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
좌표위치위도 is highly overall correlated with nox and 1 other fieldsHigh correlation
co is highly overall correlated with nox and 3 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 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 16:23:13.579304
Analysis finished2024-04-16 16:23:20.455452
Duration6.88 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
2024-04-17T01:23:20.516015image/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
2024-04-17T01:23:20.673011image/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

2024-04-17T01:23:20.776569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:20.849911image/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
2024-04-17T01:23:21.018927image/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%
2024-04-17T01:23:21.317107image/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 74
9.2%
3 72
9.0%
4 44
 
5.5%
9 24
 
3.0%
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%
2 106
21.2%
1 74
14.8%
3 72
14.4%
4 44
 
8.8%
9 24
 
4.8%
6 22
 
4.4%
7 18
 
3.6%
5 16
 
3.2%
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 74
9.2%
3 72
9.0%
4 44
 
5.5%
9 24
 
3.0%
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%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 74
9.2%
3 72
9.0%
4 44
 
5.5%
9 24
 
3.0%
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

2024-04-17T01:23:21.430381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:21.504344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%
Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:23:21.671812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.16
Min length4

Characters and Unicode

Total characters516
Distinct characters85
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%
유구-사곡 2
 
2.0%
장평-신풍 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%
태안-서산 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T01:23:22.001875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.4%
38
 
7.4%
20
 
3.9%
14
 
2.7%
14
 
2.7%
12
 
2.3%
12
 
2.3%
10
 
1.9%
10
 
1.9%
8
 
1.6%
Other values (75) 278
53.9%

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 (%)
38
 
9.3%
20
 
4.9%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.5%
10
 
2.5%
8
 
2.0%
8
 
2.0%
Other values (72) 262
64.2%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
I 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 (%)
38
 
9.3%
20
 
4.9%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.5%
10
 
2.5%
8
 
2.0%
8
 
2.0%
Other values (72) 262
64.2%
Latin
ValueCountFrequency (%)
C 4
50.0%
I 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%
C 4
 
3.7%
I 4
 
3.7%
Hangul
ValueCountFrequency (%)
38
 
9.3%
20
 
4.9%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.5%
10
 
2.5%
8
 
2.0%
8
 
2.0%
Other values (72) 262
64.2%

연장
Real number (ℝ)

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9
Minimum1.8
Maximum23.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:22.410069image/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 deviation3.9587775
Coefficient of variation (CV)0.50111107
Kurtosis3.3454605
Mean7.9
Median Absolute Deviation (MAD)2.2
Skewness1.4704191
Sum790
Variance15.671919
MonotonicityNot monotonic
2024-04-17T01:23:22.507366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
4.9 6
 
6.0%
6.2 4
 
4.0%
9.7 4
 
4.0%
8.3 4
 
4.0%
9.3 4
 
4.0%
6.0 4
 
4.0%
6.6 4
 
4.0%
11.5 2
 
2.0%
14.2 2
 
2.0%
7.2 2
 
2.0%
Other values (32) 64
64.0%
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%
12.9 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
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2024-04-17T01:23:22.604186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:22.678383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 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

2024-04-17T01:23:22.785583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:22.873027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.500273
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:22.959740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.25196
median36.500575
Q336.75792
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.50596

Descriptive statistics

Standard deviation0.28102358
Coefficient of variation (CV)0.0076992185
Kurtosis-1.2962207
Mean36.500273
Median Absolute Deviation (MAD)0.249765
Skewness0.0040770365
Sum3650.0273
Variance0.078974253
MonotonicityNot monotonic
2024-04-17T01:23:23.075312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.87646 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%
36.13314 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%
36.85256 2
2.0%
36.83292 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.94943
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:23.205136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.75145
median126.93537
Q3127.15746
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.40601

Descriptive statistics

Standard deviation0.29560327
Coefficient of variation (CV)0.002328512
Kurtosis-0.30834863
Mean126.94943
Median Absolute Deviation (MAD)0.2172
Skewness-0.162562
Sum12694.943
Variance0.087381296
MonotonicityNot monotonic
2024-04-17T01:23:23.350695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
126.86923 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%
126.60118 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%
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%
Mean5019.9241
Minimum348.86
Maximum14794.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:23.475504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum348.86
5-th percentile625.6535
Q12046.05
median4826.855
Q37626.9075
95-th percentile10072.505
Maximum14794.74
Range14445.88
Interquartile range (IQR)5580.8575

Descriptive statistics

Standard deviation3351.7919
Coefficient of variation (CV)0.66769772
Kurtosis0.20086468
Mean5019.9241
Median Absolute Deviation (MAD)2784.535
Skewness0.68112311
Sum501992.41
Variance11234509
MonotonicityNot monotonic
2024-04-17T01:23:23.607859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3634.3 1
 
