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 13 (13.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:29:57.689940
Analysis finished2023-12-10 13:30:10.483869
Duration12.79 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
2023-12-10T22:30:10.595792image/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
2023-12-10T22:30:10.770982image/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

2023-12-10T22:30:10.971941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:30:11.118818image/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
2023-12-10T22:30:11.512611image/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[0104-0]
ValueCountFrequency (%)
0101-0 2
 
2.0%
1810-1 2
 
2.0%
2306-0 2
 
2.0%
1701-0 2
 
2.0%
1704-0 2
 
2.0%
1705-1 2
 
2.0%
1706-3 2
 
2.0%
1707-1 2
 
2.0%
1801-4 2
 
2.0%
1805-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:30:12.058437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 148
18.5%
0 142
17.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 82
10.2%
3 36
 
4.5%
5 22
 
2.7%
8 18
 
2.2%
4 14
 
1.7%
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%
0 142
28.3%
2 82
16.3%
3 36
 
7.2%
5 22
 
4.4%
8 18
 
3.6%
4 14
 
2.8%
6 14
 
2.8%
7 14
 
2.8%
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 802
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 148
18.5%
0 142
17.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 82
10.2%
3 36
 
4.5%
5 22
 
2.7%
8 18
 
2.2%
4 14
 
1.7%
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%
0 142
17.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 82
10.2%
3 36
 
4.5%
5 22
 
2.7%
8 18
 
2.2%
4 14
 
1.7%
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

2023-12-10T22:30:12.284429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:30:12.432734image/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
남-동
 
2
조성-별량
 
2
학교-장산
 
2
광주-장성
 
2
Other values (44)
88 

Length

Max length6
Median length5
Mean length5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광양-하동 4
 
4.0%
남-동 2
 
2.0%
조성-별량 2
 
2.0%
학교-장산 2
 
2.0%
광주-장성 2
 
2.0%
금계-강진 2
 
2.0%
목포-학산 2
 
2.0%
목포-청계 2
 
2.0%
암태-신안 2
 
2.0%
성전-강진 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T22:30:12.572993image/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%
여수-순천 2
 
2.0%
순천-황전 2
 
2.0%
구례구-용방 2
 
2.0%
구례-곡성 2
 
2.0%
백동-죽림 2
 
2.0%
황산-해남 2
 
2.0%
옥천-강진 2
 
2.0%
보성-복내 2
 
2.0%
Other values (39) 78
78.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.718
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:30:13.073441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.2
Q15.6
median10.2
Q313.5
95-th percentile21.8
Maximum33.8
Range31.2
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.1503943
Coefficient of variation (CV)0.57383787
Kurtosis2.6505723
Mean10.718
Median Absolute Deviation (MAD)4
Skewness1.3323343
Sum1071.8
Variance37.827349
MonotonicityNot monotonic
2023-12-10T22:30:13.257769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
5.4 6
 
6.0%
11.4 6
 
6.0%
12.5 4
 
4.0%
7.4 4
 
4.0%
10.3 4
 
4.0%
7.9 4
 
4.0%
10.2 4
 
4.0%
5.2 2
 
2.0%
14.5 2
 
2.0%
24.3 2
 
2.0%
Other values (31) 62
62.0%
ValueCountFrequency (%)
2.6 2
 
2.0%
2.7 2
 
2.0%
3.2 2
 
2.0%
3.5 2
 
2.0%
4.2 2
 
2.0%
4.3 2
 
2.0%
4.4 2
 
2.0%
4.5 2
 
2.0%
5.2 2
 
2.0%
5.4 6
6.0%
ValueCountFrequency (%)
33.8 2
2.0%
24.3 2
2.0%
21.8 2
2.0%
21.3 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%
14.5 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

2023-12-10T22:30:13.460862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:30:13.578562image/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
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

2023-12-10T22:30:13.714744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:30:13.912796image/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%
Mean34.909841
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:30:14.084316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38107
5-th percentile34.46151
Q134.72412
median34.91025
Q335.06978
95-th percentile35.28858
Maximum35.34926
Range0.96819
Interquartile range (IQR)0.34566

Descriptive statistics

Standard deviation0.24694311
Coefficient of variation (CV)0.0070737391
Kurtosis-0.6637706
Mean34.909841
Median Absolute Deviation (MAD)0.18223
Skewness-0.22373513
Sum3490.9841
Variance0.060980898
MonotonicityNot monotonic
2023-12-10T22:30:14.317382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85192 2
 
