Overview

Dataset statistics

Number of variables14
Number of observations2967
Missing cells8262
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory350.7 KiB
Average record size in memory121.0 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-21210/S/1/datasetView.do

Alerts

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 유형 코드 is highly overall correlated with 링크 ID and 1 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 WKT has 1202 (40.5%) missing valuesMissing
링크 WKT has 1765 (59.5%) missing valuesMissing
시작노드 ID has 1765 (59.5%) missing valuesMissing
종료노드 ID has 1765 (59.5%) missing valuesMissing
링크 길이 has 1765 (59.5%) missing valuesMissing
노드 ID has 1202 (40.5%) zerosZeros
링크 ID has 1765 (59.5%) zerosZeros

Reproduction

Analysis started2024-05-04 00:40:52.366768
Analysis finished2024-05-04 00:41:13.114038
Duration20.75 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 size23.3 KiB
NODE
1765 
LINK
1202 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
NODE 1765
59.5%
LINK 1202
40.5%

Length

2024-05-04T00:41:13.345249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:41:13.703221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
node 1765
59.5%
link 1202
40.5%

노드 WKT
Text

MISSING 

Distinct1765
Distinct (%)100.0%
Missing1202
Missing (%)40.5%
Memory size23.3 KiB
2024-05-04T00:41:14.291861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length43
Mean length43.06289
Min length35

Characters and Unicode

Total characters76006
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

Unique1765 ?
Unique (%)100.0%

Sample

1st rowPOINT(126.96830521004509 37.60624806420481)
2nd rowPOINT(126.96428168289928 37.56851721499537)
3rd rowPOINT(126.96108651984895 37.602128831272644)
4th rowPOINT(127.00930344261518 37.56988138272497)
5th rowPOINT(127.01014673600021 37.56987659240611)
ValueCountFrequency (%)
point(126.97924075439786 1
 
< 0.1%
37.49259283487618 1
 
< 0.1%
point(126.82445403512692 1
 
< 0.1%
37.49199892873403 1
 
< 0.1%
point(126.8817312834239 1
 
< 0.1%
37.50201391992696 1
 
< 0.1%
point(126.84373988203829 1
 
< 0.1%
37.49367016734308 1
 
< 0.1%
point(126.84415878916266 1
 
< 0.1%
37.49388126780886 1
 
< 0.1%
Other values (3520) 3520
99.7%
2024-05-04T00:41:15.707024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 7139
9.4%
3 6366
 
8.4%
1 6298
 
8.3%
2 6241
 
8.2%
6 6008
 
7.9%
5 5802
 
7.6%
8 5226
 
6.9%
4 5174
 
6.8%
9 5128
 
6.7%
0 4974
 
6.5%
Other values (9) 17650
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58356
76.8%
Uppercase Letter 8825
 
11.6%
Other Punctuation 3530
 
4.6%
Space Separator 1765
 
2.3%
Open Punctuation 1765
 
2.3%
Close Punctuation 1765
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 7139
12.2%
3 6366
10.9%
1 6298
10.8%
2 6241
10.7%
6 6008
10.3%
5 5802
9.9%
8 5226
9.0%
4 5174
8.9%
9 5128
8.8%
0 4974
8.5%
Uppercase Letter
ValueCountFrequency (%)
P 1765
20.0%
O 1765
20.0%
T 1765
20.0%
N 1765
20.0%
I 1765
20.0%
Other Punctuation
ValueCountFrequency (%)
. 3530
100.0%
Space Separator
ValueCountFrequency (%)
1765
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1765
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67181
88.4%
Latin 8825
 
11.6%

Most frequent character per script

Common
ValueCountFrequency (%)
7 7139
10.6%
3 6366
9.5%
1 6298
9.4%
2 6241
9.3%
6 6008
8.9%
5 5802
8.6%
8 5226
7.8%
4 5174
7.7%
9 5128
7.6%
0 4974
7.4%
Other values (4) 8825
13.1%
Latin
ValueCountFrequency (%)
P 1765
20.0%
O 1765
20.0%
T 1765
20.0%
N 1765
20.0%
I 1765
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 7139
9.4%
3 6366
 
