Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells1727
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory996.1 KiB
Average record size in memory102.0 B

Variable types

Text5
Categorical2
Numeric4

Dataset

Description관리_전유_공용_면적_pk,호별명세_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-15665/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 (93.8%)Imbalance
층_구분_코드 has 320 (3.2%) missing valuesMissing
기타_용도 has 1336 (13.4%) missing valuesMissing
층_번호 is highly skewed (γ1 = 33.6697486)Skewed
면적 is highly skewed (γ1 = 35.78791434)Skewed
관리_전유_공용_면적_pk has unique valuesUnique
층_번호 has 5521 (55.2%) zerosZeros

Reproduction

Analysis started2024-05-10 23:26:40.228379
Analysis finished2024-05-10 23:26:50.727843
Duration10.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_전유_공용_면적_pk
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T23:26:51.234650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length13.9095
Min length7

Characters and Unicode

Total characters139095
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 row11000-100026311
2nd row11000-100001480
3rd row11000-100008544
4th row11110-19912
5th row11000-1713
ValueCountFrequency (%)
11000-100026311 1
 
< 0.1%
11110-100016724 1
 
< 0.1%
11140-1000000000000000751645 1
 
< 0.1%
11110-2013 1
 
< 0.1%
11000-100011668 1
 
< 0.1%
11000-100006933 1
 
< 0.1%
11000-4317 1
 
< 0.1%
11110-100022324 1
 
< 0.1%
11110-9953 1
 
< 0.1%
11000-100002429 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-10T23:26:52.250609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 51056
36.7%
1 41824
30.1%
- 10000
 
7.2%
2 6866
 
4.9%
6 4582
 
3.3%
8 4232
 
3.0%
9 4208
 
3.0%
4 4167
 
3.0%
7 4118
 
3.0%
5 4085
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129095
92.8%
Dash Punctuation 10000
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51056
39.5%
1 41824
32.4%
2 6866
 
5.3%
6 4582
 
3.5%
8 4232
 
3.3%
9 4208
 
3.3%
4 4167
 
3.2%
7 4118
 
3.2%
5 4085
 
3.2%
3 3957
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 139095
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51056
36.7%
1 41824
30.1%
- 10000
 
7.2%
2 6866
 
4.9%
6 4582
 
3.3%
8 4232
 
3.0%
9 4208
 
3.0%
4 4167
 
3.0%
7 4118
 
3.0%
5 4085
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139095
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51056
36.7%
1 41824
30.1%
- 10000
 
7.2%
2 6866
 
4.9%
6 4582
 
3.3%
8 4232
 
3.0%
9 4208
 
3.0%
4 4167
 
3.0%
7 4118
 
3.0%
5 4085
 
2.9%

호별명세_pk
Text

Distinct1056
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T23:26:52.762300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length11.4847
Min length7

Characters and Unicode

Total characters114847
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

Unique350 ?
Unique (%)3.5%

Sample

1st row11000-139
2nd row11000-106
3rd row11000-100004025
4th row11110-3477
5th row11000-18
ValueCountFrequency (%)
11000-100004025 400
 
4.0%
11000-65 273
 
2.7%
11000-131 267
 
2.7%
11000-72 251
 
2.5%
11110-100017332 194
 
1.9%
11000-92 173
 
1.7%
11000-56 162
 
1.6%
11110-2502 151
 
1.5%
11000-100004246 141
 
1.4%
11000-33 136
 
1.4%
Other values (1046) 7852
78.5%
2024-05-10T23:26:53.644714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 38351
33.4%
0 38073
33.2%
- 10000
 
8.7%
2 4958
 
4.3%
3 3991
 
3.5%
4 3785
 
3.3%
5 3630
 
3.2%
7 3405
 
3.0%
6 3008
 
2.6%
9 3002
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104847
91.3%
Dash Punctuation 10000
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38351
36.6%
0 38073
36.3%
2 4958
 
