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
co2((g/km)) has unique valuesUnique
pm((g/km)) has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:22:04.297447
Analysis finished2023-12-10 11:22:18.166043
Duration13.87 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
2023-12-10T20:22:18.279824image/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
2023-12-10T20:22:18.511967image/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

2023-12-10T20:22:18.712033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:18.858980image/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
2023-12-10T20:22:19.149056image/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[0134-0]
2nd row[0134-0]
3rd row[0142-0]
4th row[0142-0]
5th row[0328-2]
ValueCountFrequency (%)
0134-0 2
 
2.0%
4304-0 2
 
2.0%
4707-0 2
 
2.0%
3906-4 2
 
2.0%
3907-1 2
 
2.0%
3918-2 2
 
2.0%
4202-1 2
 
2.0%
4205-1 2
 
2.0%
4206-2 2
 
2.0%
4207-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:22:19.655421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 126
15.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
4 62
7.8%
1 60
7.5%
2 54
6.8%
7 30
 
3.8%
8 26
 
3.2%
Other values (3) 60
7.5%

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 (%)
0 126
25.2%
3 82
16.4%
4 62
12.4%
1 60
12.0%
2 54
10.8%
7 30
 
6.0%
8 26
 
5.2%
6 22
 
4.4%
5 22
 
4.4%
9 16
 
3.2%
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 (%)
0 126
15.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
4 62
7.8%
1 60
7.5%
2 54
6.8%
7 30
 
3.8%
8 26
 
3.2%
Other values (3) 60
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 126
15.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
4 62
7.8%
1 60
7.5%
2 54
6.8%
7 30
 
3.8%
8 26
 
3.2%
Other values (3) 60
7.5%

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

2023-12-10T20:22:19.869683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:20.026815image/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
2023-12-10T20:22:20.356607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.24
Min length4

Characters and Unicode

Total characters524
Distinct characters83
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 row송탄-오산
2nd row송탄-오산
3rd row당동-파평
4th row당동-파평
5th row이천-장호원
ValueCountFrequency (%)
송탄-오산 2
 
2.0%
죽전-문형 2
 
2.0%
내각-부평 2
 
2.0%
발안ic-청북ic 2
 
2.0%
팔탄-비봉 2
 
2.0%
일영-의정부 2
 
2.0%
시흥-목감 2
 
2.0%
보라-용인 2
 
2.0%
용인-마장 2
 
2.0%
부발-여주 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:22:21.075851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.1%
평 28
 
5.3%
천 22
 
4.2%
부 18
 
3.4%
안 14
 
2.7%
주 12
 
2.3%
동 12
 
2.3%
성 12
 
2.3%
중 12
 
2.3%
양 12
 
2.3%
Other values (73) 282
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 416
79.4%
Dash Punctuation 100
 
19.1%
Uppercase Letter 8
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
평 28
 
6.7%
천 22
 
5.3%
부 18
 
4.3%
안 14
 
3.4%
주 12
 
2.9%
동 12
 
2.9%
성 12
 
2.9%
중 12
 
2.9%
양 12
 
2.9%
산 10
 
2.4%
Other values (70) 264
63.5%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 416
79.4%
Common 100
 
19.1%
Latin 8
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
평 28
 
6.7%
천 22
 
5.3%
부 18
 
4.3%
안 14
 
3.4%
주 12
 
2.9%
동 12
 
2.9%
성 12
 
2.9%
중 12
 
2.9%
양 12
 
2.9%
산 10
 
2.4%
Other values (70) 264
63.5%
Latin
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 416
79.4%
ASCII 108
 
20.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
C 4
 
3.7%
I 4
 
3.7%
Hangul
ValueCountFrequency (%)
평 28
 
6.7%
천 22
 
5.3%
부 18
 
4.3%
안 14
 
3.4%
주 12
 
2.9%
동 12
 
2.9%
성 12
 
2.9%
중 12
 
2.9%
양 12
 
2.9%
산 10
 
2.4%
Other values (70) 264
63.5%

연장((km))
Real number (ℝ)

