th percentile35.32476
Maximum35.34926
Range0.96819
Interquartile range (IQR)0.36713

Descriptive statistics

Standard deviation0.25025144
Coefficient of variation (CV)0.0071607372
Kurtosis-0.58476728
Mean34.94772
Median Absolute Deviation (MAD)0.188515
Skewness-0.30248922
Sum3494.772
Variance0.062625785
MonotonicityNot monotonic
2023-12-10T22:28:57.419369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85192 2
 
2.0%
35.06978 2
 
2.0%
34.38107 2
 
2.0%
34.61562 2
 
2.0%
34.80445 2
 
2.0%
34.83385 2
 
2.0%
35.05426 2
 
2.0%
35.21934 2
 
2.0%
35.34926 2
 
2.0%
35.22404 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.38107 2
2.0%
34.38392 2
2.0%
34.55042 2
2.0%
34.55273 2
2.0%
34.58881 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.67935 2
2.0%
34.71151 2
2.0%
34.7172 2
2.0%
ValueCountFrequency (%)
35.34926 2
2.0%
35.34316 2
2.0%
35.32476 2
2.0%
35.29816 2
2.0%
35.28858 2
2.0%
35.28584 2
2.0%
35.27304 2
2.0%
35.22404 2
2.0%
35.21934 2
2.0%
35.21885 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.95553
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:57.689827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.2341
Q1126.59409
median126.97424
Q3127.31619
95-th percentile127.61069
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.7221

Descriptive statistics

Standard deviation0.42035996
Coefficient of variation (CV)0.0033110803
Kurtosis-1.1238951
Mean126.95553
Median Absolute Deviation (MAD)0.34604
Skewness0.013551243
Sum12695.553
Variance0.1767025
MonotonicityNot monotonic
2023-12-10T22:28:57.958543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.42727 2
 
2.0%
127.25441 2
 
2.0%
126.21616 2
 
2.0%
126.74515 2
 
2.0%
127.10201 2
 
2.0%
127.09515 2
 
2.0%
127.26851 2
 
2.0%
127.48499 2
 
2.0%
126.46074 2
 
2.0%
126.5425 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.21616 2
2.0%
126.23118 2
2.0%
126.2341 2
2.0%
126.36491 2
2.0%
126.42727 2
2.0%
126.43865 2
2.0%
126.46074 2
2.0%
126.47853 2
2.0%
126.50315 2
2.0%
126.53424 2
2.0%
ValueCountFrequency (%)
127.75881 2
2.0%
127.67417 2
2.0%
127.61069 2
2.0%
127.55961 2
2.0%
127.48499 2
2.0%
127.46572 2
2.0%
127.46028 2
2.0%
127.4424 2
2.0%
127.43777 2
2.0%
127.37931 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.3391
Minimum0
Maximum142.26
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:58.242716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.6675
median10.89
Q324.3525
95-th percentile45.078
Maximum142.26
Range142.26
Interquartile range (IQR)21.685

Descriptive statistics

Standard deviation22.748919
Coefficient of variation (CV)1.3120011
Kurtosis14.984852
Mean17.3391
Median Absolute Deviation (MAD)9.135
Skewness3.2901031
Sum1733.91
Variance517.5133
MonotonicityNot monotonic
2023-12-10T22:28:58.485608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
6.0%
0.65 3
 
3.0%
2.26 2
 
2.0%
39.58 2
 
2.0%
3.33 2
 
2.0%
12.32 2
 
2.0%
1.78 2
 
2.0%
1.73 2
 
2.0%
2.83 2
 
2.0%
3.31 2
 
2.0%
Other values (74) 75
75.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.52 1
 
1.0%
0.65 3
3.0%
1.05 2
 
2.0%
1.26 1
 
1.0%
1.3 1
 
1.0%
1.38 1
 
1.0%
1.57 1
 
1.0%
1.69 1
 
1.0%
1.73 2
 
2.0%
ValueCountFrequency (%)
142.26 1
1.0%
135.67 1
1.0%
60.52 1
1.0%
50.8 1
1.0%
48.84 1
1.0%
44.88 1
1.0%
42.19 1
1.0%
41.99 1
1.0%
41.85 1
1.0%
41.34 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.2599
Minimum0
Maximum282.51
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:58.737533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.7075
median9.69
Q325.0125
95-th percentile45.0405
Maximum282.51
Range282.51
Interquartile range (IQR)23.305

Descriptive statistics

Standard deviation39.138303
Coefficient of variation (CV)2.0321135
Kurtosis34.332506
Mean19.2599
Median Absolute Deviation (MAD)8.485
Skewness5.5461403
Sum1925.99
Variance1531.8067
MonotonicityNot monotonic
2023-12-10T22:28:58.975591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
6.0%
2.06 4
 
