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 13:29:33.256797
Analysis finished2023-12-10 13:29:46.492812
Duration13.24 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
2023-12-10T22:29:46.633406image/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
2023-12-10T22:29:46.933267image/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

2023-12-10T22:29:47.121000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:29:47.271851image/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
2023-12-10T22:29:47.601076image/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%
1812-1 2
 
2.0%
2311-1 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%
1806-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:29:48.138420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 152
19.0%
0 140
17.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 82
10.2%
3 34
 
4.2%
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 (%)
1 152
30.3%
0 140
27.9%
2 82
16.3%
3 34
 
6.8%
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 (%)
1 152
19.0%
0 140
17.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 82
10.2%
3 34
 
4.2%
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 (%)
1 152
19.0%
0 140
17.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 82
10.2%
3 34
 
4.2%
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

2023-12-10T22:29:48.335930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:29:48.479443image/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
화순-북
 
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%
화순-북 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

2023-12-10T22:29:48.654631image/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%
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%
미력-문덕 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%
Mean10.526
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:48.901852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.2
Q15.4
median9.65
Q313.5
95-th percentile21.8
Maximum33.8
Range31.2
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation6.2136315
Coefficient of variation (CV)0.5903127
Kurtosis2.612769
Mean10.526
Median Absolute Deviation (MAD)4.15
Skewness1.3638393
Sum1052.6
Variance38.609216
MonotonicityNot monotonic
2023-12-10T22:29:49.135387image/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%
7.4 4
 
4.0%
10.3 4
 
4.0%
7.9 4
 
4.0%
4.4 4
 
4.0%
5.2 2
 
2.0%
10.2 2
 
2.0%
24.3 2
 
2.0%
8.0 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%
3.5 2
 
2.0%
4.2 2
 
2.0%
4.3 2
 
2.0%
4.4 4
4.0%
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
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T22:29:49.390528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:29:49.570879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 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

2023-12-10T22:29:49.745925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:29:49.890632image/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%
Mean34.93851
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:50.090290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38107
5-th percentile34.55042
Q134.80189
median34.944735
Q335.16902
95-th percentile35.29816
Maximum35.34926
Range0.96819
Interquartile range (IQR)0.36713

Descriptive statistics

Standard deviation0.24519095
Coefficient of variation (CV)0.0070177849
Kurtosis-0.57418543
Mean34.93851
Median Absolute Deviation (MAD)0.18919
Skewness-0.29604558
Sum3493.851
Variance0.060118601
MonotonicityNot monotonic
2023-12-10T22:29:50.338417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85192 2
 
2.0%
35.21934 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%
34.83385 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%
34.61562 2
2.0%
34.64175 2
2.0%
34.67935 2
2.0%
34.70297 2
2.0%
34.7172 2
2.0%
ValueCountFrequency (%)
35.34926 2
2.0%
35.32476 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%
35.21885 2
2.0%
35.18146 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.94133
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:50.596138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.36491
Q1126.59409
median126.87648
Q3127.31619
95-th percentile127.61069
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.7221

Descriptive statistics

Standard deviation0.41565705
Coefficient of variation (CV)0.0032744029
Kurtosis-1.1463461
Mean126.94133
Median Absolute Deviation (MAD)0.351965
Skewness0.16357972
Sum12694.133
Variance0.17277078
MonotonicityNot monotonic
2023-12-10T22:29:50.865976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.42727 2
 
2.0%
127.48499 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%
127.09515 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%
126.50315 2
2.0%
126.51479 2
2.0%
126.53424 2
2.0%
ValueCountFrequency (%)
127.75881 2
2.0%
127.67417 2
2.0%
127.61069 2
2.0%
127.55961 2
2.0%
127.48499 2
2.0%
127.46572 2
2.0%
127.46028 2
2.0%
127.4424 2
2.0%
127.43777 2
2.0%
127.37931 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.2822
Minimum0
Maximum152.12
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:51.136355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52
Q12.8175
median11.165
Q330.2
95-th percentile68.1255
Maximum152.12
Range152.12
Interquartile range (IQR)27.3825

Descriptive statistics

Standard deviation28.713722
Coefficient of variation (CV)1.2886395
Kurtosis6.9391104
Mean22.2822
Median Absolute Deviation (MAD)9.405
Skewness2.3666829
Sum2228.22
Variance824.47786
MonotonicityNot monotonic
2023-12-10T22:29:51.388875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.26 6
 
