Overview

Dataset statistics

Number of variables13
Number of observations1426
Missing cells119
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory149.1 KiB
Average record size in memory107.1 B

Variable types

Categorical4
Text6
Numeric3

Dataset

Description대구광역시 경로당현황(2014년말)
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15006977&dataSetDetailId=1500697718a98ba5ce17d&provdMethod=FILE

Alerts

등록회원 수 계 is highly overall correlated with 등록회원 수(남) and 1 other fieldsHigh correlation
등록회원 수(남) is highly overall correlated with 등록회원 수 계High correlation
등록회원 수(여) is highly overall correlated with 등록회원 수 계 and 1 other fieldsHigh correlation
소 유 별(사설) is highly overall correlated with 등록회원 수(여)High correlation
소 유 별(공설) is highly imbalanced (65.7%)Imbalance
소 유 별(사설) is highly imbalanced (60.6%)Imbalance
대지규모 has 53 (3.7%) missing valuesMissing
등록회원 수(남) has 48 (3.4%) missing valuesMissing
등록회원 수(여) has 15 (1.1%) missing valuesMissing
등록회원 수 계 is highly skewed (γ1 = 26.41700277)Skewed
등록회원 수(여) is highly skewed (γ1 = 28.54934252)Skewed
등록회원 수(남) has 101 (7.1%) zerosZeros

Reproduction

Analysis started2023-12-10 19:08:15.166726
Analysis finished2023-12-10 19:08:18.589838
Duration3.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Categorical

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
대구광역시 달성군
278 
대구광역시 북구
266 
대구광역시 달서구
259 
대구광역시 수성구
242 
대구광역시 동구
198 
Other values (3)
183 

Length

Max length9
Median length9
Mean length8.5462833
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 중구
2nd row대구광역시 중구
3rd row대구광역시 중구
4th row대구광역시 중구
5th row대구광역시 중구

Common Values

ValueCountFrequency (%)
대구광역시 달성군 278
19.5%
대구광역시 북구 266
18.7%
대구광역시 달서구 259
18.2%
대구광역시 수성구 242
17.0%
대구광역시 동구 198
13.9%
대구광역시 서구 80
 
5.6%
대구광역시 남구 60
 
4.2%
대구광역시 중구 43
 
3.0%

Length

2023-12-11T04:08:18.667407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T04:08:18.796855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 1426
50.0%
달성군 278
 
9.7%
북구 266
 
9.3%
달서구 259
 
9.1%
수성구 242
 
8.5%
동구 198
 
6.9%
서구 80
 
2.8%
남구 60
 
2.1%
중구 43
 
1.5%

행정동명
Text

Distinct139
Distinct (%)9.8%
Missing1
Missing (%)0.1%
Memory size11.3 KiB
2023-12-11T04:08:19.198667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.642807
Min length2

Characters and Unicode

Total characters5191
Distinct characters101
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row동인동
2nd row동인동
3rd row동인동
4th row동인동
5th row동인동
ValueCountFrequency (%)
다사읍 51
 
3.5%
화원읍 46
 
3.2%
진천동 39
 
2.7%
논공읍 33
 
2.3%
현풍면 30
 
2.1%
가창면 29
 
2.0%
공산동 28
 
1.9%
안심3.4동 28
 
1.9%
고산3동 27
 
1.9%
하빈면 27
 
1.9%
Other values (130) 1104
76.6%
2023-12-11T04:08:19.792448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1141
22.0%
1 283
 
5.5%
산 254
 
4.9%
2 244
 
4.7%
읍 152
 
2.9%
면 148
 
2.9%
3 146
 
2.8%
성 94
 
1.8%
4 90
 
1.7%
현 82
 
1.6%
Other values (91) 2557
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4320
83.2%
Decimal Number 795
 
15.3%
Other Punctuation 59
 
1.1%
Space Separator 17
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1141
26.4%
산 254
 
5.9%
읍 152
 
3.5%
면 148
 
3.4%
성 94
 
2.2%
현 82
 
1.9%
천 82
 
1.9%
고 79
 
1.8%
가 78
 
1.8%
원 73
 
1.7%
Other values (79) 2137
49.5%
Decimal Number
ValueCountFrequency (%)
1 283
35.6%
2 244
30.7%
3 146
18.4%
4 90
 
