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 3 other fieldsHigh correlation
hc is highly overall correlated with co and 3 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with co and 3 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
pm has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:29:05.857475
Analysis finished2023-12-10 13:29:22.102120
Duration16.24 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
2023-12-10T22:29:22.234083image/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
2023-12-10T22:29:22.480328image/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

2023-12-10T22:29:22.754074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:29:22.904045image/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
2023-12-10T22:29:23.157166image/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%
1801-4 2
 
2.0%
1805-2 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%
2023-12-10T22:29:23.713650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 146
18.2%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 92
11.5%
3 34
 
4.2%
5 22
 
2.7%
4 20
 
2.5%
8 18
 
2.2%
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 (%)
1 146
29.1%
0 130
25.9%
2 92
18.3%
3 34
 
6.8%
5 22
 
4.4%
4 20
 
4.0%
8 18
 
3.6%
7 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 (%)
1 146
18.2%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 92
11.5%
3 34
 
4.2%
5 22
 
2.7%
4 20
 
2.5%
8 18
 
2.2%
Other values (3) 40
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 146
18.2%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 92
11.5%
3 34
 
4.2%
5 22
 
2.7%
4 20
 
2.5%
8 18
 
2.2%
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

2023-12-10T22:29:23.911487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:29:24.068285image/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
구례구-용방
 
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%
구례구-용방 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

2023-12-10T22:29:24.240310image/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%
황산-해남 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 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.726
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:24.425734image/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 deviation6.3094545
Coefficient of variation (CV)0.58823928
Kurtosis2.1746475
Mean10.726
Median Absolute Deviation (MAD)4.25
Skewness1.2745349
Sum1072.6
Variance39.809216
MonotonicityNot monotonic
2023-12-10T22:29:24.619712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5.4 6
 
6.0%
11.4 6
 
6.0%
4.4 6
 
6.0%
7.9 4
 
4.0%
10.3 4
 
4.0%
19.0 2
 
2.0%
4.5 2
 
2.0%
15.8 2
 
2.0%
11.7 2
 
2.0%
6.8 2
 
2.0%
Other values (32) 64
64.0%
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
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

2023-12-10T22:29:24.800400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:29:24.959837image/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
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

2023-12-10T22:29:25.130174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:29:25.286123image/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%
Mean34.945236
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:25.805290image/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 deviation0.25321327
Coefficient of variation (CV)0.0072460027
Kurtosis-0.64127711
Mean34.945236
Median Absolute Deviation (MAD)0.188515
Skewness-0.30468336
Sum3494.5236
Variance0.064116962
MonotonicityNot monotonic
2023-12-10T22:29:26.035510image/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%
34.58728 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%
34.58728 2
2.0%
34.58881 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.67935 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%
Mean126.93886
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:26.278466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.2341
Q1126.57464
median126.93796
Q3127.2868
95-th percentile127.61069
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.71216

Descriptive statistics

Standard deviation0.42098375
Coefficient of variation (CV)0.0033164293
Kurtosis-1.1204736
Mean126.93886
Median Absolute Deviation (MAD)0.35608
Skewness0.094380385
Sum12693.886
Variance0.17722732
MonotonicityNot monotonic
2023-12-10T22:29:26.570183image/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%
126.51479 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%
126.51479 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 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.8252
Minimum0.52
Maximum109.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:26.930258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile0.65
Q13.1675
median8.425
Q320.7425
95-th percentile39.487
Maximum109.63
Range109.11
Interquartile range (IQR)17.575

Descriptive statistics

Standard deviation17.835197
Coefficient of variation (CV)1.2030325
Kurtosis10.22512
Mean14.8252
Median Absolute Deviation (MAD)6.82
Skewness2.7139657
Sum1482.52
Variance318.09426
MonotonicityNot monotonic
2023-12-10T22:29:27.219714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.78 4
 
4.0%
0.65 4
 
4.0%
1.57 3
 
3.0%
4.35 3
 
3.0%
0.52 3
 
3.0%
11.33 2
 
2.0%
5.93 2
 
2.0%
3.28 2
 
2.0%
8.5 2
 
2.0%
7.14 2
 
2.0%
Other values (70) 73
73.0%
ValueCountFrequency (%)
0.52 3
3.0%
0.65 4
4.0%
1.05 1
 
