Overview

Dataset statistics

Number of variables6
Number of observations331
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.0 KiB
Average record size in memory49.4 B

Variable types

Numeric1
Text4
Categorical1

Dataset

Description대구광역시 서구_부동산중개업소현황_20240110
Author대구광역시 서구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3075608&dataSetDetailId=30756081dcf91b7f067d&provdMethod=FILE

Alerts

중개업소 구분 is highly imbalanced (89.4%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-13 13:41:38.305994
Analysis finished2024-03-13 13:41:38.916472
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct331
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166
Minimum1
Maximum331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-13T22:41:38.982393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.5
Q183.5
median166
Q3248.5
95-th percentile314.5
Maximum331
Range330
Interquartile range (IQR)165

Descriptive statistics

Standard deviation95.695698
Coefficient of variation (CV)0.57648011
Kurtosis-1.2
Mean166
Median Absolute Deviation (MAD)83
Skewness0
Sum54946
Variance9157.6667
MonotonicityStrictly increasing
2024-03-13T22:41:39.128340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
229 1
 
0.3%
227 1
 
0.3%
226 1
 
0.3%
225 1
 
0.3%
224 1
 
0.3%
223 1
 
0.3%
222 1
 
0.3%
221 1
 
0.3%
220 1
 
0.3%
Other values (321) 321
97.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
331 1
0.3%
330 1
0.3%
329 1
0.3%
328 1
0.3%
327 1
0.3%
326 1
0.3%
325 1
0.3%
324 1
0.3%
323 1
0.3%
322 1
0.3%

등록번호
Text

Distinct330
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-13T22:41:39.393437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length14.543807
Min length9

Characters and Unicode

Total characters4814
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)99.4%

Sample

1st row나-13-0327
2nd row나-13-0534
3rd row나-13-0702
4th row가-13-2215
5th row가-13-2212
ValueCountFrequency (%)
27170-2021-00053 2
 
0.6%
27170-2020-00038 1
 
0.3%
27170-2020-00052 1
 
0.3%
27170-2020-00054 1
 
0.3%
27170-2020-00058 1
 
0.3%
27170-2020-00059 1
 
0.3%
27170-2020-00060 1
 
0.3%
27170-2020-00061 1
 
0.3%
27170-2020-00063 1
 
0.3%
27170-2020-00071 1
 
0.3%
Other values (320) 320
96.7%
2024-03-13T22:41:39.810900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1424
29.6%
2 858
17.8%
- 662
13.8%
7 592
12.3%
1 567
 
11.8%
3 237
 
4.9%
5 105
 
2.2%
4 83
 
1.7%
8 77
 
1.6%
9 73
 
1.5%
Other values (3) 136
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4083
84.8%
Dash Punctuation 662
 
13.8%
Other Letter 69
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1424
34.9%
2 858
21.0%
7 592
14.5%
1 567
 
13.9%
3 237
 
5.8%
5 105
 
2.6%
4 83
 
2.0%
8 77
 
1.9%
9 73
 
1.8%
6 67
 
1.6%
Other Letter
ValueCountFrequency (%)
가 66
95.7%
나 3
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 662
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4745
98.6%
Hangul 69
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1424
30.0%
2 858
18.1%
- 662
14.0%
7 592
12.5%
1 567
 
11.9%
3 237
 
5.0%
5 105
 
2.2%
4 83
 
1.7%
8 77
 
1.6%
9 73
 
1.5%
Hangul
ValueCountFrequency (%)
가 66
95.7%
나 3
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4745
98.6%
Hangul 69
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1424
30.0%
2 858
18.1%
- 662
14.0%
7 592
12.5%
1 567
 
11.9%
3 237
 
5.0%
5 105
 
2.2%
4 83
 
1.7%
8 77
 
1.6%
9 73
 
1.5%
Hangul
ValueCountFrequency (%)
가 66
95.7%
나 3
 
4.3%

중개업소 구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
공인중개사
324 
법인
 
4
중개인
 
3

Length

Max length5
Median length5
Mean length4.9456193
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중개인
2nd row중개인
3rd row중개인
4th row공인중개사
5th row공인중개사

Common Values

ValueCountFrequency (%)
공인중개사 324
97.9%
법인 4
 
1.2%
중개인 3
 
0.9%

Length

2024-03-13T22:41:39.953094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:41:40.054775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 324
97.9%
법인 4
 