2.0%
35.18107 2
 
2.0%
34.93904 2
 
2.0%
35.01799 2
 
2.0%
35.18146 2
 
2.0%
35.17397 2
 
2.0%
34.38107 2
 
2.0%
34.58728 2
 
2.0%
34.61562 2
 
2.0%
34.80445 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.38107 2
2.0%
34.38392 2
2.0%
34.46151 2
2.0%
34.55042 2
2.0%
34.55273 2
2.0%
34.58728 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.64863 2
2.0%
34.67935 2
2.0%
ValueCountFrequency (%)
35.34926 2
2.0%
35.29816 2
2.0%
35.28858 2
2.0%
35.27304 2
2.0%
35.2553 2
2.0%
35.22404 2
2.0%
35.21934 2
2.0%
35.18146 2
2.0%
35.18107 2
2.0%
35.17397 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.95006
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:30:14.514973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.36491
Q1126.64976
median126.88985
Q3127.31619
95-th percentile127.61069
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.66643

Descriptive statistics

Standard deviation0.4098768
Coefficient of variation (CV)0.003228646
Kurtosis-1.0840819
Mean126.95006
Median Absolute Deviation (MAD)0.35148
Skewness0.13688721
Sum12695.006
Variance0.16799899
MonotonicityNot monotonic
2023-12-10T22:30:14.728020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.42727 2
 
2.0%
127.36361 2
 
2.0%
127.55961 2
 
2.0%
127.46028 2
 
2.0%
127.46572 2
 
2.0%
127.43777 2
 
2.0%
126.21616 2
 
2.0%
126.51479 2
 
2.0%
126.74515 2
 
2.0%
127.10201 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.21616 2
2.0%
126.2341 2
2.0%
126.36491 2
2.0%
126.36721 2
2.0%
126.42727 2
2.0%
126.46074 2
2.0%
126.47853 2
2.0%
126.51479 2
2.0%
126.53424 2
2.0%
126.5425 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 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.7978
Minimum0.52
Maximum726.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:30:14.983688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile1.05
Q16.5225
median22.965
Q350.58
95-th percentile91.476
Maximum726.81
Range726.29
Interquartile range (IQR)44.0575

Descriptive statistics

Standard deviation80.421558
Coefficient of variation (CV)1.9712229
Kurtosis55.328167
Mean40.7978
Median Absolute Deviation (MAD)19.87
Skewness6.8227502
Sum4079.78
Variance6467.6271
MonotonicityNot monotonic
2023-12-10T22:30:15.199167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.05 5
 
5.0%
6.53 2
 
2.0%
1.57 2
 
2.0%
52.09 1
 
1.0%
7.55 1
 
1.0%
51.54 1
 
1.0%
726.81 1
 
1.0%
23.03 1
 
1.0%
23.72 1
 
1.0%
17.52 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.52 1
 
1.0%
0.65 1
 
1.0%
0.87 1
 
1.0%
1.05 5
5.0%
1.26 1
 
1.0%
1.3 1
 
1.0%
1.57 2
 
2.0%
1.78 1
 
1.0%
1.98 1
 
1.0%
2.26 1
 
1.0%
ValueCountFrequency (%)
726.81 1
1.0%
319.7 1
1.0%
126.03 1
1.0%
100.86 1
1.0%
100.14 1
1.0%
91.02 1
1.0%
90.24 1
1.0%
88.35 1
1.0%
82.84 1
1.0%
75.22 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.508
Minimum0.28
Maximum470.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:30:15.416517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.55
Q13.795
median15.63
Q341.2475
95-th percentile91.4955
Maximum470.23
Range469.95
Interquartile range (IQR)37.4525

Descriptive statistics

Standard deviation66.098813
Coefficient of variation (CV)1.9154635
Kurtosis31.59217
Mean34.508
Median Absolute Deviation (MAD)14.445
Skewness5.2657051
Sum3450.8
Variance4369.0531
MonotonicityNot monotonic
2023-12-10T22:30:15.640959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.55 5
 
