Overview

Dataset statistics

Number of variables10
Number of observations643
Missing cells180
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.5 KiB
Average record size in memory85.2 B

Variable types

Categorical3
Text3
Numeric4

Dataset

Description노드링크 유형,노드 WKT,노드 ID,노드 유형 코드,시군구코드,시군구명,읍면동코드,읍면동명,지하철역코드,지하철역명
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21212/S/1/datasetView.do

Alerts

노드링크 유형 has constant value ""Constant
시군구코드 is highly overall correlated with 읍면동코드 and 1 other fieldsHigh correlation
읍면동코드 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
지하철역코드 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 시군구코드 and 2 other fieldsHigh correlation
지하철역코드 has 90 (14.0%) missing valuesMissing
지하철역명 has 90 (14.0%) missing valuesMissing
노드 WKT has unique valuesUnique
노드 ID has unique valuesUnique

Reproduction

Analysis started2024-05-03 19:59:27.843156
Analysis finished2024-05-03 19:59:33.763113
Duration5.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노드링크 유형
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
NODE
643 

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 643
100.0%

Length

2024-05-03T19:59:33.880890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:59:34.054066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
node 643
100.0%

노드 WKT
Text

UNIQUE 

Distinct643
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2024-05-03T19:59:34.415238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length43
Mean length43.046656
Min length39

Characters and Unicode

Total characters27679
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

Unique643 ?
Unique (%)100.0%

Sample

1st rowPOINT(127.01049296509703 37.571417966123946)
2nd rowPOINT(127.01744971746365 37.57329704981851)
3rd rowPOINT(127.01551499790813 37.57951303710329)
4th rowPOINT(127.00225226116126 37.58169863717817)
5th rowPOINT(127.00904036229613 37.57079391329676)
ValueCountFrequency (%)
point(127.01049296509703 1
 
0.1%
point(126.90687722062857 1
 
0.1%
point(126.95379404109858 1
 
0.1%
37.495995707189685 1
 
0.1%
point(126.95416941763811 1
 
0.1%
37.50321256138417 1
 
0.1%
point(126.9760906816393 1
 
0.1%
37.48802792998867 1
 
0.1%
point(126.98202481192345 1
 
0.1%
37.51492146932141 1
 
0.1%
Other values (1276) 1276
99.2%
2024-05-03T19:59:35.153807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 2660
9.6%
1 2379
 
8.6%
2 2338
 
8.4%
3 2318
 
8.4%
5 2138
 
7.7%
6 2115
 
7.6%
4 1894
 
6.8%
0 1841
 
6.7%
8 1788
 
6.5%
9 1778
 
6.4%
Other values (9) 6430
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21249
76.8%
Uppercase Letter 3215
 
11.6%
Other Punctuation 1286
 
4.6%
Open Punctuation 643
 
2.3%
Space Separator 643
 
2.3%
Close Punctuation 643
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 2660
12.5%
1 2379
11.2%
2 2338
11.0%
3 2318
10.9%
5 2138
10.1%
6 2115
10.0%
4 1894
8.9%
0 1841
8.7%
8 1788
8.4%
9 1778
8.4%
Uppercase Letter
ValueCountFrequency (%)
O 643
20.0%
T 643
20.0%
N 643
20.0%
I 643
20.0%
P 643
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1286
100.0%
Open Punctuation
ValueCountFrequency (%)
( 643
100.0%
Space Separator
ValueCountFrequency (%)
643
100.0%
Close Punctuation
ValueCountFrequency (%)
) 643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24464
88.4%
Latin 3215
 
11.6%

Most frequent character per script

Common
ValueCountFrequency (%)
7 2660
10.9%
1 2379
9.7%
2 2338
9.6%
3 2318
9.5%
5 2138
8.7%
6 2115
8.6%
4 1894
7.7%
0 1841
7.5%
8 1788
7.3%
9 1778
7.3%
Other values (4) 3215
13.1%
Latin
ValueCountFrequency (%)
O 643
20.0%
T 643
20.0%
N 643
20.0%
I 643
20.0%
P 643
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 2660
9.6%
1 2379
 
8.6%
2 2338
 
8.4%
3 2318
 
8.4%
5 2138
 
7.7%
6 2115
 
7.6%
4 1894
 
6.8%
0 1841
 
6.7%
8 1788
 
6.5%
9 1778
 
6.4%
Other values (9) 6430
23.2%

노드 ID
Real number (ℝ)

