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 co and 4 other fieldsHigh correlation
co is highly overall correlated with 좌표위치위도 and 4 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
기본키 has unique valuesUnique
co has unique valuesUnique
nox has unique valuesUnique
hc has unique valuesUnique
co2 has unique valuesUnique

Reproduction

Analysis started2024-04-16 16:22:19.359807
Analysis finished2024-04-16 16:22:26.599823
Duration7.24 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
2024-04-17T01:22:26.657748image/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
2024-04-17T01:22:26.767527image/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

2024-04-17T01:22:26.868941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:26.945686image/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
2024-04-17T01:22:27.106268image/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[0124-0]
4th row[0124-0]
5th row[0127-2]
ValueCountFrequency (%)
0122-2 2
 
2.0%
3206-3 2
 
2.0%
3905-0 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%
2024-04-17T01:22:27.388497image/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 76
9.5%
3 66
8.2%
4 46
 
5.8%
6 26
 
3.2%
9 24
 
3.0%
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 76
15.2%
3 66
13.2%
4 46
 
9.2%
6 26
 
5.2%
9 24
 
4.8%
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%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 76
9.5%
3 66
8.2%
4 46
 
5.8%
6 26
 
3.2%
9 24
 
3.0%
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 76
9.5%
3 66
8.2%
4 46
 
5.8%
6 26
 
3.2%
9 24
 
3.0%
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

2024-04-17T01:22:27.501612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:27.615309image/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
2024-04-17T01:22:27.795988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.16
Min length4

Characters and Unicode

Total characters516
Distinct characters86
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 row논산-반포
4th row논산-반포
5th row금남-조치원
ValueCountFrequency (%)
연무-논산 2
 
2.0%
유구-사곡 2
 
2.0%
염치-권관 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%
2024-04-17T01:22:28.131431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.4%
40
 
7.8%
18
 
3.5%
14
 
2.7%
14
 
2.7%
12
 
2.3%
12
 
2.3%
10
 
1.9%
10
 
1.9%
8
 
1.6%
Other values (76) 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 (%)
40
 
9.8%
18
 
4.4%
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 (73) 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 (%)
40
 
9.8%
18
 
4.4%
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 (73) 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 (%)
40
 
9.8%
18
 
4.4%
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 (73) 262
64.2%

연장
Real number (ℝ)

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.596
Minimum1.8
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:28.268589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.2
Q15.4
median6.75
Q39.5
95-th percentile14.2
Maximum17.6
Range15.8
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation3.3471809
Coefficient of variation (CV)0.44065047
Kurtosis0.51107781
Mean7.596
Median Absolute Deviation (MAD)2.2
Skewness0.65945966
Sum759.6
Variance11.20362
MonotonicityNot monotonic
2024-04-17T01:22:28.404427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
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%
8.4 2
 
2.0%
2.2 2
 
2.0%
7.2 2
 
2.0%
Other values (34) 68
68.0%
ValueCountFrequency (%)
1.8 2
2.0%
2.0 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 2
2.0%
ValueCountFrequency (%)
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%
10.0 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2024-04-17T01:22:28.525529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:28.836109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 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

2024-04-17T01:22:28.907583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:28.977301image/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%
Mean36.497515
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:29.064882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.25196
median36.500575
Q336.75627
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.50431

Descriptive statistics

Standard deviation0.2795019
Coefficient of variation (CV)0.0076581077
Kurtosis-1.3225964
Mean36.497515
Median Absolute Deviation (MAD)0.249765
Skewness-0.010449822
Sum3649.7515
Variance0.078121312
MonotonicityNot monotonic
2024-04-17T01:22:29.178321image/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%
36.89461 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%
36.78489 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93791
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:29.300993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.71493
median126.89799
Q3127.15435
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.43942

Descriptive statistics

Standard deviation0.29521512
Coefficient of variation (CV)0.0023256655
Kurtosis-0.32232425
Mean126.93791
Median Absolute Deviation (MAD)0.23154
Skewness-0.083692215
Sum12693.791
Variance0.087151965
MonotonicityNot monotonic
2024-04-17T01:22:29.437745image/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%
126.66451 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%
127.25821 2
2.0%
127.24084 2
2.0%
127.22854 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6662.7035
Minimum617.17
Maximum16642.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:29.554015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum617.17
5-th percentile900.8925
Q12703.05
median6191.47
Q310026.467
95-th percentile13300.246
Maximum16642.28
Range16025.11
Interquartile range (IQR)7323.4175

Descriptive statistics

Standard deviation4175.8382
Coefficient of variation (CV)0.62674831
Kurtosis-0.86005006
Mean6662.7035
Median Absolute Deviation (MAD)3570.265
Skewness0.3141255
Sum666270.35
Variance17437625
MonotonicityNot monotonic
2024-04-17T01:22:29.673920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5801.78 1
 
