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
측정시분 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 co and 5 other fieldsHigh correlation
좌표위치경도 is highly overall correlated with 측정구간High correlation
co is highly overall correlated with 좌표위치위도 and 5 other fieldsHigh correlation
nox is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
hc is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
pm is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
co2 is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
기본키 has unique valuesUnique
pm has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:10:05.136180
Analysis finished2023-12-10 12:10:15.385451
Duration10.25 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
2023-12-10T21:10:15.509201image/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
2023-12-10T21:10:15.668494image/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

2023-12-10T21:10:15.817262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:15.913712image/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
2023-12-10T21:10:16.110155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
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[0122-2]
2nd row[0122-2]
3rd row[0123-2]
4th row[0123-2]
5th row[0124-0]
ValueCountFrequency (%)
0122-2 2
 
2.0%
3206-3 2
 
2.0%
3902-2 2
 
2.0%
2918-0 2
 
2.0%
2921-3 2
 
2.0%
2922-0 2
 
2.0%
2923-0 2
 
2.0%
2924-2 2
 
2.0%
3201-0 2
 
2.0%
3203-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:10:16.488746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
15.0%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 74
9.2%
3 74
9.2%
4 42
 
5.2%
9 26
 
3.2%
6 22
 
2.8%
Other values (3) 38
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120
24.0%
2 104
20.8%
1 74
14.8%
3 74
14.8%
4 42
 
8.4%
9 26
 
5.2%
6 22
 
4.4%
7 18
 
3.6%
5 14
 
2.8%
8 6
 
1.2%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 120
15.0%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 74
9.2%
3 74
9.2%
4 42
 
5.2%
9 26
 
3.2%
6 22
 
2.8%
Other values (3) 38
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
15.0%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 74
9.2%
3 74
9.2%
4 42
 
5.2%
9 26
 
3.2%
6 22
 
2.8%
Other values (3) 38
 
4.8%

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

2023-12-10T21:10:16.643945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:16.726438image/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
신평-인주
 
4
공주-유성
 
4
부여-논산
 
2
논산-반포
 
2
금남-조치원
 
2
Other values (43)
86 

Length

Max length7
Median length5
Mean length5.1
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연무-논산
2nd row연무-논산
3rd row두마-금남
4th row두마-금남
5th row논산-반포

Common Values

ValueCountFrequency (%)
신평-인주 4
 
4.0%
공주-유성 4
 
4.0%
부여-논산 2
 
2.0%
논산-반포 2
 
2.0%
금남-조치원 2
 
2.0%
전동-쌍전 2
 
2.0%
천안-성환 2
 
2.0%
성환-천안 2
 
2.0%
둔포-평택 2
 
2.0%
장항-마서 2
 
2.0%
Other values (38) 76
76.0%

Length

2023-12-10T21:10:16.812738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신평-인주 4
 
4.0%
공주-유성 4
 
4.0%
유구-사곡 2
 
2.0%
연무-논산 2
 
2.0%
청양-홍성 2
 
2.0%
홍성-고북 2
 
2.0%
고북-서산 2
 
2.0%
서산-지곡 2
 
2.0%
만리포-태안 2
 
2.0%
태안-서산 2
 
2.0%
Other values (38) 76
76.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.966
Minimum1.8
Maximum23.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:16.916992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.7
Q15.4
median6.75
Q39.7
95-th percentile14.6
Maximum23.2
Range21.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.9304874
Coefficient of variation (CV)0.49340791
Kurtosis3.4206142
Mean7.966
Median Absolute Deviation (MAD)2.2
Skewness1.46173
Sum796.6
Variance15.448731
MonotonicityNot monotonic
2023-12-10T21:10:17.027438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
6.6 6
 
6.0%
4.9 4
 
4.0%
6.2 4
 
4.0%
9.7 4
 
4.0%
9.3 4
 
4.0%
8.3 4
 
4.0%
2.2 2
 
2.0%
7.2 2
 
2.0%
4.3 2
 
2.0%
9.5 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
1.8 2
2.0%
2.2 2
2.0%
2.7 2
2.0%
3.6 2
2.0%
4.0 2
2.0%
4.2 2
2.0%
4.3 2
2.0%
4.4 2
2.0%
4.9 4
4.0%
5.2 2
2.0%
ValueCountFrequency (%)
23.2 2
2.0%
17.6 2
2.0%
14.6 2
2.0%
14.2 2
2.0%
12.9 2
2.0%
12.4 2
2.0%
12.2 2
2.0%
11.5 2
2.0%
10.6 2
2.0%
10.2 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210301
2nd row20210301
3rd row20210301
4th row20210301
5th row20210301