1.2%
중개인 3
 
0.9%

중개업소명
Text

Distinct330
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-13T22:41:40.217810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length11.220544
Min length7

Characters and Unicode

Total characters3714
Distinct characters230
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)99.4%

Sample

1st row한일부동산중개인사무소
2nd row고령부동산중개인사무소
3rd row형제부동산중개인사무소
4th row열린공인중개사사무소
5th row이현공인중개사사무소
ValueCountFrequency (%)
사무소 6
 
1.7%
정문부동산중개 2
 
0.6%
공인중개사 2
 
0.6%
대화공인중개사사무소 1
 
0.3%
유진공인중개사사무소 1
 
0.3%
뉴화성부동산중개사무소 1
 
0.3%
대우공인중개사사무소 1
 
0.3%
화성톡공인중개사사무소 1
 
0.3%
탑센트럴자이공인중개사사무소 1
 
0.3%
대양공인중개사사무소 1
 
0.3%
Other values (330) 330
95.1%
2024-03-13T22:41:40.623528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 556
15.0%
중 336
 
9.0%
개 332
 
8.9%
무 296
 
8.0%
소 295
 
7.9%
인 264
 
7.1%
공 260
 
7.0%
동 124
 
3.3%
부 124
 
3.3%
산 122
 
3.3%
Other values (220) 1005
27.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3651
98.3%
Decimal Number 19
 
0.5%
Space Separator 16
 
0.4%
Lowercase Letter 12
 
0.3%
Uppercase Letter 8
 
0.2%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 556
15.2%
중 336
 
9.2%
개 332
 
9.1%
무 296
 
8.1%
소 295
 
8.1%
인 264
 
7.2%
공 260
 
7.1%
동 124
 
3.4%
부 124
 
3.4%
산 122
 
3.3%
Other values (202) 942
25.8%
Decimal Number
ValueCountFrequency (%)
1 11
57.9%
3 3
 
15.8%
4 2
 
10.5%
2 1
 
5.3%
5 1
 
5.3%
6 1
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
T 3
37.5%
N 2
25.0%
M 1
 
12.5%
E 1
 
12.5%
H 1
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
e 7
58.3%
h 2
 
16.7%
w 2
 
16.7%
k 1
 
8.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3648
98.2%
Common 43
 
1.2%
Latin 20
 
0.5%
Han 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 556
15.2%
중 336
 
9.2%
개 332
 
9.1%
무 296
 
8.1%
소 295
 
8.1%
인 264
 
7.2%
공 260
 
7.1%
동 124
 
3.4%
부 124
 
3.4%
산 122
 
3.3%
Other values (200) 939
25.7%
Common
ValueCountFrequency (%)
16
37.2%
1 11
25.6%
) 4
 
9.3%
( 4
 
9.3%
3 3
 
7.0%
4 2
 
4.7%
2 1
 
2.3%
5 1
 
2.3%
6 1
 
2.3%
Latin
ValueCountFrequency (%)
e 7
35.0%
T 3
15.0%
N 2
 
10.0%
h 2
 
10.0%
w 2
 
10.0%
M 1
 
5.0%
E 1
 
5.0%
H 1
 
5.0%
k 1
 
5.0%
Han
ValueCountFrequency (%)
新 2
66.7%
正 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3648
98.2%
ASCII 63
 
1.7%
CJK 3
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 556
15.2%
중 336
 
9.2%
개 332
 
9.1%
무 296
 
8.1%
소 295
 
8.1%
인 264
 
7.2%
공 260
 
7.1%
동 124
 
3.4%
부 124
 
3.4%
산 122
 
3.3%
Other values (200) 939
25.7%
ASCII
ValueCountFrequency (%)
16
25.4%
1 11
17.5%
e 7
11.1%
) 4
 
6.3%
( 4
 
6.3%
T 3
 
4.8%
3 3
 
4.8%
N 2
 
3.2%
h 2
 
3.2%
w 2
 
3.2%
Other values (8) 9
14.3%
CJK
ValueCountFrequency (%)
新 2
66.7%
正 1
33.3%

중개업자명
Text

Distinct324
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-13T22:41:40.928760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9909366
Min length2

Characters and Unicode

Total characters990
Distinct characters151
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)95.8%

Sample

1st row안복원
2nd row박한수
3rd row김재오
4th row임향숙
5th row구본국
ValueCountFrequency (%)
김경호 2
 
