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
Categorical5
Text2

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
기본키 is highly overall correlated with 주소High correlation
연장((km)) is highly overall correlated with 주소High correlation
좌표위치위도((°)) is highly overall correlated with 주소High correlation
좌표위치경도((°)) is highly overall correlated with 주소High correlation
co((g/km)) is highly overall correlated with nox((g/km)) and 4 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 4 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 4 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
기본키 has unique valuesUnique
co((g/km)) has unique valuesUnique
nox((g/km)) has unique valuesUnique
pm((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2024-04-16 09:20:52.230147
Analysis finished2024-04-16 09:20:59.345098
Duration7.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:20:59.422621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2024-04-16T18:20:59.793837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2024-04-16T18:20:59.919508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:21:00.007430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-16T18:21:00.177793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0526-3]
2nd row[0526-3]
3rd row[0527-2]
4th row[0527-2]
5th row[0529-0]
ValueCountFrequency (%)
0526-3 2
 
2.0%
4313-0 2
 
2.0%
5602-0 2
 
2.0%
3813-1 2
 
2.0%
3814-0 2
 
2.0%
3818-0 2
 
2.0%
4209-1 2
 
2.0%
4209-2 2
 
2.0%
4212-1 2
 
2.0%
4213-1 2
 
2.0%
Other values (40) 80
80.0%
2024-04-16T18:21:00.463190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 60
7.5%
4 52
6.5%
6 36
 
4.5%
5 30
 
3.8%
Other values (3) 50
6.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106
21.2%
1 102
20.4%
3 64
12.8%
2 60
12.0%
4 52
10.4%
6 36
 
7.2%
5 30
 
6.0%
7 22
 
4.4%
8 18
 
3.6%
9 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 (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 60
7.5%
4 52
6.5%
6 36
 
4.5%
5 30
 
3.8%
Other values (3) 50
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
13.2%
1 102
12.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 64
8.0%
2 60
7.5%
4 52
6.5%
6 36
 
4.5%
5 30
 
3.8%
Other values (3) 50
6.2%

방향
Categorical

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2024-04-16T18:21:00.573844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:21:00.660883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%
Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-16T18:21:00.843327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.18
Min length4

Characters and Unicode

Total characters518
Distinct characters90
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row원주-소초
2nd row원주-소초
3rd row원주-횡성
4th row원주-횡성
5th row공근-동산
ValueCountFrequency (%)
원주-소초 2
 
2.0%
문혜-김화 2
 
2.0%
사내-화천 2
 
2.0%
신동-사북 2
 
2.0%
남-사북 2
 
2.0%
도계-고천 2
 
2.0%
원주-새말 2
 
2.0%
문막-원주 2
 
2.0%
안흥-운교 2
 
2.0%
노론-용탄 2
 
2.0%
Other values (40) 80
80.0%
2024-04-16T18:21:01.157276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.3%
26
 
5.0%
18
 
3.5%
16
 
3.1%
12
 
2.3%
12
 
2.3%
12
 
2.3%
12
 
2.3%
12
 
2.3%
10
 
1.9%
Other values (80) 288
55.6%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
 
6.3%
18
 
4.3%
16
 
3.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (77) 274
66.2%
Uppercase Letter
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

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

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
 
6.3%
18
 
4.3%
16
 
3.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (77) 274
66.2%
Latin
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
96.2%
I 2
 
1.9%
C 2
 
1.9%
Hangul
ValueCountFrequency (%)
26
 
6.3%
18
 
4.3%
16
 
3.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (77) 274
66.2%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.194
Minimum0.4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:01.271905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.3
Q14.8
median8.95
Q313.3
95-th percentile23
Maximum27
Range26.6
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation6.372143
Coefficient of variation (CV)0.6250876
Kurtosis0.035432841
Mean10.194
Median Absolute Deviation (MAD)4.3
Skewness0.75165879
Sum1019.4
Variance40.604206
MonotonicityNot monotonic
2024-04-16T18:21:01.381250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
7.2 4
 