1.0%
1232.99 1
 
1.0%
2315.75 1
 
1.0%
1993.27 1
 
1.0%
2034.86 1
 
1.0%
3577.57 1
 
1.0%
3637.43 1
 
1.0%
8779.46 1
 
1.0%
8752.66 1
 
1.0%
6200.24 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
348.86 1
1.0%
383.67 1
1.0%
562.28 1
1.0%
587.04 1
1.0%
596.65 1
1.0%
627.18 1
1.0%
653.91 1
1.0%
677.16 1
1.0%
951.24 1
1.0%
964.4 1
1.0%
ValueCountFrequency (%)
14794.74 1
1.0%
14677.38 1
1.0%
13943.22 1
1.0%
12789.5 1
1.0%
10514.36 1
1.0%
10049.25 1
1.0%
9565.78 1
1.0%
8988.63 1
1.0%
8878.95 1
1.0%
8779.46 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.8915
Minimum274.38
Maximum20326.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:23.739478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum274.38
5-th percentile549.473
Q11941.0525
median4599.715
Q37200.2825
95-th percentile12779.144
Maximum20326.22
Range20051.84
Interquartile range (IQR)5259.23

Descriptive statistics

Standard deviation3833.9855
Coefficient of variation (CV)0.74796461
Kurtosis2.0489271
Mean5125.8915
Median Absolute Deviation (MAD)2663.8
Skewness1.1642514
Sum512589.15
Variance14699444
MonotonicityNot monotonic
2024-04-17T01:23:23.886446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4471.6 1
 
1.0%
983.15 1
 
1.0%
2412.49 1
 
1.0%
1861.25 1
 
1.0%
2004.04 1
 
1.0%
5281.23 1
 
1.0%
4148.35 1
 
1.0%
13226.49 1
 
1.0%
12755.6 1
 
1.0%
5677.73 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
274.38 1
1.0%
286.06 1
1.0%
465.97 1
1.0%
490.55 1
1.0%
506.4 1
1.0%
551.74 1
1.0%
561.08 1
1.0%
609.42 1
1.0%
624.33 1
1.0%
779.34 1
1.0%
ValueCountFrequency (%)
20326.22 1
1.0%
17040.54 1
1.0%
13600.39 1
1.0%
13443.06 1
1.0%
13226.49 1
1.0%
12755.6 1
1.0%
10077.82 1
1.0%
9894.97 1
1.0%
9880.35 1
1.0%
9453.98 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.9962
Minimum39.49
Maximum2331.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:24.022175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39.49
5-th percentile76.9745
Q1261.195
median579.135
Q3978.68
95-th percentile1288.6805
Maximum2331.37
Range2291.88
Interquartile range (IQR)717.485

Descriptive statistics

Standard deviation456.87557
Coefficient of variation (CV)0.70180989
Kurtosis1.0843829
Mean650.9962
Median Absolute Deviation (MAD)334.41
Skewness0.88872974
Sum65099.62
Variance208735.29
MonotonicityNot monotonic
2024-04-17T01:23:24.136986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
516.63 1
 
1.0%
128.84 1
 
1.0%
300.83 1
 
1.0%
255.78 1
 
1.0%
286.12 1
 
1.0%
580.44 1
 
1.0%
518.64 1
 
1.0%
1310.92 1
 
1.0%
1287.51 1
 
1.0%
724.53 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
39.49 1
1.0%
40.09 1
1.0%
67.11 1
1.0%
69.03 1
1.0%
75.92 1
1.0%
77.03 1
1.0%
77.56 1
1.0%
79.23 1
1.0%
92.75 1
1.0%
107.17 1
1.0%
ValueCountFrequency (%)
2331.37 1
1.0%
1986.49 1
1.0%
1719.9 1
1.0%
1675.48 1
1.0%
1310.92 1
1.0%
1287.51 1
1.0%
1262.32 1
1.0%
1258.38 1
1.0%
1234.33 1
1.0%
1229.29 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.5691
Minimum23.93
Maximum1122.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:24.269203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.93
5-th percentile37.882
Q1113.995
median273.65
Q3402.23
95-th percentile737.673
Maximum1122.95
Range1099.02
Interquartile range (IQR)288.235

Descriptive statistics

Standard deviation220.39158
Coefficient of variation (CV)0.73324762
Kurtosis1.5965568
Mean300.5691
Median Absolute Deviation (MAD)157.735
Skewness1.123116
Sum30056.91
Variance48572.447
MonotonicityNot monotonic
2024-04-17T01:23:24.397725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.35 1
 
