Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells541
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1005.9 KiB
Average record size in memory103.0 B

Variable types

Text4
Categorical2
Numeric5

Dataset

Description관리_호별_전유_공용_면적_pk,관리_호별_명세_pk,평형_구분_명,전유_공용_구분_코드,주_부속_구분_코드,층_구분_코드,층_번호,구조_코드,주_용도_코드,기타_용도,면적
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15662/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
전유_공용_구분_코드 is highly overall correlated with 층_구분_코드High correlation
주_부속_구분_코드 is highly imbalanced (77.8%)Imbalance
기타_용도 has 541 (5.4%) missing valuesMissing
면적 is highly skewed (γ1 = 46.54549634)Skewed
관리_호별_전유_공용_면적_pk has unique valuesUnique
층_번호 has 4734 (47.3%) zerosZeros

Reproduction

Analysis started2024-05-11 06:58:50.470009
Analysis finished2024-05-11 06:58:56.554754
Duration6.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_호별_전유_공용_면적_pk
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:58:56.850136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length14.1032
Min length9

Characters and Unicode

Total characters141032
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 row11000-100058484
2nd row11000-108660
3rd row11000-100142576
4th row11000-100141103
5th row11000-100040832
ValueCountFrequency (%)
11000-100058484 1
 
< 0.1%
11000-104359 1
 
< 0.1%
11000-100144821 1
 
< 0.1%
11000-100015554 1
 
< 0.1%
11000-100017492 1
 
< 0.1%
11000-100053685 1
 
< 0.1%
11000-122116 1
 
< 0.1%
11000-100059443 1
 
< 0.1%
11000-100014006 1
 
< 0.1%
11000-100059625 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-11T15:58:57.533425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 55112
39.1%
1 38663
27.4%
- 10000
 
7.1%
4 5699
 
4.0%
3 5531
 
3.9%
2 5500
 
3.9%
5 5122
 
3.6%
6 4045
 
2.9%
7 3885
 
2.8%
9 3761
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 131032
92.9%
Dash Punctuation 10000
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55112
42.1%
1 38663
29.5%
4 5699
 
4.3%
3 5531
 
4.2%
2 5500
 
4.2%
5 5122
 
3.9%
6 4045
 
3.1%
7 3885
 
3.0%
9 3761
 
2.9%
8 3714
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 141032
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55112
39.1%
1 38663
27.4%
- 10000
 
7.1%
4 5699
 
4.0%
3 5531
 
3.9%
2 5500
 
3.9%
5 5122
 
3.6%
6 4045
 
2.9%
7 3885
 
2.8%
9 3761
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55112
39.1%
1 38663
27.4%
- 10000
 
7.1%
4 5699
 
4.0%
3 5531
 
3.9%
2 5500
 
3.9%
5 5122
 
3.6%
6 4045
 
2.9%
7 3885
 
2.8%
9 3761
 
2.7%

관리_호별_명세_pk
Text

Distinct7874
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:58:57.889251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length13.8076
Min length8

Characters and Unicode

Total characters138076
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

Unique6067 ?
Unique (%)60.7%

Sample

1st row11000-100014890
2nd row11000-19568
3rd row11000-100033046
4th row11000-100032707
5th row11000-100010477
ValueCountFrequency (%)
11000-100033511 6
 
0.1%
11000-100033103 6
 
0.1%
11000-100032186 5
 
< 0.1%
11000-100033130 5
 
< 0.1%
11000-100032234 5
 
< 0.1%
11000-100032171 5
 
< 0.1%
11000-100033514 5
 
< 0.1%
11000-100032236 5
 
< 0.1%
11000-18508 4
 
< 0.1%
11000-100032051 4
 
< 0.1%
Other values (7864) 9950
99.5%
2024-05-11T15:58:58.558857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58278
42.2%
1 35356
25.6%
- 10000
 
7.2%
2 6545
 
4.7%
3 5559
 
4.0%
9 4481
 
3.2%
8 4104
 
3.0%
4 3965
 
2.9%
5 3555
 
2.6%
7 3331
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128076
92.8%
Dash Punctuation 10000
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58278
45.5%
1 35356
27.6%
2 6545
 