4.0%
12.0 4
 
4.0%
3.5 4
 
4.0%
8.8 4
 
4.0%
4.8 2
 
2.0%
14.4 2
 
2.0%
3.8 2
 
2.0%
5.9 2
 
2.0%
16.1 2
 
2.0%
10.3 2
 
2.0%
Other values (36) 72
72.0%
ValueCountFrequency (%)
0.4 2
2.0%
2.0 2
2.0%
2.3 2
2.0%
2.4 2
2.0%
2.6 2
2.0%
2.9 2
2.0%
3.5 4
4.0%
3.6 2
2.0%
3.8 2
2.0%
4.0 2
2.0%
ValueCountFrequency (%)
27.0 2
2.0%
24.7 2
2.0%
23.0 2
2.0%
22.7 2
2.0%
21.1 2
2.0%
18.1 2
2.0%
17.4 2
2.0%
16.4 2
2.0%
16.1 2
2.0%
15.3 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2024-04-16T18:21:01.482076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:21:01.558897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-04-16T18:21:01.637582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T18:21:01.713186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.695204
Minimum37.08588
Maximum38.38086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:01.811827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.3568097
Coefficient of variation (CV)0.0094656524
Kurtosis-1.2932456
Mean37.695204
Median Absolute Deviation (MAD)0.32117
Skewness0.12471941
Sum3769.5204
Variance0.12731316
MonotonicityNot monotonic
2024-04-16T18:21:01.954652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3551 2
 
2.0%
38.02855 2
 
2.0%
37.25108 2
 
2.0%
37.30412 2
 
2.0%
37.40802 2
 
2.0%
37.32395 2
 
2.0%
37.4163 2
 
2.0%
37.32703 2
 
2.0%
37.4489 2
 
2.0%
37.48273 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
37.08588 2
2.0%
37.18474 2
2.0%
37.18732 2
2.0%
37.19159 2
2.0%
37.21543 2
2.0%
37.25108 2
2.0%
37.28643 2
2.0%
37.30412 2
2.0%
37.32395 2
2.0%
37.32703 2
2.0%
ValueCountFrequency (%)
38.38086 2
2.0%
38.25247 2
2.0%
38.23094 2
2.0%
38.19136 2
2.0%
38.18502 2
2.0%
38.17869 2
2.0%
38.14989 2
2.0%
38.11527 2
2.0%
38.08778 2
2.0%
38.07373 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.23238
Minimum127.35058
Maximum129.20253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:02.068059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.35058
5-th percentile127.47033
Q1127.86266
median128.16257
Q3128.62245
95-th percentile129.07044
Maximum129.20253
Range1.85195
Interquartile range (IQR)0.75979

Descriptive statistics

Standard deviation0.47550385
Coefficient of variation (CV)0.0037081418
Kurtosis-0.88039804
Mean128.23238
Median Absolute Deviation (MAD)0.3488
Skewness0.20298234
Sum12823.238
Variance0.22610391
MonotonicityNot monotonic
2024-04-16T18:21:02.188361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.99487 2
 
2.0%
128.1302 2
 
2.0%
128.7796 2
 
2.0%
129.07044 2
 
2.0%
127.99641 2
 
2.0%
127.83897 2
 
2.0%
128.2034 2
 
2.0%
128.51597 2
 
2.0%
128.66017 2
 
2.0%
129.09293 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
127.35058 2
2.0%
127.41894 2
2.0%
127.47033 2
2.0%
127.60419 2
2.0%
127.62463 2
2.0%
127.63815 2
2.0%
127.67987 2
2.0%
127.77663 2
2.0%
127.81252 2
2.0%
127.81502 2
2.0%
ValueCountFrequency (%)
129.20253 2
2.0%
129.09293 2
2.0%
129.07044 2
2.0%
129.02671 2
2.0%
128.84543 2
2.0%
128.84271 2
2.0%
128.83913 2
2.0%
128.81034 2
2.0%
128.79017 2
2.0%
128.7796 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2467.7592
Minimum237.66
Maximum9936.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:02.310224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum237.66
5-th percentile324.3285
Q1750.1275
median1167.88
Q33454.4825
95-th percentile7111.6905
Maximum9936.98
Range9699.32
Interquartile range (IQR)2704.355

