Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells9660
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory908.2 KiB
Average record size in memory93.0 B

Variable types

Text5
Numeric4
Categorical1

Dataset

Description관리_층별_개요_PK,관리_동별_개요_PK,층_번호,주_용도_코드,기타_용도,구조_코드,기타_구조,층_면적,층_구분_코드,작업_일자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15406/S/1/datasetView.do

Alerts

층_구분_코드 is highly imbalanced (62.4%)Imbalance
기타_용도 has 4643 (46.4%) missing valuesMissing
기타_구조 has 5014 (50.1%) missing valuesMissing
층_면적 is highly skewed (γ1 = 99.84695324)Skewed
관리_층별_개요_PK has unique valuesUnique

Reproduction

Analysis started2024-05-11 08:12:26.359845
Analysis finished2024-05-11 08:12:36.278404
Duration9.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_층별_개요_PK
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T08:12:36.823602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length16.7989
Min length8

Characters and Unicode

Total characters167989
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 row11000-100022887
2nd row11000-100058144
3rd row11290-100036504
4th row11650-1000000000000000702008
5th row11000-100039702
ValueCountFrequency (%)
11000-100022887 1
 
< 0.1%
11000-100036753 1
 
< 0.1%
11710-1000000000000000595305 1
 
< 0.1%
11000-100032321 1
 
< 0.1%
11000-100035789 1
 
< 0.1%
11000-100055240 1
 
< 0.1%
11000-100056037 1
 
< 0.1%
11560-100017540 1
 
< 0.1%
11200-100019741 1
 
< 0.1%
11560-100028630 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-11T08:12:38.241945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 65634
39.1%
1 37994
22.6%
- 10000
 
6.0%
2 8820
 
5.3%
3 8068
 
4.8%
5 7990
 
4.8%
4 6737
 
4.0%
6 6623
 
3.9%
7 5895
 
3.5%
9 5127
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 157989
94.0%
Dash Punctuation 10000
 
6.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65634
41.5%
1 37994
24.0%
2 8820
 
5.6%
3 8068
 
5.1%
5 7990
 
5.1%
4 6737
 
4.3%
6 6623
 
4.2%
7 5895
 
3.7%
9 5127
 
3.2%
8 5101
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 167989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65634
39.1%
1 37994
22.6%
- 10000
 
6.0%
2 8820
 
5.3%
3 8068
 
4.8%
5 7990
 
4.8%
4 6737
 
4.0%
6 6623
 
3.9%
7 5895
 
3.5%
9 5127
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65634
39.1%
1 37994
22.6%
- 10000
 
6.0%
2 8820
 
5.3%
3 8068
 
4.8%
5 7990
 
4.8%
4 6737
 
4.0%
6 6623
 
3.9%
7 5895
 
3.5%
9 5127
 
3.1%

관리_동별_개요_PK
Text

Distinct5241
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T08:12:39.239595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length16.6493
Min length7

Characters and Unicode

Total characters166493
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

Unique2810 ?
Unique (%)28.1%

Sample

1st row11000-100005342
2nd row11000-100011293
3rd row11290-100005588
4th row11650-1000000000000000164943
5th row11000-100007427
ValueCountFrequency (%)
11230-1000000000000000101255 17
 
0.2%
11140-100004821 15
 
0.1%
11305-100005404 11
 
0.1%
11305-100006084 11
 
0.1%
11000-100012361 11
 
0.1%
11000-100012776 10
 
0.1%
11140-1000000000000000066327 10
 
0.1%
11230-100009108 10
 
0.1%
11140-100005541 10
 
0.1%
11560-1000000000000000078706 9
 
0.1%
Other values (5231) 9886
98.9%
2024-05-11T08:12:40.559820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 70537
42.4%
1 38263
23.0%
- 10000
 
6.0%
5 7731
 
4.6%
6 6856
 
4.1%
7 6191
 
3.7%
3 5851
 
3.5%
4 5734
 
3.4%
2 5639
 
3.4%
8 4926
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 156493
94.0%
Dash Punctuation 10000
 
6.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 70537
45.1%
1 38263
24.5%
5 7731
 
