Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells28879
Missing cells (%)20.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory129.0 B

Variable types

Categorical4
Text3
Numeric7

Dataset

Description노드링크 유형,노드 WKT,노드 ID,노드 유형 코드,링크 WKT,링크 ID,링크 유형 코드,시작노드 ID,종료노드 ID,링크 길이,시군구코드,시군구명,읍면동코드,읍면동명
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21209/S/1/datasetView.do

Alerts

노드 유형 코드 is highly overall correlated with 링크 ID and 1 other fieldsHigh correlation
링크 유형 코드 is highly overall correlated with 노드 ID and 1 other fieldsHigh correlation
노드링크 유형 is highly overall correlated with 노드 ID and 6 other fieldsHigh correlation
노드 ID is highly overall correlated with 링크 ID and 2 other fieldsHigh correlation
링크 ID is highly overall correlated with 노드 ID and 2 other fieldsHigh correlation
시작노드 ID is highly overall correlated with 종료노드 ID and 1 other fieldsHigh correlation
종료노드 ID is highly overall correlated with 시작노드 ID and 1 other fieldsHigh correlation
링크 길이 is highly overall correlated with 노드링크 유형High correlation
시군구코드 is highly overall correlated with 읍면동코드 and 1 other fieldsHigh correlation
읍면동코드 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
노드 유형 코드 is highly imbalanced (53.9%)Imbalance
노드 WKT has 3707 (37.1%) missing valuesMissing
링크 WKT has 6293 (62.9%) missing valuesMissing
시작노드 ID has 6293 (62.9%) missing valuesMissing
종료노드 ID has 6293 (62.9%) missing valuesMissing
링크 길이 has 6293 (62.9%) missing valuesMissing
노드 ID has 3707 (37.1%) zerosZeros
링크 ID has 6293 (62.9%) zerosZeros

Reproduction

Analysis started2024-05-18 09:47:51.278319
Analysis finished2024-05-18 09:48:07.979343
Duration16.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노드링크 유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
NODE
6293 
LINK
3707 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
NODE 6293
62.9%
LINK 3707
37.1%

Length

2024-05-18T18:48:08.279703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:48:08.540132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
node 6293
62.9%
link 3707
37.1%

노드 WKT
Text

MISSING 

Distinct6293
Distinct (%)100.0%
Missing3707
Missing (%)37.1%
Memory size156.2 KiB
2024-05-18T18:48:09.127244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length43
Mean length43.03639
Min length23

Characters and Unicode

Total characters270828
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6293 ?
Unique (%)100.0%

Sample

1st rowPOINT(126.83515429200594 37.55802429568897)
2nd rowPOINT(127.0161550655481 37.647762875611775)
3rd rowPOINT(127.01771335920874 37.48234109664415)
4th rowPOINT(127.12139315962087 37.49408874255733)
5th rowPOINT(127.11155185739744 37.490285304051085)
ValueCountFrequency (%)
point(127.08436497422292 2
 
< 0.1%
point(126.98678463864071 1
 
< 0.1%
point(127.04743793490103 1
 
< 0.1%
37.62677650902781 1
 
< 0.1%
point(126.93572970862486 1
 
< 0.1%
37.5554996645453 1
 
< 0.1%
point(126.92886640325582 1
 
< 0.1%
37.52433676705406 1
 
< 0.1%
point(126.84248113863693 1
 
< 0.1%
37.48426109371383 1
 
< 0.1%
Other values (12575) 12575
99.9%
2024-05-18T18:48:10.110631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 25831
9.5%
1 23306
 
8.6%
3 22659
 
8.4%
2 22599
 
8.3%
6 21055
 
7.8%
5 20977
 
7.7%
4 18491
 
6.8%
9 17896
 
6.6%
8 17602
 
6.5%
0 17482
 
6.5%
Other values (9) 62930
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 207898
76.8%
Uppercase Letter 31465
 
11.6%
Other Punctuation 12586
 
4.6%
Space Separator 6293
 
2.3%
Open Punctuation 6293
 
2.3%
Close Punctuation 6293
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 25831
12.4%
1 23306
11.2%
3 22659
10.9%
2 22599
10.9%
6 21055
10.1%
5 20977
10.1%
4 18491
8.9%
9 17896
8.6%
8 17602
8.5%
0 17482
8.4%
Uppercase Letter
ValueCountFrequency (%)
P 6293
20.0%
O 6293
20.0%
T 6293
20.0%
N 6293
20.0%
I 6293
20.0%
Other Punctuation
ValueCountFrequency (%)
. 12586
100.0%
Space Separator
ValueCountFrequency (%)
6293
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6293
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6293
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 239363
88.4%
Latin 31465
 