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.814
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:21.311005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median7.45
Q310.9
95-th percentile18.8
Maximum27.5
Range26
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.1650735
Coefficient of variation (CV)0.58600788
Kurtosis2.0186887
Mean8.814
Median Absolute Deviation (MAD)2.8
Skewness1.2326609
Sum881.4
Variance26.677984
MonotonicityNot monotonic
2023-12-10T20:22:21.525419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
6.0 4
 
4.0%
5.4 4
 
4.0%
10.6 4
 
4.0%
6.1 4
 
4.0%
9.1 2
 
2.0%
2.7 2
 
2.0%
18.8 2
 
2.0%
27.5 2
 
2.0%
7.2 2
 
2.0%
4.1 2
 
2.0%
Other values (36) 72
72.0%
ValueCountFrequency (%)
1.5 2
2.0%
2.0 2
2.0%
2.6 2
2.0%
2.7 2
2.0%
2.9 2
2.0%
3.1 2
2.0%
3.4 2
2.0%
4.1 2
2.0%
4.3 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
27.5 2
2.0%
19.0 2
2.0%
18.8 2
2.0%
17.6 2
2.0%
15.5 2
2.0%
15.4 2
2.0%
14.7 2
2.0%
14.6 2
2.0%
14.1 2
2.0%
12.9 2
2.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2023-12-10T20:22:21.750340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:21.922935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 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

2023-12-10T20:22:22.096379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:22.252817image/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%
Mean37.431928
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:22.459908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.380095
Q337.70419
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.53917

Descriptive statistics

Standard deviation0.33503729
Coefficient of variation (CV)0.0089505753
Kurtosis-1.0057936
Mean37.431928
Median Absolute Deviation (MAD)0.266565
Skewness0.38815809
Sum3743.1928
Variance0.11224999
MonotonicityNot monotonic
2023-12-10T20:22:22.688582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.468 2
 
2.0%
37.23589 2
 
2.0%
37.71834 2
 
2.0%
37.39258 2
 
2.0%
37.23653 2
 
2.0%
37.2375 2
 
2.0%
37.29776 2
 
2.0%
37.14026 2
 
2.0%
37.19991 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
36.95627 2
2.0%
36.95866 2
2.0%
36.96033 2
2.0%
36.98521 2
2.0%
37.00613 2
2.0%
37.01644 2
2.0%
37.02727 2
2.0%
37.05786 2
2.0%
37.08149 2
2.0%
37.09529 2
2.0%
ValueCountFrequency (%)
38.06264 2
2.0%
38.06053 2
2.0%
38.0169 2
2.0%
37.99285 2
2.0%
37.98354 2
2.0%
37.96019 2
2.0%
37.91419 2
2.0%
37.87708 2
2.0%
37.83225 2
2.0%
37.76396 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.2124
Minimum126.77946
Maximum127.6367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:22.937986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.85818
Q1127.06364
median127.22912
Q3127.37739
95-th percentile127.61201
Maximum127.6367
Range0.85724
Interquartile range (IQR)0.31375

Descriptive statistics

Standard deviation0.2312289
Coefficient of variation (CV)0.00181766
Kurtosis-0.81245353
Mean127.2124
Median Absolute Deviation (MAD)0.16088
Skewness0.019330949
Sum12721.24
Variance0.053466803
MonotonicityNot monotonic
2023-12-10T20:22:23.212602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
127.2475 2
 
2.0%
126.88236 2
 
2.0%
126.92818 2
 
2.0%
126.85818 2
 
2.0%
127.166 2
 
2.0%
127.3107 2
 
2.0%
127.60231 2
 
2.0%
126.91531 2
 
2.0%
126.9889 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.77946 2
2.0%
126.82904 2
2.0%
126.85818 2
2.0%
126.85908 2
2.0%
126.88236 2
2.0%
126.88641 2
2.0%
126.91531 2
2.0%
126.92256 2
2.0%
126.92508 2
2.0%
126.92818 2
2.0%
ValueCountFrequency (%)
127.6367 2
2.0%
127.62684 2
2.0%
127.61201 2
2.0%
127.60231 2
2.0%
127.56566 2
2.0%
127.55994 2
2.0%
127.49045 2
2.0%
127.44335 2
2.0%
127.44132 2
2.0%
127.42763 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.3396
Minimum0.52
Maximum536.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:23.461858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile9.6925
Q134.9525
median70.28
Q3118.755
95-th percentile207.7255
Maximum536.83
Range536.31
Interquartile range (IQR)83.8025

