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
측정시간 has constant value ""Constant
co((g/km)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
co((g/km)) has unique valuesUnique
nox((g/km)) has unique valuesUnique
hc((g/km)) has unique valuesUnique
pm((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2024-04-16 09:20:10.747767
Analysis finished2024-04-16 09:20:18.032809
Duration7.29 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
2024-04-16T18:20:18.096735image/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
2024-04-16T18:20:18.229800image/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

2024-04-16T18:20:18.345398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:18.414142image/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
2024-04-16T18:20:18.583647image/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[0526-3]
2nd row[0526-3]
3rd row[0527-2]
4th row[0527-2]
5th row[0529-0]
ValueCountFrequency (%)
0526-3 2
 
2.0%
4215-1 2
 
2.0%
4710-0 2
 
2.0%
3517-1 2
 
2.0%
3811-0 2
 
2.0%
3813-1 2
 
2.0%
3814-0 2
 
2.0%
3818-0 2
 
2.0%
4209-1 2
 
2.0%
4209-2 2
 
2.0%
Other values (40) 80
80.0%
2024-04-16T18:20:18.870287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 62
7.8%
4 52
6.5%
6 32
 
4.0%
5 26
 
3.2%
Other values (3) 56
7.0%

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 (%)
0 106
21.2%
1 102
20.4%
3 64
12.8%
2 62
12.4%
4 52
10.4%
6 32
 
6.4%
5 26
 
5.2%
7 26
 
5.2%
8 18
 
3.6%
9 12
 
2.4%
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 (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 62
7.8%
4 52
6.5%
6 32
 
4.0%
5 26
 
3.2%
Other values (3) 56
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 62
7.8%
4 52
6.5%
6 32
 
4.0%
5 26
 
3.2%
Other values (3) 56
7.0%

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

2024-04-16T18:20:18.976080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:19.053563image/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
2024-04-16T18:20:19.228942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.18
Min length4

Characters and Unicode

Total characters518
Distinct characters89
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 row원주-소초
2nd row원주-소초
3rd row원주-횡성
4th row원주-횡성
5th row공근-동산
ValueCountFrequency (%)
원주-소초 2
 
2.0%
정선-임계 2
 
2.0%
이동-근남 2
 
2.0%
송현-성산 2
 
2.0%
쌍용-남 2
 
2.0%
신동-사북 2
 
2.0%
남-사북 2
 
2.0%
도계-고천 2
 
2.0%
원주-새말 2
 
2.0%
문막-원주 2
 
2.0%
Other values (40) 80
80.0%
2024-04-16T18:20:19.553556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.3%
22
 
4.2%
18
 
3.5%
16
 
3.1%
14
 
2.7%
12
 
2.3%
12
 
2.3%
12
 
2.3%
12
 
2.3%
12
 
2.3%
Other values (79) 288
55.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 414
79.9%
Dash Punctuation 100
 
19.3%
Uppercase Letter 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
5.3%
18
 
4.3%
16
 
3.9%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
Other values (76) 274
66.2%
Uppercase Letter
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 414
79.9%
Common 100
 
19.3%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22
 
5.3%
18
 
4.3%
16
 
3.9%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
Other values (76) 274
66.2%
Latin
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 414
79.9%
ASCII 104
 
20.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
96.2%
I 2
 
1.9%
C 2
 
1.9%
Hangul
ValueCountFrequency (%)
22
 
5.3%
18
 
4.3%
16
 
3.9%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
Other values (76) 274
66.2%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.67
Minimum0.4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:19.669822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.3
Q14.4
median8.8
Q312.7
95-th percentile23
Maximum27
Range26.6
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation6.1808845
Coefficient of variation (CV)0.63918144
Kurtosis0.52372158
Mean9.67
Median Absolute Deviation (MAD)4.2
Skewness0.9201318
Sum967
Variance38.203333
MonotonicityNot monotonic
2024-04-16T18:20:19.789664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
8.0 4
 