8.4%
1 6298
 
8.3%
2 6241
 
8.2%
6 6008
 
7.9%
5 5802
 
7.6%
8 5226
 
6.9%
4 5174
 
6.8%
9 5128
 
6.7%
0 4974
 
6.5%
Other values (9) 17650
23.2%

노드 ID
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1766
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69789.986
Minimum0
Maximum215565
Zeros1202
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-05-04T00:41:16.336693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median35168
Q3136414.5
95-th percentile212233.4
Maximum215565
Range215565
Interquartile range (IQR)136414.5

Descriptive statistics

Standard deviation77374.147
Coefficient of variation (CV)1.1086712
Kurtosis-1.1431191
Mean69789.986
Median Absolute Deviation (MAD)35168
Skewness0.6084491
Sum2.0706689 × 108
Variance5.9867586 × 109
MonotonicityNot monotonic
2024-05-04T00:41:16.773659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1202
40.5%
16171 1
 
< 0.1%
122074 1
 
< 0.1%
137486 1
 
< 0.1%
97341 1
 
< 0.1%
122068 1
 
< 0.1%
122069 1
 
< 0.1%
122070 1
 
< 0.1%
122071 1
 
< 0.1%
101857 1
 
< 0.1%
Other values (1756) 1756
59.2%
ValueCountFrequency (%)
0 1202
40.5%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
12 1
 
< 0.1%
15 1
 
< 0.1%
19 1
 
< 0.1%
24 1
 
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
215565 1
< 0.1%
215564 1
< 0.1%
215456 1
< 0.1%
215446 1
< 0.1%
215409 1
< 0.1%
215012 1
< 0.1%
215011 1
< 0.1%
214930 1
< 0.1%
214929 1
< 0.1%
214886 1
< 0.1%

노드 유형 코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
0
1650 
<NA>
1202 
1
 
63
2
 
29
3
 
23

Length

Max length4
Median length1
Mean length2.2153691
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
0 1650
55.6%
<NA> 1202
40.5%
1 63
 
2.1%
2 29
 
1.0%
3 23
 
0.8%

Length

2024-05-04T00:41:17.366030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:41:17.806680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1650
55.6%
na 1202
40.5%
1 63
 
2.1%
2 29
 
1.0%
3 23
 
0.8%

링크 WKT
Text

MISSING 

Distinct1202
Distinct (%)100.0%
Missing1765
Missing (%)59.5%
Memory size23.3 KiB
2024-05-04T00:41:18.447927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length752
Mean length104.40765
Min length77

Characters and Unicode

Total characters125498
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

Unique1202 ?
Unique (%)100.0%

Sample

1st rowLINESTRING(127.00046603020982 37.56933322150643,127.00047232147018 37.569311312454516)
2nd rowLINESTRING(126.96439976075217 37.56841438437971,126.9645267774511 37.56827639221446)
3rd rowLINESTRING(127.01561353995251 37.56975576530333,127.01565920244559 37.569726273858045)
4th rowLINESTRING(126.96185800185964 37.603777083785324,126.96140414155813 37.603608977428664)
5th rowLINESTRING(126.96213626388878 37.60352381491358,126.96196790370622 37.6034166933586)
ValueCountFrequency (%)
linestring(127.10613428561375 4
 
0.1%
linestring(126.98906354909147 3
 
0.1%
linestring(126.89572324823102 3
 
0.1%
linestring(126.87261336266316 3
 
0.1%
linestring(126.99985543146114 3
 
0.1%
37.49138105271298 3
 
0.1%
linestring(127.09509533713153 3
 
0.1%
37.47834357729815 3
 
0.1%
linestring(126.87589966567619 3
 
0.1%
linestring(126.89523867316785 3
 
0.1%
Other values (3914) 4200
99.3%
2024-05-04T00:41:19.733493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 12325
9.8%
1 10888
8.7%
3 10883
8.7%
2 10659
8.5%
6 10319
8.2%
5 10079
 