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2023-12-10T21:10:17.128770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:17.203108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 100
100.0%

측정시분
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

2023-12-10T21:10:17.294004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:17.379046image/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%
Mean36.511733
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:17.476050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.25536
median36.500575
Q336.76405
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.50869

Descriptive statistics

Standard deviation0.28100671
Coefficient of variation (CV)0.00769634
Kurtosis-1.2857598
Mean36.511733
Median Absolute Deviation (MAD)0.249765
Skewness-0.021396592
Sum3651.1733
Variance0.078964772
MonotonicityNot monotonic
2023-12-10T21:10:17.612187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.89111 2
 
2.0%
36.47481 2
 
2.0%
36.58635 2
 
2.0%
36.72496 2
 
2.0%
36.83292 2
 
2.0%
36.75792 2
 
2.0%
36.78489 2
 
2.0%
36.90325 2
 
2.0%
36.87015 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
36.02784 2
2.0%
36.05892 2
2.0%
36.07008 2
2.0%
36.08997 2
2.0%
36.1228 2
2.0%
36.14511 2
2.0%
36.16232 2
2.0%
36.18861 2
2.0%
36.1897 2
2.0%
36.21913 2
2.0%
ValueCountFrequency (%)
36.95295 2
2.0%
36.9261 2
2.0%
36.90325 2
2.0%
36.89991 2
2.0%
36.89461 2
2.0%
36.89111 2
2.0%
36.87646 2
2.0%
36.87015 2
2.0%
36.86711 2
2.0%
36.85256 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.96016
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:17.738905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.75712
median126.95748
Q3127.20167
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.44455

Descriptive statistics

Standard deviation0.29397234
Coefficient of variation (CV)0.0023154693
Kurtosis-0.24844547
Mean126.96016
Median Absolute Deviation (MAD)0.2032
Skewness-0.23112933
Sum12696.016
Variance0.086419737
MonotonicityNot monotonic
2023-12-10T21:10:17.852652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
126.81017 2
 
2.0%
126.79526 2
 
2.0%
126.61312 2
 
2.0%
126.5301 2
 
2.0%
126.44223 2
 
2.0%
126.18913 2
 
2.0%
126.37641 2
 
2.0%
126.64887 2
 
2.0%
126.75145 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.18913 2
2.0%
126.37641 2
2.0%
126.44223 2
2.0%
126.5301 2
2.0%
126.61312 2
2.0%
126.64274 2
2.0%
126.64887 2
2.0%
126.66796 2
2.0%
126.70896 2
2.0%
126.71196 2
2.0%
ValueCountFrequency (%)
127.49717 2
2.0%
127.49544 2
2.0%
127.47469 2
2.0%
127.42036 2
2.0%
127.28963 2
2.0%
127.28536 2
2.0%
127.27513 2
2.0%
127.25961 2
2.0%
127.25821 2
2.0%
127.24084 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.3301
Minimum0
Maximum158.26
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:17.967376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.2835
Q113.4925
median26.535
Q348.95
95-th percentile72.537
Maximum158.26
Range158.26
Interquartile range (IQR)35.4575

Descriptive statistics

Standard deviation28.952099
Coefficient of variation (CV)0.86864723
Kurtosis5.7240675
Mean33.3301
Median Absolute Deviation (MAD)15.75
Skewness1.9667319
Sum3333.01
Variance838.22404
MonotonicityNot monotonic
2023-12-10T21:10:18.084987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65 2
 
2.0%
4.35 2
 
2.0%
6.97 2
 
2.0%
25.17 1
 
1.0%
16.21 1
 
1.0%
52.9 1
 
1.0%
11.11 1
 
1.0%
21.02 1
 
1.0%
13.74 1
 
1.0%
57.99 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.65 2
2.0%
1.26 1
1.0%
1.78 1
1.0%
2.31 1
1.0%
2.63 1
1.0%
2.68 1
1.0%
3.33 1
1.0%
4.35 2
2.0%
5.14 1
1.0%
ValueCountFrequency (%)
158.26 1
1.0%
151.13 1
1.0%
133.94 1
1.0%
75.41 1
1.0%
75.14 1
1.0%
72.4 1
1.0%
72.13 1
1.0%
71.53 1
1.0%
67.87 1
1.0%
67.82 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.4397
Minimum0
Maximum122.98
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:18.228753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5865
Q18.66
median20.075
Q333.965
95-th percentile53.3985
Maximum122.98
Range122.98
Interquartile range (IQR)25.305

