Overview

Dataset statistics

Number of variables10
Number of observations27
Missing cells150
Missing cells (%)55.6%
Duplicate rows1
Duplicate rows (%)3.7%
Total size in memory2.4 KiB
Average record size in memory92.9 B

Variable types

Text2
Numeric8

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15279/F/1/datasetView.do

Alerts

Dataset has 1 (3.7%) duplicate rowsDuplicates
강변북로 is highly overall correlated with 북부간선도로 and 4 other fieldsHigh correlation
북부간선도로 is highly overall correlated with 강변북로 and 4 other fieldsHigh correlation
올림픽대로 is highly overall correlated with 강변북로 and 4 other fieldsHigh correlation
동부간선도로 is highly overall correlated with 강남순환로High correlation
분당수서로 is highly overall correlated with 강변북로 and 4 other fieldsHigh correlation
경부고속도로 is highly overall correlated with 강변북로 and 3 other fieldsHigh correlation
강남순환로 is highly overall correlated with 강변북로 and 4 other fieldsHigh correlation
구분 has 15 (55.6%) missing valuesMissing
내부순환로 has 15 (55.6%) missing valuesMissing
강변북로 has 15 (55.6%) missing valuesMissing
북부간선도로 has 15 (55.6%) missing valuesMissing
올림픽대로 has 15 (55.6%) missing valuesMissing
동부간선도로 has 15 (55.6%) missing valuesMissing
분당수서로 has 15 (55.6%) missing valuesMissing
경부고속도로 has 15 (55.6%) missing valuesMissing
서부간선도로 has 15 (55.6%) missing valuesMissing
강남순환로 has 15 (55.6%) missing valuesMissing

Reproduction

Analysis started2023-12-11 06:48:42.074573
Analysis finished2023-12-11 06:48:49.684939
Duration7.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Memory size348.0 B
2023-12-11T15:48:49.824258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.25
Min length2

Characters and Unicode

Total characters27
Distinct characters11
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

Unique12 ?
Unique (%)100.0%

Sample

1st row1월
2nd row2월
3rd row3월
4th row4월
5th row5월
ValueCountFrequency (%)
1월 1
8.3%
2월 1
8.3%
3월 1
8.3%
4월 1
8.3%
5월 1
8.3%
6월 1
8.3%
7월 1
8.3%
8월 1
8.3%
9월 1
8.3%
10월 1
8.3%
Other values (2) 2
16.7%
2023-12-11T15:48:50.144694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
월 12
44.4%
1 5
18.5%
2 2
 
7.4%
3 1
 
3.7%
4 1
 
3.7%
5 1
 
3.7%
6 1
 
3.7%
7 1
 
3.7%
8 1
 
3.7%
9 1
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15
55.6%
Other Letter 12
44.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
33.3%
2 2
 
13.3%
3 1
 
6.7%
4 1
 
6.7%
5 1
 
6.7%
6 1
 
6.7%
7 1
 
6.7%
8 1
 
6.7%
9 1
 
6.7%
0 1
 
6.7%
Other Letter
ValueCountFrequency (%)
월 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
55.6%
Hangul 12
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
2 2
 
13.3%
3 1
 
6.7%
4 1
 
6.7%
5 1
 
6.7%
6 1
 
6.7%
7 1
 
6.7%
8 1
 
6.7%
9 1
 
6.7%
0 1
 
6.7%
Hangul
ValueCountFrequency (%)
월 12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
55.6%
Hangul 12
44.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
월 12
100.0%
ASCII
ValueCountFrequency (%)
1 5
33.3%
2 2
 
13.3%
3 1
 
6.7%
4 1
 
6.7%
5 1
 
6.7%
6 1
 
6.7%
7 1
 
6.7%
8 1
 
6.7%
9 1
 
6.7%
0 1
 
6.7%

내부순환로
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)83.3%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean55.483333
Minimum46.4
Maximum65.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T15:48:50.296579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46.4
5-th percentile49.425
Q154.65
median55.35
Q357
95-th percentile61.23
Maximum65.3
Range18.9
Interquartile range (IQR)2.35

Descriptive statistics

Standard deviation4.3295671
Coefficient of variation (CV)0.078033652
Kurtosis3.2836267
Mean55.483333
Median Absolute Deviation (MAD)1.65
Skewness0.22157062
Sum665.8
Variance18.745152
MonotonicityNot monotonic
2023-12-11T15:48:50.410315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
55.2 2
 
