Overview

Dataset statistics

Number of variables13
Number of observations288
Missing cells9
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.8 KiB
Average record size in memory109.5 B

Variable types

Numeric5
Text4
Categorical2
DateTime2

Dataset

Description광주광역시 광산구 관내에 위치한 공동주택현황(아파트명, 행정동, 소재지도로명주소, 세대수, 층수, 동수, 사업승인일, 사용검사일, 관리사무소 전화번호 등) 정보를 제공합니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15100132/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 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
행정동 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
전화번호(관리사무소) has 7 (2.4%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-17 18:38:33.739317
Analysis finished2024-04-17 18:38:36.096745
Duration2.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct288
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.5
Minimum1
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-18T03:38:36.157929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.35
Q172.75
median144.5
Q3216.25
95-th percentile273.65
Maximum288
Range287
Interquartile range (IQR)143.5

Descriptive statistics

Standard deviation83.282651
Coefficient of variation (CV)0.57635053
Kurtosis-1.2
Mean144.5
Median Absolute Deviation (MAD)72
Skewness0
Sum41616
Variance6936
MonotonicityStrictly increasing
2024-04-18T03:38:36.272560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
146 1
 
0.3%
198 1
 
0.3%
197 1
 
0.3%
196 1
 
0.3%
195 1
 
0.3%
194 1
 
0.3%
193 1
 
0.3%
192 1
 
0.3%
191 1
 
0.3%
Other values (278) 278
96.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
288 1
0.3%
287 1
0.3%
286 1
0.3%
285 1
0.3%
284 1
0.3%
283 1
0.3%
282 1
0.3%
281 1
0.3%
280 1
0.3%
279 1
0.3%

아파트명
Text

Distinct286
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-18T03:38:36.470545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length17
Mean length7.3194444
Min length2

Characters and Unicode

Total characters2108
Distinct characters218
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique284 ?
Unique (%)98.6%

Sample

1st row금호사원
2nd row현대
3rd row명동
4th row대천아파트
5th row공항연립주택
ValueCountFrequency (%)
수완 13
 
3.2%
2차 9
 
2.2%
첨단 6
 
1.5%
주상복합 4
 
1.0%
3차 4
 
1.0%
쌍암동 4
 
1.0%
모아엘가 4
 
1.0%
1단지 4
 
1.0%
운남2 4
 
1.0%
도산 4
 
1.0%
Other values (301) 348
86.1%
2024-04-18T03:38:36.779500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
116
 
5.5%
차 94
 
4.5%
단 83
 
3.9%
수 56
 
2.7%
아 56
 
2.7%
2 56
 
2.7%
지 53
 
2.5%
첨 51
 
2.4%
완 45
 
2.1%
1 42
 
2.0%
Other values (208) 1456
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1768
83.9%
Decimal Number 154
 
7.3%
Space Separator 116
 
5.5%
Uppercase Letter 30
 
1.4%
Lowercase Letter 15
 
0.7%
Close Punctuation 9
 
0.4%
Open Punctuation 9
 
0.4%
Dash Punctuation 7
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
차 94
 
5.3%
단 83
 
4.7%
수 56
 
3.2%
아 56
 
3.2%
지 53
 
3.0%
첨 51
 
2.9%
완 45
 
2.5%
대 35
 
2.0%
라 35
 
2.0%
주 34
 
1.9%
Other values (183) 1226
69.3%
Decimal Number
ValueCountFrequency (%)
2 56
36.4%
1 42
27.3%
3 26
16.9%
6 8
 
5.2%
5 8
 
5.2%
7 5
 
3.2%
8 3
 
1.9%
4 3
 
1.9%
9 2
 
1.3%
0 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
S 10
33.3%
G 5
16.7%
L 5
16.7%
H 4
 
13.3%
E 4
 
13.3%
C 1
 
3.3%
A 1
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
e 6
40.0%
t 4
26.7%
h 4
26.7%
s 1
 
6.7%
Space Separator
ValueCountFrequency (%)
116
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1768
83.9%
Common 295
 
14.0%
Latin 45
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
차 94
 
5.3%
단 83
 
4.7%
수 56
 
3.2%
아 56
 
3.2%
지 53
 
3.0%
첨 51
 
2.9%
완 45
 
2.5%
대 35
 
2.0%
라 35
 
2.0%
주 34
 
1.9%
Other values (183) 1226
69.3%
Common
ValueCountFrequency (%)
116
39.3%
2 56
19.0%
1 42
 