5.1%
3 5559
 
4.3%
9 4481
 
3.5%
8 4104
 
3.2%
4 3965
 
3.1%
5 3555
 
2.8%
7 3331
 
2.6%
6 2902
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 138076
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58278
42.2%
1 35356
25.6%
- 10000
 
7.2%
2 6545
 
4.7%
3 5559
 
4.0%
9 4481
 
3.2%
8 4104
 
3.0%
4 3965
 
2.9%
5 3555
 
2.6%
7 3331
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58278
42.2%
1 35356
25.6%
- 10000
 
7.2%
2 6545
 
4.7%
3 5559
 
4.0%
9 4481
 
3.2%
8 4104
 
3.0%
4 3965
 
2.9%
5 3555
 
2.6%
7 3331
 
2.4%

평형_구분_명
Text

Distinct2041
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:58:59.183442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length3.9467
Min length1

Characters and Unicode

Total characters39467
Distinct characters101
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1052 ?
Unique (%)10.5%

Sample

1st row67.97
2nd row48H12
3rd rowI01
4th row57
5th row84.35
ValueCountFrequency (%)
66.95 289
 
2.9%
66.83 265
 
2.6%
32a 160
 
1.6%
67.97 158
 
1.6%
66.79a 152
 
1.5%
73.07 137
 
1.4%
a 132
 
1.3%
40a 125
 
1.2%
84.35 118
 
1.2%
56w 104
 
1.0%
Other values (2014) 8378
83.6%
2024-05-11T15:58:59.911595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 4554
11.5%
. 3704
9.4%
1 3608
 
9.1%
3 3136
 
7.9%
5 2957
 
7.5%
7 2889
 
7.3%
2 2833
 
7.2%
4 2686
 
6.8%
8 2128
 
5.4%
0 1900
 
4.8%
Other values (91) 9072
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28517
72.3%
Uppercase Letter 5310
 
13.5%
Other Punctuation 3743
 
9.5%
Dash Punctuation 713
 
1.8%
Lowercase Letter 583
 
1.5%
Other Letter 569
 
1.4%
Space Separator 18
 
< 0.1%
Open Punctuation 7
 
< 0.1%
Close Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 37
 
6.5%
워 35
 
6.2%
타 35
 
6.2%
설 33
 
5.8%
동 32
 
5.6%
드 21
 
3.7%
층 20
 
3.5%
매 19
 
3.3%
판 19
 
3.3%
이 18
 
3.2%
Other values (43) 300
52.7%
Uppercase Letter
ValueCountFrequency (%)
A 1579
29.7%
B 1459
27.5%
C 456
 
8.6%
D 280
 
5.3%
F 271
 
5.1%
S 194
 
3.7%
H 188
 
3.5%
E 155
 
2.9%
I 153
 
2.9%
J 147
 
2.8%
Other values (13) 428
 
8.1%
Decimal Number
ValueCountFrequency (%)
6 4554
16.0%
1 3608
12.7%
3 3136
11.0%
5 2957
10.4%
7 2889
10.1%
2 2833
9.9%
4 2686
9.4%
8 2128
7.5%
0 1900
6.7%
9 1826
6.4%
Lowercase Letter
ValueCountFrequency (%)
a 394
67.6%
b 122
 
20.9%
c 35
 
6.0%
d 19
 
3.3%
e 5
 
0.9%
f 3
 
0.5%
i 2
 
0.3%
g 2
 
0.3%
h 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 3704
99.0%
* 39
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 713
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33005
83.6%
Latin 5893
 
14.9%
Hangul 569
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 37
 
6.5%
워 35
 
6.2%
타 35
 
6.2%
설 33
 
5.8%
동 32
 
5.6%
드 21
 
3.7%
층 20
 
3.5%
매 19
 
3.3%
판 19
 
3.3%
이 18
 
3.2%
Other values (43) 300
52.7%
Latin
ValueCountFrequency (%)
A 1579
26.8%
B 1459
24.8%
C 456
 
7.7%
a 394
 
6.7%
D 280
 
4.8%
F 271
 
4.6%
S 194
 
3.3%
H 188
 
3.2%
E 155
 
2.6%
I 153
 
2.6%
Other values (22) 764
13.0%
Common
ValueCountFrequency (%)
6 4554
13.8%
. 3704
11.2%
1 3608
10.9%
3 3136
9.5%
5 2957
9.0%
7 2889
8.8%
2 2833
8.6%
4 2686
8.1%
8 2128
6.4%
0 1900
5.8%
Other values (6) 2610
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38898
98.6%
Hangul 569
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 4554
11.7%
. 3704
9.5%
1 3608
9.3%
3 3136
 