4.9%
6 6856
 
4.4%
7 6191
 
4.0%
3 5851
 
3.7%
4 5734
 
3.7%
2 5639
 
3.6%
8 4926
 
3.1%
9 4765
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 166493
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 70537
42.4%
1 38263
23.0%
- 10000
 
6.0%
5 7731
 
4.6%
6 6856
 
4.1%
7 6191
 
3.7%
3 5851
 
3.5%
4 5734
 
3.4%
2 5639
 
3.4%
8 4926
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70537
42.4%
1 38263
23.0%
- 10000
 
6.0%
5 7731
 
4.6%
6 6856
 
4.1%
7 6191
 
3.7%
3 5851
 
3.5%
4 5734
 
3.4%
2 5639
 
3.4%
8 4926
 
3.0%

층_번호
Real number (ℝ)

Distinct60
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9.3144314
Minimum-2
Maximum81
Zeros2
Zeros (%)< 0.1%
Negative3
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-11T08:12:41.166369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1
Q13
median7
Q314
95-th percentile26
Maximum81
Range83
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.2237381
Coefficient of variation (CV)0.88290286
Kurtosis2.3149399
Mean9.3144314
Median Absolute Deviation (MAD)5
Skewness1.3175581
Sum93135
Variance67.629869
MonotonicityNot monotonic
2024-05-11T08:12:41.642749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1534
15.3%
2 935
 
9.3%
3 742
 
7.4%
4 540
 
5.4%
5 526
 
5.3%
6 468
 
4.7%
7 447
 
4.5%
8 435
 
4.3%
9 385
 
3.9%
13 350
 
3.5%
Other values (50) 3637
36.4%
ValueCountFrequency (%)
-2 1
 
< 0.1%
-1 2
 
< 0.1%
0 2
 
< 0.1%
1 1534
15.3%
2 935
9.3%
3 742
7.4%
4 540
 
5.4%
5 526
 
5.3%
6 468
 
4.7%
7 447
 
4.5%
ValueCountFrequency (%)
81 1
 
< 0.1%
63 1
 
< 0.1%
61 1
 
< 0.1%
58 1
 
< 0.1%
55 1
 
< 0.1%
53 1
 
< 0.1%
52 1
 
< 0.1%
50 2
< 0.1%
49 1
 
< 0.1%
48 4
< 0.1%

주_용도_코드
Text

Distinct68
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2024-05-11T08:12:42.054852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters49995
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

Unique23 ?
Unique (%)0.2%

Sample

1st row02001
2nd row02001
3rd row02001
4th row02001
5th row02001
ValueCountFrequency (%)
02001 8256
82.6%
02005 577
 
5.8%
02006 255
 
2.6%
02003 234
 
2.3%
04999 100
 
1.0%
02002 95
 
1.0%
14202 85
 
0.9%
03999 47
 
0.5%
03001 46
 
0.5%
07201 33
 
0.3%
Other values (58) 271
 
2.7%
2024-05-11T08:12:43.002579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29095
58.2%
2 9843
 
19.7%
1 8676
 
17.4%
9 684
 
1.4%
5 611
 
1.2%
3 380
 
0.8%
4 376
 
0.8%
6 257
 
0.5%
7 71
 
0.1%
Z 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49994
> 99.9%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29095
58.2%
2 9843
 
19.7%
1 8676
 
17.4%
9 684
 
1.4%
5 611
 
1.2%
3 380
 
0.8%
4 376
 
0.8%
6 257
 
0.5%
7 71
 
0.1%
8 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
Z 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49994
> 99.9%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29095
58.2%
2 9843
 
19.7%
1 8676
 
17.4%
9 684
 
1.4%
5 611
 
1.2%
3 380
 
0.8%
4 376
 
0.8%
6 257
 
0.5%
7 71
 
0.1%
8 1
 
< 0.1%
Latin
ValueCountFrequency (%)
Z 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29095
58.2%
2 9843
 
19.7%
1 8676
 
17.4%
9 684
 
1.4%
5 611
 
1.2%
3 380
 
0.8%
4 376
 
0.8%
6 257
 
0.5%
7 71
 
0.1%
Z 1
 
< 0.1%

기타_용도
Text

MISSING 

Distinct779
Distinct (%)14.5%
Missing4643
Missing (%)46.4%
Memory size156.2 KiB
2024-05-11T08:12:43.819337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length38
Mean length6.5034534
Min length1

