Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells10243
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory120.0 B

Variable types

Text4
Categorical3
Numeric6

Dataset

Description관리_전유_공용_pk,관리_형별_개요_pk,관리_호별_명세_pk,전유_공용_구분_코드,주_부속_구분_코드,층_구분_코드,층_번호,구조_코드,기타_구조,용도_코드,기타_용도,면적,작업_일자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15673/S/1/datasetView.do

Alerts

층_구분_코드 is highly overall correlated with 층_번호 and 1 other fieldsHigh correlation
층_번호 is highly overall correlated with 층_구분_코드High correlation
구조_코드 is highly overall correlated with 기타_구조High correlation
전유_공용_구분_코드 is highly overall correlated with 층_구분_코드High correlation
기타_구조 is highly overall correlated with 구조_코드High correlation
전유_공용_구분_코드 is highly imbalanced (53.7%)Imbalance
기타_구조 is highly imbalanced (74.0%)Imbalance
관리_호별_명세_pk has 9968 (99.7%) missing valuesMissing
기타_용도 has 226 (2.3%) missing valuesMissing
면적 is highly skewed (γ1 = 62.43615799)Skewed
관리_전유_공용_pk has unique valuesUnique
층_번호 has 7110 (71.1%) zerosZeros

Reproduction

Analysis started2024-05-11 03:38:05.311354
Analysis finished2024-05-11 03:38:19.512190
Duration14.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T03:38:19.854597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length19.7717
Min length9

Characters and Unicode

Total characters197717
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 row11710-100010604
2nd row11260-1000000000000000142658
3rd row11380-1000000000000000102908
4th row11410-100006970
5th row11440-100006644
ValueCountFrequency (%)
11710-100010604 1
 
< 0.1%
11590-1000000000000000189925 1
 
< 0.1%
11000-100002209 1
 
< 0.1%
11590-1000000000000000189997 1
 
< 0.1%
11410-100007759 1
 
< 0.1%
11680-100008061 1
 
< 0.1%
11440-100006858 1
 
< 0.1%
11740-100006408 1
 
< 0.1%
11260-1000000000000000064518 1
 
< 0.1%
11545-100002289 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-11T03:38:20.790955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 98798
50.0%
1 39294
 
19.9%
- 10000
 
5.1%
5 7772
 
3.9%
6 7636
 
3.9%
4 6670
 
3.4%
2 5818
 
2.9%
7 5730
 
2.9%
3 5724
 
2.9%
8 5550
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 187717
94.9%
Dash Punctuation 10000
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98798
52.6%
1 39294
 
20.9%
5 7772
 
4.1%
6 7636
 
4.1%
4 6670
 
3.6%
2 5818
 
3.1%
7 5730
 
3.1%
3 5724
 
3.0%
8 5550
 
3.0%
9 4725
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 197717
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98798
50.0%
1 39294
 
19.9%
- 10000
 
5.1%
5 7772
 
3.9%
6 7636
 
3.9%
4 6670
 
3.4%
2 5818
 
2.9%
7 5730
 
2.9%
3 5724
 
2.9%
8 5550
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98798
50.0%
1 39294
 
19.9%
- 10000
 
5.1%
5 7772
 
3.9%
6 7636
 
3.9%
4 6670
 
3.4%
2 5818
 
2.9%
7 5730
 
2.9%
3 5724
 
2.9%
8 5550
 
2.8%
Distinct6706
Distinct (%)67.3%
Missing32
Missing (%)0.3%
Memory size156.2 KiB
2024-05-11T03:38:21.287595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length19.010734
Min length9

Characters and Unicode

Total characters189499
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

Unique4283 ?
Unique (%)43.0%

Sample

1st row11710-100004977
2nd row11260-1000000000000000082107
3rd row11380-100003861
4th row11410-100004347
5th row11440-100004121
ValueCountFrequency (%)
11590-100002581 10
 
