Overview

Dataset statistics

Number of variables15
Number of observations351
Missing cells238
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.0 KiB
Average record size in memory128.4 B

Variable types

Numeric6
Categorical7
Text2

Dataset

Description서울특별시 서초구 공동주택 주차장 허용 면수에 대한 정보(주소, 대장종류, 건물명, 용도, 면수 등)를 제공합니다.
URLhttps://www.data.go.kr/data/15112793/fileData.do

Alerts

대장종류 has constant value ""Constant
시군구명 has constant value ""Constant
대지구분 has constant value ""Constant
주용도 has constant value ""Constant
옥내 자주식 대수 is highly overall correlated with 옥외 자주식 대수 and 2 other fieldsHigh correlation
옥외 자주식 대수 is highly overall correlated with 옥내 자주식 대수 and 1 other fieldsHigh correlation
총 주차수 is highly overall correlated with 옥내 자주식 대수 and 2 other fieldsHigh correlation
옥내 기계식 대수 is highly overall correlated with 옥내 자주식 대수 and 1 other fieldsHigh correlation
옥내 기계식 대수 is highly imbalanced (57.5%)Imbalance
건물명 has 112 (31.9%) missing valuesMissing
옥내 자주식 대수 has 23 (6.6%) missing valuesMissing
옥외 자주식 대수 has 81 (23.1%) missing valuesMissing
총 주차수 has 19 (5.4%) missing valuesMissing
순번 has unique valuesUnique
부번 has 85 (24.2%) zerosZeros
옥내 자주식 대수 has 120 (34.2%) zerosZeros
옥외 자주식 대수 has 121 (34.5%) zerosZeros
총 주차수 has 119 (33.9%) zerosZeros

Reproduction

Analysis started2023-12-12 00:46:51.305397
Analysis finished2023-12-12 00:46:57.256275
Duration5.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct351
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176
Minimum1
Maximum351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-12T09:46:57.337495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.5
Q188.5
median176
Q3263.5
95-th percentile333.5
Maximum351
Range350
Interquartile range (IQR)175

Descriptive statistics

Standard deviation101.46921
Coefficient of variation (CV)0.57652959
Kurtosis-1.2
Mean176
Median Absolute Deviation (MAD)88
Skewness0
Sum61776
Variance10296
MonotonicityStrictly increasing
2023-12-12T09:46:57.544345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
2 1
 
0.3%
241 1
 
0.3%
240 1
 
0.3%
239 1
 
0.3%
238 1
 
0.3%
237 1
 
0.3%
236 1
 
0.3%
235 1
 
0.3%
234 1
 
0.3%
Other values (341) 341
97.2%
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 (%)
351 1
0.3%
350 1
0.3%
349 1
0.3%
348 1
0.3%
347 1
0.3%
346 1
0.3%
345 1
0.3%
344 1
0.3%
343 1
0.3%
342 1
0.3%

대장종류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
총괄표제부
351 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
총괄표제부 351
100.0%

Length

2023-12-12T09:46:57.747885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:46:57.886336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
총괄표제부 351
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
서울특별시 서초구
351 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 서초구 351
100.0%

Length

2023-12-12T09:46:58.009946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:46:58.118047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 351
50.0%
서초구 351
50.0%

법정동명
Categorical

Distinct9
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
방배동
128 
서초동
88 
반포동
44 
양재동
36 
잠원동
26 
Other values (4)
29 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row방배동
2nd row서초동
3rd row양재동
4th row방배동
5th row반포동

Common Values

ValueCountFrequency (%)
방배동 128
36.5%
서초동 88
25.1%
반포동 44
 
12.5%
양재동 36
 
10.3%
잠원동 26
 
7.4%
우면동 21
 
6.0%
신원동 5
 
1.4%
내곡동 2
 
0.6%
염곡동 1
 
0.3%

Length

2023-12-12T09:46:58.249438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:46:58.387290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
방배동 128
36.5%
서초동 88
25.1%
반포동 44
 
12.5%
양재동 36
 
10.3%
잠원동 26
 
7.4%
우면동 21
 
6.0%
신원동 5
 
1.4%
내곡동 2
 
0.6%
염곡동 1
 
0.3%

대지구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
대지
351 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대지 351
100.0%

Length

2023-12-12T09:46:58.542548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:46:58.641357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대지 351
100.0%

본번
Real number (ℝ)