7.4%
57.0 2
 
7.4%
55.8 1
 
3.7%
55.5 1
 
3.7%
57.9 1
 
3.7%
65.3 1
 
3.7%
51.9 1
 
3.7%
53.6 1
 
3.7%
46.4 1
 
3.7%
55.0 1
 
3.7%
(Missing) 15
55.6%
ValueCountFrequency (%)
46.4 1
3.7%
51.9 1
3.7%
53.6 1
3.7%
55.0 1
3.7%
55.2 2
7.4%
55.5 1
3.7%
55.8 1
3.7%
57.0 2
7.4%
57.9 1
3.7%
65.3 1
3.7%
ValueCountFrequency (%)
65.3 1
3.7%
57.9 1
3.7%
57.0 2
7.4%
55.8 1
3.7%
55.5 1
3.7%
55.2 2
7.4%
55.0 1
3.7%
53.6 1
3.7%
51.9 1
3.7%
46.4 1
3.7%

강변북로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)91.7%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean51.283333
Minimum44
Maximum54.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T15:48:50.555096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile45.595
Q149.35
median52.8
Q353.875
95-th percentile54.58
Maximum54.8
Range10.8
Interquartile range (IQR)4.525

Descriptive statistics

Standard deviation3.4028954
Coefficient of variation (CV)0.066354801
Kurtosis0.24003228
Mean51.283333
Median Absolute Deviation (MAD)1.75
Skewness-1.0462205
Sum615.4
Variance11.579697
MonotonicityNot monotonic
2023-12-11T15:48:50.679842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
52.8 2
 
7.4%
54.8 1
 
3.7%
52.9 1
 
3.7%
48.3 1
 
3.7%
54.1 1
 
3.7%
49.7 1
 
3.7%
54.4 1
 
3.7%
46.9 1
 
3.7%
50.9 1
 
3.7%
44.0 1
 
3.7%
(Missing) 15
55.6%
ValueCountFrequency (%)
44.0 1
3.7%
46.9 1
3.7%
48.3 1
3.7%
49.7 1
3.7%
50.9 1
3.7%
52.8 2
7.4%
52.9 1
3.7%
53.8 1
3.7%
54.1 1
3.7%
54.4 1
3.7%
ValueCountFrequency (%)
54.8 1
3.7%
54.4 1
3.7%
54.1 1
3.7%
53.8 1
3.7%
52.9 1
3.7%
52.8 2
7.4%
50.9 1
3.7%
49.7 1
3.7%
48.3 1
3.7%
46.9 1
3.7%

북부간선도로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean51.175
Minimum47.1
Maximum54.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T15:48:50.799150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.1
5-th percentile47.54
Q148.725
median51.25
Q353.4
95-th percentile54.635
Maximum54.8
Range7.7
Interquartile range (IQR)4.675

Descriptive statistics

Standard deviation2.7173266
Coefficient of variation (CV)0.053098711
Kurtosis-1.5762986
Mean51.175
Median Absolute Deviation (MAD)2.45
Skewness-0.10622426
Sum614.1
Variance7.3838636
MonotonicityNot monotonic
2023-12-11T15:48:50.921658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
54.8 1
 
3.7%
53.3 1
 
3.7%
54.5 1
 
3.7%
48.5 1
 
3.7%
48.8 1
 
3.7%
47.1 1
 
3.7%
53.7 1
 
3.7%
51.2 1
 
3.7%
49.8 1
 
3.7%
51.3 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
47.1 1
3.7%
47.9 1
3.7%
48.5 1
3.7%
48.8 1
3.7%
49.8 1
3.7%
51.2 1
3.7%
51.3 1
3.7%
53.2 1
3.7%
53.3 1
3.7%
53.7 1
3.7%
ValueCountFrequency (%)
54.8 1
3.7%
54.5 1
3.7%
53.7 1
3.7%
53.3 1
3.7%
53.2 1
3.7%
51.3 1
3.7%
51.2 1
3.7%
49.8 1
3.7%
48.8 1
3.7%
48.5 1
3.7%

올림픽대로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)91.7%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean54.491667
Minimum48.3
Maximum57.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T15:48:51.031050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48.3
5-th percentile49.18
Q152.825
median55.5
Q356.5
95-th percentile57.535
Maximum57.7
Range9.4
Interquartile range (IQR)3.675