11.6%

Most frequent character per script

Common
ValueCountFrequency (%)
7 25831
10.8%
1 23306
9.7%
3 22659
9.5%
2 22599
9.4%
6 21055
8.8%
5 20977
8.8%
4 18491
7.7%
9 17896
7.5%
8 17602
7.4%
0 17482
7.3%
Other values (4) 31465
13.1%
Latin
ValueCountFrequency (%)
P 6293
20.0%
O 6293
20.0%
T 6293
20.0%
N 6293
20.0%
I 6293
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 25831
9.5%
1 23306
 
8.6%
3 22659
 
8.4%
2 22599
 
8.3%
6 21055
 
7.8%
5 20977
 
7.7%
4 18491
 
6.8%
9 17896
 
6.6%
8 17602
 
6.5%
0 17482
 
6.5%
Other values (9) 62930
23.2%

노드 ID
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6294
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68624.833
Minimum0
Maximum215540
Zeros3707
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:48:10.453631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median43304.5
Q3130014
95-th percentile200805.5
Maximum215540
Range215540
Interquartile range (IQR)130014

Descriptive statistics

Standard deviation72418.548
Coefficient of variation (CV)1.055282
Kurtosis-1.1422476
Mean68624.833
Median Absolute Deviation (MAD)43304.5
Skewness0.57112299
Sum6.8624833 × 108
Variance5.244446 × 109
MonotonicityNot monotonic
2024-05-18T18:48:10.873120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3707
37.1%
188156 1
 
< 0.1%
118889 1
 
< 0.1%
188902 1
 
< 0.1%
84509 1
 
< 0.1%
190670 1
 
< 0.1%
98182 1
 
< 0.1%
116842 1
 
< 0.1%
167790 1
 
< 0.1%
146260 1
 
< 0.1%
Other values (6284) 6284
62.8%
ValueCountFrequency (%)
0 3707
37.1%
89 1
 
< 0.1%
99 1
 
< 0.1%
120 1
 
< 0.1%
130 1
 
< 0.1%
132 1
 
< 0.1%
139 1
 
< 0.1%
140 1
 
< 0.1%
151 1
 
< 0.1%
190 1
 
< 0.1%
ValueCountFrequency (%)
215540 1
< 0.1%
215522 1
< 0.1%
215497 1
< 0.1%
215476 1
< 0.1%
215460 1
< 0.1%
215435 1
< 0.1%
215418 1
< 0.1%
215337 1
< 0.1%
215292 1
< 0.1%
215288 1
< 0.1%

노드 유형 코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6137 
<NA>
3707 
2
 
126
1
 
26
3
 
4

Length

Max length4
Median length1
Mean length2.1121
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6137
61.4%
<NA> 3707
37.1%
2 126
 
1.3%
1 26
 
0.3%
3 4
 
< 0.1%

Length

2024-05-18T18:48:11.301829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:48:11.646402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6137
61.4%
na 3707
37.1%
2 126
 
1.3%
1 26
 
0.3%
3 4
 
< 0.1%

링크 WKT
Text

MISSING 

Distinct3707
Distinct (%)100.0%
Missing6293
Missing (%)62.9%
Memory size156.2 KiB
2024-05-18T18:48:12.158671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length196
Median length194
Mean length86.625034
Min length66

Characters and Unicode

Total characters321119
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3707 ?
Unique (%)100.0%

Sample

1st rowLINESTRING(127.07453015485862 37.572017173995185,127.0743806470536 37.57202184534189)
2nd rowLINESTRING(127.09610796345048 37.60200044780578,127.09617702331438 37.6020209881845)
3rd rowLINESTRING(127.08027172528098 37.57185586510061,127.07995835934942 37.571888117137846)
4th rowLINESTRING(127.02540848101617 37.62599098672322,127.02537334664729 37.62608762011456)
5th rowLINESTRING(127.0173313565474 37.483062712080994,127.01765476236837 37.48318448580289)
ValueCountFrequency (%)
linestring(127.10703461808987 3
 