6.0%
0.52 4
 
4.0%
0.0 4
 
4.0%
2.83 2
 
2.0%
2.31 2
 
2.0%
3.93 2
 
2.0%
0.65 2
 
2.0%
26.29 1
 
1.0%
16.49 1
 
1.0%
8.55 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.52 4
4.0%
0.65 2
 
2.0%
1.05 1
 
1.0%
1.3 1
 
1.0%
1.57 1
 
1.0%
1.95 1
 
1.0%
1.98 1
 
1.0%
2.26 6
6.0%
2.31 2
 
2.0%
ValueCountFrequency (%)
152.12 1
1.0%
147.17 1
1.0%
98.75 1
1.0%
98.46 1
1.0%
83.81 1
1.0%
67.3 1
1.0%
64.99 1
1.0%
62.8 1
1.0%
60.36 1
1.0%
59.61 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.0319
Minimum0
Maximum227.5
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:51.652549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q11.8575
median7.9
Q326.8425
95-th percentile69.338
Maximum227.5
Range227.5
Interquartile range (IQR)24.985

Descriptive statistics

Standard deviation33.836827
Coefficient of variation (CV)1.6088336
Kurtosis17.50697
Mean21.0319
Median Absolute Deviation (MAD)7.58
Skewness3.6661079
Sum2103.19
Variance1144.9308
MonotonicityNot monotonic
2023-12-10T22:29:51.892146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.51 6
 
6.0%
0.0 4
 
4.0%
0.28 4
 
4.0%
0.32 2
 
2.0%
1.88 2
 
2.0%
2.28 2
 
2.0%
5.85 2
 
2.0%
3.02 2
 
2.0%
1.6 2
 
2.0%
13.02 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.28 4
4.0%
0.32 2
 
2.0%
0.55 1
 
1.0%
0.64 1
 
1.0%
0.83 1
 
1.0%
0.96 1
 
1.0%
1.32 1
 
1.0%
1.51 6
6.0%
1.6 2
 
2.0%
ValueCountFrequency (%)
227.5 1
1.0%
177.5 1
1.0%
103.84 1
1.0%
85.88 1
1.0%
78.04 1
1.0%
68.88 1
1.0%
68.83 1
1.0%
56.93 1
1.0%
56.63 1
1.0%
53.68 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8099
Minimum0
Maximum23.72
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:52.106996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.2675
median1.135
Q33.6725
95-th percentile9.965
Maximum23.72
Range23.72
Interquartile range (IQR)3.405

Descriptive statistics

Standard deviation4.0661901
Coefficient of variation (CV)1.4470942
Kurtosis10.661234
Mean2.8099
Median Absolute Deviation (MAD)1.06
Skewness2.8957733
Sum280.99
Variance16.533902
MonotonicityNot monotonic
2023-12-10T22:29:52.360954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22 6
 
6.0%
0.04 4
 
4.0%
0.0 4
 
4.0%
0.44 3
 
3.0%
0.27 2
 
2.0%
0.38 2
 
2.0%
0.06 2
 
2.0%
0.26 2
 
2.0%
0.84 2
 
2.0%
1.14 2
 
2.0%
Other values (70) 71
71.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.04 4
4.0%
0.06 2
 
2.0%
0.09 1
 
1.0%
0.12 1
 
1.0%
0.13 1
 
1.0%
0.17 1
 
1.0%
0.2 1
 
1.0%
0.22 6
6.0%
0.23 2
 
2.0%
ValueCountFrequency (%)
23.72 1
1.0%
21.59 1
1.0%
13.48 1
1.0%
12.52 1
1.0%
10.82 1
1.0%
9.92 1
1.0%
9.12 1
1.0%
8.08 1
1.0%
8.03 1
1.0%
7.04 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.273
Minimum0
Maximum13.63
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:52.608901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.56
Q31.7375
95-th percentile3.674
Maximum13.63
Range13.63
Interquartile range (IQR)1.6075

Descriptive statistics

Standard deviation1.9337667
Coefficient of variation (CV)1.5190626
Kurtosis18.490597
Mean1.273
Median Absolute Deviation (MAD)0.56
Skewness3.6676132
Sum127.3
Variance3.7394535
MonotonicityNot monotonic
2023-12-10T22:29:53.222154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
0.13 13
 
13.0%
0.14 7
 
7.0%
0.27 6
 
6.0%
1.46 3
 
3.0%
0.7 3
 
3.0%
0.4 3
 
3.0%
1.81 2
 
2.0%
1.35 2
 
2.0%
0.79 2
 
2.0%
Other values (42) 45
45.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.13 13
13.0%
0.14 7
7.0%
0.27 6
6.0%
0.28 2
 
2.0%
0.4 3
 
3.0%
0.41 1
 
1.0%
0.42 1
 
1.0%
0.54 2
 
2.0%
0.56 2
 
2.0%
ValueCountFrequency (%)
13.63 1
1.0%
9.01 1
1.0%
6.42 1
1.0%
5.03 1
1.0%
4.32 1
1.0%
3.64 1
1.0%
3.6 1
1.0%
3.51 1
1.0%
3.32 1
1.0%
3.14 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5485.2275
Minimum0
Maximum41885.56
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:29:53.444848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138.68
Q1738.8825
median2749.12
Q37429.005
95-th percentile18293.677
Maximum41885.56
Range41885.56
Interquartile range (IQR)6690.1225