4.7%
3 3991
 
3.8%
4 3785
 
3.6%
5 3630
 
3.5%
7 3405
 
3.2%
6 3008
 
2.9%
9 3002
 
2.9%
8 2644
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114847
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 38351
33.4%
0 38073
33.2%
- 10000
 
8.7%
2 4958
 
4.3%
3 3991
 
3.5%
4 3785
 
3.3%
5 3630
 
3.2%
7 3405
 
3.0%
6 3008
 
2.6%
9 3002
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 38351
33.4%
0 38073
33.2%
- 10000
 
8.7%
2 4958
 
4.3%
3 3991
 
3.5%
4 3785
 
3.3%
5 3630
 
3.2%
7 3405
 
3.0%
6 3008
 
2.6%
9 3002
 
2.6%

평형_구분_명
Text

Distinct5174
Distinct (%)51.8%
Missing6
Missing (%)0.1%
Memory size156.2 KiB
2024-05-10T23:26:54.157768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length3.8323994
Min length1

Characters and Unicode

Total characters38301
Distinct characters167
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3367 ?
Unique (%)33.7%

Sample

1st rowC-2N
2nd row2115
3rd row370.92
4th rowSB-24
5th row3C
ValueCountFrequency (%)
a 100
 
1.0%
b 72
 
0.7%
c 49
 
0.5%
201 48
 
0.5%
d 48
 
0.5%
a동 44
 
0.4%
101 38
 
0.4%
301 38
 
0.4%
203 36
 
0.4%
402 34
 
0.3%
Other values (4982) 9654
95.0%
2024-05-10T23:26:55.182019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5718
14.9%
0 3874
 
10.1%
2 3789
 
9.9%
3 2745
 
7.2%
4 2278
 
5.9%
. 2049
 
5.3%
5 2020
 
5.3%
6 1955
 
5.1%
7 1729
 
4.5%
8 1569
 
4.1%
Other values (157) 10575
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27030
70.6%
Uppercase Letter 5483
 
14.3%
Other Punctuation 2075
 
5.4%
Other Letter 1678
 
4.4%
Dash Punctuation 947
 
2.5%
Lowercase Letter 798
 
2.1%
Space Separator 167
 
0.4%
Close Punctuation 58
 
0.2%
Open Punctuation 58
 
0.2%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 402
24.0%
동 136
 
8.1%
층 120
 
7.2%
시 67
 
4.0%
설 57
 
3.4%
타 53
 
3.2%
업 44
 
2.6%
워 43
 
2.6%
형 43
 
2.6%
무 42
 
2.5%
Other values (87) 671
40.0%
Uppercase Letter
ValueCountFrequency (%)
B 1304
23.8%
A 1281
23.4%
S 623
11.4%
C 460
 
8.4%
D 281
 
5.1%
O 234
 
4.3%
E 202
 
3.7%
F 170
 
3.1%
P 121
 
2.2%
T 103
 
1.9%
Other values (16) 704
12.8%
Lowercase Letter
ValueCountFrequency (%)
b 162
20.3%
s 144
18.0%
a 144
18.0%
f 116
14.5%
c 36
 
4.5%
o 33
 
4.1%
p 33
 
4.1%
e 31
 
3.9%
y 22
 
2.8%
d 20
 
2.5%
Other values (14) 57
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 5718
21.2%
0 3874
14.3%
2 3789
14.0%
3 2745
10.2%
4 2278
 
8.4%
5 2020
 
7.5%
6 1955
 
7.2%
7 1729
 
6.4%
8 1569
 
5.8%
9 1353
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 2049
98.7%
* 13
 
0.6%
, 12
 
0.6%
/ 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 947
100.0%
Space Separator
ValueCountFrequency (%)
167
100.0%
Close Punctuation
ValueCountFrequency (%)
) 58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 58
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30342
79.2%
Latin 6281
 
