Overview

Dataset statistics

Number of variables17
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.9 KiB
Average record size in memory142.7 B

Variable types

Categorical11
Text4
Numeric2

Alerts

OFFICE_DONG has constant value ""Constant
STD_DATE has constant value ""Constant
OFFICE_NM is highly overall correlated with PRICE and 8 other fieldsHigh correlation
BLD_CD is highly overall correlated with PRICE and 8 other fieldsHigh correlation
OFFICE_CD is highly overall correlated with PRICE and 8 other fieldsHigh correlation
HOUS_ID is highly overall correlated with PRICE and 8 other fieldsHigh correlation
OFFICE_DONG_CD is highly overall correlated with PRICE and 8 other fieldsHigh correlation
ROAD_ADDR is highly overall correlated with PRICE and 8 other fieldsHigh correlation
BLK_CD is highly overall correlated with PRICE and 8 other fieldsHigh correlation
Y_AXIS is highly overall correlated with PRICE and 8 other fieldsHigh correlation
PRICE is highly overall correlated with OFFICE_CD and 8 other fieldsHigh correlation
OFFICE_FLR is highly overall correlated with PRICE and 8 other fieldsHigh correlation
HOUS_ADDR has unique valuesUnique
X_AXIS has unique valuesUnique
OFFICE_HO_CD has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:36:55.380096
Analysis finished2023-12-10 06:36:57.855100
Duration2.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

OFFICE_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
OI00019954
85 
OI00000327
74 
OI00002781
22 
OI00019762
19 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOI00019954
2nd rowOI00019954
3rd rowOI00019954
4th rowOI00019954
5th rowOI00019954

Common Values

ValueCountFrequency (%)
OI00019954 85
42.5%
OI00000327 74
37.0%
OI00002781 22
 
11.0%
OI00019762 19
 
9.5%

Length

2023-12-10T15:36:57.975760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:58.145685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
oi00019954 85
42.5%
oi00000327 74
37.0%
oi00002781 22
 
11.0%
oi00019762 19
 
9.5%

OFFICE_DONG_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
OD10019954
85 
OD10000327
74 
OD10002781
22 
OD10019762
19 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOD10019954
2nd rowOD10019954
3rd rowOD10019954
4th rowOD10019954
5th rowOD10019954

Common Values

ValueCountFrequency (%)
OD10019954 85
42.5%
OD10000327 74
37.0%
OD10002781 22
 
11.0%
OD10019762 19
 
9.5%

Length

2023-12-10T15:36:58.358443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:58.699713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
od10019954 85
42.5%
od10000327 74
37.0%
od10002781 22
 
11.0%
od10019762 19
 
9.5%

HOUS_ID
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1150010200006310000
85 
1150010400014790009
74 
4117110100006740062
22 
4117110100006760001
19 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1150010200006310000
2nd row1150010200006310000
3rd row1150010200006310000
4th row1150010200006310000
5th row1150010200006310000

Common Values

ValueCountFrequency (%)
1150010200006310000 85
42.5%
1150010400014790009 74
37.0%
4117110100006740062 22
 
11.0%
4117110100006760001 19
 
9.5%

Length

2023-12-10T15:36:58.926760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:59.139876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1150010200006310000 85
42.5%
1150010400014790009 74
37.0%
4117110100006740062 22
 
11.0%
4117110100006760001 19
 
9.5%

BLD_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1150010200106310001027250
85 
1150010400114790009009680
74 
4117110100106740063008387
22 
4117110100106760296007990
19 

Length

Max length25
Median length25
Mean length25
Min length25

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1150010200106310001027250
2nd row1150010200106310001027250
3rd row1150010200106310001027250
4th row1150010200106310001027250
5th row1150010200106310001027250

Common Values

ValueCountFrequency (%)
1150010200106310001027250 85
42.5%
1150010400114790009009680 74
37.0%
4117110100106740063008387 22
 