Characters and Unicode

Total characters34839
Distinct characters303
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique536 ?
Unique (%)10.0%

Sample

1st row공동주택(계단실)
2nd row아파트-4세대
3rd row공동주택(아파트)
4th row아파트
5th row주차장
ValueCountFrequency (%)
아파트 1593
28.3%
공동주택(아파트 1438
25.6%
지하주차장 155
 
2.8%
주차장 102
 
1.8%
근린생활시설 78
 
1.4%
계단실 75
 
1.3%
경비실 69
 
1.2%
도시형생활주택 52
 
0.9%
및 51
 
0.9%
복도 42
 
0.7%
Other values (757) 1969
35.0%
2024-05-11T08:12:45.305446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 3317
 
9.5%
아 3294
 
9.5%
파 3287
 
9.4%
주 2249
 
6.5%
( 2012
 
5.8%
) 2012
 
5.8%
택 1813
 
5.2%
공 1709
 
4.9%
동 1631
 
4.7%
실 814
 
2.3%
Other values (293) 12701
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28923
83.0%
Open Punctuation 2014
 
5.8%
Close Punctuation 2014
 
5.8%
Decimal Number 569
 
1.6%
Other Punctuation 488
 
1.4%
Uppercase Letter 331
 
1.0%
Space Separator 268
 
0.8%
Dash Punctuation 182
 
0.5%
Control 42
 
0.1%
Lowercase Letter 3
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 3317
 
11.5%
아 3294
 
11.4%
파 3287
 
11.4%
주 2249
 
7.8%
택 1813
 
6.3%
공 1709
 
5.9%
동 1631
 
5.6%
실 814
 
2.8%
시 577
 
2.0%
대 465
 
1.6%
Other values (249) 9767
33.8%
Uppercase Letter
ValueCountFrequency (%)
E 73
22.1%
D 47
14.2%
F 45
13.6%
M 45
13.6%
V 38
11.5%
L 35
10.6%
A 14
 
4.2%
B 10
 
3.0%
T 4
 
1.2%
I 4
 
1.2%
Other values (7) 16
 
4.8%
Decimal Number
ValueCountFrequency (%)
2 118
20.7%
4 105
18.5%
1 104
18.3%
3 93
16.3%
5 52
9.1%
6 28
 
4.9%
0 23
 
4.0%
8 22
 
3.9%
7 19
 
3.3%
9 5
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 369
75.6%
/ 78
 
16.0%
. 31
 
6.4%
# 10
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
33.3%
d 1
33.3%
f 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 2012
99.9%
[ 2
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 2012
99.9%
] 2
 
0.1%
Space Separator
ValueCountFrequency (%)
268
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182
100.0%
Control
ValueCountFrequency (%)
42
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28923
83.0%
Common 5582
 
16.0%
Latin 334
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 3317
 
11.5%
아 3294
 
11.4%
파 3287
 
11.4%
주 2249
 
7.8%
택 1813
 
6.3%
공 1709
 
5.9%
동 1631
 
5.6%
실 814
 
2.8%
시 577
 
2.0%
대 465
 
1.6%
Other values (249) 9767
33.8%
Common
ValueCountFrequency (%)
( 2012
36.0%
) 2012
36.0%
, 369
 
6.6%
268
 
4.8%
- 182
 
3.3%
2 118
 
2.1%
4 105
 
1.9%
1 104
 
1.9%
3 93
 
1.7%
/ 78
 
1.4%
Other values (14) 241
 
4.3%
Latin
ValueCountFrequency (%)
E 73
21.9%
D 47
14.1%
F 45
13.5%
M 45
13.5%
V 38
11.4%
L 35
10.5%
A 14
 
4.2%
B 10
 
3.0%
T 4
 
1.2%
I 4
 
1.2%
Other values (10) 19
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28923
83.0%
ASCII 5915
 
