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 5 (5.0%) zerosZeros
nox((g/km)) has 5 (5.0%) zerosZeros
hc((g/km)) has 5 (5.0%) zerosZeros
pm((g/km)) has 10 (10.0%) zerosZeros
co2((g/km)) has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:15:40.222391
Analysis finished2023-12-10 12:15:47.617400
Duration7.4 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
2023-12-10T21:15:47.677314image/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
2023-12-10T21:15:47.788060image/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

2023-12-10T21:15:47.887004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:48.154802image/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
2023-12-10T21:15:48.309771image/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%
2023-12-10T21:15:48.600953image/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

2023-12-10T21:15:48.712244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:48.784706image/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
2023-12-10T21:15:48.965653image/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%
2023-12-10T21:15:49.288796image/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
2023-12-10T21:15:49.400293image/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
2023-12-10T21:15:49.508155image/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

2023-12-10T21:15:49.614657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:49.683528image/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
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T21:15:49.754413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:49.825413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
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
2023-12-10T21:15:49.906174image/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
2023-12-10T21:15:50.012064image/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
2023-12-10T21:15:50.120075image/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
2023-12-10T21:15:50.235486image/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  ZEROS 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.1607
Minimum0
Maximum134.5
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:50.364594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.494
Q14.815
median12.33
Q330.88
95-th percentile97.4875
Maximum134.5
Range134.5
Interquartile range (IQR)26.065

Descriptive statistics

Standard deviation30.572987
Coefficient of variation (CV)1.2151088
Kurtosis2.0342464
Mean25.1607
Median Absolute Deviation (MAD)8.99
Skewness1.6710405
Sum2516.07
Variance934.70753
MonotonicityNot monotonic
2023-12-10T21:15:50.503859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.52 3
 
3.0%
1.78 3
 
3.0%
9.81 3
 
3.0%
12.69 2
 
2.0%
6.53 1
 
1.0%
1.73 1
 
1.0%
48.65 1
 
1.0%
58.06 1
 
1.0%
11.11 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.52 3
3.0%
0.65 1
 
1.0%
1.05 1
 
1.0%
1.73 1
 
1.0%
1.78 3
3.0%
2.26 1
 
1.0%
2.31 1
 
1.0%
2.68 1
 
1.0%
3.31 1
 
1.0%
ValueCountFrequency (%)
134.5 1
1.0%
108.91 1
1.0%
107.36 1
1.0%
107.15 1
1.0%
98.39 1
1.0%
97.44 1
1.0%
89.78 1
1.0%
85.88 1
1.0%
83.99 1
1.0%
79.78 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.1974
Minimum0
Maximum96.08
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:50.627413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.266
Q13.1225
median7.28
Q326.04
95-th percentile64.485
Maximum96.08
Range96.08
Interquartile range (IQR)22.9175

Descriptive statistics

Standard deviation21.477608
Coefficient of variation (CV)1.2488869
Kurtosis1.988992
Mean17.1974
Median Absolute Deviation (MAD)5.615
Skewness1.6435943
Sum1719.74
Variance461.28765
MonotonicityNot monotonic
2023-12-10T21:15:50.742618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.28 3
 
3.0%
1.33 3
 
3.0%
2.06 2
 
2.0%
7.85 2
 
2.0%
5.53 2
 
2.0%
3.57 1
 
1.0%
1.73 1
 
1.0%
36.81 1
 
1.0%
40.28 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.28 3
3.0%
0.32 1
 
1.0%
0.55 1
 
1.0%
1.23 1
 
1.0%
1.33 3
3.0%
1.51 1
 
1.0%
1.6 1
 
1.0%
1.73 1
 
1.0%
2.06 2
 
2.0%
ValueCountFrequency (%)
96.08 1
1.0%
78.49 1
1.0%
74.4 1
1.0%
69.6 1
1.0%
67.43 1
1.0%
64.33 1
1.0%
59.75 1
1.0%
58.16 1
1.0%
55.46 1
1.0%
55.32 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4473
Minimum0
Maximum13.45
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:50.852232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.038
Q10.455
median1.145
Q33.2125
95-th percentile9.152
Maximum13.45
Range13.45
Interquartile range (IQR)2.7575

Descriptive statistics

Standard deviation2.9893977
Coefficient of variation (CV)1.2215085
Kurtosis2.060509
Mean2.4473
Median Absolute Deviation (MAD)0.83
Skewness1.6601856
Sum244.73
Variance8.9364987
MonotonicityNot monotonic
2023-12-10T21:15:50.982273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.18 4
 