1.0%
62.99 1
 
1.0%
139.94 1
 
1.0%
116.67 1
 
1.0%
115.16 1
 
1.0%
321.93 1
 
1.0%
253.52 1
 
1.0%
756.16 1
 
1.0%
736.7 1
 
1.0%
332.43 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
23.93 1
1.0%
25.23 1
1.0%
33.51 1
1.0%
34.16 1
1.0%
34.88 1
1.0%
38.04 1
1.0%
38.73 1
1.0%
40.61 1
1.0%
41.44 1
1.0%
45.82 1
1.0%
ValueCountFrequency (%)
1122.95 1
1.0%
948.38 1
1.0%
863.12 1
1.0%
815.29 1
1.0%
756.16 1
1.0%
736.7 1
1.0%
633.44 1
1.0%
615.94 1
1.0%
588.37 1
1.0%
575.16 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1282479.2
Minimum89518.61
Maximum3814202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:24.544475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89518.61
5-th percentile150217.31
Q1514369
median1206400.3
Q31859982.1
95-th percentile2576551.7
Maximum3814202
Range3724683.4
Interquartile range (IQR)1345613.1

Descriptive statistics

Standard deviation865037.49
Coefficient of variation (CV)0.67450411
Kurtosis0.25409493
Mean1282479.2
Median Absolute Deviation (MAD)696101.79
Skewness0.71113521
Sum1.2824792 × 108
Variance7.4828985 × 1011
MonotonicityNot monotonic
2024-04-17T01:23:24.670965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
934010.24 1
 
1.0%
318116.2 1
 
1.0%
584796.15 1
 
1.0%
492248.45 1
 
1.0%
489522.33 1
 
1.0%
970137.87 1
 
1.0%
941271.52 1
 
1.0%
2523899.54 1
 
1.0%
2486452.78 1
 
1.0%
1580606.35 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
89518.61 1
1.0%
100004.76 1
1.0%
141040.27 1
1.0%
146147.07 1
1.0%
147557.59 1
1.0%
150357.29 1
1.0%
164326.94 1
1.0%
173684.84 1
1.0%
247338.92 1
1.0%
249527.5 1
1.0%
ValueCountFrequency (%)
3814202.02 1
1.0%
3729720.1 1
1.0%
3633195.71 1
1.0%
3355242.72 1
1.0%
2650870.45 1
1.0%
2572640.22 1
1.0%
2523899.54 1
1.0%
2486452.78 1
1.0%
2405171.29 1
1.0%
2281740.06 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:23:24.926566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.94
Min length8

Characters and Unicode

Total characters1094
Distinct characters106
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%
공주 10
 
2.5%
금산 10
 
2.5%
서천 10
 
2.5%
청양 10
 
2.5%
아산 8
 
2.0%
예산 8
 
2.0%
부여 8
 
2.0%
세종 6
 
1.5%
Other values (96) 216
54.3%
2024-04-17T01:23:25.251983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
102
 
9.3%
100
 
9.1%
52
 
4.8%
28
 
2.6%
24
 
2.2%
22
 
2.0%
16
 
1.5%
16
 
1.5%
14
 
1.3%
Other values (96) 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 (%)
102
 
12.8%
100
 
12.6%
52
 
6.5%
28
 
3.5%
24
 
3.0%
22
 
2.8%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (95) 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 (%)
102
 
12.8%
100
 
12.6%
52
 
6.5%
28
 
3.5%
24
 
3.0%
22
 
2.8%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (95) 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 (%)
102
 