1.0%
2005.99 1
 
1.0%
3106.26 1
 
1.0%
2838.4 1
 
1.0%
2721.02 1
 
1.0%
5861.41 1
 
1.0%
5731.92 1
 
1.0%
10806.7 1
 
1.0%
10544.71 1
 
1.0%
10022.66 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
617.17 1
1.0%
643.07 1
1.0%
792.1 1
1.0%
867.61 1
1.0%
890.49 1
1.0%
901.44 1
1.0%
913.93 1
1.0%
1038.79 1
1.0%
1265.26 1
1.0%
1336.2 1
1.0%
ValueCountFrequency (%)
16642.28 1
1.0%
16585.75 1
1.0%
15100.76 1
1.0%
14867.29 1
1.0%
14521.5 1
1.0%
13235.97 1
1.0%
12383.15 1
1.0%
12367.66 1
1.0%
11989.39 1
1.0%
11984.37 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7763.0549
Minimum595.44
Maximum30073.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:29.801503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum595.44
5-th percentile953.999
Q13039.935
median7082.03
Q310619.353
95-th percentile17854.944
Maximum30073.09
Range29477.65
Interquartile range (IQR)7579.4175

Descriptive statistics

Standard deviation5742.9272
Coefficient of variation (CV)0.73977671
Kurtosis1.4278263
Mean7763.0549
Median Absolute Deviation (MAD)3935.99
Skewness1.1044336
Sum776305.49
Variance32981213
MonotonicityNot monotonic
2024-04-17T01:22:29.956419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7307.56 1
 
1.0%
1684.82 1
 
1.0%
3873.39 1
 
1.0%
2747.01 1
 
1.0%
2770.14 1
 
1.0%
10439.62 1
 
1.0%
8722.54 1
 
1.0%
17427.09 1
 
1.0%
17120.39 1
 
1.0%
10597.87 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
595.44 1
1.0%
683.44 1
1.0%
758.54 1
1.0%
833.06 1
1.0%
910.28 1
1.0%
956.3 1
1.0%
984.75 1
1.0%
1149.29 1
1.0%
1354.74 1
1.0%
1438.79 1
1.0%
ValueCountFrequency (%)
30073.09 1
1.0%
22477.6 1
1.0%
20181.31 1
1.0%
19648.25 1
1.0%
19040.81 1
1.0%
17792.53 1
1.0%
17427.09 1
1.0%
17305.81 1
1.0%
17261.37 1
1.0%
17120.39 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean968.48
Minimum81.92
Maximum3287.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:30.068584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81.92
5-th percentile128.9095
Q1403.0225
median893.895
Q31362.615
95-th percentile2131.794
Maximum3287.52
Range3205.6
Interquartile range (IQR)959.5925

Descriptive statistics

Standard deviation659.42108
Coefficient of variation (CV)0.68088249
Kurtosis0.47814982
Mean968.48
Median Absolute Deviation (MAD)476.32
Skewness0.80096084
Sum96848
Variance434836.16
MonotonicityNot monotonic
2024-04-17T01:22:30.207603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
907.36 1
 
1.0%
231.96 1
 
1.0%
466.59 1
 
1.0%
373.18 1
 
1.0%
381.1 1
 
1.0%
1136.69 1
 
1.0%
993.14 1
 
1.0%
1752.58 1
 
1.0%
1746.18 1
 
1.0%
1373.31 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
81.92 1
1.0%
90.29 1
1.0%
105.06 1
1.0%
114.55 1
1.0%
127.38 1
1.0%
128.99 1
1.0%
145.29 1
1.0%
161.67 1
1.0%
166.09 1
1.0%
183.3 1
1.0%
ValueCountFrequency (%)
3287.52 1
1.0%
2576.23 1
1.0%
2324.59 1
1.0%
2185.78 1
1.0%
2174.05 1
1.0%
2129.57 1
1.0%
2109.44 1
1.0%
2107.26 1
1.0%
2061.77 1
1.0%
2053.42 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean504.2994
Minimum54.41
Maximum2115.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:30.345144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54.41
5-th percentile77.016
Q1213.4025
median455.975
Q3684.7425
95-th percentile1123.45
Maximum2115.11
Range2060.7
Interquartile range (IQR)471.34

Descriptive statistics

Standard deviation370.85713
Coefficient of variation (CV)0.73539078
Kurtosis2.7972861
Mean504.2994
Median Absolute Deviation (MAD)242.535
Skewness1.3501844
Sum50429.94
Variance137535.01
MonotonicityNot monotonic
2024-04-17T01:22:30.452400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
217.78 2
 