Descriptive statistics

Standard deviation7226.9523
Coefficient of variation (CV)1.3175301
Kurtosis8.7523881
Mean5485.2275
Median Absolute Deviation (MAD)2310.565
Skewness2.6013083
Sum548522.75
Variance52228839
MonotonicityNot monotonic
2023-12-10T22:29:53.663865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
595.97 6
 
6.0%
138.68 4
 
4.0%
0.0 4
 
4.0%
740.29 2
 
2.0%
601.6 2
 
2.0%
948.33 2
 
2.0%
153.68 2
 
2.0%
6176.82 1
 
1.0%
3811.54 1
 
1.0%
2076.57 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 4
4.0%
138.68 4
4.0%
153.68 2
 
2.0%
277.37 1
 
1.0%
307.36 1
 
1.0%
416.06 1
 
1.0%
461.05 1
 
1.0%
487.28 1
 
1.0%
595.97 6
6.0%
601.6 2
 
2.0%
ValueCountFrequency (%)
41885.56 1
1.0%
36109.2 1
1.0%
24186.16 1
1.0%
23642.76 1
1.0%
20683.25 1
1.0%
18167.91 1
1.0%
15219.07 1
1.0%
14267.76 1
1.0%
14097.96 1
1.0%
13856.54 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:29:54.071653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.82
Min length8

Characters and Unicode

Total characters1082
Distinct characters108
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전남 무안 삼향 왕산
2nd row전남 무안 삼향 왕산
3rd row전남 목포 죽교
4th row전남 목포 죽교
5th row전남 나주 다시 복암
ValueCountFrequency (%)
전남 100
25.3%
순천 12
 
3.0%
강진 10
 
2.5%
영광 8
 
2.0%
보성 8
 
2.0%
화순 8
 
2.0%
구례 6
 
1.5%
광양 6
 
1.5%
곡성 6
 
1.5%
해남 6
 
1.5%
Other values (97) 226
57.1%
2023-12-10T22:29:54.690184image/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%
순 20
 
1.8%
천 20
 
1.8%
산 18
 
1.7%
영 14
 
1.3%
안 14
 
1.3%
진 14
 
1.3%
Other values (98) 438
40.5%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 110
 
14.0%
남 110
 
14.0%
성 28
 
3.6%
순 20
 
2.5%
천 20
 
2.5%
산 18
 
2.3%
영 14
 
1.8%
안 14
 
1.8%
진 14
 
1.8%
신 14
 
1.8%
Other values (97) 424
53.9%
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%
순 20
 
2.5%
천 20
 
2.5%
산 18
 
2.3%
영 14
 
1.8%
안 14
 
1.8%
진 14
 
1.8%
신 14
 
1.8%
Other values (97) 424
53.9%
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%
순 20
 