Descriptive statistics

Standard deviation83.594241
Coefficient of variation (CV)0.95711729
Kurtosis10.541307
Mean87.3396
Median Absolute Deviation (MAD)39.415
Skewness2.6959504
Sum8733.96
Variance6987.9971
MonotonicityNot monotonic
2023-12-10T20:22:23.686531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124.6 1
 
1.0%
74.55 1
 
1.0%
57.63 1
 
1.0%
182.76 1
 
1.0%
158.0 1
 
1.0%
97.58 1
 
1.0%
118.34 1
 
1.0%
45.67 1
 
1.0%
25.24 1
 
1.0%
62.29 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.52 1
1.0%
0.65 1
1.0%
1.95 1
1.0%
3.98 1
1.0%
5.56 1
1.0%
9.91 1
1.0%
10.56 1
1.0%
11.15 1
1.0%
11.44 1
1.0%
12.63 1
1.0%
ValueCountFrequency (%)
536.83 1
1.0%
436.82 1
1.0%
335.1 1
1.0%
255.34 1
1.0%
213.72 1
1.0%
207.41 1
1.0%
191.71 1
1.0%
182.76 1
1.0%
175.43 1
1.0%
174.59 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.377
Minimum0.28
Maximum522.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:24.222600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile5.9165
Q130.8275
median69.2
Q3116.78
95-th percentile224.248
Maximum522.01
Range521.73
Interquartile range (IQR)85.9525

Descriptive statistics

Standard deviation80.121206
Coefficient of variation (CV)0.92757569
Kurtosis9.2081932
Mean86.377
Median Absolute Deviation (MAD)41.735
Skewness2.4272069
Sum8637.7
Variance6419.4076
MonotonicityNot monotonic
2023-12-10T20:22:24.455475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.74 1
 
1.0%
92.22 1
 
1.0%
49.47 1
 
1.0%
137.89 1
 
1.0%
133.37 1
 
1.0%
79.11 1
 
1.0%
110.6 1
 
1.0%
40.68 1
 
1.0%
23.33 1
 
1.0%
58.78 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.28 1
1.0%
0.32 1
1.0%
0.96 1
1.0%
2.38 1
1.0%
3.57 1
1.0%
6.04 1
1.0%
9.97 1
1.0%
10.81 1
1.0%
11.28 1
1.0%
12.68 1
1.0%
ValueCountFrequency (%)
522.01 1
1.0%
345.59 1
1.0%
334.06 1
1.0%
259.44 1
1.0%
234.85 1
1.0%
223.69 1
1.0%
183.22 1
1.0%
177.23 1
1.0%
174.87 1
1.0%
165.84 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.6413
Minimum0.04
Maximum73.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:24.703273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.986
Q14.5225
median9.745
Q315.13
95-th percentile27.0975
Maximum73.35
Range73.31
Interquartile range (IQR)10.6075

Descriptive statistics

Standard deviation10.927533
Coefficient of variation (CV)0.93868667
Kurtosis10.871337
Mean11.6413
Median Absolute Deviation (MAD)5.355
Skewness2.6585428
Sum1164.13
Variance119.41098
MonotonicityNot monotonic
2023-12-10T20:22:24.964219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.25 2
 
