Overview

Dataset statistics

Number of variables17
Number of observations207
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.8 KiB
Average record size in memory142.6 B

Variable types

Text4
Numeric6
Categorical6
DateTime1

Dataset

Description경상남도 양산시 공동주택(아파트) 연한별 현황에 대한 데이터로 아파트명, 위도, 경도, 층수, 동수, 세대수, 유형, 난방방식, 승강기, 주차, 관리방법, 승인일, 준공일, 의무관리대상 등의 항목을 제공합니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15074023

Alerts

출처 has constant value ""Constant
기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도High correlation
동수 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
세대수 is highly overall correlated with 동수 and 3 other fieldsHigh correlation
승강기 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
주차 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
난방방식 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
유형 is highly imbalanced (77.0%)Imbalance
위치 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
승강기 has 38 (18.4%) zerosZeros
주차 has 8 (3.9%) zerosZeros

Reproduction

Analysis started2023-12-10 23:29:34.055419
Analysis finished2023-12-10 23:29:37.691110
Duration3.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct205
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T08:29:37.862589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length7.6908213
Min length4

Characters and Unicode

Total characters1592
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

Unique203 ?
Unique (%)98.1%

Sample

1st row주공2차아파트
2nd row주공3차아파트
3rd row삼전무지개아파트
4th row삼위로얄맨션아파트
5th row덕산타운아파트
ValueCountFrequency (%)
두산 3
 
1.2%
휴먼시아 3
 
1.2%
위브 3
 
1.2%
e편한세상 2
 
0.8%
서창 2
 
0.8%
이지더원 2
 
0.8%
월드메르디앙 2
 
0.8%
양산 2
 
0.8%
해강아파트 2
 
0.8%
일동미라주아파트 2
 
0.8%
Other values (223) 223
90.7%
2023-12-11T08:29:38.231766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 135
 
8.5%
트 132
 
8.3%
파 127
 
8.0%
차 74
 
4.6%
39
 
2.4%
대 35
 
2.2%
산 32
 
2.0%
양 30
 
1.9%
2 30
 
1.9%
동 30
 
1.9%
Other values (208) 928
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1430
89.8%
Decimal Number 88
 
5.5%
Space Separator 39
 
2.4%
Close Punctuation 10
 
0.6%
Open Punctuation 10
 
0.6%
Uppercase Letter 9
 
0.6%
Lowercase Letter 4
 
0.3%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 135
 
9.4%
트 132
 
9.2%
파 127
 
8.9%
차 74
 
5.2%
대 35
 
2.4%
산 32
 
2.2%
양 30
 
2.1%
동 30
 
2.1%
신 24
 
1.7%
우 24
 
1.7%
Other values (191) 787
55.0%
Decimal Number
ValueCountFrequency (%)
2 30
34.1%
1 23
26.1%
3 13
14.8%
5 7
 
8.0%
4 5
 
5.7%
6 4
 
4.5%
8 3
 
3.4%
7 3
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
H 3
33.3%
L 3
33.3%
C 2
22.2%
K 1
 
11.1%
Space Separator
ValueCountFrequency (%)
39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1430
89.8%
Common 149
 
9.4%
Latin 13
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 135
 
9.4%
트 132
 
9.2%
파 127
 
8.9%
차 74
 
5.2%
대 35
 
2.4%
산 32
 
2.2%
양 30
 
2.1%
동 30
 
2.1%
신 24
 
1.7%
우 24
 
1.7%
Other values (191) 787
55.0%
Common
ValueCountFrequency (%)
39
26.2%
2 30
20.1%
1 23
15.4%
3 13
 
8.7%
) 10
 
6.7%
( 10
 
6.7%
5 7
 
4.7%
4 5
 
3.4%
6 4
 
2.7%
8 3
 
2.0%
Other values (2) 5
 
3.4%
Latin
ValueCountFrequency (%)
e 4
30.8%
H 3
23.1%
L 3
23.1%
C 2
15.4%
K 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1430
89.8%
ASCII 162
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 135
 
9.4%
트 132
 
9.2%
파 127
 
8.9%
차 74
 
5.2%
대 35
 
2.4%
산 32
 
2.2%
양 30
 
2.1%
동 30
 
2.1%
신 24
 
1.7%
우 24
 
1.7%
Other values (191) 787
55.0%
ASCII
ValueCountFrequency (%)
39
24.1%
2 30
18.5%
1 23
14.2%
3 13
 
