Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells19983
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory820.3 KiB
Average record size in memory84.0 B

Variable types

Text5
Numeric2
Categorical1
Unsupported1

Dataset

Description관리_호별_명세_pk,관리_동별_개요_pk,호_번호,호_명칭,평형_구분_명,층_번호,층_구분_코드,관리_건축물대장_참조_pk,변경_구분_코드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15664/S/1/datasetView.do

Alerts

층_구분_코드 is highly imbalanced (78.9%)Imbalance
관리_건축물대장_참조_pk has 9935 (99.4%) missing valuesMissing
변경_구분_코드 has 10000 (100.0%) missing valuesMissing
관리_호별_명세_pk has unique valuesUnique
변경_구분_코드 is an unsupported type, check if it needs cleaning or further analysisUnsupported
호_번호 has 2544 (25.4%) zerosZeros

Reproduction

Analysis started2024-05-18 03:21:29.254520
Analysis finished2024-05-18 03:21:34.347825
Duration5.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_호별_명세_pk
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T12:21:34.913461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length13.6772
Min length7

Characters and Unicode

Total characters136772
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 row11110-8270
2nd row11000-100001935
3rd row11110-3117
4th row11110-100010190
5th row11110-2262
ValueCountFrequency (%)
11110-8270 1
 
< 0.1%
11110-100022908 1
 
< 0.1%
11110-1000000000000000440107 1
 
< 0.1%
11000-15070 1
 
< 0.1%
11110-100011367 1
 
< 0.1%
11000-20713 1
 
< 0.1%
11000-22251 1
 
< 0.1%
11110-702 1
 
< 0.1%
11000-33353 1
 
< 0.1%
11140-1000000000000000191020 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-18T12:21:36.207028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 48806
35.7%
1 41527
30.4%
- 10000
 
7.3%
2 5995
 
4.4%
4 5978
 
4.4%
3 4788
 
3.5%
9 4088
 
3.0%
5 4001
 
2.9%
8 3954
 
2.9%
7 3881
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126772
92.7%
Dash Punctuation 10000
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48806
38.5%
1 41527
32.8%
2 5995
 
4.7%
4 5978
 
4.7%
3 4788
 
3.8%
9 4088
 
3.2%
5 4001
 
3.2%
8 3954
 
3.1%
7 3881
 
3.1%
6 3754
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 136772
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48806
35.7%
1 41527
30.4%
- 10000
 
7.3%
2 5995
 
4.4%
4 5978
 
4.4%
3 4788
 
3.5%
9 4088
 
3.0%
5 4001
 
2.9%
8 3954
 
2.9%
7 3881
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48806
35.7%
1 41527
30.4%
- 10000
 
7.3%
2 5995
 
4.4%
4 5978
 
4.4%
3 4788
 
3.5%
9 4088
 
3.0%
5 4001
 
2.9%
8 3954
 
2.9%
7 3881
 
2.8%

관리_동별_개요_pk
Text

Distinct1092
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T12:21:36.959515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length12.7359
Min length7

Characters and Unicode

Total characters127359
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

Unique480 ?
Unique (%)4.8%

Sample

1st row11110-1179
2nd row11000-100004785
3rd row11110-775
4th row11110-100020542
5th row11110-716
ValueCountFrequency (%)
11000-100005238 514
 
5.1%
11140-100007179 467
 
4.7%
11000-131 309
 
3.1%
11000-100005237 264
 
2.6%
11000-45 156
 
1.6%
11000-82 139
 
1.4%
11110-1252 135
 
1.4%
11110-3036 118
 
1.2%
11000-5 104
 
1.0%
11000-76 97
 
1.0%
Other values (1082) 7697
77.0%
2024-05-18T12:21:38.438948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 47222
37.1%
1 39481
31.0%
- 10000
 
7.9%
7 4756
 
3.7%
2 4649
 
3.7%
4 4290
 
3.4%
5 3928
 
3.1%
3 3782
 
3.0%
8 3333
 
2.6%
6 3089
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117359
92.1%
Dash Punctuation 10000
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47222
40.2%
1 39481
33.6%
7 4756
 
4.1%
2 4649
 
4.0%
4 4290
 
3.7%
5 3928
 
3.3%
3 3782
 
3.2%
8 3333
 
2.8%
6 3089
 
2.6%
9 2829
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 127359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47222
37.1%
1 39481
31.0%
- 10000
 