5.0%
3.57 2
 
2.0%
0.83 2
 
2.0%
38.78 1
 
1.0%
4.37 1
 
1.0%
56.18 1
 
1.0%
433.68 1
 
1.0%
14.13 1
 
1.0%
17.68 1
 
1.0%
11.55 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.28 1
 
1.0%
0.32 1
 
1.0%
0.49 1
 
1.0%
0.55 5
5.0%
0.64 1
 
1.0%
0.83 2
 
2.0%
1.05 1
 
1.0%
1.32 1
 
1.0%
1.33 1
 
1.0%
1.51 1
 
1.0%
ValueCountFrequency (%)
470.23 1
1.0%
433.68 1
1.0%
144.75 1
1.0%
110.27 1
1.0%
93.88 1
1.0%
91.37 1
1.0%
81.48 1
1.0%
70.01 1
1.0%
68.82 1
1.0%
68.24 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6453
Minimum0.04
Maximum71.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:30:15.893416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.09
Q10.61
median2.195
Q35.33
95-th percentile12.3565
Maximum71.99
Range71.95
Interquartile range (IQR)4.72

Descriptive statistics

Standard deviation8.9649175
Coefficient of variation (CV)1.9298899
Kurtosis38.198044
Mean4.6453
Median Absolute Deviation (MAD)2.035
Skewness5.7164431
Sum464.53
Variance80.369746
MonotonicityNot monotonic
2023-12-10T22:30:16.107452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.09 5
 
5.0%
1.68 2
 
2.0%
1.2 2
 
2.0%
0.13 2
 
2.0%
1.84 2
 
2.0%
5.27 2
 
2.0%
1.03 2
 
2.0%
0.61 2
 
2.0%
0.45 1
 
1.0%
0.67 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.04 1
 
1.0%
0.06 1
 
1.0%
0.07 1
 
1.0%
0.09 5
5.0%
0.12 1
 
1.0%
0.13 2
 
2.0%
0.14 1
 
1.0%
0.18 1
 
1.0%
0.2 1
 
1.0%
0.22 1
 
1.0%
ValueCountFrequency (%)
71.99 1
1.0%
50.02 1
1.0%
14.29 1
1.0%
14.14 1
1.0%
12.86 1
1.0%
12.33 1
1.0%
11.3 1
1.0%
10.02 1
1.0%
10.01 1
1.0%
9.62 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8678
Minimum0
Maximum27.75
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:30:16.319777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.14
median0.835
Q32.0525
95-th percentile5.481
Maximum27.75
Range27.75
Interquartile range (IQR)1.9125

Descriptive statistics

Standard deviation3.2781393
Coefficient of variation (CV)1.7550805
Kurtosis39.544207
Mean1.8678
Median Absolute Deviation (MAD)0.74
Skewness5.4298855
Sum186.78
Variance10.746197
MonotonicityNot monotonic
2023-12-10T22:30:16.522848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
13.0%
0.14 7
 
7.0%
0.13 6
 
6.0%
0.4 4
 
4.0%
0.84 3
 
3.0%
0.54 3
 
3.0%
0.83 2
 
2.0%
0.27 2
 
2.0%
0.41 2
 
2.0%
0.81 2
 
2.0%
Other values (53) 56
56.0%
ValueCountFrequency (%)
0.0 13
13.0%
0.13 6
6.0%
0.14 7
7.0%
0.27 2
 
2.0%
0.28 1
 
1.0%
0.4 4
 
4.0%
0.41 2
 
2.0%
0.42 1
 
1.0%
0.44 1
 
1.0%
0.54 3
 
3.0%
ValueCountFrequency (%)
27.75 1
1.0%
10.19 1
1.0%
9.6 1
1.0%
5.7 1
1.0%
5.69 1
1.0%
5.47 1
1.0%
5.45 1
1.0%
5.12 1
1.0%
4.99 1
1.0%
4.85 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10127.873
Minimum138.68
Maximum170751.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:30:16.747509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile277.37
Q11695.62
median5575.56
Q312819.752
95-th percentile23103.189
Maximum170751.33
Range170612.65
Interquartile range (IQR)11124.132

Descriptive statistics

Standard deviation19308.392
Coefficient of variation (CV)1.9064608
Kurtosis50.599371
Mean10127.873
Median Absolute Deviation (MAD)4980.275
Skewness6.4976781
Sum1012787.3
Variance3.7281401 × 108
MonotonicityNot monotonic
2023-12-10T22:30:16.951226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
277.37 5
 