< 0.1%
37.535937843454896 2
 
< 0.1%
37.55732062931826 2
 
< 0.1%
linestring(126.90181223287333 2
 
< 0.1%
linestring(127.01349262440904 2
 
< 0.1%
linestring(127.00809909466585 2
 
< 0.1%
37.55448671961763 2
 
< 0.1%
37.50101773069214 2
 
< 0.1%
37.50702107895057 2
 
< 0.1%
37.53874339361398 2
 
< 0.1%
Other values (11106) 11255
99.8%
2024-05-18T18:48:13.189979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 31191
9.7%
1 27793
 
8.7%
3 27271
 
8.5%
2 27222
 
8.5%
6 25482
 
7.9%
5 24939
 
7.8%
4 22207
 
6.9%
0 21357
 
6.7%
9 21344
 
6.6%
8 21260
 
6.6%
Other values (13) 71053
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 250066
77.9%
Uppercase Letter 37070
 
11.5%
Other Punctuation 19000
 
5.9%
Space Separator 7569
 
2.4%
Open Punctuation 3707
 
1.2%
Close Punctuation 3707
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 31191
12.5%
1 27793
11.1%
3 27271
10.9%
2 27222
10.9%
6 25482
10.2%
5 24939
10.0%
4 22207
8.9%
0 21357
8.5%
9 21344
8.5%
8 21260
8.5%
Uppercase Letter
ValueCountFrequency (%)
N 7414
20.0%
I 7414
20.0%
L 3707
10.0%
G 3707
10.0%
R 3707
10.0%
T 3707
10.0%
S 3707
10.0%
E 3707
10.0%
Other Punctuation
ValueCountFrequency (%)
. 15138
79.7%
, 3862
 
20.3%
Space Separator
ValueCountFrequency (%)
7569
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3707
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 284049
88.5%
Latin 37070
 
11.5%

Most frequent character per script

Common
ValueCountFrequency (%)
7 31191
11.0%
1 27793
9.8%
3 27271
9.6%
2 27222
9.6%
6 25482
9.0%
5 24939
8.8%
4 22207
7.8%
0 21357
7.5%
9 21344
7.5%
8 21260
7.5%
Other values (5) 33983
12.0%
Latin
ValueCountFrequency (%)
N 7414
20.0%
I 7414
20.0%
L 3707
10.0%
G 3707
10.0%
R 3707
10.0%
T 3707
10.0%
S 3707
10.0%
E 3707
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 31191
9.7%
1 27793
 
8.7%
3 27271
 
8.5%
2 27222
 
8.5%
6 25482
 
7.9%
5 24939
 
7.8%
4 22207
 
6.9%
0 21357
 
6.7%
9 21344
 
6.6%
8 21260
 
6.6%
Other values (13) 71053
22.1%

링크 ID
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3708
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51947.284
Minimum0
Maximum282265
Zeros6293
Zeros (%)62.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:48:13.599480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q389828
95-th percentile245638.4
Maximum282265
Range282265
Interquartile range (IQR)89828

Descriptive statistics

Standard deviation84031.151
Coefficient of variation (CV)1.6176236
Kurtosis0.48914343
Mean51947.284
Median Absolute Deviation (MAD)0
Skewness1.3931162
Sum5.1947284 × 108
Variance7.0612343 × 109
MonotonicityNot monotonic
2024-05-18T18:48:14.018574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6293
62.9%
95171 1
 
< 0.1%
195435 1
 
< 0.1%
112740 1
 
< 0.1%
154156 1
 
< 0.1%
97787 1
 
< 0.1%
265213 1
 
< 0.1%
170543 1
 
< 0.1%
193453 1
 
< 0.1%
178603 1
 
< 0.1%
Other values (3698) 3698
37.0%
ValueCountFrequency (%)
0 6293
62.9%
75 1
 
< 0.1%
130 1
 
< 0.1%
153 1
 
< 0.1%
308 1
 
< 0.1%
316 1
 
< 0.1%
451 1
 
< 0.1%
503 1
 
< 0.1%
673 1
 
< 0.1%
685 1
 
< 0.1%
ValueCountFrequency (%)
282265 1
< 0.1%
282244 1
< 0.1%
282221 1
< 0.1%
282188 1
< 0.1%
282140 1
< 0.1%
281953 1
< 0.1%
281930 1
< 0.1%
281727 1
< 0.1%
281717 1
< 0.1%
281710 1
< 0.1%

