Overview

Dataset statistics

Number of variables11
Number of observations3669
Missing cells0
Missing cells (%)0.0%
Duplicate rows24
Duplicate rows (%)0.7%
Total size in memory333.3 KiB
Average record size in memory93.0 B

Variable types

Categorical1
Text3
DateTime2
Numeric5

Dataset

Description주택관리공단 예비입주자 모집(임대상가)에 대한 데이터로 모집공고일, 당첨자발표일, 계약금, 잔금, 월임대료 등에 대한 정보를 제공합니다.
Author주택관리공단(주)
URLhttps://www.data.go.kr/data/15069062/fileData.do

Alerts

모집구분 has constant value ""Constant
Dataset has 24 (0.7%) duplicate rowsDuplicates
전용면적(제곱미터) is highly overall correlated with 계약금(원) and 2 other fieldsHigh correlation
계약금(원) is highly overall correlated with 전용면적(제곱미터) and 2 other fieldsHigh correlation
잔금(원) is highly overall correlated with 전용면적(제곱미터) and 2 other fieldsHigh correlation
월임대료(원) is highly overall correlated with 전용면적(제곱미터) and 2 other fieldsHigh correlation
모집호수 has 38 (1.0%) zerosZeros
계약금(원) has 50 (1.4%) zerosZeros
잔금(원) has 65 (1.8%) zerosZeros
월임대료(원) has 38 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-12 17:48:53.492609
Analysis finished2023-12-12 17:48:58.445278
Duration4.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

모집구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
임대상가
3669 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임대상가 3669
100.0%

Length

2023-12-13T02:48:58.517531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:58.621239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임대상가 3669
100.0%

관리소명
Text

Distinct145
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2023-12-13T02:48:58.856663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length7.9888253
Min length7

Characters and Unicode

Total characters29311
Distinct characters169
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

Unique15 ?
Unique (%)0.4%

Sample

1st row서울번동3관리소
2nd row충주연수2관리소
3rd row수원우만3관리소
4th row광주하남1관리소
5th row부산금곡1관리소
ValueCountFrequency (%)
공주옥룡1관리소 316
 
8.6%
광명하안13관리소 308
 
8.4%
여수문수1관리소 153
 
4.2%
대전산내관리소 129
 
3.5%
인천만수7관리소 115
 
3.1%
부산모라1관리소 111
 
3.0%
대전둔산1관리소 106
 
2.9%
천안성정4관리소 97
 
2.6%
천안쌍용1관리소 83
 
2.3%
부산반송관리소 81
 
2.2%
Other values (135) 2170
59.1%
2023-12-13T02:48:59.298025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 3694
 
12.6%
소 3689
 
12.6%
관 3674
 
12.5%
1 1743
 
5.9%
산 1092
 
3.7%
주 773
 
2.6%
3 765
 
2.6%
대 649
 
2.2%
수 592
 
2.0%
안 537
 
1.8%
Other values (159) 12103
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25717
87.7%
Decimal Number 3516
 
12.0%
Dash Punctuation 78
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 3694
 
14.4%
소 3689
 
14.3%
관 3674
 
14.3%
산 1092
 
4.2%
주 773
 
3.0%
대 649
 
2.5%
수 592
 
2.3%
안 537
 
2.1%
천 530
 
2.1%
전 497
 
1.9%
Other values (149) 9990
38.8%
Decimal Number
ValueCountFrequency (%)
1 1743
49.6%
3 765
21.8%
2 472
 
13.4%
4 266
 
7.6%
7 123
 
3.5%
5 79
 
2.2%
9 52
 
1.5%
6 13
 
0.4%
8 3
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25717
87.7%
Common 3594
 
12.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 3694
 
14.4%
소 3689
 
14.3%
관 3674
 
14.3%
산 1092
 
4.2%
주 773
 
3.0%
대 649
 
2.5%
수 592
 
2.3%
안 537
 
2.1%
천 530
 
2.1%
전 497
 
1.9%
Other values (149) 9990
38.8%
Common
ValueCountFrequency (%)
1 1743
48.5%
3 765
21.3%
2 472
 