Descriptive statistics

Standard deviation3.0485342
Coefficient of variation (CV)0.055944961
Kurtosis-0.0081989932
Mean54.491667
Median Absolute Deviation (MAD)1.75
Skewness-1.0079615
Sum653.9
Variance9.2935606
MonotonicityNot monotonic
2023-12-11T15:48:51.167958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
54.9 2
 
7.4%
57.4 1
 
3.7%
56.1 1
 
3.7%
56.3 1
 
3.7%
52.0 1
 
3.7%
56.2 1
 
3.7%
53.1 1
 
3.7%
57.7 1
 
3.7%
49.9 1
 
3.7%
48.3 1
 
3.7%
(Missing) 15
55.6%
ValueCountFrequency (%)
48.3 1
3.7%
49.9 1
3.7%
52.0 1
3.7%
53.1 1
3.7%
54.9 2
7.4%
56.1 1
3.7%
56.2 1
3.7%
56.3 1
3.7%
57.1 1
3.7%
57.4 1
3.7%
ValueCountFrequency (%)
57.7 1
3.7%
57.4 1
3.7%
57.1 1
3.7%
56.3 1
3.7%
56.2 1
3.7%
56.1 1
3.7%
54.9 2
7.4%
53.1 1
3.7%
52.0 1
3.7%
49.9 1
3.7%

동부간선도로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean48.333333
Minimum43.5
Maximum53.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T15:48:51.299634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43.5
5-th percentile44.16
Q146.475
median47.15
Q350.725
95-th percentile53.405
Maximum53.9
Range10.4
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation3.2955434
Coefficient of variation (CV)0.068183656
Kurtosis-0.84365745
Mean48.333333
Median Absolute Deviation (MAD)2
Skewness0.45323249
Sum580
Variance10.860606
MonotonicityNot monotonic
2023-12-11T15:48:51.440757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
48.7 1
 
3.7%
47.4 1
 
3.7%
46.4 1
 
3.7%
43.5 1
 
3.7%
46.5 1
 
3.7%
44.7 1
 
3.7%
53.0 1
 
3.7%
52.0 1
 
3.7%
50.3 1
 
3.7%
46.9 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
43.5 1
3.7%
44.7 1
3.7%
46.4 1
3.7%
46.5 1
3.7%
46.7 1
3.7%
46.9 1
3.7%
47.4 1
3.7%
48.7 1
3.7%
50.3 1
3.7%
52.0 1
3.7%
ValueCountFrequency (%)
53.9 1
3.7%
53.0 1
3.7%
52.0 1
3.7%
50.3 1
3.7%
48.7 1
3.7%
47.4 1
3.7%
46.9 1
3.7%
46.7 1
3.7%
46.5 1
3.7%
46.4 1
3.7%

분당수서로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean57.216667
Minimum47.2
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T15:48:51.575465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.2
5-th percentile50.94
Q156.575
median58.05
Q359.2
95-th percentile60.835
Maximum61
Range13.8
Interquartile range (IQR)2.625

Descriptive statistics

Standard deviation3.7201743
Coefficient of variation (CV)0.065019068
Kurtosis4.6783171
Mean57.216667
Median Absolute Deviation (MAD)1.55
Skewness-1.9385543
Sum686.6
Variance13.839697
MonotonicityNot monotonic
2023-12-11T15:48:51.727586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
60.7 1
 
3.7%
59.8 1
 
3.7%
61.0 1
 
3.7%
56.2 1
 
3.7%
58.8 1
 
3.7%
56.7 1
 
3.7%
58.2 1
 
3.7%
59.0 1
 
3.7%
54.0 1
 
3.7%
57.9 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
47.2 1
3.7%
54.0 1
3.7%
56.2 1
3.7%
56.7 1
3.7%
57.1 1
3.7%
57.9 1
3.7%
58.2 1
3.7%
58.8 1
3.7%
59.0 1
3.7%
59.8 1
3.7%
ValueCountFrequency (%)
61.0 1
3.7%
60.7 1
3.7%
59.8 1
3.7%
59.0 1
3.7%
58.8 1
3.7%
58.2 1
3.7%
57.9 1
3.7%
57.1 1
3.7%
56.7 1
3.7%
56.2 1
3.7%