17.0%
CJK Compat 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 3317
 
11.5%
아 3294
 
11.4%
파 3287
 
11.4%
주 2249
 
7.8%
택 1813
 
6.3%
공 1709
 
5.9%
동 1631
 
5.6%
실 814
 
2.8%
시 577
 
2.0%
대 465
 
1.6%
Other values (249) 9767
33.8%
ASCII
ValueCountFrequency (%)
( 2012
34.0%
) 2012
34.0%
, 369
 
6.2%
268
 
4.5%
- 182
 
3.1%
2 118
 
2.0%
4 105
 
1.8%
1 104
 
1.8%
3 93
 
1.6%
/ 78
 
1.3%
Other values (33) 574
 
9.7%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%

구조_코드
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21.870487
Minimum11
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T08:12:45.823537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile21
Q121
median21
Q321
95-th percentile21
Maximum99
Range88
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.9349974
Coefficient of variation (CV)0.22564643
Kurtosis101.98123
Mean21.870487
Median Absolute Deviation (MAD)0
Skewness8.4175439
Sum218683
Variance24.354199
MonotonicityNot monotonic
2024-05-11T08:12:46.233256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
21 9566
95.7%
42 311
 
3.1%
26 38
 
0.4%
41 21
 
0.2%
99 16
 
0.2%
31 11
 
0.1%
43 8
 
0.1%
20 8
 
0.1%
11 7
 
0.1%
29 4
 
< 0.1%
Other values (4) 9
 
0.1%
ValueCountFrequency (%)
11 7
 
0.1%
12 1
 
< 0.1%
20 8
 
0.1%
21 9566
95.7%
22 2
 
< 0.1%
26 38
 
0.4%
29 4
 
< 0.1%
31 11
 
0.1%
32 4
 
< 0.1%
40 2
 
< 0.1%
ValueCountFrequency (%)
99 16
 
0.2%
43 8
 
0.1%
42 311
3.1%
41 21
 
0.2%
40 2
 
< 0.1%
32 4
 
< 0.1%
31 11
 
0.1%
29 4
 
< 0.1%
26 38
 
0.4%
22 2
 
< 0.1%

기타_구조
Text

MISSING 

Distinct57
Distinct (%)1.1%
Missing5014
Missing (%)50.1%
Memory size156.2 KiB
2024-05-11T08:12:46.667044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length8
Mean length7.433213
Min length2

Characters and Unicode

Total characters37062
Distinct characters64
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.4%

Sample

1st row벽식
2nd row철골철근콘크리트합성구조
3rd row철근콘크리트구조
4th row철근콘크리트구조
5th row철근콘크리트구조
ValueCountFrequency (%)
철근콘크리트구조 3610
72.0%
벽식구조 295
 
5.9%
벽식 207
 
4.1%
철골철근콘크리트구조 115
 
2.3%
철근콘크리트조 102
 
2.0%
무량판구조 94
 
1.9%
라멘구조 87
 
1.7%
철근콘크리트라멘구조 70
 
1.4%
철근콘크리트 56
 
1.1%
라멘조 53
 
1.1%
Other values (49) 328
 
6.5%
2024-05-11T08:12:47.938875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
조 4692
12.7%
구 4472
12.1%
철 4265
11.5%
리 4111
11.1%
콘 4109
11.1%
크 4109
11.1%
트 4109
11.1%
근 4103
11.1%
벽 574
 
1.5%
식 573
 
1.5%
Other values (54) 1945
5.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36807
99.3%
Close Punctuation 78
 
0.2%
Open Punctuation 74
 
0.2%
Math Symbol 44
 
0.1%
Space Separator 31
 
0.1%
Other Punctuation 28
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
조 4692
12.7%
구 4472
12.1%
철 4265
11.6%
리 4111
11.2%
콘 4109
11.2%
크 4109
11.2%
트 4109
11.2%
근 4103
11.1%
벽 574
 
1.6%
식 573
 
1.6%
Other values (48) 1690
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 25
89.3%
. 3
 
10.7%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Open Punctuation
ValueCountFrequency (%)
( 74
100.0%
Math Symbol
ValueCountFrequency (%)
+ 44
100.0%
Space Separator
ValueCountFrequency (%)
31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36807
99.3%
Common 255
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
조 4692
12.7%
구 4472
12.1%
철 4265
11.6%
리 4111
11.2%
콘 4109
11.2%
크 4109
11.2%
트 4109
11.2%
근 4103
11.1%
벽 574
 