8.1%
5 2957
 
7.6%
7 2889
 
7.4%
2 2833
 
7.3%
4 2686
 
6.9%
8 2128
 
5.5%
0 1900
 
4.9%
Other values (38) 8503
21.9%
Hangul
ValueCountFrequency (%)
시 37
 
6.5%
워 35
 
6.2%
타 35
 
6.2%
설 33
 
5.8%
동 32
 
5.6%
드 21
 
3.7%
층 20
 
3.5%
매 19
 
3.3%
판 19
 
3.3%
이 18
 
3.2%
Other values (43) 300
52.7%

전유_공용_구분_코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
7902 
1
2098 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 7902
79.0%
1 2098
 
21.0%

Length

2024-05-11T15:59:00.110173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:59:00.252643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 7902
79.0%
1 2098
 
21.0%

주_부속_구분_코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9644 
1
 
356

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 9644
96.4%
1 356
 
3.6%

Length

2024-05-11T15:59:00.401935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:59:00.533726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9644
96.4%
1 356
 
3.6%

층_구분_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.0806
Minimum10
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:59:00.670504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q120
median20
Q340
95-th percentile40
Maximum60
Range50
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.3766
Coefficient of variation (CV)0.44075268
Kurtosis-1.3817363
Mean28.0806
Median Absolute Deviation (MAD)10
Skewness-0.044902456
Sum280806
Variance153.18022
MonotonicityNot monotonic
2024-05-11T15:59:00.818243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40 4690
46.9%
20 3531
35.3%
10 1661
 
16.6%
60 89
 
0.9%
22 27
 
0.3%
21 2
 
< 0.1%
ValueCountFrequency (%)
10 1661
 
16.6%
20 3531
35.3%
21 2
 
< 0.1%
22 27
 
0.3%
40 4690
46.9%
60 89
 
0.9%
ValueCountFrequency (%)
60 89
 
0.9%
40 4690
46.9%
22 27
 
0.3%
21 2
 
< 0.1%
20 3531
35.3%
10 1661
 
16.6%

층_번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5923
Minimum0
Maximum113
Zeros4734
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:59:00.999020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile19
Maximum113
Range113
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.2296824
Coefficient of variation (CV)2.0125497
Kurtosis27.776698
Mean3.5923
Median Absolute Deviation (MAD)1
Skewness4.1439348
Sum35923
Variance52.268308
MonotonicityNot monotonic
2024-05-11T15:59:01.272210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4734
47.3%
1 1257
 
12.6%
3 718
 
7.2%
4 681
 
6.8%
2 427
 
4.3%
5 334
 
3.3%
6 283
 
2.8%
7 256
 
2.6%
8 192
 
1.9%
9 182
 
1.8%
Other values (53) 936
 
9.4%
ValueCountFrequency (%)
0 4734
47.3%
1 1257
 
12.6%
2 427
 
4.3%
3 718
 
7.2%
4 681
 
6.8%
5 334
 
3.3%
6 283
 
2.8%
7 256
 
2.6%
8 192
 
1.9%
9 182
 
1.8%
ValueCountFrequency (%)
113 1
 
< 0.1%
112 1
 
< 0.1%
109 1
 
< 0.1%
68 1
 
< 0.1%
62 2
< 0.1%
61 1
 
< 0.1%
57 2
< 0.1%
56 4
< 0.1%
55 1
 
< 0.1%
54 4
< 0.1%

구조_코드
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.7501
Minimum21
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:59:01.464725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q121
median21
Q321
95-th percentile42
Maximum42
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.8562928
Coefficient of variation (CV)0.2886848
Kurtosis3.0097716
Mean23.7501
Median Absolute Deviation (MAD)0
Skewness2.2271329
Sum237501
Variance47.008751
MonotonicityNot monotonic
2024-05-11T15:59:01.585272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
21 7696
77.0%
42 1031
 