12.8%
100
 
12.6%
52
 
6.5%
28
 
3.5%
24
 
3.0%
22
 
2.8%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (95) 408
51.3%

Interactions

2024-04-17T01:23:19.487232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.048988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.669685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.279599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.922334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.648536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.512632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.192908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.849576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.558339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.108838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.733115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.347782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.996112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.714272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.584935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.253114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.913898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.624474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.163916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.790341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.417910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.069819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.777309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.648058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.312801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.971202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.699314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.223467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.852836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.478993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.136625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.844149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.719604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.373533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.044990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.780964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.307358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.936640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.548769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.211298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.151368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.792893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.452246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.118282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.864527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.399381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.011724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.616540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.290710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.217219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.873833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.559595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.189127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.944731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.470947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.088156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.689351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.395249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.290833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.953964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.652879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.260307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:20.012765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.532869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.147518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.748862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.502049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.356897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.028727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.717029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.339206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:20.091233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:14.594774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.209569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:15.815371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:16.568780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:17.431089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.107195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:18.775997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:19.414606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T01:23:25.339717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.5950.8500.8490.7020.5260.5570.7440.6111.000
지점1.0001.0000.0001.0001.0001.0001.0000.9730.9280.9310.9620.9821.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9730.9280.9310.9620.9821.000
연장0.5951.0000.0001.0001.0000.5920.6140.4480.3670.2790.3780.4941.000
좌표위치위도0.8501.0000.0001.0000.5921.0000.8440.5840.3950.3540.4940.4911.000
좌표위치경도0.8491.0000.0001.0000.6140.8441.0000.7030.5790.5300.6590.6211.000
co0.7020.9730.0000.9730.4480.5840.7031.0000.8590.8860.9380.9850.973
nox0.5260.9280.0000.9280.3670.3950.5790.8591.0000.9380.9460.8650.928
hc0.5570.9310.0000.9310.2790.3540.5300.8860.9381.0000.9460.9670.931
pm0.7440.9620.0000.9620.3780.4940.6590.9380.9460.9461.0000.8810.962
co20.6110.9820.0000.9820.4940.4910.6210.9850.8650.9670.8811.0000.982
주소1.0001.0000.0001.0001.0001.0001.0000.9730.9280.9310.9620.9821.000
2024-04-17T01:23:25.450148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.000-0.0200.231-0.210-0.271-0.220-0.217-0.252-0.2730.000
연장-0.0201.0000.089-0.1280.019-0.039-0.030-0.0300.0250.000
좌표위치위도0.2310.0891.000-0.1100.4430.5210.5100.4860.4480.000
좌표위치경도-0.210-0.128-0.1101.0000.3640.3430.3610.3660.3560.000
co-0.2710.0190.4430.3641.0000.9540.9720.9170.9980.000
nox-0.220-0.0390.5210.3430.9541.0000.9940.9790.9540.000
hc-0.217-0.0300.5100.3610.9720.9941.0000.9660.9710.000
pm-0.252-0.0300.4860.3660.9170.9790.9661.0000.9180.000
co2-0.2730.0250.4480.3560.9980.9540.9710.9181.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T01:23:20.223926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T01:23:20.396443image/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-논산11.520210201036.14511127.105013634.34471.6516.63330.35934010.24충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210201036.14511127.105013706.725196.36566.88376.25940936.1충남 논산 은진 토양
23건기연[0123-2]1두마-금남4.920210201036.37685127.2596110514.369053.741234.33456.612650870.45충남 공주 반포 온천
34건기연[0123-2]2두마-금남4.920210201036.37685127.259619565.788183.891116.53389.732405171.29충남 공주 반포 온천
45건기연[0124-0]1논산-반포10.220210201036.24966127.228548066.746266.43887.12364.752066089.84충남 논산 연산 천호
56건기연[0124-0]2논산-반포10.220210201036.24966127.228548362.737093.78981.41487.642134585.68충남 논산 연산 천호
67건기연[0127-2]1금남-조치원12.220210201036.56218127.285368878.957519.79997.84389.192281740.06충남 세종 연서 봉암
78건기연[0127-2]2금남-조치원12.220210201036.56218127.285368715.966872.43972.56359.382232172.59충남 세종 연서 봉암
89건기연[0127-7]1공주-유성5.820210201036.40916127.258218372.968412.851077.13484.172118312.85충남 공주 반포 성강
910건기연[0127-7]2공주-유성5.820210201036.40916127.258217928.437897.881047.89412.041986383.34충남 공주 반포 성강
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3707-0]1추부-군서1.820210201036.21913127.495441969.172014.35279.87148.6475471.75충남 금산 추부 요광
9192건기연[3707-0]2추부-군서1.820210201036.21913127.495442085.981891.89262.46123.51527141.03충남 금산 추부 요광
9293건기연[3901-4]1은산-청양IC6.220210201036.35819126.91585348.86286.0639.4925.2389518.61충남 청양 장평 은곡
9394건기연[3901-4]2은산-청양IC6.220210201036.35819126.91585383.67274.3840.0923.93100004.76충남 청양 장평 은곡
9495건기연[3902-0]1유구-아산23.220210201036.60979126.970311204.591028.76141.9657.6302655.26충남 공주 유구 추계
9596건기연[3902-0]2유구-아산23.220210201036.60979126.97031964.4779.34107.1741.44249527.5충남 공주 유구 추계
9697건기연[3902-2]1장평-신풍12.920210201036.43536126.9549627.18490.5577.5634.88146147.07충남 청양 정산 해남
9798건기연[3902-2]2장평-신풍12.920210201036.43536126.9549587.04465.9767.1140.61150357.29충남 청양 정산 해남
9899건기연[3905-0]1염치-권관8.320210201036.85256126.960078274.399880.351262.32568.452044541.44충남 아산 영인 아산
99100건기연[3905-0]2염치-권관8.320210201036.85256126.960077971.588982.831139.24467.462017250.47충남 아산 영인 아산