Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells10834
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory119.0 B

Variable types

Text5
Categorical3
Numeric5

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15673/S/1/datasetView.do

Alerts

층_구분_코드 is highly overall correlated with 층_번호High correlation
층_번호 is highly overall correlated with 층_구분_코드 and 1 other fieldsHigh correlation
구조_코드 is highly overall correlated with 기타_구조High correlation
기타_구조 is highly overall correlated with 층_번호 and 1 other fieldsHigh correlation
기타_구조 is highly imbalanced (67.1%)Imbalance
관리_호별_명세_PK has 9951 (99.5%) missing valuesMissing
층_구분_코드 has 106 (1.1%) missing valuesMissing
기타_용도 has 708 (7.1%) missing valuesMissing
층_번호 is highly skewed (γ1 = 48.40575)Skewed
면적 is highly skewed (γ1 = 97.23576694)Skewed
관리_전유_공용_PK has unique valuesUnique
층_번호 has 6098 (61.0%) zerosZeros

Reproduction

Analysis started2024-05-11 03:37:16.663095
Analysis finished2024-05-11 03:37:32.224027
Duration15.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_전유_공용_PK
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T03:37:32.678435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length13.6952
Min length7

Characters and Unicode

Total characters136952
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

Unique10000 ?
Unique (%)100.0%

Sample

1st row11740-2381
2nd row11290-100006344
3rd row11140-100002568
4th row11380-100007406
5th row11000-100006503
ValueCountFrequency (%)
11740-2381 1
 
< 0.1%
11500-2626 1
 
< 0.1%
11440-100001673 1
 
< 0.1%
11740-975 1
 
< 0.1%
11200-100002972 1
 
< 0.1%
11200-100001859 1
 
< 0.1%
11200-100002072 1
 
< 0.1%
11290-100006477 1
 
< 0.1%
11380-100008919 1
 
< 0.1%
11350-1911 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-11T03:37:34.266502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45416
33.2%
1 35785
26.1%
- 10000
 
7.3%
2 7728
 
5.6%
3 6732
 
4.9%
4 6585
 
4.8%
5 6139
 
4.5%
8 5147
 
3.8%
6 5014
 
3.7%
7 4292
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126952
92.7%
Dash Punctuation 10000
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45416
35.8%
1 35785
28.2%
2 7728
 
6.1%
3 6732
 
5.3%
4 6585
 
5.2%
5 6139
 
4.8%
8 5147
 
4.1%
6 5014
 
3.9%
7 4292
 
3.4%
9 4114
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 136952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45416
33.2%
1 35785
26.1%
- 10000
 
7.3%
2 7728
 
5.6%
3 6732
 
4.9%
4 6585
 
4.8%
5 6139
 
4.5%
8 5147
 
3.8%
6 5014
 
3.7%
7 4292
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45416
33.2%
1 35785
26.1%
- 10000
 
7.3%
2 7728
 
5.6%
3 6732
 
4.9%
4 6585
 
4.8%
5 6139
 
4.5%
8 5147
 
3.8%
6 5014
 
3.7%
7 4292
 
3.1%

관리_형별_개요_PK
Text

Distinct6514
Distinct (%)65.5%
Missing49
Missing (%)0.5%
Memory size156.2 KiB
2024-05-11T03:37:35.458178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length13.290725
Min length7

Characters and Unicode

Total characters132256
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

Unique4043 ?
Unique (%)40.6%

Sample

1st row11740-715
2nd row11290-100003188
3rd row11140-100001618
4th row11380-100002966
5th row11000-100005370
ValueCountFrequency (%)
11590-131 28
 
0.3%
11290-100 26
 
0.3%
11305-41 24
 
0.2%
11590-130 22
 
0.2%
11290-99 19
 
0.2%
11410-63 9
 
0.1%
11305-42 8
 
0.1%
11350-75 7
 
0.1%
11410-66 7
 
0.1%
11350-73 7
 
0.1%
Other values (6504) 9794
98.4%
2024-05-11T03:37:37.008394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 42343
32.0%
1 36507
27.6%
- 9951
 