UNIQUE 

Distinct643
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170836.26
Minimum408
Maximum215561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T19:59:35.580046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum408
5-th percentile19371.8
Q1137684.5
median211761
Q3212889.5
95-th percentile213856.7
Maximum215561
Range215153
Interquartile range (IQR)75205

Descriptive statistics

Standard deviation65799.169
Coefficient of variation (CV)0.38515928
Kurtosis0.37809447
Mean170836.26
Median Absolute Deviation (MAD)1665
Skewness-1.3522183
Sum1.0984771 × 108
Variance4.3295307 × 109
MonotonicityNot monotonic
2024-05-03T19:59:35.996543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212410 1
 
0.2%
212810 1
 
0.2%
115583 1
 
0.2%
214532 1
 
0.2%
211456 1
 
0.2%
138069 1
 
0.2%
211438 1
 
0.2%
211437 1
 
0.2%
211425 1
 
0.2%
211435 1
 
0.2%
Other values (633) 633
98.4%
ValueCountFrequency (%)
408 1
0.2%
5647 1
0.2%
5803 1
0.2%
5885 1
0.2%
5948 1
0.2%
6110 1
0.2%
6154 1
0.2%
6504 1
0.2%
6510 1
0.2%
6734 1
0.2%
ValueCountFrequency (%)
215561 1
0.2%
215427 1
0.2%
215413 1
0.2%
215412 1
0.2%
215410 1
0.2%
215393 1
0.2%
215382 1
0.2%
215380 1
0.2%
215358 1
0.2%
215357 1
0.2%

노드 유형 코드
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
1
458 
0
183 
2
 
2

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 (%)
1 458
71.2%
0 183
 
28.5%
2 2
 
0.3%

Length

2024-05-03T19:59:36.276932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:59:36.460351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 458
71.2%
0 183
 
28.5%
2 2
 
0.3%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1445202 × 109
Minimum1.111 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T19:59:36.644625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.111 × 109
Q11.126 × 109
median1.144 × 109
Q31.165 × 109
95-th percentile1.171 × 109
Maximum1.174 × 109
Range63000000
Interquartile range (IQR)39000000

Descriptive statistics

Standard deviation20271723
Coefficient of variation (CV)0.017711983
Kurtosis-1.3400108
Mean1.1445202 × 109
Median Absolute Deviation (MAD)21000000
Skewness-0.15629015
Sum7.359265 × 1011
Variance4.1094274 × 1014
MonotonicityIncreasing
2024-05-03T19:59:37.008454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1168000000 75
 
11.7%
1171000000 50
 
7.8%
1144000000 39
 
6.1%
1165000000 38
 
5.9%
1135000000 36
 
5.6%
1114000000 36
 
5.6%
1111000000 35
 
5.4%
1150000000 33
 
5.1%
1153000000 26
 
4.0%
1156000000 25
 
3.9%
Other values (15) 250
38.9%
ValueCountFrequency (%)
1111000000 35
5.4%
1114000000 36
5.6%
1117000000 22
3.4%
1120000000 21
3.3%
1121500000 13
 
2.0%
1123000000 19
3.0%
1126000000 17
2.6%
1129000000 17
2.6%
1130500000 21
3.3%
1132000000 20
3.1%
ValueCountFrequency (%)
1174000000 20
 
3.1%
1171000000 50
7.8%
1168000000 75
11.7%
1165000000 38
5.9%
1162000000 11
 
1.7%
1159000000 22
 
3.4%
1156000000 25
 
3.9%
1154500000 7
 
1.1%
1153000000 26
 
4.0%
1150000000 33
5.1%

시군구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
강남구
75 
송파구
50 
마포구
 
39
서초구
 
38
노원구
 
36
Other values (20)
405 

Length

Max length4
Median length3
Mean length3.0311042
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강남구 75
 
11.7%
송파구 50
 
7.8%
마포구 39
 
6.1%
서초구 38
 
5.9%
노원구 36
 
5.6%
중구 36
 
5.6%
종로구 35
 
5.4%
강서구 33
 
5.1%
구로구 26
 
4.0%
영등포구 25
 
3.9%
Other values (15) 250
38.9%

Length

2024-05-03T19:59:37.449162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 75
 