16.4%
Hangul 1678
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 402
24.0%
동 136
 
8.1%
층 120
 
7.2%
시 67
 
4.0%
설 57
 
3.4%
타 53
 
3.2%
업 44
 
2.6%
워 43
 
2.6%
형 43
 
2.6%
무 42
 
2.5%
Other values (87) 671
40.0%
Latin
ValueCountFrequency (%)
B 1304
20.8%
A 1281
20.4%
S 623
9.9%
C 460
 
7.3%
D 281
 
4.5%
O 234
 
3.7%
E 202
 
3.2%
F 170
 
2.7%
b 162
 
2.6%
s 144
 
2.3%
Other values (40) 1420
22.6%
Common
ValueCountFrequency (%)
1 5718
18.8%
0 3874
12.8%
2 3789
12.5%
3 2745
9.0%
4 2278
 
7.5%
. 2049
 
6.8%
5 2020
 
6.7%
6 1955
 
6.4%
7 1729
 
5.7%
8 1569
 
5.2%
Other values (10) 2616
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36620
95.6%
Hangul 1677
 
4.4%
CJK Compat 3
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5718
15.6%
0 3874
10.6%
2 3789
10.3%
3 2745
 
7.5%
4 2278
 
6.2%
. 2049
 
5.6%
5 2020
 
5.5%
6 1955
 
5.3%
7 1729
 
4.7%
8 1569
 
4.3%
Other values (59) 8894
24.3%
Hangul
ValueCountFrequency (%)
호 402
24.0%
동 136
 
8.1%
층 120
 
7.2%
시 67
 
4.0%
설 57
 
3.4%
타 53
 
3.2%
업 44
 
2.6%
워 43
 
2.6%
형 43
 
2.6%
무 42
 
2.5%
Other values (86) 670
40.0%
CJK Compat
ValueCountFrequency (%)
㎡ 3
100.0%
Compat Jamo
ValueCountFrequency (%)
ㄴ 1
100.0%

전유_공용_구분_코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
7598 
1
2401 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0003
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
2 7598
76.0%
1 2401
 
24.0%
<NA> 1
 
< 0.1%

Length

2024-05-10T23:26:55.614412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:26:55.992286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 7598
76.0%
1 2401
 
24.0%
na 1
 
< 0.1%

주_부속_구분_코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9877 
1
 
120
<NA>
 
3

Length

Max length4
Median length1
Mean length1.0009
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9877
98.8%
1 120
 
1.2%
<NA> 3
 
< 0.1%

Length

2024-05-10T23:26:56.360282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:26:56.713339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9877
98.8%
1 120
 
1.2%
na 3
 
< 0.1%

층_구분_코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.1%
Missing320
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean25.37531
Minimum0
Maximum60
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:26:57.119699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q120
median20
Q340
95-th percentile40
Maximum60
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.402426
Coefficient of variation (CV)0.4887596
Kurtosis-1.6152213
Mean25.37531
Median Absolute Deviation (MAD)10
Skewness0.13527137
Sum245633
Variance153.82018
MonotonicityNot monotonic
2024-05-10T23:26:57.483422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
40 3804
38.0%
20 3382
33.8%
10 2403
24.0%
22 48
 
0.5%
21 27
 
0.3%
0 11
 
0.1%
30 4
 
< 0.1%
60 1
 
< 0.1%
(Missing) 320
 
3.2%
ValueCountFrequency (%)
0 11
 
0.1%
10 2403
24.0%
20 3382
33.8%
21 27
 
0.3%
22 48
 
0.5%
30 4
 
< 0.1%
40 3804
38.0%
60 1
 
< 0.1%
ValueCountFrequency (%)
60 1
 
< 0.1%
40 3804
38.0%
30 4
 
< 0.1%
22 48
 
0.5%
21 27
 
0.3%
20 3382
33.8%
10 2403
24.0%
0 11
 
0.1%

층_번호
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0788
Minimum0
Maximum902
Zeros5521
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:26:57.935105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum902
Range902
Interquartile range (IQR)2