Descriptive statistics

Standard deviation2341.275
Coefficient of variation (CV)0.94874531
Kurtosis0.62471352
Mean2467.7592
Median Absolute Deviation (MAD)655.18
Skewness1.234823
Sum246775.92
Variance5481568.4
MonotonicityNot monotonic
2024-04-16T18:21:02.423383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3646.61 1
 
1.0%
1101.88 1
 
1.0%
6555.24 1
 
1.0%
1162.1 1
 
1.0%
1173.66 1
 
1.0%
941.26 1
 
1.0%
836.92 1
 
1.0%
1185.7 1
 
1.0%
1112.02 1
 
1.0%
579.67 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
237.66 1
1.0%
255.37 1
1.0%
275.81 1
1.0%
286.08 1
1.0%
300.93 1
1.0%
325.56 1
1.0%
462.23 1
1.0%
497.1 1
1.0%
528.3 1
1.0%
530.25 1
1.0%
ValueCountFrequency (%)
9936.98 1
1.0%
8902.45 1
1.0%
8176.25 1
1.0%
7888.75 1
1.0%
7767.2 1
1.0%
7077.19 1
1.0%
6996.14 1
1.0%
6555.24 1
1.0%
6149.02 1
1.0%
6033.05 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2313.1344
Minimum238.17
Maximum12204.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:02.534607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum238.17
5-th percentile371.555
Q1701.7125
median1196.225
Q33199.6725
95-th percentile7026.9655
Maximum12204.24
Range11966.07
Interquartile range (IQR)2497.96

Descriptive statistics

Standard deviation2319.667
Coefficient of variation (CV)1.0028241
Kurtosis2.5768503
Mean2313.1344
Median Absolute Deviation (MAD)752.185
Skewness1.5903757
Sum231313.44
Variance5380854.9
MonotonicityNot monotonic
2024-04-16T18:21:02.671824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2856.14 1
 
1.0%
914.8 1
 
1.0%
5475.63 1
 
1.0%
1088.84 1
 
1.0%
1085.28 1
 
1.0%
1494.36 1
 
1.0%
1098.49 1
 
1.0%
899.69 1
 
1.0%
853.6 1
 
1.0%
579.01 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
238.17 1
1.0%
286.47 1
1.0%
286.97 1
1.0%
305.11 1
1.0%
310.09 1
1.0%
374.79 1
1.0%
411.1 1
1.0%
414.94 1
1.0%
430.73 1
1.0%
435.02 1
1.0%
ValueCountFrequency (%)
12204.24 1
1.0%
7860.1 1
1.0%
7488.57 1
1.0%
7177.67 1
1.0%
7043.22 1
1.0%
7026.11 1
1.0%
6915.08 1
1.0%
6813.12 1
1.0%
6504.81 1
1.0%
6280.58 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.7748
Minimum31.88
Maximum1448.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:02.812746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.88
5-th percentile45.9675
Q183.305
median157.325
Q3464.1325
95-th percentile897.5095
Maximum1448.19
Range1416.31
Interquartile range (IQR)380.8275

Descriptive statistics

Standard deviation288.27643
Coefficient of variation (CV)0.96486192
Kurtosis1.6156452
Mean298.7748
Median Absolute Deviation (MAD)99.99
Skewness1.383412
Sum29877.48
Variance83103.298
MonotonicityNot monotonic
2024-04-16T18:21:02.956387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.85 2
 