8.0%
4 8935
 
7.1%
9 8828
 
7.0%
8 8827
 
7.0%
0 8416
 
6.7%
Other values (13) 25339
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100159
79.8%
Uppercase Letter 12020
 
9.6%
Other Punctuation 7887
 
6.3%
Space Separator 3029
 
2.4%
Open Punctuation 1202
 
1.0%
Close Punctuation 1201
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 12325
12.3%
1 10888
10.9%
3 10883
10.9%
2 10659
10.6%
6 10319
10.3%
5 10079
10.1%
4 8935
8.9%
9 8828
8.8%
8 8827
8.8%
0 8416
8.4%
Uppercase Letter
ValueCountFrequency (%)
N 2404
20.0%
I 2404
20.0%
L 1202
10.0%
G 1202
10.0%
R 1202
10.0%
T 1202
10.0%
S 1202
10.0%
E 1202
10.0%
Other Punctuation
ValueCountFrequency (%)
. 6059
76.8%
, 1828
 
23.2%
Space Separator
ValueCountFrequency (%)
3029
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1202
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113478
90.4%
Latin 12020
 
9.6%

Most frequent character per script

Common
ValueCountFrequency (%)
7 12325
10.9%
1 10888
9.6%
3 10883
9.6%
2 10659
9.4%
6 10319
9.1%
5 10079
8.9%
4 8935
7.9%
9 8828
7.8%
8 8827
7.8%
0 8416
7.4%
Other values (5) 13319
11.7%
Latin
ValueCountFrequency (%)
N 2404
20.0%
I 2404
20.0%
L 1202
10.0%
G 1202
10.0%
R 1202
10.0%
T 1202
10.0%
S 1202
10.0%
E 1202
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 12325
9.8%
1 10888
8.7%
3 10883
8.7%
2 10659
8.5%
6 10319
8.2%
5 10079
 
8.0%
4 8935
 
7.1%
9 8828
 
7.0%
8 8827
 
7.0%
0 8416
 
6.7%
Other values (13) 25339
20.2%

링크 ID
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1203
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56180.851
Minimum0
Maximum282149
Zeros1765
Zeros (%)59.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-05-04T00:41:20.198673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3110201.5
95-th percentile241055.6
Maximum282149
Range282149
Interquartile range (IQR)110201.5

Descriptive statistics

Standard deviation84680.24
Coefficient of variation (CV)1.5072794
Kurtosis0.11968723
Mean56180.851
Median Absolute Deviation (MAD)0
Skewness1.2455451
Sum1.6668858 × 108
Variance7.170743 × 109
MonotonicityNot monotonic
2024-05-04T00:41:20.740005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1765
59.5%
177083 1
 
< 0.1%
178970 1
 
< 0.1%
173416 1
 
< 0.1%
123963 1
 
< 0.1%
110885 1
 
< 0.1%
120264 1
 
< 0.1%
109237 1
 
< 0.1%
125147 1
 
< 0.1%
14830 1
 
< 0.1%
Other values (1193) 1193
40.2%
ValueCountFrequency (%)
0 1765
59.5%
69 1
 
< 0.1%
366 1
 
< 0.1%
791 1
 
< 0.1%
900 1
 
< 0.1%
1120 1
 
< 0.1%
1728 1
 
< 0.1%
2026 1
 
< 0.1%
2078 1
 
< 0.1%
2261 1
 
< 0.1%
ValueCountFrequency (%)
282149 1
< 0.1%
282144 1
< 0.1%
281828 1
< 0.1%
281541 1
< 0.1%
281369 1
< 0.1%
281354 1
< 0.1%
281094 1
< 0.1%
280785 1
< 0.1%
280729 1
< 0.1%
280711 1
< 0.1%

링크 유형 코드
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
<NA>
1765 
1000
901 
1011
294 
1010
 
5
11
 
1

Length

Max length4
Median length4
Mean length3.9993259
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row1000
5th row1000

Common Values

ValueCountFrequency (%)
<NA> 1765
59.5%
1000 901
30.4%
1011 294
 
9.9%
1010 5
 
0.2%
11 1
 
< 0.1%
1111 1
 
< 0.1%

Length

2024-05-04T00:41:21.241243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:41:21.770386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1765
59.5%
1000 901
30.4%
1011 294
 