4.0%
0.04 3
 
3.0%
1.57 2
 
2.0%
0.44 2
 
2.0%
0.62 2
 
2.0%
0.93 2
 
2.0%
1.17 2
 
2.0%
0.06 1
 
1.0%
6.17 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.04 3
3.0%
0.06 1
 
1.0%
0.09 1
 
1.0%
0.18 4
4.0%
0.22 1
 
1.0%
0.23 1
 
1.0%
0.27 1
 
1.0%
0.31 1
 
1.0%
0.32 1
 
1.0%
ValueCountFrequency (%)
13.45 1
1.0%
10.97 1
1.0%
10.18 1
1.0%
10.02 1
1.0%
9.38 1
1.0%
9.14 1
1.0%
8.95 1
1.0%
8.53 1
1.0%
7.89 1
1.0%
7.5 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7618
Minimum0
Maximum4.03
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:51.127950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.14
median0.4
Q31.0875
95-th percentile2.5615
Maximum4.03
Range4.03
Interquartile range (IQR)0.9475

Descriptive statistics

Standard deviation0.90837226
Coefficient of variation (CV)1.1924025
Kurtosis1.8466121
Mean0.7618
Median Absolute Deviation (MAD)0.27
Skewness1.605973
Sum76.18
Variance0.82514016
MonotonicityNot monotonic
2023-12-10T21:15:51.249418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.13 12
 
12.0%
0.14 10
 
10.0%
0.0 10
 
10.0%
0.27 9
 
9.0%
0.4 7
 
7.0%
0.28 7
 
7.0%
0.42 4
 
4.0%
2.41 2
 
2.0%
0.54 2
 
2.0%
0.41 2
 
2.0%
Other values (34) 35
35.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.13 12
12.0%
0.14 10
10.0%
0.27 9
9.0%
0.28 7
7.0%
0.39 1
 
1.0%
0.4 7
7.0%
0.41 2
 
2.0%
0.42 4
 
4.0%
0.54 2
 
2.0%
ValueCountFrequency (%)
4.03 1
1.0%
3.54 1
1.0%
3.04 1
1.0%
2.95 1
1.0%
2.78 1
1.0%
2.55 1
1.0%
2.5 1
1.0%
2.42 1
1.0%
2.41 2
2.0%
2.36 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6287.6303
Minimum0
Maximum31648.87
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:51.359073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile131.746
Q11267.8525
median3157.88
Q37817.24
95-th percentile23468.345
Maximum31648.87
Range31648.87
Interquartile range (IQR)6549.3875

Descriptive statistics

Standard deviation7604.5827
Coefficient of variation (CV)1.2094513
Kurtosis1.7043034
Mean6287.6303
Median Absolute Deviation (MAD)2316.5
Skewness1.622504
Sum628763.03
Variance57829677
MonotonicityNot monotonic
2023-12-10T21:15:51.468970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
138.68 3
 
3.0%
462.92 3
 
3.0%
3330.35 2
 
2.0%
2357.7 2
 
2.0%
1563.06 1
 
1.0%
457.29 1
 
1.0%
12425.34 1
 
1.0%
13594.61 1
 
1.0%
2665.07 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
0.0 5
5.0%
138.68 3
3.0%
153.68 1
 
1.0%
277.37 1
 
1.0%
457.29 1
 
1.0%
462.92 3
3.0%
595.97 1
 
1.0%
601.6 1
 
1.0%
646.6 1
 
1.0%
800.28 1
 
1.0%
ValueCountFrequency (%)
31648.87 1
1.0%
28009.39 1
1.0%
25769.71 1
1.0%
25555.24 1
1.0%
25325.89 1
1.0%
23370.58 1
1.0%
23324.7 1
1.0%
21939.91 1
1.0%
21889.59 1
1.0%
20881.18 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:15:51.691403image/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%
2023-12-10T21:15:52.019707image/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

