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 started2023-12-10 12:42:45.414243
Analysis finished2023-12-10 12:42:53.861350
Duration8.45 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
2023-12-10T21:42:53.935365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T21:42:54.076959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T21:42:54.199109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:54.285444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:54.481186image/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[0216-2]
2nd row[0216-2]
3rd row[0218-1]
4th row[0218-1]
5th row[0220-2]
ValueCountFrequency (%)
0216-2 2
 
2.0%
3301-4 2
 
2.0%
7701-4 2
 
2.0%
2010-0 2
 
2.0%
2416-1 2
 
2.0%
2417-2 2
 
2.0%
2419-1 2
 
2.0%
2421-0 2
 
2.0%
2422-0 2
 
2.0%
2423-1 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:42:54.933882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 98
12.2%
2 80
10.0%
3 52
 
6.5%
4 40
 
5.0%
5 26
 
3.2%
7 18
 
2.2%
Other values (3) 36
 
4.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 150
30.0%
1 98
19.6%
2 80
16.0%
3 52
 
10.4%
4 40
 
8.0%
5 26
 
5.2%
7 18
 
3.6%
9 14
 
2.8%
6 12
 
2.4%
8 10
 
2.0%
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 (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 98
12.2%
2 80
10.0%
3 52
 
6.5%
4 40
 
5.0%
5 26
 
3.2%
7 18
 
2.2%
Other values (3) 36
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 98
12.2%
2 80
10.0%
3 52
 
6.5%
4 40
 
5.0%
5 26
 
3.2%
7 18
 
2.2%
Other values (3) 36
 
4.5%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2023-12-10T21:42:55.064416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:55.168340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:55.406981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1
Min length5

Characters and Unicode

Total characters510
Distinct characters84
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 row북천-완사
2nd row북천-완사
3rd row진주-사봉
4th row진주-사봉
5th row일반성-진북
ValueCountFrequency (%)
북천-완사 2
 
2.0%
상리-고성 2
 
2.0%
도산-거제 2
 
2.0%
유어-창녕 2
 
2.0%
언양-울산 2
 
2.0%
봉산-금양 2
 
2.0%
초계-적교 2
 
2.0%
창녕-청도 2
 
2.0%
가산-남기 2
 
2.0%
부북-금곡 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:42:55.798502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.6%
산 34
 
6.7%
남 16
 
3.1%
성 14
 
2.7%
양 14
 
2.7%
창 12
 
2.4%
천 12
 
2.4%
진 10
 
2.0%
부 10
 
2.0%
거 10
 
2.0%
Other values (74) 278
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
79.6%
Dash Punctuation 100
 
19.6%
Uppercase Letter 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 34
 
8.4%
남 16
 
3.9%
성 14
 
3.4%
양 14
 
3.4%
창 12
 
3.0%
천 12
 
3.0%
진 10
 
2.5%
부 10
 
2.5%
거 10
 
2.5%
해 8
 
2.0%
Other values (71) 266
65.5%
Uppercase Letter
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
79.6%
Common 100
 
19.6%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 34
 
8.4%
남 16
 
3.9%
성 14
 
3.4%
양 14
 
3.4%
창 12
 
3.0%
천 12
 
3.0%
진 10
 
2.5%
부 10
 
2.5%
거 10
 
2.5%
해 8
 
2.0%
Other values (71) 266
65.5%
Latin
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
79.6%
ASCII 104
 
20.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
96.2%
I 2
 
1.9%
C 2
 
1.9%
Hangul
ValueCountFrequency (%)
산 34
 
8.4%
남 16
 
3.9%
성 14
 
3.4%
양 14
 
3.4%
창 12
 
3.0%
천 12
 
3.0%
진 10
 
2.5%
부 10
 
2.5%
거 10
 
2.5%
해 8
 
2.0%
Other values (71) 266
65.5%

연장((km))
Real number (ℝ)