7.5%
2 8332
 
6.3%
4 6629
 
5.0%
3 6030
 
4.6%
5 5848
 
4.4%
6 5255
 
4.0%
8 4529
 
3.4%
7 3609
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 122305
92.5%
Dash Punctuation 9951
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42343
34.6%
1 36507
29.8%
2 8332
 
6.8%
4 6629
 
5.4%
3 6030
 
4.9%
5 5848
 
4.8%
6 5255
 
4.3%
8 4529
 
3.7%
7 3609
 
3.0%
9 3223
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 9951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 132256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42343
32.0%
1 36507
27.6%
- 9951
 
7.5%
2 8332
 
6.3%
4 6629
 
5.0%
3 6030
 
4.6%
5 5848
 
4.4%
6 5255
 
4.0%
8 4529
 
3.4%
7 3609
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42343
32.0%
1 36507
27.6%
- 9951
 
7.5%
2 8332
 
6.3%
4 6629
 
5.0%
3 6030
 
4.6%
5 5848
 
4.4%
6 5255
 
4.0%
8 4529
 
3.4%
7 3609
 
2.7%

관리_호별_명세_PK
Text

MISSING 

Distinct36
Distinct (%)73.5%
Missing9951
Missing (%)99.5%
Memory size156.2 KiB
2024-05-11T03:37:37.793750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters735
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

Unique23 ?
Unique (%)46.9%

Sample

1st row11440-100033074
2nd row11440-100033016
3rd row11440-100033041
4th row11440-100033080
5th row11440-100033062
ValueCountFrequency (%)
11440-100033038 2
 
4.1%
11440-100033050 2
 
4.1%
11440-100033065 2
 
4.1%
11440-100033045 2
 
4.1%
11440-100033057 2
 
4.1%
11440-100033048 2
 
4.1%
11440-100033026 2
 
4.1%
11440-100033064 2
 
4.1%
11440-100033073 2
 
4.1%
11440-100033072 2
 
4.1%
Other values (26) 29
59.2%
2024-05-11T03:37:38.982804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 249
33.9%
1 157
21.4%
4 110
15.0%
3 108
14.7%
- 49
 
6.7%
6 15
 
2.0%
5 12
 
1.6%
7 12
 
1.6%
2 10
 
1.4%
8 9
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 686
93.3%
Dash Punctuation 49
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249
36.3%
1 157
22.9%
4 110
16.0%
3 108
15.7%
6 15
 
2.2%
5 12
 
1.7%
7 12
 
1.7%
2 10
 
1.5%
8 9
 
1.3%
9 4
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249
33.9%
1 157
21.4%
4 110
15.0%
3 108
14.7%
- 49
 
6.7%
6 15
 
2.0%
5 12
 
1.6%
7 12
 
1.6%
2 10
 
1.4%
8 9
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249
33.9%
1 157
21.4%
4 110
15.0%
3 108
14.7%
- 49
 
6.7%
6 15
 
2.0%
5 12
 
1.6%
7 12
 
1.6%
2 10
 
1.4%
8 9
 
1.2%

전유_공용_구분_코드
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
7881 
1
2119 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 7881
78.8%
1 2119
 
21.2%

Length

2024-05-11T03:37:39.510116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:37:39.817483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 7881
78.8%
1 2119
 
21.2%

주_부속_구분_코드
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
7092 
1
2908 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 7092
70.9%
1 2908
29.1%

Length

2024-05-11T03:37:40.279163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:37:40.664532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7092
70.9%
1 2908
29.1%

층_구분_코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)0.1%
Missing106
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean26.011421
Minimum0
Maximum60
Zeros28
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:37:41.087427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q110
median20
Q340
95-th percentile40
Maximum60
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation12.945593
Coefficient of variation (CV)0.49768879
Kurtosis-1.6420857
Mean26.011421
Median Absolute Deviation (MAD)10
Skewness0.023435402
Sum257357
Variance167.58837
MonotonicityNot monotonic
2024-05-11T03:37:41.495765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
40 4267
42.7%
20 2898
29.0%
10 2632
26.3%
0 28
 