0.1%
11590-100003867 8
 
0.1%
11590-100002588 8
 
0.1%
11590-100003866 7
 
0.1%
11000-100008534 6
 
0.1%
11500-100005922 6
 
0.1%
11680-100003372 6
 
0.1%
11590-100002591 6
 
0.1%
11000-100008275 5
 
0.1%
11230-1000000000000000034910 5
 
0.1%
Other values (6696) 9901
99.3%
2024-05-11T03:38:22.203341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 93246
49.2%
1 35802
 
18.9%
- 9968
 
5.3%
5 8390
 
4.4%
4 7575
 
4.0%
3 7355
 
3.9%
6 6880
 
3.6%
2 6815
 
3.6%
8 5347
 
2.8%
9 4074
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 179531
94.7%
Dash Punctuation 9968
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93246
51.9%
1 35802
 
19.9%
5 8390
 
4.7%
4 7575
 
4.2%
3 7355
 
4.1%
6 6880
 
3.8%
2 6815
 
3.8%
8 5347
 
3.0%
9 4074
 
2.3%
7 4047
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 9968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189499
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93246
49.2%
1 35802
 
18.9%
- 9968
 
5.3%
5 8390
 
4.4%
4 7575
 
4.0%
3 7355
 
3.9%
6 6880
 
3.6%
2 6815
 
3.6%
8 5347
 
2.8%
9 4074
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189499
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93246
49.2%
1 35802
 
18.9%
- 9968
 
5.3%
5 8390
 
4.4%
4 7575
 
4.0%
3 7355
 
3.9%
6 6880
 
3.6%
2 6815
 
3.6%
8 5347
 
2.8%
9 4074
 
2.1%
Distinct30
Distinct (%)93.8%
Missing9968
Missing (%)99.7%
Memory size156.2 KiB
2024-05-11T03:38:22.812097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters480
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

Unique28 ?
Unique (%)87.5%

Sample

1st row11500-100054551
2nd row11500-100054490
3rd row11500-100054561
4th row11500-100054504
5th row11500-100054514
ValueCountFrequency (%)
11500-100054496 2
 
6.2%
11500-100054523 2
 
6.2%
11500-100054491 1
 
3.1%
11500-100054541 1
 
3.1%
11500-100054513 1
 
3.1%
11500-100054530 1
 
3.1%
11500-100054554 1
 
3.1%
11500-100054555 1
 
3.1%
11500-100054522 1
 
3.1%
11500-100054512 1
 
3.1%
Other values (20) 20
62.5%
2024-05-11T03:38:23.954278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 166
34.6%
1 105
21.9%
5 96
20.0%
4 46
 
9.6%
- 32
 
6.7%
2 11
 
2.3%
3 8
 
1.7%
9 7
 
1.5%
6 4
 
0.8%
8 3
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 448
93.3%
Dash Punctuation 32
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
37.1%
1 105
23.4%
5 96
21.4%
4 46
 
10.3%
2 11
 
2.5%
3 8
 
1.8%
9 7
 
1.6%
6 4
 
0.9%
8 3
 
0.7%
7 2
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
34.6%
1 105
21.9%
5 96
20.0%
4 46
 
9.6%
- 32
 
6.7%
2 11
 
2.3%
3 8
 
1.7%
9 7
 
1.5%
6 4
 
0.8%
8 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
34.6%
1 105
21.9%
5 96
20.0%
4 46
 
9.6%
- 32
 
6.7%
2 11
 
2.3%
3 8
 
1.7%
9 7
 
1.5%
6 4
 
0.8%
8 3
 
0.6%

전유_공용_구분_코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
7960 
1
2037 
<NA>
 
3

Length

Max length4
Median length1
Mean length1.0009
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 7960
79.6%
1 2037
 
20.4%
<NA> 3
 
< 0.1%

Length

2024-05-11T03:38:24.527542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:38:24.844571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 7960
79.6%
1 2037
 
20.4%
na 3
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6769 
1
3228 
<NA>
 
3

Length

Max length4
Median length1
Mean length1.0009
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 6769
67.7%
1 3228
32.3%
<NA> 3
 
< 0.1%

Length

2024-05-11T03:38:25.171946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:38:25.526598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6769
67.7%
1 3228
32.3%
na 3
 