4.0%
0.32 3
 
3.0%
1.33 2
 
2.0%
0.55 2
 
2.0%
1.23 2
 
2.0%
1.51 2
 
2.0%
1.88 2
 
2.0%
11.84 2
 
2.0%
19.14 1
 
1.0%
Other values (74) 74
74.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.28 1
 
1.0%
0.32 3
3.0%
0.55 2
 
2.0%
0.64 1
 
1.0%
0.83 1
 
1.0%
1.05 1
 
1.0%
1.09 1
 
1.0%
1.18 1
 
1.0%
1.23 2
 
2.0%
ValueCountFrequency (%)
282.51 1
1.0%
260.07 1
1.0%
75.27 1
1.0%
50.09 1
1.0%
45.81 1
1.0%
45.0 1
1.0%
41.28 1
1.0%
38.73 1
1.0%
38.33 1
1.0%
36.77 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4541
Minimum0
Maximum27.12
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:59.244130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.26
median1.395
Q33.4725
95-th percentile5.542
Maximum27.12
Range27.12
Interquartile range (IQR)3.2125

Descriptive statistics

Standard deviation3.9240613
Coefficient of variation (CV)1.5989818
Kurtosis25.815357
Mean2.4541
Median Absolute Deviation (MAD)1.245
Skewness4.5668983
Sum245.41
Variance15.398257
MonotonicityNot monotonic
2023-12-10T22:28:59.529971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
6.0%
0.18 4
 
4.0%
1.77 3
 
3.0%
0.06 3
 
3.0%
0.27 3
 
3.0%
0.31 2
 
2.0%
0.09 2
 
2.0%
0.22 2
 
2.0%
3.12 2
 
2.0%
5.2 2
 
2.0%
Other values (67) 71
71.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.04 1
 
1.0%
0.06 3
3.0%
0.09 2
 
2.0%
0.12 1
 
1.0%
0.13 1
 
1.0%
0.14 1
 
1.0%
0.16 1
 
1.0%
0.17 1
 
1.0%
0.18 4
4.0%
ValueCountFrequency (%)
27.12 1
1.0%
25.15 1
1.0%
7.56 1
1.0%
7.22 1
1.0%
6.15 1
1.0%
5.51 1
1.0%
5.43 2
2.0%
5.33 1
1.0%
5.24 1
1.0%
5.21 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2748
Minimum0
Maximum16.61
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:59.803531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.775
Q31.79
95-th percentile2.9285
Maximum16.61
Range16.61
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation2.3720219
Coefficient of variation (CV)1.8607012
Kurtosis32.115805
Mean1.2748
Median Absolute Deviation (MAD)0.645
Skewness5.303665
Sum127.48
Variance5.6264878
MonotonicityNot monotonic
2023-12-10T22:29:00.150408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
0.13 12
 
12.0%
0.14 11
 
11.0%
0.93 3
 
3.0%
0.98 2
 
2.0%
0.83 2
 
2.0%
0.27 2
 
2.0%
0.84 2
 
2.0%
1.36 2
 
2.0%
2.15 2
 
2.0%
Other values (46) 48
48.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.13 12
12.0%
0.14 11
11.0%
0.27 2
 
2.0%
0.28 1
 
1.0%
0.4 2
 
2.0%
0.41 1
 
1.0%
0.54 1
 
1.0%
0.57 1
 
1.0%
0.65 1
 
1.0%
ValueCountFrequency (%)
16.61 1
1.0%
16.1 1
1.0%
4.44 1
1.0%
3.28 2
2.0%
2.91 1
1.0%
2.86 1
1.0%
2.82 1
1.0%
2.57 1
1.0%
2.54 1
1.0%
2.53 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4316.7058
Minimum0
Maximum41739.32
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:00.471204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1645.19
median2802.93
Q35779.8925
95-th percentile10983.874
Maximum41739.32
Range41739.32
Interquartile range (IQR)5134.7025

Descriptive statistics

Standard deviation6297.9666
Coefficient of variation (CV)1.4589752
Kurtosis20.618974
Mean4316.7058
Median Absolute Deviation (MAD)2206.96
Skewness3.9983927
Sum431670.58
Variance39664383
MonotonicityNot monotonic
2023-12-10T22:29:01.133724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
6.0%
153.68 3
 