1.0%
1.21 1
 
1.0%
1.3 2
2.0%
1.57 3
3.0%
1.73 2
2.0%
2.1 1
 
1.0%
2.26 1
 
1.0%
2.31 2
2.0%
ValueCountFrequency (%)
109.63 1
1.0%
91.11 1
1.0%
65.33 1
1.0%
45.45 1
1.0%
39.81 1
1.0%
39.47 1
1.0%
37.43 1
1.0%
36.98 1
1.0%
36.77 1
1.0%
36.64 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.3053
Minimum0.28
Maximum109.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:27.456220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.32
Q11.94
median5.435
Q314.63
95-th percentile34.3075
Maximum109.42
Range109.14
Interquartile range (IQR)12.69

Descriptive statistics

Standard deviation16.768633
Coefficient of variation (CV)1.4832541
Kurtosis17.469383
Mean11.3053
Median Absolute Deviation (MAD)4.4
Skewness3.6754678
Sum1130.53
Variance281.18706
MonotonicityNot monotonic
2023-12-10T22:29:27.719364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.79 4
 
4.0%
0.32 4
 
4.0%
0.83 3
 
3.0%
2.62 3
 
3.0%
0.28 3
 
3.0%
6.62 2
 
2.0%
3.45 2
 
2.0%
1.96 2
 
2.0%
5.64 2
 
2.0%
4.4 2
 
2.0%
Other values (70) 73
73.0%
ValueCountFrequency (%)
0.28 3
3.0%
0.32 4
4.0%
0.55 1
 
1.0%
0.64 2
2.0%
0.83 3
3.0%
0.96 1
 
1.0%
1.11 1
 
1.0%
1.23 2
2.0%
1.51 1
 
1.0%
1.6 2
2.0%
ValueCountFrequency (%)
109.42 1
1.0%
97.26 1
1.0%
46.14 1
1.0%
43.55 1
1.0%
35.4 1
1.0%
34.25 1
1.0%
30.26 1
1.0%
29.92 1
1.0%
27.46 1
1.0%
27.29 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5555
Minimum0.04
Maximum12.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:27.958778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.06
Q10.3
median0.805
Q32.1275
95-th percentile4.6705
Maximum12.96
Range12.92
Interquartile range (IQR)1.8275

Descriptive statistics

Standard deviation2.0919067
Coefficient of variation (CV)1.3448452
Kurtosis13.904874
Mean1.5555
Median Absolute Deviation (MAD)0.66
Skewness3.2397879
Sum155.55
Variance4.3760735
MonotonicityNot monotonic
2023-12-10T22:29:28.233458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06 4
 
4.0%
0.26 4
 
4.0%
0.13 4
 
4.0%
0.32 3
 
3.0%
0.4 3
 
3.0%
0.04 3
 
3.0%
0.18 3
 
3.0%
0.12 2
 
2.0%
0.44 2
 
2.0%
0.66 2
 
2.0%
Other values (64) 70
70.0%
ValueCountFrequency (%)
0.04 3
3.0%
0.06 4
4.0%
0.09 1
 
1.0%
0.12 2
2.0%
0.13 4
4.0%
0.18 3
3.0%
0.22 1
 
1.0%
0.23 2
2.0%
0.26 4
4.0%
0.27 1
 
1.0%
ValueCountFrequency (%)
12.96 1
1.0%
12.01 1
1.0%
6.82 1
1.0%
5.06 1
1.0%
4.87 1
1.0%
4.66 1
1.0%
4.1 1
1.0%
3.93 1
1.0%
3.86 1
1.0%
3.73 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.627
Minimum0
Maximum5.3
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:28.466465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.28
Q30.8
95-th percentile2.0005
Maximum5.3
Range5.3
Interquartile range (IQR)0.67

Descriptive statistics

Standard deviation0.85317766
Coefficient of variation (CV)1.3607299
Kurtosis12.674683
Mean0.627
Median Absolute Deviation (MAD)0.26
Skewness3.1134056
Sum62.7
Variance0.72791212
MonotonicityNot monotonic
2023-12-10T22:29:28.694330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.13 19
19.0%
0.0 14
14.0%
0.14 6
 
6.0%
0.28 6
 
6.0%
0.54 6
 
6.0%
0.27 6
 
6.0%
0.4 6
 
6.0%
0.8 4
 
4.0%
0.56 3
 
3.0%
0.42 2
 
2.0%
Other values (25) 28
28.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.13 19
19.0%
0.14 6
 
