Overview

Dataset statistics

Number of variables10
Number of observations83
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory86.6 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-21211/S/1/datasetView.do

Alerts

노드링크 유형 has constant value ""Constant
시군구코드 is highly overall correlated with 읍면동코드 and 2 other fieldsHigh correlation
읍면동코드 is highly overall correlated with 시군구코드 and 2 other fieldsHigh correlation
지하철역코드 is highly overall correlated with 시군구코드 and 3 other fieldsHigh correlation
노드 유형 코드 is highly overall correlated with 지하철역코드 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 시군구코드 and 3 other fieldsHigh correlation
노드 유형 코드 is highly imbalanced (72.1%)Imbalance
노드 WKT has unique valuesUnique
노드 ID has unique valuesUnique

Reproduction

Analysis started2024-05-18 04:06:29.965550
Analysis finished2024-05-18 04:06:34.909601
Duration4.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노드링크 유형
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
NODE
83 

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 83
100.0%

Length

2024-05-18T13:06:35.106932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:06:35.291033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
node 83
100.0%

노드 WKT
Text

UNIQUE 

Distinct83
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-05-18T13:06:35.655545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length43
Mean length42.915663
Min length39

Characters and Unicode

Total characters3562
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

Unique83 ?
Unique (%)100.0%

Sample

1st rowPOINT(127.01587272865775 37.57169713324989)
2nd rowPOINT(127.01520097725069 37.5727631714696)
3rd rowPOINT(126.99215227845464 37.57106843929747)
4th rowPOINT(126.99186553544038 37.5705742122576)
5th rowPOINT(127.01550585346979 37.573234568417995)
ValueCountFrequency (%)
point(127.01587272865775 1
 
0.6%
point(126.98263119503251 1
 
0.6%
point(127.02748750839716 1
 
0.6%
37.49883559708308 1
 
0.6%
point(127.08493968575138 1
 
0.6%
37.48365867102188 1
 
0.6%
point(127.08508924242753 1
 
0.6%
37.48385309111214 1
 
0.6%
point(127.08349207345061 1
 
0.6%
37.48401662304801 1
 
0.6%
Other values (156) 156
94.0%
2024-05-18T13:06:36.526680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 353
9.9%
1 303
 
8.5%
3 302
 
8.5%
5 283
 
7.9%
2 282
 
7.9%
4 258
 
7.2%
6 254
 
7.1%
0 247
 
6.9%
8 232
 
6.5%
9 218
 
6.1%
Other values (9) 830
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2732
76.7%
Uppercase Letter 415
 
11.7%
Other Punctuation 166
 
4.7%
Space Separator 83
 
2.3%
Open Punctuation 83
 
2.3%
Close Punctuation 83
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 353
12.9%
1 303
11.1%
3 302
11.1%
5 283
10.4%
2 282
10.3%
4 258
9.4%
6 254
9.3%
0 247
9.0%
8 232
8.5%
9 218
8.0%
Uppercase Letter
ValueCountFrequency (%)
P 83
20.0%
O 83
20.0%
T 83
20.0%
N 83
20.0%
I 83
20.0%
Other Punctuation
ValueCountFrequency (%)
. 166
100.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 83
100.0%
Close Punctuation
ValueCountFrequency (%)
) 83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3147
88.3%
Latin 415
 
11.7%

Most frequent character per script

Common
ValueCountFrequency (%)
7 353
11.2%
1 303
9.6%
3 302
9.6%
5 283
9.0%
2 282
9.0%
4 258
8.2%
6 254
8.1%
0 247
7.8%
8 232
7.4%
9 218
6.9%
Other values (4) 415
13.2%
Latin
ValueCountFrequency (%)
P 83
20.0%
O 83
20.0%
T 83
20.0%
N 83
20.0%
I 83
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 353
9.9%
1 303
 
8.5%
3 302
 
8.5%
5 283
 
7.9%
2 282
 
7.9%
4 258
 
7.2%
6 254
 
7.1%
0 247
 
6.9%
8 232
 
6.5%
9 218
 
6.1%
Other values (9) 830
23.3%

노드 ID
Real number (ℝ)

UNIQUE 

Distinct83
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143681.45
Minimum6504
Maximum214896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-18T13:06:36.965838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6504
5-th percentile15362.8
Q1106326
median160421
Q3202968.5
95-th percentile212454.7
Maximum214896
Range208392
Interquartile range (IQR)96642.5

