Overview

Dataset statistics

Number of variables10
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory87.6 B

Variable types

Numeric5
Categorical1
Text3
DateTime1

Dataset

Description"21~"22년 도내 입주자모집공고를 참고하여 아파트별 공급세대수, 접수건수, 분양경쟁률, 평균분양가 등을 산출한 자료입니다.
URLhttps://www.data.go.kr/data/15107235/fileData.do

Alerts

연번 is highly overall correlated with 시군High correlation
공급세대 is highly overall correlated with 접수건수High correlation
접수건수 is highly overall correlated with 공급세대High correlation
평균 분양가(천원) is highly overall correlated with 평당 분양가(천원)High correlation
평당 분양가(천원) is highly overall correlated with 평균 분양가(천원)High correlation
시군 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
단지명 has unique valuesUnique
주소 has unique valuesUnique
평균 분양가(천원) has unique valuesUnique
평당 분양가(천원) has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:22:43.026187
Analysis finished2023-12-12 22:22:46.517727
Duration3.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-13T07:22:46.583542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.45
Q113.25
median25.5
Q337.75
95-th percentile47.55
Maximum50
Range49
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation14.57738
Coefficient of variation (CV)0.57166195
Kurtosis-1.2
Mean25.5
Median Absolute Deviation (MAD)12.5
Skewness0
Sum1275
Variance212.5
MonotonicityStrictly increasing
2023-12-13T07:22:46.713447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

시군
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
원주
11 
강릉
11 
속초
7 
평창
5 
춘천
4 
Other values (8)
12 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique5 ?
Unique (%)10.0%

Sample

1st row춘천
2nd row춘천
3rd row춘천
4th row춘천
5th row원주

Common Values

ValueCountFrequency (%)
원주 11
22.0%
강릉 11
22.0%
속초 7
14.0%
평창 5
10.0%
춘천 4
 
8.0%
홍천 3
 
6.0%
동해 2
 
4.0%
철원 2
 
4.0%
삼척 1
 
2.0%
횡성 1
 
2.0%
Other values (3) 3
 
6.0%

Length

2023-12-13T07:22:46.852245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
원주 11
22.0%
강릉 11
22.0%
속초 7
14.0%
평창 5
10.0%
춘천 4
 
8.0%
홍천 3
 
6.0%
동해 2
 
4.0%
철원 2
 
4.0%
삼척 1
 
2.0%
횡성 1
 
2.0%
Other values (3) 3
 
6.0%

단지명
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-13T07:22:47.104520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length17
Mean length10.68
Min length4

Characters and Unicode

Total characters534
Distinct characters163
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

Unique50 ?
Unique (%)100.0%

Sample

1st row파밀리에 리버파크
2nd row모아엘가 그랑데
3rd row하우스디 시그니처 98
4th row삼부르네상스 더테라스
5th row세경3차아파트
ValueCountFrequency (%)
더 5
 
4.1%
아파트 3
 
2.4%
원주 3
 
2.4%
롯데캐슬 3
 
2.4%
경남아너스빌 2
 
1.6%
리치먼드 2
 
1.6%
디오션 2
 
1.6%
휴티스 2
 
1.6%
엘크루 2
 
1.6%
더퍼스트 2
 
1.6%
Other values (91) 97
78.9%
2023-12-13T07:22:47.608993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
73
 
13.7%
스 28
 
5.2%
트 16
 
3.0%
더 13
 
2.4%
아 12
 
2.2%
파 11
 
2.1%
이 10
 
1.9%
시 10
 
1.9%
라 9
 
1.7%
리 8
 
1.5%
Other values (153) 344
64.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 434
81.3%
Space Separator 73
 
13.7%
Decimal Number 17
 
3.2%
Uppercase Letter 7
 
1.3%
Dash Punctuation 2
 
0.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 28
 
6.5%
트 16
 
3.7%
더 13
 
3.0%
아 12
 
2.8%
파 11
 
2.5%
이 10
 
2.3%
시 10
 
2.3%
라 9
 
2.1%
리 8
 
1.8%
티 8
 
1.8%
Other values (135) 309
71.2%
Decimal Number
ValueCountFrequency (%)
2 6
35.3%
0 2
 
11.8%
3 2
 
11.8%
8 2
 
11.8%
7 1
 
5.9%
9 1
 
5.9%
4 1
 
5.9%
5 1
 
5.9%
1 1
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
A 1
14.3%
L 1
14.3%
K 1
14.3%
T 1
14.3%
X 1
14.3%
Space Separator
ValueCountFrequency (%)
73
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 434
81.3%
Common 93
 