11.0%
4117110100106760296007990 19
 
9.5%

Length

2023-12-10T15:36:59.344091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:59.525664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1150010200106310001027250 85
42.5%
1150010400114790009009680 74
37.0%
4117110100106740063008387 22
 
11.0%
4117110100106760296007990 19
 
9.5%

HOUS_ADDR
Text

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:36:59.885967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length34.155
Min length31

Characters and Unicode

Total characters6831
Distinct characters57
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)100.0%

Sample

1st row서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 632호
2nd row서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 701호
3rd row서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 702호
4th row서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 703호
5th row서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 704호
ValueCountFrequency (%)
서울특별시 159
12.8%
강서구 159
12.8%
등촌동 85
 
6.8%
631번지 85
 
6.8%
등촌두산위브센티움 85
 
6.8%
휴먼빌 74
 
6.0%
1479-9번지 74
 
6.0%
가양동 74
 
6.0%
안양시 41
 
3.3%
경기도 41
 
3.3%
Other values (183) 364
29.3%
2023-12-10T15:37:00.410049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1041
 
15.2%
서 318
 
4.7%
1 296
 
4.3%
6 215
 
3.1%
지 200
 
2.9%
시 200
 
2.9%
번 200
 
2.9%
구 200
 
2.9%
호 200
 
2.9%
동 200
 
2.9%
Other values (47) 3761
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3960
58.0%
Decimal Number 1468
 
21.5%
Space Separator 1041
 
15.2%
Uppercase Letter 247
 
3.6%
Dash Punctuation 115
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 318
 
8.0%
지 200
 
5.1%
시 200
 
5.1%
번 200
 
5.1%
구 200
 
5.1%
호 200
 
5.1%
동 200
 
5.1%
촌 170
 
4.3%
등 170
 
4.3%
강 159
 
4.0%
Other values (26) 1943
49.1%
Decimal Number
ValueCountFrequency (%)
1 296
20.2%
6 215
14.6%
9 184
12.5%
7 177
12.1%
0 140
9.5%
4 137
9.3%
3 129
8.8%
2 81
 
5.5%
5 60
 
4.1%
8 49
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
T 38
15.4%
E 38
15.4%
O 38
15.4%
R 38
15.4%
P 19
7.7%
J 19
7.7%
C 19
7.7%
W 19
7.7%
B 19
7.7%
Space Separator
ValueCountFrequency (%)
1041
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3960
58.0%
Common 2624
38.4%
Latin 247
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 318
 
8.0%
지 200
 
5.1%
시 200
 
5.1%
번 200
 
5.1%
구 200
 
5.1%
호 200
 
5.1%
동 200
 
5.1%
촌 170
 
4.3%
등 170
 
4.3%
강 159
 
4.0%
Other values (26) 1943
49.1%
Common
ValueCountFrequency (%)
1041
39.7%
1 296
 
11.3%
6 215
 
8.2%
9 184
 
7.0%
7 177
 
6.7%
0 140
 
5.3%
4 137
 
5.2%
3 129
 
4.9%
- 115
 
4.4%
2 81
 
3.1%
Other values (2) 109
 
4.2%
Latin
ValueCountFrequency (%)
T 38
15.4%
E 38
15.4%
O 38
15.4%
R 38
15.4%
P 19
7.7%
J 19
7.7%
C 19
7.7%
W 19
7.7%
B 19
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3960
58.0%
ASCII 2871
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1041
36.3%
1 296
 
10.3%
6 215
 
7.5%
9 184
 
6.4%
7 177
 
6.2%
0 140
 
4.9%
4 137
 
4.8%
3 129
 
4.5%
- 115
 
4.0%
2 81
 
2.8%
Other values (11) 356
 
12.4%
Hangul
ValueCountFrequency (%)
서 318
 
8.0%
지 200
 
5.1%
시 200
 
5.1%
번 200
 
5.1%
구 200
 
5.1%
호 200
 
5.1%
동 200
 
5.1%
촌 170
 
4.3%
등 170
 
4.3%
강 159
 
4.0%
Other values (26) 1943
49.1%

ROAD_ADDR
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
서울특별시 강서구 양천로 564
85 
서울특별시 강서구 양천로57길 9-13
74 
경기도 안양시 만안구 장내로139번길 52
22 
경기도 안양시 만안구 안양로 311
19 