14.2%
3 26
 
8.8%
) 9
 
3.1%
( 9
 
3.1%
6 8
 
2.7%
5 8
 
2.7%
- 7
 
2.4%
7 5
 
1.7%
Other values (4) 9
 
3.1%
Latin
ValueCountFrequency (%)
S 10
22.2%
e 6
13.3%
G 5
11.1%
L 5
11.1%
H 4
 
8.9%
E 4
 
8.9%
t 4
 
8.9%
h 4
 
8.9%
C 1
 
2.2%
A 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1768
83.9%
ASCII 340
 
16.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116
34.1%
2 56
16.5%
1 42
 
12.4%
3 26
 
7.6%
S 10
 
2.9%
) 9
 
2.6%
( 9
 
2.6%
6 8
 
2.4%
5 8
 
2.4%
- 7
 
2.1%
Other values (15) 49
14.4%
Hangul
ValueCountFrequency (%)
차 94
 
5.3%
단 83
 
4.7%
수 56
 
3.2%
아 56
 
3.2%
지 53
 
3.0%
첨 51
 
2.9%
완 45
 
2.5%
대 35
 
2.0%
라 35
 
2.0%
주 34
 
1.9%
Other values (183) 1226
69.3%

행정동
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
수완동
49 
어룡동
39 
첨단1동
34 
첨단2동
27 
우산동
22 
Other values (12)
117 

Length

Max length4
Median length3
Mean length3.3055556
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신흥동
2nd row신흥동
3rd row송정2동
4th row송정2동
5th row송정1동

Common Values

ValueCountFrequency (%)
수완동 49
17.0%
어룡동 39
13.5%
첨단1동 34
11.8%
첨단2동 27
9.4%
우산동 22
7.6%
도산동 20
6.9%
신창동 15
 
5.2%
하남동 14
 
4.9%
운남동 12
 
4.2%
송정1동 12
 
4.2%
Other values (7) 44
15.3%

Length

2024-04-18T03:38:36.890091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수완동 49
17.0%
어룡동 39
13.5%
첨단1동 34
11.8%
첨단2동 27
9.4%
우산동 22
7.6%
도산동 20
6.9%
신창동 15
 
5.2%
하남동 14
 
4.9%
송정1동 12
 
4.2%
운남동 12
 
4.2%
Other values (7) 44
15.3%

소재지도로명주소
Text

Distinct283
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-18T03:38:37.140011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length20.256944
Min length15

Characters and Unicode

Total characters5834
Distinct characters88
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

Unique278 ?
Unique (%)96.5%

Sample

1st row광주광역시 광산구 사암로27번길 41
2nd row광주광역시 광산구 신흥동길 22-3
3rd row광주광역시 광산구 송정로 39
4th row광주광역시 광산구 송정로51번길 15
5th row광주광역시 광산구 내상로 78
ValueCountFrequency (%)
광주광역시 288
25.3%
광산구 288
25.3%
첨단중앙로181번길 14
 
1.2%
임방울대로 11
 
1.0%
월계로 11
 
1.0%
월곡산정로 8
 
0.7%
15 7
 
0.6%
16 6
 
0.5%
목련로 6
 
0.5%
22 6
 
0.5%
Other values (324) 492
43.3%
2024-04-18T03:38:37.479652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
광 865
14.8%
849
14.6%
산 313
 
5.4%
역 288
 
4.9%
시 288
 
4.9%
구 288
 
4.9%
주 288
 
4.9%
로 277
 
4.7%
1 261
 
4.5%
2 196
 
3.4%
Other values (78) 1921
32.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3684
63.1%
Decimal Number 1226
 
21.0%
Space Separator 849
 
14.6%
Dash Punctuation 75
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 865
23.5%
산 313
 
8.5%
역 288
 
7.8%
시 288
 
7.8%
구 288
 
7.8%
주 288
 
7.8%
로 277
 
7.5%
길 188
 
5.1%
번 175
 
4.8%
대 43
 
1.2%
Other values (66) 671
18.2%
Decimal Number
ValueCountFrequency (%)
1 261
21.3%
2 196
16.0%
3 126
10.3%
5 111
9.1%
7 104
 