2.0%
414.56 1
 
1.0%
120.52 1
 
1.0%
690.38 1
 
1.0%
151.75 1
 
1.0%
150.54 1
 
1.0%
173.47 1
 
1.0%
155.29 1
 
1.0%
126.71 1
 
1.0%
117.58 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
31.88 1
1.0%
34.9 1
1.0%
37.39 1
1.0%
39.89 1
1.0%
42.12 1
1.0%
46.17 1
1.0%
53.27 1
1.0%
54.97 1
1.0%
55.76 1
1.0%
56.89 1
1.0%
ValueCountFrequency (%)
1448.19 1
1.0%
930.07 1
1.0%
928.03 1
1.0%
922.86 1
1.0%
921.06 1
1.0%
896.27 1
1.0%
841.05 1
1.0%
806.32 1
1.0%
744.13 1
1.0%
727.38 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.2422
Minimum13.27
Maximum745.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:03.103065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.27
5-th percentile20.27
Q142.6025
median71.52
Q3162.9875
95-th percentile370.7125
Maximum745.63
Range732.36
Interquartile range (IQR)120.385

Descriptive statistics

Standard deviation125.63642
Coefficient of variation (CV)1.0112218
Kurtosis5.4631934
Mean124.2422
Median Absolute Deviation (MAD)42.775
Skewness2.0202931
Sum12424.22
Variance15784.51
MonotonicityNot monotonic
2024-04-16T18:21:03.221047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152.8 1
 
1.0%
58.18 1
 
1.0%
245.32 1
 
1.0%
69.07 1
 
1.0%
67.11 1
 
1.0%
88.3 1
 
1.0%
63.72 1
 
1.0%
43.57 1
 
1.0%
46.35 1
 
1.0%
35.88 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
13.27 1
1.0%
16.35 1
1.0%
18.36 1
1.0%
18.46 1
1.0%
18.56 1
1.0%
20.36 1
1.0%
21.24 1
1.0%
21.78 1
1.0%
23.28 1
1.0%
23.53 1
1.0%
ValueCountFrequency (%)
745.63 1
1.0%
461.29 1
1.0%
416.89 1
1.0%
396.31 1
1.0%
393.18 1
1.0%
369.53 1
1.0%
366.18 1
1.0%
336.56 1
1.0%
321.06 1
1.0%
307.02 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617202.72
Minimum56992.97
Maximum2533511.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-16T18:21:03.342103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56992.97
5-th percentile81337.515
Q1179281.89
median285793.85
Q3883292.32
95-th percentile1811853.1
Maximum2533511.5
Range2476518.5
Interquartile range (IQR)704010.43

Descriptive statistics

Standard deviation591648.79
Coefficient of variation (CV)0.95859718
Kurtosis0.73972684
Mean617202.72
Median Absolute Deviation (MAD)160131.06
Skewness1.2682442
Sum61720272
Variance3.5004829 × 1011
MonotonicityNot monotonic
2024-04-16T18:21:03.463205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
923978.94 1
 
1.0%
281731.93 1
 
1.0%
1687136.77 1
 
1.0%
285969.84 1
 
1.0%
289847.19 1
 
1.0%
240712.09 1
 
1.0%
183718.33 1
 
1.0%
303463.38 1
 
1.0%
285617.86 1
 
1.0%
141921.49 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
56992.97 1
1.0%
57006.16 1
1.0%
60867.69 1
1.0%
69810.18 1
1.0%
72464.41 1
1.0%
81804.52 1
1.0%
118202.18 1
1.0%
123831.09 1
1.0%
127494.49 1
1.0%
135355.77 1
1.0%
ValueCountFrequency (%)
2533511.51 1
1.0%
2287932.97 1
1.0%
2107561.49 1
1.0%
1951150.25 1
1.0%
1851095.34 1
1.0%
1809787.71 1
1.0%
1740473.46 1
1.0%
1687136.77 1
1.0%
1562741.62 1
1.0%
1536911.2 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원 춘천 사북 오탄
 