Length

Max length23
Median length21
Mean length19.33
Min length17

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 강서구 양천로 564
2nd row서울특별시 강서구 양천로 564
3rd row서울특별시 강서구 양천로 564
4th row서울특별시 강서구 양천로 564
5th row서울특별시 강서구 양천로 564

Common Values

ValueCountFrequency (%)
서울특별시 강서구 양천로 564 85
42.5%
서울특별시 강서구 양천로57길 9-13 74
37.0%
경기도 안양시 만안구 장내로139번길 52 22
 
11.0%
경기도 안양시 만안구 안양로 311 19
 
9.5%

Length

2023-12-10T15:37:00.692662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:00.951742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 159
18.9%
강서구 159
18.9%
양천로 85
10.1%
564 85
10.1%
양천로57길 74
8.8%
9-13 74
8.8%
경기도 41
 
4.9%
안양시 41
 
4.9%
만안구 41
 
4.9%
장내로139번길 22
 
2.6%
Other values (3) 60
 
7.1%

X_AXIS
Text

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:37:01.656923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length15
Mean length13.115
Min length9

Characters and Unicode

Total characters2623
Distinct characters39
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)100.0%

Sample

1st row 등촌두산위브센티움 632호
2nd row 등촌두산위브센티움 701호
3rd row 등촌두산위브센티움 702호
4th row 등촌두산위브센티움 703호
5th row 등촌두산위브센티움 704호
ValueCountFrequency (%)
등촌두산위브센티움 85
21.2%
휴먼빌 74
 
18.5%
더존아카데미 22
 
5.5%
project500tower 19
 
4.8%
702호 2
 
0.5%
713호 2
 
0.5%
602호 2
 
0.5%
601호 2
 
0.5%
503호 2
 
0.5%
502호 2
 
0.5%
Other values (171) 188
47.0%
2023-12-10T15:37:02.632683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
400
 
15.2%
호 200
 
7.6%
0 140
 
5.3%
1 118
 
4.5%
센 85
 
3.2%
등 85
 
3.2%
티 85
 
3.2%
움 85
 
3.2%
브 85
 
3.2%
위 85
 
3.2%
Other values (29) 1255
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1319
50.3%
Decimal Number 657
25.0%
Space Separator 400
 
15.2%
Uppercase Letter 247
 
9.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 200
15.2%
센 85
 
6.4%
등 85
 
6.4%
티 85
 
6.4%
움 85
 
6.4%
브 85
 
6.4%
위 85
 
6.4%
산 85
 
6.4%
두 85
 
6.4%
촌 85
 
6.4%
Other values (9) 354
26.8%
Decimal Number
ValueCountFrequency (%)
0 140
21.3%
1 118
18.0%
7 62
9.4%
5 60
9.1%
2 59
9.0%
8 49
 
7.5%
6 48
 
7.3%
3 44
 
6.7%
4 41
 
6.2%
9 36
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
T 38
15.4%
E 38
15.4%
O 38
15.4%
R 38
15.4%
P 19
7.7%
J 19
7.7%
C 19
7.7%
W 19
7.7%
B 19
7.7%
Space Separator
ValueCountFrequency (%)
400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1319
50.3%
Common 1057
40.3%
Latin 247
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 200
15.2%
센 85
 