5.0%
1563.06 2
 
2.0%
416.06 2
 
2.0%
13560.85 1
 
1.0%
1988.46 1
 
1.0%
12095.19 1
 
1.0%
170751.33 1
 
1.0%
6077.8 1
 
1.0%
5641.07 1
 
1.0%
4580.42 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
138.68 1
 
1.0%
153.68 1
 
1.0%
254.3 1
 
1.0%
277.37 5
5.0%
307.36 1
 
1.0%
324.24 1
 
1.0%
416.06 2
 
2.0%
462.92 1
 
1.0%
487.28 1
 
1.0%
595.97 1
 
1.0%
ValueCountFrequency (%)
170751.33 1
1.0%
83493.97 1
1.0%
33153.5 1
1.0%
25879.92 1
1.0%
24125.76 1
1.0%
23049.37 1
1.0%
21675.74 1
1.0%
21610.71 1
1.0%
19547.24 1
1.0%
19288.42 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:30:17.357878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.8
Min length8

Characters and Unicode

Total characters1080
Distinct characters105
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전남 무안 삼향 왕산
2nd row전남 무안 삼향 왕산
3rd row전남 목포 죽교
4th row전남 목포 죽교
5th row전남 나주 다시 복암
ValueCountFrequency (%)
전남 100
25.3%
순천 12
 
3.0%
강진 10
 
2.5%
보성 8
 
2.0%
해남 8
 
2.0%
화순 8
 
2.0%
미력 6
 
1.5%
광양 6
 
1.5%
주암 6
 
1.5%
구례 6
 
1.5%
Other values (96) 226
57.1%
2023-12-10T22:30:17.955228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
27.4%
남 112
 
10.4%
전 110
 
10.2%
성 28
 
2.6%
천 20
 
1.9%
순 20
 
1.9%
산 18
 
1.7%
곡 16
 
1.5%
신 16
 
1.5%
해 14
 
1.3%
Other values (95) 430
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 784
72.6%
Space Separator 296
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 112
 
14.3%
전 110
 
14.0%
성 28
 
3.6%
천 20
 
2.6%
순 20
 
2.6%
산 18
 
2.3%
곡 16
 
2.0%
신 16
 
2.0%
해 14
 
1.8%
진 14
 
1.8%
Other values (94) 416
53.1%
Space Separator
ValueCountFrequency (%)
296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 784
72.6%
Common 296
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 112
 
14.3%
전 110
 
14.0%
성 28
 
3.6%
천 20
 
2.6%
순 20
 
2.6%
산 18
 
2.3%
곡 16
 
2.0%
신 16
 
2.0%
해 14
 
1.8%
진 14
 
1.8%
Other values (94) 416
53.1%
Common
ValueCountFrequency (%)
296
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 784
72.6%
ASCII 296
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296
100.0%
Hangul
ValueCountFrequency (%)
남 112
 
