Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory75.3 B

Variable types

Text1
Categorical8

Dataset

Description샘플 데이터
Author오픈메이트
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=12

Alerts

대학가_여부(UNVTW_AT) is highly imbalanced (80.6%)Imbalance

Reproduction

Analysis started2023-12-10 14:49:28.036083
Analysis finished2023-12-10 14:49:28.790294
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

블록_코드(BLCK_CD)
Text

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T23:49:28.973255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.77
Min length4

Characters and Unicode

Total characters577
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)79.0%

Sample

1st row2*4*4*
2nd row2*3*9*
3rd row3*2*7*
4th row2*2*8*
5th row2*4*3
ValueCountFrequency (%)
2*1*6 3
 
3.0%
2*9*0 3
 
3.0%
3*3*2 3
 
3.0%
2*1*4 3
 
3.0%
2*2*1 2
 
2.0%
2*2*0 2
 
2.0%
1*3*0 2
 
2.0%
2*0*5 2
 
2.0%
2*2*8 2
 
2.0%
2*0*4 2
 
2.0%
Other values (74) 76
76.0%
2023-12-10T23:49:29.407501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 278
48.2%
2 82
 
14.2%
1 45
 
7.8%
3 35
 
6.1%
4 27
 
4.7%
0 24
 
4.2%
9 21
 
3.6%
8 21
 
3.6%
6 16
 
2.8%
5 15
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 299
51.8%
Other Punctuation 278
48.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 82
27.4%
1 45
15.1%
3 35
11.7%
4 27
 
9.0%
0 24
 
8.0%
9 21
 
7.0%
8 21
 
7.0%
6 16
 
5.4%
5 15
 
5.0%
7 13
 
4.3%
Other Punctuation
ValueCountFrequency (%)
* 278
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 278
48.2%
2 82
 
14.2%
1 45
 
7.8%
3 35
 
6.1%
4 27
 
4.7%
0 24
 
4.2%
9 21
 
3.6%
8 21
 
3.6%
6 16
 
2.8%
5 15
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 278
48.2%
2 82
 
14.2%
1 45
 
7.8%
3 35
 
6.1%
4 27
 
4.7%
0 24
 
4.2%
9 21
 
3.6%
8 21
 
3.6%
6 16
 
2.8%
5 15
 
2.6%

블록_유형_대분류_코드(BLCK_TY_LCLAS_CD)
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
주거지역
60 
상업지역
27 
오피스지역
7 
기타지역
 
6

Length

Max length5
Median length4
Mean length4.07
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상업지역
2nd row주거지역
3rd row주거지역
4th row상업지역
5th row주거지역

Common Values

ValueCountFrequency (%)
주거지역 60
60.0%
상업지역 27
27.0%
오피스지역 7
 
7.0%
기타지역 6
 
6.0%

Length

2023-12-10T23:49:29.546017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:29.644555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거지역 60
60.0%
상업지역 27
27.0%
오피스지역 7
 
7.0%
기타지역 6
 
6.0%

블록_유형_중분류_코드(BLCK_TY_MLSFC_CD)
Categorical

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
고밀주거지역
23 
중밀주거지역
21 
기타지역
15 
주택상업지
11 
혼합지역
10 
Other values (7)
20 

Length

Max length6
Median length5
Mean length5.13
Min length1

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row고밀주거지역
2nd row주택상업지
3rd row중밀주거지역
4th row일반상업지
5th row중밀주거지역

Common Values

ValueCountFrequency (%)
고밀주거지역 23
23.0%
중밀주거지역 21
21.0%
기타지역 15
15.0%
주택상업지 11
11.0%
혼합지역 10
10.0%
일반상업지 9
 
9.0%
저밀주거지역 4
 
4.0%
복합상업지 2
 
2.0%
학교시설 2
 
2.0%
공원 1
 
1.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T23:49:29.768945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고밀주거지역 23
23.0%
중밀주거지역 21
21.0%
기타지역 15
15.0%
주택상업지 11
11.0%
혼합지역 10
10.0%
일반상업지 9
 
9.0%
저밀주거지역 4
 
4.0%
복합상업지 2
 
2.0%
학교시설 2
 
2.0%
공원 1
 
1.0%
Other values (2) 2
 
2.0%

블록_유형_소분류_코드(BLCK_TY_SCLAS_CD)
Categorical

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
고밀주거지역
23 
중밀주거지역
18 
혼합지역
12 
기타지역
10 
고밀주거상업
9 
Other values (7)
28 

