Overview

Dataset statistics

Number of variables13
Number of observations322
Missing cells66
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.0 KiB
Average record size in memory111.4 B

Variable types

Categorical4
Text3
Numeric6

Dataset

Description한국토지주택공사 청약센터에서 분양하는 주택에 대한 공고종류, 공고구분, 공고명, 소재지, 세대수, 분양가격 등 가격공고에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15119323/fileData.do

Alerts

공고구분 has constant value ""Constant
분양가격 is highly overall correlated with 계약금 and 4 other fieldsHigh correlation
계약금 is highly overall correlated with 분양가격 and 4 other fieldsHigh correlation
중도금합계 is highly overall correlated with 분양가격 and 2 other fieldsHigh correlation
잔금 is highly overall correlated with 분양가격 and 2 other fieldsHigh correlation
공고종류 is highly overall correlated with 입주예정월High correlation
융자금 is highly overall correlated with 분양가격 and 2 other fieldsHigh correlation
입주예정월 is highly overall correlated with 분양가격 and 5 other fieldsHigh correlation
중도금합계 has 66 (20.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:26:47.689154
Analysis finished2023-12-12 22:26:52.296615
Duration4.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공고종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
일반공고
193 
정정공고
129 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반공고 193
59.9%
정정공고 129
40.1%

Length

2023-12-13T07:26:52.385132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:26:52.516521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반공고 193
59.9%
정정공고 129
40.1%

공고구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
신규최초모집(부동산원대행)
322 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신규최초모집(부동산원대행)
2nd row신규최초모집(부동산원대행)
3rd row신규최초모집(부동산원대행)
4th row신규최초모집(부동산원대행)
5th row신규최초모집(부동산원대행)

Common Values

ValueCountFrequency (%)
신규최초모집(부동산원대행) 322
100.0%

Length

2023-12-13T07:26:52.630119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:26:52.761260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규최초모집(부동산원대행 322
100.0%
Distinct64
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-13T07:26:53.003170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length37
Mean length29.993789
Min length18

Characters and Unicode

Total characters9658
Distinct characters154
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

Unique0 ?
Unique (%)0.0%

Sample

1st row전주만성 A1블록 공공분양주택 입주자 모집공고
2nd row전주만성 A1블록 공공분양주택 입주자 모집공고
3rd row전주만성 A1블록 공공분양주택 입주자 모집공고
4th row전주만성 A1블록 공공분양주택 입주자 모집공고
5th row행정중심복합도시 3-3생활권 M6블록 공공분양주택 입주자모집공고
ValueCountFrequency (%)
공공분양주택 230
 
15.7%
입주자모집공고 200
 
13.7%
모집공고 91
 
6.2%
입주자 91
 
6.2%
공공분양 56
 
3.8%
입주자모집 24
 
1.6%
공고 21
 
1.4%
1블록 19
 
1.3%
s3bl 14
 
1.0%
의정부고산 14
 
1.0%
Other values (116) 705
48.1%
2023-12-13T07:26:53.457821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1158
 
12.0%
1083
 
11.2%
626
 
6.5%
514
 
5.3%
382
 
4.0%
329
 
3.4%
327
 
3.4%
322
 
3.3%
322
 
3.3%
322
 
3.3%
Other values (144) 4273
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6854
71.0%
Space Separator 1158
 
12.0%
Decimal Number 651
 
6.7%
Uppercase Letter 457
 
4.7%
Close Punctuation 194
 
2.0%
Open Punctuation 194
 
2.0%
Dash Punctuation 131
 
1.4%
Other Punctuation 14
 
0.1%
Lowercase Letter 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1083
15.8%
626
 
9.1%
514
 
7.5%
382
 
5.6%
329
 
4.8%
327
 
4.8%
322
 
4.7%
322
 
4.7%
322
 
4.7%
295
 
4.3%
Other values (120) 2332
34.0%
Decimal Number
ValueCountFrequency (%)
3 169
26.0%
1 145
22.3%
2 139
21.4%
4 53
 