8.0%
) 10
 
6.2%
( 10
 
6.2%
5 7
 
4.3%
4 5
 
3.1%
6 4
 
2.5%
e 4
 
2.5%
Other values (7) 17
10.5%

위치
Text

UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T08:29:38.592062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length17.816425
Min length14

Characters and Unicode

Total characters3688
Distinct characters109
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

Unique207 ?
Unique (%)100.0%

Sample

1st row경상남도 양산시 물금읍 동중1길21
2nd row경상남도 양산시 물금읍 동중1길7
3rd row경상남도 양산시 물금읍 원동로 59
4th row경상남도 양산시 물금읍 동중7길21
5th row경상남도 양산시 물금읍 오봉로 29
ValueCountFrequency (%)
경상남도 207
22.8%
양산시 207
22.8%
물금읍 53
 
5.8%
동면 17
 
1.9%
상북면 15
 
1.7%
하북면 10
 
1.1%
양주로 10
 
1.1%
오봉로 7
 
0.8%
14 7
 
0.8%
야리로 6
 
0.7%
Other values (241) 369
40.6%
2023-12-11T08:29:39.037331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
702
19.0%
산 233
 
6.3%
상 226
 
6.1%
양 220
 
6.0%
남 215
 
5.8%
도 209
 
5.7%
경 207
 
5.6%
시 207
 
5.6%
1 147
 
4.0%
로 115
 
3.1%
Other values (99) 1207
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2423
65.7%
Space Separator 702
 
19.0%
Decimal Number 550
 
14.9%
Dash Punctuation 13
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 233
 
9.6%
상 226
 
9.3%
양 220
 
9.1%
남 215
 
8.9%
도 209
 
8.6%
경 207
 
8.5%
시 207
 
8.5%
로 115
 
4.7%
길 92
 
3.8%
금 70
 
2.9%
Other values (87) 629
26.0%
Decimal Number
ValueCountFrequency (%)
1 147
26.7%
3 64
11.6%
5 59
10.7%
2 55
 
10.0%
4 48
 
8.7%
7 46
 
8.4%
6 41
 
7.5%
0 31
 
5.6%
9 31
 
5.6%
8 28
 
5.1%
Space Separator
ValueCountFrequency (%)
702
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2423
65.7%
Common 1265
34.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 233
 
9.6%
상 226
 
9.3%
양 220
 
9.1%
남 215
 
8.9%
도 209
 
8.6%
경 207
 
8.5%
시 207
 
8.5%
로 115
 
4.7%
길 92
 
3.8%
금 70
 
2.9%
Other values (87) 629
26.0%
Common
ValueCountFrequency (%)
702
55.5%
1 147
 
11.6%
3 64
 
5.1%
5 59
 
4.7%
2 55
 
4.3%
4 48
 
3.8%
7 46
 
3.6%
6 41
 
3.2%
0 31
 
2.5%
9 31
 
2.5%
Other values (2) 41
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2423
65.7%
ASCII 1265
34.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
702
55.5%
1 147
 
11.6%
3 64
 
5.1%
5 59
 
4.7%
2 55
 
4.3%
4 48
 
3.8%
7 46
 
3.6%
6 41
 
3.2%
0 31
 
2.5%
9 31
 
2.5%
Other values (2) 41
 
3.2%
Hangul
ValueCountFrequency (%)
산 233
 
9.6%
상 226
 
9.3%
양 220
 
9.1%
남 215
 
8.9%
도 209
 
8.6%
경 207
 
8.5%
시 207
 
8.5%
로 115
 
4.7%
길 92
 
3.8%
금 70
 
2.9%
Other values (87) 629
26.0%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.363975
Minimum35.302472
Maximum35.495539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:39.178423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.302472
5-th percentile35.312271
Q135.32967
median35.351133
Q335.392955
95-th percentile35.422162
Maximum35.495539
Range0.193067
Interquartile range (IQR)0.0632855

