Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Numeric9
Categorical5
Text2

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
기본키 is highly overall correlated with 측정구간High correlation
연장 is highly overall correlated with 측정구간High correlation
좌표위치위도 is highly overall correlated with 측정구간High correlation
좌표위치경도 is highly overall correlated with 측정구간High correlation
co is highly overall correlated with nox and 3 other fieldsHigh correlation
nox is highly overall correlated with co and 3 other fieldsHigh correlation
hc is highly overall correlated with co and 3 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with co and 3 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
pm has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:28:10.735034
Analysis finished2023-12-10 13:28:26.794536
Duration16.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:26.929906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T22:28:27.181784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T22:28:27.403075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:27.541162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:28:27.839848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.02
Min length8

Characters and Unicode

Total characters802
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0101-0]
2nd row[0101-0]
3rd row[0101-1]
4th row[0101-1]
5th row[0201-4]
ValueCountFrequency (%)
0101-0 2
 
2.0%
2212-2 2
 
2.0%
2702-1 2
 
2.0%
1707-1 2
 
2.0%
1806-2 2
 
2.0%
1809-2 2
 
2.0%
1812-1 2
 
2.0%
1815-0 2
 
2.0%
2206-0 2
 
2.0%
2207-1 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:28:28.379277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 144
18.0%
0 134
16.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 94
11.7%
3 34
 
4.2%
5 22
 
2.7%
7 22
 
2.7%
4 18
 
2.2%
Other values (3) 34
 
4.2%

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 (%)
1 144
28.7%
0 134
26.7%
2 94
18.7%
3 34
 
6.8%
5 22
 
4.4%
7 22
 
4.4%
4 18
 
3.6%
8 12
 
2.4%
6 12
 
2.4%
9 10
 
2.0%
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 (%)
1 144
18.0%
0 134
16.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 94
11.7%
3 34
 
4.2%
5 22
 
2.7%
7 22
 
2.7%
4 18
 
2.2%
Other values (3) 34
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 144
18.0%
0 134
16.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 94
11.7%
3 34
 
4.2%
5 22
 
2.7%
7 22
 
2.7%
4 18
 
2.2%
Other values (3) 34
 
4.2%

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

2023-12-10T22:28:28.601414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:28.769201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
옥과-주암
 
4
광양-하동
 
4
원진-옥천
 
2
금계-강진
 
2
목포-학산
 
2
Other values (43)
86 

Length

Max length6
Median length5
Mean length4.98
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
옥과-주암 4
 
4.0%
광양-하동 4
 
4.0%
원진-옥천 2
 
2.0%
금계-강진 2
 
2.0%
목포-학산 2
 
2.0%
목포-청계 2
 
2.0%
암태-신안 2
 
2.0%
강진-장흥 2
 
2.0%
장흥-보성 2
 
2.0%
조성-별량 2
 
2.0%
Other values (38) 76
76.0%

Length

2023-12-10T22:28:28.969336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
옥과-주암 4
 
4.0%
광양-하동 4
 
4.0%
주암-승주 2
 
2.0%
동복-승주 2
 
2.0%
옥천-강진 2
 
2.0%
보성-복내 2
 
2.0%
송광-목사동 2
 
2.0%
마산-구례읍 2
 
2.0%
공음-영광 2
 
2.0%
법성-해보 2
 
2.0%
Other values (38) 76
76.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.08
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:29.170618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.2
Q15.4
median8.35
Q313
95-th percentile21.3
Maximum33.8
Range31.2
Interquartile range (IQR)7.6

Descriptive statistics

Standard deviation6.0857509
Coefficient of variation (CV)0.60374513
Kurtosis3.0755984
Mean10.08
Median Absolute Deviation (MAD)3.9
Skewness1.4601751
Sum1008
Variance37.036364
MonotonicityNot monotonic
2023-12-10T22:28:29.384213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5.4 6
 
6.0%
4.4 6
 
6.0%
7.9 4
 
4.0%
10.3 4
 
4.0%
7.4 4
 
4.0%
7.5 4
 
4.0%
4.9 2
 
2.0%
11.4 2
 
2.0%
8.0 2
 
2.0%
4.5 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 6
6.0%
4.5 2
 
2.0%
4.9 2
 
2.0%
5.4 6
6.0%
ValueCountFrequency (%)
33.8 2
2.0%
21.8 2
2.0%
21.3 2
2.0%
19.5 2
2.0%
19.0 2
2.0%
18.0 2
2.0%
17.0 2
2.0%
16.0 2
2.0%
14.5 2
2.0%
14.3 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210501
2nd row20210501
3rd row20210501
4th row20210501
5th row20210501