Distinct273
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean836.02564
Minimum1
Maximum3283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-12T09:46:58.763217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q1157.5
median807
Q31346.5
95-th percentile1686.5
Maximum3283
Range3282
Interquartile range (IQR)1189

Descriptive statistics

Standard deviation674.21541
Coefficient of variation (CV)0.80645303
Kurtosis1.6475538
Mean836.02564
Median Absolute Deviation (MAD)618
Skewness0.95753134
Sum293445
Variance454566.43
MonotonicityNot monotonic
2023-12-12T09:46:58.937828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 6
 
1.7%
18 4
 
1.1%
593 4
 
1.1%
65 4
 
1.1%
850 3
 
0.9%
730 3
 
0.9%
1 3
 
0.9%
725 3
 
0.9%
70 3
 
0.9%
541 3
 
0.9%
Other values (263) 315
89.7%
ValueCountFrequency (%)
1 3
0.9%
2 1
 
0.3%
3 1
 
0.3%
5 2
 
0.6%
6 1
 
0.3%
9 1
 
0.3%
10 1
 
0.3%
12 2
 
0.6%
15 1
 
0.3%
16 6
1.7%
ValueCountFrequency (%)
3283 1
0.3%
3282 1
0.3%
3279 1
0.3%
3278 1
0.3%
3277 1
0.3%
3276 1
0.3%
3275 1
0.3%
2626 1
0.3%
2525 1
0.3%
2233 1
0.3%

부번
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.618234
Minimum0
Maximum216
Zeros85
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-12T09:46:59.118638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q313.5
95-th percentile48.5
Maximum216
Range216
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation21.045993
Coefficient of variation (CV)1.8114624
Kurtosis32.811778
Mean11.618234
Median Absolute Deviation (MAD)5
Skewness4.7119346
Sum4078
Variance442.93384
MonotonicityNot monotonic
2023-12-12T09:46:59.297843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85
24.2%
1 43
 
12.3%
2 17
 
4.8%
3 15
 
4.3%
4 13
 
3.7%
9 12
 
3.4%
5 12
 
3.4%
12 11
 
3.1%
10 11
 
3.1%
8 11
 
3.1%
Other values (44) 121
34.5%
ValueCountFrequency (%)
0 85
24.2%
1 43
12.3%
2 17
 
4.8%
3 15
 
4.3%
4 13
 
3.7%
5 12
 
3.4%
6 9
 
2.6%
7 11
 
3.1%
8 11
 
3.1%
9 12
 
3.4%
ValueCountFrequency (%)
216 1
0.3%
150 1
0.3%
113 1
0.3%
94 1
0.3%
88 1
0.3%
81 1
0.3%
79 1
0.3%
73 2
0.6%
63 2
0.6%
62 1
0.3%

건물명
Text

MISSING 

Distinct226
Distinct (%)94.6%
Missing112
Missing (%)31.9%
Memory size2.9 KiB
2023-12-12T09:46:59.593449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length7.3472803
Min length2

Characters and Unicode

Total characters1756
Distinct characters220
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique221 ?
Unique (%)92.5%

Sample

1st row신동아아파트
2nd row도곡한신빌라
3rd row삼호가든맨션
4th row반포미도아파트
5th row반포미도2차아파트,상가
ValueCountFrequency (%)
방배 13
 
3.9%
서리풀 9
 
2.7%
래미안 8
 
2.4%
서초 8
 
2.4%
신반포아파트 7
 
2.1%
반포 5
 
1.5%
아파트 4
 
1.2%
삼호아파트 4
 
1.2%
방배동 4
 
1.2%
양재 3
 
0.9%
Other values (247) 265
80.3%
2023-12-12T09:47:00.023996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 96
 
5.5%
92
 
5.2%
아 91
 
5.2%
서 73
 
4.2%
파 73
 
4.2%
초 60
 
3.4%
스 53
 
3.0%
방 46
 
2.6%
배 46
 
2.6%
이 41
 
2.3%
Other values (210) 1085
61.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1588
90.4%
Space Separator 92
 
5.2%
Decimal Number 50
 
2.8%
Dash Punctuation 8
 
0.5%
Uppercase Letter 7
 
0.4%
Lowercase Letter 6
 
0.3%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 96
 
6.0%
아 91
 
5.7%
서 73
 
4.6%
파 73
 
4.6%
초 60
 
3.8%
스 53
 
3.3%
방 46
 
2.9%
배 46
 
2.9%
이 41
 
2.6%
리 41
 
2.6%
Other values (189) 968
61.0%
Decimal Number
ValueCountFrequency (%)
2 14
28.0%
3 9
18.0%
1 7
14.0%
5 5
 