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.526
Minimum3
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:55.966771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.5
Q15
median7.5
Q310.7
95-th percentile17.6
Maximum20.1
Range17.1
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.1903598
Coefficient of variation (CV)0.49148015
Kurtosis0.51208521
Mean8.526
Median Absolute Deviation (MAD)2.85
Skewness0.96279362
Sum852.6
Variance17.559115
MonotonicityNot monotonic
2023-12-10T21:42:56.348964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
10.5 4
 
4.0%
10.2 4
 
4.0%
6.8 4
 
4.0%
11.9 4
 
4.0%
4.2 2
 
2.0%
19.3 2
 
2.0%
7.6 2
 
2.0%
6.1 2
 
2.0%
5.2 2
 
2.0%
3.3 2
 
2.0%
Other values (36) 72
72.0%
ValueCountFrequency (%)
3.0 2
2.0%
3.3 2
2.0%
3.5 2
2.0%
3.8 2
2.0%
3.9 2
2.0%
4.1 2
2.0%
4.2 2
2.0%
4.5 2
2.0%
4.6 2
2.0%
4.7 2
2.0%
ValueCountFrequency (%)
20.1 2
2.0%
19.3 2
2.0%
17.6 2
2.0%
17.2 2
2.0%
13.5 2
2.0%
13.3 2
2.0%
13.2 2
2.0%
12.2 2
2.0%
11.9 4
4.0%
11.2 2
2.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2023-12-10T21:42:56.485508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:56.588780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 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

2023-12-10T21:42:56.685874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:56.768439image/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%
Mean35.279952
Minimum34.86496
Maximum35.72809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:56.864982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.86496
5-th percentile34.87506
Q135.10107
median35.30868
Q335.51362
95-th percentile35.62274
Maximum35.72809
Range0.86313
Interquartile range (IQR)0.41255

Descriptive statistics

Standard deviation0.24696272
Coefficient of variation (CV)0.0070000865
Kurtosis-1.1139119
Mean35.279952
Median Absolute Deviation (MAD)0.206275
Skewness-0.13889743
Sum3527.9952
Variance0.060990583
MonotonicityNot monotonic
2023-12-10T21:42:56.977626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.12031 2
 
2.0%
35.51362 2
 
2.0%
35.57341 2
 
2.0%
35.65095 2
 
2.0%
35.55894 2
 
2.0%
35.57918 2
 
2.0%
35.51777 2
 
2.0%
35.50716 2
 
2.0%
35.32823 2
 
2.0%
35.39903 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.86496 2
2.0%
34.86778 2
2.0%
34.87506 2
2.0%
34.89878 2
2.0%
34.90831 2
2.0%
34.92968 2
2.0%
34.93132 2
2.0%
34.95217 2
2.0%
34.97974 2
2.0%
34.98771 2
2.0%
ValueCountFrequency (%)
35.72809 2
2.0%
35.65095 2
2.0%
35.62274 2
2.0%
35.61557 2
2.0%
35.61463 2
2.0%
35.58349 2
2.0%
35.57918 2
2.0%
35.57341 2
2.0%
35.55894 2
2.0%
35.53342 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.40318
Minimum127.78878
Maximum129.33586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:57.107352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.78878
5-th percentile127.80269
Q1128.00958
median128.33924
Q3128.71274
95-th percentile129.24056
Maximum129.33586
Range1.54708
Interquartile range (IQR)0.70316

Descriptive statistics

Standard deviation0.4497458
Coefficient of variation (CV)0.0035026063
Kurtosis-0.77232364
Mean128.40318
Median Absolute Deviation (MAD)0.337485
Skewness0.54350588
Sum12840.318
Variance0.20227128
MonotonicityNot monotonic
2023-12-10T21:42:57.289127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.97011 2
 