6.4%
등 85
 
6.4%
티 85
 
6.4%
움 85
 
6.4%
브 85
 
6.4%
위 85
 
6.4%
산 85
 
6.4%
두 85
 
6.4%
촌 85
 
6.4%
Other values (9) 354
26.8%
Common
ValueCountFrequency (%)
400
37.8%
0 140
 
13.2%
1 118
 
11.2%
7 62
 
5.9%
5 60
 
5.7%
2 59
 
5.6%
8 49
 
4.6%
6 48
 
4.5%
3 44
 
4.2%
4 41
 
3.9%
Latin
ValueCountFrequency (%)
T 38
15.4%
E 38
15.4%
O 38
15.4%
R 38
15.4%
P 19
7.7%
J 19
7.7%
C 19
7.7%
W 19
7.7%
B 19
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1319
50.3%
ASCII 1304
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
400
30.7%
0 140
 
10.7%
1 118
 
9.0%
7 62
 
4.8%
5 60
 
4.6%
2 59
 
4.5%
8 49
 
3.8%
6 48
 
3.7%
3 44
 
3.4%
4 41
 
3.1%
Other values (10) 283
21.7%
Hangul
ValueCountFrequency (%)
호 200
15.2%
센 85
 
6.4%
등 85
 
6.4%
티 85
 
6.4%
움 85
 
6.4%
브 85
 
6.4%
위 85
 
6.4%
산 85
 
6.4%
두 85
 
6.4%
촌 85
 
6.4%
Other values (9) 354
26.8%

Y_AXIS
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
299592
85 
298704
74 
304721
22 
304597
19 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row299592
2nd row299592
3rd row299592
4th row299592
5th row299592

Common Values

ValueCountFrequency (%)
299592 85
42.5%
298704 74
37.0%
304721 22
 
11.0%
304597 19
 
9.5%

Length

2023-12-10T15:37:02.867289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:03.025894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
299592 85
42.5%
298704 74
37.0%
304721 22
 
11.0%
304597 19
 
9.5%

BLK_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
551138
85 
551879
74 
533637
22 
533623
19 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row551138
2nd row551138
3rd row551138
4th row551138
5th row551138

Common Values

ValueCountFrequency (%)
551138 85
42.5%
551879 74
37.0%
533637 22
 
11.0%
533623 19
 
9.5%

Length

2023-12-10T15:37:03.250717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:03.385238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
551138 85
42.5%
551879 74
37.0%
533637 22
 
11.0%
533623 19
 
9.5%

OFFICE_HO_CD
Text

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:37:03.687503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2000
Distinct characters12
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

Unique200 ?
Unique (%)100.0%

Sample

1st rowOH00000501
2nd rowOH00000502
3rd rowOH00000503
4th rowOH00000504
5th rowOH00000505
ValueCountFrequency (%)
oh00000501 1
 
0.5%
oh00000011 1
 
0.5%
oh00000023 1
 
0.5%
oh00000002 1
 
0.5%
oh00000003 1
 
0.5%
oh00000004 1
 
0.5%
oh00000005 1
 
0.5%
oh00000006 1
 
0.5%
oh00000007 1
 
0.5%
oh00000008 1
 
0.5%
Other values (190) 190
95.0%
2023-12-10T15:37:04.275139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1121
56.0%
O 200
 
10.0%
H 200
 
10.0%
5 139
 
7.0%
6 67
 
3.4%
1 51
 
2.5%
2 48
 
2.4%
4 41
 
2.1%
3 41
 
2.1%
7 34
 
1.7%
Other values (2) 58
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1600
80.0%
Uppercase Letter 400
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1121
70.1%
5 139
 
8.7%
6 67
 
4.2%
1 51
 
3.2%
2 48
 
3.0%
4 41
 
2.6%
3 41
 
2.6%
7 34
 
2.1%
8 29
 
1.8%
9 29
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
O 200
50.0%
H 200
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1600
80.0%
Latin 400
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1121
70.1%
5 139
 
8.7%
6 67
 
4.2%
1 51
 
3.2%
2 48
 
3.0%
4 41
 
2.6%
3 41
 
2.6%
7 34
 
2.1%
8 29
 
1.8%
9 29
 
1.8%
Latin
ValueCountFrequency (%)
O 200
50.0%
H 200
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1121
56.0%
O 200
 