3.0%
873.34 2
 
2.0%
277.37 2
 
2.0%
740.29 2
 
2.0%
457.29 2
 
2.0%
595.97 2
 
2.0%
462.92 2
 
2.0%
800.28 2
 
2.0%
2970.56 2
 
2.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 6
6.0%
138.68 1
 
1.0%
153.68 3
3.0%
277.37 2
 
2.0%
307.36 1
 
1.0%
324.24 1
 
1.0%
339.23 1
 
1.0%
416.06 1
 
1.0%
457.29 2
 
2.0%
460.9 1
 
1.0%
ValueCountFrequency (%)
41739.32 1
1.0%
39130.66 1
1.0%
14718.58 1
1.0%
13919.61 1
1.0%
13177.32 1
1.0%
10868.43 1
1.0%
10285.64 1
1.0%
9985.36 1
1.0%
9889.71 1
1.0%
9640.83 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:29:01.694166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.82
Min length8

Characters and Unicode

Total characters1082
Distinct characters108
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 row전남 무안 삼향 왕산
2nd row전남 무안 삼향 왕산
3rd row전남 목포 죽교
4th row전남 목포 죽교
5th row전남 무안 삼향 용포
ValueCountFrequency (%)
전남 100
25.3%
순천 12
 
3.0%
강진 10
 
2.5%
고흥 8
 
2.0%
영광 8
 
2.0%
화순 8
 
2.0%
보성 8
 
2.0%
무안 6
 
1.5%
미력 6
 
1.5%
주암 6
 
1.5%
Other values (98) 224
56.6%
2023-12-10T22:29:02.532459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
27.4%
전 110
 
10.2%
남 110
 
10.2%
성 28
 
2.6%
천 20
 
1.8%
순 20
 
1.8%
안 16
 
1.5%
광 16
 
1.5%
양 14
 
1.3%
산 14
 
1.3%
Other values (98) 438
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 786
72.6%
Space Separator 296
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 110
 
14.0%
남 110
 
14.0%
성 28
 
3.6%
천 20
 
2.5%
순 20
 
2.5%
안 16
 
2.0%
광 16
 
2.0%
양 14
 
1.8%
산 14
 
1.8%
동 12
 
1.5%
Other values (97) 426
54.2%
Space Separator
ValueCountFrequency (%)
296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 786
72.6%
Common 296
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 110
 
14.0%
남 110
 
14.0%
성 28
 
3.6%
천 20
 
2.5%
순 20
 
2.5%
안 16
 
2.0%
광 16
 
2.0%
양 14
 
1.8%
산 14
 
1.8%
동 12
 
1.5%
Other values (97) 426
54.2%
Common
ValueCountFrequency (%)
296
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 786
72.6%
ASCII 296
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296
100.0%
Hangul
ValueCountFrequency (%)
전 110
 