8.5%
6 104
 
8.5%
0 94
 
7.7%
8 92
 
7.5%
4 78
 
6.4%
9 60
 
4.9%
Space Separator
ValueCountFrequency (%)
849
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3684
63.1%
Common 2150
36.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 865
23.5%
산 313
 
8.5%
역 288
 
7.8%
시 288
 
7.8%
구 288
 
7.8%
주 288
 
7.8%
로 277
 
7.5%
길 188
 
5.1%
번 175
 
4.8%
대 43
 
1.2%
Other values (66) 671
18.2%
Common
ValueCountFrequency (%)
849
39.5%
1 261
 
12.1%
2 196
 
9.1%
3 126
 
5.9%
5 111
 
5.2%
7 104
 
4.8%
6 104
 
4.8%
0 94
 
4.4%
8 92
 
4.3%
4 78
 
3.6%
Other values (2) 135
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3684
63.1%
ASCII 2150
36.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
광 865
23.5%
산 313
 
8.5%
역 288
 
7.8%
시 288
 
7.8%
구 288
 
7.8%
주 288
 
7.8%
로 277
 
7.5%
길 188
 
5.1%
번 175
 
4.8%
대 43
 
1.2%
Other values (66) 671
18.2%
ASCII
ValueCountFrequency (%)
849
39.5%
1 261
 
12.1%
2 196
 
9.1%
3 126
 
5.9%
5 111
 
5.2%
7 104
 
4.8%
6 104
 
4.8%
0 94
 
4.4%
8 92
 
4.3%
4 78
 
3.6%
Other values (2) 135
 
6.3%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct238
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean448.3125
Minimum29
Maximum1956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-18T03:38:37.592900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile68.35
Q1200
median348.5
Q3576.75
95-th percentile1209.05
Maximum1956
Range1927
Interquartile range (IQR)376.75

Descriptive statistics

Standard deviation363.15834
Coefficient of variation (CV)0.81005624
Kurtosis2.9176246
Mean448.3125
Median Absolute Deviation (MAD)171.5
Skewness1.6371991
Sum129114
Variance131883.98
MonotonicityNot monotonic
2024-04-18T03:38:37.699481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 7
 
2.4%
590 4
 
1.4%
180 4
 
1.4%
298 4
 
1.4%
64 3
 
1.0%
300 3
 
1.0%
299 3
 
1.0%
76 3
 
1.0%
310 2
 
0.7%
567 2
 
0.7%
Other values (228) 253
87.8%
ValueCountFrequency (%)
29 1
0.3%
30 1
0.3%
35 1
0.3%
40 1
0.3%
43 1
0.3%
44 1
0.3%
45 1
0.3%
50 1
0.3%
57 1
0.3%
60 1
0.3%
ValueCountFrequency (%)
1956 1
0.3%
1884 1
0.3%
1792 1
0.3%
1673 1
0.3%
1660 1
0.3%
1620 1
0.3%
1511 1
0.3%
1500 1
0.3%
1350 1
0.3%
1344 1
0.3%

층수
Text

Distinct88
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-18T03:38:37.888292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.1736111
Min length1

Characters and Unicode

Total characters914
Distinct characters14
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

Unique49 ?
Unique (%)17.0%

Sample

1st row5~6
2nd row5
3rd row5
4th row5
5th row3
ValueCountFrequency (%)
15 41
 
14.2%
5 30
 
10.4%
13 19
 
6.6%
13~15 11
 
3.8%
12~15 10
 
3.5%
4 10
 
3.5%
14 9
 
3.1%
20 8
 
2.8%
12 8
 
2.8%
14~15 8
 
2.8%
Other values (78) 134
46.5%
2024-04-18T03:38:38.183659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 292
31.9%
5 146
16.0%
~ 131
14.3%
2 122
13.3%
3 55
 
6.0%
0 51
 
5.6%
4 45
 
4.9%
9 20
 
2.2%
8 19
 
2.1%
6 15
 
1.6%
Other values (4) 18
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 776
84.9%
Math Symbol 131
 
14.3%
Other Letter 6
 
0.7%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 292
37.6%
5 146
18.8%
2 122
15.7%
3 55
 
7.1%
0 51
 
6.6%
4 45
 
5.8%
9 20
 
2.6%
8 19
 
2.4%
6 15
 
1.9%
7 11
 
1.4%
Other Letter
ValueCountFrequency (%)
지 3
50.0%
층 3
50.0%
Math Symbol
ValueCountFrequency (%)
~ 131
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 908
99.3%
Hangul 6
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 292
32.2%
5 146
16.1%
~ 131
14.4%
2 122
13.4%
3 55
 