7.9%
7 4756
 
3.7%
2 4649
 
3.7%
4 4290
 
3.4%
5 3928
 
3.1%
3 3782
 
3.0%
8 3333
 
2.6%
6 3089
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47222
37.1%
1 39481
31.0%
- 10000
 
7.9%
7 4756
 
3.7%
2 4649
 
3.7%
4 4290
 
3.4%
5 3928
 
3.1%
3 3782
 
3.0%
8 3333
 
2.6%
6 3089
 
2.4%

호_번호
Real number (ℝ)

ZEROS 

Distinct1862
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean435.212
Minimum0
Maximum5339
Zeros2544
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T12:21:38.874159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94
Q3353
95-th percentile2761.05
Maximum5339
Range5339
Interquartile range (IQR)353

Descriptive statistics

Standard deviation902.70813
Coefficient of variation (CV)2.0741802
Kurtosis9.5539665
Mean435.212
Median Absolute Deviation (MAD)94
Skewness3.1000898
Sum4352120
Variance814881.97
MonotonicityNot monotonic
2024-05-18T12:21:39.478672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2544
 
25.4%
1 104
 
1.0%
2 85
 
0.9%
4 84
 
0.8%
3 79
 
0.8%
7 76
 
0.8%
5 72
 
0.7%
6 62
 
0.6%
8 52
 
0.5%
9 43
 
0.4%
Other values (1852) 6799
68.0%
ValueCountFrequency (%)
0 2544
25.4%
1 104
 
1.0%
2 85
 
0.9%
3 79
 
0.8%
4 84
 
0.8%
5 72
 
0.7%
6 62
 
0.6%
7 76
 
0.8%
8 52
 
0.5%
9 43
 
0.4%
ValueCountFrequency (%)
5339 1
< 0.1%
5324 1
< 0.1%
5320 1
< 0.1%
5289 1
< 0.1%
5286 1
< 0.1%
5275 1
< 0.1%
5254 1
< 0.1%
5246 1
< 0.1%
5237 1
< 0.1%
5228 1
< 0.1%

호_명칭
Text

Distinct5464
Distinct (%)54.7%
Missing19
Missing (%)0.2%
Memory size156.2 KiB
2024-05-18T12:21:40.575325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length4.8019237
Min length1

Characters and Unicode

Total characters47928
Distinct characters122
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

Unique4537 ?
Unique (%)45.5%

Sample

1st row1713
2nd rowS1004
3rd row오피스텔-1413
4th row301
5th rowb03
ValueCountFrequency (%)
401 116
 
1.1%
101 107
 
1.0%
201 106
 
1.0%
오피스텔 105
 
1.0%
301 100
 
1.0%
302 99
 
1.0%
202 98
 
1.0%
402 62
 
0.6%
501 62
 
0.6%
아파트 57
 
0.6%
Other values (5271) 9292
91.1%
2024-05-18T12:21:42.158372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9153
19.1%
1 9087
19.0%
2 5211
10.9%
3 3482
 
7.3%
- 3002
 
6.3%
4 2670
 
5.6%
5 2074
 
4.3%
6 1794
 
3.7%
7 1598
 
3.3%
8 1425
 
3.0%
Other values (112) 8432
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37775
78.8%
Other Letter 4079
 
8.5%
Dash Punctuation 3002
 
6.3%
Uppercase Letter 2763
 
5.8%
Space Separator 223
 
0.5%
Close Punctuation 33
 
0.1%
Open Punctuation 33
 
0.1%
Lowercase Letter 15
 
< 0.1%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 1229
30.1%
스 247
 
6.1%
텔 229
 
5.6%
오 228
 
5.6%
피 228
 
5.6%
이 207
 
5.1%
에 152
 
3.7%
동 134
 
3.3%
비 125
 
3.1%
지 109
 
2.7%
Other values (69) 1191
29.2%
Uppercase Letter
ValueCountFrequency (%)
B 952
34.5%
F 467
16.9%
A 358
 
13.0%
T 178
 
6.4%
L 159
 
5.8%
C 159
 
5.8%
D 156
 
5.6%
Y 117
 
4.2%
E 110
 
4.0%
S 54
 
2.0%
Other values (11) 53
 
1.9%
Decimal Number
ValueCountFrequency (%)
0 9153
24.2%
1 9087
24.1%
2 5211
13.8%
3 3482
 
9.2%
4 2670
 
7.1%
5 2074
 
5.5%
6 1794
 
4.7%
7 1598
 
4.2%
8 1425
 
3.8%
9 1281
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
b 7
46.7%
c 2
 
13.3%
e 2
 
13.3%
r 2
 
13.3%
m 1
 
6.7%
i 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 3002
100.0%
Space Separator
ValueCountFrequency (%)
223
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41071
85.7%
Hangul 4079
 