Descriptive statistics

Standard deviation22.058413
Coefficient of variation (CV)0.90256479
Kurtosis6.8210149
Mean24.4397
Median Absolute Deviation (MAD)12.87
Skewness2.1128846
Sum2443.97
Variance486.57357
MonotonicityNot monotonic
2023-12-10T21:10:18.400546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 2
 
2.0%
2.62 2
 
2.0%
4.0 2
 
2.0%
24.59 1
 
1.0%
9.51 1
 
1.0%
43.1 1
 
1.0%
6.17 1
 
1.0%
15.27 1
 
1.0%
8.41 1
 
1.0%
41.74 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.32 2
2.0%
1.05 1
1.0%
1.33 1
1.0%
1.6 1
1.0%
1.64 1
1.0%
1.73 1
1.0%
2.06 1
1.0%
2.62 2
2.0%
3.25 1
1.0%
ValueCountFrequency (%)
122.98 1
1.0%
120.67 1
1.0%
96.05 1
1.0%
64.04 1
1.0%
55.65 1
1.0%
53.28 1
1.0%
51.14 1
1.0%
50.68 1
1.0%
47.22 1
1.0%
46.63 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5276
Minimum0
Maximum16.98
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:18.531898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2275
Q11.3625
median2.805
Q35.235
95-th percentile7.584
Maximum16.98
Range16.98
Interquartile range (IQR)3.8725

Descriptive statistics

Standard deviation3.1366285
Coefficient of variation (CV)0.88916786
Kurtosis5.8923012
Mean3.5276
Median Absolute Deviation (MAD)1.81
Skewness2.0004296
Sum352.76
Variance9.8384386
MonotonicityNot monotonic
2023-12-10T21:10:18.662979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06 2
 
2.0%
0.4 2
 
2.0%
6.09 2
 
2.0%
3.04 2
 
2.0%
2.91 2
 
2.0%
0.62 2
 
2.0%
6.25 1
 
1.0%
5.31 1
 
1.0%
6.07 1
 
1.0%
1.05 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.06 2
2.0%
0.14 1
1.0%
0.18 1
1.0%
0.23 1
1.0%
0.26 1
1.0%
0.27 1
1.0%
0.33 1
1.0%
0.4 2
2.0%
0.49 1
1.0%
ValueCountFrequency (%)
16.98 1
1.0%
16.77 1
1.0%
13.76 1
1.0%
10.36 1
1.0%
8.61 1
1.0%
7.53 1
1.0%
7.31 1
1.0%
7.22 1
1.0%
7.03 1
1.0%
6.81 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1958
Minimum0
Maximum7.33
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:18.796740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.4
median1.08
Q31.6475
95-th percentile2.87
Maximum7.33
Range7.33
Interquartile range (IQR)1.2475

Descriptive statistics

Standard deviation1.1017793
Coefficient of variation (CV)0.92137419
Kurtosis10.853528
Mean1.1958
Median Absolute Deviation (MAD)0.63
Skewness2.5510983
Sum119.58
Variance1.2139175
MonotonicityNot monotonic
2023-12-10T21:10:19.161684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14 6
 
6.0%
0.13 6
 
6.0%
0.54 5
 
5.0%
0.27 5
 
5.0%
0.28 4
 
4.0%
1.08 3
 
3.0%
0.0 3
 
3.0%
1.51 2
 
2.0%
1.64 2
 
2.0%
0.67 2
 
2.0%
Other values (55) 62
62.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.13 6
6.0%
0.14 6
6.0%
0.27 5
5.0%
0.28 4
4.0%
0.4 2
 
2.0%
0.41 1
 
1.0%
0.42 1
 
1.0%
0.54 5
5.0%
0.56 1
 
1.0%
ValueCountFrequency (%)
7.33 1
1.0%
5.67 1
1.0%
3.27 1
1.0%
3.1 1
1.0%
3.06 1
1.0%
2.86 1
1.0%
2.69 1
1.0%
2.5 1
1.0%
2.32 1
1.0%
2.28 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8251.1072
Minimum0
Maximum37107.1
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:19.290758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile594.666
Q13537.4475
median6678.56
Q311538.77
95-th percentile17618.31
Maximum37107.1
Range37107.1
Interquartile range (IQR)8001.3225

Descriptive statistics

Standard deviation7036.6351
Coefficient of variation (CV)0.85281101
Kurtosis4.9985682
Mean8251.1072
Median Absolute Deviation (MAD)4007.86
Skewness1.8465468
Sum825110.72
Variance49514233
MonotonicityNot monotonic
2023-12-10T21:10:19.415689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
153.68 2
 