6.0%
0.27 6
 
6.0%
0.28 6
 
6.0%
0.4 6
 
6.0%
0.42 2
 
2.0%
0.54 6
 
6.0%
0.56 3
 
3.0%
0.67 1
 
1.0%
ValueCountFrequency (%)
5.3 1
1.0%
4.58 1
1.0%
2.83 1
1.0%
2.66 1
1.0%
2.01 1
1.0%
2.0 1
1.0%
1.8 1
1.0%
1.64 1
1.0%
1.55 1
1.0%
1.45 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3733.6163
Minimum138.68
Maximum27819.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:28.898178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile153.68
Q1781.06
median2212.42
Q35151.93
95-th percentile9622.8155
Maximum27819.39
Range27680.71
Interquartile range (IQR)4370.87

Descriptive statistics

Standard deviation4469.8205
Coefficient of variation (CV)1.1971826
Kurtosis10.904604
Mean3733.6163
Median Absolute Deviation (MAD)1706.405
Skewness2.7829664
Sum373361.63
Variance19979295
MonotonicityNot monotonic
2023-12-10T22:29:29.131796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
734.66 4
 
4.0%
153.68 4
 
4.0%
416.06 3
 
3.0%
1150.71 3
 
3.0%
138.68 3
 
3.0%
2994.85 2
 
2.0%
1566.77 2
 
2.0%
794.65 2
 
2.0%
2220.87 2
 
2.0%
1885.37 2
 
2.0%
Other values (70) 73
73.0%
ValueCountFrequency (%)
138.68 3
3.0%
153.68 4
4.0%
277.37 1
 
1.0%
307.36 2
2.0%
318.6 1
 
1.0%
416.06 3
3.0%
457.29 2
2.0%
554.74 1
 
1.0%
595.97 1
 
1.0%
601.6 2
2.0%
ValueCountFrequency (%)
27819.39 1
1.0%
23417.0 1
1.0%
15263.19 1
1.0%
10687.7 1
1.0%
10419.97 1
1.0%
9580.86 1
1.0%
9559.6 1
1.0%
9363.25 1
1.0%
9086.12 1
1.0%
9051.04 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:29:29.553657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.82
Min length8

Characters and Unicode

Total characters1082
Distinct characters109
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%
화순 8
 
2.0%
영광 8
 
2.0%
보성 8
 
2.0%
담양 6
 
1.5%
해남 6
 
1.5%
광양 6
 
1.5%
고흥 6
 
1.5%
Other values (98) 226
57.1%
2023-12-10T22:29:30.256682image/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%
광 16
 
1.5%
산 16
 
1.5%
안 16
 
1.5%
진 12
 
1.1%
Other values (99) 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%
광 16
 
2.0%
산 16
 
2.0%
안 16
 
2.0%
진 12
 
1.5%
동 12
 
1.5%
Other values (98) 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%
광 16
 
2.0%
산 16
 
2.0%
안 16
 
2.0%
진 12
 
1.5%
동 12
 
1.5%
Other values (98) 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%
광 16
 