링크 유형 코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6293 
1000
2589 
1011
1115 
1111
 
2
1100
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6293
62.9%
1000 2589
25.9%
1011 1115
 
11.2%
1111 2
 
< 0.1%
1100 1
 
< 0.1%

Length

2024-05-18T18:48:14.439680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:48:14.757646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6293
62.9%
1000 2589
25.9%
1011 1115
 
11.2%
1111 2
 
< 0.1%
1100 1
 
< 0.1%

시작노드 ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3616
Distinct (%)97.5%
Missing6293
Missing (%)62.9%
Infinite0
Infinite (%)0.0%
Mean110259.58
Minimum78
Maximum215554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:48:15.349564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile9888.8
Q155636
median109629
Q3164367.5
95-th percentile205793.9
Maximum215554
Range215476
Interquartile range (IQR)108731.5

Descriptive statistics

Standard deviation62931.327
Coefficient of variation (CV)0.57075608
Kurtosis-1.1589663
Mean110259.58
Median Absolute Deviation (MAD)54203
Skewness-0.037777249
Sum4.0873227 × 108
Variance3.9603519 × 109
MonotonicityNot monotonic
2024-05-18T18:48:15.941834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196565 3
 
< 0.1%
166406 2
 
< 0.1%
89109 2
 
< 0.1%
175704 2
 
< 0.1%
77467 2
 
< 0.1%
140829 2
 
< 0.1%
84400 2
 
< 0.1%
171977 2
 
< 0.1%
167787 2
 
< 0.1%
172941 2
 
< 0.1%
Other values (3606) 3686
36.9%
(Missing) 6293
62.9%
ValueCountFrequency (%)
78 1
< 0.1%
151 1
< 0.1%
193 1
< 0.1%
377 1
< 0.1%
380 1
< 0.1%
457 1
< 0.1%
458 1
< 0.1%
666 1
< 0.1%
667 1
< 0.1%
808 1
< 0.1%
ValueCountFrequency (%)
215554 1
< 0.1%
215548 1
< 0.1%
215528 1
< 0.1%
215522 1
< 0.1%
215497 1
< 0.1%
215448 1
< 0.1%
215414 1
< 0.1%
215375 1
< 0.1%
215335 1
< 0.1%
215330 1
< 0.1%

종료노드 ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3640
Distinct (%)98.2%
Missing6293
Missing (%)62.9%
Infinite0
Infinite (%)0.0%
Mean109477.12
Minimum39
Maximum215563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:48:16.470400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile9827.8
Q153613.5
median109624
Q3163365.5
95-th percentile204958.3
Maximum215563
Range215524
Interquartile range (IQR)109752

Descriptive statistics

Standard deviation62697.157
Coefficient of variation (CV)0.57269645
Kurtosis-1.1554482
Mean109477.12
Median Absolute Deviation (MAD)53978
Skewness-0.040114683
Sum4.0583168 × 108
Variance3.9309335 × 109
MonotonicityNot monotonic
2024-05-18T18:48:16.807175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65899 2
 
< 0.1%
7507 2
 
< 0.1%
102882 2
 
< 0.1%
203450 2
 
< 0.1%
205916 2
 
< 0.1%
133144 2
 
< 0.1%
9832 2
 
< 0.1%
105747 2
 
< 0.1%
5446 2
 
< 0.1%
183743 2
 
< 0.1%
Other values (3630) 3687
36.9%
(Missing) 6293
62.9%
ValueCountFrequency (%)
39 1
< 0.1%
78 1
< 0.1%
120 1
< 0.1%
378 1
< 0.1%
453 1
< 0.1%
467 1
< 0.1%
635 1
< 0.1%
636 1
< 0.1%
648 1
< 0.1%
665 1
< 0.1%
ValueCountFrequency (%)
215563 1
< 0.1%
215554 1
< 0.1%
215540 1
< 0.1%
215530 1
< 0.1%
215529 1
< 0.1%
215522 1
< 0.1%
215449 1
< 0.1%
215411 1
< 0.1%
215374 1
< 0.1%
215331 1
< 0.1%