13.1%
4 266
 
7.4%
7 123
 
3.4%
5 79
 
2.2%
- 78
 
2.2%
9 52
 
1.4%
6 13
 
0.4%
8 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25717
87.7%
ASCII 3594
 
12.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 3694
 
14.4%
소 3689
 
14.3%
관 3674
 
14.3%
산 1092
 
4.2%
주 773
 
3.0%
대 649
 
2.5%
수 592
 
2.3%
안 537
 
2.1%
천 530
 
2.1%
전 497
 
1.9%
Other values (149) 9990
38.8%
ASCII
ValueCountFrequency (%)
1 1743
48.5%
3 765
21.3%
2 472
 
13.1%
4 266
 
7.4%
7 123
 
3.4%
5 79
 
2.2%
- 78
 
2.2%
9 52
 
1.4%
6 13
 
0.4%
8 3
 
0.1%

모집공고명
Text

Distinct1065
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2023-12-13T02:48:59.630120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length142
Median length80
Mean length26.503679
Min length9

Characters and Unicode

Total characters97242
Distinct characters248
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique535 ?
Unique (%)14.6%

Sample

1st row서울번동3단지 영구임대상가 입점자 모집공고
2nd row충북 충주연수2아파트 임대상가 예비입점자 모집안내(13.10.31수정본)
3rd row수원우만3단지 임대상가 입점자 모집공고(2013.11.06)
4th row광주하남주공1단지 영구임대상가 입점자 모집공고
5th row부산금곡1 상가 입점자 모집 공고
ValueCountFrequency (%)
입점자 2735
 
15.4%
임대상가 2470
 
13.9%
모집공고 1673
 
9.4%
모집 783
 
4.4%
영구임대상가 563
 
3.2%
공고 492
 
2.8%
상가 257
 
1.4%
제지층 250
 
1.4%
공주옥룡1 187
 
1.1%
11호 163
 
0.9%
Other values (869) 8177
46.1%
2023-12-13T02:49:00.081589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14641
 
15.1%
1 4743
 
4.9%
대 4245
 
4.4%
공 3937
 
4.0%
가 3881
 
4.0%
모 3863
 
4.0%
상 3826
 
3.9%
집 3743
 
3.8%
임 3650
 
3.8%
고 3432
 
3.5%
Other values (238) 47281
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 62655
64.4%
Space Separator 14641
 
15.1%
Decimal Number 14115
 
14.5%
Other Punctuation 2426
 
2.5%
Open Punctuation 1535
 
1.6%
Close Punctuation 1516
 
1.6%
Dash Punctuation 189
 
0.2%
Connector Punctuation 109
 
0.1%
Uppercase Letter 38
 
< 0.1%
Math Symbol 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 4245
 
6.8%
공 3937
 
6.3%
가 3881
 
6.2%
모 3863
 
6.2%
상 3826
 
6.1%
집 3743
 
6.0%
임 3650
 
5.8%
고 3432
 
5.5%
점 3180
 
5.1%
자 3169
 
5.1%
Other values (209) 25729
41.1%
Decimal Number
ValueCountFrequency (%)
1 4743
33.6%
2 2448
17.3%
0 2347
16.6%
3 1392
 
9.9%
4 834
 
5.9%
7 798
 
5.7%
6 611
 
4.3%
5 487
 
3.5%
8 252
 
1.8%
9 203
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 1838
75.8%
. 570
 
23.5%
' 8
 
0.3%
* 5
 
0.2%
; 3
 
0.1%
/ 2
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 21
55.3%
L 7
 
18.4%
H 7
 
18.4%
A 3
 
7.9%
Open Punctuation
ValueCountFrequency (%)
( 1516
98.8%
[ 19
 
1.2%
Close Punctuation
ValueCountFrequency (%)
) 1497
98.7%
] 19
 
1.3%
Space Separator
ValueCountFrequency (%)
14641
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 109
100.0%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%
Control
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 62655
64.4%
Common 34549
35.5%
Latin 38
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 4245
 