2.0%
11.78 2
 
2.0%
15.52 2
 
2.0%
12.26 2
 
2.0%
15.02 1
 
1.0%
15.12 1
 
1.0%
19.33 1
 
1.0%
15.16 1
 
1.0%
5.17 1
 
1.0%
2.62 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.04 1
1.0%
0.06 1
1.0%
0.17 1
1.0%
0.39 1
1.0%
0.53 1
1.0%
1.01 1
1.0%
1.42 1
1.0%
1.48 1
1.0%
1.7 1
1.0%
1.79 1
1.0%
ValueCountFrequency (%)
73.35 1
1.0%
48.43 1
1.0%
45.0 1
1.0%
36.99 1
1.0%
30.28 1
1.0%
26.93 1
1.0%
26.13 1
1.0%
23.68 1
1.0%
23.43 1
1.0%
22.79 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4462
Minimum0
Maximum34.41
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:25.207440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2795
Q12.0275
median4.34
Q37.335
95-th percentile14.271
Maximum34.41
Range34.41
Interquartile range (IQR)5.3075

Descriptive statistics

Standard deviation4.9962195
Coefficient of variation (CV)0.91737717
Kurtosis10.881394
Mean5.4462
Median Absolute Deviation (MAD)2.495
Skewness2.5372564
Sum544.62
Variance24.96221
MonotonicityNot monotonic
2023-12-10T20:22:25.433884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
4.12 2
 
2.0%
1.88 2
 
2.0%
1.24 2
 
2.0%
2.42 2
 
2.0%
6.45 1
 
1.0%
5.27 1
 
1.0%
7.46 1
 
1.0%
8.34 1
 
1.0%
4.4 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.14 1
 
1.0%
0.27 1
 
1.0%
0.28 1
 
1.0%
0.64 1
 
1.0%
0.67 1
 
1.0%
0.7 1
 
1.0%
0.82 1
 
1.0%
0.86 1
 
1.0%
0.98 1
 
1.0%
ValueCountFrequency (%)
34.41 1
1.0%
19.0 1
1.0%
17.27 1
1.0%
17.07 1
1.0%
15.05 1
1.0%
14.23 1
1.0%
12.13 1
1.0%
11.8 1
1.0%
11.58 1
1.0%
11.44 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21167.77
Minimum138.68
Maximum125859.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:25.641864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile2305.6525
Q17585.63
median16365.78
Q328780.395
95-th percentile52787.993
Maximum125859.26
Range125720.58
Interquartile range (IQR)21194.765

Descriptive statistics

Standard deviation20302.397
Coefficient of variation (CV)0.95911838
Kurtosis10.153482
Mean21167.77
Median Absolute Deviation (MAD)9450.39
Skewness2.6641298
Sum2116777
Variance4.1218732 × 108
MonotonicityNot monotonic
2023-12-10T20:22:25.891475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30871.14 1
 
1.0%
18883.53 1
 
1.0%
14689.59 1
 
1.0%
42245.19 1
 
1.0%
39294.8 1
 
1.0%
22527.03 1
 
1.0%
29032.31 1
 
1.0%
11506.97 1
 
1.0%
6461.66 1
 
1.0%
15360.55 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
138.68 1
1.0%
153.68 1
1.0%
461.05 1
1.0%
953.96 1
1.0%
1469.32 1
1.0%
2349.67 1
1.0%
2387.24 1
1.0%
2522.65 1
1.0%
2539.56 1
1.0%
2808.34 1
1.0%
ValueCountFrequency (%)
125859.26 1
1.0%
112357.15 1
1.0%
80054.96 1
1.0%
58444.11 1
1.0%
54193.86 1
1.0%
52714.0 1
1.0%
43047.61 1
1.0%
43003.57 1
1.0%
42926.46 1
1.0%
42659.88 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:22:26.376888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

Total characters1070
Distinct characters109
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기 평택 진위 신
2nd row경기 평택 진위 신
3rd row경기 파주 문산 당동
4th row경기 파주 문산 당동
5th row경기 여주 가남 심석
ValueCountFrequency (%)
경기 100
25.8%
평택 14
 
3.6%
용인 12
 
3.1%
광주 10
 
2.6%
여주 8
 
2.1%
가평 8
 
2.1%
포천 8
 
2.1%
화성 6
 
1.5%
안성 6
 
1.5%
남양주 6
 
1.5%
Other values (95) 210
54.1%
2023-12-10T20:22:27.153640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
288
26.9%
기 102
 