8.1%
0 34
 
5.2%
6 33
 
5.1%
5 33
 
5.1%
7 23
 
3.5%
9 12
 
1.8%
8 10
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
A 180
39.4%
B 130
28.4%
L 74
16.2%
S 39
 
8.5%
M 20
 
4.4%
H 14
 
3.1%
Close Punctuation
ValueCountFrequency (%)
] 154
79.4%
) 40
 
20.6%
Open Punctuation
ValueCountFrequency (%)
[ 154
79.4%
( 40
 
20.6%
Space Separator
ValueCountFrequency (%)
1158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 131
100.0%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6854
71.0%
Common 2342
 
24.2%
Latin 462
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1083
15.8%
626
 
9.1%
514
 
7.5%
382
 
5.6%
329
 
4.8%
327
 
4.8%
322
 
4.7%
322
 
4.7%
322
 
4.7%
295
 
4.3%
Other values (120) 2332
34.0%
Common
ValueCountFrequency (%)
1158
49.4%
3 169
 
7.2%
] 154
 
6.6%
[ 154
 
6.6%
1 145
 
6.2%
2 139
 
5.9%
- 131
 
5.6%
4 53
 
2.3%
) 40
 
1.7%
( 40
 
1.7%
Other values (7) 159
 
6.8%
Latin
ValueCountFrequency (%)
A 180
39.0%
B 130
28.1%
L 74
16.0%
S 39
 
8.4%
M 20
 
4.3%
H 14
 
3.0%
a 5
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6854
71.0%
ASCII 2804
29.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1158
41.3%
A 180
 
6.4%
3 169
 
6.0%
] 154
 
5.5%
[ 154
 
5.5%
1 145
 
5.2%
2 139
 
5.0%
- 131
 
4.7%
B 130
 
4.6%
L 74
 
2.6%
Other values (14) 370
 
13.2%
Hangul
ValueCountFrequency (%)
1083
15.8%
626
 
9.1%
514
 
7.5%
382
 
5.6%
329
 
4.8%
327
 
4.8%
322
 
4.7%
322
 
4.7%
322
 
4.7%
295
 
4.3%
Other values (120) 2332
34.0%
Distinct66
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-13T07:26:53.796751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length37
Mean length25.996894
Min length13

Characters and Unicode

Total characters8371
Distinct characters201
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

Unique0 ?
Unique (%)0.0%

Sample

1st row전라북도 전주시 덕진구 만성동로 16 (만성동)
2nd row전라북도 전주시 덕진구 만성동로 16 (만성동)
3rd row전라북도 전주시 덕진구 만성동로 16 (만성동)
4th row전라북도 전주시 덕진구 만성동로 16 (만성동)
5th row세종특별자치시 소담동 일원
ValueCountFrequency (%)
경기도 180
 
10.0%
일원 78
 
4.3%
51
 
2.8%
인천광역시 40
 
2.2%
시흥시 27
 
1.5%
하남시 25
 
1.4%
화성시 25
 
1.4%
30 20
 
1.1%
세종특별자치시 20
 
1.1%
감일동 20
 
1.1%
Other values (218) 1312
73.0%
2023-12-13T07:26:54.287027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1558
 
18.6%
438
 
5.2%
367
 
4.4%
242
 
2.9%
197
 
2.4%
195
 
2.3%
180
 
2.2%
168
 
2.0%
1 165
 
2.0%
3 158
 
1.9%
Other values (191) 4703
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5549
66.3%
Space Separator 1558
 
18.6%
Decimal Number 830
 
9.9%
Uppercase Letter 192
 
2.3%
Open Punctuation 95
 
1.1%
Close Punctuation 95
 
1.1%
Dash Punctuation 45
 
0.5%
Lowercase Letter 5
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
438
 
7.9%
367
 
6.6%
242
 
4.4%
197
 
3.6%
195
 
3.5%
180
 
3.2%
168
 
3.0%
131
 
2.4%
127
 
2.3%
116
 
2.1%
Other values (169) 3388
61.1%
Decimal Number
ValueCountFrequency (%)
1 165
19.9%
3 158
19.0%
2 108
13.0%
4 73
8.8%
0 73
8.8%
6 59
 
