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-15278/F/1/datasetView.do

Alerts

Dataset has 1 (3.7%) duplicate rowsDuplicates
내부순환로 is highly overall correlated with 강변북로 and 4 other fieldsHigh correlation
강변북로 is highly overall correlated with 내부순환로 and 4 other fieldsHigh correlation
올림픽대로 is highly overall correlated with 내부순환로 and 4 other fieldsHigh correlation
동부간선도로 is highly overall correlated with 내부순환로 and 4 other fieldsHigh correlation
분당수서로 is highly overall correlated with 내부순환로 and 4 other fieldsHigh correlation
경부고속도로 is highly overall correlated with 내부순환로 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 07:50:39.710549
Analysis finished2023-12-11 07:50:48.263099
Duration8.55 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
2023-12-11T16:50:48.388068image/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%
2023-12-11T16:50:48.745645image/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)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean55.733333
Minimum51.2
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T16:50:48.879844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.2
5-th percentile52.135
Q153.65
median55.85
Q357.075
95-th percentile60.305
Maximum63
Range11.8
Interquartile range (IQR)3.425

Descriptive statistics

Standard deviation3.0952554
Coefficient of variation (CV)0.055536879
Kurtosis1.7691284
Mean55.733333
Median Absolute Deviation (MAD)1.95
Skewness0.9529367
Sum668.8
Variance9.5806061
MonotonicityNot monotonic
2023-12-11T16:50:48.995925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
57.3 1
 
3.7%
56.3 1
 
3.7%
58.1 1
 
3.7%
56.6 1
 
3.7%
57.0 1
 
3.7%
63.0 1
 
3.7%
53.2 1
 
3.7%
52.9 1
 
3.7%
51.2 1
 
3.7%
54.0 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
51.2 1
3.7%
52.9 1
3.7%
53.2 1
3.7%
53.8 1
3.7%
54.0 1
3.7%
55.4 1
3.7%
56.3 1
3.7%
56.6 1
3.7%
57.0 1
3.7%
57.3 1
3.7%
ValueCountFrequency (%)
63.0 1
3.7%
58.1 1
3.7%
57.3 1
3.7%
57.0 1
3.7%
56.6 1
3.7%
56.3 1
3.7%
55.4 1
3.7%
54.0 1
3.7%
53.8 1
3.7%
53.2 1
3.7%

강변북로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)91.7%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean49.775
Minimum45.5
Maximum53.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T16:50:49.109839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45.5
5-th percentile45.775
Q148.125
median50.45
Q351.3
95-th percentile53.08
Maximum53.3
Range7.8
Interquartile range (IQR)3.175

Descriptive statistics

Standard deviation2.7143139
Coefficient of variation (CV)0.054531671
Kurtosis-0.96778223
Mean49.775
Median Absolute Deviation (MAD)2
Skewness-0.42630318
Sum597.3
Variance7.3675
MonotonicityNot monotonic
2023-12-11T16:50:49.234563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
50.6 2
 
7.4%
52.9 1
 
3.7%
53.3 1
 
3.7%
50.8 1
 
3.7%
52.8 1
 
3.7%
46.0 1
 
3.7%
46.1 1
 
3.7%
45.5 1
 
3.7%
49.6 1
 
3.7%
48.8 1
 
3.7%
(Missing) 15
55.6%
ValueCountFrequency (%)
45.5 1
3.7%
46.0 1
3.7%
46.1 1
3.7%
48.8 1
3.7%
49.6 1
3.7%
50.3 1
3.7%
50.6 2
7.4%
50.8 1
3.7%
52.8 1
3.7%
52.9 1
3.7%
ValueCountFrequency (%)
53.3 1
3.7%
52.9 1
3.7%
52.8 1
3.7%
50.8 1
3.7%
50.6 2
7.4%
50.3 1
3.7%
49.6 1
3.7%
48.8 1
3.7%
46.1 1
3.7%
46.0 1
3.7%

북부간선도로
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean55.791667
Minimum51.8
Maximum58.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T16:50:49.418884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.8
5-th percentile52.185
Q154.175
median56.4
Q357.175
95-th percentile58.405
Maximum58.9
Range7.1
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2084377
Coefficient of variation (CV)0.039583648
Kurtosis-0.58932942
Mean55.791667
Median Absolute Deviation (MAD)1.45
Skewness-0.57734665
Sum669.5
Variance4.877197
MonotonicityNot monotonic
2023-12-11T16:50:49.558356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
58.0 1
 