9.5%
경 100
 
9.3%
평 34
 
3.2%
주 32
 
3.0%
성 22
 
2.1%
양 20
 
1.9%
천 18
 
1.7%
가 16
 
1.5%
인 16
 
1.5%
Other values (99) 422
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 782
73.1%
Space Separator 288
 
26.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 102
 
13.0%
경 100
 
12.8%
평 34
 
4.3%
주 32
 
4.1%
성 22
 
2.8%
양 20
 
2.6%
천 18
 
2.3%
가 16
 
2.0%
인 16
 
2.0%
용 16
 
2.0%
Other values (98) 406
51.9%
Space Separator
ValueCountFrequency (%)
288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 782
73.1%
Common 288
 
26.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 102
 
13.0%
경 100
 
12.8%
평 34
 
4.3%
주 32
 
4.1%
성 22
 
2.8%
양 20
 
2.6%
천 18
 
2.3%
가 16
 
2.0%
인 16
 
2.0%
용 16
 
2.0%
Other values (98) 406
51.9%
Common
ValueCountFrequency (%)
288
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 782
73.1%
ASCII 288
 
26.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
288
100.0%
Hangul
ValueCountFrequency (%)
기 102
 
13.0%
경 100
 
12.8%
평 34
 
4.3%
주 32
 
4.1%
성 22
 
2.8%
양 20
 
2.6%
천 18
 
2.3%
가 16
 
2.0%
인 16
 
2.0%
용 16
 
2.0%
Other values (98) 406
51.9%

Interactions

2023-12-10T20:22:16.286349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:05.149352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:06.800491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:08.090984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:09.412515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:10.678554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:12.007061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:13.316791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:14.587370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:16.440671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:05.291572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:06.947854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:08.227621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:09.533754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:10.833287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:12.165450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:13.436458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:14.736484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:16.580085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:05.420741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:07.078161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:08.361604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:09.646658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:10.972131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:12.295227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:13.563404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:14.868480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:16.739607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:05.904642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:07.238006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:08.518390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:09.796011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:11.114193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:12.432381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:13.689447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:15.000835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:16.882942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:06.037788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:07.368823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:08.668184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:09.940417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:11.255397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:12.590105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:13.834143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:15.466558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:17.025509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:06.235013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:07.503132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:08.823374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:10.082989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:11.396164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:12.723971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:13.988382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:15.679858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:17.164911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:06.370390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:07.635373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:08.978054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:10.228235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:11.541368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:12.871560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:14.137344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:15.827136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:17.367593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:06.503449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:07.791502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:09.138290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:10.380206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:11.691807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:13.019968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:14.294802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:15.971133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:17.514714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:06.649293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:07.933470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:09.285655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:10.528932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:11.836251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:13.170556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:14.445575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:16.128050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:22:27.346625image/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
기본키1.0001.0000.0001.0000.5660.8200.8530.2220.2990.3420.2640.3201.000
지점1.0001.0000.0001.0001.0001.0001.0000.7370.7420.7840.7250.7651.000
방향0.0000.0001.0000.0000.0000.0000.0000.1340.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.7370.7420.7840.7250.7651.000
연장((km))0.5661.0000.0001.0001.0000.6660.6150.4560.1640.4470.0000.4121.000
좌표위치위도((°))0.8201.0000.0001.0000.6661.0000.8050.3990.3920.4870.3800.3531.000
좌표위치경도((°))0.8531.0000.0001.0000.6150.8051.0000.2400.0000.1200.0000.2901.000
co((g/km))0.2220.7370.1340.7370.4560.3990.2401.0000.9120.9860.8990.9960.737
nox((g/km))0.2990.7420.0000.7420.1640.3920.0000.9121.0000.9550.9990.9120.742
hc((g/km))0.3420.7840.0000.7840.4470.4870.1200.9860.9551.0000.9460.9780.784
pm((g/km))0.2640.7250.0000.7250.0000.3800.0000.8990.9990.9461.0000.9030.725
co2((g/km))0.3200.7650.0000.7650.4120.3530.2900.9960.9120.9780.9031.0000.765
주소1.0001.0000.0001.0001.0001.0001.0000.7370.7420.7840.7250.7651.000
2023-12-10T20:22:27.565485image/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
기본키1.000-0.2210.034-0.039-0.006-0.035-0.028-0.017-0.0040.000
연장((km))-0.2211.0000.243-0.0750.0360.0330.0240.0240.0430.000
좌표위치위도((°))0.0340.2431.0000.117-0.279-0.356-0.340-0.367-0.2810.000
좌표위치경도((°))-0.039-0.0750.1171.000-0.144-0.176-0.158-0.170-0.1640.000
co((g/km))-0.0060.036-0.279-0.1441.0000.9640.9760.9390.9980.094
nox((g/km))-0.0350.033-0.356-0.1760.9641.0000.9940.9900.9680.000
hc((g/km))-0.0280.024-0.340-0.1580.9760.9941.0000.9840.9750.000
pm((g/km))-0.0170.024-0.367-0.1700.9390.9900.9841.0000.9420.000
co2((g/km))-0.0040.043-0.281-0.1640.9980.9680.9750.9421.0000.000
방향0.0000.0000.0000.0000.0940.0000.0000.0000.0001.000

