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
co has 4 (4.0%) zerosZeros
nox has 4 (4.0%) zerosZeros
hc has 4 (4.0%) zerosZeros
pm has 14 (14.0%) zerosZeros
co2 has 4 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:27:42.799710
Analysis finished2023-12-10 13:27:58.175006
Duration15.38 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
2023-12-10T22:27:58.301023image/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
2023-12-10T22:27:58.533756image/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

2023-12-10T22:27:58.731097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:27:58.874086image/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
2023-12-10T22:27:59.180670image/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%
1803-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:28:00.276896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 152
19.0%
0 146
18.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 74
9.2%
3 36
 
4.5%
5 22
 
2.7%
8 20
 
2.5%
6 14
 
1.7%
Other values (3) 38
 
4.7%

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 (%)
1 152
30.3%
0 146
29.1%
2 74
14.7%
3 36
 
7.2%
5 22
 
4.4%
8 20
 
4.0%
6 14
 
2.8%
7 14
 
2.8%
9 12
 
2.4%
4 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 (%)
1 152
19.0%
0 146
18.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 74
9.2%
3 36
 
4.5%
5 22
 
2.7%
8 20
 
2.5%
6 14
 
1.7%
Other values (3) 38
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 152
19.0%
0 146
18.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 74
9.2%
3 36
 
4.5%
5 22
 
2.7%
8 20
 
2.5%
6 14
 
1.7%
Other values (3) 38
 
4.7%

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

2023-12-10T22:28:00.513729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:00.679168image/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
장성-북하
 
2
Other values (44)
88 

Length

Max length7
Median length5
Mean length5.04
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%
장성-북하 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%
Other values (39) 78
78.0%

Length

2023-12-10T22:28:00.873257image/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%
진도-진도대교 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 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.504
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:01.063048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile2.9
Q15.4
median9.65
Q313.5
95-th percentile21.8
Maximum33.8
Range31.2
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation6.2729807
Coefficient of variation (CV)0.59719923
Kurtosis2.4490319
Mean10.504
Median Absolute Deviation (MAD)4.15
Skewness1.3160924
Sum1050.4
Variance39.350287
MonotonicityNot monotonic
2023-12-10T22:28:01.282484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5.4 6
 
6.0%
12.5 6
 
6.0%
7.4 4
 
4.0%
10.3 4
 
4.0%
7.9 4
 
4.0%
11.7 2
 
2.0%
10.2 2
 
2.0%
11.4 2
 
2.0%
24.3 2
 
2.0%
6.9 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
2.6 2
2.0%
2.7 2
2.0%
2.9 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%
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
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2023-12-10T22:28:01.559580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:01.771680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 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

2023-12-10T22:28:01.928539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:02.071618image/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%
Mean34.923505
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:02.242916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38107
5-th percentile34.46151
Q134.73192
median34.935545
Q335.1103
95-th percentile35.29816
Maximum35.34926
Range0.96819
Interquartile range (IQR)0.37838

Descriptive statistics

Standard deviation0.25197101
Coefficient of variation (CV)0.0072149404
Kurtosis-0.62085649
Mean34.923505
Median Absolute Deviation (MAD)0.18919
Skewness-0.26347505
Sum3492.3505
Variance0.063489389
MonotonicityNot monotonic
2023-12-10T22:28:02.492823image/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%
34.54517 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%
34.54517 2
2.0%
34.55042 2
2.0%
34.55273 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.70297 2
2.0%
34.71151 2
2.0%
ValueCountFrequency (%)
35.34926 2
2.0%
35.3418 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%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.96137
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:02.705121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.29822
Q1126.64976
median126.93796
Q3127.32438
95-th percentile127.61069
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.67462

Descriptive statistics

Standard deviation0.41653525
Coefficient of variation (CV)0.0032808031
Kurtosis-1.1014778
Mean126.96137
Median Absolute Deviation (MAD)0.35608
Skewness0.019049325
Sum12696.137
Variance0.17350161
MonotonicityNot monotonic
2023-12-10T22:28:02.949679image/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%
126.29822 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%
126.29822 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%
126.53424 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  ZEROS 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.2047
Minimum0
Maximum131.36
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:03.234335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52
Q12.68
median12.17
Q329.27
95-th percentile45.245
Maximum131.36
Range131.36
Interquartile range (IQR)26.59

Descriptive statistics

Standard deviation21.079626
Coefficient of variation (CV)1.1579222
Kurtosis9.8501856
Mean18.2047
Median Absolute Deviation (MAD)10.515
Skewness2.5214287
Sum1820.47
Variance444.35063
MonotonicityNot monotonic
2023-12-10T22:28:03.501189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52 6
 