14.3%
전 110
 
14.0%
성 28
 
3.6%
천 20
 
2.6%
순 20
 
2.6%
산 18
 
2.3%
곡 16
 
2.0%
신 16
 
2.0%
해 14
 
1.8%
진 14
 
1.8%
Other values (94) 416
53.1%

Interactions

2023-12-10T22:30:08.801119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:58.497355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:59.585324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:00.974697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:02.234700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:03.596762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:05.163310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:06.239966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:07.512646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:08.932382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:58.643569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:59.704008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:01.110968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:02.375120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:03.729846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:05.284197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:06.363043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:07.751729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:09.067245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:58.761526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:59.844609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:01.242901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:02.523822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:03.872820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:05.420918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:06.490187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:07.894726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:09.191335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:58.862510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:59.973278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:01.374430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:02.675380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:04.091484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:05.548125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:06.613898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:08.029456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:09.362262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:58.996457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:00.114345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:01.529102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:02.846723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:04.234783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:05.655677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:06.785301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:08.171757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:09.503420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:59.104080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:00.233914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:01.678878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:03.027715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:04.684240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:05.791385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:06.927202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:08.312436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:09.601939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:59.209192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:00.360584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:01.817600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:03.176808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:04.781869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:05.889791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:07.066613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:08.427060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:09.756966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:59.342036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:00.551921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:01.967642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:03.350455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:04.912278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:06.005343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:07.206320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:08.566250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:09.893686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:59.453527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:00.811594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:02.093453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:03.471967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:05.021268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:06.113029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:07.319645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:30:08.676931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:30:18.120013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.4860.8360.8000.2090.3430.2850.5180.2531.000
지점1.0001.0000.0001.0001.0001.0001.0000.6480.7440.7080.6930.7101.000
방향0.0000.0001.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9991.0001.0000.6420.6990.6690.6350.7111.000
연장0.4861.0000.0000.9991.0000.5500.6510.0000.2140.1410.0000.1881.000
좌표위치위도0.8361.0000.0001.0000.5501.0000.7860.0000.0000.0240.4080.0001.000
좌표위치경도0.8001.0000.0001.0000.6510.7861.0000.2140.3710.2310.3330.2191.000
co0.2090.6480.0490.6420.0000.0000.2141.0000.7460.9950.8030.9980.648
nox0.3430.7440.0000.6990.2140.0000.3710.7461.0000.8160.9420.7560.744
hc0.2850.7080.0000.6690.1410.0240.2310.9950.8161.0000.8440.9970.708
pm0.5180.6930.0000.6350.0000.4080.3330.8030.9420.8441.0000.8040.693
co20.2530.7100.0000.7110.1880.0000.2190.9980.7560.9970.8041.0000.710
주소1.0001.0000.0001.0001.0001.0001.0000.6480.7440.7080.6930.7101.000
2023-12-10T22:30:18.295370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:30:18.436232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.0000.0390.2840.191-0.427-0.433-0.437-0.391-0.4250.0000.753
연장0.0391.000-0.025-0.127-0.143-0.173-0.170-0.190-0.1310.0000.734
좌표위치위도0.284-0.0251.0000.284-0.190-0.202-0.211-0.247-0.1860.0000.753
좌표위치경도0.191-0.1270.2841.000-0.143-0.118-0.131-0.095-0.1500.0000.753
co-0.427-0.143-0.190-0.1431.0000.9880.9920.9380.9980.0270.268
nox-0.433-0.173-0.202-0.1180.9881.0000.9950.9680.9850.0000.290
hc-0.437-0.170-0.211-0.1310.9920.9951.0000.9630.9880.0000.286
pm-0.391-0.190-0.247-0.0950.9380.9680.9631.0000.9340.0000.248
co2-0.425-0.131-0.186-0.1500.9980.9850.9880.9341.0000.0000.322
방향0.0000.0000.0000.0000.0270.0000.0000.0000.0001.0000.000
측정구간0.7530.7340.7530.7530.2680.2900.2860.2480.3220.0001.000

Missing values

2023-12-10T22:30:10.071919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:30:10.377965image/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-무안5.420210101134.85192126.4272752.0938.784.991.7413560.85전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210101134.85192126.4272764.9543.416.351.6115384.72전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210101134.80189126.3649157.7937.285.271.3415176.5전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210101134.80189126.3649191.0257.328.762.0221610.71전남 목포 죽교
45건기연[0104-0]1학교-장산12.520210101134.99062126.6548810.566.341.030.412552.49전남 나주 다시 복암
56건기연[0104-0]2학교-장산12.520210101134.99062126.6548815.048.451.420.43613.39전남 나주 다시 복암
67건기연[0109-0]1광주-장성7.420210101135.2553126.812172.0367.539.074.9917881.04전남 장성 진원 산정
78건기연[0109-0]2광주-장성7.420210101135.2553126.812170.0663.168.724.7617370.95전남 장성 진원 산정
89건기연[0201-4]1금계-강진12.620210101134.70297126.6497626.2819.422.50.816834.78전남 영암 학산 묵동
910건기연[0201-4]2금계-강진12.620210101134.70297126.6497621.7726.263.421.85073.17전남 영암 학산 묵동
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2213-1]1동복-승주17.020210101135.05208127.28681.050.550.090.0277.37전남 순천 주암 창촌
9192건기연[2213-1]2동복-승주17.020210101135.05208127.28682.261.510.220.13595.97전남 순천 주암 창촌
9293건기연[2302-1]1마량-관산12.520210101134.46151126.865261.261.050.140.14324.24전남 장흥 대덕 신
9394건기연[2302-1]2마량-관산12.520210101134.46151126.865261.981.320.20.13487.28전남 장흥 대덕 신
9495건기연[2305-3]1금정-나주5.420210101134.88845126.748243.992.740.40.271053.26전남 영암 금정 와운
9596건기연[2305-3]2금정-나주5.420210101134.88845126.748248.035.580.80.542112.15전남 영암 금정 와운
9697건기연[2306-0]1나주-상방9.720210101134.95043126.6470424.1821.423.071.986003.02전남 나주 왕곡 신포
9798건기연[2306-0]2나주-상방9.720210101134.95043126.6470414.759.671.50.833566.58전남 나주 왕곡 신포
9899건기연[2309-0]1동강-함평5.620210101135.03781126.5342417.8614.171.840.684211.1전남 함평 학교 사거
99100건기연[2309-0]2동강-함평5.620210101135.03781126.5342410.556.31.030.412543.25전남 함평 학교 사거