Overview

Dataset statistics

Number of variables10
Number of observations21
Missing cells2
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory90.3 B

Variable types

Text4
Numeric4
DateTime2

Dataset

Description경기도 수원시 공공임대주택 제공현황 데이터로 아파트명, 소재지도로명주소, 소재지지번주소, 위도, 경도, 세대수, 사용검사일, 동수, 층수, 데이터기준일자를 포함합니다.
URLhttps://www.data.go.kr/data/15102712/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
세대수 is highly overall correlated with 동수High correlation
동수 is highly overall correlated with 세대수High correlation
위도 has 1 (4.8%) missing valuesMissing
경도 has 1 (4.8%) missing valuesMissing
아파트명 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
소재지지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:55:07.547027
Analysis finished2023-12-12 23:55:09.183994
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:55:09.284850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length9.1904762
Min length4

Characters and Unicode

Total characters193
Distinct characters69
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row우만주공3단지
2nd row백설주공2단지
3rd row매탄주공 그린빌6단지
4th row밤꽃마을주공
5th row상송마을주공
ValueCountFrequency (%)
행복주택 2
 
7.7%
수원광교 2
 
7.7%
우만주공3단지 1
 
3.8%
백설주공2단지 1
 
3.8%
보훈복지타운 1
 
3.8%
경기행복주택 1
 
3.8%
광교원천 1
 
3.8%
수원영통 1
 
3.8%
공공실버주택 1
 
3.8%
수원고등lh2단지(행복주택 1
 
3.8%
Other values (14) 14
53.8%
2023-12-13T08:55:09.563976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 14
 
7.3%
단 12
 
6.2%
주 10
 
5.2%
공 7
 
3.6%
시 7
 
3.6%
실 7
 
3.6%
2 6
 
3.1%
교 6
 
3.1%
먼 6
 
3.1%
아 6
 
3.1%
Other values (59) 112
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 167
86.5%
Decimal Number 17
 
8.8%
Space Separator 5
 
2.6%
Uppercase Letter 2
 
1.0%
Close Punctuation 1
 
0.5%
Open Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 14
 
8.4%
단 12
 
7.2%
주 10
 
6.0%
공 7
 
4.2%
시 7
 
4.2%
실 7
 
4.2%
교 6
 
3.6%
먼 6
 
3.6%
아 6
 
3.6%
광 6
 
3.6%
Other values (46) 86
51.5%
Decimal Number
ValueCountFrequency (%)
2 6
35.3%
1 2
 
11.8%
3 2
 
11.8%
6 2
 
11.8%
4 2
 
11.8%
0 1
 
5.9%
7 1
 
5.9%
8 1
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
H 1
50.0%
L 1
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 167
86.5%
Common 24
 
12.4%
Latin 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 14
 
8.4%
단 12
 
7.2%
주 10
 
6.0%
공 7
 
4.2%
시 7
 
4.2%
실 7
 
4.2%
교 6
 
3.6%
먼 6
 
3.6%
아 6
 
3.6%
광 6
 
3.6%
Other values (46) 86
51.5%
Common
ValueCountFrequency (%)
2 6
25.0%
5
20.8%
1 2
 
8.3%
3 2
 
8.3%
6 2
 
8.3%
4 2
 
8.3%
) 1
 
4.2%
( 1
 
4.2%
0 1
 
4.2%
7 1
 
4.2%
Latin
ValueCountFrequency (%)
H 1
50.0%
L 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 167
86.5%
ASCII 26
 
13.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 14
 
8.4%
단 12
 
7.2%
주 10
 
6.0%
공 7
 
4.2%
시 7
 
4.2%
실 7
 
4.2%
교 6
 
3.6%
먼 6
 
3.6%
아 6
 
3.6%
광 6
 
3.6%
Other values (46) 86
51.5%
ASCII
ValueCountFrequency (%)
2 6
23.1%
5
19.2%
1 2
 