2.0%
산 16
 
2.0%
안 16
 
2.0%
진 12
 
1.5%
동 12
 
1.5%
Other values (98) 426
54.2%

Interactions

2023-12-10T22:29:20.192726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:06.852401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:08.242017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:09.667139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:11.345396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:13.127001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:15.600861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:17.506600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:18.915099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:20.314650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:07.010469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:08.394529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:09.805872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:11.500129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:13.271515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:16.466543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:17.638262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:19.046276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:20.455395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:07.205521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:08.547320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:09.958553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:11.662444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:13.536692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:16.597916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:17.789208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:19.198327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:20.588941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:07.365851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:08.724272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:10.104219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:11.809037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:13.680982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:16.719004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:17.928809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:19.326424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:20.742136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:07.538629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:08.893086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:10.257740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:12.020913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:13.879210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:16.872479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:18.204117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:19.491269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:20.871405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:07.699893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:09.051955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:10.389723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:12.252870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:14.132245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:16.988223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:18.368937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:19.620489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:21.015386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:07.835574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:09.204119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:10.510730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:12.445485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:14.404222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:17.117882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:18.506623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:19.752594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:21.162491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:07.982693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:09.367190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:10.793771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:12.729108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:14.725087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:17.255625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:18.646290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:19.908828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:21.400509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:08.110517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:09.517952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:11.173600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:12.920153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:15.189277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:17.386172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:18.785581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:20.062358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:29:30.452923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9980.5390.8250.8200.3900.3270.4260.3630.3191.000
지점1.0001.0000.0001.0001.0001.0001.0000.7980.7550.9250.6830.8011.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.1070.0000.0000.0000.000
측정구간0.9981.0000.0001.0000.9991.0001.0000.7770.6160.9010.6010.7931.000
연장0.5391.0000.0000.9991.0000.5830.6880.3330.1630.3860.0150.2341.000
좌표위치위도0.8251.0000.0001.0000.5831.0000.7620.0000.0000.2550.2230.0001.000
좌표위치경도0.8201.0000.0001.0000.6880.7621.0000.3290.4060.4660.3560.3791.000
co0.3900.7980.0000.7770.3330.0000.3291.0000.9220.9380.8900.9750.798
nox0.3270.7550.1070.6160.1630.0000.4060.9221.0000.8810.9800.9770.755
hc0.4260.9250.0000.9010.3860.2550.4660.9380.8811.0000.8550.9120.925
pm0.3630.6830.0000.6010.0150.2230.3560.8900.9800.8551.0000.9610.683
co20.3190.8010.0000.7930.2340.0000.3790.9750.9770.9120.9611.0000.801
주소1.0001.0000.0001.0001.0001.0001.0000.7980.7550.9250.6830.8011.000
2023-12-10T22:29:30.664817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:29:30.816549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.0350.441-0.075-0.340-0.345-0.333-0.336-0.3420.0000.736
연장-0.0351.0000.012-0.182-0.071-0.087-0.097-0.092-0.0590.0000.732
좌표위치위도0.4410.0121.0000.084-0.141-0.165-0.151-0.192-0.1490.0000.753
좌표위치경도-0.075-0.1820.0841.000-0.101-0.084-0.094-0.086-0.1080.0000.753
co-0.340-0.071-0.141-0.1011.0000.9950.9960.9520.9990.0000.310
nox-0.345-0.087-0.165-0.0840.9951.0000.9980.9660.9940.1090.198
hc-0.333-0.097-0.151-0.0940.9960.9981.0000.9620.9940.0000.478
pm-0.336-0.092-0.192-0.0860.9520.9660.9621.0000.9500.0000.190
co2-0.342-0.059-0.149-0.1080.9990.9940.9940.9501.0000.0000.339
방향0.0000.0000.0000.0000.0000.1090.0000.0000.0001.0000.000
측정구간0.7360.7320.7530.7530.3100.1980.4780.1900.3390.0001.000

Missing values

2023-12-10T22:29:21.651026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:29:21.975258image/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-무안5.420210301134.85192126.4272726.1420.92.430.987321.83전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210301134.85192126.4272736.9829.923.641.359559.6전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210301134.80189126.3649136.7726.173.481.119580.86전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210301134.80189126.3649139.8127.463.731.2110419.97전남 목포 죽교
45건기연[0201-8]1목포-학산4.220210301134.82996126.4785365.3343.556.821.5515263.19전남 무안 삼향 용포
56건기연[0201-8]2목포-학산4.220210301134.82996126.4785336.6425.973.931.279363.25전남 무안 삼향 용포
67건기연[0201-11]1암태-신안21.320210301134.86017126.23415.43.170.480.131428.08전남 신안 압해 송공
78건기연[0201-11]2암태-신안21.320210301134.86017126.23415.933.450.530.131566.77전남 신안 압해 송공
89건기연[0202-2]1성전-강진11.420210301134.67935126.7219218.1411.121.660.564764.07전남 강진 성전 도림
910건기연[0202-2]2성전-강진11.420210301134.67935126.721929.235.510.840.272440.11전남 강진 성전 도림
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2401-0]1지도-해제19.520210301135.06002126.231182.261.510.220.13595.97전남 신안 지도 광정
9192건기연[2401-0]2지도-해제19.520210301135.06002126.231185.663.760.550.281480.58전남 신안 지도 광정
9293건기연[2404-1]1현경-함평13.020210301135.02245126.438650.520.280.040.0138.68전남 무안 현경 평산
9394건기연[2404-1]2현경-함평13.020210301135.02245126.438650.650.320.060.0153.68전남 무안 현경 평산
9495건기연[2406-3]1삼계-장성9.420210301135.28584126.742916.4613.292.120.83764.7전남 장성 동화 용정
9596건기연[2406-3]2삼계-장성9.420210301135.28584126.742917.2210.151.660.564123.2전남 장성 동화 용정
9697건기연[2408-2]1담양-순창4.920210301135.34316127.045415.933.450.530.131566.77전남 담양 금성 봉서
9798건기연[2408-2]2담양-순창4.920210301135.34316127.045414.352.620.40.131150.71전남 담양 금성 봉서
9899건기연[2701-2]1도양-고흥4.420210301134.58881127.2659815.5112.621.920.543833.98전남 고흥 고흥 등암
99100건기연[2701-2]2도양-고흥4.420210301134.58881127.2659811.957.081.070.283144.8전남 고흥 고흥 등암