1.6%
식 573
 
1.6%
Other values (48) 1690
 
4.6%
Common
ValueCountFrequency (%)
) 78
30.6%
( 74
29.0%
+ 44
17.3%
31
 
12.2%
, 25
 
9.8%
. 3
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36807
99.3%
ASCII 255
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
조 4692
12.7%
구 4472
12.1%
철 4265
11.6%
리 4111
11.2%
콘 4109
11.2%
크 4109
11.2%
트 4109
11.2%
근 4103
11.1%
벽 574
 
1.6%
식 573
 
1.6%
Other values (48) 1690
 
4.6%
ASCII
ValueCountFrequency (%)
) 78
30.6%
( 74
29.0%
+ 44
17.3%
31
 
12.2%
, 25
 
9.8%
. 3
 
1.2%

층_면적
Real number (ℝ)

SKEWED 

Distinct5743
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1286.7861
Minimum-18
Maximum6772508
Zeros43
Zeros (%)0.4%
Negative4
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-11T08:12:48.594714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile34.877
Q1244.81
median408.2031
Q3527.0074
95-th percentile1060.0163
Maximum6772508
Range6772526
Interquartile range (IQR)282.1974

Descriptive statistics

Standard deviation67753.705
Coefficient of variation (CV)52.653434
Kurtosis9979.5117
Mean1286.7861
Median Absolute Deviation (MAD)135.2175
Skewness99.846953
Sum12867861
Variance4.5905646 × 109
MonotonicityNot monotonic
2024-05-11T08:12:49.252464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 43
 
0.4%
437.654 33
 
0.3%
484.213 31
 
0.3%
437.16 31
 
0.3%
542.1444 29
 
0.3%
331.72 28
 
0.3%
542.4347 27
 
0.3%
543.4206 23
 
0.2%
563.53 21
 
0.2%
455.1096 19
 
0.2%
Other values (5733) 9715
97.2%
ValueCountFrequency (%)
-18.0 1
 
< 0.1%
-5.94 2
 
< 0.1%
-4.58 1
 
< 0.1%
0.0 43
0.4%
0.5597 1
 
< 0.1%
0.5916 1
 
< 0.1%
0.68 1
 
< 0.1%
0.83 1
 
< 0.1%
0.8791 1
 
< 0.1%
1.3203 1
 
< 0.1%
ValueCountFrequency (%)
6772508.0 1
< 0.1%
74344.17 2
< 0.1%
60721.55 1
< 0.1%
60539.09 1
< 0.1%
56923.42 1
< 0.1%
48979.16 1
< 0.1%
45459.965 1
< 0.1%
33852.27 1
< 0.1%
31739.38 1
< 0.1%
30159.5 1
< 0.1%

층_구분_코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20
8712 
10
1212 
30
 
76

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 8712
87.1%
10 1212
 
12.1%
30 76
 
0.8%

Length

2024-05-11T08:12:49.782952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:12:50.193666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 8712
87.1%
10 1212
 
12.1%
30 76
 
0.8%

작업_일자
Real number (ℝ)

Distinct167
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20226299
Minimum20201201
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T08:12:50.694431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20201201
5-th percentile20210916
Q120211211
median20230207
Q320240327
95-th percentile20240510
Maximum20240510
Range39309
Interquartile range (IQR)29116

Descriptive statistics

Standard deviation12182.34
Coefficient of variation (CV)0.00060230197
Kurtosis-1.4220927
Mean20226299
Median Absolute Deviation (MAD)10213
Skewness-0.14717574
Sum2.0226299 × 1011
Variance1.484094 × 108
MonotonicityNot monotonic
2024-05-11T08:12:51.261250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240510 2202
22.0%
20211029 1590
 