6.8%
공 3937
 
6.3%
가 3881
 
6.2%
모 3863
 
6.2%
상 3826
 
6.1%
집 3743
 
6.0%
임 3650
 
5.8%
고 3432
 
5.5%
점 3180
 
5.1%
자 3169
 
5.1%
Other values (209) 25729
41.1%
Common
ValueCountFrequency (%)
14641
42.4%
1 4743
 
13.7%
2 2448
 
7.1%
0 2347
 
6.8%
, 1838
 
5.3%
( 1516
 
4.4%
) 1497
 
4.3%
3 1392
 
4.0%
4 834
 
2.4%
7 798
 
2.3%
Other values (15) 2495
 
7.2%
Latin
ValueCountFrequency (%)
B 21
55.3%
L 7
 
18.4%
H 7
 
18.4%
A 3
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 62654
64.4%
ASCII 34587
35.6%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14641
42.3%
1 4743
 
13.7%
2 2448
 
7.1%
0 2347
 
6.8%
, 1838
 
5.3%
( 1516
 
4.4%
) 1497
 
4.3%
3 1392
 
4.0%
4 834
 
2.4%
7 798
 
2.3%
Other values (19) 2533
 
7.3%
Hangul
ValueCountFrequency (%)
대 4245
 
6.8%
공 3937
 
6.3%
가 3881
 
6.2%
모 3863
 
6.2%
상 3826
 
6.1%
집 3743
 
6.0%
임 3650
 
5.8%
고 3432
 
5.5%
점 3180
 
5.1%
자 3169
 
5.1%
Other values (208) 25728
41.1%
Compat Jamo
ValueCountFrequency (%)
ㄱ 1
100.0%

모집공고일
Date

Distinct1077
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
Minimum2013-10-29 00:00:00
Maximum2023-09-20 00:00:00
2023-12-13T02:49:00.207227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:00.332133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

당첨자발표일
Date

Distinct1095
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
Minimum2013-11-06 00:00:00
Maximum2023-10-13 00:00:00
2023-12-13T02:49:00.445330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:00.565026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

문의전화
Text

Distinct145
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2023-12-13T02:49:00.766127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.854729
Min length11

Characters and Unicode

Total characters43495
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

Unique15 ?
Unique (%)0.4%

Sample

1st row02-984-6152
2nd row043-852-3808
3rd row031-241-2872
4th row062-951-6691
5th row051-363-2116
ValueCountFrequency (%)
041-856-5898 316
 
8.6%
02-892-1651 308
 
8.4%
061-653-1954 153
 
4.2%
042-271-8241 129
 
3.5%
032-462-9901 115
 
3.1%
051-302-4946 111
 
3.0%
042-484-2592 106
 
2.9%
041-575-5171 97
 
2.6%
041-571-3676 83
 
2.3%
051-542-7784 81
 
2.2%
Other values (135) 2170
59.1%
2023-12-13T02:49:01.072772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7338
16.9%
0 5147
11.8%
5 4466
10.3%
1 4364
10.0%
2 4352
10.0%
4 3854
8.9%
6 3752
8.6%
8 3196
7.3%
3 2996
6.9%
9 2192
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36157
83.1%
Dash Punctuation 7338
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5147
14.2%
5 4466
12.4%
1 4364
12.1%
2 4352
12.0%
4 3854
10.7%
6 3752
10.4%
8 3196
8.8%
3 2996
8.3%
9 2192
6.1%
7 1838
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 7338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 7338
16.9%
0 5147
11.8%
5 4466
10.3%
1 4364
10.0%
2 4352
10.0%
4 3854
8.9%
6 3752
8.6%
8 3196
7.3%
3 2996
6.9%
9 2192
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7338
16.9%
0 5147
11.8%
5 4466
10.3%
1 4364
10.0%
2 4352
10.0%
4 3854
8.9%
6 3752
8.6%
8 3196
7.3%
3 2996
6.9%
9 2192
 