3.7%
56.2 1
 
3.7%
56.8 1
 
3.7%
54.2 1
 
3.7%
52.5 1
 
3.7%
51.8 1
 
3.7%
57.7 1
 
3.7%
55.7 1
 
3.7%
56.6 1
 
3.7%
58.9 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
51.8 1
3.7%
52.5 1
3.7%
54.1 1
3.7%
54.2 1
3.7%
55.7 1
3.7%
56.2 1
3.7%
56.6 1
3.7%
56.8 1
3.7%
57.0 1
3.7%
57.7 1
3.7%
ValueCountFrequency (%)
58.9 1
3.7%
58.0 1
3.7%
57.7 1
3.7%
57.0 1
3.7%
56.8 1
3.7%
56.6 1
3.7%
56.2 1
3.7%
55.7 1
3.7%
54.2 1
3.7%
54.1 1
3.7%

올림픽대로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)91.7%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean52.941667
Minimum49.2
Maximum56.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T16:50:49.720632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49.2
5-th percentile49.2
Q151.6
median53.55
Q354.4
95-th percentile55.605
Maximum56.1
Range6.9
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.360839
Coefficient of variation (CV)0.044593212
Kurtosis-0.80086552
Mean52.941667
Median Absolute Deviation (MAD)1.4
Skewness-0.66953365
Sum635.3
Variance5.5735606
MonotonicityNot monotonic
2023-12-11T16:50:49.859994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
49.2 2
 
7.4%
55.2 1
 
3.7%
53.5 1
 
3.7%
56.1 1
 
3.7%
54.2 1
 
3.7%
55.0 1
 
3.7%
53.6 1
 
3.7%
49.8 1
 
3.7%
54.0 1
 
3.7%
52.2 1
 
3.7%
(Missing) 15
55.6%
ValueCountFrequency (%)
49.2 2
7.4%
49.8 1
3.7%
52.2 1
3.7%
53.3 1
3.7%
53.5 1
3.7%
53.6 1
3.7%
54.0 1
3.7%
54.2 1
3.7%
55.0 1
3.7%
55.2 1
3.7%
ValueCountFrequency (%)
56.1 1
3.7%
55.2 1
3.7%
55.0 1
3.7%
54.2 1
3.7%
54.0 1
3.7%
53.6 1
3.7%
53.5 1
3.7%
53.3 1
3.7%
52.2 1
3.7%
49.8 1
3.7%

동부간선도로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean50.791667
Minimum47.7
Maximum53.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T16:50:49.988002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.7
5-th percentile48.03
Q149.55
median50.6
Q352.525
95-th percentile53.245
Maximum53.3
Range5.6
Interquartile range (IQR)2.975

Descriptive statistics

Standard deviation1.8889431
Coefficient of variation (CV)0.03719002
Kurtosis-1.1155793
Mean50.791667
Median Absolute Deviation (MAD)1.5
Skewness-0.10183671
Sum609.5
Variance3.5681061
MonotonicityNot monotonic
2023-12-11T16:50:50.125842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
51.4 1
 
3.7%
50.1 1
 
3.7%
49.4 1
 
3.7%
47.7 1
 
3.7%
48.3 1
 
3.7%
49.6 1
 
3.7%
52.9 1
 
3.7%
52.4 1
 
3.7%
50.4 1
 
3.7%
50.8 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
47.7 1
3.7%
48.3 1
3.7%
49.4 1
3.7%
49.6 1
3.7%
50.1 1
3.7%
50.4 1
3.7%
50.8 1
3.7%
51.4 1
3.7%
52.4 1
3.7%
52.9 1
3.7%
ValueCountFrequency (%)
53.3 1
3.7%
53.2 1
3.7%
52.9 1
3.7%
52.4 1
3.7%
51.4 1
3.7%
50.8 1
3.7%
50.4 1
3.7%
50.1 1
3.7%
49.6 1
3.7%
49.4 1
3.7%

분당수서로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean53.1
Minimum48.7
Maximum57.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T16:50:50.261966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48.7
5-th percentile49.525
Q150.675
median53.4
Q354.925
95-th percentile57.005
Maximum57.5
Range8.8
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation2.7627391
Coefficient of variation (CV)0.052028985
Kurtosis-1.1040422
Mean53.1
Median Absolute Deviation (MAD)2.3
Skewness0.021324405
Sum637.2
Variance7.6327273
MonotonicityNot monotonic
2023-12-11T16:50:50.400450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
57.5 1
 