15.9%
20240208 479
 
4.8%
20240327 214
 
2.1%
20231028 171
 
1.7%
20211123 167
 
1.7%
20230704 157
 
1.6%
20210106 145
 
1.5%
20230616 145
 
1.5%
20230818 135
 
1.4%
Other values (157) 4595
46.0%
ValueCountFrequency (%)
20201201 35
 
0.4%
20201216 133
1.3%
20201230 7
 
0.1%
20210106 145
1.5%
20210119 56
 
0.6%
20210126 10
 
0.1%
20210130 22
 
0.2%
20210309 1
 
< 0.1%
20210515 9
 
0.1%
20210701 1
 
< 0.1%
ValueCountFrequency (%)
20240510 2202
22.0%
20240507 7
 
0.1%
20240425 12
 
0.1%
20240420 111
 
1.1%
20240417 61
 
0.6%
20240416 4
 
< 0.1%
20240411 34
 
0.3%
20240406 6
 
0.1%
20240402 33
 
0.3%
20240330 11
 
0.1%

Interactions

2024-05-11T08:12:33.036323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:28.858227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:30.228826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:31.570412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:33.567639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:29.114702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:30.559790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:31.906552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:33.920768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:29.433861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:30.918773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:32.233905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:34.357703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:29.769160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:31.277010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:32.584285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T08:12:51.586675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_번호주_용도_코드구조_코드기타_구조층_면적층_구분_코드작업_일자
층_번호1.0000.3640.1040.2000.0000.5310.117
주_용도_코드0.3641.0000.6120.8560.0000.6920.236
구조_코드0.1040.6121.0000.9870.0000.1810.136
기타_구조0.2000.8560.9871.0000.0000.5760.491
층_면적0.0000.0000.0000.0001.0000.0140.035
층_구분_코드0.5310.6920.1810.5760.0141.0000.079
작업_일자0.1170.2360.1360.4910.0350.0791.000
2024-05-11T08:12:51.911511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_번호구조_코드층_면적작업_일자층_구분_코드
층_번호1.000-0.0540.235-0.0490.275
구조_코드-0.0541.0000.044-0.0270.138
층_면적0.2350.0441.0000.0200.023
작업_일자-0.049-0.0270.0201.0000.035
층_구분_코드0.2750.1380.0230.0351.000

Missing values

2024-05-11T08:12:34.964254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T08:12:35.610492image/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.
2024-05-11T08:12:36.009620image/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_층별_개요_PK관리_동별_개요_PK층_번호주_용도_코드기타_용도구조_코드기타_구조층_면적층_구분_코드작업_일자
1286411000-10002288711000-100005342202001<NA>21<NA>252.32020240510
8134511000-10005814411000-100011293102001<NA>21<NA>61.222020240510
6047411290-10003650411290-1000055881702001<NA>21<NA>245.862020211029
6386011650-100000000000000070200811650-1000000000000000164943602001<NA>21<NA>368.882020240420
370711000-10003970211000-1000074271402001<NA>21벽식220.6062020240510
4618511680-10004105111680-100009589102001공동주택(계단실)43철골철근콘크리트합성구조33.431020211029
8625811710-10009958011710-1000193043102001<NA>21<NA>480.4772020230818
1151911215-10001460911215-100005364602001아파트-4세대21철근콘크리트구조424.73312020220111
5644411590-10003107411590-1000077591202001공동주택(아파트)21철근콘크리트구조323.1632020211029
4567211560-10002329611560-100009850302001아파트21철근콘크리트구조449.542020211029
관리_층별_개요_PK관리_동별_개요_PK층_번호주_용도_코드기타_용도구조_코드기타_구조층_면적층_구분_코드작업_일자
3539511650-100000000000000043170711650-10000000000000001137133202001공동주택(아파트)21철근콘크리트구조452.462020240417
1230511000-10004207311000-100004973202001아파트21철근콘크리트구조194.042020240510
2533111545-10001806111545-100005935102005MDF21철근콘크리트구조42.45732020221012
706611710-10008839111710-100014546902001<NA>21<NA>498.712020211123
2883711000-10002807711000-100006130202001<NA>21라멘구조0.01020240510
5278211680-10002241311680-1000056961002001<NA>21<NA>483.75392020230509
2479611000-10005675711000-100010936202001아파트(계단실,복도)21<NA>29.571020230623
1381111560-10002822411560-100011187302001<NA>21<NA>405.62172020220226
3659911380-10003484411380-100008797402001<NA>21<NA>382.25742020211029
3517711410-100000000000000042660411410-1000000000000000112918102004구매및생활시설,제2종근린생활시설21철근콘크리트조893.32020230831