11.7%
송파구 50
 
7.8%
마포구 39
 
6.1%
서초구 38
 
5.9%
노원구 36
 
5.6%
중구 36
 
5.6%
종로구 35
 
5.4%
강서구 33
 
5.1%
구로구 26
 
4.0%
영등포구 25
 
3.9%
Other values (15) 250
38.9%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct211
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1445315 × 109
Minimum1.1110107 × 109
Maximum1.174011 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T19:59:37.796900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110107 × 109
5-th percentile1.1110175 × 109
Q11.1260106 × 109
median1.1440127 × 109
Q31.1650107 × 109
95-th percentile1.1710111 × 109
Maximum1.174011 × 109
Range63000300
Interquartile range (IQR)39000100

Descriptive statistics

Standard deviation20270872
Coefficient of variation (CV)0.017711065
Kurtosis-1.3401258
Mean1.1445315 × 109
Median Absolute Deviation (MAD)20997400
Skewness-0.15620538
Sum7.3593377 × 1011
Variance4.1090825 × 1014
MonotonicityNot monotonic
2024-05-03T19:59:38.073008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1135010500 14
 
2.2%
1168010600 13
 
2.0%
1171011100 11
 
1.7%
1165010800 11
 
1.7%
1165010700 10
 
1.6%
1168010100 10
 
1.6%
1168011400 10
 
1.6%
1130510100 10
 
1.6%
1144012500 9
 
1.4%
1150010500 9
 
1.4%
Other values (201) 536
83.4%
ValueCountFrequency (%)
1111010700 1
 
0.2%
1111012700 1
 
0.2%
1111013400 1
 
0.2%
1111013700 1
 
0.2%
1111013800 3
0.5%
1111015100 5
0.8%
1111015300 2
 
0.3%
1111015600 3
0.5%
1111016100 1
 
0.2%
1111016300 1
 
0.2%
ValueCountFrequency (%)
1174011000 1
 
0.2%
1174010900 1
 
0.2%
1174010800 4
0.6%
1174010700 1
 
0.2%
1174010600 4
0.6%
1174010500 1
 
0.2%
1174010300 3
0.5%
1174010200 3
0.5%
1174010100 2
0.3%
1171011400 2
0.3%

읍면동명
Text

Distinct211
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2024-05-03T19:59:38.784038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2410575
Min length2

Characters and Unicode

Total characters2084
Distinct characters167
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

Unique72 ?
Unique (%)11.2%

Sample

1st row창신동
2nd row숭인동
3rd row숭인동
4th row동숭동
5th row종로6가
ValueCountFrequency (%)
상계동 14
 
2.2%
대치동 13
 
2.0%
방이동 11
 
1.7%
서초동 11
 
1.7%
미아동 10
 
1.6%
일원동 10
 
1.6%
반포동 10
 
1.6%
역삼동 10
 
1.6%
성산동 9
 
1.4%
개포동 9
 
1.4%
Other values (201) 536
83.4%
2024-05-03T19:59:39.657878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 617
29.6%
가 97
 
4.7%
신 43
 
2.1%
로 43
 
2.1%
상 35
 
1.7%
산 34
 
1.6%
방 31
 
1.5%
계 30
 
1.4%
성 29
 
1.4%
수 27
 
1.3%
Other values (157) 1098
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2006
96.3%
Decimal Number 78
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 617
30.8%
가 97
 
4.8%
신 43
 
2.1%
로 43
 
2.1%
상 35
 
1.7%
산 34
 
1.7%
방 31
 
1.5%
계 30
 
1.5%
성 29
 
1.4%
수 27
 
1.3%
Other values (150) 1020
50.8%
Decimal Number
ValueCountFrequency (%)
1 23
29.5%
2 20
25.6%
5 14
17.9%
3 9
 
11.5%
4 6
 
7.7%
6 4
 
5.1%
7 2
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2006
96.3%
Common 78
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 617
30.8%
가 97
 
4.8%
신 43
 
2.1%
로 43
 
2.1%
상 35
 
1.7%
산 34
 
1.7%
방 31
 
1.5%
계 30
 
1.5%
성 29
 
1.4%
수 27
 
1.3%
Other values (150) 1020
50.8%
Common
ValueCountFrequency (%)
1 23
29.5%
2 20
25.6%
5 14
17.9%
3 9
 
11.5%
4 6
 
7.7%
6 4
 
5.1%
7 2
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2006
96.3%
ASCII 78
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 617
30.8%
가 97
 
4.8%
신 43
 
2.1%
로 43
 
2.1%
상 35
 
1.7%
산 34
 
1.7%
방 31
 
1.5%
계 30
 
1.5%
성 29
 
1.4%
수 27
 
1.3%
Other values (150) 1020
50.8%
ASCII
ValueCountFrequency (%)
1 23
29.5%
2 20
25.6%
5 14
17.9%
3 9
 