5.0%

전용면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct379
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.173038
Minimum7.41
Maximum5451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-13T02:49:01.198896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.41
5-th percentile17.4
Q127.23
median32.1
Q346.51
95-th percentile240.22
Maximum5451
Range5443.59
Interquartile range (IQR)19.28

Descriptive statistics

Standard deviation136.62946
Coefficient of variation (CV)2.1290789
Kurtosis687.1369
Mean64.173038
Median Absolute Deviation (MAD)7.05
Skewness19.614393
Sum235450.88
Variance18667.61
MonotonicityNot monotonic
2023-12-13T02:49:01.320893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.84 314
 
8.6%
27.75 201
 
5.5%
54.51 120
 
3.3%
37.95 104
 
2.8%
37.41 92
 
2.5%
126.65 84
 
2.3%
240.22 79
 
2.2%
33.51 73
 
2.0%
32.1 73
 
2.0%
401.5 68
 
1.9%
Other values (369) 2461
67.1%
ValueCountFrequency (%)
7.41 4
 
0.1%
8.44 2
 
0.1%
8.56 1
 
< 0.1%
10.42 3
 
0.1%
11.85 1
 
< 0.1%
12.72 19
0.5%
14.1 43
1.2%
14.17 2
 
0.1%
14.39 5
 
0.1%
14.43 4
 
0.1%
ValueCountFrequency (%)
5451.0 1
 
< 0.1%
2313.0 1
 
< 0.1%
1760.0 1
 
< 0.1%
613.15 1
 
< 0.1%
583.4 46
1.3%
583.04 1
 
< 0.1%
470.84 1
 
< 0.1%
407.97 4
 
0.1%
404.65 14
 
0.4%
404.09 2
 
0.1%

모집호수
Real number (ℝ)

ZEROS 

Distinct56
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24330.985
Minimum-1
Maximum7702206
Zeros38
Zeros (%)1.0%
Negative2
Negative (%)0.1%
Memory size32.4 KiB
2023-12-13T02:49:01.441850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q3105
95-th percentile206
Maximum7702206
Range7702207
Interquartile range (IQR)104

Descriptive statistics

Standard deviation424155.17
Coefficient of variation (CV)17.432717
Kurtosis319.85421
Mean24330.985
Median Absolute Deviation (MAD)0
Skewness17.868667
Sum89270383
Variance1.7990761 × 1011
MonotonicityNot monotonic
2023-12-13T02:49:01.569847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2066
56.3%
102 98
 
2.7%
204 94
 
2.6%
203 91
 
2.5%
103 89
 
2.4%
104 86
 
2.3%
202 73
 
2.0%
206 71
 
1.9%
105 67
 
1.8%
2 66
 
1.8%
Other values (46) 868
23.7%
ValueCountFrequency (%)
-1 2
 
0.1%
0 38
 
1.0%
1 2066
56.3%
2 66
 
1.8%
3 14
 
0.4%
4 16
 
0.4%
5 16
 
0.4%
6 9
 
0.2%
7 26
 
0.7%
8 7
 
0.2%
ValueCountFrequency (%)
7702206 2
0.1%
7702202 4
0.1%
7702201 3
0.1%
7701203 1
 
< 0.1%
7701202 1
 
< 0.1%
2384000 1
 
< 0.1%
1964000 1
 
< 0.1%
1083 1
 
< 0.1%
624 1
 
< 0.1%
305 1
 
< 0.1%

계약금(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1086
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2069846.5
Minimum0
Maximum20800000
Zeros50
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-13T02:49:01.698935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile379000
Q1874000
median1530000
Q32420000
95-th percentile5300000
Maximum20800000
Range20800000
Interquartile range (IQR)1546000