4.0%
12.0 4
 
4.0%
3.5 4
 
4.0%
8.8 4
 
4.0%
7.2 4
 
4.0%
2.0 2
 
2.0%
14.0 2
 
2.0%
6.5 2
 
2.0%
14.4 2
 
2.0%
3.8 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
0.4 2
2.0%
2.0 2
2.0%
2.3 2
2.0%
2.4 2
2.0%
2.6 2
2.0%
2.9 2
2.0%
3.3 2
2.0%
3.5 4
4.0%
3.6 2
2.0%
3.8 2
2.0%
ValueCountFrequency (%)
27.0 2
2.0%
24.7 2
2.0%
23.0 2
2.0%
22.7 2
2.0%
18.1 2
2.0%
17.4 2
2.0%
16.1 2
2.0%
15.3 2
2.0%
14.4 2
2.0%
14.0 2
2.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2024-04-16T18:20:19.884823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:19.955606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 100
100.0%

측정시간
Categorical

CONSTANT 

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

2024-04-16T18:20:20.042267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:20:20.115060image/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%
Mean37.694698
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:20.212371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.08588
5-th percentile37.18732
Q137.40743
median37.65705
Q338.04937
95-th percentile38.23094
Maximum38.38086
Range1.29498
Interquartile range (IQR)0.64194

Descriptive statistics

Standard deviation0.3577231
Coefficient of variation (CV)0.0094900111
Kurtosis-1.2945624
Mean37.694698
Median Absolute Deviation (MAD)0.32117
Skewness0.13857335
Sum3769.4698
Variance0.12796582
MonotonicityNot monotonic
2024-04-16T18:20:20.339294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3551 2
 
2.0%
38.23094 2
 
2.0%
37.18732 2
 
2.0%
37.21543 2
 
2.0%
37.25108 2
 
2.0%
37.30412 2
 
2.0%
37.40802 2
 
2.0%
37.32395 2
 
2.0%
37.4163 2
 
2.0%
37.32703 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
37.08588 2
2.0%
37.18474 2
2.0%
37.18732 2
2.0%
37.19159 2
2.0%
37.21543 2
2.0%
37.25108 2
2.0%
37.28643 2
2.0%
37.30412 2
2.0%
37.32395 2
2.0%
37.32703 2
2.0%
ValueCountFrequency (%)
38.38086 2
2.0%
38.25247 2
2.0%
38.23094 2
2.0%
38.19136 2
2.0%
38.18502 2
2.0%
38.17869 2
2.0%
38.14989 2
2.0%
38.11527 2
2.0%
38.10576 2
2.0%
38.08778 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.27445
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:20.452680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.35058
5-th percentile127.47033
Q1127.89528
median128.20314
Q3128.64197
95-th percentile129.07044
Maximum129.20253
Range1.85195
Interquartile range (IQR)0.74669

Descriptive statistics

Standard deviation0.47844126
Coefficient of variation (CV)0.003729825
Kurtosis-0.93775591
Mean128.27445
Median Absolute Deviation (MAD)0.36472
Skewness0.074427266
Sum12827.445
Variance0.22890604
MonotonicityNot monotonic
2024-04-16T18:20:20.570351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.99487 2
 
2.0%
127.35058 2
 
2.0%
128.38975 2
 
2.0%
128.64197 2
 
2.0%
128.7796 2
 
2.0%
129.07044 2
 
2.0%
127.99641 2
 
2.0%
127.83897 2
 
2.0%
128.2034 2
 
2.0%
128.51597 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
127.35058 2
2.0%
127.41894 2
2.0%
127.47033 2
2.0%
127.62463 2
2.0%
127.63815 2
2.0%
127.67987 2
2.0%
127.77663 2
2.0%
127.81252 2
2.0%
127.81502 2
2.0%
127.83787 2
2.0%
ValueCountFrequency (%)
129.20253 2
2.0%
129.09293 2
2.0%
129.07044 2
2.0%
129.02671 2
2.0%
128.98396 2
2.0%
128.84543 2
2.0%
128.84271 2
2.0%
128.83913 2
2.0%
128.81034 2
2.0%
128.79017 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3837.9811
Minimum437.07
Maximum12540.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:20.712034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437.07
5-th percentile647.0345
Q11309.12
median2553.665
Q35561.055
95-th percentile10748.987
Maximum12540.31
Range12103.24
Interquartile range (IQR)4251.935