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2023-12-10T22:28:29.586474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:29.735465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 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

2023-12-10T22:28:29.893536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:28:30.034151image/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%
Mean34.948464
Minimum34.38392
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:30.215138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38392
5-th percentile34.55042
Q134.80189
median34.951145
Q335.1103
95-th percentile35.32476
Maximum35.34926
Range0.96534
Interquartile range (IQR)0.30841

Descriptive statistics

Standard deviation0.24189515
Coefficient of variation (CV)0.0069214818
Kurtosis-0.70273729
Mean34.948464
Median Absolute Deviation (MAD)0.154205
Skewness-0.21905684
Sum3494.8464
Variance0.058513266
MonotonicityNot monotonic
2023-12-10T22:28:30.461663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85192 2
 
2.0%
34.88845 2
 
2.0%
34.61562 2
 
2.0%
34.80445 2
 
2.0%
35.05426 2
 
2.0%
35.21934 2
 
2.0%
35.34926 2
 
2.0%
35.22404 2
 
2.0%
35.16902 2
 
2.0%
35.04504 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.38392 2
2.0%
34.51686 2
2.0%
34.55042 2
2.0%
34.55273 2
2.0%
34.58881 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.70297 2
2.0%
34.71151 2
2.0%
34.7172 2
2.0%
ValueCountFrequency (%)
35.34926 2
2.0%
35.34316 2
2.0%
35.32476 2
2.0%
35.29816 2
2.0%
35.28858 2
2.0%
35.27304 2
2.0%
35.25002 2
2.0%
35.22404 2
2.0%
35.21934 2
2.0%
35.21885 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.96215
Minimum126.23118
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:30.695122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.23118
5-th percentile126.36491
Q1126.59409
median127.0162
Q3127.2868
95-th percentile127.61069
Maximum127.75881
Range1.52763
Interquartile range (IQR)0.69271

Descriptive statistics

Standard deviation0.41251194
Coefficient of variation (CV)0.0032490937
Kurtosis-1.1436232
Mean126.96215
Median Absolute Deviation (MAD)0.35493
Skewness-0.0054444053
Sum12696.215
Variance0.1701661
MonotonicityNot monotonic
2023-12-10T22:28:31.264977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.42727 2
 
2.0%
126.74824 2
 
2.0%
126.74515 2
 
2.0%
127.10201 2
 
2.0%
127.26851 2
 
2.0%
127.48499 2
 
2.0%
126.46074 2
 
2.0%
126.5425 2
 
2.0%
126.66536 2
 
2.0%
126.987 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.23118 2
2.0%
126.2341 2
2.0%
126.36491 2
2.0%
126.36721 2
2.0%
126.42727 2
2.0%
126.43865 2
2.0%
126.46074 2
2.0%
126.47853 2
2.0%
126.50315 2
2.0%
126.53424 2
2.0%
ValueCountFrequency (%)
127.75881 2
2.0%
127.67417 2
2.0%
127.61069 2
2.0%
127.55961 2
2.0%
127.48499 2
2.0%
127.46028 2
2.0%
127.4424 2
2.0%
127.43777 2
2.0%
127.37931 2
2.0%
127.34844 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.0224
Minimum0
Maximum240.98
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:31.492344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.05
Q14.565
median14.67
Q330.915
95-th percentile64.313
Maximum240.98
Range240.98
Interquartile range (IQR)26.35

Descriptive statistics

Standard deviation32.189528
Coefficient of variation (CV)1.3399797
Kurtosis21.299073
Mean24.0224
Median Absolute Deviation (MAD)11.89
Skewness3.825542
Sum2402.24
Variance1036.1657
MonotonicityNot monotonic
2023-12-10T22:28:31.718544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.78 3
 
3.0%
1.57 3
 
3.0%
1.95 3
 
3.0%
4.52 2
 
2.0%
5.98 2
 
2.0%
0.52 2
 
2.0%
1.05 2
 
2.0%
5.28 2
 
2.0%
7.21 1
 
1.0%
29.4 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.52 2
2.0%
0.65 1
 
1.0%
1.05 2
2.0%
1.3 1
 
1.0%
1.57 3
3.0%
1.95 3
3.0%
2.26 1
 
1.0%
2.6 1
 
1.0%
2.68 1
 
1.0%
ValueCountFrequency (%)
240.98 1
1.0%
130.02 1
1.0%
114.0 1
1.0%
79.27 1
1.0%
70.26 1
1.0%
64.0 1
1.0%
61.13 1
1.0%
60.18 1
1.0%
54.49 1
1.0%
53.81 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.5153
Minimum0
Maximum330.47
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:31.942805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q13.0125
median9.15
Q324.905
95-th percentile48.045
Maximum330.47
Range330.47
Interquartile range (IQR)21.8925