링크 길이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3195
Distinct (%)86.2%
Missing6293
Missing (%)62.9%
Infinite0
Infinite (%)0.0%
Mean21.605812
Minimum2.896
Maximum164.992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:48:17.230721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.896
5-th percentile8.3086
Q113.854
median19.429
Q327.229
95-th percentile42.0721
Maximum164.992
Range162.096
Interquartile range (IQR)13.375

Descriptive statistics

Standard deviation11.116265
Coefficient of variation (CV)0.51450345
Kurtosis11.249723
Mean21.605812
Median Absolute Deviation (MAD)6.495
Skewness1.9111509
Sum80092.746
Variance123.57135
MonotonicityNot monotonic
2024-05-18T18:48:17.712820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.229 6
 
0.1%
14.811 6
 
0.1%
9.919 5
 
0.1%
9.772 5
 
0.1%
19.76 5
 
0.1%
19.23 4
 
< 0.1%
17.634 4
 
< 0.1%
14.244 4
 
< 0.1%
8.443 4
 
< 0.1%
18.316 4
 
< 0.1%
Other values (3185) 3660
36.6%
(Missing) 6293
62.9%
ValueCountFrequency (%)
2.896 1
< 0.1%
3.228 1
< 0.1%
3.302 1
< 0.1%
3.541 1
< 0.1%
3.556 1
< 0.1%
3.599 1
< 0.1%
3.665 2
< 0.1%
3.698 1
< 0.1%
4.035 1
< 0.1%
4.266 1
< 0.1%
ValueCountFrequency (%)
164.992 1
< 0.1%
120.259 1
< 0.1%
102.589 1
< 0.1%
83.195 1
< 0.1%
76.279 1
< 0.1%
76.052 1
< 0.1%
73.223 1
< 0.1%
72.403 1
< 0.1%
70.026 1
< 0.1%
69.803 1
< 0.1%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1449962 × 109
Minimum1.111 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:48:18.128572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.114 × 109
Q11.129 × 109
median1.147 × 109
Q31.162 × 109
95-th percentile1.174 × 109
Maximum1.174 × 109
Range63000000
Interquartile range (IQR)33000000

Descriptive statistics

Standard deviation18933340
Coefficient of variation (CV)0.016535724
Kurtosis-1.1801314
Mean1.1449962 × 109
Median Absolute Deviation (MAD)18000000
Skewness-0.14421067
Sum1.1449962 × 1013
Variance3.5847136 × 1014
MonotonicityNot monotonic
2024-05-18T18:48:18.799901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1168000000 699
 
7.0%
1171000000 602
 
6.0%
1156000000 576
 
5.8%
1147000000 567
 
5.7%
1144000000 543
 
5.4%
1174000000 534
 
5.3%
1150000000 528
 
5.3%
1165000000 492
 
4.9%
1153000000 482
 
4.8%
1126000000 414
 
4.1%
Other values (15) 4563
45.6%
ValueCountFrequency (%)
1111000000 345
3.5%
1114000000 390
3.9%
1117000000 252
2.5%
1120000000 260
2.6%
1121500000 297
3.0%
1123000000 413
4.1%
1126000000 414
4.1%
1129000000 373
3.7%
1130500000 248
2.5%
1132000000 249
2.5%
ValueCountFrequency (%)
1174000000 534
5.3%
1171000000 602
6.0%
1168000000 699
7.0%
1165000000 492
4.9%
1162000000 277
 
2.8%
1159000000 233
 
2.3%
1156000000 576
5.8%
1154500000 280
2.8%
1153000000 482
4.8%
1150000000 528
5.3%

시군구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강남구
 
699
송파구
 
602
영등포구
 
576
양천구
 
567
마포구
 
543
Other values (20)
7013 

Length

Max length4
Median length3
Mean length3.0821
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강서구
2nd row강북구
3rd row서초구
4th row송파구
5th row송파구

Common Values

ValueCountFrequency (%)
강남구 699
 
7.0%
송파구 602
 
6.0%
영등포구 576
 
5.8%
양천구 567
 
5.7%
마포구 543
 
5.4%
강동구 534
 
5.3%
강서구 528
 
5.3%
서초구 492
 
4.9%
구로구 482
 
4.8%
중랑구 414
 
4.1%
Other values (15) 4563
45.6%

Length

2024-05-18T18:48:19.525967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 699
 