10.0%
H 200
 
10.0%
5 139
 
7.0%
6 67
 
3.4%
1 51
 
2.5%
2 48
 
2.4%
4 41
 
2.1%
3 41
 
2.1%
7 34
 
1.7%
Other values (2) 58
 
2.9%

OFFICE_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
등촌두산위브센티움
85 
휴먼빌
74 
더존아카데미
22 
PROJECT500TOWER
19 

Length

Max length15
Median length9
Mean length7.02
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등촌두산위브센티움 85
42.5%
휴먼빌 74
37.0%
더존아카데미 22
 
11.0%
PROJECT500TOWER 19
 
9.5%

Length

2023-12-10T15:37:04.534439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:04.729025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등촌두산위브센티움 85
42.5%
휴먼빌 74
37.0%
더존아카데미 22
 
11.0%
project500tower 19
 
9.5%

OFFICE_DONG
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
동명없음
200 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동명없음 200
100.0%

Length

2023-12-10T15:37:04.930652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:05.157120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동명없음 200
100.0%

OFFICE_HO
Text

Distinct177
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:37:05.746771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.095
Min length3

Characters and Unicode

Total characters619
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

Unique154 ?
Unique (%)77.0%

Sample

1st row632
2nd row701
3rd row702
4th row703
5th row704
ValueCountFrequency (%)
501 2
 
1.0%
707 2
 
1.0%
401 2
 
1.0%
602 2
 
1.0%
402 2
 
1.0%
404 2
 
1.0%
601 2
 
1.0%
502 2
 
1.0%
503 2
 
1.0%
714 2
 
1.0%
Other values (167) 180
90.0%
2023-12-10T15:37:06.991303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 118
19.1%
0 102
16.5%
7 62
10.0%
2 59
9.5%
8 49
7.9%
6 48
7.8%
3 44
 
7.1%
5 41
 
6.6%
4 41
 
6.6%
9 36
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
96.9%
Uppercase Letter 19
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 118
19.7%
0 102
17.0%
7 62
10.3%
2 59
9.8%
8 49
8.2%
6 48
8.0%
3 44
 
7.3%
5 41
 
6.8%
4 41
 
6.8%
9 36
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
B 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
96.9%
Latin 19
 
3.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 118
19.7%
0 102
17.0%
7 62
10.3%
2 59
9.8%
8 49
8.2%
6 48
8.0%
3 44
 
7.3%
5 41
 
6.8%
4 41
 
6.8%
9 36
 
6.0%
Latin
ValueCountFrequency (%)
B 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 118
19.1%
0 102
16.5%
7 62
10.0%
2 59
9.5%
8 49
7.9%
6 48
7.8%
3 44
 
7.1%
5 41
 
6.6%
4 41
 
6.6%
9 36
 
5.8%

OFFICE_FLR
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
지상층7
46 
지상층8
32 
지상층9
20 
지상층4
20 
지상층6
19 
Other values (4)
63 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지상층6
2nd row지상층7
3rd row지상층7
4th row지상층7
5th row지상층7

Common Values

ValueCountFrequency (%)
지상층7 46
23.0%
지상층8 32
16.0%
지상층9 20
10.0%
지상층4 20
10.0%
지상층6 19
9.5%
지상층5 19
9.5%
지하층1 19
9.5%
지상층3 14
 
7.0%
지상층2 11
 
5.5%

Length

2023-12-10T15:37:07.189882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:07.380638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상층7 46
23.0%
지상층8 32
16.0%
지상층9 20
10.0%
지상층4 20
10.0%
지상층6 19
9.5%
지상층5 19
9.5%
지하층1 19
9.5%
지상층3 14
 
7.0%
지상층2 11
 
5.5%

AREA
Real number (ℝ)