Descriptive statistics

Standard deviation69581.061
Coefficient of variation (CV)0.48427311
Kurtosis-0.75096508
Mean143681.45
Median Absolute Deviation (MAD)44472
Skewness-0.76891534
Sum11925560
Variance4.8415241 × 109
MonotonicityNot monotonic
2024-05-18T13:06:37.419436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99451 1
 
1.2%
104255 1
 
1.2%
16189 1
 
1.2%
204923 1
 
1.2%
204893 1
 
1.2%
204890 1
 
1.2%
202612 1
 
1.2%
202611 1
 
1.2%
202492 1
 
1.2%
202490 1
 
1.2%
Other values (73) 73
88.0%
ValueCountFrequency (%)
6504 1
1.2%
6510 1
1.2%
7702 1
1.2%
8619 1
1.2%
15271 1
1.2%
16189 1
1.2%
18333 1
1.2%
18343 1
1.2%
18350 1
1.2%
18696 1
1.2%
ValueCountFrequency (%)
214896 1
1.2%
213333 1
1.2%
213332 1
1.2%
212532 1
1.2%
212481 1
1.2%
212218 1
1.2%
211979 1
1.2%
211943 1
1.2%
211765 1
1.2%
211761 1
1.2%

노드 유형 코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size796.0 B
1
79 
0
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 79
95.2%
0 4
 
4.8%

Length

2024-05-18T13:06:37.828545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:06:38.142077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 79
95.2%
0 4
 
4.8%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1430241 × 109
Minimum1.111 × 109
Maximum1.171 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-18T13:06:38.442518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.111 × 109
Q11.114 × 109
median1.144 × 109
Q31.168 × 109
95-th percentile1.171 × 109
Maximum1.171 × 109
Range60000000
Interquartile range (IQR)54000000

Descriptive statistics

Standard deviation23497912
Coefficient of variation (CV)0.02055767
Kurtosis-1.5749972
Mean1.1430241 × 109
Median Absolute Deviation (MAD)24000000
Skewness-0.17354755
Sum9.4871 × 1010
Variance5.5215185 × 1014
MonotonicityIncreasing
2024-05-18T13:06:38.838387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1168000000 22
26.5%
1111000000 16
19.3%
1135000000 12
14.5%
1171000000 8
 
9.6%
1114000000 6
 
7.2%
1144000000 6
 
7.2%
1153000000 4
 
4.8%
1123000000 2
 
2.4%
1120000000 1
 
1.2%
1121500000 1
 
1.2%
Other values (5) 5
 
6.0%
ValueCountFrequency (%)
1111000000 16
19.3%
1114000000 6
 
7.2%
1120000000 1
 
1.2%
1121500000 1
 
1.2%
1123000000 2
 
2.4%
1135000000 12
14.5%
1138000000 1
 
1.2%
1144000000 6
 
7.2%
1150000000 1
 
1.2%
1153000000 4
 
4.8%
ValueCountFrequency (%)
1171000000 8
 
9.6%
1168000000 22
26.5%
1165000000 1
 
1.2%
1156000000 1
 
1.2%
1154500000 1
 
1.2%
1153000000 4
 
4.8%
1150000000 1
 
1.2%
1144000000 6
 
7.2%
1138000000 1
 
1.2%
1135000000 12
14.5%

시군구명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size796.0 B
강남구
22 
종로구
16 
노원구
12 
송파구
8 
중구
6 
Other values (10)
19 

Length

Max length4
Median length3
Mean length2.9638554
Min length2

Unique

Unique7 ?
Unique (%)8.4%

Sample

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

Common Values

ValueCountFrequency (%)
강남구 22
26.5%
종로구 16
19.3%
노원구 12
14.5%
송파구 8
 
9.6%
중구 6
 
7.2%
마포구 6
 
7.2%
구로구 4
 
4.8%
동대문구 2
 
2.4%
성동구 1
 
1.2%
광진구 1
 
1.2%
Other values (5) 5
 
6.0%

Length

2024-05-18T13:06:39.291945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 22
26.5%
종로구 16
19.3%
노원구 12
14.5%
송파구 8
 