6.1%
0 51
 
5.6%
4 45
 
5.0%
9 20
 
2.2%
8 19
 
2.1%
6 15
 
1.7%
Other values (2) 12
 
1.3%
Hangul
ValueCountFrequency (%)
지 3
50.0%
층 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 908
99.3%
Hangul 6
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 292
32.2%
5 146
16.1%
~ 131
14.4%
2 122
13.4%
3 55
 
6.1%
0 51
 
5.6%
4 45
 
5.0%
9 20
 
2.2%
8 19
 
2.1%
6 15
 
1.7%
Other values (2) 12
 
1.3%
Hangul
ValueCountFrequency (%)
지 3
50.0%
층 3
50.0%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4097222
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-18T03:38:38.284976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile16
Maximum32
Range31
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1166371
Coefficient of variation (CV)0.7982619
Kurtosis4.511933
Mean6.4097222
Median Absolute Deviation (MAD)3
Skewness1.74245
Sum1846
Variance26.179975
MonotonicityNot monotonic
2024-04-18T03:38:38.583115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 41
14.2%
4 37
12.8%
2 27
9.4%
3 24
8.3%
6 24
8.3%
8 22
7.6%
7 21
7.3%
5 18
 
6.2%
10 16
 
5.6%
9 13
 
4.5%
Other values (14) 45
15.6%
ValueCountFrequency (%)
1 41
14.2%
2 27
9.4%
3 24
8.3%
4 37
12.8%
5 18
6.2%
6 24
8.3%
7 21
7.3%
8 22
7.6%
9 13
 
4.5%
10 16
 
5.6%
ValueCountFrequency (%)
32 1
 
0.3%
30 1
 
0.3%
26 1
 
0.3%
25 1
 
0.3%
24 1
 
0.3%
22 2
 
0.7%
19 3
1.0%
17 1
 
0.3%
16 5
1.7%
15 3
1.0%

사업승인일자
Date

Distinct243
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
Minimum1980-07-11 00:00:00
Maximum2022-04-02 00:00:00
2024-04-18T03:38:38.680500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:38.787249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용검사일자
Date

Distinct264
Distinct (%)92.3%
Missing2
Missing (%)0.7%
Memory size2.4 KiB
Minimum1981-07-06 00:00:00
Maximum2022-08-29 00:00:00
2024-04-18T03:38:38.893606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:39.002613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호(관리사무소)
Text

MISSING 

Distinct277
Distinct (%)98.6%
Missing7
Missing (%)2.4%
Memory size2.4 KiB
2024-04-18T03:38:39.195328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters3372
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

Unique274 ?
Unique (%)97.5%

Sample

1st row062-941-5526
2nd row062-953-9396
3rd row062-951-7958
4th row062-941-7201
5th row062-952-6448
ValueCountFrequency (%)
062-974-4564 3
 
1.1%
062-945-7677 2
 
0.7%
062-942-4312 2
 
0.7%
062-952-5002 1
 
0.4%
062-953-4020 1
 
0.4%
062-961-7451 1
 
0.4%
062-955-7868 1
 
0.4%
062-952-9613 1
 
0.4%
062-951-0937 1
 
0.4%
062-951-0180 1
 
0.4%
Other values (267) 267
95.0%
2024-04-18T03:38:39.481221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 562
16.7%
2 483
14.3%
0 447
13.3%
6 437
13.0%
9 374
11.1%
5 254
7.5%
1 209
 
6.2%
4 188
 
5.6%
7 168
 
5.0%
3 153
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2810
83.3%
Dash Punctuation 562
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 483
17.2%
0 447
15.9%
6 437
15.6%
9 374
13.3%
5 254
9.0%
1 209
7.4%
4 188
 
6.7%
7 168
 
6.0%
3 153
 
5.4%
8 97
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 562
16.7%
2 483
14.3%
0 447
13.3%
6 437
13.0%
9 374
11.1%
5 254
7.5%
1 209
 
6.2%
4 188
 
5.6%
7 168
 
5.0%
3 153
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 562
16.7%
2 483
14.3%
0 447
13.3%
6 437
13.0%
9 374
11.1%
5 254
7.5%
1 209
 