경부고속도로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean42.091667
Minimum34.2
Maximum46.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T15:48:51.869831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.2
5-th percentile36.235
Q141.15
median42.7
Q344.55
95-th percentile45.695
Maximum46.3
Range12.1
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation3.4286118
Coefficient of variation (CV)0.081455833
Kurtosis1.3386962
Mean42.091667
Median Absolute Deviation (MAD)1.9
Skewness-1.178215
Sum505.1
Variance11.755379
MonotonicityNot monotonic
2023-12-11T15:48:52.317220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
45.2 1
 
3.7%
43.8 1
 
3.7%
46.3 1
 
3.7%
39.8 1
 
3.7%
44.7 1
 
3.7%
41.6 1
 
3.7%
44.5 1
 
3.7%
42.4 1
 
3.7%
37.9 1
 
3.7%
41.7 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
34.2 1
3.7%
37.9 1
3.7%
39.8 1
3.7%
41.6 1
3.7%
41.7 1
3.7%
42.4 1
3.7%
43.0 1
3.7%
43.8 1
3.7%
44.5 1
3.7%
44.7 1
3.7%
ValueCountFrequency (%)
46.3 1
3.7%
45.2 1
3.7%
44.7 1
3.7%
44.5 1
3.7%
43.8 1
3.7%
43.0 1
3.7%
42.4 1
3.7%
41.7 1
3.7%
41.6 1
3.7%
39.8 1
3.7%

서부간선도로
Text

MISSING 

Distinct11
Distinct (%)91.7%
Missing15
Missing (%)55.6%
Memory size348.0 B
2023-12-11T15:48:52.493144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6666667
Min length1

Characters and Unicode

Total characters56
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

Unique10 ?
Unique (%)83.3%

Sample

1st row35.8
2nd row33.4
3rd row-
4th row27.4
5th row34.3
ValueCountFrequency (%)
27.4 2
16.7%
35.8 1
8.3%
33.4 1
8.3%
1
8.3%
34.3 1
8.3%
29.9 1
8.3%
33.1 1
8.3%
31.7 1
8.3%
28.6 1
8.3%
25.1 1
8.3%
2023-12-11T15:48:52.830722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 11
19.6%
11
19.6%
3 9
16.1%
2 5
8.9%
4 4
 
7.1%
7 3
 
5.4%
1 3
 
5.4%
5 2
 
3.6%
8 2
 
3.6%
9 2
 
3.6%
Other values (3) 4
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33
58.9%
Other Punctuation 11
 
19.6%
Space Separator 11
 
19.6%
Dash Punctuation 1
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 9
27.3%
2 5
15.2%
4 4
12.1%
7 3
 
9.1%
1 3
 
9.1%
5 2
 
6.1%
8 2
 
6.1%
9 2
 
6.1%
6 2
 
6.1%
0 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 11
19.6%
11
19.6%
3 9
16.1%
2 5
8.9%
4 4
 
7.1%
7 3
 
5.4%
1 3
 
5.4%
5 2
 
3.6%
8 2
 
3.6%
9 2
 
3.6%
Other values (3) 4
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 11
19.6%
11
19.6%
3 9
16.1%
2 5
8.9%
4 4
 
7.1%
7 3
 
5.4%
1 3
 
5.4%
5 2
 
3.6%
8 2
 
3.6%
9 2
 
3.6%
Other values (3) 4
 
7.1%

강남순환로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean85.941667
Minimum77.9
Maximum91.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T15:48:52.970604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77.9
5-th percentile79.385
Q183.475
median85.45
Q389.45
95-th percentile91.405
Maximum91.9
Range14
Interquartile range (IQR)5.975

Descriptive statistics

Standard deviation4.3976147
Coefficient of variation (CV)0.051169763
Kurtosis-0.85299512
Mean85.941667
Median Absolute Deviation (MAD)3.6
Skewness-0.31010511
Sum1031.3
Variance19.339015
MonotonicityNot monotonic
2023-12-11T15:48:53.104462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
91.0 1
 