Descriptive statistics

Standard deviation37.450923
Coefficient of variation (CV)1.9190544
Kurtosis49.490579
Mean19.5153
Median Absolute Deviation (MAD)8.19
Skewness6.3406213
Sum1951.53
Variance1402.5716
MonotonicityNot monotonic
2023-12-10T22:28:32.179388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.02 4
 
4.0%
0.83 3
 
3.0%
1.79 3
 
3.0%
0.96 3
 
3.0%
3.54 2
 
2.0%
0.28 2
 
2.0%
0.55 2
 
2.0%
4.24 1
 
1.0%
43.46 1
 
1.0%
330.47 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.28 2
2.0%
0.32 1
 
1.0%
0.55 2
2.0%
0.64 1
 
1.0%
0.83 3
3.0%
0.96 3
3.0%
1.28 1
 
1.0%
1.51 1
 
1.0%
1.73 1
 
1.0%
ValueCountFrequency (%)
330.47 1
1.0%
147.74 1
1.0%
73.8 1
1.0%
55.86 1
1.0%
53.46 1
1.0%
47.76 1
1.0%
47.14 1
1.0%
45.29 1
1.0%
43.46 1
1.0%
42.19 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6803
Minimum0
Maximum35.39
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:32.470139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09
Q10.44
median1.415
Q33.5175
95-th percentile6.756
Maximum35.39
Range35.39
Interquartile range (IQR)3.0775

Descriptive statistics

Standard deviation4.2611955
Coefficient of variation (CV)1.5898203
Kurtosis35.727177
Mean2.6803
Median Absolute Deviation (MAD)1.19
Skewness5.1386427
Sum268.03
Variance18.157787
MonotonicityNot monotonic
2023-12-10T22:28:32.716941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44 3
 
3.0%
0.26 3
 
3.0%
0.17 3
 
3.0%
0.13 3
 
3.0%
2.59 2
 
2.0%
0.04 2
 
2.0%
0.48 2
 
2.0%
0.54 2
 
2.0%
1.19 2
 
2.0%
0.09 2
 
2.0%
Other values (75) 76
76.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.04 2
2.0%
0.06 1
 
1.0%
0.09 2
2.0%
0.12 1
 
1.0%
0.13 3
3.0%
0.17 3
3.0%
0.22 1
 
1.0%
0.23 1
 
1.0%
0.26 3
3.0%
ValueCountFrequency (%)
35.39 1
1.0%
16.47 1
1.0%
11.87 1
1.0%
8.3 1
1.0%
8.01 1
1.0%
6.69 1
1.0%
6.58 1
1.0%
6.37 1
1.0%
6.3 1
1.0%
6.04 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.091
Minimum0
Maximum19.14
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:32.982013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.14
median0.55
Q31.38
95-th percentile2.8365
Maximum19.14
Range19.14
Interquartile range (IQR)1.24

Descriptive statistics

Standard deviation2.1190328
Coefficient of variation (CV)1.9422849
Kurtosis54.238746
Mean1.091
Median Absolute Deviation (MAD)0.525
Skewness6.6449915
Sum109.1
Variance4.4903
MonotonicityNot monotonic
2023-12-10T22:28:33.211832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
0.13 10
 
10.0%
0.14 9
 
9.0%
0.27 7
 
7.0%
1.34 3
 
3.0%
0.54 3
 
3.0%
0.42 3
 
3.0%
1.38 2
 
2.0%
0.4 2
 
2.0%
0.81 2
 
2.0%
Other values (42) 45
45.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.13 10
10.0%
0.14 9
9.0%
0.27 7
7.0%
0.28 2
 
2.0%
0.4 2
 
2.0%
0.42 3
 
3.0%
0.54 3
 
3.0%
0.56 2
 
2.0%
0.67 2
 
2.0%
ValueCountFrequency (%)
19.14 1
1.0%
7.2 1
1.0%
4.13 1
1.0%
3.06 1
1.0%
2.96 1
1.0%
2.83 1
1.0%
2.78 1
1.0%
2.71 1
1.0%
2.63 1
1.0%
2.62 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6003.674
Minimum0
Maximum63617.22
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:28:33.408475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile277.37
Q11191.95
median3750.695
Q37709.075
95-th percentile14751.402
Maximum63617.22
Range63617.22
Interquartile range (IQR)6517.125