Descriptive statistics

Standard deviation0.044208361
Coefficient of variation (CV)0.0012500959
Kurtosis0.70467489
Mean35.363975
Median Absolute Deviation (MAD)0.030722
Skewness0.94663711
Sum7320.3429
Variance0.0019543792
MonotonicityNot monotonic
2023-12-11T08:29:39.322731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.329976 1
 
0.5%
35.328111 1
 
0.5%
35.351441 1
 
0.5%
35.348472 1
 
0.5%
35.343464 1
 
0.5%
35.342885 1
 
0.5%
35.383569 1
 
0.5%
35.381855 1
 
0.5%
35.380897 1
 
0.5%
35.346728 1
 
0.5%
Other values (197) 197
95.2%
ValueCountFrequency (%)
35.302472 1
0.5%
35.304909 1
0.5%
35.306804 1
0.5%
35.309101 1
0.5%
35.310138 1
0.5%
35.310884 1
0.5%
35.310896 1
0.5%
35.311415 1
0.5%
35.311453 1
0.5%
35.311911 1
0.5%
ValueCountFrequency (%)
35.495539 1
0.5%
35.495259 1
0.5%
35.495159 1
0.5%
35.492663 1
0.5%
35.491743 1
0.5%
35.490722 1
0.5%
35.489653 1
0.5%
35.489566 1
0.5%
35.467095 1
0.5%
35.444517 1
0.5%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.06857
Minimum128.98649
Maximum129.18025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:39.757967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.98649
5-th percentile128.99477
Q1129.01896
median129.04146
Q3129.14533
95-th percentile129.16994
Maximum129.18025
Range0.193758
Interquartile range (IQR)0.1263755

Descriptive statistics

Standard deviation0.063312415
Coefficient of variation (CV)0.00049053315
Kurtosis-1.3243022
Mean129.06857
Median Absolute Deviation (MAD)0.034728
Skewness0.55430134
Sum26717.195
Variance0.0040084619
MonotonicityNot monotonic
2023-12-11T08:29:39.896662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.002416 1
 
0.5%
129.002984 1
 
0.5%
129.035971 1
 
0.5%
129.050212 1
 
0.5%
129.026198 1
 
0.5%
129.025639 1
 
0.5%
129.028274 1
 
0.5%
129.027944 1
 
0.5%
129.027353 1
 
0.5%
129.021567 1
 
0.5%
Other values (197) 197
95.2%
ValueCountFrequency (%)
128.986489 1
0.5%
128.989723 1
0.5%
128.989858 1
0.5%
128.990193 1
0.5%
128.991534 1
0.5%
128.991953 1
0.5%
128.992663 1
0.5%
128.992769 1
0.5%
128.992863 1
0.5%
128.993844 1
0.5%
ValueCountFrequency (%)
129.180247 1
0.5%
129.178242 1
0.5%
129.175255 1
0.5%
129.174292 1
0.5%
129.172692 1
0.5%
129.171906 1
0.5%
129.171874 1
0.5%
129.170941 1
0.5%
129.170729 1
0.5%
129.170237 1
0.5%

층수
Text

Distinct88
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T08:29:40.100650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.2173913
Min length1

Characters and Unicode

Total characters666
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

Unique57 ?
Unique (%)27.5%

Sample

1st row5
2nd row5
3rd row15
4th row14
5th row15
ValueCountFrequency (%)
15 46
22.2%
5 20
 
9.7%
6 14
 
6.8%
17~20 5
 
2.4%
14 4
 
1.9%
20~25 4
 
1.9%
20 4
 
1.9%
14~15 3
 
1.4%
24 3
 
1.4%
13~15 3
 
1.4%
Other values (78) 101
48.8%
2023-12-11T08:29:40.468152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 153
23.0%
2 130
19.5%
5 110
16.5%
~ 98
14.7%
0 39
 
5.9%
6 32
 
4.8%
8 24
 
3.6%
3 24
 
3.6%
4 23
 
3.5%
9 17
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 568
85.3%
Math Symbol 98
 
14.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 153
26.9%
2 130
22.9%
5 110
19.4%
0 39
 
6.9%
6 32
 
5.6%
8 24
 
4.2%
3 24
 
4.2%
4 23
 
4.0%
9 17
 
3.0%
7 16
 
2.8%
Math Symbol
ValueCountFrequency (%)
~ 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 666
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 153
23.0%
2 130
19.5%
5 110
16.5%
~ 98
14.7%
0 39
 