2023-12-10T21:15:46.701991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:40.693808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.476615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.202472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.967235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.718194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.651006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.306811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.004444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.766933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:40.783579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.550683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.281256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.044080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.781246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.715441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.380970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.094591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.837440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:40.862078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.626692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.356745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.143587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.063377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.784574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.453258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.179825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.898772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:40.933488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.695421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.435158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.236567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.134707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.857018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.525182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.243612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.975290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.036505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.778188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.530903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.328775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.247798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.938158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.619867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.322338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:47.048574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.115026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.854149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.630174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.405820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.349928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.011731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.702542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.398291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:47.116720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.195866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.928620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.725542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.482097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.428671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.084287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.769281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.472888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:47.185757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.300580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.042077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.823275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.566372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.505369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.157164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.840477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.552664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:47.264135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:41.403076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.128017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:42.899887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:43.644384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:44.582852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.238819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:45.914496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:46.629515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:15:52.106929image/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
기본키1.0001.0000.0001.0000.5830.7450.6890.5050.5580.5310.4710.5101.000
지점1.0001.0000.0001.0001.0001.0001.0000.8520.7710.9180.8280.8811.000
방향0.0000.0001.0000.0000.0000.0000.0000.1470.0000.0000.0000.2420.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.8520.7710.9180.8280.8811.000
연장((km))0.5831.0000.0001.0001.0000.5680.6910.4030.2540.4400.3780.4301.000
좌표위치위도((°))0.7451.0000.0001.0000.5681.0000.6320.3220.1890.5540.5680.4971.000
좌표위치경도((°))0.6891.0000.0001.0000.6910.6321.0000.4340.3780.5260.4140.4991.000
co((g/km))0.5050.8520.1470.8520.4030.3220.4341.0000.9730.9900.9270.9920.852
nox((g/km))0.5580.7710.0000.7710.2540.1890.3780.9731.0000.9700.9550.9740.771
hc((g/km))0.5310.9180.0000.9180.4400.5540.5260.9900.9701.0000.9510.9920.918
pm((g/km))0.4710.8280.0000.8280.3780.5680.4140.9270.9550.9511.0000.9440.828
co2((g/km))0.5100.8810.2420.8810.4300.4970.4990.9920.9740.9920.9441.0000.881
주소1.0001.0000.0001.0001.0001.0001.0000.8520.7710.9180.8280.8811.000
2023-12-10T21:15:52.220875image/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
기본키1.000-0.0440.2010.172-0.383-0.372-0.382-0.356-0.3790.000
연장((km))-0.0441.0000.2670.0650.0440.0680.0500.1270.0450.000
좌표위치위도((°))0.2010.2671.0000.211-0.396-0.394-0.387-0.344-0.3960.000
좌표위치경도((°))0.1720.0650.2111.0000.3550.3490.3570.3110.3560.000
co((g/km))-0.3830.044-0.3960.3551.0000.9960.9990.9450.9990.104
nox((g/km))-0.3720.068-0.3940.3490.9961.0000.9970.9580.9960.000
hc((g/km))-0.3820.050-0.3870.3570.9990.9971.0000.9480.9980.000
pm((g/km))-0.3560.127-0.3440.3110.9450.9580.9481.0000.9460.000
co2((g/km))-0.3790.045-0.3960.3560.9990.9960.9980.9461.0000.175
방향0.0000.0000.0000.0000.1040.0000.0000.0000.1751.000

Missing values

2023-12-10T21:15:47.365792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:15:47.540651image/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-완사4.220210301135.12031127.970116.533.570.610.131563.06경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210301135.12031127.970114.42.710.40.141156.34경남 사천 곤명 작팔
23건기연[0218-1]1진주-사봉3.520210301135.16901128.18735.883.250.550.131409.37경남 진주 문산 상문
34건기연[0218-1]2진주-사봉3.520210301135.16901128.187311.336.621.010.272994.85경남 진주 문산 상문
45건기연[0220-2]1일반성-진북4.820210301135.10632128.4421844.5828.44.311.0810590.77경남 창원 진전 근곡
56건기연[0220-2]2일반성-진북4.820210301135.10632128.4421848.2538.765.262.3612279.21경남 창원 진전 근곡
67건기연[0222-1]1마산-부산9.420210301135.1839128.6395134.596.0813.453.5431648.87경남 창원 양곡
78건기연[0222-1]2마산-부산9.420210301135.1839128.639589.7867.438.532.4123324.7경남 창원 양곡
89건기연[0302-4]1상죽-사천10.220210301134.87506128.0095861.6533.685.761.1114695.42경남 남해 창선 동대
910건기연[0302-4]2상죽-사천10.220210301134.87506128.0095811.977.241.090.43178.38경남 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-단성6.920210301135.28806127.835553.312.060.310.13873.34경남 산청 삼장 대하
9192건기연[5903-0]2시천-단성6.920210301135.28806127.835550.520.280.040.0138.68경남 산청 삼장 대하
9293건기연[5904-4]1합천-거창7.920210301135.61463128.02870.00.00.00.00.0경남 합천 봉산 봉계
9394건기연[5904-4]2합천-거창7.920210301135.61463128.02870.00.00.00.00.0경남 합천 봉산 봉계
9495건기연[7701-0]1고성-마산5.620210301135.10107128.447096.974.00.620.131844.14경남 창원 진전 이명
9596건기연[7701-0]2고성-마산5.620210301135.10107128.4470915.08.11.390.273587.16경남 창원 진전 이명
9697건기연[7701-4]1도산-거제8.120210301134.90831128.4093315.1812.441.570.543564.5경남 통영 광도 노산
9798건기연[7701-4]2도산-거제8.120210301134.90831128.4093316.014.441.660.734065.35경남 통영 광도 노산
9899건기연[7702-0]1통영-고성6.420210301134.89878128.355381.781.330.180.14462.92경남 통영 도산 오륜
99100건기연[7702-0]2통영-고성6.420210301134.89878128.355382.261.510.220.13595.97경남 통영 도산 오륜