Missing values

2023-12-10T20:22:17.726349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:22:18.018875image/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[0134-0]1송탄-오산6.220210601137.09529127.06364124.6119.7415.026.4530871.14경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210601137.09529127.06364170.14147.7418.946.8742926.46경기 평택 진위 신
23건기연[0142-0]1당동-파평14.720210601137.87708126.7794613.2812.681.420.823378.92경기 파주 문산 당동
34건기연[0142-0]2당동-파평14.720210601137.87708126.7794610.566.041.010.282522.65경기 파주 문산 당동
45건기연[0328-2]1이천-장호원9.620210601137.19066127.5599431.4328.94.412.417163.27경기 여주 가남 심석
56건기연[0328-2]2이천-장호원9.620210601137.19066127.5599432.2927.134.251.997443.16경기 여주 가남 심석
67건기연[0330-1]1이천-광주15.520210601137.31793127.42763107.1133.2217.159.0726172.92경기 이천 신둔 수하
78건기연[0330-1]2이천-광주15.520210601137.31793127.42763150.06164.7123.6811.4435091.02경기 이천 신둔 수하
89건기연[0331-0]1성남-이천7.720210601137.37777127.30168335.1334.0645.019.080054.96경기 광주 초월 용수
910건기연[0331-0]2성남-이천7.720210601137.37777127.30168213.72223.6930.2814.2352714.0경기 광주 초월 용수
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4509-0]1광주-팔당19.020210601137.4815127.2810122.217.972.691.245594.2경기 광주 남종 삼성
9192건기연[4509-0]2광주-팔당19.020210601137.4815127.2810122.0218.312.71.285485.7경기 광주 남종 삼성
9293건기연[4512-1]1화도-청평8.320210601137.68735127.3799750.6147.616.172.9912572.3경기 가평 청평 대성
9394건기연[4512-1]2화도-청평8.320210601137.68735127.3799777.2869.9110.124.1917536.05경기 가평 청평 대성
9495건기연[4606-2]1청평-가평2.920210601137.76396127.4433558.4256.228.263.6913078.06경기 가평 청평 상천
9596건기연[4606-2]2청평-가평2.920210601137.76396127.4433579.2379.2111.525.1217693.46경기 가평 청평 상천
9697건기연[4707-0]1내각-부평6.820210601137.70419127.17103174.59174.8723.4310.9642496.84경기 남양주 진접 내각
9798건기연[4707-0]2내각-부평6.820210601137.70419127.17103148.03145.5319.229.4736381.99경기 남양주 진접 내각
9899건기연[4708-0]1일동-이동7.920210601137.98354127.3344526.5231.474.251.886100.55경기 포천 일동 사직
99100건기연[4708-0]2일동-이동7.920210601137.98354127.3344513.5712.761.90.863270.46경기 포천 일동 사직