4
강원 정선 임계 봉산
 
2
강원 강릉 연곡 동덕
 
2
강원 홍천 홍천 삼마치
 
2
강원 홍천 북방 부사원
 
2
Other values (44)
88 

Length

Max length12
Median length11
Mean length10.72
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원 원주 봉산
2nd row강원 원주 봉산
3rd row강원 원주 소초 장양
4th row강원 원주 소초 장양
5th row강원 홍천 홍천 삼마치

Common Values

ValueCountFrequency (%)
강원 춘천 사북 오탄 4
 
4.0%
강원 정선 임계 봉산 2
 
2.0%
강원 강릉 연곡 동덕 2
 
2.0%
강원 홍천 홍천 삼마치 2
 
2.0%
강원 홍천 북방 부사원 2
 
2.0%
강원 춘천 동내 거두 2
 
2.0%
강원 춘천 신북 용산 2
 
2.0%
강원 횡성 서원 유현 2
 
2.0%
강원 횡성 횡성 조곡 2
 
2.0%
강원 정선 용평 용전 2
 
2.0%
Other values (39) 78
78.0%

Length

2024-04-16T18:21:03.582204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원 100
25.4%
인제 14
 
3.6%
횡성 12
 
3.0%
홍천 12
 
3.0%
정선 10
 
2.5%
원주 10
 
2.5%
춘천 10
 
2.5%
평창 8
 
2.0%
영월 8
 
2.0%
8
 
2.0%
Other values (83) 202
51.3%

Interactions

2024-04-16T18:20:58.397359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:52.680753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.376797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.036152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.664311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.631423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.297179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.994212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.744714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.468121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:52.743137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.450748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.104492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.733054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.695561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.376046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.062260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.813995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.535947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:52.815240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.511396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.169258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.800670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.759299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.466107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.144396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.884147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.610219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:52.889465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.581180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.238646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.880563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.830264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.558715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.229085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.959374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.682542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:52.970875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.652984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.310467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.972081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.903888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.639112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.333175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.039454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.760189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.051800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.720127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.373760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.328080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.968639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.706101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.420116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.110676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.841361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.136309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.812334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.450150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.399505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.043832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.775810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.507134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.179281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.928549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.209308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.889148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.521200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.478788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.139039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.848197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.581413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.249560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:59.018074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.290178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:53.968127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:54.597182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:55.560017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.214621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:56.922155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:57.671177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T18:20:58.324031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T18:21:03.909593image/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
기본키1.0001.0000.0001.0000.7380.8620.7780.6320.4220.4080.4420.6390.998
지점1.0001.0000.0001.0001.0001.0001.0000.9550.9170.9470.9060.9601.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9550.9170.9470.9060.9601.000
연장((km))0.7381.0000.0001.0001.0000.7920.7700.0520.0000.1810.2360.2660.998
좌표위치위도((°))0.8621.0000.0001.0000.7921.0000.7940.5940.3930.3880.4430.5861.000
좌표위치경도((°))0.7781.0000.0001.0000.7700.7941.0000.6400.3240.3850.2970.5931.000
co((g/km))0.6320.9550.0000.9550.0520.5940.6401.0000.8910.9350.8890.9890.945
nox((g/km))0.4220.9170.0000.9170.0000.3930.3240.8911.0000.9840.9830.8740.921
hc((g/km))0.4080.9470.0000.9470.1810.3880.3850.9350.9841.0000.9690.8960.949
pm((g/km))0.4420.9060.0000.9060.2360.4430.2970.8890.9830.9691.0000.8220.911
co2((g/km))0.6390.9600.0000.9600.2660.5860.5930.9890.8740.8960.8221.0000.950
주소0.9981.0000.0001.0000.9981.0001.0000.9450.9210.9490.9110.9501.000
2024-04-16T18:21:04.011663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향
주소1.0000.000
방향0.0001.000
2024-04-16T18:21:04.084163image/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))방향주소
기본키1.0000.2230.365-0.085-0.076-0.071-0.080-0.093-0.0790.0000.736
연장((km))0.2231.0000.1490.166-0.253-0.261-0.243-0.297-0.2630.0000.732
좌표위치위도((°))0.3650.1491.000-0.425-0.373-0.460-0.454-0.449-0.3610.0000.753
좌표위치경도((°))-0.0850.166-0.4251.0000.0570.1110.1220.0660.0420.0000.753
co((g/km))-0.076-0.253-0.3730.0571.0000.9740.9810.9480.9970.0000.526
nox((g/km))-0.071-0.261-0.4600.1110.9741.0000.9950.9860.9660.0000.494
hc((g/km))-0.080-0.243-0.4540.1220.9810.9951.0000.9810.9720.0000.550
pm((g/km))-0.093-0.297-0.4490.0660.9480.9860.9811.0000.9400.0000.473
co2((g/km))-0.079-0.263-0.3610.0420.9970.9660.9720.9401.0000.0000.540
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
주소0.7360.7320.7530.7530.5260.4940.5500.4730.5400.0001.000