7.0%
송파구 602
 
6.0%
영등포구 576
 
5.8%
양천구 567
 
5.7%
마포구 543
 
5.4%
강동구 534
 
5.3%
강서구 528
 
5.3%
서초구 492
 
4.9%
구로구 482
 
4.8%
중랑구 414
 
4.1%
Other values (15) 4563
45.6%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct414
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1450263 × 109
Minimum1.1110101 × 109
Maximum1.174011 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:48:20.055526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 109
5-th percentile1.1140146 × 109
Q11.1290133 × 109
median1.1470102 × 109
Q31.1620101 × 109
95-th percentile1.1740101 × 109
Maximum1.174011 × 109
Range63000900
Interquartile range (IQR)32996800

Descriptive statistics

Standard deviation18928206
Coefficient of variation (CV)0.016530805
Kurtosis-1.1811071
Mean1.1450263 × 109
Median Absolute Deviation (MAD)17996400
Skewness-0.14412849
Sum1.1450263 × 1013
Variance3.5827699 × 1014
MonotonicityNot monotonic
2024-05-18T18:48:20.524374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1147010100 236
 
2.4%
1147010200 208
 
2.1%
1153010200 192
 
1.9%
1150010500 187
 
1.9%
1162010200 155
 
1.6%
1156011000 138
 
1.4%
1165010800 135
 
1.4%
1135010500 119
 
1.2%
1147010300 118
 
1.2%
1126010100 115
 
1.1%
Other values (404) 8397
84.0%
ValueCountFrequency (%)
1111010100 6
0.1%
1111010200 6
0.1%
1111010300 2
 
< 0.1%
1111010400 1
 
< 0.1%
1111010500 2
 
< 0.1%
1111010600 4
< 0.1%
1111010800 5
0.1%
1111011000 7
0.1%
1111011100 1
 
< 0.1%
1111011300 7
0.1%
ValueCountFrequency (%)
1174011000 75
0.8%
1174010900 68
0.7%
1174010800 74
0.7%
1174010700 50
0.5%
1174010600 33
0.3%
1174010500 53
0.5%
1174010300 66
0.7%
1174010200 57
0.6%
1174010100 59
0.6%
1171011400 22
 
0.2%

읍면동명
Text

Distinct412
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T18:48:21.151020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1827
Min length2

Characters and Unicode

Total characters31827
Distinct characters206
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

Unique36 ?
Unique (%)0.4%

Sample

1st row마곡동
2nd row수유동
3rd row서초동
4th row가락동
5th row가락동
ValueCountFrequency (%)
신정동 240
 
2.4%
목동 208
 
2.1%
구로동 192
 
1.9%
마곡동 187
 
1.9%
신림동 155
 
1.6%
여의도동 138
 
1.4%
서초동 135
 
1.4%
상계동 119
 
1.2%
신월동 118
 
1.2%
면목동 115
 
1.1%
Other values (402) 8393
83.9%
2024-05-18T18:48:22.205695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9763
30.7%
가 1148
 
3.6%
신 1027
 
3.2%
곡 552
 
1.7%
로 483
 
1.5%
정 481
 
1.5%
도 446
 
1.4%
상 420
 
1.3%
산 388
 
1.2%
방 358
 
1.1%
Other values (196) 16761
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30964
97.3%
Decimal Number 863
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9763
31.5%
가 1148
 
3.7%
신 1027
 
3.3%
곡 552
 
1.8%
로 483
 
1.6%
정 481
 
1.6%
도 446
 
1.4%
상 420
 
1.4%
산 388
 
1.3%
방 358
 
1.2%
Other values (188) 15898
51.3%
Decimal Number
ValueCountFrequency (%)
1 222
25.7%
2 208
24.1%
3 143
16.6%
4 113
13.1%
5 80
 
9.3%
6 63
 
7.3%
7 23
 
2.7%
8 11
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30964
97.3%
Common 863
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9763
31.5%
가 1148
 
3.7%
신 1027
 
3.3%
곡 552
 
1.8%
로 483
 
1.6%
정 481
 
1.6%
도 446
 
1.4%
상 420
 
1.4%
산 388
 
1.3%
방 358
 
1.2%
Other values (188) 15898
51.3%
Common
ValueCountFrequency (%)
1 222
25.7%
2 208
24.1%
3 143
16.6%
4 113
13.1%
5 80
 