Descriptive statistics

Standard deviation3161.5722
Coefficient of variation (CV)0.82375919
Kurtosis0.23636841
Mean3837.9811
Median Absolute Deviation (MAD)1649.14
Skewness1.0391558
Sum383798.11
Variance9995538.7
MonotonicityNot monotonic
2024-04-16T18:20:20.847146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5174.19 1
 
1.0%
4211.34 1
 
1.0%
1428.16 1
 
1.0%
2637.84 1
 
1.0%
2681.87 1
 
1.0%
1195.77 1
 
1.0%
1462.83 1
 
1.0%
2056.4 1
 
1.0%
2339.88 1
 
1.0%
8379.74 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
437.07 1
1.0%
451.91 1
1.0%
542.77 1
1.0%
586.18 1
1.0%
586.89 1
1.0%
650.2 1
1.0%
672.95 1
1.0%
692.61 1
1.0%
821.21 1
1.0%
833.49 1
1.0%
ValueCountFrequency (%)
12540.31 1
1.0%
12498.32 1
1.0%
11711.17 1
1.0%
11028.55 1
1.0%
10950.34 1
1.0%
10738.39 1
1.0%
9891.02 1
1.0%
9598.23 1
1.0%
9067.23 1
1.0%
9037.71 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3091.1097
Minimum403.31
Maximum11306.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:20.973921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum403.31
5-th percentile614.3225
Q1968.575
median1916.585
Q34599.0225
95-th percentile8265.6155
Maximum11306.86
Range10903.55
Interquartile range (IQR)3630.4475

Descriptive statistics

Standard deviation2549.7807
Coefficient of variation (CV)0.82487552
Kurtosis0.30292527
Mean3091.1097
Median Absolute Deviation (MAD)1242.905
Skewness1.0476757
Sum309110.97
Variance6501381.8
MonotonicityNot monotonic
2024-04-16T18:20:21.088475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3539.95 1
 
1.0%
2785.4 1
 
1.0%
1798.62 1
 
1.0%
1662.5 1
 
1.0%
1753.07 1
 
1.0%
921.37 1
 
1.0%
1520.46 1
 
1.0%
1384.08 1
 
1.0%
1695.26 1
 
1.0%
8262.36 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
403.31 1
1.0%
406.54 1
1.0%
441.59 1
1.0%
485.78 1
1.0%
495.05 1
1.0%
620.6 1
1.0%
637.61 1
1.0%
646.2 1
1.0%
655.12 1
1.0%
692.24 1
1.0%
ValueCountFrequency (%)
11306.86 1
1.0%
8883.23 1
1.0%
8859.4 1
1.0%
8819.68 1
1.0%
8327.47 1
1.0%
8262.36 1
1.0%
8256.96 1
1.0%
7961.26 1
1.0%
7793.82 1
1.0%
7217.11 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean425.684
Minimum62.13
Maximum1422.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:21.446523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62.13
5-th percentile83.973
Q1137.905
median263.565
Q3639.655
95-th percentile1106.5355
Maximum1422.04
Range1359.91
Interquartile range (IQR)501.75

Descriptive statistics

Standard deviation344.75222
Coefficient of variation (CV)0.80987826
Kurtosis-0.026172563
Mean425.684
Median Absolute Deviation (MAD)171.01
Skewness0.9605465
Sum42568.4
Variance118854.09
MonotonicityNot monotonic
2024-04-16T18:20:21.560304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
512.96 1
 