< 0.1%

층_구분_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing11
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.309941
Minimum10
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:38:25.827657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q120
median20
Q340
95-th percentile40
Maximum40
Range30
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.450416
Coefficient of variation (CV)0.45589319
Kurtosis-1.6796124
Mean27.309941
Median Absolute Deviation (MAD)10
Skewness-0.12655079
Sum272799
Variance155.01286
MonotonicityNot monotonic
2024-05-11T03:38:26.192577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40 4686
46.9%
20 3215
32.1%
10 2074
20.7%
22 7
 
0.1%
21 5
 
0.1%
30 2
 
< 0.1%
(Missing) 11
 
0.1%
ValueCountFrequency (%)
10 2074
20.7%
20 3215
32.1%
21 5
 
0.1%
22 7
 
0.1%
30 2
 
< 0.1%
40 4686
46.9%
ValueCountFrequency (%)
40 4686
46.9%
30 2
 
< 0.1%
22 7
 
0.1%
21 5
 
0.1%
20 3215
32.1%
10 2074
20.7%

층_번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5406
Minimum0
Maximum29
Zeros7110
Zeros (%)71.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:38:26.630624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum29
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.425256
Coefficient of variation (CV)2.6364337
Kurtosis132.75806
Mean0.5406
Median Absolute Deviation (MAD)0
Skewness8.6702864
Sum5406
Variance2.0313548
MonotonicityNot monotonic
2024-05-11T03:38:27.117999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 7110
71.1%
1 1883
 
18.8%
2 514
 
5.1%
3 176
 
1.8%
5 117
 
1.2%
4 78
 
0.8%
6 64
 
0.6%
7 18
 
0.2%
9 11
 
0.1%
8 9
 
0.1%
Other values (5) 20
 
0.2%
ValueCountFrequency (%)
0 7110
71.1%
1 1883
 
18.8%
2 514
 
5.1%
3 176
 
1.8%
4 78
 
0.8%
5 117
 
1.2%
6 64
 
0.6%
7 18
 
0.2%
8 9
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
29 3
 
< 0.1%
26 7
 
0.1%
25 1
 
< 0.1%
11 5
 
0.1%
10 4
 
< 0.1%
9 11
 
0.1%
8 9
 
0.1%
7 18
 
0.2%
6 64
0.6%
5 117
1.2%

구조_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22.386216
Minimum21
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:38:27.565297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q121
median21
Q321
95-th percentile42
Maximum99
Range78
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.3763691
Coefficient of variation (CV)0.24016426
Kurtosis24.90416
Mean22.386216
Median Absolute Deviation (MAD)0
Skewness4.2644394
Sum223795
Variance28.905344
MonotonicityNot monotonic
2024-05-11T03:38:27.971339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21 9345
93.5%
42 621
 
6.2%
43 11
 
0.1%
40 7
 
0.1%
31 5
 
0.1%
41 4
 
< 0.1%
99 4
 
< 0.1%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
21 9345
93.5%
31 5
 
0.1%
40 7
 
0.1%
41 4
 
< 0.1%
42 621
 
6.2%
43 11
 
0.1%
99 4
 
< 0.1%
ValueCountFrequency (%)
99 4
 
< 0.1%
43 11
 
0.1%
42 621
 
6.2%
41 4
 
< 0.1%
40 7
 
0.1%
31 5
 
0.1%
21 9345
93.5%

기타_구조
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
철근콘크리트구조
7739 
<NA>
1506 
철골철근콘크리트구조
 
627
무량판구조
 
49
철근콘크리트조
 
27
Other values (12)
 
52

Length

Max length21
Median length8
Mean length7.5372
Min length3

Unique

Unique8 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
철근콘크리트구조 7739
77.4%
<NA> 1506
 
