Overview

Dataset statistics

Number of variables6
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory51.9 B

Variable types

Numeric2
Categorical3
Text1

Dataset

Description서울특별시 영등포구 주택종류별 법정동 통계현황입니다. 제공데이터: 순번, 시군구, 법정동, 주용도, 주택수, 데이터기준일자
Author서울특별시 영등포구
URLhttps://www.data.go.kr/data/15108151/fileData.do

Alerts

시군구 has constant value ""Constant
데이터기준일자 has constant value ""Constant
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:28:42.374704
Analysis finished2023-12-12 14:28:43.199647
Duration0.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.5
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T23:28:43.303282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.35
Q117.75
median34.5
Q351.25
95-th percentile64.65
Maximum68
Range67
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation19.77372
Coefficient of variation (CV)0.5731513
Kurtosis-1.2
Mean34.5
Median Absolute Deviation (MAD)17
Skewness0
Sum2346
Variance391
MonotonicityStrictly increasing
2023-12-12T23:28:43.476525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
45 1
 
1.5%
51 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
44 1
 
1.5%
36 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
68 1
1.5%
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
서울특별시 영등포구
68 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 영등포구
2nd row서울특별시 영등포구
3rd row서울특별시 영등포구
4th row서울특별시 영등포구
5th row서울특별시 영등포구

Common Values

ValueCountFrequency (%)
서울특별시 영등포구 68
100.0%

Length

2023-12-12T23:28:43.642183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:43.735341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 68
50.0%
영등포구 68
50.0%

법정동
Text

Distinct34
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
2023-12-12T23:28:43.915416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8235294
Min length3

Characters and Unicode

Total characters328
Distinct characters27
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

Unique3 ?
Unique (%)4.4%

Sample

1st row당산동
2nd row당산동
3rd row당산동1가
4th row당산동1가
5th row당산동2가
ValueCountFrequency (%)
도림동 3
 
4.4%
신길동 3
 
4.4%
영등포동 3
 
4.4%
영등포동6가 2
 
2.9%
영등포동5가 2
 
2.9%
영등포동4가 2
 
2.9%
영등포동3가 2
 
2.9%
영등포동2가 2
 
2.9%
당산동1가 2
 
2.9%
당산동 2
 
2.9%
Other values (24) 45
66.2%
2023-12-12T23:28:44.262745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 68
20.7%
가 52
15.9%
영 19
 
5.8%
등 19
 
5.8%
포 19
 
5.8%
당 14
 
4.3%
양 14
 
4.3%
산 14
 
4.3%
평 13
 
4.0%
래 12
 
3.7%
Other values (17) 84
25.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 276
84.1%
Decimal Number 52
 
15.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 68
24.6%
가 52
18.8%
영 19
 
6.9%
등 19
 
6.9%
포 19
 
6.9%
당 14
 
5.1%
양 14
 
5.1%
산 14
 
5.1%
평 13
 
4.7%
래 12
 
4.3%
Other values (9) 32
11.6%
Decimal Number
ValueCountFrequency (%)
1 8
15.4%
2 8
15.4%
4 8
15.4%
5 8
15.4%
6 8
15.4%
3 8
15.4%
8 2
 
3.8%
7 2
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 276
84.1%
Common 52
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 68
24.6%
가 52
18.8%
영 19
 
6.9%
등 19
 
6.9%
포 19
 
6.9%
당 14
 
5.1%
양 14
 
5.1%
산 14
 
5.1%
평 13
 
4.7%
래 12
 
4.3%
Other values (9) 32
11.6%
Common
ValueCountFrequency (%)
1 8
15.4%
2 8
15.4%
4 8
15.4%
5 8
15.4%
6 8
15.4%
3 8
15.4%
8 2
 
3.8%
7 2
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 276
84.1%
ASCII 52
 
15.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 68
24.6%
가 52
18.8%
영 19
 
6.9%
등 19
 
6.9%
포 19
 
6.9%
당 14
 
5.1%
양 14
 
5.1%
산 14
 
5.1%
평 13
 
4.7%
래 12
 
4.3%
Other values (9) 32
11.6%
ASCII
ValueCountFrequency (%)
1 8
15.4%
2 8
15.4%
4 8
15.4%
5 8
15.4%
6 8
15.4%
3 8
15.4%
8 2
 