2.0%
128.17297 2
 
2.0%
129.2158 2
 
2.0%
128.12366 2
 
2.0%
128.32045 2
 
2.0%
128.51771 2
 
2.0%
128.72421 2
 
2.0%
128.83128 2
 
2.0%
128.71274 2
 
2.0%
129.33586 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
127.78878 2
2.0%
127.78997 2
2.0%
127.80269 2
2.0%
127.83555 2
2.0%
127.86709 2
2.0%
127.89437 2
2.0%
127.90225 2
2.0%
127.93378 2
2.0%
127.95798 2
2.0%
127.97011 2
2.0%
ValueCountFrequency (%)
129.33586 2
2.0%
129.28105 2
2.0%
129.24056 2
2.0%
129.2158 2
2.0%
129.17553 2
2.0%
129.12842 2
2.0%
129.09913 2
2.0%
129.02747 2
2.0%
128.87085 2
2.0%
128.83128 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2781.7503
Minimum153.39
Maximum9930.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:57.472309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum153.39
5-th percentile367.364
Q11006.4075
median2025.185
Q33794.035
95-th percentile7241.848
Maximum9930.27
Range9776.88
Interquartile range (IQR)2787.6275

Descriptive statistics

Standard deviation2332.809
Coefficient of variation (CV)0.83861195
Kurtosis0.80514704
Mean2781.7503
Median Absolute Deviation (MAD)1098.3
Skewness1.2315327
Sum278175.03
Variance5441998
MonotonicityNot monotonic
2023-12-10T21:42:57.624688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1910.85 1
 
1.0%
1512.94 1
 
1.0%
2188.97 1
 
1.0%
2931.73 1
 
1.0%
2416.71 1
 
1.0%
2641.68 1
 
1.0%
2792.94 1
 
1.0%
5862.36 1
 
1.0%
5877.95 1
 
1.0%
2814.24 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
153.39 1
1.0%
211.01 1
1.0%
311.71 1
1.0%
317.29 1
1.0%
356.61 1
1.0%
367.93 1
1.0%
371.58 1
1.0%
377.92 1
1.0%
399.66 1
1.0%
412.23 1
1.0%
ValueCountFrequency (%)
9930.27 1
1.0%
9682.66 1
1.0%
9149.85 1
1.0%
7809.18 1
1.0%
7450.62 1
1.0%
7230.86 1
1.0%
7114.05 1
1.0%
7080.86 1
1.0%
6609.98 1
1.0%
6425.77 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2132.1707
Minimum106.32
Maximum8690.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:57.762038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum106.32
5-th percentile286.696
Q1893.325
median1575.82
Q33054.57
95-th percentile5306.8845
Maximum8690.65
Range8584.33
Interquartile range (IQR)2161.245

Descriptive statistics

Standard deviation1734.2582
Coefficient of variation (CV)0.81337679
Kurtosis1.7843536
Mean2132.1707
Median Absolute Deviation (MAD)819.71
Skewness1.3559004
Sum213217.07
Variance3007651.3
MonotonicityNot monotonic
2023-12-10T21:42:57.898467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1207.36 1
 
1.0%
952.72 1
 
1.0%
2071.22 1
 
1.0%
1940.39 1
 
1.0%
1727.97 1
 
1.0%
2349.9 1
 
1.0%
2397.69 1
 
1.0%
4283.98 1
 
1.0%
4583.16 1
 
1.0%
2002.65 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
106.32 1
1.0%
150.1 1
1.0%
217.5 1
1.0%
247.93 1
1.0%
276.93 1
1.0%
287.21 1
1.0%
293.57 1
1.0%
320.32 1
1.0%
322.71 1
1.0%
369.51 1
1.0%
ValueCountFrequency (%)
8690.65 1
1.0%
7756.09 1
1.0%
5712.68 1
1.0%
5431.24 1
1.0%
5406.72 1
1.0%
5301.63 1
1.0%
5037.04 1
1.0%
5036.92 1
1.0%
5025.91 1
1.0%
4956.12 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.8334
Minimum15.31
Maximum1047.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:58.042987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.31
5-th percentile37.0185
Q1111.48
median213.535
Q3391.2325
95-th percentile693.312
Maximum1047.84
Range1032.53
Interquartile range (IQR)279.7525

Descriptive statistics

Standard deviation231.86006
Coefficient of variation (CV)0.81401991
Kurtosis1.0987821
Mean284.8334
Median Absolute Deviation (MAD)112.825
Skewness1.254074
Sum28483.34
Variance53759.086
MonotonicityNot monotonic
2023-12-10T21:42:58.172985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.78 1
 