9.9%
1010 5
 
0.2%
11 1
 
< 0.1%
1111 1
 
< 0.1%

시작노드 ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1012
Distinct (%)84.2%
Missing1765
Missing (%)59.5%
Infinite0
Infinite (%)0.0%
Mean117710.45
Minimum5
Maximum215565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-05-04T00:41:22.700435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5469.25
Q165886.5
median120390.5
Q3174517.25
95-th percentile213060.1
Maximum215565
Range215560
Interquartile range (IQR)108630.75

Descriptive statistics

Standard deviation65852.088
Coefficient of variation (CV)0.55944131
Kurtosis-1.0608339
Mean117710.45
Median Absolute Deviation (MAD)54487.5
Skewness-0.16583835
Sum1.4148796 × 108
Variance4.3364975 × 109
MonotonicityNot monotonic
2024-05-04T00:41:23.182602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127844 4
 
0.1%
94854 3
 
0.1%
14925 3
 
0.1%
206166 3
 
0.1%
99116 3
 
0.1%
204383 3
 
0.1%
137607 3
 
0.1%
89121 3
 
0.1%
163531 3
 
0.1%
211859 3
 
0.1%
Other values (1002) 1171
39.5%
(Missing) 1765
59.5%
ValueCountFrequency (%)
5 1
< 0.1%
19 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
47 1
< 0.1%
49 1
< 0.1%
51 1
< 0.1%
57 1
< 0.1%
61 1
< 0.1%
64 1
< 0.1%
ValueCountFrequency (%)
215565 1
< 0.1%
215564 1
< 0.1%
215456 1
< 0.1%
215446 1
< 0.1%
215348 1
< 0.1%
215011 1
< 0.1%
214930 1
< 0.1%
214883 1
< 0.1%
214882 1
< 0.1%
214819 1
< 0.1%

종료노드 ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1091
Distinct (%)90.8%
Missing1765
Missing (%)59.5%
Infinite0
Infinite (%)0.0%
Mean115930.21
Minimum6
Maximum215409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-05-04T00:41:23.631743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7469.05
Q162969.5
median114938.5
Q3174998.75
95-th percentile212865.95
Maximum215409
Range215403
Interquartile range (IQR)112029.25

Descriptive statistics

Standard deviation66411.014
Coefficient of variation (CV)0.5728534
Kurtosis-1.1276466
Mean115930.21
Median Absolute Deviation (MAD)55623
Skewness-0.096325895
Sum1.3934811 × 108
Variance4.4104228 × 109
MonotonicityNot monotonic
2024-05-04T00:41:24.127773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56340 3
 
0.1%
37348 3
 
0.1%
19238 3
 
0.1%
105462 3
 
0.1%
215012 3
 
0.1%
143792 3
 
0.1%
110917 3
 
0.1%
214929 3
 
0.1%
185926 3
 
0.1%
15996 2
 
0.1%
Other values (1081) 1173
39.5%
(Missing) 1765
59.5%
ValueCountFrequency (%)
6 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
32 1
< 0.1%
40 1
< 0.1%
41 1
< 0.1%
45 1
< 0.1%
46 1
< 0.1%
ValueCountFrequency (%)
215409 1
 
< 0.1%
215349 1
 
< 0.1%
215012 3
0.1%
214930 2
0.1%
214929 3
0.1%
214886 1
 
< 0.1%
214735 1
 
< 0.1%
214718 1
 
< 0.1%
214708 1
 
< 0.1%
214638 1
 
< 0.1%

링크 길이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1179
Distinct (%)98.1%
Missing1765
Missing (%)59.5%
Infinite0
Infinite (%)0.0%
Mean31.948605
Minimum1.142
Maximum532.262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-05-04T00:41:24.565437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.142
5-th percentile5.52445
Q113.165
median19.249
Q334.201
95-th percentile102.03135
Maximum532.262
Range531.12
Interquartile range (IQR)21.036