15.1%
철골철근콘크리트구조 627
 
6.3%
무량판구조 49
 
0.5%
철근콘크리트조 27
 
0.3%
철근콘크리트구조,철골철근콘크리트구조 25
 
0.2%
철골철근콘크리트합성구조 10
 
0.1%
(벽식구조) 6
 
0.1%
라멘구조 3
 
< 0.1%
철근콘크리트벽식구조 1
 
< 0.1%
Other values (7) 7
 
0.1%

Length

2024-05-11T03:38:28.342406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
철근콘크리트구조 7739
77.4%
na 1506
 
15.1%
철골철근콘크리트구조 627
 
6.3%
무량판구조 49
 
0.5%
철근콘크리트조 27
 
0.3%
철근콘크리트구조,철골철근콘크리트구조 25
 
0.2%
철골철근콘크리트합성구조 10
 
0.1%
벽식구조 6
 
0.1%
라멘구조 3
 
< 0.1%
철근콘크리트벽식구조 1
 
< 0.1%
Other values (7) 7
 
0.1%

용도_코드
Real number (ℝ)

Distinct58
Distinct (%)0.6%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3900.5868
Minimum1003
Maximum20001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:38:28.798159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile2001
Q12001
median2005
Q34999
95-th percentile14202
Maximum20001
Range18998
Interquartile range (IQR)2998

Descriptive statistics

Standard deviation3161.2254
Coefficient of variation (CV)0.81044867
Kurtosis4.3443515
Mean3900.5868
Median Absolute Deviation (MAD)4
Skewness2.180108
Sum38994166
Variance9993345.9
MonotonicityNot monotonic
2024-05-11T03:38:29.271876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2001 3544
35.4%
4999 1772
17.7%
2005 936
 
9.4%
2003 586
 
5.9%
7201 426
 
4.3%
14204 386
 
3.9%
3001 376
 
3.8%
2006 344
 
3.4%
3999 322
 
3.2%
7999 307
 
3.1%
Other values (48) 998
 
10.0%
ValueCountFrequency (%)
1003 14
 
0.1%
2001 3544
35.4%
2002 234
 
2.3%
2003 586
 
5.9%
2004 17
 
0.2%
2005 936
 
9.4%
2006 344
 
3.4%
3001 376
 
3.8%
3002 22
 
0.2%
3005 71
 
0.7%
ValueCountFrequency (%)
20001 2
 
< 0.1%
15104 2
 
< 0.1%
14204 386
3.9%
14202 189
1.9%
14199 7
 
0.1%
14102 3
 
< 0.1%
14101 1
 
< 0.1%
13999 21
 
0.2%
13104 5
 
0.1%
13011 7
 
0.1%

기타_용도
Text

MISSING 

Distinct983
Distinct (%)10.1%
Missing226
Missing (%)2.3%
Memory size156.2 KiB
2024-05-11T03:38:29.917341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length84
Median length72
Mean length11.049928
Min length2

Characters and Unicode

Total characters108002
Distinct characters303
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

Unique257 ?
Unique (%)2.6%

Sample

1st row계단실,화장실,홀,경비실,방재실
2nd row주차장
3rd row공동주택(아파트)
4th row주민공동시설,외부ELEV
5th row지하주차장
ValueCountFrequency (%)
지하주차장 802
 
7.7%
주차장 599
 
5.7%
벽체 546
 
5.2%
근린생활시설 543
 
5.2%
계단실 452
 
4.3%
296
 
2.8%
공동주택(아파트 283
 
2.7%
아파트 227
 
2.2%
기계실,전기실 217
 
2.1%
경비실 161
 
1.5%
Other values (945) 6293
60.4%
2024-05-11T03:38:31.179064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 12548
 
11.6%
9764
 
9.0%
4551
 
4.2%
3228
 
3.0%
3084
 
2.9%
2569
 
2.4%
2421
 
2.2%
2328
 
2.2%
2029
 
1.9%
1936
 
1.8%
Other values (293) 63544
58.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87421
80.9%
Other Punctuation 12828
 