0.6%
김일환 2
 
0.6%
박영순 2
 
0.6%
박혜정 2
 
0.6%
이동식 2
 
0.6%
이은주 2
 
0.6%
김민정 2
 
0.6%
신효욱 1
 
0.3%
제은영 1
 
0.3%
김재경 1
 
0.3%
Other values (314) 314
94.9%
2024-03-13T22:41:41.365253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 62
 
6.3%
김 59
 
6.0%
정 45
 
4.5%
영 32
 
3.2%
희 31
 
3.1%
현 25
 
2.5%
최 23
 
2.3%
박 23
 
2.3%
경 21
 
2.1%
호 19
 
1.9%
Other values (141) 650
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 990
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 62
 
6.3%
김 59
 
6.0%
정 45
 
4.5%
영 32
 
3.2%
희 31
 
3.1%
현 25
 
2.5%
최 23
 
2.3%
박 23
 
2.3%
경 21
 
2.1%
호 19
 
1.9%
Other values (141) 650
65.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 990
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 62
 
6.3%
김 59
 
6.0%
정 45
 
4.5%
영 32
 
3.2%
희 31
 
3.1%
현 25
 
2.5%
최 23
 
2.3%
박 23
 
2.3%
경 21
 
2.1%
호 19
 
1.9%
Other values (141) 650
65.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 990
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 62
 
6.3%
김 59
 
6.0%
정 45
 
4.5%
영 32
 
3.2%
희 31
 
3.1%
현 25
 
2.5%
최 23
 
2.3%
박 23
 
2.3%
경 21
 
2.1%
호 19
 
1.9%
Other values (141) 650
65.7%

도로명주소(지번)
Text

Distinct302
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-13T22:41:41.617069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length42
Mean length22.450151
Min length15

Characters and Unicode

Total characters7431
Distinct characters135
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

Unique274 ?
Unique (%)82.8%

Sample

1st row대구광역시 서구 평리로50길 9
2nd row대구광역시 서구 평리로 371
3rd row대구광역시 서구 국채보상로48길 10
4th row대구광역시 서구 통학로 111
5th row대구광역시 서구 평리로35길 58
ValueCountFrequency (%)
대구광역시 331
22.0%
서구 331
22.0%
달서로 30
 
2.0%
문화로 28
 
1.9%
국채보상로 19
 
1.3%
당산로 16
 
1.1%
30 14
 
0.9%
고성로 14
 
0.9%
서대구로 14
 
0.9%
평리로 14
 
0.9%
Other values (360) 695
46.1%
2024-03-13T22:41:41.985530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1393
18.7%
구 750
 
10.1%
서 448
 
6.0%
대 424
 
5.7%
역 340
 
4.6%
광 336
 
4.5%
시 332
 
4.5%
로 330
 
4.4%
1 304
 
4.1%
3 225
 
3.0%
Other values (125) 2549
34.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4349
58.5%
Decimal Number 1514
 
20.4%
Space Separator 1393
 
18.7%
Dash Punctuation 53
 
0.7%
Open Punctuation 37
 
0.5%
Close Punctuation 36
 
0.5%
Lowercase Letter 28
 
0.4%
Other Punctuation 12
 
0.2%
Uppercase Letter 8
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 750
17.2%
서 448
10.3%
대 424
9.7%
역 340
 
7.8%
광 336
 
7.7%
시 332
 
7.6%
로 330
 
7.6%
길 149
 
3.4%
상 97
 
2.2%
동 97
 
2.2%
Other values (101) 1046
24.1%
Decimal Number
ValueCountFrequency (%)
1 304
20.1%
3 225
14.9%
2 215
14.2%
0 166
11.0%
4 134
8.9%
5 112
 
7.4%
6 109
 
7.2%
7 101
 
6.7%
9 78
 
5.2%
8 70
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
k 9
32.1%
t 9
32.1%
x 9
32.1%
e 1
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
A 4
50.0%
B 2
25.0%
P 1
 
12.5%
T 1
 
12.5%
Space Separator
ValueCountFrequency (%)
1393
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4349
58.5%
Common 3046
41.0%
Latin 36
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 750
17.2%
서 448
10.3%
대 424
9.7%
역 340
 