2.5%
천 20
 
2.5%
산 18
 
2.3%
영 14
 
1.8%
안 14
 
1.8%
진 14
 
1.8%
신 14
 
1.8%
Other values (97) 424
53.9%

Interactions

2023-12-10T22:29:44.578351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:34.135921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:35.523843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:37.147469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:38.384147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:39.729992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:40.913310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:41.997778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:43.250581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:45.034497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:34.256121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:35.679397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:37.235768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:38.524727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:39.851180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:41.024884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:42.131413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:43.402524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:45.159304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:34.393449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:35.838145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:37.368608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:38.668811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:39.979322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:41.161442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:42.270537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:43.548990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:45.266497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:34.526146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:35.978443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:37.481664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:38.801549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:40.111170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:41.257909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:42.400901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:43.703863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:45.388912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:34.696047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:36.476920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:37.621398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:38.979275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:40.259104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:41.387181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:42.533160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:43.866404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:45.522099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:34.946761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:36.618475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:37.754872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:39.156887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:40.413498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:41.522119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:42.698529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:44.009491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:45.610536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:35.094281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:36.745340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:37.880199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:39.333254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:40.532838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:41.627872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:42.870589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:44.159537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:45.716110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:35.225832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:36.875323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:38.005421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:39.466002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:40.653591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:41.742479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:43.001316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:44.285516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:45.862790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:35.394305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:37.031835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:38.172502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:39.601053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:40.788879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:41.871120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:43.133561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:29:44.429238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:29:54.876082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.4670.7850.8400.4320.3800.4490.4010.3841.000
지점1.0001.0000.0001.0001.0001.0001.0000.8530.7600.8710.7150.7841.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.1150.0000.0000.000
측정구간1.0001.0000.0001.0000.9991.0001.0000.8410.7280.8700.7430.7471.000
연장0.4671.0000.0000.9991.0000.5460.6470.4000.0000.3950.1110.3231.000
좌표위치위도0.7851.0000.0001.0000.5461.0000.7810.0000.0000.0830.0000.0001.000
좌표위치경도0.8401.0000.0001.0000.6470.7811.0000.2790.2530.3680.3010.2401.000
co0.4320.8530.0000.8410.4000.0000.2791.0000.8840.9440.8940.9850.853
nox0.3800.7600.0000.7280.0000.0000.2530.8841.0000.9630.9890.9440.760
hc0.4490.8710.1150.8700.3950.0830.3680.9440.9631.0000.9650.9120.871
pm0.4010.7150.0000.7430.1110.0000.3010.8940.9890.9651.0000.9180.715
co20.3840.7840.0000.7470.3230.0000.2400.9850.9440.9120.9181.0000.784
주소1.0001.0000.0001.0001.0001.0001.0000.8530.7600.8710.7150.7841.000
2023-12-10T22:29:55.081626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:29:55.236732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.0200.4060.108-0.365-0.394-0.380-0.403-0.3710.0000.753
연장-0.0201.000-0.043-0.090-0.0030.0040.002-0.002-0.0080.0000.733
좌표위치위도0.406-0.0431.0000.190-0.117-0.118-0.115-0.145-0.1250.0000.753
좌표위치경도0.108-0.0900.1901.000-0.135-0.123-0.132-0.154-0.1360.0000.753
co-0.365-0.003-0.117-0.1351.0000.9950.9960.9810.9990.0000.374
nox-0.3940.004-0.118-0.1230.9951.0000.9980.9880.9940.0000.267
hc-0.3800.002-0.115-0.1320.9960.9981.0000.9860.9930.1170.400
pm-0.403-0.002-0.145-0.1540.9810.9880.9861.0000.9810.0000.278
co2-0.371-0.008-0.125-0.1360.9990.9940.9930.9811.0000.0000.285
방향0.0000.0000.0000.0000.0000.0000.1170.0000.0001.0000.000
측정구간0.7530.7330.7530.7530.3740.2670.4000.2780.2850.0001.000

Missing values

2023-12-10T22:29:46.051904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:29:46.366429image/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-무안5.420210201134.85192126.4272762.856.638.033.0414267.76전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210201134.85192126.4272748.0948.896.382.5811674.97전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210201134.80189126.3649167.368.888.083.618167.91전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210201134.80189126.3649148.940.455.672.1112345.29전남 목포 죽교
45건기연[0104-0]1학교-장산12.520210201134.99062126.6548812.5211.271.760.792836.98전남 나주 다시 복암
56건기연[0104-0]2학교-장산12.520210201134.99062126.654884.573.110.450.271197.58전남 나주 다시 복암
67건기연[0109-0]1광주-장성7.420210201135.2553126.812183.8178.0410.825.0320683.25전남 장성 진원 산정
78건기연[0109-0]2광주-장성7.420210201135.2553126.812198.75103.8413.486.4223642.76전남 장성 진원 산정
89건기연[0201-4]1금계-강진12.620210201134.70297126.6497628.4733.24.482.156604.7전남 영암 학산 묵동
910건기연[0201-4]2금계-강진12.620210201134.70297126.6497610.7910.491.650.72385.87전남 영암 학산 묵동
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2305-3]1금정-나주5.420210201134.88845126.748242.311.60.230.14601.6전남 영암 금정 와운
9192건기연[2305-3]2금정-나주5.420210201134.88845126.748244.632.70.440.141107.64전남 영암 금정 와운
9293건기연[2306-0]1나주-상방9.720210201134.95043126.6470426.2921.473.251.926176.82전남 나주 왕곡 신포
9394건기연[2306-0]2나주-상방9.720210201134.95043126.6470414.159.441.450.843418.53전남 나주 왕곡 신포
9495건기연[2309-0]1동강-함평5.620210201135.03781126.534245.232.920.490.131255.69전남 함평 학교 사거
9596건기연[2309-0]2동강-함평5.620210201135.03781126.534247.44.990.720.411937.87전남 함평 학교 사거
9697건기연[2311-1]1신광-영광8.720210201135.21885126.503152.631.640.260.13640.96전남 영광 불갑 안맹
9798건기연[2311-1]2신광-영광8.720210201135.21885126.503152.261.510.220.13595.97전남 영광 불갑 안맹
9899건기연[2314-0]1마전-성송4.420210201135.32476126.594093.362.160.320.14878.97전남 영광 대마 홍교
99100건기연[2314-0]2마전-성송4.420210201135.32476126.594092.261.510.220.13595.97전남 영광 대마 홍교