Descriptive statistics

Standard deviation16.052344
Coefficient of variation (CV)7.7219282
Kurtosis1403.8883
Mean2.0788
Median Absolute Deviation (MAD)0
Skewness33.669749
Sum20788
Variance257.67776
MonotonicityNot monotonic
2024-05-10T23:26:58.387885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5521
55.2%
1 1641
 
16.4%
2 744
 
7.4%
3 601
 
6.0%
4 500
 
5.0%
5 296
 
3.0%
6 190
 
1.9%
7 129
 
1.3%
10 107
 
1.1%
8 69
 
0.7%
Other values (43) 202
 
2.0%
ValueCountFrequency (%)
0 5521
55.2%
1 1641
 
16.4%
2 744
 
7.4%
3 601
 
6.0%
4 500
 
5.0%
5 296
 
3.0%
6 190
 
1.9%
7 129
 
1.3%
8 69
 
0.7%
9 45
 
0.4%
ValueCountFrequency (%)
902 1
 
< 0.1%
501 2
< 0.1%
402 1
 
< 0.1%
401 4
< 0.1%
304 1
 
< 0.1%
303 1
 
< 0.1%
302 1
 
< 0.1%
201 1
 
< 0.1%
121 1
 
< 0.1%
102 1
 
< 0.1%

구조_코드
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean27.342371
Minimum11
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:26:58.755611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile21
Q121
median21
Q342
95-th percentile42
Maximum42
Range31
Interquartile range (IQR)21

Descriptive statistics

Standard deviation9.5286787
Coefficient of variation (CV)0.34849497
Kurtosis-1.2288961
Mean27.342371
Median Absolute Deviation (MAD)0
Skewness0.85557761
Sum273287
Variance90.795718
MonotonicityNot monotonic
2024-05-10T23:26:59.126623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
21 6624
66.2%
42 2793
27.9%
22 254
 
2.5%
31 145
 
1.5%
41 124
 
1.2%
40 30
 
0.3%
11 13
 
0.1%
32 7
 
0.1%
26 4
 
< 0.1%
39 1
 
< 0.1%
(Missing) 5
 
0.1%
ValueCountFrequency (%)
11 13
 
0.1%
21 6624
66.2%
22 254
 
2.5%
26 4
 
< 0.1%
31 145
 
1.5%
32 7
 
0.1%
39 1
 
< 0.1%
40 30
 
0.3%
41 124
 
1.2%
42 2793
27.9%
ValueCountFrequency (%)
42 2793
27.9%
41 124
 
1.2%
40 30
 
0.3%
39 1
 
< 0.1%
32 7
 
0.1%
31 145
 
1.5%
26 4
 
< 0.1%
22 254
 
2.5%
21 6624
66.2%
11 13
 
0.1%

주_용도_코드
Text

Distinct119
Distinct (%)1.2%
Missing60
Missing (%)0.6%
Memory size156.2 KiB
2024-05-10T23:26:59.675458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9991952
Min length4

Characters and Unicode

Total characters49692
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

Unique21 ?
Unique (%)0.2%

Sample

1st row02001
2nd row03001
3rd row07201
4th row04001
5th row14202
ValueCountFrequency (%)
14202 1671
16.8%
02001 1205
12.1%
02003 1096
11.0%
07999 1036
10.4%
07201 897
 
9.0%
04001 437
 
4.4%
z6999 316
 
3.2%
15101 306
 
3.1%
14204 296
 
3.0%
07001 295
 
3.0%
Other values (109) 2385
24.0%
2024-05-10T23:27:00.515787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18986
38.2%
2 7843
15.8%
1 7118
 