3.7%
55.3 1
 
3.7%
56.6 1
 
3.7%
54.4 1
 
3.7%
54.8 1
 
3.7%
52.8 1
 
3.7%
50.7 1
 
3.7%
50.6 1
 
3.7%
48.7 1
 
3.7%
54.0 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
48.7 1
3.7%
50.2 1
3.7%
50.6 1
3.7%
50.7 1
3.7%
51.6 1
3.7%
52.8 1
3.7%
54.0 1
3.7%
54.4 1
3.7%
54.8 1
3.7%
55.3 1
3.7%
ValueCountFrequency (%)
57.5 1
3.7%
56.6 1
3.7%
55.3 1
3.7%
54.8 1
3.7%
54.4 1
3.7%
54.0 1
3.7%
52.8 1
3.7%
51.6 1
3.7%
50.7 1
3.7%
50.6 1
3.7%

경부고속도로
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean39.783333
Minimum35.6
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T16:50:50.549179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.6
5-th percentile36.535
Q138.625
median40.4
Q341.075
95-th percentile42.34
Maximum43
Range7.4
Interquartile range (IQR)2.45

Descriptive statistics

Standard deviation2.0866822
Coefficient of variation (CV)0.052451164
Kurtosis0.0038753888
Mean39.783333
Median Absolute Deviation (MAD)1.4
Skewness-0.56040889
Sum477.4
Variance4.3542424
MonotonicityNot monotonic
2023-12-11T16:50:50.692430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
41.3 1
 
3.7%
40.3 1
 
3.7%
43.0 1
 
3.7%
41.0 1
 
3.7%
41.8 1
 
3.7%
40.7 1
 
3.7%
38.8 1
 
3.7%
37.3 1
 
3.7%
35.6 1
 
3.7%
40.5 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
35.6 1
3.7%
37.3 1
3.7%
38.1 1
3.7%
38.8 1
3.7%
39.0 1
3.7%
40.3 1
3.7%
40.5 1
3.7%
40.7 1
3.7%
41.0 1
3.7%
41.3 1
3.7%
ValueCountFrequency (%)
43.0 1
3.7%
41.8 1
3.7%
41.3 1
3.7%
41.0 1
3.7%
40.7 1
3.7%
40.5 1
3.7%
40.3 1
3.7%
39.0 1
3.7%
38.8 1
3.7%
38.1 1
3.7%

서부간선도로
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Memory size348.0 B
2023-12-11T16:50:50.881683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6666667
Min length1

Characters and Unicode

Total characters56
Distinct characters11
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

Unique12 ?
Unique (%)100.0%

Sample

1st row31.8
2nd row31.0
3rd row-
4th row29.1
5th row31.1
ValueCountFrequency (%)
31.8 1
8.3%
31.0 1
8.3%
1
8.3%
29.1 1
8.3%
31.1 1
8.3%
31.3 1
8.3%
28.6 1
8.3%
28.1 1
8.3%
26.8 1
8.3%
28.3 1
8.3%
Other values (2) 2
16.7%
2023-12-11T16:50:51.593489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 11
19.6%
11
19.6%
2 8
14.3%
1 7
12.5%
3 6
10.7%
8 6
10.7%
6 3
 
5.4%
0 1
 
1.8%
- 1
 
1.8%
9 1
 
1.8%

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 (%)
2 8
24.2%
1 7
21.2%
3 6
18.2%
8 6
18.2%
6 3
 
9.1%
0 1
 
3.0%
9 1
 
3.0%
7 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%
2 8
14.3%
1 7
12.5%
3 6
10.7%
8 6
10.7%
6 3
 
5.4%
0 1
 
1.8%
- 1
 
1.8%
9 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 11
19.6%
11
19.6%
2 8
14.3%
1 7
12.5%
3 6
10.7%
8 6
10.7%
6 3
 
5.4%
0 1
 
1.8%
- 1
 
1.8%
9 1
 
1.8%

강남순환로
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)100.0%
Missing15
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean88.875
Minimum85.6
Maximum91.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T16:50:51.755546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum85.6
5-th percentile85.655
Q187.475
median89
Q390.625
95-th percentile91.415
Maximum91.8
Range6.2
Interquartile range (IQR)3.15

Descriptive statistics

Standard deviation2.1209453
Coefficient of variation (CV)0.023864364
Kurtosis-1.2420234
Mean88.875
Median Absolute Deviation (MAD)1.7
Skewness-0.26618043
Sum1066.5
Variance4.4984091
MonotonicityNot monotonic
2023-12-11T16:50:51.921435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
91.1 1
 