2.0%
1150.71 2
 
2.0%
1844.14 2
 
2.0%
5785.43 1
 
1.0%
4284.25 1
 
1.0%
13302.45 1
 
1.0%
2665.07 1
 
1.0%
4819.87 1
 
1.0%
3607.72 1
 
1.0%
14934.74 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.0 1
1.0%
153.68 2
2.0%
324.24 1
1.0%
462.92 1
1.0%
601.6 1
1.0%
640.96 1
1.0%
646.6 1
1.0%
800.28 1
1.0%
1150.71 2
2.0%
1341.9 1
1.0%
ValueCountFrequency (%)
37107.1 1
1.0%
35418.74 1
1.0%
34365.01 1
1.0%
19835.57 1
1.0%
17894.19 1
1.0%
17603.79 1
1.0%
17281.65 1
1.0%
17001.59 1
1.0%
16887.3 1
1.0%
16753.15 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:10:19.662184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.94
Min length8

Characters and Unicode

Total characters1094
Distinct characters104
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 row충남 논산 은진 토양
2nd row충남 논산 은진 토양
3rd row충남 공주 반포 온천
4th row충남 공주 반포 온천
5th row충남 논산 연산 천호
ValueCountFrequency (%)
충남 100
25.1%
천안 12
 
3.0%
청양 10
 
2.5%
금산 10
 
2.5%
공주 10
 
2.5%
아산 8
 
2.0%
부여 8
 
2.0%
서천 8
 
2.0%
세종 8
 
2.0%
서산 6
 
1.5%
Other values (94) 218
54.8%
2023-12-10T21:10:20.037046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
남 102
 
9.3%
충 100
 
9.1%
산 50
 
4.6%
천 26
 
2.4%
서 22
 
2.0%
성 22
 
2.0%
안 16
 
1.5%
청 16
 
1.5%
부 14
 
1.3%
Other values (94) 428
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 796
72.8%
Space Separator 298
 
27.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 102
 
12.8%
충 100
 
12.6%
산 50
 
6.3%
천 26
 
3.3%
서 22
 
2.8%
성 22
 
2.8%
안 16
 
2.0%
청 16
 
2.0%
부 14
 
1.8%
주 14
 
1.8%
Other values (93) 414
52.0%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 796
72.8%
Common 298
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 102
 
12.8%
충 100
 
12.6%
산 50
 
6.3%
천 26
 
3.3%
서 22
 
2.8%
성 22
 
2.8%
안 16
 
2.0%
청 16
 
2.0%
부 14
 
1.8%
주 14
 
1.8%
Other values (93) 414
52.0%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 796
72.8%
ASCII 298
 