Length

Max length6
Median length6
Mean length5.05
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row혼합지역
2nd row고밀주거지역
3rd row고밀주거지역
4th row오피스가
5th row중밀주거지역

Common Values

ValueCountFrequency (%)
고밀주거지역 23
23.0%
중밀주거지역 18
18.0%
혼합지역 12
12.0%
기타지역 10
10.0%
고밀주거상업 9
 
9.0%
오피스가 7
 
7.0%
일반상업지 7
 
7.0%
저밀주거지역 4
 
4.0%
저밀주거상업 3
 
3.0%
공원 3
 
3.0%
Other values (2) 4
 
4.0%

Length

2023-12-10T23:49:29.881216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고밀주거지역 23
23.0%
중밀주거지역 18
18.0%
혼합지역 12
12.0%
기타지역 10
10.0%
고밀주거상업 9
 
9.0%
오피스가 7
 
7.0%
일반상업지 7
 
7.0%
저밀주거지역 4
 
4.0%
저밀주거상업 3
 
3.0%
공원 3
 
3.0%
Other values (2) 4
 
4.0%

역세권_여부(RASTSP_AT)
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
76 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 76
76.0%
1 24
 
24.0%

Length

2023-12-10T23:49:29.997297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:30.095718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 76
76.0%
1 24
 
24.0%

대학가_여부(UNVTW_AT)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
97 
1
 
3

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 (%)
0 97
97.0%
1 3
 
3.0%

Length

2023-12-10T23:49:30.202394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:30.315843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 97
97.0%
1 3
 
3.0%

배후지_유형_대분류_코드(HILND_TY_LCLAS_CD)
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
주거지역
76 
상업지역
19 
기타지역
 
4
오피스지역
 
1

Length

Max length5
Median length4
Mean length4.01
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row주거지역
2nd row상업지역
3rd row주거지역
4th row주거지역
5th row주거지역

Common Values

ValueCountFrequency (%)
주거지역 76
76.0%
상업지역 19
 
19.0%
기타지역 4
 
4.0%
오피스지역 1
 
1.0%

Length

2023-12-10T23:49:30.425370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:30.532545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거지역 76
76.0%
상업지역 19
 
19.0%
기타지역 4
 
4.0%
오피스지역 1
 
1.0%

배후지_유형_중분류_코드(HILND_TY_MLSFC_CD)
Categorical

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
중밀주거지역
31 
저밀주거지역
27 
고밀주거지역
22 
고밀주거상업
7 
저밀주거상업
6 
Other values (3)
7 

Length

Max length6
Median length6
Mean length5.89
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row주거공업지
2nd row저밀주거지역
3rd row저밀주거지역
4th row중밀주거지역
5th row중밀주거지역

Common Values

ValueCountFrequency (%)
중밀주거지역 31
31.0%
저밀주거지역 27
27.0%
고밀주거지역 22
22.0%
고밀주거상업 7
 
7.0%
저밀주거상업 6
 
6.0%
기타지역 4
 
4.0%
주거공업지 2
 
2.0%
복합상업지 1
 
1.0%

Length

2023-12-10T23:49:30.653254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:30.800893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중밀주거지역 31
31.0%
저밀주거지역 27
27.0%
고밀주거지역 22
22.0%
고밀주거상업 7
 
7.0%
저밀주거상업 6
 
6.0%
기타지역 4
 
4.0%
주거공업지 2
 
2.0%
복합상업지 1
 
1.0%

배후지_유형_소분류_코드(HILND_TY_SCLAS_CD)
Categorical

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
중밀주거지역
32 
고밀주거지역
27 
저밀주거지역
18 
저밀주거상업
8 
고밀주거상업
6 
Other values (6)
9 

Length

Max length6
Median length6
Mean length5.94
Min length4

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row고밀주거지역
2nd row저밀주거지역
3rd row고밀주거지역
4th row고밀주거지역
5th row고밀주거상업

Common Values

ValueCountFrequency (%)
중밀주거지역 32
32.0%
고밀주거지역 27
27.0%
저밀주거지역 18
18.0%
저밀주거상업 8
 
8.0%
고밀주거상업 6
 
6.0%
주택오피스가 3
 
3.0%
기타지역 2
 
2.0%
복합상업지 1
 
1.0%
혼합상업지역 1
 
1.0%
주거공업지역 1
 
1.0%

Length

2023-12-10T23:49:30.952495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중밀주거지역 32
32.0%
고밀주거지역 27
27.0%
저밀주거지역 18
18.0%
저밀주거상업 8
 