9.3%
6 63
 
7.3%
7 23
 
2.7%
8 11
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30964
97.3%
ASCII 863
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9763
31.5%
가 1148
 
3.7%
신 1027
 
3.3%
곡 552
 
1.8%
로 483
 
1.6%
정 481
 
1.6%
도 446
 
1.4%
상 420
 
1.4%
산 388
 
1.3%
방 358
 
1.2%
Other values (188) 15898
51.3%
ASCII
ValueCountFrequency (%)
1 222
25.7%
2 208
24.1%
3 143
16.6%
4 113
13.1%
5 80
 
9.3%
6 63
 
7.3%
7 23
 
2.7%
8 11
 
1.3%

Interactions

2024-05-18T18:48:04.749348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:54.065809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:56.028002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:57.625115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:59.346003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:00.973989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:03.027170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:05.012879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:54.317134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:56.190247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:57.910019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:59.629906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:01.476903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:03.209486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:05.279374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:54.570034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:56.378963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:58.168587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:59.886243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:01.749712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:03.482331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:05.521256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:54.841413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:56.551667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:58.338151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:00.144813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:02.013805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:03.747408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:05.690665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:55.107760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:56.793591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:58.494860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:00.325261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:02.284303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:04.007668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:06.037082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:55.425742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:57.069515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:58.801305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:00.514661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:02.571725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:04.211673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:06.317632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:55.754574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:57.348242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:47:59.073608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:00.694878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:02.814250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:48:04.469883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T18:48:22.469813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드링크 유형노드 ID노드 유형 코드링크 ID링크 유형 코드시작노드 ID종료노드 ID링크 길이시군구코드시군구명읍면동코드
노드링크 유형1.0000.978NaN0.991NaNNaNNaNNaN0.0000.0000.000
노드 ID0.9781.0000.0000.674NaNNaNNaNNaN0.5310.6320.535
노드 유형 코드NaN0.0001.000NaNNaNNaNNaNNaN0.0900.1530.085
링크 ID0.9910.674NaN1.0000.0000.0610.0410.0000.0370.0460.038
링크 유형 코드NaNNaNNaN0.0001.0000.3380.3520.0000.4360.7030.420
시작노드 IDNaNNaNNaN0.0610.3381.0000.9640.0800.6820.7650.666
종료노드 IDNaNNaNNaN0.0410.3520.9641.0000.0620.6710.7590.659
링크 길이NaNNaNNaN0.0000.0000.0800.0621.0000.1870.3220.181
시군구코드0.0000.5310.0900.0370.4360.6820.6710.1871.0001.0001.000
시군구명0.0000.6320.1530.0460.7030.7650.7590.3221.0001.0001.000
읍면동코드0.0000.5350.0850.0380.4200.6660.6590.1811.0001.0001.000
2024-05-18T18:48:22.806276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명노드 유형 코드링크 유형 코드노드링크 유형
시군구명1.0000.0810.4540.000
노드 유형 코드0.0811.000NaN1.000
링크 유형 코드0.454NaN1.0001.000
노드링크 유형0.0001.0001.0001.000
2024-05-18T18:48:23.098911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 ID링크 ID시작노드 ID종료노드 ID링크 길이시군구코드읍면동코드노드링크 유형노드 유형 코드링크 유형 코드시군구명
노드 ID1.000-0.829NaNNaNNaN0.0850.0830.8710.0001.0000.278
링크 ID-0.8291.000-0.022-0.0120.005-0.016-0.0140.9181.0000.0000.016
시작노드 IDNaN-0.0221.0000.6670.0120.1990.1981.0000.0000.2080.391
종료노드 IDNaN-0.0120.6671.0000.0190.1870.1881.0000.0000.2170.384
링크 길이NaN0.0050.0120.0191.0000.0600.0591.0000.0000.0000.133
시군구코드0.085-0.0160.1990.1870.0601.0000.9990.0000.0510.2640.999
읍면동코드0.083-0.0140.1980.1880.0590.9991.0000.0000.0510.2630.996
노드링크 유형0.8710.9181.0001.0001.0000.0000.0001.0001.0001.0000.000
노드 유형 코드0.0001.0000.0000.0000.0000.0510.0511.0001.0000.0000.081
링크 유형 코드1.0000.0000.2080.2170.0000.2640.2631.0000.0001.0000.454
시군구명0.2780.0160.3910.3840.1330.9990.9960.0000.0810.4541.000