9.6%
중구 6
 
7.2%
마포구 6
 
7.2%
구로구 4
 
4.8%
동대문구 2
 
2.4%
성동구 1
 
1.2%
광진구 1
 
1.2%
Other values (5) 5
 
6.0%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1430362 × 109
Minimum1.1110137 × 109
Maximum1.1710114 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-18T13:06:39.727173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110137 × 109
5-th percentile1.1110153 × 109
Q11.1140154 × 109
median1.1440115 × 109
Q31.1680106 × 109
95-th percentile1.1710111 × 109
Maximum1.1710114 × 109
Range59997700
Interquartile range (IQR)53995200

Descriptive statistics

Standard deviation23496343
Coefficient of variation (CV)0.02055608
Kurtosis-1.5750793
Mean1.1430362 × 109
Median Absolute Deviation (MAD)23999900
Skewness-0.17344895
Sum9.4872002 × 1010
Variance5.5207815 × 1014
MonotonicityNot monotonic
2024-05-18T13:06:40.256899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1168011400 7
 
8.4%
1135010500 6
 
7.2%
1135010200 5
 
6.0%
1168010600 5
 
6.0%
1111017500 4
 
4.8%
1168010300 3
 
3.6%
1111017400 3
 
3.6%
1168011800 3
 
3.6%
1144011500 2
 
2.4%
1168010100 2
 
2.4%
Other values (36) 43
51.8%
ValueCountFrequency (%)
1111013700 1
 
1.2%
1111013800 1
 
1.2%
1111015100 2
2.4%
1111015300 2
2.4%
1111015600 1
 
1.2%
1111016100 1
 
1.2%
1111016300 1
 
1.2%
1111017400 3
3.6%
1111017500 4
4.8%
1114011800 1
 
1.2%
ValueCountFrequency (%)
1171011400 2
 
2.4%
1171011200 1
 
1.2%
1171011100 2
 
2.4%
1171010900 1
 
1.2%
1171010300 1
 
1.2%
1171010200 1
 
1.2%
1168011800 3
3.6%
1168011500 2
 
2.4%
1168011400 7
8.4%
1168010600 5
6.0%

읍면동명
Text

Distinct46
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-05-18T13:06:40.744327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2168675
Min length2

Characters and Unicode

Total characters267
Distinct characters69
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

Unique29 ?
Unique (%)34.9%

Sample

1st row숭인동
2nd row창신동
3rd row묘동
4th row종로3가
5th row창신동
ValueCountFrequency (%)
일원동 7
 
8.4%
상계동 6
 
7.2%
월계동 5
 
6.0%
대치동 5
 
6.0%
숭인동 4
 
4.8%
개포동 3
 
3.6%
창신동 3
 
3.6%
도곡동 3
 
3.6%
묘동 2
 
2.4%
충무로2가 2
 
2.4%
Other values (36) 43
51.8%
2024-05-18T13:06:41.636655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 76
28.5%
가 13
 
4.9%
계 12
 
4.5%
상 9
 
3.4%
원 8
 
3.0%
로 8
 
3.0%
일 7
 
2.6%
대 7
 
2.6%
치 5
 
1.9%
인 5
 
1.9%
Other values (59) 117
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 257
96.3%
Decimal Number 10
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 76
29.6%
가 13
 
5.1%
계 12
 
4.7%
상 9
 
3.5%
원 8
 
3.1%
로 8
 
3.1%
일 7
 
2.7%
대 7
 
2.7%
치 5
 
1.9%
인 5
 
1.9%
Other values (54) 107
41.6%
Decimal Number
ValueCountFrequency (%)
2 4
40.0%
5 2
20.0%
3 2
20.0%
4 1
 
10.0%
1 1
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 257
96.3%
Common 10
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 76
29.6%
가 13
 
5.1%
계 12
 
4.7%
상 9
 
3.5%
원 8
 
3.1%
로 8
 
3.1%
일 7
 
2.7%
대 7
 
2.7%
치 5
 
1.9%
인 5
 
1.9%
Other values (54) 107
41.6%
Common
ValueCountFrequency (%)
2 4
40.0%
5 2
20.0%
3 2
20.0%
4 1
 
10.0%
1 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 257
96.3%
ASCII 10
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 76
29.6%
가 13
 
5.1%
계 12
 
4.7%
상 9
 
3.5%
원 8
 
3.1%
로 8
 
3.1%
일 7
 
2.7%
대 7
 
2.7%
치 5
 
1.9%
인 5
 
1.9%
Other values (54) 107
41.6%
ASCII
ValueCountFrequency (%)
2 4
40.0%
5 2
20.0%
3 2
20.0%
4 1
 