1.0%
405.58 1
 
1.0%
245.36 1
 
1.0%
248.25 1
 
1.0%
259.42 1
 
1.0%
125.98 1
 
1.0%
178.78 1
 
1.0%
197.63 1
 
1.0%
235.72 1
 
1.0%
1046.56 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
62.13 1
1.0%
62.77 1
1.0%
64.32 1
1.0%
66.95 1
1.0%
72.44 1
1.0%
84.58 1
1.0%
90.5 1
1.0%
91.01 1
1.0%
91.9 1
1.0%
93.21 1
1.0%
ValueCountFrequency (%)
1422.04 1
1.0%
1246.38 1
1.0%
1195.08 1
1.0%
1191.86 1
1.0%
1167.63 1
1.0%
1103.32 1
1.0%
1101.69 1
1.0%
1090.44 1
1.0%
1046.56 1
1.0%
1021.16 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.5614
Minimum12.51
Maximum689.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:21.688447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.51
5-th percentile29.133
Q157.49
median113.975
Q3228.62
95-th percentile399.4075
Maximum689.89
Range677.38
Interquartile range (IQR)171.13

Descriptive statistics

Standard deviation128.75993
Coefficient of variation (CV)0.8224245
Kurtosis2.1983105
Mean156.5614
Median Absolute Deviation (MAD)63.335
Skewness1.4060444
Sum15656.14
Variance16579.12
MonotonicityNot monotonic
2024-04-16T18:20:21.804517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.3 1
 
1.0%
140.17 1
 
1.0%
106.04 1
 
1.0%
71.06 1
 
1.0%
79.03 1
 
1.0%
62.03 1
 
1.0%
81.74 1
 
1.0%
58.62 1
 
1.0%
95.77 1
 
1.0%
474.14 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
12.51 1
1.0%
13.94 1
1.0%
23.37 1
1.0%
24.25 1
1.0%
27.29 1
1.0%
29.23 1
1.0%
31.5 1
1.0%
31.51 1
1.0%
31.77 1
1.0%
37.74 1
1.0%
ValueCountFrequency (%)
689.89 1
1.0%
480.27 1
1.0%
474.14 1
1.0%
435.54 1
1.0%
417.41 1
1.0%
398.46 1
1.0%
379.37 1
1.0%
366.34 1
1.0%
363.13 1
1.0%
359.39 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978415.48
Minimum102672.71
Maximum3277616.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:21.944822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102672.71
5-th percentile168772.47
Q1334021.31
median666428.03
Q31433113.1
95-th percentile2695910
Maximum3277616.1
Range3174943.4
Interquartile range (IQR)1099091.7

Descriptive statistics

Standard deviation812381
Coefficient of variation (CV)0.83030268
Kurtosis0.35936702
Mean978415.48
Median Absolute Deviation (MAD)422093.21
Skewness1.0725657
Sum97841548
Variance6.5996288 × 1011
MonotonicityNot monotonic
2024-04-16T18:20:22.114236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1340976.22 1
 
1.0%
1100985.04 1
 
1.0%
334819.35 1
 
1.0%
691315.63 1
 
1.0%
699709.58 1
 
1.0%
308714.54 1
 
1.0%
384025.51 1
 
1.0%
536244.9 1
 
1.0%
607249.32 1
 
1.0%
2125577.89 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
102672.71 1
1.0%
105095.28 1
1.0%
124074.53 1
1.0%
135137.35 1
1.0%
151743.28 1
1.0%
169668.74 1
1.0%
175382.2 1
1.0%
176209.08 1
1.0%
193758.28 1
1.0%
211793.36 1
1.0%
ValueCountFrequency (%)
3277616.06 1
1.0%
3252100.84 1
1.0%
3017974.03 1
1.0%
2860684.3 1
1.0%
2829416.09 1
1.0%
2688883.32 1
1.0%
2516313.83 1
1.0%
2471048.75 1
1.0%
2314831.3 1
1.0%
2304184.98 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-16T18:20:22.343983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