10.0%
4 5
 
10.0%
6 4
 
8.0%
7 3
 
6.0%
9 1
 
2.0%
8 1
 
2.0%
0 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
I 3
42.9%
R 1
 
14.3%
A 1
 
14.3%
P 1
 
14.3%
K 1
 
14.3%
Space Separator
ValueCountFrequency (%)
92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1588
90.4%
Common 155
 
8.8%
Latin 13
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 96
 
6.0%
아 91
 
5.7%
서 73
 
4.6%
파 73
 
4.6%
초 60
 
3.8%
스 53
 
3.3%
방 46
 
2.9%
배 46
 
2.9%
이 41
 
2.6%
리 41
 
2.6%
Other values (189) 968
61.0%
Common
ValueCountFrequency (%)
92
59.4%
2 14
 
9.0%
3 9
 
5.8%
- 8
 
5.2%
1 7
 
4.5%
5 5
 
3.2%
4 5
 
3.2%
6 4
 
2.6%
7 3
 
1.9%
) 2
 
1.3%
Other values (5) 6
 
3.9%
Latin
ValueCountFrequency (%)
e 6
46.2%
I 3
23.1%
R 1
 
7.7%
A 1
 
7.7%
P 1
 
7.7%
K 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1588
90.4%
ASCII 168
 
9.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 96
 
6.0%
아 91
 
5.7%
서 73
 
4.6%
파 73
 
4.6%
초 60
 
3.8%
스 53
 
3.3%
방 46
 
2.9%
배 46
 
2.9%
이 41
 
2.6%
리 41
 
2.6%
Other values (189) 968
61.0%
ASCII
ValueCountFrequency (%)
92
54.8%
2 14
 
8.3%
3 9
 
5.4%
- 8
 
4.8%
1 7
 
4.2%
e 6
 
3.6%
5 5
 
3.0%
4 5
 
3.0%
6 4
 
2.4%
7 3
 
1.8%
Other values (11) 15
 
8.9%

주용도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
공동주택
351 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공동주택 351
100.0%

Length

2023-12-12T09:47:00.211689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:47:00.360142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동주택 351
100.0%

기타용도
Text

Distinct71
Distinct (%)20.4%
Missing3
Missing (%)0.9%
Memory size2.9 KiB
2023-12-12T09:47:00.624198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length8.2241379
Min length2

Characters and Unicode

Total characters2862
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)15.8%

Sample

1st row공동주택
2nd row공동주택
3rd row공동주택
4th row공동주택
5th row공동주택
ValueCountFrequency (%)
공동주택 171
43.8%
공동주택(아파트 78
20.0%
상가 14
 
3.6%
아파트 10
 
2.6%
다세대주택 9
 
2.3%
도시형생활주택(단지형다세대 8
 
2.1%
도시형생활주택(단지형다세대주택 6
 
1.5%
공동주택,아파트 5
 
1.3%
도시형생활주택-단지형다세대 5
 
1.3%
판매시설 5
 
1.3%
Other values (58) 79
20.3%
2023-12-12T09:47:01.413117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 361
 
12.6%
택 361
 
12.6%
공 271
 
9.5%
동 271
 
9.5%
) 140
 
4.9%
( 139
 
4.9%
형 101
 
3.5%
아 98
 
3.4%
파 98
 
3.4%
트 98
 
3.4%
Other values (54) 924
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2439
85.2%
Close Punctuation 140
 
4.9%
Open Punctuation 139
 
4.9%
Other Punctuation 54
 
1.9%
Space Separator 42
 
1.5%
Decimal Number 32
 
1.1%
Dash Punctuation 15
 
0.5%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 361
14.8%
택 361
14.8%
공 271
11.1%
동 271
11.1%
형 101
 
4.1%
아 98
 
4.0%
파 98
 
4.0%
트 98
 
4.0%
시 82
 
3.4%
세 79
 
3.2%
Other values (38) 619
25.4%
Decimal Number
ValueCountFrequency (%)
2 12
37.5%
1 8
25.0%
0 3
 