8.5%
Latin 2778
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 1229
30.1%
스 247
 
6.1%
텔 229
 
5.6%
오 228
 
5.6%
피 228
 
5.6%
이 207
 
5.1%
에 152
 
3.7%
동 134
 
3.3%
비 125
 
3.1%
지 109
 
2.7%
Other values (69) 1191
29.2%
Latin
ValueCountFrequency (%)
B 952
34.3%
F 467
16.8%
A 358
 
12.9%
T 178
 
6.4%
L 159
 
5.7%
C 159
 
5.7%
D 156
 
5.6%
Y 117
 
4.2%
E 110
 
4.0%
S 54
 
1.9%
Other values (17) 68
 
2.4%
Common
ValueCountFrequency (%)
0 9153
22.3%
1 9087
22.1%
2 5211
12.7%
3 3482
 
8.5%
- 3002
 
7.3%
4 2670
 
6.5%
5 2074
 
5.0%
6 1794
 
4.4%
7 1598
 
3.9%
8 1425
 
3.5%
Other values (6) 1575
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43849
91.5%
Hangul 4079
 
8.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9153
20.9%
1 9087
20.7%
2 5211
11.9%
3 3482
 
7.9%
- 3002
 
6.8%
4 2670
 
6.1%
5 2074
 
4.7%
6 1794
 
4.1%
7 1598
 
3.6%
8 1425
 
3.2%
Other values (33) 4353
9.9%
Hangul
ValueCountFrequency (%)
호 1229
30.1%
스 247
 
6.1%
텔 229
 
5.6%
오 228
 
5.6%
피 228
 
5.6%
이 207
 
5.1%
에 152
 
3.7%
동 134
 
3.3%
비 125
 
3.1%
지 109
 
2.7%
Other values (69) 1191
29.2%

평형_구분_명
Text

Distinct3701
Distinct (%)37.1%
Missing29
Missing (%)0.3%
Memory size156.2 KiB
2024-05-18T12:21:43.090508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length3.49654
Min length1

Characters and Unicode

Total characters34864
Distinct characters154
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

Unique2486 ?
Unique (%)24.9%

Sample

1st row22C2
2nd row1004
3rd rowF
4th row62.21
5th row10.90
ValueCountFrequency (%)
a 424
 
4.2%
b 176
 
1.7%
c 126
 
1.2%
1ob 81
 
0.8%
type 77
 
0.8%
d 72
 
0.7%
66.95 66
 
0.6%
17ta 66
 
0.6%
19 55
 
0.5%
g 55
 
0.5%
Other values (3589) 8964
88.2%
2024-05-18T12:21:44.844923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3998
 
11.5%
2 2957
 
8.5%
3 2740
 
7.9%
A 2243
 
6.4%
4 2224
 
6.4%
6 2057
 
5.9%
5 1980
 
5.7%
0 1897
 
5.4%
. 1843
 
5.3%
B 1665
 
4.8%
Other values (144) 11260
32.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22271
63.9%
Uppercase Letter 7955
 
22.8%
Other Punctuation 1867
 
5.4%
Lowercase Letter 1055
 
3.0%
Other Letter 759
 
2.2%
Dash Punctuation 680
 
2.0%
Space Separator 191
 
0.5%
Close Punctuation 39
 
0.1%
Open Punctuation 39
 
0.1%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 113
14.9%
형 102
 
13.4%
생 80
 
10.5%
숙 58
 
7.6%
오 31
 
4.1%
동 30
 
4.0%
층 25
 
3.3%
전 20
 
2.6%
근 19
 
2.5%
유 19
 
2.5%
Other values (73) 262
34.5%
Lowercase Letter
ValueCountFrequency (%)
a 212
20.1%
b 163
15.5%
e 106
10.0%
p 105
10.0%
o 103
9.8%
y 94
8.9%
f 74
 
7.0%
c 53
 
5.0%
s 34
 
3.2%
t 26
 
2.5%
Other values (16) 85
8.1%
Uppercase Letter
ValueCountFrequency (%)
A 2243
28.2%
B 1665
20.9%
C 750
 
9.4%
F 673
 
8.5%
O 520
 
6.5%
D 489
 
6.1%
S 259
 
3.3%
E 246
 
3.1%
T 227
 
2.9%
G 144
 
1.8%
Other values (15) 739
 
9.3%
Decimal Number
ValueCountFrequency (%)
1 3998
18.0%
2 2957
13.3%
3 2740
12.3%
4 2224
10.0%
6 2057
9.2%
5 1980
8.9%
0 1897
8.5%
7 1605
7.2%
8 1531
 