10.0%
1 1
 
10.0%

지하철역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.80723
Minimum5
Maximum284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-18T13:06:42.236388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q128
median122
Q3265
95-th percentile275.7
Maximum284
Range279
Interquartile range (IQR)237

Descriptive statistics

Standard deviation99.535563
Coefficient of variation (CV)0.71194861
Kurtosis-1.4605645
Mean139.80723
Median Absolute Deviation (MAD)99
Skewness0.095334921
Sum11604
Variance9907.3282
MonotonicityNot monotonic
2024-05-18T13:06:42.682502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
265 6
 
7.2%
268 5
 
6.0%
105 4
 
4.8%
8 4
 
4.8%
23 4
 
4.8%
19 4
 
4.8%
17 3
 
3.6%
88 3
 
3.6%
96 3
 
3.6%
209 2
 
2.4%
Other values (34) 45
54.2%
ValueCountFrequency (%)
5 2
2.4%
6 2
2.4%
8 4
4.8%
10 2
2.4%
17 3
3.6%
19 4
4.8%
23 4
4.8%
33 1
 
1.2%
61 1
 
1.2%
79 1
 
1.2%
ValueCountFrequency (%)
284 2
 
2.4%
282 1
 
1.2%
276 2
 
2.4%
273 1
 
1.2%
272 1
 
1.2%
270 2
 
2.4%
268 5
6.0%
266 2
 
2.4%
265 6
7.2%
232 1
 
1.2%

지하철역명
Text

Distinct44
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-05-18T13:06:43.268096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length3.3253012
Min length2

Characters and Unicode

Total characters276
Distinct characters84
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)27.7%

Sample

1st row동묘앞
2nd row동묘앞
3rd row종로3가
4th row종로3가
5th row동묘앞
ValueCountFrequency (%)
종로3가 6
 
7.2%
동묘앞 5
 
6.0%
수락산 4
 
4.8%
대청 4
 
4.8%
대치 4
 
4.8%
일원 4
 
4.8%
도곡 3
 
3.6%
남구로 3
 
3.6%
월계 3
 
3.6%
디지털미디어시티 2
 
2.4%
Other values (34) 45
54.2%
2024-05-18T13:06:44.295211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 17
 
6.2%
로 11
 
4.0%
구 11
 
4.0%
동 11
 
4.0%
수 10
 
3.6%
종 9
 
3.3%
청 8
 
2.9%
원 8
 
2.9%
가 8
 
2.9%
산 7
 
2.5%
Other values (74) 176
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 258
93.5%
Decimal Number 8
 
2.9%
Close Punctuation 5
 
1.8%
Open Punctuation 5
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 17
 
6.6%
로 11
 
4.3%
구 11
 
4.3%
동 11
 
4.3%
수 10
 
3.9%
종 9
 
3.5%
청 8
 
3.1%
원 8
 
3.1%
가 8
 
3.1%
산 7
 
2.7%
Other values (70) 158
61.2%
Decimal Number
ValueCountFrequency (%)
3 6
75.0%
5 2
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 258
93.5%
Common 18
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 17
 
6.6%
로 11
 
4.3%
구 11
 
4.3%
동 11
 
4.3%
수 10
 
3.9%
종 9
 
3.5%
청 8
 
3.1%
원 8
 
3.1%
가 8
 
3.1%
산 7
 
2.7%
Other values (70) 158
61.2%
Common
ValueCountFrequency (%)
3 6
33.3%
) 5
27.8%
( 5
27.8%
5 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 258
93.5%
ASCII 18
 
6.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 17
 
6.6%
로 11
 
4.3%
구 11
 
4.3%
동 11
 
4.3%
수 10
 
3.9%
종 9
 
3.5%
청 8
 
3.1%
원 8
 
3.1%
가 8
 
3.1%
산 7
 
2.7%
Other values (70) 158
61.2%
ASCII
ValueCountFrequency (%)
3 6
33.3%
) 5
27.8%
( 5
27.8%
5 2
 