10.3%
22 1000
 
10.0%
41 122
 
1.2%
40 100
 
1.0%
31 51
 
0.5%
ValueCountFrequency (%)
21 7696
77.0%
22 1000
 
10.0%
31 51
 
0.5%
40 100
 
1.0%
41 122
 
1.2%
42 1031
 
10.3%
ValueCountFrequency (%)
42 1031
 
10.3%
41 122
 
1.2%
40 100
 
1.0%
31 51
 
0.5%
22 1000
 
10.0%
21 7696
77.0%

주_용도_코드
Real number (ℝ)

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6215.0545
Minimum2001
Maximum27004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:59:01.747944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12001
median7001
Q37201
95-th percentile14202
Maximum27004
Range25003
Interquartile range (IQR)5200

Descriptive statistics

Standard deviation4642.335
Coefficient of variation (CV)0.74695
Kurtosis0.14467319
Mean6215.0545
Median Absolute Deviation (MAD)5000
Skewness0.96810743
Sum62150545
Variance21551274
MonotonicityNot monotonic
2024-05-11T15:59:01.920443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2001 3838
38.4%
7001 2260
22.6%
14202 1132
 
11.3%
7201 1042
 
10.4%
2005 453
 
4.5%
7999 374
 
3.7%
18001 332
 
3.3%
10301 158
 
1.6%
14204 71
 
0.7%
10004 59
 
0.6%
Other values (40) 281
 
2.8%
ValueCountFrequency (%)
2001 3838
38.4%
2003 2
 
< 0.1%
2005 453
 
4.5%
3001 28
 
0.3%
3002 2
 
< 0.1%
3003 4
 
< 0.1%
3005 10
 
0.1%
3014 1
 
< 0.1%
3015 1
 
< 0.1%
3105 1
 
< 0.1%
ValueCountFrequency (%)
27004 6
 
0.1%
20001 15
 
0.1%
18001 332
 
3.3%
15999 7
 
0.1%
15201 9
 
0.1%
14299 11
 
0.1%
14204 71
 
0.7%
14202 1132
11.3%
14201 6
 
0.1%
13999 5
 
0.1%

기타_용도
Text

MISSING 

Distinct258
Distinct (%)2.7%
Missing541
Missing (%)5.4%
Memory size156.2 KiB
2024-05-11T15:59:02.230654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length85
Median length65
Mean length14.191458
Min length2

Characters and Unicode

Total characters134237
Distinct characters214
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)0.4%

Sample

1st row판매시설(도매시장)
2nd row계단,승강기홀
3rd row기계실,전기실
4th row공동주택(아파트)
5th row판매시설(도매시장)
ValueCountFrequency (%)
주차장 1351
 
14.1%
계단실,복도,로비,화장실,공조실 631
 
6.6%
기계실,전기실,창고,재활용창고,용역원실,휴게실,오락실,주차관제실,체력단련실,검수실,방재센터,경비실,유아실,사무실 586
 
6.1%
판매시설(도매시장 570
 
5.9%
아파트 304
 
3.2%
기계실,전기실,발전기실,기사대기실,용역원실,주차관리실,로비,경비실,용역원식당,운영사무실등 284
 
3.0%
지하주차장 242
 
2.5%
판매시설 237
 
2.5%
계단실,복도,공조실등 235
 
2.5%
기계실,전기실 230
 
2.4%
Other values (244) 4917
51.3%
2024-05-11T15:59:02.790324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 20736
 
15.4%
실 16712
 
12.4%
기 5920
 
4.4%
계 4246
 
3.2%
장 3690
 
2.7%
주 3372
 
2.5%
차 2945
 
2.2%
단 2933
 
2.2%
전 2786
 
2.1%
비 2538
 
1.9%
Other values (204) 68359
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102534
76.4%
Other Punctuation 21088
 
15.7%
Decimal Number 3134
 
2.3%
Uppercase Letter 2715
 
2.0%
Open Punctuation 1797
 
1.3%
Close Punctuation 1797
 
1.3%
Math Symbol 514
 
0.4%
Space Separator 423
 
0.3%
Dash Punctuation 235
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 16712
 
16.3%
기 5920
 
5.8%
계 4246
 
4.1%
장 3690
 
3.6%
주 3372
 
3.3%
차 2945
 
2.9%
단 2933
 
2.9%
전 2786
 
2.7%
비 2538
 
2.5%
시 2517
 
2.5%
Other values (175) 54875
53.5%
Uppercase Letter
ValueCountFrequency (%)
E 556
20.5%
F 467
17.2%
D 467
17.2%
M 463
17.1%
V 282
10.4%
L 280
10.3%
C 92
 