11.5%
4 6
 
7.7%
6 4
 
5.1%
7 2
 
2.6%

지하철역코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct271
Distinct (%)49.0%
Missing90
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean143.22604
Minimum1
Maximum299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T19:59:40.081425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q172
median138
Q3214
95-th percentile284
Maximum299
Range298
Interquartile range (IQR)142

Descriptive statistics

Standard deviation84.427063
Coefficient of variation (CV)0.58946727
Kurtosis-1.1030727
Mean143.22604
Median Absolute Deviation (MAD)71
Skewness0.1219151
Sum79204
Variance7127.9289
MonotonicityNot monotonic
2024-05-03T19:59:40.441698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87 7
 
1.1%
246 6
 
0.9%
169 6
 
0.9%
284 6
 
0.9%
102 6
 
0.9%
71 5
 
0.8%
124 5
 
0.8%
231 5
 
0.8%
110 5
 
0.8%
119 5
 
0.8%
Other values (261) 497
77.3%
(Missing) 90
 
14.0%
ValueCountFrequency (%)
1 2
0.3%
2 1
0.2%
3 2
0.3%
4 2
0.3%
5 2
0.3%
6 2
0.3%
7 2
0.3%
8 2
0.3%
9 2
0.3%
10 2
0.3%
ValueCountFrequency (%)
299 1
 
0.2%
298 1
 
0.2%
297 1
 
0.2%
295 1
 
0.2%
294 2
0.3%
293 2
0.3%
292 1
 
0.2%
291 3
0.5%
290 2
0.3%
289 3
0.5%

지하철역명
Text

MISSING 

Distinct271
Distinct (%)49.0%
Missing90
Missing (%)14.0%
Memory size5.2 KiB
2024-05-03T19:59:41.000519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length4.1500904
Min length2

Characters and Unicode

Total characters2295
Distinct characters252
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)17.7%

Sample

1st row동대문
2nd row동묘앞
3rd row창신
4th row혜화
5th row동대문
ValueCountFrequency (%)
온수(성공회대입구 7
 
1.3%
강남 6
 
1.1%
삼각지 6
 
1.1%
노원 6
 
1.1%
서울역 6
 
1.1%
마곡나루 5
 
0.9%
사당 5
 
0.9%
신림 5
 
0.9%
당산 5
 
0.9%
방학 5
 
0.9%
Other values (262) 501
89.9%
2024-05-03T19:59:41.738619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 100
 
4.4%
) 100
 
4.4%
구 97
 
4.2%
대 83
 
3.6%
신 54
 
2.4%
동 49
 
2.1%
청 49
 
2.1%
입 42
 
1.8%
산 39
 
1.7%
원 38
 
1.7%
Other values (242) 1644
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2062
89.8%
Open Punctuation 100
 
4.4%
Close Punctuation 100
 
4.4%
Decimal Number 20
 
0.9%
Other Punctuation 9
 
0.4%
Space Separator 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 97
 
4.7%
대 83
 
4.0%
신 54
 
2.6%
동 49
 
2.4%
청 49
 
2.4%
입 42
 
2.0%
산 39
 
1.9%
원 38
 
1.8%
서 36
 
1.7%
지 33
 
1.6%
Other values (232) 1542
74.8%
Decimal Number
ValueCountFrequency (%)
4 5
25.0%
3 5
25.0%
2 4
20.0%
9 3
15.0%
1 3
15.0%
Other Punctuation
ValueCountFrequency (%)
. 5
55.6%
, 4
44.4%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2062
89.8%
Common 233
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 97
 
4.7%
대 83
 
4.0%
신 54
 
2.6%
동 49
 
2.4%
청 49
 
2.4%
입 42
 
2.0%
산 39
 
1.9%
원 38
 
1.8%
서 36
 
1.7%
지 33
 
1.6%
Other values (232) 1542
74.8%
Common
ValueCountFrequency (%)
( 100
42.9%
) 100
42.9%
. 5
 
2.1%
4 5
 
2.1%
3 5
 
2.1%
2 4
 
1.7%
, 4
 
1.7%
4
 
1.7%
9 3
 
1.3%
1 3
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2062
89.8%
ASCII 233
 
10.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 100
42.9%
) 100
42.9%
. 5
 