9.4%
9 2
 
6.2%
6 2
 
6.2%
7 2
 
6.2%
4 1
 
3.1%
8 1
 
3.1%
3 1
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 51
94.4%
/ 3
 
5.6%
Close Punctuation
ValueCountFrequency (%)
) 140
100.0%
Open Punctuation
ValueCountFrequency (%)
( 139
100.0%
Space Separator
ValueCountFrequency (%)
42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2439
85.2%
Common 423
 
14.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 361
14.8%
택 361
14.8%
공 271
11.1%
동 271
11.1%
형 101
 
4.1%
아 98
 
4.0%
파 98
 
4.0%
트 98
 
4.0%
시 82
 
3.4%
세 79
 
3.2%
Other values (38) 619
25.4%
Common
ValueCountFrequency (%)
) 140
33.1%
( 139
32.9%
, 51
 
12.1%
42
 
9.9%
- 15
 
3.5%
2 12
 
2.8%
1 8
 
1.9%
0 3
 
0.7%
/ 3
 
0.7%
9 2
 
0.5%
Other values (6) 8
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2439
85.2%
ASCII 423
 
14.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 361
14.8%
택 361
14.8%
공 271
11.1%
동 271
11.1%
형 101
 
4.1%
아 98
 
4.0%
파 98
 
4.0%
트 98
 
4.0%
시 82
 
3.4%
세 79
 
3.2%
Other values (38) 619
25.4%
ASCII
ValueCountFrequency (%)
) 140
33.1%
( 139
32.9%
, 51
 
12.1%
42
 
9.9%
- 15
 
3.5%
2 12
 
2.8%
1 8
 
1.9%
0 3
 
0.7%
/ 3
 
0.7%
9 2
 
0.5%
Other values (6) 8
 
1.9%

옥내 기계식 대수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
182 
0
165 
65
 
1
22
 
1
56
 
1

Length

Max length4
Median length4
Mean length2.5669516
Min length1

Unique

Unique4 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 182
51.9%
0 165
47.0%
65 1
 
0.3%
22 1
 
0.3%
56 1
 
0.3%
83 1
 
0.3%

Length

2023-12-12T09:47:01.662312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:47:01.867981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 182
51.9%
0 165
47.0%
65 1
 
0.3%
22 1
 
0.3%
56 1
 
0.3%
83 1
 
0.3%

옥외 기계식 대수
Categorical

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
184 
0
166 
2
 
1

Length

Max length4
Median length4
Mean length2.5726496
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row0
2nd row<NA>
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 184
52.4%
0 166
47.3%
2 1
 
0.3%

Length

2023-12-12T09:47:02.148626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:47:02.389610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 184
52.4%
0 166
47.3%
2 1
 
0.3%

옥내 자주식 대수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct153
Distinct (%)46.6%
Missing23
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean239.50305
Minimum0
Maximum6075
Zeros120
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-12T09:47:02.608611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q3227
95-th percentile1187.8
Maximum6075
Range6075
Interquartile range (IQR)227

Descriptive statistics

Standard deviation581.32113
Coefficient of variation (CV)2.4271972
Kurtosis41.073936
Mean239.50305
Median Absolute Deviation (MAD)15
Skewness5.4288683
Sum78557
Variance337934.26
MonotonicityNot monotonic
2023-12-12T09:47:02.805194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120
34.2%
8 10
 
2.8%
15 5
 
1.4%
21 4
 
1.1%
12 4
 
1.1%
11 4
 
1.1%
27 4
 
1.1%
9 4
 
1.1%
18 3
 
0.9%
112 3
 
0.9%
Other values (143) 167
47.6%
(Missing) 23
 
6.6%
ValueCountFrequency (%)
0 120
34.2%
1 1
 
0.3%
2 3
 
0.9%
3 2
 
0.6%
4 2
 
0.6%
5 1
 
0.3%
6 1
 
0.3%
7 3
 
0.9%
8 10
 
2.8%
9 4
 
1.1%
ValueCountFrequency (%)
6075 1
0.3%
4368 1
0.3%
2973 1
0.3%
2892 1
0.3%
2516 1
0.3%
1759 1
0.3%
1722 1
0.3%
1637 1
0.3%
1523 1
0.3%
1511 1
0.3%

옥외 자주식 대수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct56
Distinct (%)20.7%
Missing81
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean16.322222
Minimum0
Maximum322
Zeros121
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-12T09:47:03.005285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313
95-th percentile84.55
Maximum322
Range322
Interquartile range (IQR)13