7.7%
3 2
 
7.7%
6 2
 
7.7%
4 2
 
7.7%
) 1
 
3.8%
( 1
 
3.8%
0 1
 
3.8%
H 1
 
3.8%
Other values (3) 3
11.5%

소재지도로명주소
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:55:09.723667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length22.142857
Min length17

Characters and Unicode

Total characters465
Distinct characters59
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row경기도 수원시 팔달구 창룡대로210번길 13
2nd row경기도 수원시 장안구 대평로89번길 10
3rd row경기도 수원시 영통구 동탄원천로881번길 19
4th row경기도 수원시 장안구 상률로12번길 28
5th row경기도 수원시 권선구 오목천로 15
ValueCountFrequency (%)
경기도 21
19.8%
수원시 21
19.8%
영통구 8
 
7.5%
권선구 7
 
6.6%
팔달구 3
 
2.8%
장안구 3
 
2.8%
13 2
 
1.9%
광교중앙로 2
 
1.9%
10 2
 
1.9%
19 2
 
1.9%
Other values (34) 35
33.0%
2023-12-13T08:55:10.000721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
18.3%
수 23
 
4.9%
원 22
 
4.7%
경 21
 
4.5%
구 21
 
4.5%
로 21
 
4.5%
기 21
 
4.5%
시 21
 
4.5%
도 21
 
4.5%
1 19
 
4.1%
Other values (49) 190
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 297
63.9%
Space Separator 85
 
18.3%
Decimal Number 83
 
17.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 23
 
7.7%
원 22
 
7.4%
경 21
 
7.1%
구 21
 
7.1%
로 21
 
7.1%
기 21
 
7.1%
시 21
 
7.1%
도 21
 
7.1%
번 14
 
4.7%
길 14
 
4.7%
Other values (38) 98
33.0%
Decimal Number
ValueCountFrequency (%)
1 19
22.9%
2 11
13.3%
0 11
13.3%
5 7
 
8.4%
8 7
 
8.4%
3 7
 
8.4%
4 7
 
8.4%
9 6
 
7.2%
6 5
 
6.0%
7 3
 
3.6%
Space Separator
ValueCountFrequency (%)
85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 297
63.9%
Common 168
36.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 23
 
7.7%
원 22
 
7.4%
경 21
 
7.1%
구 21
 
7.1%
로 21
 
7.1%
기 21
 
7.1%
시 21
 
7.1%
도 21
 
7.1%
번 14
 
4.7%
길 14
 
4.7%
Other values (38) 98
33.0%
Common
ValueCountFrequency (%)
85
50.6%
1 19
 
11.3%
2 11
 
6.5%
0 11
 
6.5%
5 7
 
4.2%
8 7
 
4.2%
3 7
 
4.2%
4 7
 
4.2%
9 6
 
3.6%
6 5
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 297
63.9%
ASCII 168
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
50.6%
1 19
 
11.3%
2 11
 
6.5%
0 11
 
6.5%
5 7
 
4.2%
8 7
 
4.2%
3 7
 
4.2%
4 7
 
4.2%
9 6
 
3.6%
6 5
 
3.0%
Hangul
ValueCountFrequency (%)
수 23
 
7.7%
원 22
 
7.4%
경 21
 
7.1%
구 21
 
7.1%
로 21
 
7.1%
기 21
 
7.1%
시 21
 
7.1%
도 21
 
7.1%
번 14
 
4.7%
길 14
 
4.7%
Other values (38) 98
33.0%

소재지지번주소
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:55:10.158436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length20
Min length18

Characters and Unicode

Total characters420
Distinct characters51
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

Unique21 ?
Unique (%)100.0%

Sample

1st row경기도 수원시 팔달구 우만동 301
2nd row경기도 수원시 장안구 정자동 871-3
3rd row경기도 수원시 영통구 매탄동 1350
4th row경기도 수원시 장안구 율전동 545
5th row경기도 수원시 권선구 오목천동 946
ValueCountFrequency (%)
경기도 21
20.0%
수원시 21
20.0%
영통구 8
 
