Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells7371
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory908.2 KiB
Average record size in memory93.0 B

Variable types

Text4
Numeric3
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15406/S/1/datasetView.do

Alerts

작업_일자 has constant value ""Constant
층_번호 is highly overall correlated with 층_구분_코드High correlation
구조_코드 is highly overall correlated with 기타_구조High correlation
층_면적 is highly overall correlated with 기타_구조High correlation
기타_구조 is highly overall correlated with 구조_코드 and 1 other fieldsHigh correlation
층_구분_코드 is highly overall correlated with 층_번호High correlation
기타_구조 is highly imbalanced (80.4%)Imbalance
층_구분_코드 is highly imbalanced (75.6%)Imbalance
기타_용도 has 7295 (73.0%) missing valuesMissing
층_면적 is highly skewed (γ1 = 74.43342532)Skewed
관리_층별_개요_PK has unique valuesUnique
층_면적 has 358 (3.6%) zerosZeros

Reproduction

Analysis started2024-05-04 01:16:39.651612
Analysis finished2024-05-04 01:16:44.827378
Duration5.18 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
2024-05-04T01:16:45.416540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length10.595
Min length7

Characters and Unicode

Total characters105950
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 row11170-113
2nd row11740-2092
3rd row11290-10509
4th row11305-5692
5th row11380-658
ValueCountFrequency (%)
11170-113 1
 
< 0.1%
11350-263 1
 
< 0.1%
11620-5233 1
 
< 0.1%
11620-5507 1
 
< 0.1%
11560-1346 1
 
< 0.1%
11350-1114 1
 
< 0.1%
11290-8271 1
 
< 0.1%
11350-1174 1
 
< 0.1%
11320-457 1
 
< 0.1%
11200-3553 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-04T01:16:46.648660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 29609
27.9%
0 19571
18.5%
- 10000
 
9.4%
2 9726
 
9.2%
3 7016
 
6.6%
5 6917
 
6.5%
9 5679
 
5.4%
4 5588
 
5.3%
7 4331
 
4.1%
6 4075
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95950
90.6%
Dash Punctuation 10000
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 29609
30.9%
0 19571
20.4%
2 9726
 
10.1%
3 7016
 
7.3%
5 6917
 
7.2%
9 5679
 
5.9%
4 5588
 
5.8%
7 4331
 
4.5%
6 4075
 
4.2%
8 3438
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 105950
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 29609
27.9%
0 19571
18.5%
- 10000
 
9.4%
2 9726
 
9.2%
3 7016
 
6.6%
5 6917
 
6.5%
9 5679
 
5.4%
4 5588
 
5.3%
7 4331
 
4.1%
6 4075
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 29609
27.9%
0 19571
18.5%
- 10000
 
9.4%
2 9726
 
9.2%
3 7016
 
6.6%
5 6917
 
6.5%
9 5679
 
5.4%
4 5588
 
5.3%
7 4331
 
4.1%
6 4075
 
3.8%

관리_동별_개요_PK
Text

Distinct2882
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T01:16:47.261584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length9
Mean length9.6138
Min length7

Characters and Unicode

Total characters96138
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

Unique1053 ?
Unique (%)10.5%

Sample

1st row11170-8
2nd row11740-199
3rd row11290-869
4th row11305-358
5th row11380-85
ValueCountFrequency (%)
11530-653 78
 
0.8%
11530-652 27
 
0.3%
11140-100003741 26
 
0.3%
11170-151 17
 
0.2%
11680-409 15
 
0.1%
11530-100005984 15
 
0.1%
11320-237 15
 
0.1%
11290-100004446 14
 
0.1%
11290-100004451 14
 
0.1%
11590-100004043 13
 
0.1%
Other values (2872) 9766
97.7%
2024-05-04T01:16:48.314150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 26694
27.8%
0 18890
19.6%
- 10000
 
10.4%
2 8733
 
9.1%
3 6703
 
7.0%
4 5661
 
5.9%
5 5428
 
5.6%
9 4909
 
5.1%
7 3535
 
3.7%
6 3384
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86138
89.6%
Dash Punctuation 10000
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26694
31.0%
0 18890
21.9%
2 8733
 
10.1%
3 6703
 
7.8%
4 5661
 
6.6%
5 5428
 
6.3%
9 4909
 
5.7%
7 3535
 
4.1%
6 3384
 
3.9%
8 2201
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 96138
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26694
27.8%
0 18890
19.6%
- 10000
 