5.9%
6 32
 
4.8%
8 24
 
3.6%
3 24
 
3.6%
4 23
 
3.5%
9 17
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 153
23.0%
2 130
19.5%
5 110
16.5%
~ 98
14.7%
0 39
 
5.9%
6 32
 
4.8%
8 24
 
3.6%
3 24
 
3.6%
4 23
 
3.5%
9 17
 
2.6%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2850242
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:40.582970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q39
95-th percentile15.7
Maximum26
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.998147
Coefficient of variation (CV)0.79524707
Kurtosis1.1824297
Mean6.2850242
Median Absolute Deviation (MAD)4
Skewness1.0768261
Sum1301
Variance24.981474
MonotonicityNot monotonic
2023-12-11T08:29:40.688047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 43
20.8%
2 23
11.1%
7 15
 
7.2%
6 15
 
7.2%
8 14
 
6.8%
4 13
 
6.3%
9 13
 
6.3%
3 12
 
5.8%
5 12
 
5.8%
13 11
 
5.3%
Other values (11) 36
17.4%
ValueCountFrequency (%)
1 43
20.8%
2 23
11.1%
3 12
 
5.8%
4 13
 
6.3%
5 12
 
5.8%
6 15
 
7.2%
7 15
 
7.2%
8 14
 
6.8%
9 13
 
6.3%
10 11
 
5.3%
ValueCountFrequency (%)
26 1
 
0.5%
24 1
 
0.5%
22 1
 
0.5%
18 4
 
1.9%
17 1
 
0.5%
16 3
 
1.4%
15 3
 
1.4%
14 3
 
1.4%
13 11
5.3%
12 3
 
1.4%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean539.97585
Minimum20
Maximum3000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:40.827533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile42.6
Q1149
median483
Q3793
95-th percentile1282.4
Maximum3000
Range2980
Interquartile range (IQR)644

Descriptive statistics

Standard deviation460.59005
Coefficient of variation (CV)0.8529827
Kurtosis4.2772086
Mean539.97585
Median Absolute Deviation (MAD)324
Skewness1.5441001
Sum111775
Variance212143.2
MonotonicityNot monotonic
2023-12-11T08:29:40.968401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 4
 
1.9%
499 3
 
1.4%
72 3
 
1.4%
160 3
 
1.4%
998 3
 
1.4%
84 3
 
1.4%
420 3
 
1.4%
324 2
 
1.0%
790 2
 
1.0%
120 2
 
1.0%
Other values (161) 179
86.5%
ValueCountFrequency (%)
20 1
 
0.5%
21 1
 
0.5%
29 1
 
0.5%
30 2
1.0%
34 1
 
0.5%
40 4
1.9%
42 1
 
0.5%
44 1
 
0.5%
48 2
1.0%
49 2
1.0%
ValueCountFrequency (%)
3000 1
0.5%
2280 1
0.5%
2130 1
0.5%
1768 1
0.5%
1724 1
0.5%
1663 1
0.5%
1414 1
0.5%
1385 1
0.5%
1337 1
0.5%
1300 1
0.5%

유형
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
분양
191 
임대
 
13
국민임대
 
2
국민
 
1

Length

Max length4
Median length2
Mean length2.0193237
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
분양 191
92.3%
임대 13
 
6.3%
국민임대 2
 
1.0%
국민 1
 
0.5%

Length

2023-12-11T08:29:41.114673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:41.229008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분양 191
92.3%
임대 13
 
6.3%
국민임대 2
 
1.0%
국민 1
 
0.5%

난방방식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
개별
148 
지역
58 
중앙
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
개별 148
71.5%
지역 58
 
28.0%
중앙 1
 
0.5%

Length

2023-12-11T08:29:41.337951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:41.428706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별 148
71.5%
지역 58
 
28.0%
중앙 1
 
0.5%

승강기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.898551
Minimum0
Maximum75
Zeros38
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:41.540550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q319
95-th percentile33
Maximum75
Range75
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.47028
Coefficient of variation (CV)0.88926891
Kurtosis3.8587403
Mean12.898551
Median Absolute Deviation (MAD)8
Skewness1.3366251
Sum2670
Variance131.56733
MonotonicityNot monotonic
2023-12-11T08:29:41.662231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 38
 