Distinct32
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.87416
Minimum8.053
Maximum583.369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:37:07.584374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.053
5-th percentile12.6493
Q151.976
median51.976
Q383.96
95-th percentile83.96
Maximum583.369
Range575.316
Interquartile range (IQR)31.984

Descriptive statistics

Standard deviation60.065987
Coefficient of variation (CV)0.95533662
Kurtosis51.053386
Mean62.87416
Median Absolute Deviation (MAD)7.956
Skewness6.5799838
Sum12574.832
Variance3607.9228
MonotonicityNot monotonic
2023-12-10T15:37:07.798284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
51.976 65
32.5%
83.96 45
22.5%
55.42 30
15.0%
16.88 11
 
5.5%
44.02 10
 
5.0%
62.434 9
 
4.5%
9.232 4
 
2.0%
16.87 2
 
1.0%
40.51 1
 
0.5%
539.623 1
 
0.5%
Other values (22) 22
 
11.0%
ValueCountFrequency (%)
8.053 1
 
0.5%
8.098 1
 
0.5%
8.113 1
 
0.5%
8.485 1
 
0.5%
9.232 4
2.0%
10.46 1
 
0.5%
12.37 1
 
0.5%
12.664 1
 
0.5%
15.72 1
 
0.5%
16.87 2
1.0%
ValueCountFrequency (%)
583.369 1
 
0.5%
539.623 1
 
0.5%
383.859 1
 
0.5%
156.326 1
 
0.5%
149.532 1
 
0.5%
85.894 1
 
0.5%
83.96 45
22.5%
76.634 1
 
0.5%
76.606 1
 
0.5%
71.786 1
 
0.5%

PRICE
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1814645
Minimum814000
Maximum2173000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:37:07.956140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum814000
5-th percentile1361450
Q11717000
median1855000
Q32022000
95-th percentile2129000
Maximum2173000
Range1359000
Interquartile range (IQR)305000

Descriptive statistics

Standard deviation247694.68
Coefficient of variation (CV)0.13649759
Kurtosis3.8318116
Mean1814645
Median Absolute Deviation (MAD)167000
Skewness-1.5883593
Sum3.62929 × 108
Variance6.1352652 × 1010
MonotonicityNot monotonic
2023-12-10T15:37:08.114526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1717000 46
23.0%
1855000 45
22.5%
2022000 40
20.0%
1631000 28
14.0%
2036000 19
9.5%
2173000 7
 
3.5%
1085000 4
 
2.0%
1031000 3
 
1.5%
2151000 2
 
1.0%
2129000 2
 
1.0%
Other values (3) 4
 
2.0%
ValueCountFrequency (%)
814000 2
 
1.0%
1031000 3
 
1.5%
1032000 1
 
0.5%
1085000 4
 
2.0%
1376000 1
 
0.5%
1631000 28
14.0%
1717000 46
23.0%
1855000 45
22.5%
2022000 40
20.0%
2036000 19
9.5%
ValueCountFrequency (%)
2173000 7
 
3.5%
2151000 2
 
1.0%
2129000 2
 
1.0%
2036000 19
9.5%
2022000 40
20.0%
1855000 45
22.5%
1717000 46
23.0%
1631000 28
14.0%
1376000 1
 
0.5%
1085000 4
 
2.0%

STD_DATE
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
20190101
200 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20190101
2nd row20190101
3rd row20190101
4th row20190101
5th row20190101

Common Values

ValueCountFrequency (%)
20190101 200
100.0%

Length

2023-12-10T15:37:08.292953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:08.449518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20190101 200
100.0%