Descriptive statistics

Standard deviation8270.7989
Coefficient of variation (CV)1.3776229
Kurtosis24.675257
Mean6003.674
Median Absolute Deviation (MAD)2925.58
Skewness4.1733734
Sum600367.4
Variance68406114
MonotonicityNot monotonic
2023-12-10T22:28:33.644123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
734.66 3
 
3.0%
416.06 3
 
3.0%
461.05 3
 
3.0%
1191.95 2
 
2.0%
1572.4 2
 
2.0%
138.68 2
 
2.0%
277.37 2
 
2.0%
1261.32 2
 
2.0%
1742.97 1
 
1.0%
7062.18 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
138.68 2
2.0%
153.68 1
 
1.0%
277.37 2
2.0%
307.36 1
 
1.0%
416.06 3
3.0%
461.05 3
3.0%
595.97 1
 
1.0%
614.73 1
 
1.0%
646.6 1
 
1.0%
ValueCountFrequency (%)
63617.22 1
1.0%
35933.42 1
1.0%
26769.93 1
1.0%
18706.47 1
1.0%
16670.63 1
1.0%
14650.39 1
1.0%
14540.13 1
1.0%
14325.98 1
1.0%
13519.88 1
1.0%
13120.81 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:28:34.029697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.8
Min length8

Characters and Unicode

Total characters1080
Distinct characters109
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전남 무안 삼향 왕산
2nd row전남 무안 삼향 왕산
3rd row전남 목포 죽교
4th row전남 목포 죽교
5th row전남 영암 학산 묵동
ValueCountFrequency (%)
전남 100
25.3%
순천 14
 
3.5%
고흥 10
 
2.5%
주암 8
 
2.0%
화순 8
 
2.0%
영광 8
 
2.0%
강진 8
 
2.0%
무안 6
 
1.5%
광양 6
 
1.5%
담양 6
 
1.5%
Other values (97) 222
56.1%
2023-12-10T22:28:34.606923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
27.4%
남 110
 
10.2%
전 108
 
10.0%
성 24
 
2.2%
순 22
 
2.0%
천 22
 
2.0%
안 18
 
1.7%
광 16
 
1.5%
양 16
 
1.5%
산 16
 
1.5%
Other values (99) 432
40.0%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 110
 
14.0%
전 108
 
13.8%
성 24
 
3.1%
순 22
 
2.8%
천 22
 
2.8%
안 18
 
2.3%
광 16
 
2.0%
양 16
 
2.0%
산 16
 
2.0%
신 14
 
1.8%
Other values (98) 418
53.3%
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 110
 
14.0%
전 108
 
13.8%
성 24
 
3.1%
순 22
 
2.8%
천 22
 
2.8%
안 18
 
2.3%
광 16
 
2.0%
양 16
 
2.0%
산 16
 
2.0%
신 14
 
1.8%
Other values (98) 418
53.3%
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 110
 