3.4%
O 92
 
3.4%
I 6
 
0.2%
P 6
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 1009
32.2%
2 518
16.5%
4 396
 
12.6%
5 394
 
12.6%
3 288
 
9.2%
8 245
 
7.8%
7 100
 
3.2%
6 86
 
2.7%
9 51
 
1.6%
0 47
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 20736
98.3%
. 267
 
1.3%
/ 85
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 1797
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1797
100.0%
Math Symbol
ValueCountFrequency (%)
~ 514
100.0%
Space Separator
ValueCountFrequency (%)
423
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102534
76.4%
Common 28988
 
21.6%
Latin 2715
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 16712
 
16.3%
기 5920
 
5.8%
계 4246
 
4.1%
장 3690
 
3.6%
주 3372
 
3.3%
차 2945
 
2.9%
단 2933
 
2.9%
전 2786
 
2.7%
비 2538
 
2.5%
시 2517
 
2.5%
Other values (175) 54875
53.5%
Common
ValueCountFrequency (%)
, 20736
71.5%
( 1797
 
6.2%
) 1797
 
6.2%
1 1009
 
3.5%
2 518
 
1.8%
~ 514
 
1.8%
423
 
1.5%
4 396
 
1.4%
5 394
 
1.4%
3 288
 
1.0%
Other values (8) 1116
 
3.8%
Latin
ValueCountFrequency (%)
E 556
20.5%
F 467
17.2%
D 467
17.2%
M 463
17.1%
V 282
10.4%
L 280
10.3%
C 92
 
3.4%
O 92
 
3.4%
I 6
 
0.2%
P 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102534
76.4%
ASCII 31703
 
23.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 20736
65.4%
( 1797
 
5.7%
) 1797
 
5.7%
1 1009
 
3.2%
E 556
 
1.8%
2 518
 
1.6%
~ 514
 
1.6%
F 467
 
1.5%
D 467
 
1.5%
M 463
 
1.5%
Other values (19) 3379
 
10.7%
Hangul
ValueCountFrequency (%)
실 16712
 
16.3%
기 5920
 
5.8%
계 4246
 
4.1%
장 3690
 
3.6%
주 3372
 
3.3%
차 2945
 
2.9%
단 2933
 
2.9%
전 2786
 
2.7%
비 2538
 
2.5%
시 2517
 
2.5%
Other values (175) 54875
53.5%

면적
Real number (ℝ)

SKEWED 

Distinct2776
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.078598
Minimum0
Maximum56315.97
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:59:02.986376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.44
Q13.0175
median21.8
Q341.705
95-th percentile152.25
Maximum56315.97
Range56315.97
Interquartile range (IQR)38.6875

Descriptive statistics

Standard deviation815.43585
Coefficient of variation (CV)9.8152336
Kurtosis2796.9211
Mean83.078598
Median Absolute Deviation (MAD)18.93
Skewness46.545496
Sum830785.98
Variance664935.62
MonotonicityNot monotonic
2024-05-11T15:59:03.172216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.41 378
 
3.8%
22.68 358
 
3.6%
21.08 316
 
3.2%
20.78 76
 
0.8%
20.66 67
 
0.7%
21.8 56
 
0.6%
2.43 56
 
0.6%
2.87 51
 
0.5%
84.98 46
 
0.5%
163.53 41
 
0.4%
Other values (2766) 8555
85.5%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.02 2
 
< 0.1%
0.054 7
 
0.1%
0.056 17
0.2%
0.06 16
0.2%
0.0603 1
 
< 0.1%
0.062 3
 
< 0.1%
0.0633 1
 
< 0.1%
0.067 30
0.3%
0.07 28
0.3%
ValueCountFrequency (%)
56315.97 1
< 0.1%
38434.35 1
< 0.1%
14335.42 1
< 0.1%
13933.39 1
< 0.1%
13342.97 2
< 0.1%
13062.16 1
< 0.1%
11126.01 1
< 0.1%
9380.13 1
< 0.1%
7356.62 1
< 0.1%
6878.91 1
< 0.1%