10.4%
2 8733
 
9.1%
3 6703
 
7.0%
4 5661
 
5.9%
5 5428
 
5.6%
9 4909
 
5.1%
7 3535
 
3.7%
6 3384
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96138
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26694
27.8%
0 18890
19.6%
- 10000
 
10.4%
2 8733
 
9.1%
3 6703
 
7.0%
4 5661
 
5.9%
5 5428
 
5.6%
9 4909
 
5.1%
7 3535
 
3.7%
6 3384
 
3.5%

층_번호
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.658866
Minimum-21
Maximum910
Zeros0
Zeros (%)0.0%
Negative661
Negative (%)6.6%
Memory size166.0 KiB
2024-05-04T01:16:48.718496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-21
5-th percentile-1
Q13
median8
Q314
95-th percentile21
Maximum910
Range931
Interquartile range (IQR)11

Descriptive statistics

Standard deviation79.417652
Coefficient of variation (CV)5.0717371
Kurtosis119.53183
Mean15.658866
Median Absolute Deviation (MAD)5
Skewness10.976458
Sum156573
Variance6307.1634
MonotonicityNot monotonic
2024-05-04T01:16:49.239472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1086
 
10.9%
2 653
 
6.5%
3 570
 
5.7%
6 478
 
4.8%
4 476
 
4.8%
5 468
 
4.7%
7 467
 
4.7%
11 448
 
4.5%
8 440
 
4.4%
10 434
 
4.3%
Other values (53) 4479
44.8%
ValueCountFrequency (%)
-21 1
 
< 0.1%
-20 1
 
< 0.1%
-18 1
 
< 0.1%
-16 1
 
< 0.1%
-14 1
 
< 0.1%
-12 1
 
< 0.1%
-11 3
< 0.1%
-9 2
< 0.1%
-7 1
 
< 0.1%
-6 1
 
< 0.1%
ValueCountFrequency (%)
910 1
 
< 0.1%
903 14
 
0.1%
902 23
0.2%
901 41
0.4%
131 1
 
< 0.1%
129 1
 
< 0.1%
128 1
 
< 0.1%
127 1
 
< 0.1%
123 1
 
< 0.1%
51 1
 
< 0.1%

주_용도_코드
Text

Distinct66
Distinct (%)0.7%
Missing34
Missing (%)0.3%
Memory size156.2 KiB
2024-05-04T01:16:49.915420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters49830
Distinct characters12
Distinct categories3 ?
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 row02005
ValueCountFrequency (%)
02001 8688
87.2%
02005 542
 
5.4%
02004 127
 
1.3%
02006 99
 
1.0%
03999 81
 
0.8%
02002 55
 
0.6%
04999 39
 
0.4%
15201 32
 
0.3%
20001 29
 
0.3%
14299 28
 
0.3%
Other values (56) 246
 
2.5%
2024-05-04T01:16:50.783675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29167
58.5%
2 9723
 
19.5%
1 9036
 
18.1%
9 664
 
1.3%
5 591
 
1.2%
4 262
 
0.5%
3 170
 
0.3%
6 114
 
0.2%
7 51
 
0.1%
Z 39
 
0.1%
Other values (2) 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49783
99.9%
Uppercase Letter 39
 
0.1%
Lowercase Letter 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29167
58.6%
2 9723
 
19.5%
1 9036
 
18.2%
9 664
 
1.3%
5 591
 
1.2%
4 262
 
0.5%
3 170
 
0.3%
6 114
 
0.2%
7 51
 
0.1%
8 5
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
Z 39
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49783
99.9%
Latin 47
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29167
58.6%
2 9723
 
19.5%
1 9036
 
18.2%
9 664
 
1.3%
5 591
 
1.2%
4 262
 
0.5%
3 170
 
0.3%
6 114
 
0.2%
7 51
 
0.1%
8 5
 
< 0.1%
Latin
ValueCountFrequency (%)
Z 39
83.0%
a 8
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29167
58.5%
2 9723
 
19.5%
1 9036
 
18.1%
9 664
 
1.3%
5 591
 
1.2%
4 262
 
0.5%
3 170
 
0.3%
6 114
 
0.2%
7 51
 
0.1%
Z 39
 
0.1%
Other values (2) 13
 
< 0.1%

기타_용도
Text

MISSING 

Distinct496
Distinct (%)18.3%
Missing7295
Missing (%)73.0%
Memory size156.2 KiB
2024-05-04T01:16:51.328251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length45
Mean length6.1031423
Min length1