6.0%
2.78 5
 
5.0%
0.0 4
 
4.0%
1.98 3
 
3.0%
2.68 3
 
3.0%
1.05 2
 
2.0%
12.07 2
 
2.0%
1.57 2
 
2.0%
6.7 2
 
2.0%
3.33 2
 
2.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.52 6
6.0%
1.05 2
 
2.0%
1.21 1
 
1.0%
1.38 1
 
1.0%
1.57 2
 
2.0%
1.73 1
 
1.0%
1.98 3
3.0%
2.03 1
 
1.0%
2.26 1
 
1.0%
ValueCountFrequency (%)
131.36 1
1.0%
106.77 1
1.0%
63.62 1
1.0%
51.51 1
1.0%
46.48 1
1.0%
45.18 1
1.0%
44.22 1
1.0%
43.21 1
1.0%
43.12 1
1.0%
42.88 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.3949
Minimum0
Maximum216.29
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:03.726625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q11.73
median11.4
Q328.2475
95-th percentile43.3295
Maximum216.29
Range216.29
Interquartile range (IQR)26.5175

Descriptive statistics

Standard deviation29.644975
Coefficient of variation (CV)1.6115866
Kurtosis26.62299
Mean18.3949
Median Absolute Deviation (MAD)10.125
Skewness4.595067
Sum1839.49
Variance878.82451
MonotonicityNot monotonic
2023-12-10T22:28:03.997916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 6
 
6.0%
1.79 5
 
5.0%
0.0 4
 
4.0%
1.32 3
 
3.0%
1.73 3
 
3.0%
4.47 3
 
3.0%
12.65 2
 
2.0%
0.55 2
 
2.0%
0.83 2
 
2.0%
2.06 2
 
2.0%
Other values (68) 68
68.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.28 6
6.0%
0.55 2
 
2.0%
0.83 2
 
2.0%
0.96 1
 
1.0%
1.09 1
 
1.0%
1.23 1
 
1.0%
1.32 3
3.0%
1.41 1
 
1.0%
1.51 1
 
1.0%
ValueCountFrequency (%)
216.29 1
1.0%
174.34 1
1.0%
66.38 1
1.0%
56.61 1
1.0%
44.46 1
1.0%
43.27 1
1.0%
40.26 1
1.0%
40.14 1
1.0%
38.22 1
1.0%
37.6 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5021
Minimum0
Maximum22.36
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:04.220430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.26
median1.685
Q33.8425
95-th percentile6.2405
Maximum22.36
Range22.36
Interquartile range (IQR)3.5825

Descriptive statistics

Standard deviation3.3450773
Coefficient of variation (CV)1.3369079
Kurtosis16.434051
Mean2.5021
Median Absolute Deviation (MAD)1.495
Skewness3.3950363
Sum250.21
Variance11.189542
MonotonicityNot monotonic
2023-12-10T22:28:04.449516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 6
 
6.0%
0.26 6
 
6.0%
0.0 4
 
4.0%
0.2 3
 
3.0%
0.13 3
 
3.0%
0.27 3
 
3.0%
0.09 2
 
2.0%
0.69 2
 
2.0%
0.33 2
 
2.0%
1.72 2
 
2.0%
Other values (67) 67
67.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.04 6
6.0%
0.09 2
 
2.0%
0.13 3
3.0%
0.16 1
 
1.0%
0.18 1
 
1.0%
0.2 3
3.0%
0.21 1
 
1.0%
0.22 1
 
1.0%
0.26 6
6.0%
ValueCountFrequency (%)
22.36 1
1.0%
18.6 1
1.0%
9.52 1
1.0%
6.56 1
1.0%
6.44 1
1.0%
6.23 1
1.0%
6.12 1
1.0%
5.6 1
1.0%
5.54 1
1.0%
5.39 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2151
Minimum0
Maximum13.83
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:04.719230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.805
Q31.99
95-th percentile2.833
Maximum13.83
Range13.83
Interquartile range (IQR)1.86

Descriptive statistics

Standard deviation1.8400398
Coefficient of variation (CV)1.5143114
Kurtosis26.646607
Mean1.2151
Median Absolute Deviation (MAD)0.675
Skewness4.494998
Sum121.51
Variance3.3857465
MonotonicityNot monotonic
2023-12-10T22:28:05.010128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
0.13 14
 
14.0%
0.14 7
 
7.0%
0.41 4
 
4.0%
0.84 3
 
3.0%
0.27 3
 
3.0%
2.13 3
 
3.0%
2.08 2
 
2.0%
2.23 2
 
2.0%
1.06 2
 
2.0%
Other values (42) 46
46.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.13 14
14.0%
0.14 7
7.0%
0.27 3
 