Descriptive statistics

Standard deviation41.397267
Coefficient of variation (CV)1.2957457
Kurtosis44.042263
Mean31.948605
Median Absolute Deviation (MAD)7.837
Skewness5.338294
Sum38402.223
Variance1713.7337
MonotonicityNot monotonic
2024-05-04T00:41:24.950107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.884 2
 
0.1%
30.103 2
 
0.1%
17.825 2
 
0.1%
12.37 2
 
0.1%
34.931 2
 
0.1%
12.495 2
 
0.1%
14.335 2
 
0.1%
4.819 2
 
0.1%
43.832 2
 
0.1%
15.583 2
 
0.1%
Other values (1169) 1182
39.8%
(Missing) 1765
59.5%
ValueCountFrequency (%)
1.142 1
< 0.1%
1.324 1
< 0.1%
1.68 2
0.1%
1.973 1
< 0.1%
2.104 1
< 0.1%
2.131 1
< 0.1%
2.178 1
< 0.1%
2.234 1
< 0.1%
2.41 1
< 0.1%
2.494 1
< 0.1%
ValueCountFrequency (%)
532.262 1
< 0.1%
508.081 1
< 0.1%
361.333 1
< 0.1%
340.834 1
< 0.1%
335.641 1
< 0.1%
289.686 1
< 0.1%
262.548 1
< 0.1%
247.385 1
< 0.1%
236.277 1
< 0.1%
229.365 1
< 0.1%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1415025 × 109
Minimum1.111 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-05-04T00:41:25.271372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.114 × 109
Q11.123 × 109
median1.147 × 109
Q31.156 × 109
95-th percentile1.168 × 109
Maximum1.174 × 109
Range63000000
Interquartile range (IQR)33000000

Descriptive statistics

Standard deviation19212275
Coefficient of variation (CV)0.01683069
Kurtosis-1.4340715
Mean1.1415025 × 109
Median Absolute Deviation (MAD)18000000
Skewness-0.05438158
Sum3.386838 × 1012
Variance3.6911151 × 1014
MonotonicityIncreasing
2024-05-04T00:41:25.574114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1153000000 395
13.3%
1117000000 266
 
9.0%
1129000000 199
 
6.7%
1165000000 187
 
6.3%
1159000000 186
 
6.3%
1123000000 182
 
6.1%
1126000000 173
 
5.8%
1147000000 171
 
5.8%
1168000000 165
 
5.6%
1154500000 129
 
4.3%
Other values (15) 914
30.8%
ValueCountFrequency (%)
1111000000 106
 
3.6%
1114000000 115
3.9%
1117000000 266
9.0%
1120000000 107
3.6%
1121500000 80
 
2.7%
1123000000 182
6.1%
1126000000 173
5.8%
1129000000 199
6.7%
1130500000 51
 
1.7%
1132000000 13
 
0.4%
ValueCountFrequency (%)
1174000000 81
 
2.7%
1171000000 51
 
1.7%
1168000000 165
5.6%
1165000000 187
6.3%
1162000000 37
 
1.2%
1159000000 186
6.3%
1156000000 97
 
3.3%
1154500000 129
 
4.3%
1153000000 395
13.3%
1150000000 54
 
1.8%

시군구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
구로구
395 
용산구
266 
성북구
199 
서초구
187 
동작구
186 
Other values (20)
1734 

Length

Max length4
Median length3
Mean length3.0707786
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
구로구 395
13.3%
용산구 266
 
9.0%
성북구 199
 
6.7%
서초구 187
 
6.3%
동작구 186
 
6.3%
동대문구 182
 
6.1%
중랑구 173
 
5.8%
양천구 171
 
5.8%
강남구 165
 
5.6%
금천구 129
 
4.3%
Other values (15) 914
30.8%

Length

2024-05-04T00:41:26.074333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구로구 395
13.3%
용산구 266
 