Descriptive statistics

Standard deviation2176895.4
Coefficient of variation (CV)1.0517183
Kurtosis21.227511
Mean2069846.5
Median Absolute Deviation (MAD)722000
Skewness3.8526668
Sum7.5942667 × 109
Variance4.7388737 × 1012
MonotonicityNot monotonic
2023-12-13T02:49:01.829493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1082000 55
 
1.5%
1726000 54
 
1.5%
2252000 52
 
1.4%
0 50
 
1.4%
4000000 44
 
1.2%
10068000 33
 
0.9%
720000 32
 
0.9%
931000 30
 
0.8%
2690000 29
 
0.8%
1639000 29
 
0.8%
Other values (1076) 3261
88.9%
ValueCountFrequency (%)
0 50
1.4%
136150 1
 
< 0.1%
139000 1
 
< 0.1%
160000 1
 
< 0.1%
182000 4
 
0.1%
182300 1
 
< 0.1%
189550 1
 
< 0.1%
212450 1
 
< 0.1%
217000 1
 
< 0.1%
224000 3
 
0.1%
ValueCountFrequency (%)
20800000 7
0.2%
19005000 1
 
< 0.1%
19000000 1
 
< 0.1%
17796000 1
 
< 0.1%
17616000 1
 
< 0.1%
14847000 6
0.2%
14693000 8
0.2%
14562000 1
 
< 0.1%
14405000 1
 
< 0.1%
13452000 6
0.2%

잔금(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1166
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8224565.2
Minimum0
Maximum1.6 × 108
Zeros65
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-13T02:49:01.945252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1506400
Q13448000
median6085000
Q39536000
95-th percentile21080000
Maximum1.6 × 108
Range1.6 × 108
Interquartile range (IQR)6088000

Descriptive statistics

Standard deviation9059124.8
Coefficient of variation (CV)1.1014716
Kurtosis43.136484
Mean8224565.2
Median Absolute Deviation (MAD)2923000
Skewness4.9524537
Sum3.017593 × 1010
Variance8.2067742 × 1013
MonotonicityNot monotonic
2023-12-13T02:49:02.077471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
1.8%
6904000 54
 
1.5%
4328000 54
 
1.5%
9008000 51
 
1.4%
16000000 43
 
1.2%
40273000 33
 
0.9%
2880000 32
 
0.9%
3724700 30
 
0.8%
10760000 29
 
0.8%
7374700 29
 
0.8%
Other values (1156) 3249
88.6%
ValueCountFrequency (%)
0 65
1.8%
557000 1
 
< 0.1%
640000 1
 
< 0.1%
730000 4
 
0.1%
872000 1
 
< 0.1%
900000 3
 
0.1%
904000 5
 
0.1%
926400 2
 
0.1%
979200 2
 
0.1%
1030000 2
 
0.1%
ValueCountFrequency (%)
160000000 1
 
< 0.1%
113094000 1
 
< 0.1%
83203000 7
0.2%
76000000 1
 
< 0.1%
72960000 1
 
< 0.1%
70464000 1
 
< 0.1%
59390500 1
 
< 0.1%
59390300 5
0.1%
58774000 8
0.2%
58248000 1
 
< 0.1%

월임대료(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1142
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287792.41
Minimum0
Maximum9809600
Zeros38
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-13T02:49:02.225367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55680
Q1123000
median217600
Q3343530
95-th percentile658970
Maximum9809600
Range9809600
Interquartile range (IQR)220530

Descriptive statistics

Standard deviation415822.33
Coefficient of variation (CV)1.444869
Kurtosis305.34205
Mean287792.41
Median Absolute Deviation (MAD)100190
Skewness14.352592
Sum1.0559104 × 109
Variance1.7290821 × 1011
MonotonicityNot monotonic
2023-12-13T02:49:02.384892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
217600 52
 