0.3%
21 21
 
0.2%
22 18
 
0.2%
60 16
 
0.2%
50 9
 
0.1%
30 5
 
0.1%
(Missing) 106
 
1.1%
ValueCountFrequency (%)
0 28
 
0.3%
10 2632
26.3%
20 2898
29.0%
21 21
 
0.2%
22 18
 
0.2%
30 5
 
0.1%
40 4267
42.7%
50 9
 
0.1%
60 16
 
0.2%
ValueCountFrequency (%)
60 16
 
0.2%
50 9
 
0.1%
40 4267
42.7%
30 5
 
0.1%
22 18
 
0.2%
21 21
 
0.2%
20 2898
29.0%
10 2632
26.3%
0 28
 
0.3%

층_번호
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2244
Minimum0
Maximum1101
Zeros6098
Zeros (%)61.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:37:41.968680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum1101
Range1101
Interquartile range (IQR)1

Descriptive statistics

Standard deviation18.627795
Coefficient of variation (CV)15.213815
Kurtosis2520.9135
Mean1.2244
Median Absolute Deviation (MAD)0
Skewness48.40575
Sum12244
Variance346.99474
MonotonicityNot monotonic
2024-05-11T03:37:42.494622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 6098
61.0%
1 2514
25.1%
2 722
 
7.2%
3 221
 
2.2%
4 127
 
1.3%
5 72
 
0.7%
7 46
 
0.5%
6 43
 
0.4%
8 28
 
0.3%
10 28
 
0.3%
Other values (22) 101
 
1.0%
ValueCountFrequency (%)
0 6098
61.0%
1 2514
25.1%
2 722
 
7.2%
3 221
 
2.2%
4 127
 
1.3%
5 72
 
0.7%
6 43
 
0.4%
7 46
 
0.5%
8 28
 
0.3%
9 23
 
0.2%
ValueCountFrequency (%)
1101 1
< 0.1%
1005 1
< 0.1%
804 1
< 0.1%
602 1
< 0.1%
301 1
< 0.1%
205 1
< 0.1%
204 1
< 0.1%
202 1
< 0.1%
41 1
< 0.1%
27 1
< 0.1%

구조_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22.526653
Minimum11
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:37:42.972013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile21
Q121
median21
Q321
95-th percentile42
Maximum42
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.7001196
Coefficient of variation (CV)0.25303891
Kurtosis7.6180502
Mean22.526653
Median Absolute Deviation (MAD)0
Skewness2.970349
Sum225244
Variance32.491363
MonotonicityNot monotonic
2024-05-11T03:37:43.685681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
21 9134
91.3%
42 764
 
7.6%
11 90
 
0.9%
31 4
 
< 0.1%
41 4
 
< 0.1%
33 1
 
< 0.1%
12 1
 
< 0.1%
19 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
11 90
 
0.9%
12 1
 
< 0.1%
19 1
 
< 0.1%
21 9134
91.3%
31 4
 
< 0.1%
33 1
 
< 0.1%
41 4
 
< 0.1%
42 764
 
7.6%
ValueCountFrequency (%)
42 764
 
7.6%
41 4
 
< 0.1%
33 1
 
< 0.1%
31 4
 
< 0.1%
21 9134
91.3%
19 1
 
< 0.1%
12 1
 
< 0.1%
11 90
 
0.9%

기타_구조
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
철근콘크리트구조
5234 
<NA>
3737 
철골철근콘크리트구조
755 
철근콘크리트구조,철골및복합구조
 
91
철근콘크리트벽식구조
 
58
Other values (20)
 
125

Length

Max length19
Median length8
Mean length6.7614
Min length3

Unique

Unique6 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
철근콘크리트구조 5234
52.3%
<NA> 3737
37.4%
철골철근콘크리트구조 755
 
7.5%
철근콘크리트구조,철골및복합구조 91
 
0.9%
철근콘크리트벽식구조 58
 
0.6%
철근콘크리트 벽식구조 29
 
0.3%
철근콘크리트(벽식구조) 16
 
0.2%
벽식구조 14
 
0.1%
철근콘크리트조 13
 
0.1%
철골철근콘크리트구조,철근콘크리트구조 9
 
0.1%
Other values (15) 44
 
0.4%

Length

2024-05-11T03:37:44.105059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
철근콘크리트구조 5234
52.2%
na 3737
37.3%
철골철근콘크리트구조 755
 