11.3%
5 11
 
1.4%
6 11
 
1.4%
0 4
 
0.5%
7 3
 
0.4%
9 3
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 53
89.8%
, 6
 
10.2%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4320
83.2%
Common 871
 
16.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1141
26.4%
산 254
 
5.9%
읍 152
 
3.5%
면 148
 
3.4%
성 94
 
2.2%
현 82
 
1.9%
천 82
 
1.9%
고 79
 
1.8%
가 78
 
1.8%
원 73
 
1.7%
Other values (79) 2137
49.5%
Common
ValueCountFrequency (%)
1 283
32.5%
2 244
28.0%
3 146
16.8%
4 90
 
10.3%
. 53
 
6.1%
17
 
2.0%
5 11
 
1.3%
6 11
 
1.3%
, 6
 
0.7%
0 4
 
0.5%
Other values (2) 6
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4320
83.2%
ASCII 871
 
16.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1141
26.4%
산 254
 
5.9%
읍 152
 
3.5%
면 148
 
3.4%
성 94
 
2.2%
현 82
 
1.9%
천 82
 
1.9%
고 79
 
1.8%
가 78
 
1.8%
원 73
 
1.7%
Other values (79) 2137
49.5%
ASCII
ValueCountFrequency (%)
1 283
32.5%
2 244
28.0%
3 146
16.8%
4 90
 
10.3%
. 53
 
6.1%
17
 
2.0%
5 11
 
1.3%
6 11
 
1.3%
, 6
 
0.7%
0 4
 
0.5%
Other values (2) 6
 
0.7%

경로당명
Text

Distinct1410
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
2023-12-11T04:08:20.132876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length7.3639551
Min length2

Characters and Unicode

Total characters10501
Distinct characters372
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

Unique1395 ?
Unique (%)97.8%

Sample

1st row동인1.2가
2nd row동인4가
3rd row동인시티타운
4th row동인3가
5th row동인삼정그린
ValueCountFrequency (%)
경로당 126
 
7.5%
제1경로당 8
 
0.5%
노인회경로당 6
 
0.4%
경로당(명곡미래빌 5
 
0.3%
제2경로당 4
 
0.2%
제1 3
 
0.2%
경로당(우방아파트 3
 
0.2%
범어2동 3
 
0.2%
성당동 3
 
0.2%
경로당(주공아파트 3
 
0.2%
Other values (1470) 1526
90.3%
2023-12-11T04:08:20.598308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
당 779
 
7.4%
로 768
 
7.3%
경 759
 
7.2%
626
 
6.0%
리 339
 
3.2%
성 236
 
2.2%
1 221
 
2.1%
2 211
 
2.0%
동 195
 
1.9%
지 167
 
1.6%
Other values (362) 6200
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8785
83.7%
Decimal Number 659
 
6.3%
Space Separator 626
 
6.0%
Uppercase Letter 141
 
1.3%
Open Punctuation 124
 
1.2%
Close Punctuation 124
 
1.2%
Other Punctuation 24
 
0.2%
Lowercase Letter 11
 
0.1%
Dash Punctuation 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
당 779
 
8.9%
로 768
 
8.7%
경 759
 
8.6%
리 339
 
3.9%
성 236
 
2.7%
동 195
 
2.2%
지 167
 
1.9%
아 156
 
1.8%
운 134
 
1.5%
타 131
 
1.5%
Other values (334) 5121
58.3%
Decimal Number
ValueCountFrequency (%)
1 221
33.5%
2 211
32.0%
3 91
13.8%
4 38
 
5.8%
5 30
 
4.6%
6 22
 
3.3%
7 17
 
2.6%
8 12
 
1.8%
0 9
 
1.4%
9 8
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
A 128
90.8%
S 3
 
2.1%
K 3
 
2.1%
C 2
 
1.4%
H 1
 
0.7%
E 1
 
0.7%
D 1
 
0.7%
U 1
 
0.7%
L 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 8
33.3%
? 7
29.2%
. 7
29.2%
@ 2
 
8.3%
Space Separator
ValueCountFrequency (%)
626
100.0%
Open Punctuation
ValueCountFrequency (%)
( 124
100.0%
Close Punctuation
ValueCountFrequency (%)
) 124
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8785
83.7%
Common 1564
 