7.1%
5 50
 
6.0%
9 50
 
6.0%
7 48
 
5.8%
8 46
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
A 59
30.7%
B 48
25.0%
L 46
24.0%
H 19
 
9.9%
S 14
 
7.3%
M 6
 
3.1%
Space Separator
ValueCountFrequency (%)
1558
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5549
66.3%
Common 2625
31.4%
Latin 197
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
438
 
7.9%
367
 
6.6%
242
 
4.4%
197
 
3.6%
195
 
3.5%
180
 
3.2%
168
 
3.0%
131
 
2.4%
127
 
2.3%
116
 
2.1%
Other values (169) 3388
61.1%
Common
ValueCountFrequency (%)
1558
59.4%
1 165
 
6.3%
3 158
 
6.0%
2 108
 
4.1%
( 95
 
3.6%
) 95
 
3.6%
4 73
 
2.8%
0 73
 
2.8%
6 59
 
2.2%
5 50
 
1.9%
Other values (5) 191
 
7.3%
Latin
ValueCountFrequency (%)
A 59
29.9%
B 48
24.4%
L 46
23.4%
H 19
 
9.6%
S 14
 
7.1%
M 6
 
3.0%
a 5
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5549
66.3%
ASCII 2822
33.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1558
55.2%
1 165
 
5.8%
3 158
 
5.6%
2 108
 
3.8%
( 95
 
3.4%
) 95
 
3.4%
4 73
 
2.6%
0 73
 
2.6%
6 59
 
2.1%
A 59
 
2.1%
Other values (12) 379
 
13.4%
Hangul
ValueCountFrequency (%)
438
 
7.9%
367
 
6.6%
242
 
4.4%
197
 
3.6%
195
 
3.5%
180
 
3.2%
168
 
3.0%
131
 
2.4%
127
 
2.3%
116
 
2.1%
Other values (169) 3388
61.1%
Distinct190
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-13T07:26:54.634668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters2898
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)42.5%

Sample

1st row059.9600A
2nd row059.9600B
3rd row059.9600C
4th row059.9300D
5th row059.0000A
ValueCountFrequency (%)
059.0000a 11
 
3.4%
059.0000b 10
 
3.1%
074.0000h 8
 
2.5%
059.0000o 8
 
2.5%
084.0000o 8
 
2.5%
059.0000c 7
 
2.2%
059.0000d 6
 
1.9%
084.0000h 6
 
1.9%
074.9900a 5
 
1.6%
059.9800a 5
 
1.6%
Other values (180) 248
77.0%
2023-12-13T07:26:55.050716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1170
40.4%
. 322
 
11.1%
9 273
 
9.4%
4 196
 
6.8%
8 185
 
6.4%
5 174
 
6.0%
7 133
 
4.6%
A 124
 
4.3%
B 80
 
2.8%
1 42
 
1.4%
Other values (12) 199
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2254
77.8%
Other Punctuation 322
 
11.1%
Uppercase Letter 322
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 124
38.5%
B 80
24.8%
C 35
 
10.9%
H 29
 
9.0%
O 21
 
6.5%
D 14
 
4.3%
E 8
 
2.5%
T 5
 
1.6%
F 3
 
0.9%
G 2
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 1170
51.9%
9 273
 
12.1%
4 196
 
8.7%
8 185
 
8.2%
5 174
 
7.7%
7 133
 
5.9%
1 42
 
1.9%
6 37
 
1.6%
2 23
 
1.0%
3 21
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2576
88.9%
Latin 322
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1170
45.4%
. 322
 
12.5%
9 273
 
10.6%
4 196
 
7.6%
8 185
 
7.2%
5 174
 
6.8%
7 133
 
5.2%
1 42
 
1.6%
6 37
 
1.4%
2 23
 
0.9%
Latin
ValueCountFrequency (%)
A 124
38.5%
B 80
24.8%
C 35
 
10.9%
H 29
 
9.0%
O 21
 
6.5%
D 14
 
4.3%
E 8
 
2.5%
T 5
 
1.6%
F 3
 
0.9%
G 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1170
40.4%
. 322
 
11.1%
9 273
 
9.4%
4 196
 
6.8%
8 185
 
6.4%
5 174
 
6.0%
7 133
 
4.6%
A 124
 
4.3%
B 80
 
2.8%
1 42
 
1.4%
Other values (12) 199
 
6.9%

세대수
Real number (ℝ)