Characters and Unicode

Total characters16509
Distinct characters234
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique326 ?
Unique (%)12.1%

Sample

1st row아파트(4세대)
2nd row경비실
3rd row경비시(19개소)
4th row백화점(계단실,복도,엘리베이터홀)
5th row아파트
ValueCountFrequency (%)
아파트 534
19.2%
경비실 168
 
6.0%
지하주차장 145
 
5.2%
주차장 133
 
4.8%
아파트(4세대 128
 
4.6%
4세대 96
 
3.5%
공동주택(아파트 79
 
2.8%
근린생활시설 74
 
2.7%
아파트(2세대 73
 
2.6%
계단실 67
 
2.4%
Other values (465) 1282
46.1%
2024-05-04T01:16:52.504624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 919
 
5.6%
트 913
 
5.5%
파 912
 
5.5%
실 781
 
4.7%
( 660
 
4.0%
) 659
 
4.0%
대 616
 
3.7%
주 550
 
3.3%
, 524
 
3.2%
세 521
 
3.2%
Other values (224) 9454
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13602
82.4%
Decimal Number 737
 
4.5%
Open Punctuation 660
 
4.0%
Close Punctuation 659
 
4.0%
Other Punctuation 640
 
3.9%
Uppercase Letter 117
 
0.7%
Space Separator 74
 
0.4%
Dash Punctuation 12
 
0.1%
Lowercase Letter 5
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 919
 
6.8%
트 913
 
6.7%
파 912
 
6.7%
실 781
 
5.7%
대 616
 
4.5%
주 550
 
4.0%
세 521
 
3.8%
시 461
 
3.4%
설 451
 
3.3%
장 389
 
2.9%
Other values (183) 7089
52.1%
Uppercase Letter
ValueCountFrequency (%)
E 24
20.5%
F 21
17.9%
D 21
17.9%
M 21
17.9%
V 12
10.3%
L 9
 
7.7%
J 2
 
1.7%
A 1
 
0.9%
T 1
 
0.9%
U 1
 
0.9%
Other values (4) 4
 
3.4%
Decimal Number
ValueCountFrequency (%)
4 259
35.1%
2 215
29.2%
1 88
 
11.9%
6 53
 
7.2%
5 34
 
4.6%
7 34
 
4.6%
3 20
 
2.7%
8 13
 
1.8%
9 12
 
1.6%
0 9
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 524
81.9%
/ 79
 
12.3%
. 31
 
4.8%
# 2
 
0.3%
: 2
 
0.3%
? 2
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
t 1
20.0%
i 1
20.0%
l 1
20.0%
e 1
20.0%
v 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 660
100.0%
Close Punctuation
ValueCountFrequency (%)
) 659
100.0%
Space Separator
ValueCountFrequency (%)
74
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13602
82.4%
Common 2785
 
16.9%
Latin 122
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 919
 
6.8%
트 913
 
6.7%
파 912
 
6.7%
실 781
 
5.7%
대 616
 
4.5%
주 550
 
4.0%
세 521
 
3.8%
시 461
 
3.4%
설 451
 
3.3%
장 389
 
2.9%
Other values (183) 7089
52.1%
Common
ValueCountFrequency (%)
( 660
23.7%
) 659
23.7%
, 524
18.8%
4 259
 
9.3%
2 215
 
7.7%
1 88
 
3.2%
/ 79
 
2.8%
74
 
2.7%
6 53
 
1.9%
5 34
 
1.2%
Other values (12) 140
 
5.0%
Latin
ValueCountFrequency (%)
E 24
19.7%
F 21
17.2%
D 21
17.2%
M 21
17.2%
V 12
9.8%
L 9
 
7.4%
J 2
 
1.6%
A 1
 
0.8%
T 1
 
0.8%
U 1
 
0.8%
Other values (9) 9
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13602
82.4%
ASCII 2905
 
17.6%
CJK Compat 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 919
 
6.8%
트 913
 
6.7%
파 912
 
6.7%
실 781
 
5.7%
대 616
 
4.5%
주 550
 
4.0%
세 521
 
3.8%
시 461
 
3.4%
설 451
 
3.3%
장 389
 
2.9%
Other values (183) 7089
52.1%
ASCII
ValueCountFrequency (%)
( 660
22.7%
) 659
22.7%
, 524
18.0%
4 259
 