7.8%
광 336
 
7.7%
시 332
 
7.6%
로 330
 
7.6%
길 149
 
3.4%
상 97
 
2.2%
동 97
 
2.2%
Other values (101) 1046
24.1%
Common
ValueCountFrequency (%)
1393
45.7%
1 304
 
10.0%
3 225
 
7.4%
2 215
 
7.1%
0 166
 
5.4%
4 134
 
4.4%
5 112
 
3.7%
6 109
 
3.6%
7 101
 
3.3%
9 78
 
2.6%
Other values (6) 209
 
6.9%
Latin
ValueCountFrequency (%)
k 9
25.0%
t 9
25.0%
x 9
25.0%
A 4
11.1%
B 2
 
5.6%
e 1
 
2.8%
P 1
 
2.8%
T 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4349
58.5%
ASCII 3082
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1393
45.2%
1 304
 
9.9%
3 225
 
7.3%
2 215
 
7.0%
0 166
 
5.4%
4 134
 
4.3%
5 112
 
3.6%
6 109
 
3.5%
7 101
 
3.3%
9 78
 
2.5%
Other values (14) 245
 
7.9%
Hangul
ValueCountFrequency (%)
구 750
17.2%
서 448
10.3%
대 424
9.7%
역 340
 
7.8%
광 336
 
7.7%
시 332
 
7.6%
로 330
 
7.6%
길 149
 
3.4%
상 97
 
2.2%
동 97
 
2.2%
Other values (101) 1046
24.1%

Interactions

2024-03-13T22:41:38.639232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:41:42.068865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번중개업소 구분
연번1.0000.286
중개업소 구분0.2861.000
2024-03-13T22:41:42.148865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번중개업소 구분
연번1.0000.172
중개업소 구분0.1721.000

Missing values

2024-03-13T22:41:38.751576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:41:38.875036image/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

연번등록번호중개업소 구분중개업소명중개업자명도로명주소(지번)
01나-13-0327중개인한일부동산중개인사무소안복원대구광역시 서구 평리로50길 9
12나-13-0534중개인고령부동산중개인사무소박한수대구광역시 서구 평리로 371
23나-13-0702중개인형제부동산중개인사무소김재오대구광역시 서구 국채보상로48길 10
34가-13-2215공인중개사열린공인중개사사무소임향숙대구광역시 서구 통학로 111
45가-13-2212공인중개사이현공인중개사사무소구본국대구광역시 서구 평리로35길 58
56가-13-2203공인중개사내당시영공인중개사사무소조창현대구광역시 서구 서대구로8길 15 점포6호
67가-13-2201공인중개사매일공인중개사 사무소김나윤대구광역시 서구 국채보상로50길 20 224동 101호(평리동, 평리푸르지오)
78가-13-2200공인중개사평리롯데공인중개사사무소나한택대구광역시 서구 국채보상로 316 평리롯데캐슬 304동 103호
89가-13-2180공인중개사두일 공인중개사 사무소이동근대구광역시 서구 평리로 415
910가-13-2178공인중개사베스트공인중개사 사무소최진영대구광역시 서구 국채보상로34길 23
연번등록번호중개업소 구분중개업소명중개업자명도로명주소(지번)
32132227170-2015-00038공인중개사벽상공인중개사사무소김근영대구광역시 서구 달서천로49안길 21
32232327170-2015-00035공인중개사남문공인중개사사무소남호태대구광역시 서구 염색공단천로 5
32332427170-2015-00034공인중개사(주)국보부동산중개사무소장극덕대구광역시 서구 달서로 200
32432527170-2015-00033공인중개사해석공인중개사사무소이정해대구광역시 서구 고성로 135
32532627170-2015-00031공인중개사조은인연공인중개사사무소이윤희대구광역시 서구 국채보상로50길 20 223동 103호(평리동, 평리푸르지오)
32632727170-2015-00026공인중개사유명부동산중개사사무소김희승대구광역시 서구 평리로 355-11
32732827170-2015-00017공인중개사대복공인중개사 사무소이규호대구광역시 서구 평리로 214
32832927170-2015-00002공인중개사삼강부동산중개사무소유은희대구광역시 서구 와룡로 367
329330227170-2023-00057공인중개사서대구역화성공인중개사사무소이정윤대구광역시 서구 서대구로29길 30 서대구역화성파크드림상가7209동10호
33033127170-2024-00001공인중개사어반공인중개사사무소김일환대구광역시 서구 평리로214, 내당광명아파트 상가6동 5호