7.6%
권선구 7
 
6.7%
호매실동 4
 
3.8%
팔달구 3
 
2.9%
하동 3
 
2.9%
장안구 3
 
2.9%
고등동 2
 
1.9%
원천동 2
 
1.9%
Other values (30) 31
29.5%
2023-12-13T08:55:10.419348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
20.0%
원 24
 
5.7%
경 21
 
5.0%
동 21
 
5.0%
도 21
 
5.0%
수 21
 
5.0%
시 21
 
5.0%
기 21
 
5.0%
구 21
 
5.0%
1 14
 
3.3%
Other values (41) 151
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 254
60.5%
Space Separator 84
 
20.0%
Decimal Number 78
 
18.6%
Dash Punctuation 4
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 24
 
9.4%
경 21
 
8.3%
동 21
 
8.3%
도 21
 
8.3%
수 21
 
8.3%
시 21
 
8.3%
기 21
 
8.3%
구 21
 
8.3%
영 8
 
3.1%
통 8
 
3.1%
Other values (29) 67
26.4%
Decimal Number
ValueCountFrequency (%)
1 14
17.9%
5 11
14.1%
0 10
12.8%
9 8
10.3%
3 7
9.0%
4 7
9.0%
6 6
7.7%
2 6
7.7%
7 5
 
6.4%
8 4
 
5.1%
Space Separator
ValueCountFrequency (%)
84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 254
60.5%
Common 166
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 24
 
9.4%
경 21
 
8.3%
동 21
 
8.3%
도 21
 
8.3%
수 21
 
8.3%
시 21
 
8.3%
기 21
 
8.3%
구 21
 
8.3%
영 8
 
3.1%
통 8
 
3.1%
Other values (29) 67
26.4%
Common
ValueCountFrequency (%)
84
50.6%
1 14
 
8.4%
5 11
 
6.6%
0 10
 
6.0%
9 8
 
4.8%
3 7
 
4.2%
4 7
 
4.2%
6 6
 
3.6%
2 6
 
3.6%
7 5
 
3.0%
Other values (2) 8
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 254
60.5%
ASCII 166
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
50.6%
1 14
 
8.4%
5 11
 
6.6%
0 10
 
6.0%
9 8
 
4.8%
3 7
 
4.2%
4 7
 
4.2%
6 6
 
3.6%
2 6
 
3.6%
7 5
 
3.0%
Other values (2) 8
 
4.8%
Hangul
ValueCountFrequency (%)
원 24
 
9.4%
경 21
 
8.3%
동 21
 
8.3%
도 21
 
8.3%
수 21
 
8.3%
시 21
 
8.3%
기 21
 
8.3%
구 21
 
8.3%
영 8
 
3.1%
통 8
 
3.1%
Other values (29) 67
26.4%

위도
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)100.0%
Missing1
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean37.275643
Minimum37.233912
Maximum37.306922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:55:10.514068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.233912
5-th percentile37.240103
Q137.262284
median37.275656
Q337.293729
95-th percentile37.29966
Maximum37.306922
Range0.07300955
Interquartile range (IQR)0.031445815

Descriptive statistics

Standard deviation0.01995214
Coefficient of variation (CV)0.00053525945
Kurtosis-0.40334657
Mean37.275643
Median Absolute Deviation (MAD)0.01705729
Skewness-0.45033992
Sum745.51286
Variance0.0003980879
MonotonicityNot monotonic
2023-12-13T08:55:10.604887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
37.29260018 1
 
