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
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시분 has constant value ""Constant
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
기본키 has unique valuesUnique
co has unique valuesUnique
nox has unique valuesUnique
hc has unique valuesUnique
pm has unique valuesUnique
co2 has unique valuesUnique

Reproduction

Analysis started2024-04-16 16:23:27.168915
Analysis finished2024-04-16 16:23:34.760345
Duration7.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

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
2024-04-17T01:23:34.836336image/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
2024-04-17T01:23:34.963650image/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

2024-04-17T01:23:35.082232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:35.169490image/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
2024-04-17T01:23:35.376796image/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%
3204-4 2
 
2.0%
3901-4 2
 
2.0%
2915-4 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%
Other values (40) 80
80.0%
2024-04-17T01:23:35.660198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
15.0%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 76
9.5%
3 70
8.8%
4 46
 
5.8%
6 26
 
3.2%
9 20
 
2.5%
Other values (3) 36
 
4.5%

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%
2 106
21.2%
1 76
15.2%
3 70
14.0%
4 46
 
9.2%
6 26
 
5.2%
9 20
 
4.0%
7 18
 
3.6%
5 14
 
2.8%
8 4
 
0.8%
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%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 76
9.5%
3 70
8.8%
4 46
 
5.8%
6 26
 
3.2%
9 20
 
2.5%
Other values (3) 36
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
15.0%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 76
9.5%
3 70
8.8%
4 46
 
5.8%
6 26
 
3.2%
9 20
 
2.5%
Other values (3) 36
 
4.5%

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

2024-04-17T01:23:35.769789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:35.853613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:23:36.027843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.16
Min length4

Characters and Unicode

Total characters516
Distinct characters81
Distinct categories3 ?
Distinct scripts3 ?
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 row연무-논산
2nd row연무-논산
3rd row두마-금남
4th row두마-금남
5th row논산-반포
ValueCountFrequency (%)
연무-논산 2
 
2.0%
당진-송악 2
 
2.0%
은산-청양ic 2
 
2.0%
화양-한산 2
 
2.0%
구룡-부여 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%
Other values (40) 80
80.0%
2024-04-17T01:23:36.356542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.4%
산 44
 
8.5%
성 20
 
3.9%
서 14
 
2.7%
천 14
 
2.7%
양 14
 
2.7%
안 12
 
2.3%
진 10
 
1.9%
홍 10
 
1.9%
주 8
 
1.6%
Other values (71) 270
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 408
79.1%
Dash Punctuation 100
 
19.4%
Uppercase Letter 8
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 44
 
10.8%
성 20
 
4.9%
서 14
 
3.4%
천 14
 
3.4%
양 14
 
3.4%
안 12
 
2.9%
진 10
 
2.5%
홍 10
 
2.5%
주 8
 
2.0%
청 8
 
2.0%
Other values (68) 254
62.3%
Uppercase Letter
ValueCountFrequency (%)
I 4
50.0%
C 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 408
79.1%
Common 100
 
19.4%
Latin 8
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 44
 
10.8%
성 20
 
4.9%
서 14
 
3.4%
천 14
 
3.4%
양 14
 
3.4%
안 12
 
2.9%
진 10
 
2.5%
홍 10
 
2.5%
주 8
 
2.0%
청 8
 
2.0%
Other values (68) 254
62.3%
Latin
ValueCountFrequency (%)
I 4
50.0%
C 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 408
79.1%
ASCII 108
 
20.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
I 4
 
3.7%
C 4
 
3.7%
Hangul
ValueCountFrequency (%)
산 44
 
10.8%
성 20
 
4.9%
서 14
 
3.4%
천 14
 
3.4%
양 14
 
3.4%
안 12
 
2.9%
진 10
 
2.5%
홍 10
 
2.5%
주 8
 
2.0%
청 8
 
2.0%
Other values (68) 254
62.3%

연장
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.874
Minimum1.8
Maximum23.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:36.473041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.7
Q15.3
median6.65
Q39.7
95-th percentile14.6
Maximum23.2
Range21.4
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation3.926538
Coefficient of variation (CV)0.49867133
Kurtosis3.5577306
Mean7.874
Median Absolute Deviation (MAD)2.2
Skewness1.4992939
Sum787.4
Variance15.417701
MonotonicityNot monotonic
2024-04-17T01:23:36.579926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4.9 6
 