18.4%
14 9
 
4.3%
13 9
 
4.3%
18 9
 
4.3%
17 8
 
3.9%
16 8
 
3.9%
3 7
 
3.4%
2 7
 
3.4%
11 7
 
3.4%
6 7
 
3.4%
Other values (30) 98
47.3%
ValueCountFrequency (%)
0 38
18.4%
1 3
 
1.4%
2 7
 
3.4%
3 7
 
3.4%
4 7
 
3.4%
5 6
 
2.9%
6 7
 
3.4%
7 4
 
1.9%
8 5
 
2.4%
9 3
 
1.4%
ValueCountFrequency (%)
75 1
 
0.5%
55 1
 
0.5%
46 1
 
0.5%
39 1
 
0.5%
36 3
1.4%
35 2
1.0%
34 1
 
0.5%
33 2
1.0%
31 2
1.0%
30 1
 
0.5%

주차
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct183
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean510.22222
Minimum0
Maximum2626
Zeros8
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:41.789237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q182
median410
Q3776.5
95-th percentile1429.3
Maximum2626
Range2626
Interquartile range (IQR)694.5

Descriptive statistics

Standard deviation481.5345
Coefficient of variation (CV)0.94377406
Kurtosis1.6198162
Mean510.22222
Median Absolute Deviation (MAD)338
Skewness1.1776719
Sum105616
Variance231875.47
MonotonicityNot monotonic
2023-12-11T08:29:41.920656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
3.9%
70 5
 
2.4%
60 3
 
1.4%
110 2
 
1.0%
15 2
 
1.0%
48 2
 
1.0%
232 2
 
1.0%
410 2
 
1.0%
100 2
 
1.0%
20 2
 
1.0%
Other values (173) 177
85.5%
ValueCountFrequency (%)
0 8
3.9%
10 1
 
0.5%
13 1
 
0.5%
15 2
 
1.0%
18 1
 
0.5%
20 2
 
1.0%
21 1
 
0.5%
23 1
 
0.5%
25 2
 
1.0%
29 1
 
0.5%
ValueCountFrequency (%)
2626 1
0.5%
2184 1
0.5%
1840 1
0.5%
1760 1
0.5%
1681 1
0.5%
1650 1
0.5%
1552 1
0.5%
1534 1
0.5%
1494 1
0.5%
1493 1
0.5%

관리방법
Categorical

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
위탁
107 
자치
98 
-
 
2

Length

Max length2
Median length2
Mean length1.9903382
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자치
2nd row자치
3rd row위탁
4th row자치
5th row자치

Common Values

ValueCountFrequency (%)
위탁 107
51.7%
자치 98
47.3%
- 2
 
1.0%

Length

2023-12-11T08:29:42.073392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:42.188625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁 107
51.7%
자치 98
47.3%
2
 
1.0%

승인일
Text

Distinct184
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T08:29:42.485839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.9613527
Min length1

Characters and Unicode

Total characters2062
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique166 ?
Unique (%)80.2%

Sample

1st row1990-03-20
2nd row1990-05-27
3rd row1992-10-24
4th row1991-08-10
5th row1991-10-31
ValueCountFrequency (%)
2004-12-30 4
 
1.9%
2012-06-21 3
 
1.4%
2005-12-13 3
 
1.4%
2013-11-07 3
 
1.4%
2006-11-30 2
 
1.0%
2013-07-04 2
 
1.0%
1991-01-26 2
 
1.0%
2016-01-29 2
 
1.0%
1991-02-13 2
 
1.0%
1990-07-07 2
 
1.0%
Other values (174) 182
87.9%
2023-12-11T08:29:42.915557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 412
20.0%
0 410
19.9%
1 371
18.0%
2 255
12.4%
9 240
11.6%
3 76
 
3.7%
4 71
 
3.4%
8 69
 
3.3%
7 56
 
2.7%
6 53
 
2.6%
Other values (2) 49
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1649
80.0%
Dash Punctuation 412
 
20.0%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 410
24.9%
1 371
22.5%
2 255
15.5%
9 240
14.6%
3 76
 