14.0%
전 108
 
13.8%
성 24
 
3.1%
순 22
 
2.8%
천 22
 
2.8%
안 18
 
2.3%
광 16
 
2.0%
양 16
 
2.0%
산 16
 
2.0%
신 14
 
1.8%
Other values (98) 418
53.3%

Interactions

2023-12-10T22:28:24.909038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:12.067059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:14.538630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:16.105051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:17.476166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:19.018812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:20.353343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:21.720640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:23.556950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:25.044462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:12.214143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:14.785586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:16.253145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:17.614729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:19.163691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:20.489146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:21.866515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:23.722866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:25.183700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:12.360265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:14.939172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:16.394052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:17.766829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:19.302823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:20.633911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:22.017721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:23.905045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:25.326517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:12.650471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:15.074891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:16.529268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:17.913007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:19.429985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:20.785085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:22.166225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:24.055231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:25.462196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:13.045833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:15.248075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:16.709087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:18.091238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:19.592292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:20.952615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:22.765025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:24.206655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:25.668692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:13.381111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:15.408828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:16.873366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:18.238031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:19.788821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:21.091434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:22.954617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:24.341447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:25.784154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:13.708046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:15.563632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:17.004571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:18.419591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:19.922199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:21.231576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:23.119659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:24.480503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:25.907817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:14.032052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:15.733243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:17.149800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:18.586051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:20.062639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:21.369323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:23.257153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:24.622520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:26.038072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:14.364529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:15.900945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:17.326584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:18.761743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:20.215815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:21.574132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:23.401319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:28:24.778883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:28:34.813827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.5540.8370.8380.3540.3920.3780.4570.3551.000
지점1.0001.0000.0001.0001.0001.0001.0000.6780.5430.6140.6210.6621.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0000.9981.0000.6770.4970.5820.5530.7011.000
연장0.5541.0000.0001.0001.0000.5300.6170.1050.0610.0000.0000.0001.000
좌표위치위도0.8371.0000.0000.9980.5301.0000.7940.2870.0000.1760.1670.0001.000
좌표위치경도0.8381.0000.0001.0000.6170.7941.0000.4230.5560.3550.5390.3291.000
co0.3540.6780.0000.6770.1050.2870.4231.0000.9810.9520.8590.9590.678
nox0.3920.5430.0000.4970.0610.0000.5560.9811.0000.9530.9770.9560.543
hc0.3780.6140.0000.5820.0000.1760.3550.9520.9531.0000.8650.9910.614
pm0.4570.6210.0000.5530.0000.1670.5390.8590.9770.8651.0000.8450.621
co20.3550.6620.0000.7010.0000.0000.3290.9590.9560.9910.8451.0000.662
주소1.0001.0000.0001.0001.0001.0001.0000.6780.5430.6140.6210.6621.000
2023-12-10T22:28:34.998730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:28:35.117604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.0650.4320.068-0.425-0.425-0.419-0.398-0.4270.0000.760
연장-0.0651.0000.094-0.0860.0450.0250.0240.0210.0500.0000.742
좌표위치위도0.4320.0941.000-0.010-0.345-0.356-0.355-0.344-0.3450.0000.745
좌표위치경도0.068-0.086-0.0101.000-0.096-0.086-0.092-0.082-0.1000.0000.760
co-0.4250.045-0.345-0.0961.0000.9910.9940.9370.9990.0000.250
nox-0.4250.025-0.356-0.0860.9911.0000.9970.9640.9900.0000.176
hc-0.4190.024-0.355-0.0920.9940.9971.0000.9540.9910.0000.194
pm-0.3980.021-0.344-0.0820.9370.9640.9541.0000.9370.0000.205
co2-0.4270.050-0.345-0.1000.9990.9900.9910.9371.0000.0000.264
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
측정구간0.7600.7420.7450.7600.2500.1760.1940.2050.2640.0001.000

Missing values

2023-12-10T22:28:26.296605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:28:26.663973image/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-무안5.420210501134.85192126.4272754.4942.036.372.1212619.46전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210501134.85192126.4272745.5631.284.281.3811887.48전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210501134.80189126.3649144.7430.444.181.3511715.0전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210501134.80189126.3649160.1836.735.761.3414325.98전남 목포 죽교
45건기연[0201-4]1금계-강진12.620210501134.70297126.6497624.3614.832.41.085901.75전남 영암 학산 묵동
56건기연[0201-4]2금계-강진12.620210501134.70297126.649767.665.580.780.561990.36전남 영암 학산 묵동
67건기연[0201-8]1목포-학산4.220210501134.82996126.47853114.073.811.872.8326769.93전남 무안 삼향 용포
78건기연[0201-8]2목포-학산4.220210501134.82996126.4785350.733.385.111.3713120.81전남 무안 삼향 용포
89건기연[0201-9]1목포-청계3.520210501134.83404126.3672127.4415.272.590.676585.82전남 신안 압해 신장
910건기연[0201-9]2목포-청계3.520210501134.83404126.3672127.9517.02.550.837348.49전남 신안 압해 신장
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2408-2]1담양-순창4.920210501135.34316127.045419.775.170.90.132331.47전남 담양 금성 봉서
9192건기연[2408-2]2담양-순창4.920210501135.34316127.045417.554.370.670.141988.46전남 담양 금성 봉서
9293건기연[2701-2]1도양-고흥4.420210501134.58881127.2659821.6212.112.040.545182.08전남 고흥 고흥 등암
9394건기연[2701-2]2도양-고흥4.420210501134.58881127.2659831.9127.753.471.478689.09전남 고흥 고흥 등암
9495건기연[2701-7]1소록도-도덕6.720210501134.51686127.126774.932.990.450.141295.03전남 고흥 도양 소록
9596건기연[2701-7]2소록도-도덕6.720210501134.51686127.1267713.748.411.260.423607.72전남 고흥 도양 소록
9697건기연[2702-1]1옥과-주암7.520210501135.09485127.244032.781.790.260.13734.66전남 순천 주암 한곡
9798건기연[2702-1]2옥과-주암7.520210501135.09485127.244034.523.020.440.271191.95전남 순천 주암 한곡
9899건기연[2703-0]1옥과-주암7.420210501135.25002127.185122.261.510.220.13595.97전남 곡성 겸 칠봉
99100건기연[2703-0]2옥과-주암7.420210501135.25002127.185124.092.930.410.281064.53전남 곡성 겸 칠봉