6.0%
6.2 4
 
4.0%
9.3 4
 
4.0%
6.0 4
 
4.0%
6.6 4
 
4.0%
9.7 4
 
4.0%
14.2 2
 
2.0%
6.4 2
 
2.0%
2.2 2
 
2.0%
7.2 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 6
6.0%
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%
12.4 2
2.0%
12.2 2
2.0%
11.5 2
2.0%
11.4 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
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2024-04-17T01:23:36.676570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:36.746190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 100
100.0%

측정시분
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-04-17T01:23:36.822855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:36.932103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.505538
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:37.041329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.25196
median36.51428
Q336.75792
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.50596

Descriptive statistics

Standard deviation0.28183552
Coefficient of variation (CV)0.0077203498
Kurtosis-1.3270696
Mean36.505538
Median Absolute Deviation (MAD)0.254345
Skewness-0.054161328
Sum3650.5538
Variance0.079431262
MonotonicityNot monotonic
2024-04-17T01:23:37.158007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.51243 2
 
2.0%
36.27491 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%
36.79488 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%
36.13314 2
2.0%
36.14511 2
2.0%
36.16232 2
2.0%
36.18861 2
2.0%
36.1897 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%
36.87646 2
2.0%
36.87015 2
2.0%
36.86711 2
2.0%
36.83292 2
2.0%
36.79488 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.94111
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:37.277297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.71493
median126.89799
Q3127.15746
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.44253

Descriptive statistics

Standard deviation0.3012401
Coefficient of variation (CV)0.0023730698
Kurtosis-0.45457419
Mean126.94111
Median Absolute Deviation (MAD)0.23154
Skewness-0.12184805
Sum12694.111
Variance0.090745598
MonotonicityNot monotonic
2024-04-17T01:23:37.429177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
126.96572 2
 
2.0%
126.85936 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%
126.53713 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%
126.53713 2
2.0%
126.60118 2
2.0%
126.61312 2
2.0%
126.64274 2
2.0%
126.64887 2
2.0%
126.66796 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  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4807.9989
Minimum357.43
Maximum14737.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:37.541523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum357.43
5-th percentile814.6605
Q12714.36
median4362.53
Q36896.4825
95-th percentile10426.177
Maximum14737.53
Range14380.1
Interquartile range (IQR)4182.1225

Descriptive statistics

Standard deviation2991.1733
Coefficient of variation (CV)0.62212438
Kurtosis0.63495311
Mean4807.9989
Median Absolute Deviation (MAD)2282.045
Skewness0.807202
Sum480799.89
Variance8947117.9
MonotonicityNot monotonic
2024-04-17T01:23:37.649617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3682.18 1
 
1.0%
6155.87 1
 
1.0%
2990.24 1
 
1.0%
9095.07 1
 
1.0%
7002.95 1
 
1.0%
8736.18 1
 
1.0%
10422.26 1
 
1.0%
6269.19 1
 
1.0%
7294.56 1
 
1.0%
1130.93 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
357.43 1
1.0%
377.77 1
1.0%
696.53 1
1.0%
748.24 1
1.0%
779.71 1
1.0%
816.5 1
1.0%
979.16 1
1.0%
988.33 1
1.0%
1064.97 1
1.0%
1130.93 1
1.0%
ValueCountFrequency (%)
14737.53 1
1.0%
13828.81 1
1.0%
11325.31 1
1.0%
10539.27 1
1.0%
10500.6 1
1.0%
10422.26 1
1.0%
9638.3 1
1.0%
9095.07 1
1.0%
8736.18 1
1.0%
8577.62 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4186.5904
Minimum240.28
Maximum14793.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:37.764883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum240.28
5-th percentile708.7445
Q11992.6275
median3506.895
Q35449.3525
95-th percentile10581.199
Maximum14793.87
Range14553.59
Interquartile range (IQR)3456.725

Descriptive statistics

Standard deviation2988.4288
Coefficient of variation (CV)0.7138097
Kurtosis2.0664493
Mean4186.5904
Median Absolute Deviation (MAD)1672.935
Skewness1.3380085
Sum418659.04
Variance8930707
MonotonicityNot monotonic
2024-04-17T01:23:37.876446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5225.46 1
 