4.6%
4 71
 
4.3%
8 69
 
4.2%
7 56
 
3.4%
6 53
 
3.2%
5 48
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 412
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 412
20.0%
0 410
19.9%
1 371
18.0%
2 255
12.4%
9 240
11.6%
3 76
 
3.7%
4 71
 
3.4%
8 69
 
3.3%
7 56
 
2.7%
6 53
 
2.6%
Other values (2) 49
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 412
20.0%
0 410
19.9%
1 371
18.0%
2 255
12.4%
9 240
11.6%
3 76
 
3.7%
4 71
 
3.4%
8 69
 
3.3%
7 56
 
2.7%
6 53
 
2.6%
Other values (2) 49
 
2.4%

준공일
Date

Distinct190
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum1981-12-22 00:00:00
Maximum2021-03-02 00:00:00
2023-12-11T08:29:43.067775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:43.231197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

의무관리대상
Categorical

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
의무
153 
<NA>
52 
비의무
 
2

Length

Max length4
Median length2
Mean length2.5120773
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의무 153
73.9%
<NA> 52
 
25.1%
비의무 2
 
1.0%

Length

2023-12-11T08:29:43.357434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:43.469348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의무 153
73.9%
na 52
 
25.1%
비의무 2
 
1.0%

출처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
기본현황
207 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기본현황 207
100.0%

Length

2023-12-11T08:29:43.563715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:43.644115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기본현황 207
100.0%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2021-08-09
207 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-08-09
2nd row2021-08-09
3rd row2021-08-09
4th row2021-08-09
5th row2021-08-09

Common Values

ValueCountFrequency (%)
2021-08-09 207
100.0%

Length

2023-12-11T08:29:43.724401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:43.807337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-08-09 207
100.0%

Interactions

2023-12-11T08:29:37.013986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:34.745681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.282475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.751260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.164806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.587712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:37.094204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:34.834415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.378353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.826241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.240736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.668067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:37.165241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:34.919748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.462008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.900648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.312613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.740902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:37.227604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.004405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.534083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.962917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.377115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.807411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:37.291467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.104462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.609114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.028999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.445083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.875950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:37.361165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.196605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:35.684203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.097958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.522514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:36.946635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:29:43.871907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도층수동수세대수유형난방방식승강기주차관리방법의무관리대상
위도1.0000.8380.0000.2940.3170.0000.6650.2400.3850.4890.273
경도0.8381.0000.0000.4150.2550.0000.7650.3290.2670.6110.072
층수0.0000.0001.0000.8920.8040.7670.4030.8390.9350.9081.000
동수0.2940.4150.8921.0000.8130.0000.7050.9500.8330.7560.000
세대수0.3170.2550.8040.8131.0000.2980.7140.8570.8760.6020.147
유형0.0000.0000.7670.0000.2981.0000.0000.0000.1790.1320.000
난방방식0.6650.7650.4030.7050.7140.0001.0000.5970.5980.7490.000
승강기0.2400.3290.8390.9500.8570.0000.5971.0000.8490.7000.105
주차0.3850.2670.9350.8330.8760.1790.5980.8491.0000.6580.000
관리방법0.4890.6110.9080.7560.6020.1320.7490.7000.6581.0000.304
의무관리대상0.2730.0721.0000.0000.1470.0000.0000.1050.0000.3041.000
2023-12-11T08:29:43.996175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형관리방법난방방식의무관리대상
유형1.0000.1240.0000.000
관리방법0.1241.0000.4070.489
난방방식0.0000.4071.0000.000
의무관리대상0.0000.4890.0001.000
2023-12-11T08:29:44.082054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도동수세대수승강기주차유형난방방식관리방법의무관리대상
위도1.0000.851-0.381-0.360-0.268-0.4200.0000.5060.3310.266
경도0.8511.000-0.262-0.263-0.157-0.3250.0000.4560.3310.054
동수-0.381-0.2621.0000.9240.8600.9170.1110.4420.4580.000
세대수-0.360-0.2630.9241.0000.9290.9550.1350.5970.4650.107
승강기-0.268-0.1570.8600.9291.0000.8930.0000.3110.4060.100
주차-0.420-0.3250.9170.9550.8931.0000.1050.4340.4990.000
유형0.0000.0000.1110.1350.0000.1051.0000.0000.1240.000
난방방식0.5060.4560.4420.5970.3110.4340.0001.0000.4070.000
관리방법0.3310.3310.4580.4650.4060.4990.1240.4071.0000.489
의무관리대상0.2660.0540.0000.1070.1000.0000.0000.0000.4891.000