14.9%
Latin 152
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
당 779
 
8.9%
로 768
 
8.7%
경 759
 
8.6%
리 339
 
3.9%
성 236
 
2.7%
동 195
 
2.2%
지 167
 
1.9%
아 156
 
1.8%
운 134
 
1.5%
타 131
 
1.5%
Other values (334) 5121
58.3%
Common
ValueCountFrequency (%)
626
40.0%
1 221
 
14.1%
2 211
 
13.5%
( 124
 
7.9%
) 124
 
7.9%
3 91
 
5.8%
4 38
 
2.4%
5 30
 
1.9%
6 22
 
1.4%
7 17
 
1.1%
Other values (8) 60
 
3.8%
Latin
ValueCountFrequency (%)
A 128
84.2%
e 11
 
7.2%
S 3
 
2.0%
K 3
 
2.0%
C 2
 
1.3%
H 1
 
0.7%
E 1
 
0.7%
D 1
 
0.7%
U 1
 
0.7%
L 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8785
83.7%
ASCII 1716
 
16.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
당 779
 
8.9%
로 768
 
8.7%
경 759
 
8.6%
리 339
 
3.9%
성 236
 
2.7%
동 195
 
2.2%
지 167
 
1.9%
아 156
 
1.8%
운 134
 
1.5%
타 131
 
1.5%
Other values (334) 5121
58.3%
ASCII
ValueCountFrequency (%)
626
36.5%
1 221
 
12.9%
2 211
 
12.3%
A 128
 
7.5%
( 124
 
7.2%
) 124
 
7.2%
3 91
 
5.3%
4 38
 
2.2%
5 30
 
1.7%
6 22
 
1.3%
Other values (18) 101
 
5.9%

소재지
Text

Distinct1416
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
2023-12-11T04:08:20.991089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length32
Mean length18.919355
Min length4

Characters and Unicode

Total characters26979
Distinct characters323
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

Unique1406 ?
Unique (%)98.6%

Sample

1st row대구광역시 중구 국채보상로 679-13(동인동2가)
2nd row대구광역시 중구 국채보상로142길 33-41(동인동4가)
3rd row대구광역시 중구 국채보상로131길55(동인동1가)
4th row대구광역시 중구 동덕로38길 85(동인동3가)
5th row대구광역시 중구 동덕로30길 53(동인동4가)
ValueCountFrequency (%)
대구광역시 640
 
13.9%
북구 266
 
5.8%
동구 190
 
4.1%
서구 80
 
1.7%
남구 60
 
1.3%
다사읍 51
 
1.1%
화원읍 45
 
1.0%
중구 43
 
0.9%
논공읍 33
 
0.7%
현풍면 30
 
0.7%
Other values (2224) 3174
68.8%
2023-12-11T04:08:21.582787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3885
 
14.4%
구 1488
 
5.5%
동 1230
 
4.6%
1 1222
 
4.5%
로 1140
 
4.2%
대 882
 
3.3%
( 841
 
3.1%
) 840
 
3.1%
2 822
 
3.0%
3 702
 
2.6%
Other values (313) 13927
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14808
54.9%
Decimal Number 5849
 
21.7%
Space Separator 3885
 
14.4%
Open Punctuation 841
 
3.1%
Close Punctuation 840
 
3.1%
Dash Punctuation 434
 
1.6%
Other Punctuation 282
 
1.0%
Uppercase Letter 35
 
0.1%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1488
 
10.0%
동 1230
 
8.3%
로 1140
 
7.7%
대 882
 
6.0%
길 693
 
4.7%
시 659
 
4.5%
광 656
 
4.4%
역 654
 
4.4%
북 319
 
2.2%
리 293
 
2.0%
Other values (283) 6794
45.9%
Decimal Number
ValueCountFrequency (%)
1 1222
20.9%
2 822
14.1%
3 702
12.0%
5 573
9.8%
4 535
9.1%
6 479
 
8.2%
0 478
 
8.2%
7 433
 
7.4%
8 311
 
5.3%
9 294
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
A 20
57.1%
B 3
 
8.6%
C 3
 
8.6%
K 2
 
5.7%
S 2
 
5.7%
D 1
 
2.9%
T 1
 
2.9%
E 1
 
2.9%
H 1
 
2.9%
L 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 225
79.8%
? 34
 
12.1%
@ 20
 
7.1%
/ 2
 
0.7%
. 1
 
0.4%
Space Separator
ValueCountFrequency (%)
3885
100.0%
Open Punctuation
ValueCountFrequency (%)
( 841
100.0%
Close Punctuation
ValueCountFrequency (%)
) 840
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 434
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14808
54.9%
Common 12131
45.0%
Latin 40
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1488
 