1.4%
136500 52
 
1.4%
283900 51
 
1.4%
0 38
 
1.0%
638720 36
 
1.0%
1201400 33
 
0.9%
277000 29
 
0.8%
126500 29
 
0.8%
227760 28
 
0.8%
124700 27
 
0.7%
Other values (1132) 3294
89.8%
ValueCountFrequency (%)
0 38
1.0%
21000 1
 
< 0.1%
25600 3
 
0.1%
31300 3
 
0.1%
32000 1
 
< 0.1%
33000 1
 
< 0.1%
35000 4
 
0.1%
38000 4
 
0.1%
39620 1
 
< 0.1%
41360 2
 
0.1%
ValueCountFrequency (%)
9809600 4
0.1%
4992580 1
 
< 0.1%
3165000 1
 
< 0.1%
2563640 1
 
< 0.1%
2384880 7
0.2%
1980000 1
 
< 0.1%
1868370 6
0.2%
1853531 1
 
< 0.1%
1853530 1
 
< 0.1%
1736000 1
 
< 0.1%

Interactions

2023-12-13T02:48:57.151273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:54.329464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:54.950145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:55.723684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:56.485502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:57.267572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:54.442766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:55.075441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:55.889874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:56.607819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:57.386206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:54.563891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:55.233981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:56.079308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:56.752426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:57.509046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:54.682348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:55.390074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:56.242954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:56.873733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:57.654380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:54.816460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:55.573023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:56.371068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:56.995063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:49:02.554932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전용면적(제곱미터)모집호수계약금(원)잔금(원)월임대료(원)
전용면적(제곱미터)1.0000.0000.7390.5510.448
모집호수0.0001.0000.0000.0000.000
계약금(원)0.7390.0001.0000.8960.794
잔금(원)0.5510.0000.8961.0000.777
월임대료(원)0.4480.0000.7940.7771.000
2023-12-13T02:49:02.657693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전용면적(제곱미터)모집호수계약금(원)잔금(원)월임대료(원)
전용면적(제곱미터)1.000-0.2030.5370.5250.603
모집호수-0.2031.000-0.121-0.112-0.166
계약금(원)0.537-0.1211.0000.9570.813
잔금(원)0.525-0.1120.9571.0000.836
월임대료(원)0.603-0.1660.8130.8361.000

Missing values

2023-12-13T02:48:58.172063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:48:58.363607image/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