8.0%
고밀주거상업 6
 
6.0%
주택오피스가 3
 
3.0%
기타지역 2
 
2.0%
복합상업지 1
 
1.0%
혼합상업지역 1
 
1.0%
주거공업지역 1
 
1.0%

Correlations

2023-12-10T23:49:31.416006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
블록_코드(BLCK_CD)블록_유형_대분류_코드(BLCK_TY_LCLAS_CD)블록_유형_중분류_코드(BLCK_TY_MLSFC_CD)블록_유형_소분류_코드(BLCK_TY_SCLAS_CD)역세권_여부(RASTSP_AT)대학가_여부(UNVTW_AT)배후지_유형_대분류_코드(HILND_TY_LCLAS_CD)배후지_유형_중분류_코드(HILND_TY_MLSFC_CD)배후지_유형_소분류_코드(HILND_TY_SCLAS_CD)
블록_코드(BLCK_CD)1.0000.0000.8890.9170.0001.0000.9230.0000.935
블록_유형_대분류_코드(BLCK_TY_LCLAS_CD)0.0001.0000.0000.0000.0000.0000.0000.1780.084
블록_유형_중분류_코드(BLCK_TY_MLSFC_CD)0.8890.0001.0000.2830.0740.0000.0000.0000.000
블록_유형_소분류_코드(BLCK_TY_SCLAS_CD)0.9170.0000.2831.0000.1500.0000.0000.0000.000
역세권_여부(RASTSP_AT)0.0000.0000.0740.1501.0000.0000.2240.0000.042
대학가_여부(UNVTW_AT)1.0000.0000.0000.0000.0001.0000.0000.1490.000
배후지_유형_대분류_코드(HILND_TY_LCLAS_CD)0.9230.0000.0000.0000.2240.0001.0000.0000.000
배후지_유형_중분류_코드(HILND_TY_MLSFC_CD)0.0000.1780.0000.0000.0000.1490.0001.0000.320
배후지_유형_소분류_코드(HILND_TY_SCLAS_CD)0.9350.0840.0000.0000.0420.0000.0000.3201.000
2023-12-10T23:49:31.557724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배후지_유형_중분류_코드(HILND_TY_MLSFC_CD)배후지_유형_대분류_코드(HILND_TY_LCLAS_CD)배후지_유형_소분류_코드(HILND_TY_SCLAS_CD)블록_유형_소분류_코드(BLCK_TY_SCLAS_CD)역세권_여부(RASTSP_AT)블록_유형_대분류_코드(BLCK_TY_LCLAS_CD)블록_유형_중분류_코드(BLCK_TY_MLSFC_CD)대학가_여부(UNVTW_AT)
배후지_유형_중분류_코드(HILND_TY_MLSFC_CD)1.0000.0000.1510.0000.0000.0740.0000.105
배후지_유형_대분류_코드(HILND_TY_LCLAS_CD)0.0001.0000.0000.0000.1460.0000.0000.000
배후지_유형_소분류_코드(HILND_TY_SCLAS_CD)0.1510.0001.0000.0000.0200.0370.0000.000
블록_유형_소분류_코드(BLCK_TY_SCLAS_CD)0.0000.0000.0001.0000.1050.0000.0760.000
역세권_여부(RASTSP_AT)0.0000.1460.0200.1051.0000.0000.0430.000
블록_유형_대분류_코드(BLCK_TY_LCLAS_CD)0.0740.0000.0370.0000.0001.0000.0000.000
블록_유형_중분류_코드(BLCK_TY_MLSFC_CD)0.0000.0000.0000.0760.0430.0001.0000.000
대학가_여부(UNVTW_AT)0.1050.0000.0000.0000.0000.0000.0001.000
2023-12-10T23:49:31.704047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
블록_유형_대분류_코드(BLCK_TY_LCLAS_CD)블록_유형_중분류_코드(BLCK_TY_MLSFC_CD)블록_유형_소분류_코드(BLCK_TY_SCLAS_CD)역세권_여부(RASTSP_AT)대학가_여부(UNVTW_AT)배후지_유형_대분류_코드(HILND_TY_LCLAS_CD)배후지_유형_중분류_코드(HILND_TY_MLSFC_CD)배후지_유형_소분류_코드(HILND_TY_SCLAS_CD)
블록_유형_대분류_코드(BLCK_TY_LCLAS_CD)1.0000.0000.0000.0000.0000.0000.0740.037
블록_유형_중분류_코드(BLCK_TY_MLSFC_CD)0.0001.0000.0760.0430.0000.0000.0000.000
블록_유형_소분류_코드(BLCK_TY_SCLAS_CD)0.0000.0761.0000.1050.0000.0000.0000.000
역세권_여부(RASTSP_AT)0.0000.0430.1051.0000.0000.1460.0000.020
대학가_여부(UNVTW_AT)0.0000.0000.0000.0001.0000.0000.1050.000
배후지_유형_대분류_코드(HILND_TY_LCLAS_CD)0.0000.0000.0000.1460.0001.0000.0000.000
배후지_유형_중분류_코드(HILND_TY_MLSFC_CD)0.0740.0000.0000.0000.1050.0001.0000.151
배후지_유형_소분류_코드(HILND_TY_SCLAS_CD)0.0370.0000.0000.0200.0000.0000.1511.000