Interactions

2023-12-10T15:36:56.893373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:56.592740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:57.117343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:56.737751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:37:08.588795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
OFFICE_CDOFFICE_DONG_CDHOUS_IDBLD_CDROAD_ADDRY_AXISBLK_CDOFFICE_NMOFFICE_FLRAREAPRICE
OFFICE_CD1.0001.0001.0001.0001.0001.0001.0001.0000.9130.4750.946
OFFICE_DONG_CD1.0001.0001.0001.0001.0001.0001.0001.0000.9130.4750.946
HOUS_ID1.0001.0001.0001.0001.0001.0001.0001.0000.9130.4750.946
BLD_CD1.0001.0001.0001.0001.0001.0001.0001.0000.9130.4750.946
ROAD_ADDR1.0001.0001.0001.0001.0001.0001.0001.0000.9130.4750.946
Y_AXIS1.0001.0001.0001.0001.0001.0001.0001.0000.9130.4750.946
BLK_CD1.0001.0001.0001.0001.0001.0001.0001.0000.9130.4750.946
OFFICE_NM1.0001.0001.0001.0001.0001.0001.0001.0000.9130.4750.946
OFFICE_FLR0.9130.9130.9130.9130.9130.9130.9130.9131.0000.3810.850
AREA0.4750.4750.4750.4750.4750.4750.4750.4750.3811.0000.722
PRICE0.9460.9460.9460.9460.9460.9460.9460.9460.8500.7221.000
2023-12-10T15:37:08.833566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
OFFICE_NMBLD_CDOFFICE_CDHOUS_IDOFFICE_DONG_CDOFFICE_FLRROAD_ADDRBLK_CDY_AXIS
OFFICE_NM1.0001.0001.0001.0001.0000.8371.0001.0001.000
BLD_CD1.0001.0001.0001.0001.0000.8371.0001.0001.000
OFFICE_CD1.0001.0001.0001.0001.0000.8371.0001.0001.000
HOUS_ID1.0001.0001.0001.0001.0000.8371.0001.0001.000
OFFICE_DONG_CD1.0001.0001.0001.0001.0000.8371.0001.0001.000
OFFICE_FLR0.8370.8370.8370.8370.8371.0000.8370.8370.837
ROAD_ADDR1.0001.0001.0001.0001.0000.8371.0001.0001.000
BLK_CD1.0001.0001.0001.0001.0000.8371.0001.0001.000
Y_AXIS1.0001.0001.0001.0001.0000.8371.0001.0001.000
2023-12-10T15:37:09.048638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AREAPRICEOFFICE_CDOFFICE_DONG_CDHOUS_IDBLD_CDROAD_ADDRY_AXISBLK_CDOFFICE_NMOFFICE_FLR
AREA1.000-0.2810.4040.4040.4040.4040.4040.4040.4040.4040.228
PRICE-0.2811.0000.8390.8390.8390.8390.8390.8390.8390.8390.572
OFFICE_CD0.4040.8391.0001.0001.0001.0001.0001.0001.0001.0000.837
OFFICE_DONG_CD0.4040.8391.0001.0001.0001.0001.0001.0001.0001.0000.837
HOUS_ID0.4040.8391.0001.0001.0001.0001.0001.0001.0001.0000.837
BLD_CD0.4040.8391.0001.0001.0001.0001.0001.0001.0001.0000.837
ROAD_ADDR0.4040.8391.0001.0001.0001.0001.0001.0001.0001.0000.837
Y_AXIS0.4040.8391.0001.0001.0001.0001.0001.0001.0001.0000.837
BLK_CD0.4040.8391.0001.0001.0001.0001.0001.0001.0001.0000.837
OFFICE_NM0.4040.8391.0001.0001.0001.0001.0001.0001.0001.0000.837
OFFICE_FLR0.2280.5720.8370.8370.8370.8370.8370.8370.8370.8371.000

Missing values

2023-12-10T15:36:57.322801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:36:57.714344image/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