3.0%
0.4 2
 
2.0%
0.41 4
 
4.0%
0.42 1
 
1.0%
0.54 1
 
1.0%
0.57 1
 
1.0%
0.69 1
 
1.0%
ValueCountFrequency (%)
13.83 1
1.0%
10.11 1
1.0%
3.79 1
1.0%
3.29 1
1.0%
3.27 1
1.0%
2.81 1
1.0%
2.7 1
1.0%
2.55 1
1.0%
2.53 1
1.0%
2.44 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4431.9601
Minimum0
Maximum36603.2
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:05.267790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138.68
Q1646.6
median2927.18
Q36721.5775
95-th percentile10862.049
Maximum36603.2
Range36603.2
Interquartile range (IQR)6074.9775

Descriptive statistics

Standard deviation5478.8491
Coefficient of variation (CV)1.2362135
Kurtosis14.541574
Mean4431.9601
Median Absolute Deviation (MAD)2490.505
Skewness3.133831
Sum443196.01
Variance30017787
MonotonicityNot monotonic
2023-12-10T22:28:05.515931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.68 6
 
6.0%
734.66 5
 
5.0%
0.0 4
 
4.0%
487.28 3
 
3.0%
646.6 3
 
3.0%
277.37 2
 
2.0%
416.06 2
 
2.0%
1626.79 2
 
2.0%
800.28 2
 
2.0%
1012.03 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 4
4.0%
138.68 6
6.0%
277.37 2
 
2.0%
318.6 1
 
1.0%
339.23 1
 
1.0%
416.06 2
 
2.0%
457.29 1
 
1.0%
487.28 3
3.0%
492.91 1
 
1.0%
595.97 1
 
1.0%
ValueCountFrequency (%)
36603.2 1
1.0%
29078.94 1
1.0%
15003.9 1
1.0%
12584.58 1
1.0%
11988.93 1
1.0%
10802.74 1
1.0%
10200.02 1
1.0%
10091.69 1
1.0%
10053.05 1
1.0%
9994.3 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:28:06.072982image/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%
화순 8
 
2.0%
보성 8
 
2.0%
강진 8
 
2.0%
미력 6
 
1.5%
광양 6
 
1.5%
주암 6
 
1.5%
곡성 6
 
1.5%
나주 6
 
1.5%
Other values (96) 230
58.1%
2023-12-10T22:28:06.742263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
27.4%
전 110
 
10.2%
남 110
 
10.2%
성 30
 
2.8%
천 20
 
1.9%
순 20
 
1.9%
산 16
 
1.5%
신 14
 
1.3%
진 14
 
1.3%
장 14
 
1.3%
Other values (95) 436
40.4%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 110
 
14.0%
남 110
 
14.0%
성 30
 
3.8%
천 20
 
2.6%
순 20
 
2.6%
산 16
 
2.0%
신 14
 
1.8%
진 14
 
1.8%
장 14
 
1.8%
곡 14
 
1.8%
Other values (94) 422
53.8%
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 (%)
전 110
 
14.0%
남 110
 
14.0%
성 30
 
3.8%
천 20
 
2.6%
순 20
 
2.6%
산 16
 
2.0%
신 14
 
1.8%
진 14
 
1.8%
장 14
 
1.8%
곡 14
 
1.8%
Other values (94) 422
53.8%
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 (%)
전 110
 