Total characters1070
Distinct characters104
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원 원주 봉산
2nd row강원 원주 봉산
3rd row강원 원주 소초 장양
4th row강원 원주 소초 장양
5th row강원 홍천 홍천 삼마치
ValueCountFrequency (%)
강원 100
25.4%
인제 14
 
3.6%
횡성 12
 
3.0%
원주 10
 
2.5%
홍천 10
 
2.5%
정선 10
 
2.5%
춘천 8
 
2.0%
8
 
2.0%
영월 8
 
2.0%
양양 8
 
2.0%
Other values (84) 206
52.3%
2024-04-16T18:20:22.686016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
294
27.5%
126
 
11.8%
110
 
10.3%
24
 
2.2%
22
 
2.1%
20
 
1.9%
16
 
1.5%
16
 
1.5%
14
 
1.3%
14
 
1.3%
Other values (94) 414
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 776
72.5%
Space Separator 294
 
27.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
126
 
16.2%
110
 
14.2%
24
 
3.1%
22
 
2.8%
20
 
2.6%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (93) 400
51.5%
Space Separator
ValueCountFrequency (%)
294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 776
72.5%
Common 294
 
27.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
126
 
16.2%
110
 
14.2%
24
 
3.1%
22
 
2.8%
20
 
2.6%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (93) 400
51.5%
Common
ValueCountFrequency (%)
294
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 776
72.5%
ASCII 294
 
27.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
294
100.0%
Hangul
ValueCountFrequency (%)
126
 
16.2%
110
 
14.2%
24
 
3.1%
22
 
2.8%
20
 
2.6%
16
 
2.1%
16
 
2.1%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (93) 400
51.5%

Interactions

2024-04-16T18:20:17.022322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.167081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.795569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.631441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.293465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.042275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.717035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.397757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.084712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:17.093229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.225719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.858128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.696908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.359802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.125039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.780573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.466053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.151644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:17.164737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.286537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.922018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.767404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.426305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.203854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.847882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.536970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.230827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:17.245459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.349607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.991342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.842505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.499592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.276383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.916543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.607330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.325207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:17.340607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.420157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.062408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.920167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.572722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.349922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.986624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.682668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.420550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:17.434943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.496340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.130062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.995949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.655350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.417829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.057386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.754202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.492866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:17.510309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.559479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.194114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.078329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.749250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.483918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.136475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.833432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.563139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:17.588046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.640162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.495525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.149940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.847371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.569330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.228202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.937985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.647046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:17.659337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:11.726298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:12.563746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.224527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:13.945524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:14.643428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:15.312274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.009441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:16.954896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T18:20:22.784837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
기본키1.0001.0000.0001.0000.5750.8450.8420.7340.5100.6380.2910.7461.000
지점1.0001.0000.0001.0001.0001.0001.0000.9670.9230.9440.8260.9701.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9670.9230.9440.8260.9701.000
연장((km))0.5751.0000.0001.0001.0000.6140.6240.4470.4990.4700.0000.4561.000
좌표위치위도((°))0.8451.0000.0001.0000.6141.0000.8050.6260.2820.4520.1230.6381.000
좌표위치경도((°))0.8421.0000.0001.0000.6240.8051.0000.6140.4130.6220.0000.6181.000
co((g/km))0.7340.9670.0000.9670.4470.6260.6141.0000.8760.9640.7860.9960.967
nox((g/km))0.5100.9230.0000.9230.4990.2820.4130.8761.0000.9430.8760.8690.923
hc((g/km))0.6380.9440.0000.9440.4700.4520.6220.9640.9431.0000.8720.9590.944
pm((g/km))0.2910.8260.0000.8260.0000.1230.0000.7860.8760.8721.0000.7580.826
co2((g/km))0.7460.9700.0000.9700.4560.6380.6180.9960.8690.9590.7581.0000.970
주소1.0001.0000.0001.0001.0001.0001.0000.9670.9230.9440.8260.9701.000
2024-04-16T18:20:22.913316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))방향
기본키1.0000.1660.300-0.030-0.055-0.028-0.038-0.056-0.0490.000
연장((km))0.1661.0000.0780.196-0.251-0.273-0.265-0.334-0.2500.000
좌표위치위도((°))0.3000.0781.000-0.369-0.161-0.223-0.231-0.249-0.1500.000
좌표위치경도((°))-0.0300.196-0.3691.0000.0640.0680.1000.0080.0480.000
co((g/km))-0.055-0.251-0.1610.0641.0000.9720.9800.9170.9980.000
nox((g/km))-0.028-0.273-0.2230.0680.9721.0000.9950.9690.9670.000
hc((g/km))-0.038-0.265-0.2310.1000.9800.9951.0000.9580.9730.000
pm((g/km))-0.056-0.334-0.2490.0080.9170.9690.9581.0000.9090.000
co2((g/km))-0.049-0.250-0.1500.0480.9980.9670.9730.9091.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-16T18:20:17.771064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T18:20:17.970015image/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