10.0%
동 1230
 
8.3%
로 1140
 
7.7%
대 882
 
6.0%
길 693
 
4.7%
시 659
 
4.5%
광 656
 
4.4%
역 654
 
4.4%
북 319
 
2.2%
리 293
 
2.0%
Other values (283) 6794
45.9%
Common
ValueCountFrequency (%)
3885
32.0%
1 1222
 
10.1%
( 841
 
6.9%
) 840
 
6.9%
2 822
 
6.8%
3 702
 
5.8%
5 573
 
4.7%
4 535
 
4.4%
6 479
 
3.9%
0 478
 
3.9%
Other values (9) 1754
14.5%
Latin
ValueCountFrequency (%)
A 20
50.0%
e 5
 
12.5%
B 3
 
7.5%
C 3
 
7.5%
K 2
 
5.0%
S 2
 
5.0%
D 1
 
2.5%
T 1
 
2.5%
E 1
 
2.5%
H 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14808
54.9%
ASCII 12171
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3885
31.9%
1 1222
 
10.0%
( 841
 
6.9%
) 840
 
6.9%
2 822
 
6.8%
3 702
 
5.8%
5 573
 
4.7%
4 535
 
4.4%
6 479
 
3.9%
0 478
 
3.9%
Other values (20) 1794
14.7%
Hangul
ValueCountFrequency (%)
구 1488
 
10.0%
동 1230
 
8.3%
로 1140
 
7.7%
대 882
 
6.0%
길 693
 
4.7%
시 659
 
4.5%
광 656
 
4.4%
역 654
 
4.4%
북 319
 
2.2%
리 293
 
2.0%
Other values (283) 6794
45.9%

등록일자
Text

Distinct1007
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
2023-12-11T04:08:21.873786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length10
Mean length9.9992987
Min length7

Characters and Unicode

Total characters14259
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique817 ?
Unique (%)57.3%

Sample

1st row1997-04-29
2nd row1999-03-18
3rd row2009-09-28
4th row1992-06-26
5th row2011-04-20
ValueCountFrequency (%)
1990-04-06 52
 
3.6%
1989-07-01 40
 
2.8%
1989-05-01 24
 
1.7%
4422-03-29 22
 
1.5%
4338-06-10 20
 
1.4%
1989-06-23 12
 
0.8%
1996-02-01 7
 
0.5%
1998-01-23 6
 
0.4%
1995-12-15 5
 
0.4%
1996-02-26 5
 
0.4%
Other values (997) 1233
86.5%
2023-12-11T04:08:22.336106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2864
20.1%
- 2846
20.0%
1 2332
16.4%
9 1818
12.7%
2 1469
10.3%
4 566
 
4.0%
3 524
 
3.7%
8 515
 
3.6%
6 460
 
3.2%
7 436
 
3.1%
Other values (4) 429
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11408
80.0%
Dash Punctuation 2846
 
20.0%
Other Punctuation 3
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2864
25.1%
1 2332
20.4%
9 1818
15.9%
2 1469
12.9%
4 566
 
5.0%
3 524
 
4.6%
8 515
 
4.5%
6 460
 
4.0%
7 436
 
3.8%
5 424
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 2846
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14259
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2864
20.1%
- 2846
20.0%
1 2332
16.4%
9 1818
12.7%
2 1469
10.3%
4 566
 
4.0%
3 524
 
3.7%
8 515
 
3.6%
6 460
 
3.2%
7 436
 
3.1%
Other values (4) 429
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2864
20.1%
- 2846
20.0%
1 2332
16.4%
9 1818
12.7%
2 1469
10.3%
4 566
 
4.0%
3 524
 
3.7%
8 515
 
3.6%
6 460
 
3.2%
7 436
 
3.1%
Other values (4) 429
 
3.0%

대지규모
Text

MISSING 

Distinct657
Distinct (%)47.9%
Missing53
Missing (%)3.7%
Memory size11.3 KiB
2023-12-11T04:08:22.724548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.7523671
Min length1

Characters and Unicode

Total characters5152
Distinct characters40
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique525 ?
Unique (%)38.2%