7.5%
철근콘크리트구조,철골및복합구조 91
 
0.9%
철근콘크리트벽식구조 58
 
0.6%
벽식구조 43
 
0.4%
철근콘크리트 34
 
0.3%
철근콘크리트(벽식구조 16
 
0.2%
철근콘크리트조 13
 
0.1%
철골철근콘크리트구조,철근콘크리트구조 9
 
0.1%
Other values (14) 39
 
0.4%

용도_코드
Text

Distinct69
Distinct (%)0.7%
Missing19
Missing (%)0.2%
Memory size156.2 KiB
2024-05-11T03:37:44.682034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters49905
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st row02001
2nd row07199
3rd row07999
4th row02001
5th row02001
ValueCountFrequency (%)
02001 4380
43.9%
02005 1779
17.8%
04999 809
 
8.1%
07999 553
 
5.5%
03999 466
 
4.7%
02006 294
 
2.9%
02003 264
 
2.6%
07199 178
 
1.8%
02002 175
 
1.8%
02004 160
 
1.6%
Other values (59) 923
 
9.2%
2024-05-11T03:37:45.820938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25050
50.2%
2 7749
 
15.5%
9 5945
 
11.9%
1 5389
 
10.8%
5 1870
 
3.7%
4 1600
 
3.2%
3 1052
 
2.1%
7 905
 
1.8%
6 337
 
0.7%
8 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49903
> 99.9%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25050
50.2%
2 7749
 
15.5%
9 5945
 
11.9%
1 5389
 
10.8%
5 1870
 
3.7%
4 1600
 
3.2%
3 1052
 
2.1%
7 905
 
1.8%
6 337
 
0.7%
8 6
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
Z 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49903
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25050
50.2%
2 7749
 
15.5%
9 5945
 
11.9%
1 5389
 
10.8%
5 1870
 
3.7%
4 1600
 
3.2%
3 1052
 
2.1%
7 905
 
1.8%
6 337
 
0.7%
8 6
 
< 0.1%
Latin
ValueCountFrequency (%)
Z 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25050
50.2%
2 7749
 
15.5%
9 5945
 
11.9%
1 5389
 
10.8%
5 1870
 
3.7%
4 1600
 
3.2%
3 1052
 
2.1%
7 905
 
1.8%
6 337
 
0.7%
8 6
 
< 0.1%

기타_용도
Text

MISSING 

Distinct1273
Distinct (%)13.7%
Missing708
Missing (%)7.1%
Memory size156.2 KiB
2024-05-11T03:37:46.399233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length60
Mean length9.3998063
Min length1

Characters and Unicode

Total characters87343
Distinct characters287
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique491 ?
Unique (%)5.3%

Sample

1st row계단실,세대내공용
2nd row판매시설(소매시장)
3rd row판매시설
4th row벽체,계단실
5th row계단실,승강기,홀 등
ValueCountFrequency (%)
지하주차장 526
 
5.5%
아파트 450
 
4.7%
경비실 390
 
4.1%
주차장 382
 
4.0%
벽체 314
 
3.3%
계단실 257
 
2.7%
근린생활시설 234
 
2.5%
계단실,승강기,홀 169
 
1.8%
창고(지4-지3 109
 
1.1%
주민공동시설 109
 
1.1%
Other values (1237) 6551
69.0%
2024-05-11T03:37:47.688815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 8783
 
10.1%
실 7657
 
8.8%
기 3767
 
4.3%
지 3646
 
4.2%
계 2769
 
3.2%
주 2417
 
2.8%
장 2327
 
2.7%
전 2014
 
2.3%
( 1936
 
2.2%
) 1933
 
2.2%
Other values (277) 50094
57.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66422
76.0%
Other Punctuation 9387
 