기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
01건기연[0526-3]1원주-소초4.820210501037.3551127.994875174.193539.95512.96180.31340976.22강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210501037.3551127.994874785.863336.31484.61177.561240895.43강원 원주 봉산
23건기연[0527-2]1원주-횡성0.420210501037.42063127.963196434.935104.21700.93256.681639642.73강원 원주 소초 장양
34건기연[0527-2]2원주-횡성0.420210501037.42063127.963196296.035505.93747.89301.261579678.26강원 원주 소초 장양
45건기연[0529-0]1공근-동산12.020210501037.62539127.895283075.132461.98369.8159.46716729.58강원 홍천 홍천 삼마치
56건기연[0529-0]2공근-동산12.020210501037.62539127.895282274.362003.53268.94111.32570284.18강원 홍천 홍천 삼마치
67건기연[0530-0]1횡성-춘천13.320210501037.73176127.837872297.431813.44256.61116.63542439.34강원 홍천 북방 부사원
78건기연[0530-0]2횡성-춘천13.320210501037.73176127.837872146.581829.64233.27121.85549760.23강원 홍천 북방 부사원
89건기연[0531-2]1동내-천전7.520210501037.86064127.776639598.237021.651021.16324.012471048.75강원 춘천 동내 거두
910건기연[0531-2]2동내-천전7.520210501037.86064127.776639067.236751.04997.16363.132314831.3강원 춘천 동내 거두
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4613-0]1양구-신남4.020210501038.08778128.04511542.77655.1291.944.71124074.53강원 양구 남 청
9192건기연[4613-0]2양구-신남4.020210501038.08778128.04511586.89692.2497.548.84135137.35강원 양구 남 청
9293건기연[4616-0]1진부령-거진11.020210501038.38086128.44452469.491682.83240.9865.3641540.43강원 고성 간성 교동
9394건기연[4616-0]2진부령-거진11.020210501038.38086128.44452228.461526.81215.3764.36580299.36강원 고성 간성 교동
9495건기연[4617-0]1북-외가평12.120210501038.19136128.317856181.04559.79605.96168.381618837.47강원 인제 북 용대
9596건기연[4617-0]2북-외가평12.120210501038.19136128.317855472.74007.58552.95154.881421764.82강원 인제 북 용대
9697건기연[4710-0]1이동-근남9.020210501038.18502127.418941333.01569.46167.1695.36356493.46강원 철원 서 자등
9798건기연[4710-0]2이동-근남9.020210501038.18502127.418941137.26992.68119.7355.55290356.93강원 철원 서 자등
9899건기연[5601-2]1김화-근남2.420210501038.25247127.47033692.61620.684.5858.81175382.2강원 철원 근남 사곡
99100건기연[5601-2]2김화-근남2.420210501038.25247127.47033586.18485.7864.3247.23151743.28강원 철원 근남 사곡