4.8%
37.29343441 1
 
4.8%
37.29927799 1
 
4.8%
37.28050366 1
 
4.8%
37.23391249 1
 
4.8%
37.27963866 1
 
4.8%
37.29488096 1
 
4.8%
37.27246521 1
 
4.8%
37.26320212 1
 
4.8%
37.2584856 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
37.23391249 1
4.8%
37.24042908 1
4.8%
37.25372527 1
4.8%
37.2584856 1
4.8%
37.25952806 1
4.8%
37.26320212 1
4.8%
37.2696826 1
4.8%
37.27202406 1
4.8%
37.27246521 1
4.8%
37.27530097 1
4.8%
ValueCountFrequency (%)
37.30692204 1
4.8%
37.29927799 1
4.8%
37.29621805 1
4.8%
37.29488096 1
4.8%
37.29461445 1
4.8%
37.29343441 1
4.8%
37.29260018 1
4.8%
37.28050366 1
4.8%
37.27963866 1
4.8%
37.27601134 1
4.8%

경도
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)100.0%
Missing1
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean127.00582
Minimum126.94262
Maximum127.073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:55:10.703161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.94262
5-th percentile126.94556
Q1126.95807
median127.01035
Q3127.04842
95-th percentile127.06787
Maximum127.073
Range0.1303747
Interquartile range (IQR)0.09034845

Descriptive statistics

Standard deviation0.047438889
Coefficient of variation (CV)0.00037351745
Kurtosis-1.7437933
Mean127.00582
Median Absolute Deviation (MAD)0.04497965
Skewness-0.033408384
Sum2540.1163
Variance0.0022504482
MonotonicityNot monotonic
2023-12-13T08:55:10.826278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
127.034043 1
 
4.8%
127.0729953 1
 
4.8%
127.0225622 1
 
4.8%
127.0499923 1
 
4.8%
127.0478937 1
 
4.8%
127.050003 1
 
4.8%
127.036812 1
 
4.8%
126.9981453 1
 
4.8%
126.9580836 1
 
4.8%
126.9580288 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
126.9426206 1
4.8%
126.9457173 1
4.8%
126.9463092 1
4.8%
126.9549755 1
4.8%
126.9580288 1
4.8%
126.9580836 1
4.8%
126.9639348 1
4.8%
126.9668134 1
4.8%
126.9919837 1
4.8%
126.9981453 1
4.8%
ValueCountFrequency (%)
127.0729953 1
4.8%
127.0676006 1
4.8%
127.0655342 1
4.8%
127.050003 1
4.8%
127.0499923 1
4.8%
127.0478937 1
4.8%
127.0422934 1
4.8%
127.036812 1
4.8%
127.034043 1
4.8%
127.0225622 1
4.8%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean721.42857
Minimum100
Maximum2289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:55:10.923512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile152
Q1341
median533
Q3980
95-th percentile1300
Maximum2289
Range2189
Interquartile range (IQR)639

Descriptive statistics

Standard deviation528.54428
Coefficient of variation (CV)0.73263564
Kurtosis2.3810796
Mean721.42857
Median Absolute Deviation (MAD)329
Skewness1.3384424
Sum15150
Variance279359.06
MonotonicityNot monotonic
2023-12-13T08:55:11.019957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
980 2
 
9.5%
1213 1
 
4.8%
712 1
 
4.8%
285 1
 
4.8%
452 1
 
4.8%
300 1
 
4.8%
100 1
 
4.8%
152 1
 
4.8%
204 1
 
4.8%
500 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
100 1
4.8%
152 1
4.8%
204 1
4.8%
285 1
4.8%
300 1
4.8%
341 1
4.8%
375 1
4.8%
389 1
4.8%
452 1
4.8%
500 1
4.8%
ValueCountFrequency (%)
2289 1
4.8%
1300 1
4.8%
1270 1
4.8%
1213 1
4.8%
1185 1
4.8%
980 2
9.5%
880 1
4.8%
712 1
4.8%
710 1
4.8%
533 1
4.8%