14.0%
남 110
 
14.0%
성 28
 
3.6%
천 20
 
2.5%
순 20
 
2.5%
안 16
 
2.0%
광 16
 
2.0%
양 14
 
1.8%
산 14
 
1.8%
동 12
 
1.5%
Other values (97) 426
54.2%

Interactions

2023-12-10T22:28:50.762522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:38.161463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:39.456707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:40.949528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:43.126137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:45.069771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:46.623436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:48.128388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:49.469479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:50.887741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:38.303121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:39.589697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:41.177143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:43.281645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:45.307194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:46.787631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:48.265398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:49.604773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:51.029358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:38.501086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:39.765468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:41.746680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:43.455760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:45.512048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:46.963343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:48.410048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:49.764641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:51.156859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:38.640949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:39.948079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:41.885808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:43.620107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:45.658037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:47.121110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:48.545360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:49.910169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:51.314570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:38.811673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:40.113977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:42.086801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:43.840168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:45.834981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:47.399140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:48.744288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:50.067013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:51.447161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:38.936762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:40.256246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:42.475403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:44.173679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:45.999727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:47.558347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:48.934992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:50.275551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:51.574146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:39.066904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:40.389465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:42.651002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:44.428319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:46.161747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:47.695438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:49.069459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:50.390419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:51.703293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:39.192298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:40.535672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:42.798207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:44.610355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:46.320991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:47.835488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:49.203216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:50.511890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:52.411846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:39.333207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:40.709231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:42.981462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:44.815871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:46.471927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:47.992469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:49.347519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:50.647159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:29:02.769529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9980.5230.8010.8180.4160.3790.5140.3950.5711.000
지점1.0001.0000.0001.0001.0001.0001.0000.8860.9160.9530.9290.9401.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0740.000
측정구간0.9981.0000.0001.0000.9991.0001.0000.8660.9030.9470.9190.9071.000
연장0.5231.0000.0000.9991.0000.5730.6900.3180.5410.4080.5680.2961.000
좌표위치위도0.8011.0000.0001.0000.5731.0000.7540.3520.1100.2960.0630.4541.000
좌표위치경도0.8181.0000.0001.0000.6900.7541.0000.4790.4920.4790.4970.6241.000
co0.4160.8860.0000.8660.3180.3520.4791.0000.9440.9660.9220.9070.886
nox0.3790.9160.0000.9030.5410.1100.4920.9441.0000.9640.9990.8460.916
hc0.5140.9530.0000.9470.4080.2960.4790.9660.9641.0000.9730.8840.953
pm0.3950.9290.0000.9190.5680.0630.4970.9220.9990.9731.0000.8470.929
co20.5710.9400.0740.9070.2960.4540.6240.9070.8460.8840.8471.0000.940
주소1.0001.0000.0001.0001.0001.0001.0000.8860.9160.9530.9290.9401.000
2023-12-10T22:29:03.114278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:29:03.278843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.0400.457-0.074-0.327-0.322-0.313-0.295-0.3230.0000.736
연장-0.0401.0000.010-0.153-0.015-0.005-0.0140.007-0.0180.0000.733
좌표위치위도0.4570.0101.0000.028-0.200-0.210-0.213-0.222-0.1970.0000.753
좌표위치경도-0.074-0.1530.0281.000-0.069-0.069-0.082-0.107-0.0570.0000.753
co-0.327-0.015-0.200-0.0691.0000.9920.9930.9730.9990.0000.431
nox-0.322-0.005-0.210-0.0690.9921.0000.9970.9810.9900.0000.506
hc-0.313-0.014-0.213-0.0820.9930.9971.0000.9800.9900.0000.577
pm-0.2950.007-0.222-0.1070.9730.9810.9801.0000.9710.0000.530
co2-0.323-0.018-0.197-0.0570.9990.9900.9900.9711.0000.0870.499
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0871.0000.000
측정구간0.7360.7330.7530.7530.4310.5060.5770.5300.4990.0001.000

Missing values

2023-12-10T22:28:52.657789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:28:53.012837image/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

기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
01건기연[0101-0]1목포-무안5.420210401134.85192126.4272737.2834.874.812.089179.45전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210401134.85192126.4272741.3435.685.241.939409.64전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210401134.80189126.3649150.875.277.224.4414718.58전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210401134.80189126.3649124.5418.042.930.985671.3전남 목포 죽교
45건기연[0201-8]1목포-학산4.220210401134.82996126.4785341.9936.775.512.1510285.64전남 무안 삼향 용포
56건기연[0201-8]2목포-학산4.220210401134.82996126.4785360.5250.097.562.9113919.61전남 무안 삼향 용포
67건기연[0201-11]1암태-신안21.320210401134.86017126.23411.731.230.180.13457.29전남 신안 압해 송공
78건기연[0201-11]2암태-신안21.320210401134.86017126.23412.261.510.220.13595.97전남 신안 압해 송공
89건기연[0202-2]1성전-강진11.420210401134.67935126.7219222.021.043.251.484907.47전남 강진 성전 도림
910건기연[0202-2]2성전-강진11.420210401134.67935126.7219219.6420.023.121.414310.7전남 강진 성전 도림
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2401-0]1지도-해제19.520210401135.06002126.231187.298.341.270.661581.28전남 신안 지도 광정
9192건기연[2401-0]2지도-해제19.520210401135.06002126.231185.717.711.160.571223.88전남 신안 지도 광정
9293건기연[2404-1]1현경-함평13.020210401135.02245126.438650.650.320.060.0153.68전남 무안 현경 평산
9394건기연[2404-1]2현경-함평13.020210401135.02245126.438651.261.050.140.14324.24전남 무안 현경 평산
9495건기연[2406-3]1삼계-장성9.420210401135.28584126.742922.6430.824.152.144990.89전남 장성 동화 용정
9596건기연[2406-3]2삼계-장성9.420210401135.28584126.742929.7338.335.22.576664.65전남 장성 동화 용정
9697건기연[2408-2]1담양-순창4.920210401135.34316127.045411.050.550.090.0277.37전남 담양 금성 봉서
9798건기연[2408-2]2담양-순창4.920210401135.34316127.045416.563.920.640.271589.29전남 담양 금성 봉서
9899건기연[2701-2]1도양-고흥4.420210401134.58881127.2659812.3211.841.770.842970.56전남 고흥 고흥 등암
99100건기연[2701-2]2도양-고흥4.420210401134.58881127.2659825.227.784.181.855798.77전남 고흥 고흥 등암