Distinct198
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.59627
Minimum1
Maximum1369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:26:55.176815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q172
median154.5
Q3313.75
95-th percentile645.35
Maximum1369
Range1368
Interquartile range (IQR)241.75

Descriptive statistics

Standard deviation212.04378
Coefficient of variation (CV)0.95689236
Kurtosis3.3070758
Mean221.59627
Median Absolute Deviation (MAD)105
Skewness1.6332674
Sum71354
Variance44962.565
MonotonicityNot monotonic
2023-12-13T07:26:55.315078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6
 
1.9%
359 5
 
1.6%
20 5
 
1.6%
156 4
 
1.2%
66 4
 
1.2%
287 4
 
1.2%
205 4
 
1.2%
116 4
 
1.2%
72 4
 
1.2%
5 4
 
1.2%
Other values (188) 278
86.3%
ValueCountFrequency (%)
1 2
 
0.6%
2 6
1.9%
3 1
 
0.3%
5 4
1.2%
6 1
 
0.3%
10 1
 
0.3%
12 1
 
0.3%
13 3
0.9%
14 1
 
0.3%
15 3
0.9%
ValueCountFrequency (%)
1369 1
 
0.3%
899 2
0.6%
869 3
0.9%
866 1
 
0.3%
844 1
 
0.3%
782 1
 
0.3%
751 3
0.9%
699 1
 
0.3%
688 3
0.9%
647 1
 
0.3%

전용면적
Real number (ℝ)

Distinct134
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.935404
Minimum49.93
Maximum84.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:26:55.452256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49.93
5-th percentile59.4415
Q159.9225
median74.73
Q384.6775
95-th percentile84.9595
Maximum84.99
Range35.06
Interquartile range (IQR)24.755

Descriptive statistics

Standard deviation11.480416
Coefficient of variation (CV)0.16184324
Kurtosis-1.6439008
Mean70.935404
Median Absolute Deviation (MAD)10.24
Skewness0.030759977
Sum22841.2
Variance131.79995
MonotonicityNot monotonic
2023-12-13T07:26:55.591912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.98 13
 
4.0%
59.96 10
 
3.1%
59.97 10
 
3.1%
59.99 10
 
3.1%
59.94 9
 
2.8%
84.98 8
 
2.5%
59.95 8
 
2.5%
84.71 8
 
2.5%
59.92 7
 
2.2%
74.99 7
 
2.2%
Other values (124) 232
72.0%
ValueCountFrequency (%)
49.93 1
0.3%
49.98 1
0.3%
51.62 1
0.3%
51.71 1
0.3%
51.76 1
0.3%
51.79 1
0.3%
51.91 1
0.3%
51.93 1
0.3%
51.94 1
0.3%
51.98 2
0.6%
ValueCountFrequency (%)
84.99 2
 
0.6%
84.98 8
2.5%
84.97 6
1.9%
84.96 1
 
0.3%
84.95 5
1.6%
84.94 4
1.2%
84.92 2
 
0.6%
84.91 2
 
0.6%
84.9 1
 
0.3%
84.89 5
1.6%

분양가격
Real number (ℝ)

HIGH CORRELATION 

Distinct309
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1717797 × 108
Minimum242000
Maximum7.27793 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:26:55.973530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum242000
5-th percentile1.9010555 × 108
Q12.415 × 108
median3.036515 × 108
Q33.8181575 × 108
95-th percentile4.9814195 × 108
Maximum7.27793 × 108
Range7.27551 × 108
Interquartile range (IQR)1.4031575 × 108