3.8%
7 2
 
3.8%

주용도
Categorical

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
단독주택
36 
공동주택
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공동주택
2nd row단독주택
3rd row공동주택
4th row단독주택
5th row공동주택

Common Values

ValueCountFrequency (%)
단독주택 36
52.9%
공동주택 32
47.1%

Length

2023-12-12T23:28:44.410782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:44.518279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독주택 36
52.9%
공동주택 32
47.1%

주택수
Real number (ℝ)

Distinct56
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean397.83824
Minimum2
Maximum6287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T23:28:44.640152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q130.5
median73.5
Q3177
95-th percentile1481
Maximum6287
Range6285
Interquartile range (IQR)146.5

Descriptive statistics

Standard deviation1138.0685
Coefficient of variation (CV)2.8606312
Kurtosis20.96818
Mean397.83824
Median Absolute Deviation (MAD)53.5
Skewness4.5160634
Sum27053
Variance1295199.8
MonotonicityNot monotonic
2023-12-12T23:28:44.796624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3
 
4.4%
51 2
 
2.9%
71 2
 
2.9%
75 2
 
2.9%
778 2
 
2.9%
29 2
 
2.9%
7 2
 
2.9%
1481 2
 
2.9%
164 2
 
2.9%
50 2
 
2.9%
Other values (46) 47
69.1%
ValueCountFrequency (%)
2 3
4.4%
3 1
 
1.5%
7 2
2.9%
8 1
 
1.5%
9 1
 
1.5%
11 1
 
1.5%
14 1
 
1.5%
15 1
 
1.5%
19 1
 
1.5%
21 1
 
1.5%
ValueCountFrequency (%)
6287 2
2.9%
3270 1
1.5%
1481 2
2.9%
952 1
1.5%
778 2
2.9%
589 1
1.5%
434 1
1.5%
423 1
1.5%
344 1
1.5%
197 1
1.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
2022-11-17
68 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-11-17
2nd row2022-11-17
3rd row2022-11-17
4th row2022-11-17
5th row2022-11-17

Common Values

ValueCountFrequency (%)
2022-11-17 68
100.0%

Length

2023-12-12T23:28:44.944365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:28:45.040072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-11-17 68
100.0%

Interactions

2023-12-12T23:28:42.774515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:42.547317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:42.880257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:42.667040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:28:45.099059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정동주용도주택수
순번1.0000.9960.0000.634
법정동0.9961.0000.0000.734
주용도0.0000.0001.0000.111
주택수0.6340.7340.1111.000
2023-12-12T23:28:45.205272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번주택수주용도
순번1.000-0.1810.000
주택수-0.1811.0000.129
주용도0.0000.1291.000

Missing values

2023-12-12T23:28:43.012490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:28:43.142719image/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

순번시군구법정동주용도주택수데이터기준일자
01서울특별시 영등포구당산동공동주택512022-11-17
12서울특별시 영등포구당산동단독주택4342022-11-17
23서울특별시 영등포구당산동1가공동주택612022-11-17
34서울특별시 영등포구당산동1가단독주택4232022-11-17
45서울특별시 영등포구당산동2가공동주택152022-11-17
56서울특별시 영등포구당산동2가단독주택962022-11-17
67서울특별시 영등포구당산동3가공동주택602022-11-17
78서울특별시 영등포구당산동3가단독주택1832022-11-17
89서울특별시 영등포구당산동4가공동주택942022-11-17
910서울특별시 영등포구당산동4가단독주택992022-11-17
순번시군구법정동주용도주택수데이터기준일자
5859서울특별시 영등포구영등포동4가공동주택82022-11-17
5960서울특별시 영등포구영등포동4가단독주택752022-11-17
6061서울특별시 영등포구영등포동5가공동주택72022-11-17
6162서울특별시 영등포구영등포동5가단독주택1972022-11-17
6263서울특별시 영등포구영등포동6가공동주택222022-11-17
6364서울특별시 영등포구영등포동6가단독주택1072022-11-17
6465서울특별시 영등포구영등포동7가공동주택582022-11-17
6566서울특별시 영등포구영등포동7가단독주택1932022-11-17
6667서울특별시 영등포구영등포동8가공동주택192022-11-17
6768서울특별시 영등포구영등포동8가단독주택812022-11-17