1.0%
140.7 1
 
1.0%
249.92 1
 
1.0%
294.72 1
 
1.0%
240.55 1
 
1.0%
309.41 1
 
1.0%
321.64 1
 
1.0%
606.0 1
 
1.0%
631.77 1
 
1.0%
291.33 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
15.31 1
1.0%
21.28 1
1.0%
29.86 1
1.0%
32.04 1
1.0%
36.61 1
1.0%
37.04 1
1.0%
37.55 1
1.0%
41.72 1
1.0%
42.62 1
1.0%
48.28 1
1.0%
ValueCountFrequency (%)
1047.84 1
1.0%
1011.82 1
1.0%
903.77 1
1.0%
744.27 1
1.0%
741.23 1
1.0%
690.79 1
1.0%
684.38 1
1.0%
683.6 1
1.0%
667.59 1
1.0%
636.62 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.6176
Minimum10.72
Maximum452.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:58.322450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.72
5-th percentile20.2105
Q150.4775
median82.055
Q3141.57
95-th percentile265.427
Maximum452.41
Range441.69
Interquartile range (IQR)91.0925

Descriptive statistics

Standard deviation80.636109
Coefficient of variation (CV)0.76347227
Kurtosis3.3276799
Mean105.6176
Median Absolute Deviation (MAD)35.39
Skewness1.6245083
Sum10561.76
Variance6502.1821
MonotonicityNot monotonic
2023-12-10T21:42:58.508979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.8 1
 
1.0%
47.66 1
 
1.0%
116.28 1
 
1.0%
94.15 1
 
1.0%
93.75 1
 
1.0%
140.19 1
 
1.0%
134.85 1
 
1.0%
179.55 1
 
1.0%
225.57 1
 
1.0%
92.8 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
10.72 1
1.0%
13.27 1
1.0%
14.24 1
1.0%
16.08 1
1.0%
18.89 1
1.0%
20.28 1
1.0%
21.17 1
1.0%
21.95 1
1.0%
24.26 1
1.0%
25.0 1
1.0%
ValueCountFrequency (%)
452.41 1
1.0%
353.17 1
1.0%
321.36 1
1.0%
287.65 1
1.0%
268.6 1
1.0%
265.26 1
1.0%
237.47 1
1.0%
234.82 1
1.0%
225.57 1
1.0%
214.4 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean718996.88
Minimum40466.22
Maximum2553189.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:58.694884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40466.22
5-th percentile95584.83
Q1269968.55
median528863.98
Q3919601.21
95-th percentile1879483.2
Maximum2553189.6
Range2512723.4
Interquartile range (IQR)649632.65

Descriptive statistics

Standard deviation594966.01
Coefficient of variation (CV)0.82749457
Kurtosis0.67049253
Mean718996.88
Median Absolute Deviation (MAD)287839.95
Skewness1.1936942
Sum71899688
Variance3.5398455 × 1011
MonotonicityNot monotonic
2023-12-10T21:42:58.847949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500968.77 1
 
1.0%
398027.19 1
 
1.0%
593784.11 1
 
1.0%
703592.45 1
 
1.0%
625475.61 1
 
1.0%
689511.5 1
 
1.0%
730851.84 1
 
1.0%
1507609.87 1
 
1.0%
1515203.4 1
 
1.0%
726353.94 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
40466.22 1
1.0%
54950.41 1
1.0%
81716.58 1
1.0%
82726.16 1
1.0%
89649.81 1
1.0%
95897.2 1
1.0%
96450.42 1
1.0%
98704.71 1
1.0%
100518.98 1
1.0%
107297.57 1
1.0%
ValueCountFrequency (%)
2553189.61 1
1.0%
2463404.22 1
1.0%
2174081.75 1
1.0%
2034722.78 1
1.0%
1940868.94 1
1.0%
1876252.33 1
1.0%
1844629.53 1
1.0%
1739065.85 1
1.0%
1710676.64 1
1.0%
1670743.94 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:59.143301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.82
Min length8