3.7%
91.9 1
 
3.7%
84.0 1
 
3.7%
77.9 1
 
3.7%
80.6 1
 
3.7%
84.2 1
 
3.7%
90.2 1
 
3.7%
86.7 1
 
3.7%
83.1 1
 
3.7%
88.9 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
77.9 1
3.7%
80.6 1
3.7%
83.1 1
3.7%
83.6 1
3.7%
84.0 1
3.7%
84.2 1
3.7%
86.7 1
3.7%
88.9 1
3.7%
89.2 1
3.7%
90.2 1
3.7%
ValueCountFrequency (%)
91.9 1
3.7%
91.0 1
3.7%
90.2 1
3.7%
89.2 1
3.7%
88.9 1
3.7%
86.7 1
3.7%
84.2 1
3.7%
84.0 1
3.7%
83.6 1
3.7%
83.1 1
3.7%

Interactions

2023-12-11T15:48:48.234635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:42.359567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.088325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.731389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.425167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:45.541595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:46.391549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:47.317624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:48.343602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:42.460657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.168674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.827088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.508444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:45.646327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:46.481291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:47.449232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:48.448908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:42.543163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.240494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.919823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.598419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:45.749136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:46.575196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:47.574719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:48.584456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:42.632931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.320780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.005361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.704068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:45.865300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:46.696863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:47.681465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:48.705825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:42.723733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.410614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.079042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.797503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:45.976968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:46.827985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:47.786348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:48.823436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:42.819452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.481540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.157279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.883607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:46.069759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:46.939692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:47.904396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:48.958608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:42.912092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.553254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.244880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.977364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:46.182690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:47.069800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:48.029469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:49.086943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.001312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:43.648510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:44.330481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:45.081402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:46.291190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:47.183922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:48:48.123561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:48:53.214319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분내부순환로강변북로북부간선도로올림픽대로동부간선도로분당수서로경부고속도로서부간선도로강남순환로
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
내부순환로1.0001.0000.9420.7800.9170.3290.8020.8380.8440.746
강변북로1.0000.9421.0000.5270.9850.7360.7950.8990.0000.736
북부간선도로1.0000.7800.5271.0000.7890.7930.7380.8270.9140.749
올림픽대로1.0000.9170.9850.7891.0000.8770.8160.9190.7510.721
동부간선도로1.0000.3290.7360.7930.8771.0000.7270.5490.8660.931
분당수서로1.0000.8020.7950.7380.8160.7271.0000.7640.8810.822
경부고속도로1.0000.8380.8990.8270.9190.5490.7641.0000.8090.549
서부간선도로1.0000.8440.0000.9140.7510.8660.8810.8091.0000.650
강남순환로1.0000.7460.7360.7490.7210.9310.8220.5490.6501.000
2023-12-11T15:48:53.366294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
내부순환로강변북로북부간선도로올림픽대로동부간선도로분당수서로경부고속도로강남순환로
내부순환로1.0000.492-0.0490.378-0.1720.3790.4250.049
강변북로0.4921.0000.7320.9670.3960.7460.9040.536
북부간선도로-0.0490.7321.0000.8090.4830.7690.7760.685
올림픽대로0.3780.9670.8091.0000.4410.7180.8860.620
동부간선도로-0.1720.3960.4830.4411.0000.1120.1400.587
분당수서로0.3790.7460.7690.7180.1121.0000.9090.510
경부고속도로0.4250.9040.7760.8860.1400.9091.0000.434
강남순환로0.0490.5360.6850.6200.5870.5100.4341.000

Missing values

2023-12-11T15:48:49.209799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:48:49.384533image/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.
2023-12-11T15:48:49.557027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분내부순환로강변북로북부간선도로올림픽대로동부간선도로분당수서로경부고속도로서부간선도로강남순환로
01월55.854.854.857.448.760.745.235.891.0
12월55.252.853.356.147.459.843.833.491.9
23월55.252.954.556.346.461.046.3-84.0
34월55.548.348.552.043.556.239.827.477.9
45월57.954.148.856.246.558.844.734.380.6
56월65.349.747.153.144.756.741.629.984.2
67월57.054.453.757.753.058.244.533.190.2
78월57.052.851.254.952.059.042.431.786.7
89월51.946.949.849.950.354.037.928.683.1
910월53.650.951.354.946.957.941.727.488.9
구분내부순환로강변북로북부간선도로올림픽대로동부간선도로분당수서로경부고속도로서부간선도로강남순환로
17<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
18<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
19<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
22<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
24<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
26<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구분내부순환로강변북로북부간선도로올림픽대로동부간선도로분당수서로경부고속도로서부간선도로강남순환로# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>15