1.0%
5902.66 1
 
1.0%
3662.84 1
 
1.0%
14793.87 1
 
1.0%
11373.42 1
 
1.0%
10237.05 1
 
1.0%
13718.1 1
 
1.0%
5028.83 1
 
1.0%
5606.26 1
 
1.0%
671.74 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
240.28 1
1.0%
257.9 1
1.0%
533.24 1
1.0%
671.74 1
1.0%
690.02 1
1.0%
709.73 1
1.0%
751.59 1
1.0%
780.19 1
1.0%
842.5 1
1.0%
945.16 1
1.0%
ValueCountFrequency (%)
14793.87 1
1.0%
13718.1 1
1.0%
12696.56 1
1.0%
11373.42 1
1.0%
10987.21 1
1.0%
10559.83 1
1.0%
10237.05 1
1.0%
9137.62 1
1.0%
8587.92 1
1.0%
8435.67 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean550.6939
Minimum34.8
Maximum1608.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:37.987130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.8
5-th percentile96.9255
Q1285.925
median473.78
Q3729.11
95-th percentile1255.625
Maximum1608.45
Range1573.65
Interquartile range (IQR)443.185

Descriptive statistics

Standard deviation364.13661
Coefficient of variation (CV)0.66123233
Kurtosis0.55747099
Mean550.6939
Median Absolute Deviation (MAD)235.805
Skewness0.93600999
Sum55069.39
Variance132595.47
MonotonicityNot monotonic
2024-04-17T01:23:38.103051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
569.9 1
 
1.0%
723.11 1
 
1.0%
380.66 1
 
1.0%
1525.92 1
 
1.0%
1177.62 1
 
1.0%
1238.15 1
 
1.0%
1514.6 1
 
1.0%
631.65 1
 
1.0%
726.41 1
 
1.0%
100.46 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
34.8 1
1.0%
35.13 1
1.0%
76.62 1
1.0%
94.48 1
1.0%
95.89 1
1.0%
96.98 1
1.0%
100.46 1
1.0%
104.01 1
1.0%
104.36 1
1.0%
137.74 1
1.0%
ValueCountFrequency (%)
1608.45 1
1.0%
1525.92 1
1.0%
1514.6 1
1.0%
1481.48 1
1.0%
1330.2 1
1.0%
1251.7 1
1.0%
1238.15 1
1.0%
1177.62 1
1.0%
1165.95 1
1.0%
1153.71 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.887
Minimum13.74
Maximum885.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:38.502265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.74
5-th percentile36.8685
Q180.055
median166.345
Q3281.9225
95-th percentile542.176
Maximum885.82
Range872.08
Interquartile range (IQR)201.8675

Descriptive statistics

Standard deviation176.97834
Coefficient of variation (CV)0.80853747
Kurtosis2.6223065
Mean218.887
Median Absolute Deviation (MAD)94.055
Skewness1.5264218
Sum21888.7
Variance31321.333
MonotonicityNot monotonic
2024-04-17T01:23:38.605139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
371.3 1
 
1.0%
342.91 1
 
1.0%
187.1 1
 
1.0%
885.82 1
 
1.0%
677.58 1
 
1.0%
616.58 1
 
1.0%
843.84 1
 
1.0%
260.17 1
 
1.0%
221.09 1
 
1.0%
20.77 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
13.74 1
1.0%
17.15 1
1.0%
20.77 1
1.0%
25.01 1
1.0%
30.76 1
1.0%
37.19 1
1.0%
38.87 1
1.0%
41.74 1
1.0%
45.07 1
1.0%
47.75 1
1.0%
ValueCountFrequency (%)
885.82 1
1.0%
843.84 1
1.0%
677.58 1
1.0%
643.88 1
1.0%
616.58 1
1.0%
538.26 1
1.0%
514.71 1
1.0%
505.34 1
1.0%
484.66 1
1.0%
473.15 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1232750.8
Minimum93573.88
Maximum3921326.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:38.713504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum93573.88
5-th percentile209955.19
Q1699613.32
median1129949
Q31745392.6
95-th percentile2532485
Maximum3921326.3
Range3827752.5
Interquartile range (IQR)1045779.2

Descriptive statistics

Standard deviation770725.36
Coefficient of variation (CV)0.62520776
Kurtosis0.95395706
Mean1232750.8
Median Absolute Deviation (MAD)596613.87
Skewness0.86861802
Sum1.2327508 × 108
Variance5.9401758 × 1011
MonotonicityNot monotonic
2024-04-17T01:23:38.839729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
958452.05 1
 