Sample

1st row대224.80
2nd row대194.40
3rd row대
4th row대156.00
5th row대
ValueCountFrequency (%)
공유면적 167
 
12.1%
157
 
11.4%
a부지 109
 
7.9%
대 25
 
1.8%
아파트 10
 
0.7%
66.05 8
 
0.6%
105.68 8
 
0.6%
165 8
 
0.6%
66 7
 
0.5%
169 7
 
0.5%
Other values (639) 870
63.2%
2023-12-11T04:08:23.228794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 590
 
11.5%
. 544
 
10.6%
2 471
 
9.1%
6 345
 
6.7%
9 327
 
6.3%
8 299
 
5.8%
5 297
 
5.8%
3 296
 
5.7%
4 266
 
5.2%
7 233
 
4.5%
Other values (30) 1484
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3347
65.0%
Other Letter 978
 
19.0%
Other Punctuation 551
 
10.7%
Dash Punctuation 155
 
3.0%
Uppercase Letter 109
 
2.1%
Space Separator 12
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 168
17.2%
공 168
17.2%
적 167
17.1%
면 167
17.1%
지 109
11.1%
부 109
11.1%
대 41
 
4.2%
트 10
 
1.0%
아 10
 
1.0%
파 10
 
1.0%
Other values (14) 19
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 590
17.6%
2 471
14.1%
6 345
10.3%
9 327
9.8%
8 299
8.9%
5 297
8.9%
3 296
8.8%
4 266
7.9%
7 233
 
7.0%
0 223
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 544
98.7%
? 4
 
0.7%
, 3
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 155
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 109
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4065
78.9%
Hangul 978
 
19.0%
Latin 109
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 168
17.2%
공 168
17.2%
적 167
17.1%
면 167
17.1%
지 109
11.1%
부 109
11.1%
대 41
 
4.2%
트 10
 
1.0%
아 10
 
1.0%
파 10
 
1.0%
Other values (14) 19
 
1.9%
Common
ValueCountFrequency (%)
1 590
14.5%
. 544
13.4%
2 471
11.6%
6 345
8.5%
9 327
8.0%
8 299
7.4%
5 297
7.3%
3 296
7.3%
4 266
6.5%
7 233
 
5.7%
Other values (5) 397
9.8%
Latin
ValueCountFrequency (%)
A 109
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4174
81.0%
Hangul 978
 
19.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 590
14.1%
. 544
13.0%
2 471
11.3%
6 345
8.3%
9 327
7.8%
8 299
7.2%
5 297
7.1%
3 296
7.1%
4 266
6.4%
7 233
 
5.6%
Other values (6) 506
12.1%
Hangul
ValueCountFrequency (%)
유 168
17.2%
공 168
17.2%
적 167
17.1%
면 167
17.1%
지 109
11.1%
부 109
11.1%
대 41
 
4.2%
트 10
 
1.0%
아 10
 
1.0%
파 10
 
1.0%
Other values (14) 19
 
1.9%

건물규모
Text

Distinct970
Distinct (%)68.1%
Missing1
Missing (%)0.1%
Memory size11.3 KiB
2023-12-11T04:08:23.634514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length32
Mean length4.437193
Min length2

Characters and Unicode

Total characters6323
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique822 ?
Unique (%)57.7%

Sample

1st row148.4
2nd row157.32
3rd row124.99
4th row158.68
5th row84.31
ValueCountFrequency (%)
1층 121
 
8.3%
2층 53
 
3.7%
건 25
 
1.7%
99 9
 
0.6%
105.68 9
 
0.6%
60 8
 
0.6%
66 8
 
0.6%
3층 8
 
0.6%
66.05 8
 
0.6%
62.75 7
 
0.5%
Other values (958) 1194
82.3%
2023-12-11T04:08:24.138008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1032
16.3%
1 971
15.4%
2 610
9.6%
6 520
8.2%
5 461
7.3%
8 455
7.2%
4 454
7.2%
9 450
7.1%
7 402
 
6.4%
3 380
 
6.0%
Other values (10) 588
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5024
79.5%
Other Punctuation 1040
 
16.4%
Other Letter 226
 
3.6%
Space Separator 25
 
0.4%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 971
19.3%
2 610
12.1%
6 520
10.4%
5 461
9.2%
8 455
9.1%
4 454
9.0%
9 450
9.0%
7 402
8.0%
3 380
 