Descriptive statistics

Standard deviation1.0603512 × 108
Coefficient of variation (CV)0.33430797
Kurtosis1.7622766
Mean3.1717797 × 108
Median Absolute Deviation (MAD)67828000
Skewness0.68563741
Sum1.0213131 × 1011
Variance1.1243448 × 1016
MonotonicityNot monotonic
2023-12-13T07:26:56.118732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194976000 3
 
0.9%
207000000 3
 
0.9%
268000000 2
 
0.6%
356400000 2
 
0.6%
189856000 2
 
0.6%
256098000 2
 
0.6%
332000 2
 
0.6%
178980000 2
 
0.6%
275153000 2
 
0.6%
304800000 2
 
0.6%
Other values (299) 300
93.2%
ValueCountFrequency (%)
242000 1
0.3%
244700 1
0.3%
332000 2
0.6%
173300000 1
0.3%
178980000 2
0.6%
179010000 1
0.3%
179040000 1
0.3%
187934000 1
0.3%
188341000 1
0.3%
188691000 1
0.3%
ValueCountFrequency (%)
727793000 1
0.3%
724971000 1
0.3%
655830000 1
0.3%
655680000 1
0.3%
628961000 1
0.3%
601105000 1
0.3%
560670000 1
0.3%
557505000 1
0.3%
555765000 1
0.3%
548574000 1
0.3%

계약금
Real number (ℝ)

HIGH CORRELATION 

Distinct299
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35067093
Minimum25000
Maximum1.20221 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:26:56.282211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25000
5-th percentile19497000
Q125492500
median32115000
Q341406250
95-th percentile61496450
Maximum1.20221 × 108
Range1.20196 × 108
Interquartile range (IQR)15913750

Descriptive statistics

Standard deviation14155928
Coefficient of variation (CV)0.40368125
Kurtosis5.4190812
Mean35067093
Median Absolute Deviation (MAD)7390000
Skewness1.4572822
Sum1.1291604 × 1010
Variance2.003903 × 1014
MonotonicityNot monotonic
2023-12-13T07:26:56.443111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17900000 4
 
1.2%
19497000 3
 
0.9%
21000000 3
 
0.9%
27000000 3
 
0.9%
33000 2
 
0.6%
38727000 2
 
0.6%
33227000 2
 
0.6%
38551000 2
 
0.6%
55030000 2
 
0.6%
28477000 2
 
0.6%
Other values (289) 297
92.2%
ValueCountFrequency (%)
25000 2
0.6%
33000 2
0.6%
17900000 4
1.2%
18000000 1
 
0.3%
18793000 1
 
0.3%
18834000 1
 
0.3%
18869000 1
 
0.3%
18905000 1
 
0.3%
18912000 1
 
0.3%
18915000 1
 
0.3%
ValueCountFrequency (%)
120221000 1
0.3%
106122000 1
0.3%
72779000 1
0.3%
72497000 1
0.3%
67280000 1
0.3%
66900000 1
0.3%
66691000 1
0.3%
65828000 1
0.3%
65745000 1
0.3%
65583000 1
0.3%

중도금합계
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct243
Distinct (%)94.9%
Missing66
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean1.125129 × 108
Minimum72600
Maximum2.59312 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:26:56.594365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72600
5-th percentile31405000
Q172100000
median1.13659 × 108
Q31.541875 × 108
95-th percentile1.970825 × 108
Maximum2.59312 × 108
Range2.592394 × 108
Interquartile range (IQR)82087500

Descriptive statistics

Standard deviation52859498
Coefficient of variation (CV)0.46980834
Kurtosis-0.60215025
Mean1.125129 × 108
Median Absolute Deviation (MAD)40659000
Skewness0.07354081
Sum2.8803302 × 1010
Variance2.7941265 × 1015
MonotonicityNot monotonic
2023-12-13T07:26:56.730958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35800000 4
 