Characters and Unicode

Total characters1082
Distinct characters107
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 row경남 사천 곤명 작팔
2nd row경남 사천 곤명 작팔
3rd row경남 진주 문산 상문
4th row경남 진주 문산 상문
5th row경남 창원 진전 근곡
ValueCountFrequency (%)
경남 100
25.3%
창원 10
 
2.5%
합천 10
 
2.5%
산청 8
 
2.0%
고성 8
 
2.0%
울주 8
 
2.0%
진주 8
 
2.0%
밀양 6
 
1.5%
남해 6
 
1.5%
창녕 4
 
1.0%
Other values (103) 228
57.6%
2023-12-10T21:42:59.564849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
27.4%
남 110
 
10.2%
경 100
 
9.2%
산 32
 
3.0%
천 24
 
2.2%
창 20
 
1.8%
양 18
 
1.7%
주 16
 
1.5%
고 16
 
1.5%
대 14
 
1.3%
Other values (97) 436
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 786
72.6%
Space Separator 296
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 110
 
14.0%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.1%
창 20
 
2.5%
양 18
 
2.3%
주 16
 
2.0%
고 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (96) 422
53.7%
Space Separator
ValueCountFrequency (%)
296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 786
72.6%
Common 296
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 110
 
14.0%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.1%
창 20
 
2.5%
양 18
 
2.3%
주 16
 
2.0%
고 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (96) 422
53.7%
Common
ValueCountFrequency (%)
296
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 786
72.6%
ASCII 296
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296
100.0%
Hangul
ValueCountFrequency (%)
남 110
 
14.0%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.1%
창 20
 
2.5%
양 18
 
2.3%
주 16
 
2.0%
고 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (96) 422
53.7%

Interactions

2023-12-10T21:42:52.715179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:45.985151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.823658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.695845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.411959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:49.313893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.350563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.058207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.870286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.796912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.059994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.929368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.766955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.528317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:49.400688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.423310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.212474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.945007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.889816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.166186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.041993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.845201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.627920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:49.514260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.505650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.296793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.035606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.964860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.245120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.130426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.926799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.724857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:49.869834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.579986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.374341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.112527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:53.058589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.373050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.226924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.037808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.831616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:49.972317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.669445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.479148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.251587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:53.151115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.475771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.334560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.117116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.939313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.048936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.747426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.566734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.367233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:53.257490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.573533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.413448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.185178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:49.033908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.132760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.816492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.649291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.446718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:53.357380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.672989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.506827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.253462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:49.124377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.208837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.887963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.722019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.529847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:53.436028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:46.745329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:47.602053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:48.323836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:49.208161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.278788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:50.973269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:51.795508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:52.630982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:42:59.699810image/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
기본키1.0001.0000.0001.0000.5830.7450.6890.5850.5690.7290.5000.7651.000
지점1.0001.0000.0001.0001.0001.0001.0000.9600.9320.9930.8440.9831.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9600.9320.9930.8440.9831.000
연장((km))0.5831.0000.0001.0001.0000.5680.6910.5390.4660.4180.5200.4351.000
좌표위치위도((°))0.7451.0000.0001.0000.5681.0000.6320.5150.4350.6960.3710.6981.000
좌표위치경도((°))0.6891.0000.0001.0000.6910.6321.0000.6740.6930.6140.5380.5811.000
co((g/km))0.5850.9600.0000.9600.5390.5150.6741.0000.9530.9450.9120.9830.960
nox((g/km))0.5690.9320.0000.9320.4660.4350.6930.9531.0000.9330.9550.9030.932
hc((g/km))0.7290.9930.0000.9930.4180.6960.6140.9450.9331.0000.8150.9910.993
pm((g/km))0.5000.8440.0410.8440.5200.3710.5380.9120.9550.8151.0000.8320.844
co2((g/km))0.7650.9830.0000.9830.4350.6980.5810.9830.9030.9910.8321.0000.983
주소1.0001.0000.0001.0001.0001.0001.0000.9600.9320.9930.8440.9831.000
2023-12-10T21:42:59.872452image/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
기본키1.000-0.0440.2010.172-0.411-0.372-0.383-0.356-0.4100.000
연장((km))-0.0441.0000.2670.065-0.136-0.097-0.132-0.089-0.1350.000
좌표위치위도((°))0.2010.2671.0000.211-0.377-0.308-0.341-0.256-0.3700.000
좌표위치경도((°))0.1720.0650.2111.0000.3490.3410.3450.2610.3490.000
co((g/km))-0.411-0.136-0.3770.3491.0000.9790.9920.9040.9990.000
nox((g/km))-0.372-0.097-0.3080.3410.9791.0000.9930.9490.9810.000
hc((g/km))-0.383-0.132-0.3410.3450.9920.9931.0000.9340.9930.000
pm((g/km))-0.356-0.089-0.2560.2610.9040.9490.9341.0000.9080.028
co2((g/km))-0.410-0.135-0.3700.3490.9990.9810.9930.9081.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0280.0001.000