9.0%
성북구 199
 
6.7%
서초구 187
 
6.3%
동작구 186
 
6.3%
동대문구 182
 
6.1%
중랑구 173
 
5.8%
양천구 171
 
5.8%
강남구 165
 
5.6%
금천구 129
 
4.3%
Other values (15) 914
30.8%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct202
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.141532 × 109
Minimum1.1110123 × 109
Maximum1.174011 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-05-04T00:41:26.493580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110123 × 109
5-th percentile1.1140129 × 109
Q11.1230103 × 109
median1.1470101 × 109
Q31.1560132 × 109
95-th percentile1.1680118 × 109
Maximum1.174011 × 109
Range62998700
Interquartile range (IQR)33002900

Descriptive statistics

Standard deviation19217017
Coefficient of variation (CV)0.016834409
Kurtosis-1.4369246
Mean1.141532 × 109
Median Absolute Deviation (MAD)18000000
Skewness-0.054079955
Sum3.3869255 × 1012
Variance3.6929374 × 1014
MonotonicityNot monotonic
2024-05-04T00:41:26.996917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153010200 132
 
4.4%
1147010100 83
 
2.8%
1153010800 74
 
2.5%
1154510100 68
 
2.3%
1126010100 65
 
2.2%
1129013300 61
 
2.1%
1168010300 56
 
1.9%
1153010700 55
 
1.9%
1165010700 54
 
1.8%
1147010200 45
 
1.5%
Other values (192) 2274
76.6%
ValueCountFrequency (%)
1111012300 11
0.4%
1111013500 6
0.2%
1111015400 3
 
0.1%
1111015500 3
 
0.1%
1111015800 5
0.2%
1111016300 3
 
0.1%
1111016400 6
0.2%
1111016900 1
 
< 0.1%
1111017400 6
0.2%
1111017500 6
0.2%
ValueCountFrequency (%)
1174011000 14
0.5%
1174010900 7
 
0.2%
1174010700 22
0.7%
1174010500 10
0.3%
1174010300 19
0.6%
1174010100 9
0.3%
1171011200 3
 
0.1%
1171010900 16
0.5%
1171010800 3
 
0.1%
1171010300 14
0.5%

읍면동명
Text

Distinct202
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
2024-05-04T00:41:27.758928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2574992
Min length2

Characters and Unicode

Total characters9665
Distinct characters160
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

Unique9 ?
Unique (%)0.3%

Sample

1st row평창동
2nd row교남동
3rd row신영동
4th row예지동
5th row교남동
ValueCountFrequency (%)
구로동 132
 
4.4%
신정동 83
 
2.8%
오류동 74
 
2.5%
가산동 68
 
2.3%
면목동 65
 
2.2%
정릉동 61
 
2.1%
개포동 56
 
1.9%
개봉동 55
 
1.9%
반포동 54
 
1.8%
목동 45
 
1.5%
Other values (192) 2274
76.6%
2024-05-04T00:41:29.093575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2958
30.6%
가 398
 
4.1%
신 304
 
3.1%
로 213
 
2.2%
산 191
 
2.0%
정 168
 
1.7%
구 160
 
1.7%
수 137
 
1.4%
포 131
 
1.4%
곡 128
 
1.3%
Other values (150) 4877
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9353
96.8%
Decimal Number 312
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2958
31.6%
가 398
 
4.3%
신 304
 
3.3%
로 213
 
2.3%
산 191
 
2.0%
정 168
 
1.8%
구 160
 
1.7%
수 137
 
1.5%
포 131
 
1.4%
곡 128
 
1.4%
Other values (143) 4565
48.8%
Decimal Number
ValueCountFrequency (%)
2 71
22.8%
1 61
19.6%
6 57
18.3%
3 52
16.7%
5 33
10.6%
4 33
10.6%
7 5
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9353
96.8%
Common 312
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2958
31.6%
가 398
 
4.3%
신 304
 
3.3%
로 213
 
2.3%
산 191
 
2.0%
정 168
 
1.8%
구 160
 
1.7%
수 137
 
1.5%
포 131
 
1.4%
곡 128
 
1.4%
Other values (143) 4565
48.8%
Common
ValueCountFrequency (%)
2 71
22.8%
1 61
19.6%
6 57
18.3%
3 52
16.7%
5 33
10.6%
4 33
10.6%
7 5
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9353
96.8%
ASCII 312
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2958
31.6%
가 398
 