모집구분관리소명모집공고명모집공고일당첨자발표일문의전화전용면적(제곱미터)모집호수계약금(원)잔금(원)월임대료(원)
0임대상가서울번동3관리소서울번동3단지 영구임대상가 입점자 모집공고2013-10-292013-11-0602-984-615223.3720210040004016000106870
1임대상가충주연수2관리소충북 충주연수2아파트 임대상가 예비입점자 모집안내(13.10.31수정본)2013-10-312013-11-11043-852-380837.4110324420009768000313700
2임대상가수원우만3관리소수원우만3단지 임대상가 입점자 모집공고(2013.11.06)2013-11-062013-11-21031-241-287231.841300000012000000364600
3임대상가광주하남1관리소광주하남주공1단지 영구임대상가 입점자 모집공고2013-11-112013-11-20062-951-669139.33109341300013654000417300
4임대상가부산금곡1관리소부산금곡1 상가 입점자 모집 공고2013-11-122013-11-20051-363-2116240.22112450000498000001041900
5임대상가부산금곡1관리소부산금곡1 상가 입점자 모집 공고2013-11-122013-11-20051-363-211623.41307400012296000240500
6임대상가부산금곡1관리소부산금곡1 상가 입점자 모집 공고2013-11-122013-11-20051-363-211629.17122760009104000165000
7임대상가부산금곡1관리소부산금곡1 상가 입점자 모집 공고2013-11-122013-11-20051-363-211618.98114800005920000107300
8임대상가부산금곡1관리소부산금곡1 상가 입점자 모집 공고2013-11-122013-11-20051-363-211622.8311230000492000097700
9임대상가목포상동1관리소목포상동1 - 영구임대 상가 입점자 모집공고 (재 공고)2013-11-142013-11-28061-278-247931.8418890003556000127900
모집구분관리소명모집공고명모집공고일당첨자발표일문의전화전용면적(제곱미터)모집호수계약금(원)잔금(원)월임대료(원)
3659임대상가익산동산관리소익산동산 임대상가 모집공고2023-08-302023-09-08063-855-963831.842065430002173000113180
3660임대상가진주평거2관리소진주평거2 임대상가 입점자모집2023-09-012023-09-13055-746-4570126.65116668006667200231500
3661임대상가진주평거2관리소진주평거2 임대상가 입점자모집2023-09-012023-09-13055-746-457031.8417704003081600107000
3662임대상가군산나운4관리소군산나운4단지 영구임대상가 입점자 모집공고2023-09-012023-09-14063-465-8264583.416280800251232001308510
3663임대상가서울번동5관리소서울 번동5단지 임대상가 입점자 모집 공고2023-09-042023-09-1802-987-358818.69111488004595200239350
3664임대상가분당목련1관리소분당목련1 임대상가(204호) 입점자 모집 공고2023-09-042023-09-14031-705-303915.015342002136800111310
3665임대상가인천만수7관리소인천만수7단지 영구임대 상가 입점자 모집공고2023-09-152023-09-22032-462-990139.5217460002986000155500
3666임대상가인천만수7관리소인천만수7단지 영구임대 상가 입점자 모집공고2023-09-152023-09-22032-462-990131.8815770002310000120290
3667임대상가제주아라관리소제주아라 임대상가(지하)입점자 모집공고2023-09-192023-10-06064-702-9741137.49015790006318000329050
3668임대상가서울중계9관리소서울중계주공9단지 임대상가 입점자 모집공고2023-09-202023-10-1302-949-551823.4122906009162400477230

Duplicate rows

Most frequently occurring

모집구분관리소명모집공고명모집공고일당첨자발표일문의전화전용면적(제곱미터)모집호수계약금(원)잔금(원)월임대료(원)# duplicates
0임대상가구미황상3관리소2022년 1차 임대상가 입점자 모집 공고2022-03-222022-04-06054-473-518123.4185600034280001190002
1임대상가대구안심1관리소대구안심1단지 임대상가 입점자 모집공고(상가 102, 103호)2021-08-172021-08-31053-964-454831.841164600065860003430002
2임대상가대구월성3관리소대구월성3 임대상가 입점자 모집 공고(207호, 208호, 209호)2016-05-112016-05-24053-633-885854.61113600045440002373202
3임대상가대전둔산1관리소대전둔산1 임대상 입점자 모집공고(장기미임대)2023-07-132023-07-25042-484-259254.511237880095152004955902
4임대상가대전둔산1관리소대전둔산1 임대상가 입점자 모집공고(장기미임대)2023-05-242023-06-08042-484-259254.511237880095152004955902
5임대상가목포상동3관리소목포상동3 임대상가 입점자 모집공고2021-07-142021-07-29061-277-243527.7513210001287000670402
6임대상가부산모라3관리소부산모라3단지 임대상가 모집공고(,나상가-지하,102,202,203,204,205,207호)2020-05-062020-05-13051-304-322526.25157100022850001190002
7임대상가분당목련1관리소분당목련1단지 복합상가 103호, 104호 입점자 모집공고2017-02-282017-03-14031-705-303932.2213220000128800004465102
8임대상가산본가야2관리소산본가야2 임대상가 입점자 모집 표준 공고문(안)2014-06-302014-07-11031-392-398937.951182200072880002045002
9임대상가산본가야2관리소임대상가입점자모집공고문2014-04-112014-04-18031-392-398937.950182200072880002045002