14.0%
남 110
 
14.0%
성 30
 
3.8%
천 20
 
2.6%
순 20
 
2.6%
산 16
 
2.0%
신 14
 
1.8%
진 14
 
1.8%
장 14
 
1.8%
곡 14
 
1.8%
Other values (94) 422
53.8%

Interactions

2023-12-10T22:27:56.102339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:43.897455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:45.362397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:46.813219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:48.243227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:50.185704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:51.841292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:53.235113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:54.648945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:56.227166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:44.138052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:45.519925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:46.949846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:48.380417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:50.332838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:52.063000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:53.433791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:54.824689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:56.374653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:44.291055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:45.713721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:47.163147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:48.554896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:50.479403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:52.268187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:53.602002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:55.002101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:56.515517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:44.424855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:45.865812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:47.270300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:48.706231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:50.611225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:52.406137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:53.784170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:55.135966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:56.668444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:44.576428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:46.066360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:47.416110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:48.857498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:50.817547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:52.571281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:53.938767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:55.322275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:56.826575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:44.717122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:46.243057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:47.578442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:49.002917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:50.988366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:52.716703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:54.065144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:55.489506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:56.952314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:44.863436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:46.375294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:47.795729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:49.144263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:51.216673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:52.837597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:54.254385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:55.626608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:57.102691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:45.006489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:46.512231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:47.945359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:49.318949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:51.391528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:52.957388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:54.366563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:55.754418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:57.312352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:45.168324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:46.664594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:48.098442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:49.541510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:51.598004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:53.088825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:54.508728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:27:55.900838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:28:06.920446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.4430.8060.8170.4370.4480.3770.4730.3121.000
지점1.0001.0000.0001.0001.0001.0001.0000.8130.6710.8110.7220.8131.000
방향0.0000.0001.0000.0000.0000.0000.0000.1720.2050.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9991.0001.0000.8030.6900.8000.7120.8231.000
연장0.4431.0000.0000.9991.0000.5880.6170.0000.0000.0000.0000.1551.000
좌표위치위도0.8061.0000.0001.0000.5881.0000.7810.2480.0000.3840.2180.1891.000
좌표위치경도0.8171.0000.0001.0000.6170.7811.0000.4930.4770.4970.6480.5011.000
co0.4370.8130.1720.8030.0000.2480.4931.0000.9470.9590.9130.9900.813
nox0.4480.6710.2050.6900.0000.0000.4770.9471.0000.9920.9470.9640.671
hc0.3770.8110.0000.8000.0000.3840.4970.9590.9921.0000.9120.9740.811
pm0.4730.7220.0000.7120.0000.2180.6480.9130.9470.9121.0000.9460.722
co20.3120.8130.0000.8230.1550.1890.5010.9900.9640.9740.9461.0000.813
주소1.0001.0000.0001.0001.0001.0001.0000.8130.6710.8110.7220.8131.000
2023-12-10T22:28:07.179638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:28:07.325873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.0000.0760.1960.154-0.271-0.279-0.269-0.262-0.2790.0000.753
연장0.0761.000-0.063-0.073-0.063-0.047-0.060-0.053-0.0650.0000.735
좌표위치위도0.196-0.0631.0000.234-0.091-0.102-0.099-0.137-0.0900.0000.753
좌표위치경도0.154-0.0730.2341.000-0.228-0.205-0.212-0.223-0.2290.0000.753
co-0.271-0.063-0.091-0.2281.0000.9940.9930.9750.9990.1770.328
nox-0.279-0.047-0.102-0.2050.9941.0000.9970.9810.9910.1430.269
hc-0.269-0.060-0.099-0.2120.9930.9971.0000.9820.9890.0000.357
pm-0.262-0.053-0.137-0.2230.9750.9810.9821.0000.9720.0000.300
co2-0.279-0.065-0.090-0.2290.9990.9910.9890.9721.0000.0000.347
방향0.0000.0000.0000.0000.1770.1430.0000.0000.0001.0000.000
측정구간0.7530.7350.7530.7530.3280.2690.3570.3000.3470.0001.000

Missing values

2023-12-10T22:27:57.664874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:27:58.048815image/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-무안5.420210601134.85192126.4272733.5932.694.461.858159.98전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210601134.85192126.4272736.7733.784.631.999107.09전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210601134.80189126.3649125.3815.712.340.846660.7전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210601134.80189126.3649146.4856.616.233.2712584.58전남 목포 죽교
45건기연[0104-0]1학교-장산12.520210601134.99062126.654882.031.410.210.14492.91전남 나주 다시 복암
56건기연[0104-0]2학교-장산12.520210601134.99062126.654886.74.470.690.411626.79전남 나주 다시 복암
67건기연[0108-0]1장성-북하2.920210601135.3418126.823913.332.060.330.14800.28전남 장성 장성 상오
78건기연[0108-0]2장성-북하2.920210601135.3418126.823918.514.890.810.272050.34전남 장성 장성 상오
89건기연[0109-0]1광주-장성7.420210601135.2553126.812134.0133.24.992.138111.46전남 장성 진원 산정
910건기연[0109-0]2광주-장성7.420210601135.2553126.812135.534.015.092.338556.9전남 장성 진원 산정
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2213-1]1동복-승주17.020210601135.05208127.28681.050.550.090.0277.37전남 순천 주암 창촌
9192건기연[2213-1]2동복-승주17.020210601135.05208127.28680.520.280.040.0138.68전남 순천 주암 창촌
9293건기연[2302-1]1마량-관산12.520210601134.46151126.865260.00.00.00.00.0전남 장흥 대덕 신
9394건기연[2302-1]2마량-관산12.520210601134.46151126.865261.381.090.160.14339.23전남 장흥 대덕 신
9495건기연[2305-3]1금정-나주5.420210601134.88845126.748246.04.050.620.411467.48전남 영암 금정 와운
9596건기연[2305-3]2금정-나주5.420210601134.88845126.748246.74.470.690.411626.79전남 영암 금정 와운
9697건기연[2306-0]1나주-상방9.720210601134.95043126.6470427.8222.223.42.136634.02전남 나주 왕곡 신포
9798건기연[2306-0]2나주-상방9.720210601134.95043126.6470423.0423.763.52.085510.08전남 나주 왕곡 신포
9899건기연[2309-0]1동강-함평5.620210601135.03781126.534247.354.90.710.411932.24전남 함평 학교 사거
99100건기연[2309-0]2동강-함평5.620210601135.03781126.534246.734.470.650.41791.53전남 함평 학교 사거