7.6%
0 321
 
6.4%
Other Letter
ValueCountFrequency (%)
층 189
83.6%
건 35
 
15.5%
지 1
 
0.4%
하 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1032
99.2%
: 7
 
0.7%
? 1
 
0.1%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6097
96.4%
Hangul 226
 
3.6%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1032
16.9%
1 971
15.9%
2 610
10.0%
6 520
8.5%
5 461
7.6%
8 455
7.5%
4 454
7.4%
9 450
7.4%
7 402
 
6.6%
3 380
 
6.2%
Other values (6) 362
 
5.9%
Hangul
ValueCountFrequency (%)
층 189
83.6%
건 35
 
15.5%
지 1
 
0.4%
하 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6097
96.4%
Hangul 226
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1032
16.9%
1 971
15.9%
2 610
10.0%
6 520
8.5%
5 461
7.6%
8 455
7.5%
4 454
7.4%
9 450
7.4%
7 402
 
6.6%
3 380
 
6.2%
Other values (6) 362
 
5.9%
Hangul
ValueCountFrequency (%)
층 189
83.6%
건 35
 
15.5%
지 1
 
0.4%
하 1
 
0.4%

등록회원 수 계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct123
Distinct (%)8.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean42.437193
Minimum6
Maximum1788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-11T04:08:24.300488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q126
median35
Q348
95-th percentile83.8
Maximum1788
Range1782
Interquartile range (IQR)22

Descriptive statistics

Standard deviation52.327557
Coefficient of variation (CV)1.2330589
Kurtosis871.16355
Mean42.437193
Median Absolute Deviation (MAD)10
Skewness26.417003
Sum60473
Variance2738.1732
MonotonicityNot monotonic
2023-12-11T04:08:24.470344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 62
 
4.3%
30 60
 
4.2%
31 47
 
3.3%
23 43
 
3.0%
26 43
 
3.0%
40 43
 
3.0%
22 41
 
2.9%
29 41
 
2.9%
32 38
 
2.7%
34 37
 
2.6%
Other values (113) 970
68.0%
ValueCountFrequency (%)
6 1
 
0.1%
10 1
 
0.1%
11 2
 
0.1%
12 4
 
0.3%
13 2
 
0.1%
14 4
 
0.3%
15 7
0.5%
16 10
0.7%
17 10
0.7%
18 16
1.1%
ValueCountFrequency (%)
1788 1
0.1%
280 1
0.1%
220 1
0.1%
206 1
0.1%
201 1
0.1%
192 1
0.1%
184 1
0.1%
181 1
0.1%
178 1
0.1%
175 1
0.1%

등록회원 수(남)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct68
Distinct (%)4.9%
Missing48
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean13.835269
Minimum0
Maximum383
Zeros101
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-11T04:08:24.638502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median11
Q317
95-th percentile35
Maximum383
Range383
Interquartile range (IQR)11

Descriptive statistics

Standard deviation15.454471
Coefficient of variation (CV)1.1170344
Kurtosis238.07236
Mean13.835269
Median Absolute Deviation (MAD)6
Skewness10.878882
Sum19065
Variance238.84067
MonotonicityNot monotonic
2023-12-11T04:08:24.805616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 101
 
7.1%
10 93
 
6.5%
6 73
 
5.1%
8 69
 
4.8%
9 68
 
4.8%
15 63
 
4.4%
11 59
 
4.1%
7 59
 
4.1%
14 58
 
4.1%
5 58
 
4.1%
Other values (58) 677
47.5%
ValueCountFrequency (%)
0 101
7.1%
1 23
 
1.6%
2 23
 
1.6%
3 45
3.2%
4 52
3.6%
5 58
4.1%
6 73
5.1%
7 59
4.1%
8 69
4.8%
9 68
4.8%
ValueCountFrequency (%)
383 1
 
0.1%
88 1
 
0.1%
87 1
 
0.1%
84 1
 
0.1%
83 1
 
0.1%
78 1
 
0.1%
74 1
 
0.1%
70 2
0.1%
68 1
 
0.1%
67 3
0.2%

등록회원 수(여)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct97
Distinct (%)6.9%
Missing15
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean29.589653
Minimum0
Maximum1405
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-11T04:08:24.963989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q119
median25
Q334
95-th percentile58
Maximum1405
Range1405
Interquartile range (IQR)15