8.9%
2 215
 
7.4%
1 88
 
3.0%
/ 79
 
2.7%
74
 
2.5%
6 53
 
1.8%
5 34
 
1.2%
Other values (30) 260
 
9.0%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%

구조_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing41
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean21.265991
Minimum11
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T01:16:52.971323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation2.7176569
Coefficient of variation (CV)0.12779357
Kurtosis49.460448
Mean21.265991
Median Absolute Deviation (MAD)0
Skewness6.4193954
Sum211788
Variance7.3856589
MonotonicityNot monotonic
2024-05-04T01:16:53.319745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
21 9703
97.0%
42 137
 
1.4%
11 63
 
0.6%
19 22
 
0.2%
41 15
 
0.1%
29 9
 
0.1%
22 4
 
< 0.1%
31 3
 
< 0.1%
32 2
 
< 0.1%
39 1
 
< 0.1%
(Missing) 41
 
0.4%
ValueCountFrequency (%)
11 63
 
0.6%
19 22
 
0.2%
21 9703
97.0%
22 4
 
< 0.1%
29 9
 
0.1%
31 3
 
< 0.1%
32 2
 
< 0.1%
39 1
 
< 0.1%
41 15
 
0.1%
42 137
 
1.4%
ValueCountFrequency (%)
42 137
 
1.4%
41 15
 
0.1%
39 1
 
< 0.1%
32 2
 
< 0.1%
31 3
 
< 0.1%
29 9
 
0.1%
22 4
 
< 0.1%
21 9703
97.0%
19 22
 
0.2%
11 63
 
0.6%

기타_구조
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8579 
철근콘크리트구조
 
774
벽식구조
 
259
철골철근콘크리트구조
 
126
철근콘크리트조
 
126
Other values (19)
 
136

Length

Max length22
Median length4
Mean length4.5087
Min length2

Unique

Unique9 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8579
85.8%
철근콘크리트구조 774
 
7.7%
벽식구조 259
 
2.6%
철골철근콘크리트구조 126
 
1.3%
철근콘크리트조 126
 
1.3%
벽식 39
 
0.4%
철근콘크리트벽식구조+철근콘크리트 라멘조 24
 
0.2%
철근콘크리트벽식구조+철근콘크리트라멘조 17
 
0.2%
철근콘크리트벽식구조 16
 
0.2%
철근콘크리트라멘조 16
 
0.2%
Other values (14) 24
 
0.2%

Length

2024-05-04T01:16:53.875098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8579
85.5%
철근콘크리트구조 774
 
7.7%
벽식구조 259
 
2.6%
철근콘크리트조 127
 
1.3%
철골철근콘크리트구조 126
 
1.3%
벽식 39
 
0.4%
라멘조 28
 
0.3%
철근콘크리트벽식구조+철근콘크리트 24
 
0.2%
철근콘크리트벽식구조+철근콘크리트라멘조 17
 
0.2%
철근콘크리트벽식구조 16
 
0.2%
Other values (12) 40
 
0.4%

층_면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3846
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4704.6807
Minimum-657.25
Maximum21567914
Zeros358
Zeros (%)3.6%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-04T01:16:54.346903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-657.25
5-th percentile10.8
Q1260.3
median398.88
Q3538.984
95-th percentile2042.6963
Maximum21567914
Range21568571
Interquartile range (IQR)278.684

Descriptive statistics

Standard deviation256645.15
Coefficient of variation (CV)54.551024
Kurtosis5784.005
Mean4704.6807
Median Absolute Deviation (MAD)138.72
Skewness74.433425
Sum47046807
Variance6.5866732 × 1010
MonotonicityNot monotonic
2024-05-04T01:16:55.063658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 358
 