1.2%
58484000 3
 
0.9%
62000000 3
 
0.9%
80000000 3
 
0.9%
21029000 2
 
0.6%
71200000 2
 
0.6%
91400000 2
 
0.6%
82400000 2
 
0.6%
187759000 1
 
0.3%
165740000 1
 
0.3%
Other values (233) 233
72.4%
(Missing) 66
 
20.5%
ValueCountFrequency (%)
72600 1
0.3%
73420 1
0.3%
132000 1
0.3%
133000 1
0.3%
18793000 1
0.3%
18834000 1
0.3%
18869000 1
0.3%
20783000 1
0.3%
21029000 2
0.6%
21049000 1
0.3%
ValueCountFrequency (%)
259312000 1
0.3%
250811000 1
0.3%
228788000 1
0.3%
224837000 1
0.3%
221646000 1
0.3%
207054000 1
0.3%
206898000 1
0.3%
201880000 1
0.3%
201449000 1
0.3%
199360000 1
0.3%

잔금
Real number (ℝ)

HIGH CORRELATION 

Distinct309
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4178918 × 108
Minimum89400
Maximum4.54455 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:26:56.860590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89400
5-th percentile61920850
Q11.0366475 × 108
median1.35388 × 108
Q31.73282 × 108
95-th percentile2.4272895 × 108
Maximum4.54455 × 108
Range4.543656 × 108
Interquartile range (IQR)69617250

Descriptive statistics

Standard deviation63354750
Coefficient of variation (CV)0.44682358
Kurtosis4.4118531
Mean1.4178918 × 108
Median Absolute Deviation (MAD)36133500
Skewness1.2082616
Sum4.5656117 × 1010
Variance4.0138243 × 1015
MonotonicityNot monotonic
2023-12-13T07:26:56.979726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61994000 3
 
0.9%
49000000 3
 
0.9%
86000000 2
 
0.6%
174600000 2
 
0.6%
86379000 2
 
0.6%
149879000 2
 
0.6%
91000 2
 
0.6%
70280000 2
 
0.6%
165122000 2
 
0.6%
108000000 2
 
0.6%
Other values (299) 300
93.2%
ValueCountFrequency (%)
89400 1
0.3%
91000 2
0.6%
91280 1
0.3%
22022000 1
0.3%
23675000 1
0.3%
24041000 1
0.3%
24683000 1
0.3%
35823000 1
0.3%
37421000 1
0.3%
39394000 1
0.3%
ValueCountFrequency (%)
454455000 1
0.3%
452480000 1
0.3%
405884000 1
0.3%
385272000 1
0.3%
349489000 1
0.3%
318497000 1
0.3%
318408000 1
0.3%
257802000 1
0.3%
256400000 1
0.3%
250800000 1
0.3%

융자금
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
75000000
129 
55000000
122 
0
67 
55000
 
2
75000
 
2

Length

Max length8
Median length8
Mean length6.5062112
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row55000000
2nd row55000000
3rd row55000000
4th row55000000
5th row55000000

Common Values

ValueCountFrequency (%)
75000000 129
40.1%
55000000 122
37.9%
0 67
20.8%
55000 2
 
0.6%
75000 2
 
0.6%

Length

2023-12-13T07:26:57.102781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:26:57.201116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
75000000 129
40.1%
55000000 122
37.9%
0 67
20.8%
55000 2
 
0.6%
75000 2
 
0.6%

입주예정월
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2021-09
 
21
2023-12
 
19
2021-11
 
18
2018-11
 
15
2019-10
 
15
Other values (39)
234 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-02
2nd row2018-02
3rd row2018-02
4th row2018-02
5th row2017-10

Common Values

ValueCountFrequency (%)
2021-09 21
 
6.5%
2023-12 19
 
5.9%
2021-11 18
 
5.6%
2018-11 15
 
4.7%
2019-10 15
 
4.7%
2026-02 15
 
4.7%
2017-10 14
 
4.3%
2023-06 13
 
4.0%
2024-02 12
 
3.7%
2020-10 11
 
3.4%
Other values (34) 169
52.5%

Length

2023-12-13T07:26:57.315122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-09 21
 