10.7%
Decimal Number 3845
 
4.4%
Uppercase Letter 2224
 
2.5%
Open Punctuation 1936
 
2.2%
Close Punctuation 1934
 
2.2%
Dash Punctuation 821
 
0.9%
Math Symbol 545
 
0.6%
Space Separator 199
 
0.2%
Lowercase Letter 30
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 7657
 
11.5%
기 3767
 
5.7%
지 3646
 
5.5%
계 2769
 
4.2%
주 2417
 
3.6%
장 2327
 
3.5%
전 2014
 
3.0%
시 1840
 
2.8%
단 1755
 
2.6%
설 1586
 
2.4%
Other values (232) 36644
55.2%
Uppercase Letter
ValueCountFrequency (%)
D 447
20.1%
M 445
20.0%
F 440
19.8%
E 383
17.2%
V 174
 
7.8%
L 144
 
6.5%
P 68
 
3.1%
S 61
 
2.7%
C 15
 
0.7%
O 12
 
0.5%
Other values (8) 35
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 1750
45.5%
2 994
25.9%
4 460
 
12.0%
3 440
 
11.4%
7 89
 
2.3%
5 68
 
1.8%
6 28
 
0.7%
0 13
 
0.3%
8 2
 
0.1%
9 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 11
36.7%
l 6
20.0%
v 6
20.0%
f 3
 
10.0%
d 2
 
6.7%
m 2
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 8783
93.6%
/ 444
 
4.7%
. 156
 
1.7%
: 3
 
< 0.1%
& 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1933
99.9%
] 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1936
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 821
100.0%
Math Symbol
ValueCountFrequency (%)
~ 545
100.0%
Space Separator
ValueCountFrequency (%)
199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66422
76.0%
Common 18667
 
21.4%
Latin 2254
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 7657
 
11.5%
기 3767
 
5.7%
지 3646
 
5.5%
계 2769
 
4.2%
주 2417
 
3.6%
장 2327
 
3.5%
전 2014
 
3.0%
시 1840
 
2.8%
단 1755
 
2.6%
설 1586
 
2.4%
Other values (232) 36644
55.2%
Latin
ValueCountFrequency (%)
D 447
19.8%
M 445
19.7%
F 440
19.5%
E 383
17.0%
V 174
 
7.7%
L 144
 
6.4%
P 68
 
3.0%
S 61
 
2.7%
C 15
 
0.7%
O 12
 
0.5%
Other values (14) 65
 
2.9%
Common
ValueCountFrequency (%)
, 8783
47.1%
( 1936
 
10.4%
) 1933
 
10.4%
1 1750
 
9.4%
2 994
 
5.3%
- 821
 
4.4%
~ 545
 
2.9%
4 460
 
2.5%
/ 444
 
2.4%
3 440
 
2.4%
Other values (11) 561
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66422
76.0%
ASCII 20921
 
24.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 8783
42.0%
( 1936
 
9.3%
) 1933
 
9.2%
1 1750
 
8.4%
2 994
 
4.8%
- 821
 
3.9%
~ 545
 
2.6%
4 460
 
2.2%
D 447
 
2.1%
M 445
 
2.1%
Other values (35) 2807
 
13.4%
Hangul
ValueCountFrequency (%)
실 7657
 
11.5%
기 3767
 
5.7%
지 3646
 
5.5%
계 2769
 
4.2%
주 2417
 
3.6%
장 2327
 
3.5%
전 2014
 
3.0%
시 1840
 
2.8%
단 1755
 
2.6%
설 1586
 
2.4%
Other values (232) 36644
55.2%

면적
Real number (ℝ)

SKEWED 

Distinct6091
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.75339
Minimum0
Maximum84913
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:37:48.109497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q11.7145
median11.9078
Q334.8525
95-th percentile84.99253
Maximum84913
Range84913
Interquartile range (IQR)33.138

Descriptive statistics

Standard deviation856.99606
Coefficient of variation (CV)22.114093
Kurtosis9624.3481
Mean38.75339
Median Absolute Deviation (MAD)11.2378
Skewness97.235767
Sum387533.9
Variance734442.25
MonotonicityNot monotonic
2024-05-11T03:37:48.540813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.19 28
 