14.3%
9 5985
 
12.0%
4 3881
 
7.8%
7 2310
 
4.6%
3 1940
 
3.9%
5 862
 
1.7%
6 368
 
0.7%
Z 322
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49370
99.4%
Uppercase Letter 322
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18986
38.5%
2 7843
15.9%
1 7118
 
14.4%
9 5985
 
12.1%
4 3881
 
7.9%
7 2310
 
4.7%
3 1940
 
3.9%
5 862
 
1.7%
6 368
 
0.7%
8 77
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
Z 322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49370
99.4%
Latin 322
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18986
38.5%
2 7843
15.9%
1 7118
 
14.4%
9 5985
 
12.1%
4 3881
 
7.9%
7 2310
 
4.7%
3 1940
 
3.9%
5 862
 
1.7%
6 368
 
0.7%
8 77
 
0.2%
Latin
ValueCountFrequency (%)
Z 322
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18986
38.2%
2 7843
15.8%
1 7118
 
14.3%
9 5985
 
12.0%
4 3881
 
7.8%
7 2310
 
4.6%
3 1940
 
3.9%
5 862
 
1.7%
6 368
 
0.7%
Z 322
 
0.6%

기타_용도
Text

MISSING 

Distinct938
Distinct (%)10.8%
Missing1336
Missing (%)13.4%
Memory size156.2 KiB
2024-05-10T23:27:00.820778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length72
Median length54
Mean length10.918744
Min length1

Characters and Unicode

Total characters94600
Distinct characters277
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

Unique342 ?
Unique (%)3.9%

Sample

1st row주차장(지6-지1)
2nd row화장실,계단실
3rd row판매시설(상점)
4th row방재센타,복도,엠디에프실(지1,2층)
5th row주차장
ValueCountFrequency (%)
주차장 993
 
10.9%
계단실 428
 
4.7%
지하주차장 262
 
2.9%
판매시설 245
 
2.7%
기계실,전기실 211
 
2.3%
복도 135
 
1.5%
계단실,elev 132
 
1.5%
기계실,전기실,창고,재활용창고,용역원실,휴게실,오락실,주차관제실,체력단련실,검수실,방재센터,경비실,유아실,사무실 117
 
1.3%
기계실 116
 
1.3%
계단실,복도,로비,화장실,공조실 112
 
1.2%
Other values (901) 6351
69.8%
2024-05-10T23:27:01.417046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 12085
 
12.8%
실 9762
 
10.3%
기 4704
 
5.0%
계 3569
 
3.8%
장 2951
 
3.1%
주 2432
 
2.6%
단 2228
 
2.4%
전 2188
 
2.3%
지 2078
 
2.2%
차 2038
 
2.2%
Other values (267) 50565
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70870
74.9%
Other Punctuation 12570
 
13.3%
Decimal Number 3351
 
3.5%
Uppercase Letter 2918
 
3.1%
Close Punctuation 1692
 
1.8%
Open Punctuation 1691
 
1.8%
Math Symbol 505
 
0.5%
Dash Punctuation 504
 
0.5%
Space Separator 492
 
0.5%
Lowercase Letter 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 9762
 
13.8%
기 4704
 
6.6%
계 3569
 
5.0%
장 2951
 
4.2%
주 2432
 
3.4%
단 2228
 
3.1%
전 2188
 
3.1%
지 2078
 
2.9%
차 2038
 
2.9%
시 1612
 
2.3%
Other values (228) 37308
52.6%
Uppercase Letter
ValueCountFrequency (%)
E 792
27.1%
V 420
14.4%
D 407
13.9%
F 406
13.9%
M 400
13.7%
L 383
13.1%
C 29
 
1.0%
O 28
 
1.0%
P 19
 
0.7%
I 15
 
0.5%
Other values (5) 19
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 1267
37.8%
2 684
20.4%
4 370
 