27.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
남 102
 
12.8%
충 100
 
12.6%
산 50
 
6.3%
천 26
 
3.3%
서 22
 
2.8%
성 22
 
2.8%
안 16
 
2.0%
청 16
 
2.0%
부 14
 
1.8%
주 14
 
1.8%
Other values (93) 414
52.0%

Interactions

2023-12-10T21:10:13.120744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:05.722303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.518752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:07.250749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.143270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.935329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.711586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.501034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:11.297273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:13.296795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:05.811514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.595608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:07.564312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.219436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.019042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.804300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.583756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:11.381414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:13.446256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:05.895187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.659960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:07.627349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.285349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.096769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.878917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.651857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:11.454774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:13.661854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:05.974242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.733179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:07.694854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.361820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.177803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.955893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.738559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:11.550331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:13.813431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.064519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.801766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:07.771260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.460781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.276418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.045894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.834864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:11.640841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:14.291429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.152671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.875134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:07.856026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.561774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.362917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.142495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.932559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:11.739943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:14.415044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.234549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.949412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:07.923599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.651242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.444220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.222284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:11.027472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:12.067451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:14.545982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.329841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:07.059905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.001047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.774779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.528129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.313226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:11.122844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:12.350182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:14.663192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:06.423053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:07.158669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.072250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:08.852710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:09.625601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:10.398846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:11.213793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:12.821589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:10:20.156661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9980.6250.8540.8670.5100.5200.4740.4340.4951.000
지점1.0001.0000.0001.0001.0001.0001.0000.9310.9140.8890.8390.9161.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.000
측정구간0.9981.0000.0001.0001.0001.0000.9980.9240.9320.8890.8360.9081.000
연장0.6251.0000.0001.0001.0000.6140.6260.3830.4420.2280.3940.3971.000
좌표위치위도0.8541.0000.0001.0000.6141.0000.8410.3660.3230.1970.3590.3541.000
좌표위치경도0.8671.0000.0000.9980.6260.8411.0000.4140.4680.3930.3900.4241.000
co0.5100.9310.0000.9240.3830.3660.4141.0000.9360.9420.9240.9840.931
nox0.5200.9140.0000.9320.4420.3230.4680.9361.0000.9790.8480.8720.914
hc0.4740.8890.0000.8890.2280.1970.3930.9420.9791.0000.8500.8810.889
pm0.4340.8390.0860.8360.3940.3590.3900.9240.8480.8501.0000.8750.839
co20.4950.9160.0000.9080.3970.3540.4240.9840.8720.8810.8751.0000.916
주소1.0001.0000.0001.0001.0001.0001.0000.9310.9140.8890.8390.9161.000
2023-12-10T21:10:20.281687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T21:10:20.365566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.0150.239-0.258-0.257-0.269-0.259-0.305-0.2450.0000.744
연장-0.0151.0000.064-0.1380.0600.0420.0450.0640.0710.0000.735
좌표위치위도0.2390.0641.000-0.1770.5460.5780.5840.5490.5420.0000.760
좌표위치경도-0.258-0.138-0.1771.0000.2030.2190.2340.2290.1910.0000.741
co-0.2570.0600.5460.2031.0000.9720.9780.9040.9960.0000.512
nox-0.2690.0420.5780.2190.9721.0000.9920.9580.9630.0000.489
hc-0.2590.0450.5840.2340.9780.9921.0000.9430.9650.0000.432
pm-0.3050.0640.5490.2290.9040.9580.9431.0000.8900.0870.386
co2-0.2450.0710.5420.1910.9960.9630.9650.8901.0000.0000.484
방향0.0000.0000.0000.0000.0000.0000.0000.0870.0001.0000.000
측정구간0.7440.7350.7600.7410.5120.4890.4320.3860.4840.0001.000

Missing values

2023-12-10T21:10:14.893262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:10:15.246781image/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

기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
01건기연[0122-2]1연무-논산11.520210301136.14511127.1050125.1724.593.261.815785.43충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210301136.14511127.1050123.2532.333.82.255305.92충남 논산 은진 토양
23건기연[0123-2]1두마-금남4.920210301136.37685127.2596159.4539.075.481.5315545.79충남 공주 반포 온천
34건기연[0123-2]2두마-금남4.920210301136.37685127.2596143.8929.364.041.0811493.85충남 공주 반포 온천
45건기연[0124-0]1논산-반포10.220210301136.24966127.2285452.0132.75.431.3812207.9충남 논산 연산 천호
56건기연[0124-0]2논산-반포10.220210301136.24966127.2285441.5730.914.12.0510840.29충남 논산 연산 천호
67건기연[0127-2]1금남-조치원12.220210301136.56218127.2853675.4147.226.811.6119835.57충남 세종 연서 봉암
78건기연[0127-2]2금남-조치원12.220210301136.56218127.2853665.9343.226.091.7817281.65충남 세종 연서 봉암
89건기연[0127-7]1공주-유성5.820210301136.40916127.2582120.619.863.011.094907.9충남 공주 반포 성강
910건기연[0127-7]2공주-유성5.820210301136.40916127.2582116.9222.222.911.273800.71충남 공주 반포 성강
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3707-0]1추부-군서1.820210301136.21913127.495443.332.060.330.14800.28충남 금산 추부 요광
9192건기연[3707-0]2추부-군서1.820210301136.21913127.4954410.465.850.990.272511.38충남 금산 추부 요광
9293건기연[3901-4]1은산-청양IC6.220210301136.35819126.915852.311.60.230.14601.6충남 청양 장평 은곡
9394건기연[3901-4]2은산-청양IC6.220210301136.35819126.915851.261.050.140.14324.24충남 청양 장평 은곡
9495건기연[3902-0]1유구-아산23.220210301136.60979126.970315.883.250.550.131409.37충남 공주 유구 추계
9596건기연[3902-0]2유구-아산23.220210301136.60979126.970318.715.230.790.272301.43충남 공주 유구 추계
9697건기연[3902-2]1장평-신풍12.920210301136.43536126.95495.143.480.50.281341.9충남 청양 정산 해남
9798건기연[3902-2]2장평-신풍12.920210301136.43536126.95491.781.330.180.14462.92충남 청양 정산 해남
9899건기연[3905-0]1염치-권관8.320210301136.85256126.9600739.7334.994.821.519831.03충남 아산 영인 아산
99100건기연[3905-0]2염치-권관8.320210301136.85256126.96007133.9496.0513.763.2734365.01충남 아산 영인 아산