Missing values

2023-12-10T21:42:53.558455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:42:53.776385image/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[0216-2]1북천-완사4.220210301035.12031127.970111910.851207.36178.7850.8500968.77경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210301035.12031127.970111828.241434.64182.2276.94496512.21경남 사천 곤명 작팔
23건기연[0218-1]1진주-사봉3.520210301035.16901128.18733125.182100.41308.08119.12815356.0경남 진주 문산 상문
34건기연[0218-1]2진주-사봉3.520210301035.16901128.18733067.132051.72294.3397.03809979.65경남 진주 문산 상문
45건기연[0220-2]1일반성-진북4.820210301035.10632128.442186609.984718.7636.62191.661739065.85경남 창원 진전 근곡
56건기연[0220-2]2일반성-진북4.820210301035.10632128.442186285.835301.63683.6321.361637988.22경남 창원 진전 근곡
67건기연[0222-1]1마산-부산9.420210301035.1839128.63956333.525037.04624.58202.11645744.51경남 창원 양곡
78건기연[0222-1]2마산-부산9.420210301035.1839128.63955953.364666.22587.9172.961535992.73경남 창원 양곡
89건기연[0302-4]1상죽-사천10.220210301034.87506128.009582910.791866.94273.9974.59761582.44경남 남해 창선 동대
910건기연[0302-4]2상죽-사천10.220210301034.87506128.009582041.681404.51194.1782.01535589.32경남 남해 창선 동대
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[5903-0]1시천-단성6.920210301035.28806127.83555505.92378.9956.1627.96129817.04경남 산청 삼장 대하
9192건기연[5903-0]2시천-단성6.920210301035.28806127.83555607.79459.7167.8935.9155962.29경남 산청 삼장 대하
9293건기연[5904-4]1합천-거창7.920210301035.61463128.0287211.01150.121.2814.2454950.41경남 합천 봉산 봉계
9394건기연[5904-4]2합천-거창7.920210301035.61463128.0287153.39106.3215.3110.7240466.22경남 합천 봉산 봉계
9495건기연[7701-0]1고성-마산5.620210301035.10107128.447091808.351128.32167.6550.26475129.3경남 창원 진전 이명
9596건기연[7701-0]2고성-마산5.620210301035.10107128.447092021.481312.94192.2859.17533007.51경남 창원 진전 이명
9697건기연[7701-4]1도산-거제8.120210301034.90831128.409331006.53938.3105.5247.16270620.32경남 통영 광도 노산
9798건기연[7701-4]2도산-거제8.120210301034.90831128.409331144.88880.35111.5942.29296196.4경남 통영 광도 노산
9899건기연[7702-0]1통영-고성6.420210301034.89878128.35538367.93287.2137.0421.1795897.2경남 통영 도산 오륜
99100건기연[7702-0]2통영-고성6.420210301034.89878128.35538412.23320.3241.7224.26107297.57경남 통영 도산 오륜