11.1%

Interactions

2024-05-18T13:06:33.488607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:30.832452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:31.783566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:32.535356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:33.625991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:31.183069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:31.996145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:32.773313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:33.787735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:31.430331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:32.158325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:33.035257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:33.947408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:31.639184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:32.325516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:06:33.290438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T13:06:44.593950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 WKT노드 ID노드 유형 코드시군구코드시군구명읍면동코드읍면동명지하철역코드지하철역명
노드 WKT1.0001.0001.0001.0001.0001.0001.0001.0001.000
노드 ID1.0001.0000.0000.6090.7050.6100.9700.6960.987
노드 유형 코드1.0000.0001.0000.4240.6090.4560.6250.8940.919
시군구코드1.0000.6090.4241.0001.0001.0001.0000.8980.989
시군구명1.0000.7050.6091.0001.0001.0001.0000.9630.996
읍면동코드1.0000.6100.4561.0001.0001.0001.0000.9020.991
읍면동명1.0000.9700.6251.0001.0001.0001.0000.9880.992
지하철역코드1.0000.6960.8940.8980.9630.9020.9881.0001.000
지하철역명1.0000.9870.9190.9890.9960.9910.9921.0001.000
2024-05-18T13:06:44.976576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명노드 유형 코드
시군구명1.0000.513
노드 유형 코드0.5131.000
2024-05-18T13:06:45.284838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 ID시군구코드읍면동코드지하철역코드노드 유형 코드시군구명
노드 ID1.0000.0240.042-0.1330.0000.342
시군구코드0.0241.0000.985-0.6620.4370.959
읍면동코드0.0420.9851.000-0.6540.4370.959
지하철역코드-0.133-0.662-0.6541.0000.6930.760
노드 유형 코드0.0000.4370.4370.6931.0000.513
시군구명0.3420.9590.9590.7600.5131.000

Missing values

2024-05-18T13:06:34.244265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T13:06:34.728424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

노드링크 유형노드 WKT노드 ID노드 유형 코드시군구코드시군구명읍면동코드읍면동명지하철역코드지하철역명
0NODEPOINT(127.01587272865775 37.57169713324989)9945111111000000종로구1111017500숭인동268동묘앞
1NODEPOINT(127.01520097725069 37.5727631714696)16066811111000000종로구1111017400창신동268동묘앞
2NODEPOINT(126.99215227845464 37.57106843929747)13562711111000000종로구1111015100묘동265종로3가
3NODEPOINT(126.99186553544038 37.5705742122576)13566811111000000종로구1111015600종로3가265종로3가
4NODEPOINT(127.01550585346979 37.573234568417995)15968511111000000종로구1111017400창신동268동묘앞
5NODEPOINT(127.00008065138539 37.57103575444844)21176511111000000종로구1111016100종로4가266종로5가
6NODEPOINT(127.01619593666405 37.57330084304634)15970511111000000종로구1111017500숭인동268동묘앞
7NODEPOINT(126.99104038125857 37.572655707708435)13556711111000000종로구1111015300돈의동265종로3가
8NODEPOINT(127.01546995294608 37.579924238231975)16734611111000000종로구1111017500숭인동270창신
9NODEPOINT(127.00062457346921 37.57083444225304)21161011111000000종로구1111016300종로5가266종로5가
노드링크 유형노드 WKT노드 ID노드 유형 코드시군구코드시군구명읍면동코드읍면동명지하철역코드지하철역명
73NODEPOINT(127.06526375831818 37.494935992423684)2381211168000000강남구1168010600대치동23대치
74NODEPOINT(127.07996535431397 37.49367975719071)20296111168000000강남구1168011400일원동8대청
75NODEPOINT(127.15220891949295 37.49470309241651)10839711171000000송파구1171011400마천동219마천
76NODEPOINT(127.13503399427564 37.49819246830591)11469811171000000송파구1171011200오금동217개롱
77NODEPOINT(127.1532273698744 37.49551879010833)19688611171000000송파구1171011400마천동219마천
78NODEPOINT(127.11326448370943 37.517709119670556)11061111171000000송파구1171011100방이동209몽촌토성(평화의문)
79NODEPOINT(127.13120675007835 37.516040328917775)19863011171000000송파구1171011100방이동204올림픽공원(한국체대)
80NODEPOINT(127.12050307295738 37.53106649864566)19865811171000000송파구1171010300풍납동33강동구청
81NODEPOINT(127.12652310857835 37.4714482540519)19872711171000000송파구1171010900장지동198복정
82NODEPOINT(127.11271174664988 37.51793850317135)11020411171000000송파구1171010200신천동209몽촌토성(평화의문)