6.9%
9 1282
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 1843
98.7%
, 13
 
0.7%
* 10
 
0.5%
' 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 680
100.0%
Space Separator
ValueCountFrequency (%)
191
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25095
72.0%
Latin 9010
 
25.8%
Hangul 759
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 113
14.9%
형 102
 
13.4%
생 80
 
10.5%
숙 58
 
7.6%
오 31
 
4.1%
동 30
 
4.0%
층 25
 
3.3%
전 20
 
2.6%
근 19
 
2.5%
유 19
 
2.5%
Other values (73) 262
34.5%
Latin
ValueCountFrequency (%)
A 2243
24.9%
B 1665
18.5%
C 750
 
8.3%
F 673
 
7.5%
O 520
 
5.8%
D 489
 
5.4%
S 259
 
2.9%
E 246
 
2.7%
T 227
 
2.5%
a 212
 
2.4%
Other values (41) 1726
19.2%
Common
ValueCountFrequency (%)
1 3998
15.9%
2 2957
11.8%
3 2740
10.9%
4 2224
8.9%
6 2057
8.2%
5 1980
7.9%
0 1897
7.6%
. 1843
7.3%
7 1605
6.4%
8 1531
 
6.1%
Other values (10) 2263
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34103
97.8%
Hangul 758
 
2.2%
CJK Compat 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3998
 
11.7%
2 2957
 
8.7%
3 2740
 
8.0%
A 2243
 
6.6%
4 2224
 
6.5%
6 2057
 
6.0%
5 1980
 
5.8%
0 1897
 
5.6%
. 1843
 
5.4%
B 1665
 
4.9%
Other values (60) 10499
30.8%
Hangul
ValueCountFrequency (%)
호 113
14.9%
형 102
 
13.5%
생 80
 
10.6%
숙 58
 
7.7%
오 31
 
4.1%
동 30
 
4.0%
층 25
 
3.3%
전 20
 
2.6%
근 19
 
2.5%
유 19
 
2.5%
Other values (72) 261
34.4%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅁ 1
100.0%

층_번호
Real number (ℝ)

Distinct64
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1807
Minimum0
Maximum402
Zeros50
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T12:21:45.475682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q313
95-th percentile27
Maximum402
Range402
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.9191475
Coefficient of variation (CV)1.0804348
Kurtosis277.18732
Mean9.1807
Median Absolute Deviation (MAD)4
Skewness8.910055
Sum91807
Variance98.389486
MonotonicityNot monotonic
2024-05-18T12:21:46.057368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1279
 
12.8%
2 1065
 
10.7%
3 856
 
8.6%
4 718
 
7.2%
5 619
 
6.2%
6 519
 
5.2%
7 501
 
5.0%
8 407
 
4.1%
9 398
 
4.0%
10 341
 
3.4%
Other values (54) 3297
33.0%
ValueCountFrequency (%)
0 50
 
0.5%
1 1279
12.8%
2 1065
10.7%
3 856
8.6%
4 718
7.2%
5 619
6.2%
6 519
5.2%
7 501
 
5.0%
8 407
 
4.1%
9 398
 
4.0%
ValueCountFrequency (%)
402 1
 
< 0.1%
203 2
< 0.1%
108 1
 
< 0.1%
66 1
 
< 0.1%
65 1
 
< 0.1%
62 3
< 0.1%
61 3
< 0.1%
57 2
< 0.1%
55 1
 
< 0.1%
54 1
 
< 0.1%

층_구분_코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20
9177 
10
 
809
<NA>
 
11
40
 
3

Length

Max length4
Median length2
Mean length2.0022
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row10
3rd row20
4th row20
5th row10

Common Values

ValueCountFrequency (%)
20 9177
91.8%
10 809
 
8.1%
<NA> 11
 
0.1%
40 3
 
< 0.1%

Length

2024-05-18T12:21:46.706166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:21:47.158300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 9177
91.8%
10 809
 
8.1%
na 11
 
0.1%
40 3
 
< 0.1%

관리_건축물대장_참조_pk
Text

MISSING 

Distinct42
Distinct (%)64.6%
Missing9935
Missing (%)99.4%
Memory size156.2 KiB
2024-05-18T12:21:47.861969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length28
Mean length24.553846
Min length11