Descriptive statistics

Standard deviation40.243086
Coefficient of variation (CV)1.3600391
Kurtosis969.34851
Mean29.589653
Median Absolute Deviation (MAD)7
Skewness28.549343
Sum41751
Variance1619.506
MonotonicityNot monotonic
2023-12-11T04:08:25.128527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 72
 
5.0%
20 68
 
4.8%
22 64
 
4.5%
25 54
 
3.8%
16 54
 
3.8%
21 53
 
3.7%
18 50
 
3.5%
15 49
 
3.4%
17 49
 
3.4%
24 44
 
3.1%
Other values (87) 854
59.9%
ValueCountFrequency (%)
0 5
 
0.4%
2 1
 
0.1%
3 1
 
0.1%
5 2
 
0.1%
6 6
 
0.4%
8 8
 
0.6%
9 10
0.7%
10 8
 
0.6%
11 22
1.5%
12 22
1.5%
ValueCountFrequency (%)
1405 1
0.1%
196 1
0.1%
140 1
0.1%
130 1
0.1%
127 2
0.1%
124 1
0.1%
123 1
0.1%
118 1
0.1%
111 2
0.1%
110 2
0.1%

소 유 별(공설)
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
<NA>
1019 
공설
317 
구
 
56
임차
 
18
대구시
 
10
Other values (6)
 
6

Length

Max length6
Median length4
Mean length3.4039271
Min length1

Unique

Unique6 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1019
71.5%
공설 317
 
22.2%
구 56
 
3.9%
임차 18
 
1.3%
대구시 10
 
0.7%
○ 1
 
0.1%
구(시) 1
 
0.1%
구(대구시) 1
 
0.1%
재무부 1
 
0.1%
시(건물) 1
 
0.1%

Length

2023-12-11T04:08:25.288661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1019
71.5%
공설 317
 
22.2%
구 56
 
3.9%
임차 18
 
1.3%
대구시 10
 
0.7%
○ 1
 
0.1%
구(시 1
 
0.1%
구(대구시 1
 
0.1%
재무부 1
 
0.1%
시(건물 1
 
0.1%

소 유 별(사설)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
사설
1011 
<NA>
407 
개인
 
6
노원금고
 
1
가정복지관
 
1

Length

Max length5
Median length2
Mean length2.5743338
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
사설 1011
70.9%
<NA> 407
28.5%
개인 6
 
0.4%
노원금고 1
 
0.1%
가정복지관 1
 
0.1%

Length

2023-12-11T04:08:25.430888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T04:08:25.592216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사설 1011
70.9%
na 407
28.5%
개인 6
 
0.4%
노원금고 1
 
0.1%
가정복지관 1
 
0.1%

비 고(공동주택, 일반)
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
공동주택
805 
일반
621 

Length

Max length4
Median length4
Mean length3.1290323
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반
2nd row일반
3rd row공동주택
4th row일반
5th row공동주택

Common Values

ValueCountFrequency (%)
공동주택 805
56.5%
일반 621
43.5%

Length

2023-12-11T04:08:25.775033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T04:08:25.926988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동주택 805
56.5%
일반 621
43.5%

Interactions

2023-12-11T04:08:17.690547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:08:16.454356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:08:16.909289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:08:17.801881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:08:16.595154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:08:17.076566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:08:17.915483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:08:16.742478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T04:08:17.554678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T04:08:26.010675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관명등록회원 수 계등록회원 수(남)등록회원 수(여)소 유 별(공설)소 유 별(사설)비 고(공동주택, 일반)
기관명1.0000.0490.1970.0000.6210.1670.474
등록회원 수 계0.0491.0000.7730.9660.0000.0000.023
등록회원 수(남)0.1970.7731.0000.7300.2470.0000.253
등록회원 수(여)0.0000.9660.7301.0000.000NaN0.013
소 유 별(공설)0.6210.0000.2470.0001.000NaN0.000
소 유 별(사설)0.1670.0000.000NaNNaN1.0000.239
비 고(공동주택, 일반)0.4740.0230.2530.0130.0000.2391.000
2023-12-11T04:08:26.159483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소 유 별(사설)소 유 별(공설)비 고(공동주택, 일반)기관명
소 유 별(사설)1.000NaN0.1590.075
소 유 별(공설)NaN1.0000.0000.355
비 고(공동주택, 일반)0.1590.0001.0000.356
기관명0.0750.3550.3561.000
2023-12-11T04:08:26.299787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록회원 수 계등록회원 수(남)등록회원 수(여)기관명소 유 별(공설)소 유 별(사설)비 고(공동주택, 일반)
등록회원 수 계1.0000.6720.8300.0310.0000.0000.038
등록회원 수(남)0.6721.0000.2590.0900.1490.0000.168
등록회원 수(여)0.8300.2591.0000.0000.0001.0000.021
기관명0.0310.0900.0001.0000.3550.0750.356
소 유 별(공설)0.0000.1490.0000.3551.0000.0000.000
소 유 별(사설)0.0000.0001.0000.0750.0001.0000.159
비 고(공동주택, 일반)0.0380.1680.0210.3560.0000.1591.000