0.3%
0.09 28
 
0.3%
0.07 27
 
0.3%
0.04 26
 
0.3%
0.17 23
 
0.2%
0.1 23
 
0.2%
84.98 23
 
0.2%
0.06 23
 
0.2%
0.05 23
 
0.2%
84.99 22
 
0.2%
Other values (6081) 9754
97.5%
ValueCountFrequency (%)
0.0 3
 
< 0.1%
0.01 2
 
< 0.1%
0.0116 1
 
< 0.1%
0.0121 1
 
< 0.1%
0.0127 1
 
< 0.1%
0.0162 1
 
< 0.1%
0.021 1
 
< 0.1%
0.0244 1
 
< 0.1%
0.025 2
 
< 0.1%
0.03 13
0.1%
ValueCountFrequency (%)
84913.0 1
< 0.1%
5147.01 1
< 0.1%
4686.87 1
< 0.1%
3791.72 1
< 0.1%
3156.74 1
< 0.1%
3066.99 1
< 0.1%
2780.16 1
< 0.1%
2443.676 1
< 0.1%
2392.17 1
< 0.1%
2285.12 1
< 0.1%

작업_일자
Real number (ℝ)

Distinct123
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20121371
Minimum20111227
Maximum20150723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:37:48.956174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20111227
5-th percentile20111227
Q120111227
median20120207
Q320121222
95-th percentile20141218
Maximum20150723
Range39496
Interquartile range (IQR)9995

Descriptive statistics

Standard deviation11418.293
Coefficient of variation (CV)0.00056747096
Kurtosis0.20529573
Mean20121371
Median Absolute Deviation (MAD)8980
Skewness1.0985288
Sum2.0121371 × 1011
Variance1.3037743 × 108
MonotonicityNot monotonic
2024-05-11T03:37:49.516092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20111227 3929
39.3%
20120207 2771
27.7%
20131114 291
 
2.9%
20120222 136
 
1.4%
20141115 136
 
1.4%
20140614 112
 
1.1%
20140625 110
 
1.1%
20121009 107
 
1.1%
20140530 100
 
1.0%
20150402 94
 
0.9%
Other values (113) 2214
22.1%
ValueCountFrequency (%)
20111227 3929
39.3%
20120112 1
 