2.0%
572.38 1
 
1.0%
405.28 1
 
1.0%
176.27 1
 
1.0%
169.85 1
 
1.0%
705.62 1
 
1.0%
579.58 1
 
1.0%
1075.56 1
 
1.0%
1079.6 1
 
1.0%
706.04 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
54.41 1
1.0%
59.25 1
1.0%
61.84 1
1.0%
64.76 1
1.0%
69.15 1
1.0%
77.43 1
1.0%
78.83 1
1.0%
82.56 1
1.0%
107.37 1
1.0%
111.23 1
1.0%
ValueCountFrequency (%)
2115.11 1
1.0%
1539.04 1
1.0%
1313.22 1
1.0%
1274.38 1
1.0%
1157.46 1
1.0%
1121.66 1
1.0%
1106.54 1
1.0%
1079.6 1
1.0%
1075.56 1
1.0%
1066.36 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1689240.3
Minimum153721.3
Maximum4261798.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:30.587908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum153721.3
5-th percentile214785.21
Q1680659.73
median1536989.5
Q32530823.9
95-th percentile3373292.7
Maximum4261798.5
Range4108077.2
Interquartile range (IQR)1850164.1

Descriptive statistics

Standard deviation1072366.1
Coefficient of variation (CV)0.63482152
Kurtosis-0.90248753
Mean1689240.3
Median Absolute Deviation (MAD)916202.66
Skewness0.31444488
Sum1.6892403 × 108
Variance1.1499691 × 1012
MonotonicityNot monotonic
2024-04-17T01:22:30.723166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1428953.18 1
 
1.0%
508842.14 1
 
1.0%
789758.85 1
 
1.0%
699607.27 1
 
1.0%
661957.9 1
 
1.0%
1564312.77 1
 
1.0%
1505834.4 1
 
1.0%
3051383.67 1
 
1.0%
2927791.18 1
 
1.0%
2501181.94 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
153721.3 1
1.0%
164374.6 1
1.0%
196598.55 1
1.0%
200540.21 1
1.0%
213004.64 1
1.0%
214878.92 1
1.0%
223635.76 1
1.0%
306895.86 1
1.0%
321135.67 1
1.0%
337306.89 1
1.0%
ValueCountFrequency (%)
4261798.52 1
1.0%
4152759.44 1
1.0%
3900122.48 1
1.0%
3765482.57 1
1.0%
3636719.72 1
1.0%
3359428.12 1
1.0%
3095978.64 1
1.0%
3070339.73 1
1.0%
3051383.67 1
1.0%
3046098.6 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:22:30.948429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.96
Min length8

Characters and Unicode

Total characters1096
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 논산 연산 천호
4th row충남 논산 연산 천호
5th row충남 세종 연서 봉암
ValueCountFrequency (%)
충남 100
25.1%
금산 10
 
2.5%
서천 10
 
2.5%
천안 10
 
2.5%
아산 10
 
2.5%
청양 10
 
2.5%
세종 8
 
2.0%
예산 8
 
2.0%
부여 8
 
2.0%
논산 6
 
1.5%
Other values (98) 218
54.8%
2024-04-17T01:22:31.294371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
102
 
9.3%
100
 
9.1%
58
 
5.3%
24
 
2.2%
24
 
2.2%
20
 
1.8%
16
 
1.5%
16
 
1.5%
14
 
1.3%
Other values (99) 424
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 798
72.8%
Space Separator 298
 
27.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
102
 
12.8%
100
 
12.5%
58
 
7.3%
24
 
3.0%
24
 
3.0%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (98) 410
51.4%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 798
72.8%
Common 298
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
102
 
12.8%
100
 
12.5%
58
 
7.3%
24
 
3.0%
24
 
3.0%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (98) 410
51.4%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 798
72.8%
ASCII 298
 