4.3%
신 304
 
3.3%
로 213
 
2.3%
산 191
 
2.0%
정 168
 
1.8%
구 160
 
1.7%
수 137
 
1.5%
포 131
 
1.4%
곡 128
 
1.4%
Other values (143) 4565
48.8%
ASCII
ValueCountFrequency (%)
2 71
22.8%
1 61
19.6%
6 57
18.3%
3 52
16.7%
5 33
10.6%
4 33
10.6%
7 5
 
1.6%

Interactions

2024-05-04T00:41:09.276275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:55.023352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:57.399391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:59.888683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:03.071092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:05.314340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:07.291769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:09.545636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:55.265759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:57.717125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:00.440451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:03.510006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:05.627208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:07.552459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:09.817933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:55.511459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:58.007023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:01.175362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:03.828405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:05.963543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:07.908205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:10.080366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:55.885253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:58.268756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:01.496677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:04.086002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:06.218727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:08.178988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:10.339799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:56.187297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:58.525686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:01.937296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:04.346690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:06.466021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:08.448194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:10.638901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:56.498607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:58.814754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:02.319806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:04.715337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:06.703520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:08.721094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:11.015679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:56.968991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:59.218051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:02.675092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:05.020393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:07.004542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:41:08.998464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T00:41:29.407378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드링크 유형노드 ID노드 유형 코드링크 ID링크 유형 코드시작노드 ID종료노드 ID링크 길이시군구코드시군구명읍면동코드
노드링크 유형1.0000.975NaN0.992NaNNaNNaNNaN0.0000.0000.000
노드 ID0.9751.0000.1380.666NaNNaNNaNNaN0.5210.6040.520
노드 유형 코드NaN0.1381.000NaNNaNNaNNaNNaN0.2760.3950.275
링크 ID0.9920.666NaN1.0000.0000.1070.0000.0000.0590.0000.062
링크 유형 코드NaNNaNNaN0.0001.0000.1260.1500.0000.3110.4200.323
시작노드 IDNaNNaNNaN0.1070.1261.0000.9390.0570.6660.7460.660
종료노드 IDNaNNaNNaN0.0000.1500.9391.0000.0470.6770.7570.673
링크 길이NaNNaNNaN0.0000.0000.0570.0471.0000.1020.3890.108
시군구코드0.0000.5210.2760.0590.3110.6660.6770.1021.0001.0001.000
시군구명0.0000.6040.3950.0000.4200.7460.7570.3891.0001.0001.000
읍면동코드0.0000.5200.2750.0620.3230.6600.6730.1081.0001.0001.000
2024-05-04T00:41:29.924029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
링크 유형 코드노드링크 유형노드 유형 코드시군구명
링크 유형 코드1.0001.000NaN0.193
노드링크 유형1.0001.0001.0000.000
노드 유형 코드NaN1.0001.0000.217
시군구명0.1930.0000.2171.000
2024-05-04T00:41:30.371451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 ID링크 ID시작노드 ID종료노드 ID링크 길이시군구코드읍면동코드노드링크 유형노드 유형 코드링크 유형 코드시군구명
노드 ID1.000-0.842NaNNaNNaN0.0290.0290.8610.0831.0000.259
링크 ID-0.8421.000-0.0110.0110.0540.0110.0120.9211.0000.0000.000
시작노드 IDNaN-0.0111.0000.505-0.1050.1520.1541.0000.0000.0520.370
종료노드 IDNaN0.0110.5051.000-0.1090.1670.1661.0000.0000.0630.381
링크 길이NaN0.054-0.105-0.1091.0000.0110.0141.0000.0000.0000.164
시군구코드0.0290.0110.1520.1670.0111.0000.9970.0000.1680.1390.997
읍면동코드0.0290.0120.1540.1660.0140.9971.0000.0000.1670.1400.990
노드링크 유형0.8610.9211.0001.0001.0000.0000.0001.0001.0001.0000.000
노드 유형 코드0.0831.0000.0000.0000.0000.1680.1671.0001.0000.0000.217
링크 유형 코드1.0000.0000.0520.0630.0000.1390.1401.0000.0001.0000.193
시군구명0.2590.0000.3700.3810.1640.9970.9900.0000.2170.1931.000