3.6%
299.84 71
 
0.7%
261.62 64
 
0.6%
397.64 59
 
0.6%
260.3 51
 
0.5%
406.16 46
 
0.5%
403.91 45
 
0.4%
410.6 43
 
0.4%
420.072 38
 
0.4%
302.12 37
 
0.4%
Other values (3836) 9188
91.9%
ValueCountFrequency (%)
-657.25 1
 
< 0.1%
-652.83 1
 
< 0.1%
0.0 358
3.6%
0.39 1
 
< 0.1%
2.27 1
 
< 0.1%
2.3 1
 
< 0.1%
2.93 1
 
< 0.1%
3.19 1
 
< 0.1%
3.37 1
 
< 0.1%
4.51 1
 
< 0.1%
ValueCountFrequency (%)
21567914.0 1
 
< 0.1%
13650023.0 1
 
< 0.1%
2117314.0 1
 
< 0.1%
839842.0 4
< 0.1%
21374.4 2
< 0.1%
19350.6 4
< 0.1%
18321.78 1
 
< 0.1%
17960.95 1
 
< 0.1%
17324.23 1
 
< 0.1%
16925.65 1
 
< 0.1%

층_구분_코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20
9070 
10
 
845
30
 
84
<NA>
 
1

Length

Max length4
Median length2
Mean length2.0002
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
20 9070
90.7%
10 845
 
8.5%
30 84
 
0.8%
<NA> 1
 
< 0.1%

Length

2024-05-04T01:16:55.656710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T01:16:56.184968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 9070
90.7%
10 845
 
8.5%
30 84
 
0.8%
na 1
 
< 0.1%

작업_일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20111227
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20111227
2nd row20111227
3rd row20111227
4th row20111227
5th row20111227

Common Values

ValueCountFrequency (%)
20111227 10000
100.0%

Length

2024-05-04T01:16:56.654010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T01:16:56.953973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20111227 10000
100.0%

Interactions

2024-05-04T01:16:43.005291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:16:41.421561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:16:42.245073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:16:43.277790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:16:41.670510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:16:42.495613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:16:43.598386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:16:41.941030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:16:42.760709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T01:16:57.158953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_번호주_용도_코드구조_코드기타_구조층_면적층_구분_코드
층_번호1.0000.0000.0000.1480.0000.934
주_용도_코드0.0001.0000.8230.8530.0000.597
구조_코드0.0000.8231.0000.9810.0000.157
기타_구조0.1480.8530.9811.000NaN0.344
층_면적0.0000.0000.000NaN1.0000.063
층_구분_코드0.9340.5970.1570.3440.0631.000
2024-05-04T01:16:57.511198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_구분_코드기타_구조
층_구분_코드1.0000.189
기타_구조0.1891.000
2024-05-04T01:16:57.813999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_번호구조_코드층_면적기타_구조층_구분_코드
층_번호1.0000.076-0.0220.1280.686
구조_코드0.0761.0000.0670.9050.065
층_면적-0.0220.0671.0001.0000.019
기타_구조0.1280.9051.0001.0000.189
층_구분_코드0.6860.0650.0190.1891.000

Missing values

2024-05-04T01:16:43.883513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T01:16:44.206443image/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.
2024-05-04T01:16:44.614570image/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
2247911170-11311170-81102001<NA>21<NA>308.352020111227
542511740-209211740-199102001<NA>21<NA>708.782020111227
3297211290-1050911290-869302001<NA>21<NA>439.382020111227
2602611305-569211305-3581002001아파트(4세대)21<NA>369.32052020111227
2475511380-65811380-85102005경비실19<NA>0.02020111227
2074611305-137211305-891402001<NA>21<NA>402.882020111227
22411200-2211200-12202001<NA>21<NA>397.642020111227
1494711620-569011620-452102005경비시(19개소)19<NA>10.082020111227
3184211290-818411290-795102001<NA>21<NA>475.72020111227
1458011290-225911290-219602001<NA>21<NA>597.542020111227
관리_층별_개요_PK관리_동별_개요_PK층_번호주_용도_코드기타_용도구조_코드기타_구조층_면적층_구분_코드작업_일자
226611200-18211200-81202001<NA>21<NA>397.642020111227
2492411200-36511200-152002001<NA>21<NA>403.912020111227
3116011290-682111290-7191102001<NA>21<NA>462.022020111227
2537411200-447811200-4021502001<NA>21<NA>199.342020111227
770511545-129511545-126602001<NA>21<NA>443.582020111227
1134911620-509611620-4061802001<NA>21<NA>353.282020111227
2770611260-70611260-71102001<NA>21<NA>742.762020111227
227411200-18611200-81602001<NA>21<NA>397.642020111227
538211290-1501811290-12201202001<NA>21벽식구조471.372020111227
2432611305-52511305-452002001<NA>21<NA>605.712020111227