Missing values

2024-04-16T18:20:59.120647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T18:20:59.280913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
01건기연[0526-3]1원주-소초4.820210201037.3551127.994873646.612856.14414.56152.8923978.94강원 원주 봉산
12건기연[0526-3]2원주-소초4.820210201037.3551127.994874270.33138.44492.6162.1992636.69강원 원주 봉산
23건기연[0527-2]1원주-횡성0.420210201037.42063127.963195433.44880.82641.38253.81360260.61강원 원주 소초 장양
34건기연[0527-2]2원주-횡성0.420210201037.42063127.963195211.124681.15613.97236.621304293.01강원 원주 소초 장양
45건기연[0529-0]1공근-동산12.020210201037.62539127.895281531.691545.34187.4879.95379055.34강원 홍천 홍천 삼마치
56건기연[0529-0]2공근-동산12.020210201037.62539127.895281610.891711.03210.6393.02392816.2강원 홍천 홍천 삼마치
67건기연[0530-0]1횡성-춘천13.320210201037.73176127.837871325.541236.19160.180.74331866.67강원 홍천 북방 부사원
78건기연[0530-0]2횡성-춘천13.320210201037.73176127.837871363.51318.66168.1887.11339670.36강원 홍천 북방 부사원
89건기연[0531-2]1동내-천전7.520210201037.86064127.776636033.054890.45660.16231.581536911.2강원 춘천 동내 거두
910건기연[0531-2]2동내-천전7.520210201037.86064127.776636149.024774.54677.19269.781562741.62강원 춘천 동내 거두
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4617-0]1북-외가평12.120210201038.19136128.317852568.671954.18256.5475.65662605.4강원 인제 북 용대
9192건기연[4617-0]2북-외가평12.120210201038.19136128.317852682.682123.18295.094.42623061.49강원 인제 북 용대
9293건기연[4710-0]1이동-근남9.020210201038.18502127.41894854.46812.296.0356.42217038.16강원 철원 서 자등
9394건기연[4710-0]2이동-근남9.020210201038.18502127.41894784.78735.083.9149.7201140.64강원 철원 서 자등
9495건기연[5601-2]1김화-근남2.420210201038.25247127.47033325.56310.0939.8926.5881804.52강원 철원 근남 사곡
9596건기연[5601-2]2김화-근남2.420210201038.25247127.47033300.93238.1731.8818.3672464.41강원 철원 근남 사곡
9697건기연[5602-0]1사내-화천16.420210201038.05058127.60419961.95861.79108.9948.43242263.9강원 춘천 사북 오탄
9798건기연[5602-0]2사내-화천16.420210201038.05058127.60419852.62728.3990.0943.83217834.78강원 춘천 사북 오탄
9899건기연[5604-0]1풍천-서석21.120210201037.79723127.909821106.531086.1137.9165.04273941.77강원 홍천 화촌 구성포
99100건기연[5604-0]2풍천-서석21.120210201037.79723127.909821084.881321.41157.4973.97273644.32강원 홍천 화촌 구성포