27.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
102
 
12.8%
100
 
12.5%
58
 
7.3%
24
 
3.0%
24
 
3.0%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (98) 410
51.4%

Interactions

2024-04-17T01:22:25.652826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:19.872464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.515960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.254055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.038618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.715372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.663029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.361021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.985382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.731088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:19.942755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.605905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.360607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.113123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.784403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.728780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.429309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.050506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.801891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.014896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.689942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.448935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.205645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.875146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.806915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.502697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.130293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.867852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.083821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.766491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.523073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.278947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.964454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.894882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.568826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.203068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.939290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.156490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.848982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.606360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.349745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.061326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.990721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.639716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.275260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:26.053188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.230096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.945526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.687677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.428384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.371216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.084996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.714661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.346317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:26.141304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.304030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.020362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.784483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.510895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.446802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.158197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.784828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.410325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:26.212262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.376588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.099949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.889085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.585169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.521549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.228152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.855997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.500950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:26.272493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:20.443820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.172193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:21.964292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:22.648073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:23.595852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.295502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:24.919306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:25.570644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T01:22:31.381763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.7430.8250.8700.7020.5380.4980.5220.7041.000
지점1.0001.0000.0001.0001.0001.0001.0000.9860.9320.9450.9080.9811.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9860.9320.9450.9080.9811.000
연장0.7431.0000.0001.0001.0000.6950.8040.6600.3380.4410.5060.6491.000
좌표위치위도0.8251.0000.0001.0000.6951.0000.8130.7010.3360.4880.3170.6721.000
좌표위치경도0.8701.0000.0001.0000.8040.8131.0000.7430.4270.4840.3720.7151.000
co0.7020.9860.0000.9860.6600.7010.7431.0000.7920.8350.7770.9980.986
nox0.5380.9320.0000.9320.3380.3360.4270.7921.0000.9800.9850.8060.932
hc0.4980.9450.0000.9450.4410.4880.4840.8350.9801.0000.9730.8330.945
pm0.5220.9080.0000.9080.5060.3170.3720.7770.9850.9731.0000.7790.908
co20.7040.9810.0000.9810.6490.6720.7150.9980.8060.8330.7791.0000.981
주소1.0001.0000.0001.0001.0001.0001.0000.9860.9320.9450.9080.9811.000
2024-04-17T01:22:31.515894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.000-0.2080.219-0.226-0.188-0.132-0.145-0.166-0.1860.000
연장-0.2081.0000.116-0.0560.1930.1110.1170.1340.1980.000
좌표위치위도0.2190.1161.000-0.0990.5410.5720.5680.5370.5530.000
좌표위치경도-0.226-0.056-0.0991.0000.3890.3440.3720.3630.3700.000
co-0.1880.1930.5410.3891.0000.9700.9780.9470.9980.000
nox-0.1320.1110.5720.3440.9701.0000.9950.9840.9710.000
hc-0.1450.1170.5680.3720.9780.9951.0000.9780.9760.000
pm-0.1660.1340.5370.3630.9470.9840.9781.0000.9490.000
co2-0.1860.1980.5530.3700.9980.9710.9760.9491.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T01:22:26.378840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T01:22:26.541047image/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-논산11.520210601036.14511127.105015801.787307.56907.36572.381428953.18충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210601036.14511127.105015418.547006.31854.4537.71347758.42충남 논산 은진 토양
23건기연[0124-0]1논산-반포10.220210601036.24966127.228549732.517740.941107.7461.322472914.65충남 논산 연산 천호
34건기연[0124-0]2논산-반포10.220210601036.24966127.2285410268.529031.481245.44699.362615310.73충남 논산 연산 천호
45건기연[0127-2]1금남-조치원12.220210601036.56218127.2853611984.3710607.871381.36641.963095978.64충남 세종 연서 봉암
56건기연[0127-2]2금남-조치원12.220210601036.56218127.2853611764.3310657.021362.28594.863031979.22충남 세종 연서 봉암
67건기연[0127-7]1공주-유성5.820210601036.40916127.258219678.8110376.891363.62616.312395355.58충남 공주 반포 성강
78건기연[0127-7]2공주-유성5.820210601036.40916127.258219539.6310026.221321.21582.02387569.07충남 공주 반포 성강
89건기연[0127-8]1전동-쌍전6.220210601036.62718127.2896310615.5613301.641579.81956.092735249.31충남 세종 조치원 신안
910건기연[0127-8]2전동-쌍전6.220210601036.62718127.2896310271.513424.831645.29955.022583912.12충남 세종 조치원 신안
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3707-0]1추부-군서1.820210601036.21913127.495442472.392799.26382.7204.58592595.16충남 금산 추부 요광
9192건기연[3707-0]2추부-군서1.820210601036.21913127.495442561.63040.9426.46214.29602101.07충남 금산 추부 요광
9293건기연[3901-4]1은산-청양IC6.220210601036.35819126.91585643.07683.4490.2964.76164374.6충남 청양 장평 은곡
9394건기연[3901-4]2은산-청양IC6.220210601036.35819126.91585617.17595.4481.9259.25153721.3충남 청양 장평 은곡
9495건기연[3902-2]1장평-신풍12.920210601036.43536126.9549901.441149.29166.0982.56200540.21충남 청양 정산 해남
9596건기연[3902-2]2장평-신풍12.920210601036.43536126.9549792.1758.54105.0669.15196598.55충남 청양 정산 해남
9697건기연[3905-0]1염치-권관8.320210601036.85256126.9600712383.1517261.372109.441042.163070339.73충남 아산 영인 아산
9798건기연[3905-0]2염치-권관8.320210601036.85256126.9600712367.6619040.812324.591157.463024609.44충남 아산 영인 아산
9899건기연[4001-2]1개화-만수2.020210601036.29677126.664512069.82613.54359.31166.48467171.28충남 보령 미산 도화담
99100건기연[4001-2]2개화-만수2.020210601036.29677126.664512292.093074.52421.72190.44504588.35충남 보령 미산 도화담