Descriptive statistics

Standard deviation40.004413
Coefficient of variation (CV)2.450917
Kurtosis21.075464
Mean16.322222
Median Absolute Deviation (MAD)2
Skewness4.2375635
Sum4407
Variance1600.353
MonotonicityNot monotonic
2023-12-12T09:47:03.229896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 121
34.5%
2 15
 
4.3%
7 11
 
3.1%
3 10
 
2.8%
8 8
 
2.3%
6 8
 
2.3%
1 6
 
1.7%
4 6
 
1.7%
15 6
 
1.7%
10 5
 
1.4%
Other values (46) 74
21.1%
(Missing) 81
23.1%
ValueCountFrequency (%)
0 121
34.5%
1 6
 
1.7%
2 15
 
4.3%
3 10
 
2.8%
4 6
 
1.7%
5 3
 
0.9%
6 8
 
2.3%
7 11
 
3.1%
8 8
 
2.3%
10 5
 
1.4%
ValueCountFrequency (%)
322 1
0.3%
222 1
0.3%
203 1
0.3%
201 1
0.3%
195 1
0.3%
185 1
0.3%
182 1
0.3%
137 1
0.3%
133 1
0.3%
113 1
0.3%

총 주차수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct148
Distinct (%)44.6%
Missing19
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean250.06627
Minimum0
Maximum6075
Zeros119
Zeros (%)33.9%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-12T09:47:03.441968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16
Q3241
95-th percentile1257.5
Maximum6075
Range6075
Interquartile range (IQR)241

Descriptive statistics

Standard deviation588.09113
Coefficient of variation (CV)2.3517412
Kurtosis38.345278
Mean250.06627
Median Absolute Deviation (MAD)16
Skewness5.1959446
Sum83022
Variance345851.18
MonotonicityNot monotonic
2023-12-12T09:47:03.635431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 119
33.9%
15 9
 
2.6%
16 7
 
2.0%
11 7
 
2.0%
12 5
 
1.4%
21 5
 
1.4%
17 4
 
1.1%
18 4
 
1.1%
24 4
 
1.1%
13 4
 
1.1%
Other values (138) 164
46.7%
(Missing) 19
 
5.4%
ValueCountFrequency (%)
0 119
33.9%
2 3
 
0.9%
3 3
 
0.9%
4 2
 
0.6%
6 2
 
0.6%
8 1
 
0.3%
9 1
 
0.3%
11 7
 
2.0%
12 5
 
1.4%
13 4
 
1.1%
ValueCountFrequency (%)
6075 1
0.3%
4368 1
0.3%
2973 1
0.3%
2892 1
0.3%
2523 1
0.3%
1821 1
0.3%
1803 1
0.3%
1684 1
0.3%
1523 1
0.3%
1511 1
0.3%

Interactions

2023-12-12T09:46:55.655870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:52.044512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:52.736003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:53.390940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:54.102329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:54.960296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:55.783427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:52.187046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:52.847382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:53.497083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:54.225429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:55.096262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:55.909111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:52.312854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:52.963588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:53.603047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:54.364820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:55.227932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:56.027191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:52.417294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:53.053680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:53.726554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:54.530990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:55.339794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:56.138228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:52.533809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:53.155437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:53.847212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:54.706009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:55.443858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:56.260685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:52.637294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:53.266556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:53.962336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:54.839974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:55.549339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:47:03.780679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정동명본번부번기타용도옥내 기계식 대수옥외 기계식 대수옥내 자주식 대수옥외 자주식 대수총 주차수
순번1.0000.4590.5160.0000.5900.6100.0000.2060.0000.142
법정동명0.4591.0000.8590.0000.0000.0000.0000.2690.0000.294
본번0.5160.8591.0000.0750.0000.2000.0000.1700.2490.215
부번0.0000.0000.0751.0000.0000.0000.0000.0000.0000.000
기타용도0.5900.0000.0000.0001.0000.9700.0000.0000.5940.000
옥내 기계식 대수0.6100.0000.2000.0000.9701.0000.0000.7730.3930.617
옥외 기계식 대수0.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
옥내 자주식 대수0.2060.2690.1700.0000.0000.7730.0001.0000.4600.999
옥외 자주식 대수0.0000.0000.2490.0000.5940.3930.0000.4601.0000.663
총 주차수0.1420.2940.2150.0000.0000.6170.0000.9990.6631.000
2023-12-12T09:47:03.963173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
옥내 기계식 대수옥외 기계식 대수법정동명
옥내 기계식 대수1.0000.0000.000
옥외 기계식 대수0.0001.0000.000
법정동명0.0000.0001.000
2023-12-12T09:47:04.376034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번본번부번옥내 자주식 대수옥외 자주식 대수총 주차수법정동명옥내 기계식 대수옥외 기계식 대수
순번1.0000.170-0.0140.3870.3420.3940.2280.2930.000
본번0.1701.0000.0180.2040.1090.2100.4440.1220.000
부번-0.0140.0181.000-0.259-0.111-0.2220.0000.0000.000
옥내 자주식 대수0.3870.204-0.2591.0000.7160.9400.1360.7790.000
옥외 자주식 대수0.3420.109-0.1110.7161.0000.7110.0000.2770.000
총 주차수0.3940.210-0.2220.9400.7111.0000.1500.5700.000
법정동명0.2280.4440.0000.1360.0000.1501.0000.0000.000
옥내 기계식 대수0.2930.1220.0000.7790.2770.5700.0001.0000.000
옥외 기계식 대수0.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T09:46:56.726827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:46:56.979778image/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.
2023-12-12T09:46:57.154091image/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