11.9%
Uppercase Letter 3050
 
2.8%
Close Punctuation 1498
 
1.4%
Open Punctuation 1497
 
1.4%
Decimal Number 857
 
0.8%
Space Separator 645
 
0.6%
Dash Punctuation 128
 
0.1%
Math Symbol 76
 
0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9764
 
11.2%
4551
 
5.2%
3228
 
3.7%
3084
 
3.5%
2569
 
2.9%
2421
 
2.8%
2328
 
2.7%
2029
 
2.3%
1936
 
2.2%
1856
 
2.1%
Other values (253) 53655
61.4%
Uppercase Letter
ValueCountFrequency (%)
M 759
24.9%
F 758
24.9%
D 758
24.9%
E 318
10.4%
V 165
 
5.4%
L 156
 
5.1%
X 42
 
1.4%
G 41
 
1.3%
B 11
 
0.4%
T 8
 
0.3%
Other values (8) 34
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 359
41.9%
2 246
28.7%
3 116
 
13.5%
5 38
 
4.4%
6 37
 
4.3%
8 30
 
3.5%
4 30
 
3.5%
9 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 12548
97.8%
/ 186
 
1.4%
. 79
 
0.6%
& 13
 
0.1%
' 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1483
99.0%
] 15
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 1482
99.0%
[ 15
 
1.0%
Math Symbol
ValueCountFrequency (%)
~ 73
96.1%
+ 3
 
3.9%
Space Separator
ValueCountFrequency (%)
645
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 128
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87421
80.9%
Common 17531
 
16.2%
Latin 3050
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9764
 
11.2%
4551
 
5.2%
3228
 
3.7%
3084
 
3.5%
2569
 
2.9%
2421
 
2.8%
2328
 
2.7%
2029
 
2.3%
1936
 
2.2%
1856
 
2.1%
Other values (253) 53655
61.4%
Common
ValueCountFrequency (%)
, 12548
71.6%
) 1483
 
8.5%
( 1482
 
8.5%
645
 
3.7%
1 359
 
2.0%
2 246
 
1.4%
/ 186
 
1.1%
- 128
 
0.7%
3 116
 
0.7%
. 79
 
0.5%
Other values (12) 259
 
1.5%
Latin
ValueCountFrequency (%)
M 759
24.9%
F 758
24.9%
D 758
24.9%
E 318
10.4%
V 165
 
5.4%
L 156
 
5.1%
X 42
 
1.4%
G 41
 
1.3%
B 11
 
0.4%
T 8
 
0.3%
Other values (8) 34
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87421
80.9%
ASCII 20581
 
19.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 12548
61.0%
) 1483
 
7.2%
( 1482
 
7.2%
M 759
 
3.7%
F 758
 
3.7%
D 758
 
3.7%
645
 
3.1%
1 359
 
1.7%
E 318
 
1.5%
2 246
 
1.2%
Other values (30) 1225
 
6.0%
Hangul
ValueCountFrequency (%)
9764
 
11.2%
4551
 
5.2%
3228
 
3.7%
3084
 
3.5%
2569
 
2.9%
2421
 
2.8%
2328
 
2.7%
2029
 
2.3%
1936
 
2.2%
1856
 
2.1%
Other values (253) 53655
61.4%

면적
Real number (ℝ)

SKEWED 

Distinct7187
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.086541
Minimum0
Maximum18531.37
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:38:31.690968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q12.36315
median10.265
Q332.37625
95-th percentile84.821
Maximum18531.37
Range18531.37
Interquartile range (IQR)30.0131

Descriptive statistics

Standard deviation223.27341
Coefficient of variation (CV)7.4210396
Kurtosis4837.0018
Mean30.086541
Median Absolute Deviation (MAD)9.3666
Skewness62.436158
Sum300865.41
Variance49851.017
MonotonicityNot monotonic
2024-05-11T03:38:32.447189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 24
 