6.5%
2023-12 19
 
5.9%
2021-11 18
 
5.6%
2018-11 15
 
4.7%
2019-10 15
 
4.7%
2026-02 15
 
4.7%
2017-10 14
 
4.3%
2023-06 13
 
4.0%
2024-02 12
 
3.7%
2020-10 11
 
3.4%
Other values (34) 169
52.5%

Interactions

2023-12-13T07:26:51.319407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:48.306807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.018393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.542224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:50.036626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:50.632387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:51.437406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:48.407325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.115215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.633671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:50.136642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:50.730035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:51.550195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:48.489609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.204700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.724539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:50.245944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:50.841591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:51.659124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:48.569846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.296433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.804711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:50.346761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:51.009467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:51.762074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:48.876383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.385400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.886986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:50.439001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:51.125044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:51.870163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:48.949914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.465776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:49.957988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:50.540107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:26:51.231488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:26:57.396882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공고종류공고명소재지세대수전용면적분양가격계약금중도금합계잔금융자금입주예정월
공고종류1.0001.0001.0000.2510.2260.3820.2720.4930.3670.1650.951
공고명1.0001.0001.0000.8640.5980.9540.9720.9560.9520.9921.000
소재지1.0001.0001.0000.8470.5680.9540.9630.9550.9520.9921.000
세대수0.2510.8640.8471.0000.0000.2180.2470.2960.1900.2400.797
전용면적0.2260.5980.5680.0001.0000.3620.3620.2730.2210.6190.570
분양가격0.3820.9540.9540.2180.3621.0000.9690.7180.9440.7680.921
계약금0.2720.9720.9630.2470.3620.9691.0000.6650.9320.8000.920
중도금합계0.4930.9560.9550.2960.2730.7180.6651.0000.6490.6340.923
잔금0.3670.9520.9520.1900.2210.9440.9320.6491.0000.4620.910
융자금0.1650.9920.9920.2400.6190.7680.8000.6340.4621.0000.934
입주예정월0.9511.0001.0000.7970.5700.9210.9200.9230.9100.9341.000
2023-12-13T07:26:57.597972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공고종류입주예정월융자금
공고종류1.0000.7870.201
입주예정월0.7871.0000.700
융자금0.2010.7001.000
2023-12-13T07:26:57.681442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수전용면적분양가격계약금중도금합계잔금공고종류융자금입주예정월
세대수1.0000.0710.0290.086-0.0770.1240.1860.1480.421
전용면적0.0711.0000.3190.1820.3250.1640.1680.4400.237
분양가격0.0290.3191.0000.8610.7940.5450.3830.5790.624
계약금0.0860.1820.8611.0000.7680.6130.2840.5640.653
중도금합계-0.0770.3250.7940.7681.0000.2920.3760.3340.604
잔금0.1240.1640.5450.6130.2921.0000.3680.3130.594
공고종류0.1860.1680.3830.2840.3760.3681.0000.2010.787
융자금0.1480.4400.5790.5640.3340.3130.2011.0000.700
입주예정월0.4210.2370.6240.6530.6040.5940.7870.7001.000

Missing values

2023-12-13T07:26:52.018619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:26:52.218159image/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