순번대장종류시군구명법정동명대지구분본번부번건물명주용도기타용도옥내 기계식 대수옥외 기계식 대수옥내 자주식 대수옥외 자주식 대수총 주차수
01총괄표제부서울특별시 서초구방배동대지88416<NA>공동주택공동주택00000
12총괄표제부서울특별시 서초구서초동대지13341신동아아파트공동주택공동주택0<NA>00<NA>
23총괄표제부서울특별시 서초구양재동대지1812도곡한신빌라공동주택공동주택00000
34총괄표제부서울특별시 서초구방배동대지4379<NA>공동주택공동주택00000
45총괄표제부서울특별시 서초구반포동대지301삼호가든맨션공동주택공동주택00000
56총괄표제부서울특별시 서초구반포동대지551<NA>공동주택공동주택0000<NA>
67총괄표제부서울특별시 서초구반포동대지604반포미도아파트공동주택공동주택, 상가00000
78총괄표제부서울특별시 서초구반포동대지605반포미도2차아파트,상가공동주택공동주택, 판매시설00000
89총괄표제부서울특별시 서초구반포동대지651신반포궁전아파트공동주택공동주택00000
910총괄표제부서울특별시 서초구반포동대지691<NA>공동주택공동주택00280<NA>
순번대장종류시군구명법정동명대지구분본번부번건물명주용도기타용도옥내 기계식 대수옥외 기계식 대수옥내 자주식 대수옥외 자주식 대수총 주차수
341342총괄표제부서울특별시 서초구반포동대지74312알파레타6차공동주택도시형생활주택(단지형 다세대주택)<NA><NA><NA>1414
342343총괄표제부서울특별시 서초구서초동대지15823서초센트럴IPARK공동주택공동주택,오피스텔,업무시설,판매시설<NA><NA>733<NA>733
343344총괄표제부서울특별시 서초구방배동대지90613방배 서리풀 e-편한세상공동주택<NA><NA><NA>937<NA>937
344345총괄표제부서울특별시 서초구반포동대지212아크로리버파크공동주택공동주택(아파트)<NA><NA>2973<NA>2973
345346총괄표제부서울특별시 서초구반포동대지13410반포 래미안아이파크공동주택공동주택(아파트)<NA><NA>133971346
346347총괄표제부서울특별시 서초구서초동대지17580서초자이르네공동주택공동주택(아파트)<NA><NA>78<NA>78
347348총괄표제부서울특별시 서초구반포동대지3026반포리체공동주택아파트<NA><NA>1637471684
348349총괄표제부서울특별시 서초구서초동대지16131<NA>공동주택다세대주택(13세대),제1,2종근린생활시설(2호)<NA><NA><NA>88
349350총괄표제부서울특별시 서초구방배동대지32780방배아트자이공동주택공동주택(아파트)<NA><NA>572<NA>572
350351총괄표제부서울특별시 서초구서초동대지135013동원베네스트아파트공동주택아파트<NA><NA>13926165