Characters and Unicode

Total characters1596
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

Unique24 ?
Unique (%)36.9%

Sample

1st row11110-1000000000000001991257
2nd row11110-1000000000000001991266
3rd row11110-1000000000000001991269
4th row11110-1000000000000001991258
5th row11110-1000000000000001991248
ValueCountFrequency (%)
11110-1000000000000001991271 4
 
6.2%
11110-1000000000000001991282 3
 
4.6%
11110-1000000000000001991248 3
 
4.6%
11110-1000000000000001991270 3
 
4.6%
11110-1000000000000001991278 2
 
3.1%
11110-1000000000000001991244 2
 
3.1%
11110-1000000000000001991251 2
 
3.1%
11110-1000000000000001991264 2
 
3.1%
11140-100196155 2
 
3.1%
11110-1000000000000001991257 2
 
3.1%
Other values (32) 40
61.5%
2024-05-18T12:21:49.174866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 780
48.9%
1 437
27.4%
9 110
 
6.9%
- 65
 
4.1%
2 61
 
3.8%
4 33
 
2.1%
7 26
 
1.6%
6 26
 
1.6%
5 23
 
1.4%
8 22
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1531
95.9%
Dash Punctuation 65
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 780
50.9%
1 437
28.5%
9 110
 
7.2%
2 61
 
4.0%
4 33
 
2.2%
7 26
 
1.7%
6 26
 
1.7%
5 23
 
1.5%
8 22
 
1.4%
3 13
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1596
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 780
48.9%
1 437
27.4%
9 110
 
6.9%
- 65
 
4.1%
2 61
 
3.8%
4 33
 
2.1%
7 26
 
1.6%
6 26
 
1.6%
5 23
 
1.4%
8 22
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 780
48.9%
1 437
27.4%
9 110
 
6.9%
- 65
 
4.1%
2 61
 
3.8%
4 33
 
2.1%
7 26
 
1.6%
6 26
 
1.6%
5 23
 
1.4%
8 22
 
1.4%

변경_구분_코드
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Interactions

2024-05-18T12:21:31.947892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:21:30.976931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:21:32.358714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:21:31.470546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T12:21:49.485417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호_번호층_번호층_구분_코드관리_건축물대장_참조_pk
호_번호1.0000.0000.119NaN
층_번호0.0001.0000.000NaN
층_구분_코드0.1190.0001.0000.000
관리_건축물대장_참조_pkNaNNaN0.0001.000
2024-05-18T12:21:49.823026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호_번호층_번호층_구분_코드
호_번호1.0000.1950.071
층_번호0.1951.0000.000
층_구분_코드0.0710.0001.000

Missing values

2024-05-18T12:21:32.992767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T12:21:33.605734image/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.
2024-05-18T12:21:34.108470image/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_호별_명세_pk관리_동별_개요_pk호_번호호_명칭평형_구분_명층_번호층_구분_코드관리_건축물대장_참조_pk변경_구분_코드
8237711110-827011110-1179639171322C21720<NA><NA>
47811000-10000193511000-1000047850S10041004110<NA><NA>
7679611110-311711110-775412오피스텔-1413F1420<NA><NA>
5602411110-10001019011110-100020542030162.21320<NA><NA>
7592311110-226211110-7163b0310.90110<NA><NA>
8423711110-994511110-12522182-126S213220<NA><NA>
5044711110-100000000000000044009911110-10000000000000008579760504호20.46 Type52011110-1000000000000001991257<NA>
1474211000-1099911000-593890229B820<NA><NA>
9099711140-10000834211140-1000071792345F3346F3346320<NA><NA>
6529111110-103011110-311350171124D11720<NA><NA>
관리_호별_명세_pk관리_동별_개요_pk호_번호호_명칭평형_구분_명층_번호층_구분_코드관리_건축물대장_참조_pk변경_구분_코드
6199111110-10002031711110-10006194801009D1020<NA><NA>
7825811110-443311110-77872101동 1603호44A1620<NA><NA>
6679911110-1167711110-1942840122.3420<NA><NA>
3305611000-2748211000-140511102-180345AAL1820<NA><NA>
7420111110-1875011110-3417210229A120<NA><NA>
7708311110-337611110-776209아파트 -8101E820<NA><NA>
616611000-10001192311000-1000052382402Y-4001102.19420<NA><NA>
6941711110-1415011110-22415302호22.18320<NA><NA>
7768211110-391511110-776748오피스텔-16131OB1620<NA><NA>
993111000-10002703611000-10000529414에이-1202421220<NA><NA>