0.2%
0.07 23
 
0.2%
0.04 20
 
0.2%
0.05 20
 
0.2%
0.54 17
 
0.2%
0.37 16
 
0.2%
0.77 16
 
0.2%
0.4 14
 
0.1%
0.02 13
 
0.1%
0.78 13
 
0.1%
Other values (7177) 9824
98.2%
ValueCountFrequency (%)
0.0 4
 
< 0.1%
0.0168 1
 
< 0.1%
0.0185 1
 
< 0.1%
0.02 13
0.1%
0.0203 1
 
< 0.1%
0.0211 2
 
< 0.1%
0.0223 2
 
< 0.1%
0.0237 2
 
< 0.1%
0.024 1
 
< 0.1%
0.0246 1
 
< 0.1%
ValueCountFrequency (%)
18531.37 1
< 0.1%
5862.676 1
< 0.1%
5551.77 1
< 0.1%
4898.77 1
< 0.1%
3628.059 1
< 0.1%
3389.464 1
< 0.1%
2551.6552 1
< 0.1%
1876.737 1
< 0.1%
1815.6 1
< 0.1%
1671.7518 1
< 0.1%

작업_일자
Real number (ℝ)

Distinct146
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20227355
Minimum20201201
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T03:38:33.116380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20201201
5-th percentile20210507
Q120220304
median20230509
Q320240102
95-th percentile20240510
Maximum20240510
Range39309
Interquartile range (IQR)19798

Descriptive statistics

Standard deviation11413.458
Coefficient of variation (CV)0.00056425858
Kurtosis-1.147869
Mean20227355
Median Absolute Deviation (MAD)9699
Skewness-0.37866414
Sum2.0227355 × 1011
Variance1.3026703 × 108
MonotonicityNot monotonic
2024-05-11T03:38:33.820942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240510 1394
 
13.9%
20211029 997
 
10.0%
20240102 644
 
6.4%
20240208 551
 
5.5%
20230704 405
 
4.0%
20230831 239
 
2.4%
20230727 220
 
2.2%
20230310 184
 
1.8%
20210106 171
 
1.7%
20240417 170
 
1.7%
Other values (136) 5025
50.2%
ValueCountFrequency (%)
20201201 62
 
0.6%
20201208 43
 
0.4%
20201216 32
 
0.3%
20210106 171
1.7%
20210115 9
 
0.1%
20210119 65
 
0.7%
20210126 13
 
0.1%
20210203 7
 
0.1%
20210224 7
 
0.1%
20210415 8
 
0.1%
ValueCountFrequency (%)
20240510 1394
13.9%
20240507 57
 
0.6%
20240425 5
 
0.1%
20240420 44
 
0.4%
20240417 170
 
1.7%
20240416 2
 
< 0.1%
20240402 29
 
0.3%
20240327 11
 
0.1%
20240309 44
 
0.4%
20240302 9
 
0.1%

Interactions

2024-05-11T03:38:16.654830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:08.237473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:10.250023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:12.053679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:14.020452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:15.346122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:16.858016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:08.571243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:10.532941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:12.369443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:14.224464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:15.619281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:17.069584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:08.924865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:10.816622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:12.654219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:14.412437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:15.813524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:17.376940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:09.220637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:11.136162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:12.965153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:14.612329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:16.014102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:17.671070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:09.601059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:11.429793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:13.178791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:14.820602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:16.256735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:17.980139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:09.932593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:11.743798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:13.635350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:15.043785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:38:16.443515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T03:38:34.281537image/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_pk1.0000.000NaNNaN1.000NaNNaNNaNNaNNaN
전유_공용_구분_코드0.0001.0000.5200.4430.0970.0570.0320.1910.0300.056
주_부속_구분_코드NaN0.5201.0000.2150.0630.0990.1600.3220.0000.302
층_구분_코드NaN0.4430.2151.0000.2120.2350.6040.2290.0000.062
층_번호1.0000.0970.0630.2121.0000.1750.1080.1330.0190.163
구조_코드NaN0.0570.0990.2350.1751.0001.0000.2560.0240.181
기타_구조NaN0.0320.1600.6040.1081.0001.0000.5950.4560.273
용도_코드NaN0.1910.3220.2290.1330.2560.5951.0000.2860.292
면적NaN0.0300.0000.0000.0190.0240.4560.2861.0000.000
작업_일자NaN0.0560.3020.0620.1630.1810.2730.2920.0001.000
2024-05-11T03:38:34.725016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기타_구조전유_공용_구분_코드주_부속_구분_코드
기타_구조1.0000.0250.126
전유_공용_구분_코드0.0251.0000.348
주_부속_구분_코드0.1260.3481.000
2024-05-11T03:38:34.983164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_구분_코드층_번호구조_코드용도_코드면적작업_일자전유_공용_구분_코드주_부속_구분_코드기타_구조
층_구분_코드1.000-0.6990.089-0.004-0.0090.0390.5380.2630.414
층_번호-0.6991.000-0.0720.007-0.234-0.0410.0700.0450.052
구조_코드0.089-0.0721.0000.1350.053-0.0030.0380.0650.999
용도_코드-0.0040.0070.1351.0000.163-0.1570.1430.2410.319
면적-0.009-0.2340.0530.1631.000-0.0360.0370.0000.253
작업_일자0.039-0.041-0.003-0.157-0.0361.0000.0400.2130.135
전유_공용_구분_코드0.5380.0700.0380.1430.0370.0401.0000.3480.025
주_부속_구분_코드0.2630.0450.0650.2410.0000.2130.3481.0000.126
기타_구조0.4140.0520.9990.3190.2530.1350.0250.1261.000