Missing values

2024-05-04T00:41:11.417538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T00:41:12.401785image/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.
2024-05-04T00:41:12.842481image/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
0NODEPOINT(126.96830521004509 37.60624806420481)161710<NA>0<NA><NA><NA><NA>1111000000종로구1111018300평창동
1NODEPOINT(126.96428168289928 37.56851721499537)1291110<NA>0<NA><NA><NA><NA>1111000000종로구1111017600교남동
2NODEPOINT(126.96108651984895 37.602128831272644)111110<NA>0<NA><NA><NA><NA>1111000000종로구1111018600신영동
3LINK<NA>0<NA>LINESTRING(127.00046603020982 37.56933322150643,127.00047232147018 37.569311312454516)3174510002124952125632.4941111000000종로구1111015800예지동
4LINK<NA>0<NA>LINESTRING(126.96439976075217 37.56841438437971,126.9645267774511 37.56827639221446)214407100012126312910918.9861111000000종로구1111017600교남동
5NODEPOINT(127.00930344261518 37.56988138272497)1030<NA>0<NA><NA><NA><NA>1111000000종로구1111016400종로6가
6LINK<NA>0<NA>LINESTRING(127.01561353995251 37.56975576530333,127.01565920244559 37.569726273858045)867651000215456161755.1951111000000종로구1114016200신당동
7LINK<NA>0<NA>LINESTRING(126.96185800185964 37.603777083785324,126.96140414155813 37.603608977428664)13527810001283851599544.2081111000000종로구1111018600신영동
8NODEPOINT(127.01014673600021 37.56987659240611)120<NA>0<NA><NA><NA><NA>1111000000종로구1111017400창신동
9NODEPOINT(127.01561353995251 37.56975576530333)2154560<NA>0<NA><NA><NA><NA>1111000000종로구1114016200신당동
노드링크 유형노드 WKT노드 ID노드 유형 코드링크 WKT링크 ID링크 유형 코드시작노드 ID종료노드 ID링크 길이시군구코드시군구명읍면동코드읍면동명
2957LINK<NA>0<NA>LINESTRING(127.17605753070418 37.56600890236503,127.1761713332575 37.565907274566435)2222971011563375633815.111174000000강동구1174011000강일동
2958LINK<NA>0<NA>LINESTRING(127.13264145473092 37.55481425752119,127.13275610761751 37.554839061676525)142321100012395312395410.4981174000000강동구1174010700암사동
2959NODEPOINT(127.13248535560409 37.55468691623478)1442240<NA>0<NA><NA><NA><NA>1174000000강동구1174010700암사동
2960NODEPOINT(127.12767432756853 37.55473076632555)475360<NA>0<NA><NA><NA><NA>1174000000강동구1174010700암사동
2961NODEPOINT(127.12751813294814 37.554695863190354)475350<NA>0<NA><NA><NA><NA>1174000000강동구1174010700암사동
2962LINK<NA>0<NA>LINESTRING(127.1369315602276 37.54041561694762,127.13696205231913 37.5405446541134)132299100017232621493014.5731174000000강동구1174010500길동
2963LINK<NA>0<NA>LINESTRING(127.12751929583888 37.55484536050233,127.12768601717084 37.55484289179214)616331000475444754314.7341174000000강동구1174010700암사동
2964NODEPOINT(127.13685660822917 37.540684320996526)1723220<NA>0<NA><NA><NA><NA>1174000000강동구1174010900천호동
2965LINK<NA>0<NA>LINESTRING(127.1191748038903 37.54286913133529,127.1187659173253 37.542388950362124,127.1185761762667 37.542357101512835,127.11785093183492 37.54173992228576,127.11760372938689 37.54171957189195)2412851011160482160483197.31174000000강동구1174010900천호동
2966NODEPOINT(127.1327759046208 37.554681228961165)694150<NA>0<NA><NA><NA><NA>1174000000강동구1174010700암사동