Interactions

2024-05-11T15:58:55.110137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:52.261061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:52.900026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:53.558337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:54.195083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:55.293324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:52.380556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:53.039612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:53.673726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:54.436961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:55.677830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:52.501598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:53.201387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:53.777129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:54.616848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:55.795910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:52.626318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:53.328492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:53.895559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:54.789828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:55.928224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:52.767321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:53.444463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:54.028548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:58:54.951785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:59:03.292535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드주_용도_코드면적
전유_공용_구분_코드1.0000.1530.8110.6200.0550.1550.032
주_부속_구분_코드0.1531.0000.0860.0680.0440.1710.000
층_구분_코드0.8110.0861.0000.5150.1900.3790.000
층_번호0.6200.0680.5151.0000.1290.2320.000
구조_코드0.0550.0440.1900.1291.0000.4730.078
주_용도_코드0.1550.1710.3790.2320.4731.0000.070
면적0.0320.0000.0000.0000.0780.0701.000
2024-05-11T15:59:03.421804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주_부속_구분_코드전유_공용_구분_코드
주_부속_구분_코드1.0000.098
전유_공용_구분_코드0.0981.000
2024-05-11T15:59:03.569661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_구분_코드층_번호구조_코드주_용도_코드면적전유_공용_구분_코드주_부속_구분_코드
층_구분_코드1.000-0.771-0.0730.0550.0390.6070.058
층_번호-0.7711.000-0.026-0.0760.1840.4700.051
구조_코드-0.073-0.0261.0000.2180.0420.0910.073
주_용도_코드0.055-0.0760.2181.0000.0990.1070.165
면적0.0390.1840.0420.0991.0000.0390.000
전유_공용_구분_코드0.6070.4700.0910.1070.0391.0000.098
주_부속_구분_코드0.0580.0510.0730.1650.0000.0981.000

Missing values

2024-05-11T15:58:56.126319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:58:56.404097image/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.

Sample

관리_호별_전유_공용_면적_pk관리_호별_명세_pk평형_구분_명전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드주_용도_코드기타_용도면적
3802411000-10005848411000-10001489067.9710202217001판매시설(도매시장)22.68
7231311000-10866011000-1956848H1220400212001계단,승강기홀31.895
6003411000-10014257611000-100033046I01204004214202기계실,전기실73.59
5865611000-10014110311000-10003270757102036212001공동주택(아파트)150.16
2035311000-10004083211000-10001047784.3510201217001판매시설(도매시장)22.68
2463711000-10004511111000-10001154686.3520400217001기계실,전기실,창고,재활용창고,용역원실,휴게실,오락실,주차관제실,체력단련실,검수실,방재센터,경비실,유아실,사무실2.46
5328811000-10012489911000-100030839112A20101212001방재센터0.13
4330911000-10010923511000-1000275104220400212001계단실,ELEV.,로비 등(1,8~48층)27.79
5369111000-10012530111000-100030896112B20203212001발전기실0.17
8901311000-12369011000-22080920104213001주차장12.01
관리_호별_전유_공용_면적_pk관리_호별_명세_pk평형_구분_명전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드주_용도_코드기타_용도면적
7316111000-10942311000-1967748H520400212001계단,승강기홀31.895
2131111000-10004178911000-10001071666.8320209217001계단실,복도,로비,화장실,공조실20.66
3598511000-10005644711000-10001438084.3520400217001기계실,전기실,창고,재활용창고,용역원실,휴게실,오락실,주차관제실,체력단련실,검수실,방재센터,경비실,유아실,사무실2.41
3297211000-10005343711000-10001362836.7220101217001계단실,복도,로비,화장실,공조실13.52
4453011000-10011046411000-1000278373820104212001기계실,전기실,비상발전기실,휀룸,창고,MDF실(지4-2층,20층,22층,25층)4.0511
8155711000-11698011000-2090049A20400212001계단실32.49
4132411000-10010725211000-10002704420420400217999계단실,ELEV.(1~8층)18.19
107411000-10000377011000-100001676245.8020101417999주차장60.92
6526111000-10231211000-18557504A20103212001방재센타,MDF실,용원실0.4312
2560811000-10004608111000-10001178966.8320204217001계단실,복도,로비,화장실,공조실20.66