3.7%
91.8 1
 
3.7%
87.9 1
 
3.7%
85.7 1
 
3.7%
85.6 1
 
3.7%
89.5 1
 
3.7%
88.5 1
 
3.7%
87.6 1
 
3.7%
87.1 1
 
3.7%
90.5 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 15
55.6%
ValueCountFrequency (%)
85.6 1
3.7%
85.7 1
3.7%
87.1 1
3.7%
87.6 1
3.7%
87.9 1
3.7%
88.5 1
3.7%
89.5 1
3.7%
90.2 1
3.7%
90.5 1
3.7%
91.0 1
3.7%
ValueCountFrequency (%)
91.8 1
3.7%
91.1 1
3.7%
91.0 1
3.7%
90.5 1
3.7%
90.2 1
3.7%
89.5 1
3.7%
88.5 1
3.7%
87.9 1
3.7%
87.6 1
3.7%
87.1 1
3.7%

Interactions

2023-12-11T16:50:46.671174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:40.052319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:41.017828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:41.938155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.757820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:43.876394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:44.743862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:45.644477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:46.778867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:40.158165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:41.130182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.039297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.856585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:43.962500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:44.843847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:45.781525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:46.894026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:40.283579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:41.231749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.136103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.957890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:44.061693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:44.972725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:45.915112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:47.024209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:40.408416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:41.368289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.252911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:43.063198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:44.168554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:45.075358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:46.038741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:47.161323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:40.537553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:41.503319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.355268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:43.156924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:44.290143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:45.191933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:46.170283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:47.277128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:40.646344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:41.602711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.451811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:43.249026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:44.391299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:45.292528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:46.294639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:47.368501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:40.752491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:41.696363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.554221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:43.338073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:44.497574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:45.383926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:46.410107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:47.494616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:40.882282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:41.813529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:42.656219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:43.435099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:44.610707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:45.533536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:50:46.534124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:50:52.041227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분내부순환로강변북로북부간선도로올림픽대로동부간선도로분당수서로경부고속도로서부간선도로강남순환로
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
내부순환로1.0001.0000.7040.0000.7480.0000.9350.3741.0000.766
강변북로1.0000.7041.0000.6160.8530.2090.6580.5601.0000.000
북부간선도로1.0000.0000.6161.0000.0000.8270.0000.4461.0000.000
올림픽대로1.0000.7480.8530.0001.0000.0000.8200.8441.0000.000
동부간선도로1.0000.0000.2090.8270.0001.0000.0000.8521.0000.000
분당수서로1.0000.9350.6580.0000.8200.0001.0000.6531.0000.845
경부고속도로1.0000.3740.5600.4460.8440.8520.6531.0001.0000.000
서부간선도로1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
강남순환로1.0000.7660.0000.0000.0000.0000.8450.0001.0001.000
2023-12-11T16:50:52.240611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
내부순환로강변북로북부간선도로올림픽대로동부간선도로분당수서로경부고속도로강남순환로
내부순환로1.0000.907-0.3080.858-0.5590.7900.8950.091
강변북로0.9071.000-0.1790.944-0.5780.9110.9460.032
북부간선도로-0.308-0.1791.0000.0280.3710.056-0.1120.462
올림픽대로0.8580.9440.0281.000-0.5850.8970.9770.025
동부간선도로-0.559-0.5780.371-0.5851.000-0.517-0.6430.497
분당수서로0.7900.9110.0560.897-0.5171.0000.8880.203
경부고속도로0.8950.946-0.1120.977-0.6430.8881.000-0.084
강남순환로0.0910.0320.4620.0250.4970.203-0.0841.000

Missing values

2023-12-11T16:50:47.674838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:50:47.872324image/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.
2023-12-11T16:50:48.092923image/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
01월57.352.958.055.251.457.541.331.891.1
12월56.350.656.253.550.155.340.331.091.8
23월58.153.356.856.149.456.643.0-87.9
34월56.650.854.254.247.754.441.029.185.7
45월57.052.852.555.048.354.841.831.185.6
56월63.050.651.853.649.652.840.731.389.5
67월53.246.057.749.852.950.738.828.688.5
78월52.946.155.749.252.450.637.328.187.6
89월51.245.556.649.250.448.735.626.887.1
910월54.049.658.954.050.854.040.528.390.5
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