Missing values

2023-12-11T04:08:18.085509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T04:08:18.312991image/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.
2023-12-11T04:08:18.484431image/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

기관명행정동명경로당명소재지등록일자대지규모건물규모등록회원 수 계등록회원 수(남)등록회원 수(여)소 유 별(공설)소 유 별(사설)비 고(공동주택, 일반)
0대구광역시 중구동인동동인1.2가대구광역시 중구 국채보상로 679-13(동인동2가)1997-04-29대224.80148.4432419공설<NA>일반
1대구광역시 중구동인동동인4가대구광역시 중구 국채보상로142길 33-41(동인동4가)1999-03-18대194.40157.3258751공설<NA>일반
2대구광역시 중구동인동동인시티타운대구광역시 중구 국채보상로131길55(동인동1가)2009-09-28대124.99722943<NA>사설공동주택
3대구광역시 중구동인동동인3가대구광역시 중구 동덕로38길 85(동인동3가)1992-06-26대156.00158.68491237공설<NA>일반
4대구광역시 중구동인동동인삼정그린대구광역시 중구 동덕로30길 53(동인동4가)2011-04-20대84.31301416<NA>사설공동주택
5대구광역시 중구삼덕동삼덕3가대구광역시 중구 달구벌대로443길 44-40(삼덕동3가)1996-01-13대248.30건 167.41601149공설<NA>일반
6대구광역시 중구삼덕동삼덕1.2가대구광역시 중구 공평로4길25(삼덕동2가)1998-02-07대225.50건 109.9824420공설<NA>일반
7대구광역시 중구삼덕동삼덕청아람대구광역시 중구 달구벌대로 447길 772013-09-09172.27172.97271017<NA>사설공동주택
8대구광역시 중구성내1동봉산동대구광역시 중구 봉산문화길1-4(봉산동)1993-01-13대158.70건52.93451233공설<NA>일반
9대구광역시 중구성내1동라이프태평아파트대구광역시 중구 태평로177(태평로1가)1995-01-16대건85.8026323<NA>사설공동주택
기관명행정동명경로당명소재지등록일자대지규모건물규모등록회원 수 계등록회원 수(남)등록회원 수(여)소 유 별(공설)소 유 별(사설)비 고(공동주택, 일반)
1416대구광역시 달성군구지면응암1리 경로당구지면 응암리 1081997-08-23207.22층411526<NA>사설일반
1417대구광역시 달성군구지면응암4리 달성2차청아람경로당구지면 응암리 11782011-04-01263.691층893257<NA>사설공동주택
1418대구광역시 달성군구지면징리 경로당구지면 징리 73-11990-12-0196.721층562234<NA>사설일반
1419대구광역시 달성군구지면창3리 경로당구지면 창리 1327-202012-10-2690.271층401525<NA>사설일반
1420대구광역시 달성군구지면구지면 노인회경로당구지면 창리 461-11989-04-13339.642층22087123공설<NA>일반
1421대구광역시 달성군구지면창4리 경로당구지면 창리 4702003-07-28201.431층361323<NA>사설일반
1422대구광역시 달성군구지면창2리 경로당구지면 창리 5401996-02-26130.881층541440<NA>사설일반
1423대구광역시 달성군구지면평촌2리 경로당구지면 평촌리 353-21996-02-2675.241층411922<NA>사설일반
1424대구광역시 달성군구지면평촌1리 경로당구지면 평촌리 460-21991-07-01138.061층722745<NA>사설일반
1425대구광역시 달성군구지면화산1리 경로당구지면 화산리 4612014-05-23111.621층562234<NA>사설일반