1.0%
1580350.97 1
 
1.0%
855179.09 1
 
1.0%
2523124.45 1
 
1.0%
1935466.43 1
 
1.0%
2236762.88 1
 
1.0%
2806459.02 1
 
1.0%
1630031.66 1
 
1.0%
1896127.71 1
 
1.0%
298351.88 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
93573.88 1
1.0%
99539.26 1
1.0%
164878.75 1
1.0%
182416.71 1
1.0%
203487.99 1
1.0%
210295.57 1
1.0%
254900.79 1
1.0%
255421.91 1
1.0%
280581.48 1
1.0%
298351.88 1
1.0%
ValueCountFrequency (%)
3921326.33 1
1.0%
3620033.73 1
1.0%
2875784.86 1
1.0%
2806459.02 1
1.0%
2710335.15 1
1.0%
2523124.45 1
1.0%
2436852.66 1
1.0%
2285429.65 1
1.0%
2236762.88 1
1.0%
2114645.4 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:23:39.117913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.94
Min length8

Characters and Unicode

Total characters1094
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 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%
금산 10
 
2.5%
서천 10
 
2.5%
공주 10
 
2.5%
서산 8
 
2.0%
청양 8
 
2.0%
부여 8
 
2.0%
예산 8
 
2.0%
세종 6
 
1.5%
Other values (97) 218
54.8%
2024-04-17T01:23:39.461200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
충 100
 
9.1%
남 100
 
9.1%
산 52
 
4.8%
천 28
 
2.6%
서 26
 
2.4%
성 22
 
2.0%
신 16
 
1.5%
안 16
 
1.5%
부 14
 
1.3%
Other values (99) 422
38.6%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
충 100
 
12.6%
남 100
 
12.6%
산 52
 
6.5%
천 28
 
3.5%
서 26
 
3.3%
성 22
 
2.8%
신 16
 
2.0%
안 16
 
2.0%
부 14
 
1.8%
청 14
 
1.8%
Other values (98) 408
51.3%
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 (%)
충 100
 
12.6%
남 100
 
12.6%
산 52
 
6.5%
천 28
 
3.5%
서 26
 
3.3%
성 22
 
2.8%
신 16
 
2.0%
안 16
 
2.0%
부 14
 
1.8%
청 14
 
1.8%
Other values (98) 408
51.3%
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 (%)
충 100
 