Missing values

2023-12-10T23:49:28.575722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:49:28.718358image/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

블록_코드(BLCK_CD)블록_유형_대분류_코드(BLCK_TY_LCLAS_CD)블록_유형_중분류_코드(BLCK_TY_MLSFC_CD)블록_유형_소분류_코드(BLCK_TY_SCLAS_CD)역세권_여부(RASTSP_AT)대학가_여부(UNVTW_AT)배후지_유형_대분류_코드(HILND_TY_LCLAS_CD)배후지_유형_중분류_코드(HILND_TY_MLSFC_CD)배후지_유형_소분류_코드(HILND_TY_SCLAS_CD)
02*4*4*상업지역고밀주거지역혼합지역10주거지역주거공업지고밀주거지역
12*3*9*주거지역주택상업지고밀주거지역00상업지역저밀주거지역저밀주거지역
23*2*7*주거지역중밀주거지역고밀주거지역10주거지역저밀주거지역고밀주거지역
32*2*8*상업지역일반상업지오피스가00주거지역중밀주거지역고밀주거지역
42*4*3주거지역중밀주거지역중밀주거지역00주거지역중밀주거지역고밀주거상업
52*0*2*상업지역주택상업지고밀주거지역00주거지역고밀주거지역고밀주거지역
63*1*1*주거지역주택상업지오피스가00주거지역저밀주거지역고밀주거지역
73*8*0*주거지역혼합지역고밀주거상업10주거지역고밀주거지역저밀주거상업
82*8*4*상업지역고밀주거지역혼합지역10주거지역중밀주거지역고밀주거지역
92*1*5*주거지역중밀주거지역고밀주거지역00주거지역중밀주거지역저밀주거상업
블록_코드(BLCK_CD)블록_유형_대분류_코드(BLCK_TY_LCLAS_CD)블록_유형_중분류_코드(BLCK_TY_MLSFC_CD)블록_유형_소분류_코드(BLCK_TY_SCLAS_CD)역세권_여부(RASTSP_AT)대학가_여부(UNVTW_AT)배후지_유형_대분류_코드(HILND_TY_LCLAS_CD)배후지_유형_중분류_코드(HILND_TY_MLSFC_CD)배후지_유형_소분류_코드(HILND_TY_SCLAS_CD)
903*5*1주거지역중밀주거지역중밀주거지역00주거지역중밀주거지역중밀주거지역
912*5*9*상업지역복합상업지중밀주거지역00주거지역중밀주거지역저밀주거지역
922*2*0*주거지역고밀주거지역고밀주거지역10주거지역중밀주거지역고밀주거지역
931*1*8주거지역중밀주거지역중밀주거지역10주거지역중밀주거지역중밀주거지역
943*4*1*주거지역주택상업지고밀주거상업01주거지역중밀주거지역중밀주거지역
958*8*주거지역고밀주거지역고밀주거지역00주거지역중밀주거지역중밀주거지역
962*2*1*주거지역저밀주거지역일반상업지10주거지역중밀주거지역고밀주거상업
974*8*3*기타지역학교시설혼합지역00상업지역중밀주거지역저밀주거상업
984*1*9*주거지역오피스가중밀주거지역00주거지역고밀주거지역고밀주거지역
993*6*6*상업지역주택상업지중밀주거지역00주거지역중밀주거지역중밀주거지역