6.2%
4 188
 
5.6%
7 168
 
5.0%
3 153
 
4.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct283
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.177345
Minimum35.125429
Maximum35.226327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-18T03:38:39.600384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.125429
5-th percentile35.130637
Q135.150972
median35.179826
Q335.200354
95-th percentile35.220894
Maximum35.226327
Range0.10089801
Interquartile range (IQR)0.049381798

Descriptive statistics

Standard deviation0.02968416
Coefficient of variation (CV)0.00084384311
Kurtosis-1.2279753
Mean35.177345
Median Absolute Deviation (MAD)0.027304825
Skewness-0.054748232
Sum10131.075
Variance0.00088114937
MonotonicityNot monotonic
2024-04-18T03:38:39.704067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.15933896 2
 
0.7%
35.2197592 2
 
0.7%
35.21113237 2
 
0.7%
35.16631715 2
 
0.7%
35.17822307 2
 
0.7%
35.1754179 1
 
0.3%
35.18004418 1
 
0.3%
35.18333101 1
 
0.3%
35.17960712 1
 
0.3%
35.18402798 1
 
0.3%
Other values (273) 273
94.8%
ValueCountFrequency (%)
35.12542946 1
0.3%
35.12609751 1
0.3%
35.12765555 1
0.3%
35.12777889 1
0.3%
35.12815313 1
0.3%
35.12839871 1
0.3%
35.12852863 1
0.3%
35.12864918 1
0.3%
35.1290516 1
0.3%
35.12913314 1
0.3%
ValueCountFrequency (%)
35.22632747 1
0.3%
35.2236981 1
0.3%
35.22309737 1
0.3%
35.22285456 1
0.3%
35.22192771 1
0.3%
35.22158489 1
0.3%
35.22154158 1
0.3%
35.22151342 1
0.3%
35.22141022 1
0.3%
35.22138056 1
0.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct283
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.81538
Minimum126.75636
Maximum126.85392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-18T03:38:39.812677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.75636
5-th percentile126.78812
Q1126.79853
median126.81403
Q3126.83365
95-th percentile126.84827
Maximum126.85392
Range0.0975579
Interquartile range (IQR)0.03511345

Descriptive statistics

Standard deviation0.020346015
Coefficient of variation (CV)0.00016043807
Kurtosis-0.69267898
Mean126.81538
Median Absolute Deviation (MAD)0.01721275
Skewness-0.060103831
Sum36522.829
Variance0.00041396032
MonotonicityNot monotonic
2024-04-18T03:38:39.944649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7923108 2
 
0.7%
126.8524674 2
 
0.7%
126.8436314 2
 
0.7%
126.8038702 2
 
0.7%
126.8276327 2
 
0.7%
126.7984485 1
 
0.3%
126.8162968 1
 
0.3%
126.815908 1
 
0.3%
126.7959151 1
 
0.3%
126.8107862 1
 
0.3%
Other values (273) 273
94.8%
ValueCountFrequency (%)
126.7563593 1
0.3%
126.7575548 1
0.3%
126.771023 1
0.3%
126.7718751 1
0.3%
126.7734652 1
0.3%
126.7742713 1
0.3%
126.7755283 1
0.3%
126.7759442 1
0.3%
126.7768992 1
0.3%
126.7779195 1
0.3%
ValueCountFrequency (%)
126.8539172 1
0.3%
126.8524674 2
0.7%
126.8513624 1
0.3%
126.8508576 1
0.3%
126.8508419 1
0.3%
126.8507344 1
0.3%
126.8504149 1
0.3%
126.8502273 1
0.3%
126.8501202 1
0.3%
126.8497065 1
0.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-05-03
288 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-03
2nd row2023-05-03
3rd row2023-05-03
4th row2023-05-03
5th row2023-05-03

Common Values

ValueCountFrequency (%)
2023-05-03 288
100.0%

Length

2024-04-18T03:38:40.060231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:38:40.165746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-03 288
100.0%