11.0%
3 320
 
9.5%
6 180
 
5.4%
5 155
 
4.6%
7 151
 
4.5%
0 107
 
3.2%
8 101
 
3.0%
9 16
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
e 2
28.6%
l 1
14.3%
v 1
14.3%
f 1
14.3%
d 1
14.3%
m 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 12085
96.1%
. 290
 
2.3%
/ 195
 
1.6%
Close Punctuation
ValueCountFrequency (%)
) 1692
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1691
100.0%
Math Symbol
ValueCountFrequency (%)
~ 505
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 504
100.0%
Space Separator
ValueCountFrequency (%)
492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70870
74.9%
Common 20805
 
22.0%
Latin 2925
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 9762
 
13.8%
기 4704
 
6.6%
계 3569
 
5.0%
장 2951
 
4.2%
주 2432
 
3.4%
단 2228
 
3.1%
전 2188
 
3.1%
지 2078
 
2.9%
차 2038
 
2.9%
시 1612
 
2.3%
Other values (228) 37308
52.6%
Latin
ValueCountFrequency (%)
E 792
27.1%
V 420
14.4%
D 407
13.9%
F 406
13.9%
M 400
13.7%
L 383
13.1%
C 29
 
1.0%
O 28
 
1.0%
P 19
 
0.6%
I 15
 
0.5%
Other values (11) 26
 
0.9%
Common
ValueCountFrequency (%)
, 12085
58.1%
) 1692
 
8.1%
( 1691
 
8.1%
1 1267
 
6.1%
2 684
 
3.3%
~ 505
 
2.4%
- 504
 
2.4%
492
 
2.4%
4 370
 
1.8%
3 320
 
1.5%
Other values (8) 1195
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70870
74.9%
ASCII 23730
 
25.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 12085
50.9%
) 1692
 
7.1%
( 1691
 
7.1%
1 1267
 
5.3%
E 792
 
3.3%
2 684
 
2.9%
~ 505
 
2.1%
- 504
 
2.1%
492
 
2.1%
V 420
 
1.8%
Other values (29) 3598
 
15.2%
Hangul
ValueCountFrequency (%)
실 9762
 
13.8%
기 4704
 
6.6%
계 3569
 
5.0%
장 2951
 
4.2%
주 2432
 
3.4%
단 2228
 
3.1%
전 2188
 
3.1%
지 2078
 
2.9%
차 2038
 
2.9%
시 1612
 
2.3%
Other values (228) 37308
52.6%

면적
Real number (ℝ)

SKEWED 

Distinct5608
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.635859
Minimum0
Maximum31603.83
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:27:01.747435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.37
Q13.4975
median15.195
Q341.41
95-th percentile201.4825
Maximum31603.83
Range31603.83
Interquartile range (IQR)37.9125

Descriptive statistics

Standard deviation596.49359
Coefficient of variation (CV)7.3067595
Kurtosis1645.1698
Mean81.635859
Median Absolute Deviation (MAD)13.311
Skewness35.787914
Sum816358.59
Variance355804.61
MonotonicityNot monotonic
2024-05-10T23:27:02.023463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.41 37
 
0.4%
1.03 33
 
0.3%
0.86 25
 
0.2%
0.11 23
 
0.2%
0.94 21
 
0.2%
2.43 20
 
0.2%
0.1 20
 
0.2%
22.04 19
 
0.2%
0.06 18
 
0.2%
2.44 17
 
0.2%
Other values (5598) 9767
97.7%
ValueCountFrequency (%)
0.0 9
0.1%
0.003 1
 
< 0.1%
0.008 1
 
< 0.1%
0.009 2
 
< 0.1%
0.01 3
 
< 0.1%
0.013 1
 
< 0.1%
0.017 1
 
< 0.1%
0.019 1
 
< 0.1%
0.02 9
0.1%
0.022 1
 
< 0.1%
ValueCountFrequency (%)
31603.83 1
< 0.1%
29264.13 1
< 0.1%
23805.23 1
< 0.1%
12823.71 1
< 0.1%
10358.53 1
< 0.1%
8664.59 1
< 0.1%
8037.58 1
< 0.1%
6832.05 1
< 0.1%
6241.222 1
< 0.1%
5982.8 1
< 0.1%