12.6%
남 100
 
12.6%
산 52
 
6.5%
천 28
 
3.5%
서 26
 
3.3%
성 22
 
2.8%
신 16
 
2.0%
안 16
 
2.0%
부 14
 
1.8%
청 14
 
1.8%
Other values (98) 408
51.3%

Interactions

2024-04-17T01:23:33.785386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:27.581606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.527112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.239160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.848473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.599567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.345477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.028456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.894017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:33.864133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:27.642003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.596366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.305973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.923915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.697864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.417452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.114274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.966350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:33.945897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:27.697580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.650768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.365770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.014420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.768383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.492033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.211786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:33.024779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:34.021051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.027905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.718444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.429433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.097975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.838217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.560002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.296842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:33.089132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:34.096740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.106888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.798838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.497550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.187765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.915074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.630890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.389981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:33.428465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:34.173842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.193235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.882825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.566883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.273161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.992973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.705693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.478946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:33.497319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:34.249169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.275890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.978727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.636766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.359170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.069778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.791185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.570805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:33.570173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:34.334274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.350375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.102569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.709857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.440870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.195197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.867711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.687525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:33.639762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:34.401768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:28.420765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.164635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:29.771199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:30.511334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.258883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:31.940269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:32.788664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:33.699382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T01:23:39.594212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.5280.8630.8350.5360.7310.7260.5290.5941.000
지점1.0001.0000.0001.0001.0001.0001.0000.9350.8970.9060.8550.9411.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9350.8970.9060.8550.9411.000
연장0.5281.0000.0001.0001.0000.5470.6490.4320.3660.3800.4150.4381.000
좌표위치위도0.8631.0000.0001.0000.5471.0000.8450.2430.5430.4030.3100.4091.000
좌표위치경도0.8351.0000.0001.0000.6490.8451.0000.4690.6380.6630.4270.5201.000
co0.5360.9350.0000.9350.4320.2430.4691.0000.8430.8530.8990.9840.935
nox0.7310.8970.0000.8970.3660.5430.6380.8431.0000.9730.8790.8510.897
hc0.7260.9060.0000.9060.3800.4030.6630.8530.9731.0000.8450.8610.906
pm0.5290.8550.0000.8550.4150.3100.4270.8990.8790.8451.0000.8880.855
co20.5940.9410.0000.9410.4380.4090.5200.9840.8510.8610.8881.0000.941
주소1.0001.0000.0001.0001.0001.0001.0000.9350.8970.9060.8550.9411.000
2024-04-17T01:23:39.714295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.000-0.0760.223-0.236-0.269-0.291-0.285-0.336-0.2580.000
연장-0.0761.0000.120-0.1610.1490.1010.1060.0950.1500.000
좌표위치위도0.2230.1201.000-0.1470.3970.4100.4110.3410.4110.000
좌표위치경도-0.236-0.161-0.1471.0000.2150.1890.2130.1330.2000.000
co-0.2690.1490.3970.2151.0000.9500.9740.8140.9980.000
nox-0.2910.1010.4100.1890.9501.0000.9920.9300.9510.000
hc-0.2850.1060.4110.2130.9740.9921.0000.9030.9730.000
pm-0.3360.0950.3410.1330.8140.9300.9031.0000.8130.000
co2-0.2580.1500.4110.2000.9980.9510.9730.8131.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T01:23:34.518613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T01:23:34.696628image/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-논산11.520210101036.14511127.105013682.185225.46569.9371.3958452.05충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210101036.14511127.105013270.875025.91548.21371.93823499.23충남 논산 은진 토양
23건기연[0123-2]1두마-금남4.920210101036.37685127.2596110500.68587.921251.7465.252436852.66충남 공주 반포 온천
34건기연[0123-2]2두마-금남4.920210101036.37685127.259618407.027537.031002.97407.142114645.4충남 공주 반포 온천
45건기연[0124-0]1논산-반포10.220210101036.24966127.228548259.97176.72977.96436.992097105.02충남 논산 연산 천호
56건기연[0124-0]2논산-반포10.220210101036.24966127.228547320.054872.42702.25241.151914044.14충남 논산 연산 천호
67건기연[0127-2]1금남-조치원12.220210101036.56218127.285367566.165957.86800.3308.851954059.33충남 세종 연서 봉암
78건기연[0127-2]2금남-조치원12.220210101036.56218127.285367471.696490.46866.99369.721912457.0충남 세종 연서 봉암
89건기연[0127-7]1공주-유성5.820210101036.40916127.258217500.86797.01907.0358.961895097.21충남 공주 반포 성강
910건기연[0127-7]2공주-유성5.820210101036.40916127.258216885.446296.69841.05282.661733235.6충남 공주 반포 성강
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3606-0]1공주-어진동4.420210101036.48872127.201677588.165125.91743.08152.231983216.61충남 세종 장군 은용
9192건기연[3606-0]2공주-어진동4.420210101036.48872127.201676524.883754.41573.9872.061719890.05충남 세종 장군 은용
9293건기연[3706-0]1진천-음성9.320210101036.1228127.497177247.255515.79737.21262.961876714.32충남 금산 군북 내부
9394건기연[3706-0]2진천-음성9.320210101036.1228127.497176753.165232.1702.56240.691743499.35충남 금산 군북 내부
9495건기연[3707-0]1추부-군서1.820210101036.21913127.495441616.671297.15166.4975.77416530.54충남 금산 추부 요광
9596건기연[3707-0]2추부-군서1.820210101036.21913127.495441992.271278.53201.0950.45470527.59충남 금산 추부 요광
9697건기연[3901-4]1은산-청양IC6.220210101036.35819126.91585377.77240.2835.1313.7499539.26충남 청양 장평 은곡
9798건기연[3901-4]2은산-청양IC6.220210101036.35819126.91585357.43257.934.817.1593573.88충남 청양 장평 은곡
9899건기연[3902-0]1유구-아산23.220210101036.60979126.970311707.371536.71211.0189.0428702.1충남 공주 유구 추계
99100건기연[3902-0]2유구-아산23.220210101036.60979126.970311064.971206.06145.2676.95280581.48충남 공주 유구 추계