Missing values

2023-12-11T08:29:37.464924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:29:37.628392image/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

아파트명위치위도경도층수동수세대수유형난방방식승강기주차관리방법승인일준공일의무관리대상출처기준일자
0주공2차아파트경상남도 양산시 물금읍 동중1길2135.329976129.00241659420분양개별0104자치1990-03-201990-03-20의무기본현황2021-08-09
1주공3차아파트경상남도 양산시 물금읍 동중1길735.328111129.002984511410분양개별0226자치1990-05-271990-05-27의무기본현황2021-08-09
2삼전무지개아파트경상남도 양산시 물금읍 원동로 5935.313073128.986489152168분양개별441위탁1992-10-241992-10-24의무기본현황2021-08-09
3삼위로얄맨션아파트경상남도 양산시 물금읍 동중7길2135.331941129.00535614188분양개별369자치1991-08-101992-11-20<NA>기본현황2021-08-09
4덕산타운아파트경상남도 양산시 물금읍 오봉로 2935.327165128.996963156483분양개별17351자치1991-10-311993-08-02의무기본현황2021-08-09
5황전아파트경상남도 양산시 물금읍 오봉로 1535.326234128.998326155496분양개별17371자치1991-10-311994-06-03의무기본현황2021-08-09
6경민아파트경상남도 양산시 물금읍 황산로 70735.332837129.008661153210분양개별7163자치1993-03-051994-10-31의무기본현황2021-08-09
7대동타운아파트경상남도 양산시 물금읍 오봉로 16535.335363129.0067511591122분양개별25997위탁1993-07-211995-09-26의무기본현황2021-08-09
8범어그린피아아파트경상남도 양산시 물금읍 오봉로 18535.334585129.00920565300분양개별0149자치1992-08-291992-08-29의무기본현황2021-08-09
9현대아파트경상남도 양산시 물금읍 오봉로 18035.333398129.006778158956분양개별25552위탁1993-09-091996-03-20의무기본현황2021-08-09
아파트명위치위도경도층수동수세대수유형난방방식승강기주차관리방법승인일준공일의무관리대상출처기준일자
197대승1차아파트경상남도 양산시 웅상대로 86635.376129129.157245246790분양개별17413위탁1991-08-101993-12-02의무기본현황2021-08-09
198대승2차아파트경상남도 양산시 덕계북길 935.384674129.157073264476분양개별11330위탁1992-05-021994-11-14의무기본현황2021-08-09
199진명웅상(웅상 쇼핑타운)경상남도 양산시 덕계로 10535.376865129.153266151121분양개별262위탁1994-03-171997-02-04<NA>기본현황2021-08-09
200부영벽산아파트경상남도 양산시 덕계11길 1235.376569129.15198422~256863분양개별18563위탁1999-02-011999-02-01의무기본현황2021-08-09
201동일스위트2차경상남도 양산시 덕계5길 1435.373344129.14782518~257790분양개별17785위탁2000-05-252003-02-08의무기본현황2021-08-09
202경동 스마트홈경상남도 양산시 신덕계로 3435.377988129.15841317~207487분양개별12490위탁2011-06-202014-01-16의무기본현황2021-08-09
203우성스마트뷰경상남도 양산시 덕계회야길 1635.371873129.14654529~348604분양개별18699위탁2016-08-122019-10-22의무기본현황2021-08-09
204두산 위브 1차경상남도 양산시 신덕계3길 3635.372561129.16160425~29131337분양개별291494위탁2015-09-082019-11-28의무기본현황2021-08-09
205두산 위브 2차(1단지)경상남도 양산시 신덕계8길 3735.375482129.16208221~279680분양개별19752위탁2013-07-042021-03-02의무기본현황2021-08-09
206두산 위브 2차(2단지)경상남도 양산시 신덕계8길 4035.375908129.16097821~278442분양개별13494위탁2013-07-042021-03-02의무기본현황2021-08-09