2.1%
4 5
 
2.1%
3 5
 
2.1%
2 4
 
1.7%
, 4
 
1.7%
4
 
1.7%
9 3
 
1.3%
1 3
 
1.3%
Hangul
ValueCountFrequency (%)
구 97
 
4.7%
대 83
 
4.0%
신 54
 
2.6%
동 49
 
2.4%
청 49
 
2.4%
입 42
 
2.0%
산 39
 
1.9%
원 38
 
1.8%
서 36
 
1.7%
지 33
 
1.6%
Other values (232) 1542
74.8%

Interactions

2024-05-03T19:59:31.633060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:28.703665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:29.474411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:30.548663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:31.883678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:28.855149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:29.710883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:30.810819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:32.161098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:29.086401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:29.982357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:31.090554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:32.432038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:29.307163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:30.259014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:59:31.365204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T19:59:41.918039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 ID노드 유형 코드시군구코드시군구명읍면동코드지하철역코드
노드 ID1.0000.3210.5590.6320.5120.493
노드 유형 코드0.3211.0000.5300.6540.5110.416
시군구코드0.5590.5301.0001.0001.0000.938
시군구명0.6320.6541.0001.0001.0000.982
읍면동코드0.5120.5111.0001.0001.0000.937
지하철역코드0.4930.4160.9380.9820.9371.000
2024-05-03T19:59:42.116023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 유형 코드시군구명
노드 유형 코드1.0000.428
시군구명0.4281.000
2024-05-03T19:59:42.280624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 ID시군구코드읍면동코드지하철역코드노드 유형 코드시군구명
노드 ID1.0000.1620.159-0.1010.2000.273
시군구코드0.1621.0000.998-0.4370.3550.988
읍면동코드0.1590.9981.000-0.4390.3550.988
지하철역코드-0.101-0.437-0.4391.0000.2730.851
노드 유형 코드0.2000.3550.3550.2731.0000.428
시군구명0.2730.9880.9880.8510.4281.000

Missing values

2024-05-03T19:59:32.803236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T19:59:33.288466image/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.
2024-05-03T19:59:33.677521image/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 코드시군구코드시군구명읍면동코드읍면동명지하철역코드지하철역명
0NODEPOINT(127.01049296509703 37.571417966123946)21241001111000000종로구1111017400창신동272동대문
1NODEPOINT(127.01744971746365 37.57329704981851)21265901111000000종로구1111017500숭인동268동묘앞
2NODEPOINT(127.01551499790813 37.57951303710329)21241801111000000종로구1111017500숭인동270창신
3NODEPOINT(127.00225226116126 37.58169863717817)21211801111000000종로구1111016800동숭동267혜화
4NODEPOINT(127.00904036229613 37.57079391329676)21211001111000000종로구1111016400종로6가272동대문
5NODEPOINT(127.00175612966481 37.58144803112957)21211701111000000종로구1111017200명륜4가267혜화
6NODEPOINT(126.98268850812248 37.57049982033886)21269111111000000종로구1111012700공평동273종각
7NODEPOINT(127.01505874969273 37.57992200287952)16735111111000000종로구1111017400창신동270창신
8NODEPOINT(126.98404302021133 37.570384160886874)21264311111000000종로구1111013800종로2가273종각
9NODEPOINT(127.0158608830301 37.57329756951232)21184201111000000종로구1111017500숭인동268동묘앞
노드링크 유형노드 WKT노드 ID노드 유형 코드시군구코드시군구명읍면동코드읍면동명지하철역코드지하철역명
633NODEPOINT(127.13978304015922 37.53782062327283)21366901174000000강동구1174010500길동28길동
634NODEPOINT(127.16790251023284 37.556847484259855)21381401174000000강동구1174010300상일동38상일동
635NODEPOINT(127.14872889116644 37.529458714253764)21184111174000000강동구1174010600둔촌동39중앙보훈병원
636NODEPOINT(127.1652598279911 37.55672080615116)21539301174000000강동구1174010200고덕동38상일동
637NODEPOINT(127.1329072079279 37.53591207885099)12667011174000000강동구1174010800성내동30강동
638NODEPOINT(127.17628284410385 37.55740515340641)21541301174000000강동구1174010300상일동31강일
639NODEPOINT(127.13820384530973 37.519775129452434)21172601174000000강동구1174010600둔촌동37둔촌오륜
640NODEPOINT(127.14283743789058 37.545800531295995)21387101174000000강동구1174010900천호동32굽은다리(강동구민회관앞)
641NODEPOINT(127.14798818319892 37.527854901583346)21442511174000000강동구1174010600둔촌동39중앙보훈병원
642NODEPOINT(127.12735700818776 37.55012621439297)1440601174000000강동구1174010700암사동27암사