< 0.1%
20120113 14
 
0.1%
20120120 10
 
0.1%
20120203 38
 
0.4%
20120207 2771
27.7%
20120208 41
 
0.4%
20120222 136
 
1.4%
20120229 16
 
0.2%
20120306 1
 
< 0.1%
ValueCountFrequency (%)
20150723 26
 
0.3%
20150721 20
 
0.2%
20150709 10
 
0.1%
20150625 2
 
< 0.1%
20150624 4
 
< 0.1%
20150505 84
0.8%
20150501 9
 
0.1%
20150430 16
 
0.2%
20150429 46
0.5%
20150402 94
0.9%

Interactions

2024-05-11T03:37:28.287066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:20.489708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:22.243802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:24.322791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:26.418519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:28.668154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:20.855145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:22.708027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:24.731079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:26.758317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:29.202459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:21.227281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:23.240417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:25.085385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:27.157419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:29.615657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:21.533355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:23.625876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:25.624084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:27.474405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:30.005894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:21.861139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:23.972685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:26.052503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:37:27.866473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T03:37:49.837148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리_호별_명세_PK전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드기타_구조용도_코드면적작업_일자
관리_호별_명세_PK1.0000.000NaN0.000NaNNaNNaN0.000NaNNaN
전유_공용_구분_코드0.0001.0000.4950.3720.0690.0920.0890.3410.0000.000
주_부속_구분_코드NaN0.4951.0000.0780.0000.3080.3630.7120.0000.131
층_구분_코드0.0000.3720.0781.0000.0000.1470.2020.2380.0000.168
층_번호NaN0.0690.0000.0001.0000.000NaN0.0000.000NaN
구조_코드NaN0.0920.3080.1470.0001.0001.0000.6630.0000.209
기타_구조NaN0.0890.3630.202NaN1.0001.0000.6380.0000.472
용도_코드0.0000.3410.7120.2380.0000.6630.6381.0000.0000.681
면적NaN0.0000.0000.0000.0000.0000.0000.0001.0000.000
작업_일자NaN0.0000.1310.168NaN0.2090.4720.6810.0001.000
2024-05-11T03:37:50.173093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기타_구조전유_공용_구분_코드주_부속_구분_코드
기타_구조1.0000.0710.288
전유_공용_구분_코드0.0711.0000.329
주_부속_구분_코드0.2880.3291.000
2024-05-11T03:37:50.434414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_구분_코드층_번호구조_코드면적작업_일자전유_공용_구분_코드주_부속_구분_코드기타_구조
층_구분_코드1.000-0.6830.1770.0320.1400.3990.0840.099
층_번호-0.6831.000-0.207-0.218-0.1510.0500.0001.000
구조_코드0.177-0.2071.0000.0260.0380.0660.2210.999
면적0.032-0.2180.0261.0000.0140.0000.0000.000
작업_일자0.140-0.1510.0380.0141.0000.0000.1250.238
전유_공용_구분_코드0.3990.0500.0660.0000.0001.0000.3290.071
주_부속_구분_코드0.0840.0000.2210.0000.1250.3291.0000.288
기타_구조0.0991.0000.9990.0000.2380.0710.2881.000

Missing values

2024-05-11T03:37:30.658467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T03:37:31.302127image/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.
2024-05-11T03:37:31.916857image/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

관리_전유_공용_PK관리_형별_개요_PK관리_호별_명세_PK전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드기타_구조용도_코드기타_용도면적작업_일자
1937111740-238111740-715<NA>2040021<NA>02001계단실,세대내공용29.8520111227
2771811290-10000634411290-100003188<NA>1020042철골철근콘크리트구조07199판매시설(소매시장)32.7620120207
3553511140-10000256811140-100001618<NA>1020042철골철근콘크리트구조07999판매시설62.0120120207
3484811380-10000740611380-100002966<NA>2040021철근콘크리트구조02001벽체,계단실17.52120120207
5311611000-10000650311000-100005370<NA>2040021철근콘크리트구조02001계단실,승강기,홀 등19.6320140614
2060711290-42311290-14<NA>2140021<NA>02001지하주차장17.787820111227
431311680-267511680-299<NA>200021<NA><NA>벽체공유6.1120111227
5109711170-10000178511170-100001996<NA>2040021철근콘크리트구조,철골및복합구조07999주차장19.61420140222
3750911380-10000674911380-100002530<NA>2110121철근콘크리트구조02001관리사무소,MDF실,문고0.6620120207
5475411545-10000101611545-100001671<NA>2040021철근콘크리트구조02001벽체,계단실31.01920140815
관리_전유_공용_PK관리_형별_개요_PK관리_호별_명세_PK전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드기타_구조용도_코드기타_용도면적작업_일자
3735611170-10000071211170-100001424<NA>2020121철근콘크리트구조02001경비실,엠디에프실0.3920120207
2975611380-10000440011380-100002142<NA>2040021철근콘크리트구조02001계단실,승강기,홀,벽체,발코니초과28.89820120207
4263411200-10000203611200-100001924<NA>2140021철근콘크리트구조04999계단,화장실25.2920120522
112311380-10000390211380-100001987<NA>1020021철근콘크리트구조02004근린생활시설40.2620111227
2438611140-10000460711140-100001931<NA>2040021철근콘크리트구조02001주차장(지2,지1)3.9520120207
3937911380-10000591411380-100002438<NA>2110221철근콘크리트구조02001전기실,발전기실,펌프실,열교환실,통신실2.5620120207
5298211500-10000483111500-100002906<NA>2110121철근콘크리트구조02004지하주차장73.4120140614
5450511470-10000196011470-100001862<NA>2010121<NA>02005전기및발전기실0.964720140724
4565411590-10000265511590-100002363<NA>1020121철근콘크리트구조04999기타제2종근생활시설25.864420130220
2305311230-214011230-161<NA>2110121<NA>02005관리실,노인정0.5320111227