Interactions

2024-05-10T23:26:47.901647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:43.461256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:45.441482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:46.573495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:48.215545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:43.951864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:45.732693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:46.924886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:48.529055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:44.549269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:45.989840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:47.280528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:48.890003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:45.044679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:46.262399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:26:47.579305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:27:02.204750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드면적
전유_공용_구분_코드1.0000.0940.7290.0210.0840.029
주_부속_구분_코드0.0941.0000.0730.0000.0410.000
층_구분_코드0.7290.0731.0000.0000.1450.000
층_번호0.0210.0000.0001.0000.0000.000
구조_코드0.0840.0410.1450.0001.0000.000
면적0.0290.0000.0000.0000.0001.000
2024-05-10T23:27:02.388783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주_부속_구분_코드전유_공용_구분_코드
주_부속_구분_코드1.0000.060
전유_공용_구분_코드0.0601.000
2024-05-10T23:27:02.761652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층_구분_코드층_번호구조_코드면적전유_공용_구분_코드주_부속_구분_코드
층_구분_코드1.000-0.6500.0930.0540.5390.052
층_번호-0.6501.000-0.066-0.1340.0220.000
구조_코드0.093-0.0661.0000.0350.1030.051
면적0.054-0.1340.0351.0000.0310.000
전유_공용_구분_코드0.5390.0220.1030.0311.0000.060
주_부속_구분_코드0.0520.0000.0510.0000.0601.000

Missing values

2024-05-10T23:26:49.386343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:26:49.978952image/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.
2024-05-10T23:26:50.450505image/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_면적_pk호별명세_pk평형_구분_명전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드주_용도_코드기타_용도면적
1540411000-10002631111000-139C-2N204004202001주차장(지6-지1)92.49
394511000-10000148011000-1062115204002103001화장실,계단실21.64
770611000-10000854411000-100004025370.92102002107201판매시설(상점)99.64
8371611110-1991211110-3477SB-24204002104001방재센타,복도,엠디에프실(지1,2층)0.47
2641711000-171311000-183C201084214202주차장43.61
5315311110-10000165611110-38633D202012114202관리실0.55
7932311110-1583011110-22961602201064215101기계,전기실8.42
7162911110-10002845011110-10005967219-42020102114202홀 복도(1,2층,6~20층)15.12
5716111110-10000867111110-100014318A204002102001관리사무소,주민공동시설(지2,2층)0.89
6881011110-10002470111110-10003055120A204002102003주차장6.07
관리_전유_공용_면적_pk호별명세_pk평형_구분_명전유_공용_구분_코드주_부속_구분_코드층_구분_코드층_번호구조_코드주_용도_코드기타_용도면적
3116311000-2140011000-80AA201012102001주민공동시설4.23
525911000-10000609711000-13179.07102002218001창고24.5
8618211110-2213011110-4797910<NA>02102003<NA>29.97
6380211110-10001743011110-10002304657.54(a)201012102003도시형생황주택(단지형다세대주택)주차장30.06
1118011000-10001201711000-10000402571.44204002107001기계실,전기실,창고,재활용창고,용역원실,휴게실,오락실,주차관제실,체력단련실,검수실,방재센터,경비실,유아실,사무실2.53
3624411000-2597511000-924.91202004207999내부통로,화장실,방풍실,공조실3.66
1733811000-10002874911000-1000043061-601204002110004지하주차장(지3-지1)2022.08
252611000-100000000000000087827911000-10000448540층102004214204업무시설(사무소)1891.78
5895111110-10001059211110-1000173321807204014215101로비,계단실,승강기,홀,린넨실,복도36.92
3524511000-2507511000-91ss030201084207201기계실,전기실,발전기실30.798