Missing values

2024-05-18T18:48:06.726271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T18:48:07.344561image/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.
2024-05-18T18:48:07.735770image/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

노드링크 유형노드 WKT노드 ID노드 유형 코드링크 WKT링크 ID링크 유형 코드시작노드 ID종료노드 ID링크 길이시군구코드시군구명읍면동코드읍면동명
17436NODEPOINT(126.83515429200594 37.55802429568897)1881560<NA>0<NA><NA><NA><NA>1150000000강서구1150010500마곡동
9113NODEPOINT(127.0161550655481 37.647762875611775)1741450<NA>0<NA><NA><NA><NA>1130500000강북구1130510300수유동
25198NODEPOINT(127.01771335920874 37.48234109664415)308730<NA>0<NA><NA><NA><NA>1165000000서초구1165010800서초동
28005NODEPOINT(127.12139315962087 37.49408874255733)1996280<NA>0<NA><NA><NA><NA>1171000000송파구1171010700가락동
28464NODEPOINT(127.11155185739744 37.490285304051085)1957800<NA>0<NA><NA><NA><NA>1171000000송파구1171010700가락동
5809LINK<NA>0<NA>LINESTRING(127.07453015485862 37.572017173995185,127.0743806470536 37.57202184534189)7855210114204420013.2181123000000동대문구1123010600장안동
13494NODEPOINT(126.95751929242654 37.554669520810705)691490<NA>0<NA><NA><NA><NA>1144000000마포구1144010100아현동
1327NODEPOINT(127.00225194823659 37.56449035350159)73460<NA>0<NA><NA><NA><NA>1114000000중구1114015400오장동
18011NODEPOINT(126.82705405497148 37.55943517013081)1192260<NA>0<NA><NA><NA><NA>1150000000강서구1150010500마곡동
6315LINK<NA>0<NA>LINESTRING(127.09610796345048 37.60200044780578,127.09617702331438 37.6020209881845)6716310001757421757466.5111126000000중랑구1126010600신내동
노드링크 유형노드 WKT노드 ID노드 유형 코드링크 WKT링크 ID링크 유형 코드시작노드 ID종료노드 ID링크 길이시군구코드시군구명읍면동코드읍면동명
15635LINK<NA>0<NA>LINESTRING(126.87061317261136 37.54439426346924,126.87072820665051 37.544355484332065)49111000228862289611.041147000000양천구1147010200목동
25142LINK<NA>0<NA>LINESTRING(127.01637527319136 37.51520482526445,127.01642702455223 37.51504092599596)8491710001104531602818.7571165000000서초구1165010600잠원동
17098NODEPOINT(126.84344764552158 37.547479335007246)1089712<NA>0<NA><NA><NA><NA>1150000000강서구1150010300화곡동
16068LINK<NA>0<NA>LINESTRING(126.88053880882924 37.54010055442121,126.88066799328928 37.54019222658315)25682310001069112556315.2931147000000양천구1147010200목동
30070LINK<NA>0<NA>LINESTRING(127.14381395569859 37.558934993357425,127.14396934073254 37.559012614034025)149573101112522218362616.2081174000000강동구1174010700암사동
3202NODEPOINT(127.02371077210518 37.56798040769763)258530<NA>0<NA><NA><NA><NA>1120000000성동구1120010100상왕십리동
6459LINK<NA>0<NA>LINESTRING(127.07939597911738 37.61673600677816,127.07941021057988 37.61659182109358)1386611000762814327016.0521126000000중랑구1126010400묵동
18659NODEPOINT(126.89431157467368 37.494382793101444)512300<NA>0<NA><NA><NA><NA>1153000000구로구1153010200구로동
26897NODEPOINT(127.05693248221691 37.486698946748035)2057280<NA>0<NA><NA><NA><NA>1168000000강남구1168010300개포동
14300LINK<NA>0<NA>LINESTRING(126.93130209774918 37.54789389990811,126.93171416933701 37.54779878323768)1552551000406824069337.9141144000000마포구1144011400창전동