Interactions

2024-04-18T03:38:35.475254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.159933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.502382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.844181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.163979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.540059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.225085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.573436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.907274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.227367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.608039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.305752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.645359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.977826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.297360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.672262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.372660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.715551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.041857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.359587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.733759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.439671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:34.779318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.103275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:38:35.417526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:38:40.244628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동세대수층수동수위도경도
연번1.0000.7170.2780.6960.3550.7090.706
행정동0.7171.0000.5050.5540.4780.9370.880
세대수0.2780.5051.0000.7720.8420.5160.376
층수0.6960.5540.7721.0000.6860.7320.690
동수0.3550.4780.8420.6861.0000.5250.514
위도0.7090.9370.5160.7320.5251.0000.806
경도0.7060.8800.3760.6900.5140.8061.000
2024-04-18T03:38:40.360906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세대수동수위도경도행정동
연번1.0000.0600.1570.1440.0790.373
세대수0.0601.0000.7560.2380.2100.220
동수0.1570.7561.0000.3820.2830.205
위도0.1440.2380.3821.0000.8650.733
경도0.0790.2100.2830.8651.0000.594
행정동0.3730.2200.2050.7330.5941.000

Missing values

2024-04-18T03:38:35.821465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:38:35.958863image/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.
2024-04-18T03:38:36.056177image/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

연번아파트명행정동소재지도로명주소세대수층수동수사업승인일자사용검사일자전화번호(관리사무소)위도경도데이터기준일자
01금호사원신흥동광주광역시 광산구 사암로27번길 411905~641980-07-111981-07-06062-941-552635.145186126.8067782023-05-03
12현대신흥동광주광역시 광산구 신흥동길 22-350511982-07-271982-12-29<NA>35.145023126.8019762023-05-03
23명동송정2동광주광역시 광산구 송정로 3935521982-08-171983-06-21<NA>35.136032126.7955732023-05-03
34대천아파트송정2동광주광역시 광산구 송정로51번길 1570522008-09-301984-07-03<NA>35.136075126.796542023-05-03
45공항연립주택송정1동광주광역시 광산구 내상로 7860321984-10-261985-06-17<NA>35.136129126.8002992023-05-03
56보양그린어룡동광주광역시 광산구 송정공원로23번길 290521985-04-081988-04-25<NA>35.14589126.798562023-05-03
67광일연립주택송정1동광주광역시 광산구 송도로270번길 7130311986-06-191988-10-21<NA>35.137537126.8027262023-05-03
78태양월곡2동광주광역시 광산구 산정공원로71번길 273001511987-08-201988-10-20062-953-939635.173776126.8052132023-05-03
89한성1차월곡1동광주광역시 광산구 월곡산정로 1087805141988-02-151989-02-15062-951-795835.165536126.8153092023-05-03
910대덕도산동광주광역시 광산구 송도로 1431501011989-11-171990-07-31062-941-720135.127779126.7915112023-05-03
연번아파트명행정동소재지도로명주소세대수층수동수사업승인일자사용검사일자전화번호(관리사무소)위도경도데이터기준일자
278279소촌동 국제미소래아파트어룡동광주광역시 광산구 소촌로 42번길 1633821592018-04-11<NA>062-942-234735.153508126.7975232023-05-03
279280쌍용 더 플래티넘 광산우산동광주광역시 광산구 우산로107번길 6776414~17132017-12-292021-01-29062-944-772735.160647126.8128172023-05-03
280281어등산 한양수자인 테라스 플러스하남동광주광역시 광산구 여대길 3405924322018-06-282021-01-29062-531-621035.167125126.7932662023-05-03
281282수완센트럴시티 서희스타힐스하남동광주광역시 광산구 용아로 436-2042715~2572018-05-172021-08-19062-956-311735.182813126.806942023-05-03
282283윤슬의아침 수완4차비아동광주광역시 광산구 북문대로619-25973222019-10-072021-09-01062-955-111235.205927126.8330562023-05-03
283284소촌3차 국제미소래어룡동광주광역시 광산구 소촌로42번길 19518415~1642019-04-19<NA>062-941-900935.155054126.7979632023-05-03
284285무진로 진아리채 리버뷰우산동광주광역시 광산구 풍영철길로 4655821~2372019-05-102022-04-07062-952-500235.16436126.8155552023-05-03
285286모아엘가 더 수완신가동광주광역시 광산구 목련로 381번길 2756714~2982019-02-142022-04-15062-953-728535.181596126.830882023-05-03
286287평동역 광신프로그레스평동광주광역시 광산구 평동로752번길 819317~2042022-04-022022-08-01062-942-896535.125429126.7563592023-05-03
287288힐스테이트 광산어룡동광주광역시 광산구 어등대로647번북길 842811~14132019-11-052022-08-29062-945-728535.146458126.7927462023-05-03