Missing values

2024-05-11T03:38:18.402089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T03:38:18.996741image/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.
2024-05-11T03:38:19.313832image/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
5761711710-10001060411710-100004977<NA>2010121철근콘크리트구조4999계단실,화장실,홀,경비실,방재실5.6420230104
1845811260-100000000000000014265811260-1000000000000000082107<NA>2140021철근콘크리트구조7201주차장93.092820240102
2409611380-100000000000000010290811380-100003861<NA>1020021철근콘크리트구조2001공동주택(아파트)49.5320230929
2806511410-10000697011410-100004347<NA>2140021철근콘크리트구조2005주민공동시설,외부ELEV3.5120240510
3028911440-10000664411440-100004121<NA>2140021철근콘크리트구조4999지하주차장11.9720211029
4066611545-10000585311545-100003053<NA>2140021철근콘크리트구조2006경로당,어린이집,아이키움0.903520210515
4641811590-10000737111590-100004229<NA>1020021철근콘크리트구조7999판매시설63.52320211103
370311000-10000473411000-100002918<NA>2140021철근콘크리트구조2001옥외승강기0.0220240510
3735911530-100000000000000008623111530-1000000000000000053536<NA>2040021철근콘크리트구조2001계단실,승강기,홀16.530320230602
4121411560-100000000000000003530511560-1000000000000000018214<NA>2040021철근콘크리트구조2001계단실13.1720221115
관리_전유_공용_pk관리_형별_개요_pk관리_호별_명세_pk전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드기타_구조용도_코드기타_용도면적작업_일자
4542311590-10000630011590-100003865<NA>2040021철근콘크리트구조2001계단실17.972520210421
3736011530-100000000000000008623211530-1000000000000000053536<NA>2120221철근콘크리트구조2001어린이집0.550820230602
2075511290-10001036011290-100004716<NA>2040021철근콘크리트구조2001계단실,승강기,홀,복도24.1720220929
4955011650-10000625211650-100002830<NA>2020021철근콘크리트구조2001벽체9.154120211029
4610311590-10000702411590-100004086<NA>2040021철근콘크리트구조2001주차장11.857220211013
1061511140-10000762911140-100003291<NA>2040042철골철근콘크리트구조4001주차장21.531220240102
1327511215-10000490911215-100003113<NA>2140021철근콘크리트구조4999기계실,전기실,발전기실,정화조관리실,우수조관리실,방재실,재활용보관창고,쓰레기처리장5.11920220104
4033911545-10000551811545-100002971<NA>2140021철근콘크리트구조14204관리사무소,경비실0.937320210507
2205811320-100000000000000003002411320-1000000000000000013707<NA>2020221<NA>1003<NA>21.0620221019
3601411500-10001186111500-100005884<NA>2040021철근콘크리트구조2001계단실15.178620230831