사용검사일
Date

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum1992-03-11 00:00:00
Maximum2022-05-03 00:00:00
2023-12-13T08:55:11.109241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:11.199832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3333333
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:55:11.290348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q310
95-th percentile13
Maximum15
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.351245
Coefficient of variation (CV)0.68703869
Kurtosis-0.8526073
Mean6.3333333
Median Absolute Deviation (MAD)3
Skewness0.55512685
Sum133
Variance18.933333
MonotonicityNot monotonic
2023-12-13T08:55:11.373965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
6 3
14.3%
5 3
14.3%
1 3
14.3%
4 2
9.5%
13 2
9.5%
11 2
9.5%
2 2
9.5%
3 1
 
4.8%
9 1
 
4.8%
10 1
 
4.8%
ValueCountFrequency (%)
1 3
14.3%
2 2
9.5%
3 1
 
4.8%
4 2
9.5%
5 3
14.3%
6 3
14.3%
9 1
 
4.8%
10 1
 
4.8%
11 2
9.5%
13 2
9.5%
ValueCountFrequency (%)
15 1
 
4.8%
13 2
9.5%
11 2
9.5%
10 1
 
4.8%
9 1
 
4.8%
6 3
14.3%
5 3
14.3%
4 2
9.5%
3 1
 
4.8%
2 2
9.5%

층수
Text

Distinct17
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:55:11.494137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.5714286
Min length1

Characters and Unicode

Total characters75
Distinct characters10
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

Unique14 ?
Unique (%)66.7%

Sample

1st row15
2nd row18~24
3rd row13~20
4th row22
5th row10~15
ValueCountFrequency (%)
15 3
14.3%
20~25 2
 
9.5%
10~20 2
 
9.5%
7~23 1
 
4.8%
14 1
 
4.8%
5 1
 
4.8%
10 1
 
4.8%
12 1
 
4.8%
25 1
 
4.8%
12~20 1
 
4.8%
Other values (7) 7
33.3%
2023-12-13T08:55:11.736250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 18
24.0%
1 17
22.7%
~ 12
16.0%
0 11
14.7%
5 8
10.7%
8 3
 
4.0%
4 2
 
2.7%
3 2
 
2.7%
6 1
 
1.3%
7 1
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63
84.0%
Math Symbol 12
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18
28.6%
1 17
27.0%
0 11
17.5%
5 8
12.7%
8 3
 
4.8%
4 2
 
3.2%
3 2
 
3.2%
6 1
 
1.6%
7 1
 
1.6%
Math Symbol
ValueCountFrequency (%)
~ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18
24.0%
1 17
22.7%
~ 12
16.0%
0 11
14.7%
5 8
10.7%
8 3
 
4.0%
4 2
 
2.7%
3 2
 
2.7%
6 1
 
1.3%
7 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18
24.0%
1 17
22.7%
~ 12
16.0%
0 11
14.7%
5 8
10.7%
8 3
 
4.0%
4 2
 
2.7%
3 2
 
2.7%
6 1
 
1.3%
7 1
 
1.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum2023-07-25 00:00:00
Maximum2023-07-25 00:00:00
2023-12-13T08:55:11.825178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:11.902875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T08:55:08.676670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:07.870569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.152337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.425246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.745393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:07.943153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.226846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.494329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.810571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.012994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.298226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.559946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.869617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.081562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.357769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:55:08.612573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:55:11.964011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아파트명소재지도로명주소소재지지번주소위도경도세대수사용검사일동수층수
아파트명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0000.0000.4670.9050.0000.860
경도1.0001.0001.0000.0001.0000.2401.0000.6830.844
세대수1.0001.0001.0000.4670.2401.0000.8860.6550.000
사용검사일1.0001.0001.0000.9051.0000.8861.0000.3040.917
동수1.0001.0001.0000.0000.6830.6550.3041.0000.739
층수1.0001.0001.0000.8600.8440.0000.9170.7391.000
2023-12-13T08:55:12.062465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도세대수동수
위도1.0000.322-0.237-0.267
경도0.3221.000-0.466-0.410
세대수-0.237-0.4661.0000.893
동수-0.267-0.4100.8931.000