공고종류공고구분공고명소재지금회분양주택형세대수전용면적분양가격계약금중도금합계잔금융자금입주예정월
0일반공고신규최초모집(부동산원대행)전주만성 A1블록 공공분양주택 입주자 모집공고전라북도 전주시 덕진구 만성동로 16 (만성동)059.9600A54659.96194847000194840005844500061917000550000002018-02
1일반공고신규최초모집(부동산원대행)전주만성 A1블록 공공분양주택 입주자 모집공고전라북도 전주시 덕진구 만성동로 16 (만성동)059.9600B5559.96194976000194970005848400061994000550000002018-02
2일반공고신규최초모집(부동산원대행)전주만성 A1블록 공공분양주택 입주자 모집공고전라북도 전주시 덕진구 만성동로 16 (만성동)059.9600C5559.96194976000194970005848400061994000550000002018-02
3일반공고신규최초모집(부동산원대행)전주만성 A1블록 공공분양주택 입주자 모집공고전라북도 전주시 덕진구 만성동로 16 (만성동)059.9300D5559.93194976000194970005848400061994000550000002018-02
4일반공고신규최초모집(부동산원대행)행정중심복합도시 3-3생활권 M6블록 공공분양주택 입주자모집공고세종특별자치시 소담동 일원059.0000A89959.92200614000207960005953600065281000550000002017-10
5일반공고신규최초모집(부동산원대행)행정중심복합도시 3-3생활권 M6블록 공공분양주택 입주자모집공고세종특별자치시 소담동 일원059.0000A89959.5199413000206100005913000064672000550000002017-10
6일반공고신규최초모집(부동산원대행)행정중심복합도시 3-3생활권 M6블록 공공분양주택 입주자모집공고세종특별자치시 소담동 일원059.0000B18459.77198928000206660005928500063976000550000002017-10
7일반공고신규최초모집(부동산원대행)행정중심복합도시 3-3생활권 M6블록 공공분양주택 입주자모집공고세종특별자치시 소담동 일원059.0000B18459.95199507000207880005950700064211000550000002017-10
8일반공고신규최초모집(부동산원대행)행정중심복합도시 3-3생활권 M6블록 공공분양주택 입주자모집공고세종특별자치시 소담동 일원059.0000C8159.7200052000206520005930400065095000550000002017-10
9일반공고신규최초모집(부동산원대행)행정중심복합도시 3-3생활권 M6블록 공공분양주택 입주자모집공고세종특별자치시 소담동 일원059.0000C8159.97200531000207930005951700065220000550000002017-10
공고종류공고구분공고명소재지금회분양주택형세대수전용면적분양가격계약금중도금합계잔금융자금입주예정월
312정정공고신규최초모집(부동산원대행)[정정공고]인천영종 A-60블록 공공분양주택 입주자모집공고인천광역시 중구 은하수로 229084.8700A40084.8738695700038695000154782000118478000750000002025-01
313정정공고신규최초모집(부동산원대행)[정정공고]인천영종 A-60블록 공공분양주택 입주자모집공고인천광역시 중구 은하수로 229084.7600B9984.7638664500038664000154658000118322000750000002025-01
314정정공고신규최초모집(부동산원대행)[정정공고]인천영종 A-60블록 공공분양주택 입주자모집공고인천광역시 중구 은하수로 229084.7400C6684.7438563300038563000154253000117816000750000002025-01
315정정공고신규최초모집(부동산원대행)[정정공고]인천영종 A-60블록 공공분양주택 입주자모집공고인천광역시 중구 은하수로 229084.8700E4484.8739153000039153000156612000120765000750000002025-01
316정정공고신규최초모집(부동산원대행)[정정공고]성남복정1지구 A1블록 공공분양주택 입주자모집공고경기도 성남시 수정구 복정동 창곡동 일원 성남복정1 공공주택지구 내 A1블록051.0000O18451.9362896100062896000125792000385272000550000002025-12
317정정공고신규최초모집(부동산원대행)[정정공고]성남복정1지구 A1블록 공공분양주택 입주자모집공고경기도 성남시 수정구 복정동 창곡동 일원 성남복정1 공공주택지구 내 A1블록059.0000O43159.9372497100072497000144994000452480000550000002025-12
318정정공고신규최초모집(부동산원대행)[정정공고]성남복정1지구 A1블록 공공분양주택 입주자모집공고경기도 성남시 수정구 복정동 창곡동 일원 성남복정1 공공주택지구 내 A1블록059.0000O43159.9972779300072779000145558000454455000550000002025-12
319일반공고신규최초모집(부동산원대행)화성태안3 B-3블록 공공분양 입주자모집공고경기도 화성시 송산동 203-1084.0000O68884.4839537400039537000118613000162223000750000002026-02
320일반공고신규최초모집(부동산원대행)화성태안3 B-3블록 공공분양 입주자모집공고경기도 화성시 송산동 203-1084.0000O68884.9739368800039368000118106000161213000750000002026-02
321일반공고신규최초모집(부동산원대행)화성태안3 B-3블록 공공분양 입주자모집공고경기도 화성시 송산동 203-1084.0000O68884.8939178700039178000117536000160072000750000002026-02