OFFICE_CDOFFICE_DONG_CDHOUS_IDBLD_CDHOUS_ADDRROAD_ADDRX_AXISY_AXISBLK_CDOFFICE_HO_CDOFFICE_NMOFFICE_DONGOFFICE_HOOFFICE_FLRAREAPRICESTD_DATE
0OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 632호서울특별시 강서구 양천로 564등촌두산위브센티움 632호299592551138OH00000501등촌두산위브센티움동명없음632지상층683.96185500020190101
1OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 701호서울특별시 강서구 양천로 564등촌두산위브센티움 701호299592551138OH00000502등촌두산위브센티움동명없음701지상층783.96185500020190101
2OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 702호서울특별시 강서구 양천로 564등촌두산위브센티움 702호299592551138OH00000503등촌두산위브센티움동명없음702지상층783.96185500020190101
3OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 703호서울특별시 강서구 양천로 564등촌두산위브센티움 703호299592551138OH00000504등촌두산위브센티움동명없음703지상층783.96185500020190101
4OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 704호서울특별시 강서구 양천로 564등촌두산위브센티움 704호299592551138OH00000505등촌두산위브센티움동명없음704지상층783.96185500020190101
5OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 705호서울특별시 강서구 양천로 564등촌두산위브센티움 705호299592551138OH00000506등촌두산위브센티움동명없음705지상층755.42202200020190101
6OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 706호서울특별시 강서구 양천로 564등촌두산위브센티움 706호299592551138OH00000507등촌두산위브센티움동명없음706지상층755.42202200020190101
7OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 707호서울특별시 강서구 양천로 564등촌두산위브센티움 707호299592551138OH00000508등촌두산위브센티움동명없음707지상층755.42202200020190101
8OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 708호서울특별시 강서구 양천로 564등촌두산위브센티움 708호299592551138OH00000509등촌두산위브센티움동명없음708지상층744.02202200020190101
9OI00019954OD1001995411500102000063100001150010200106310001027250서울특별시 강서구 등촌동 631번지 등촌두산위브센티움 709호서울특별시 강서구 양천로 564등촌두산위브센티움 709호299592551138OH00000510등촌두산위브센티움동명없음709지상층744.02202200020190101
OFFICE_CDOFFICE_DONG_CDHOUS_IDBLD_CDHOUS_ADDRROAD_ADDRX_AXISY_AXISBLK_CDOFFICE_HO_CDOFFICE_NMOFFICE_DONGOFFICE_HOOFFICE_FLRAREAPRICESTD_DATE
190OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 705호서울특별시 강서구 양천로57길 9-13휴먼빌 705호298704551879OH00000065휴먼빌동명없음705지상층751.976171700020190101
191OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 706호서울특별시 강서구 양천로57길 9-13휴먼빌 706호298704551879OH00000066휴먼빌동명없음706지상층751.976171700020190101
192OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 707호서울특별시 강서구 양천로57길 9-13휴먼빌 707호298704551879OH00000067휴먼빌동명없음707지상층751.976171700020190101
193OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 708호서울특별시 강서구 양천로57길 9-13휴먼빌 708호298704551879OH00000068휴먼빌동명없음708지상층751.976171700020190101
194OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 709호서울특별시 강서구 양천로57길 9-13휴먼빌 709호298704551879OH00000069휴먼빌동명없음709지상층762.434171700020190101
195OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 710호서울특별시 강서구 양천로57길 9-13휴먼빌 710호298704551879OH00000070휴먼빌동명없음710지상층751.976171700020190101
196OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 711호서울특별시 강서구 양천로57길 9-13휴먼빌 711호298704551879OH00000071휴먼빌동명없음711지상층751.976171700020190101
197OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 712호서울특별시 강서구 양천로57길 9-13휴먼빌 712호298704551879OH00000072휴먼빌동명없음712지상층751.976171700020190101
198OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 713호서울특별시 강서구 양천로57길 9-13휴먼빌 713호298704551879OH00000073휴먼빌동명없음713지상층751.976171700020190101
199OI00000327OD1000032711500104000147900091150010400114790009009680서울특별시 강서구 가양동 1479-9번지 휴먼빌 714호서울특별시 강서구 양천로57길 9-13휴먼빌 714호298704551879OH00000074휴먼빌동명없음714지상층751.976171700020190101