Missing values

2023-12-13T08:55:08.956879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:55:09.063432image/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.
2023-12-13T08:55:09.147510image/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

아파트명소재지도로명주소소재지지번주소위도경도세대수사용검사일동수층수데이터기준일자
0우만주공3단지경기도 수원시 팔달구 창룡대로210번길 13경기도 수원시 팔달구 우만동 30137.2926127.03404312131992-03-116152023-07-25
1백설주공2단지경기도 수원시 장안구 대평로89번길 10경기도 수원시 장안구 정자동 871-337.296218126.9919843412001-08-06318~242023-07-25
2매탄주공 그린빌6단지경기도 수원시 영통구 동탄원천로881번길 19경기도 수원시 영통구 매탄동 135037.253725127.0422937102001-12-11613~202023-07-25
3밤꽃마을주공경기도 수원시 장안구 상률로12번길 28경기도 수원시 장안구 율전동 54537.306922126.9668133892005-09-084222023-07-25
4상송마을주공경기도 수원시 권선구 오목천로 15경기도 수원시 권선구 오목천동 94637.240429126.96393511852005-10-311310~152023-07-25
5호매실휴먼시아4단지경기도 수원시 권선구 금곡로 137경기도 수원시 권선구 금곡동 107237.275301126.9463099802011-07-19910~202023-07-25
6호매실휴먼시아7단지경기도 수원시 권선구 금곡로102번길 126경기도 수원시 권선구 호매실동 134037.269683126.9426219802011-08-191011~202023-07-25
7호매실휴먼시아8단지경기도 수원시 권선구 금곡로140번길 29경기도 수원시 권선구 금곡동 109537.272024126.94571712702011-08-191112~202023-07-25
8광교휴먼시아20단지경기도 수원시 영통구 광교호수로 84경기도 수원시 영통구 하동 103437.276011127.0676013752011-10-2457~232023-07-25
9광교휴먼시아32단지경기도 수원시 영통구 광교중앙로 247경기도 수원시 영통구 하동 95637.294614127.06553422892011-11-071320~252023-07-25
아파트명소재지도로명주소소재지지번주소위도경도세대수사용검사일동수층수데이터기준일자
11광교상록경기도 수원시 영통구 법조로150번길 19경기도 수원시 영통구 하동 99237.293434127.0729955332012-06-08618~262023-07-25
12호매실능실마을22단지경기도 수원시 권선구 호매실로166번길 10경기도 수원시 권선구 호매실동 141537.258486126.9580297122016-01-29420~252023-07-25
13벨섬시티14단지경기도 수원시 권선구 호매실로218번길 22경기도 수원시 권선구 호매실동 138037.263202126.95808413002017-12-0715252023-07-25
14수원고등LH2단지(행복주택)경기도 수원시 팔달구 고등로 8경기도 수원시 팔달구 고등동 272-5037.272465126.9981455002021-01-152152023-07-25
15수원광교 행복주택경기도 수원시 영통구 창룡대로 250경기도 수원시 영통구 이의동 128437.294881127.0368122042018-06-181122023-07-25
16수원광교 공공실버주택경기도 수원시 영통구 월드컵로150번길 55경기도 수원시 영통구 원천동 56537.279639127.0500031522019-02-181102023-07-25
17수원영통 행복주택경기도 수원시 영통구 동탄지성로470번길 34경기도 수원시 영통구 망포동 76937.233912127.0478941002019-06-07152023-07-25
18광교원천 경기행복주택경기도 수원시 영통구 광교중앙로 49번길 40경기도 수원시 영통구 원천동 55937.280504127.0499923002020-10-272142023-07-25
19보훈복지타운경기도 수원시 장안구 수일로336번길 9경기도 수원시 장안구 조원동 476-137.299278127.0225624521998-06-2958~122023-07-25
20수원역푸르지오더스마트경기도 수원시 팔달구 고등로 13경기도 수원시 팔달구 고등동 201-60<NA><NA>2852022-05-035152023-07-25