17.4%
Latin 7
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 28
 
6.5%
트 16
 
3.7%
더 13
 
3.0%
아 12
 
2.8%
파 11
 
2.5%
이 10
 
2.3%
시 10
 
2.3%
라 9
 
2.1%
리 8
 
1.8%
티 8
 
1.8%
Other values (135) 309
71.2%
Common
ValueCountFrequency (%)
73
78.5%
2 6
 
6.5%
0 2
 
2.2%
3 2
 
2.2%
8 2
 
2.2%
- 2
 
2.2%
7 1
 
1.1%
9 1
 
1.1%
4 1
 
1.1%
5 1
 
1.1%
Other values (2) 2
 
2.2%
Latin
ValueCountFrequency (%)
B 2
28.6%
A 1
14.3%
L 1
14.3%
K 1
14.3%
T 1
14.3%
X 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 434
81.3%
ASCII 100
 
18.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
73
73.0%
2 6
 
6.0%
0 2
 
2.0%
3 2
 
2.0%
8 2
 
2.0%
B 2
 
2.0%
- 2
 
2.0%
7 1
 
1.0%
9 1
 
1.0%
4 1
 
1.0%
Other values (8) 8
 
8.0%
Hangul
ValueCountFrequency (%)
스 28
 
6.5%
트 16
 
3.7%
더 13
 
3.0%
아 12
 
2.8%
파 11
 
2.5%
이 10
 
2.3%
시 10
 
2.3%
라 9
 
2.1%
리 8
 
1.8%
티 8
 
1.8%
Other values (135) 309
71.2%

주소
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-13T07:22:47.935876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length22.64
Min length18

Characters and Unicode

Total characters1132
Distinct characters96
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

Unique50 ?
Unique (%)100.0%

Sample

1st row강원특별자치도 춘천시 근화동 752-11
2nd row강원특별자치도 춘천시 학곡리 860
3rd row강원특별자치도 춘천시 효자동 302-14번지
4th row강원특별자치도 춘천시 온의동 산5-1번지
5th row강원특별자치도 원주시 단계동 807-1번지
ValueCountFrequency (%)
강원특별자치도 50
22.8%
원주시 11
 
5.0%
강릉시 11
 
5.0%
속초시 7
 
3.2%
평창군 5
 
2.3%
교동 5
 
2.3%
춘천시 4
 
1.8%
홍천읍 3
 
1.4%
홍천군 3
 
1.4%
반곡동 3
 
1.4%
Other values (103) 117
53.4%
2023-12-13T07:22:48.362548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
169
 
14.9%
원 64
 
5.7%
강 61
 
5.4%
1 56
 
4.9%
자 51
 
4.5%
특 50
 
4.4%
별 50
 
4.4%
치 50
 
4.4%
도 50
 
4.4%
동 38
 
3.4%
Other values (86) 493
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 742
65.5%
Decimal Number 192
 
17.0%
Space Separator 169
 
14.9%
Dash Punctuation 29
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 64
 
8.6%
강 61
 
8.2%
자 51
 
6.9%
특 50
 
6.7%
별 50
 
6.7%
치 50
 
6.7%
도 50
 
6.7%
동 38
 
5.1%
시 36
 
4.9%
지 27
 
3.6%
Other values (74) 265
35.7%
Decimal Number
ValueCountFrequency (%)
1 56
29.2%
2 24
12.5%
6 21
 
10.9%
3 19
 
9.9%
7 16
 
8.3%
9 12
 
6.2%
4 12
 
6.2%
5 11
 
5.7%
8 11
 
5.7%
0 10
 
5.2%
Space Separator
ValueCountFrequency (%)
169
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 742
65.5%
Common 390
34.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 64
 
8.6%
강 61
 
8.2%
자 51
 
6.9%
특 50
 
6.7%
별 50
 
6.7%
치 50
 
6.7%
도 50
 
6.7%
동 38
 
5.1%
시 36
 
4.9%
지 27
 
3.6%
Other values (74) 265
35.7%
Common
ValueCountFrequency (%)
169
43.3%
1 56
 
14.4%
- 29
 
7.4%
2 24
 
6.2%
6 21
 
5.4%
3 19
 
4.9%
7 16
 
4.1%
9 12
 
3.1%
4 12
 
3.1%
5 11
 
2.8%
Other values (2) 21
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 742
65.5%
ASCII 390
34.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
169
43.3%
1 56
 
14.4%
- 29
 
7.4%
2 24
 
6.2%
6 21
 
5.4%
3 19
 
4.9%
7 16
 
4.1%
9 12
 
3.1%
4 12
 
3.1%
5 11
 
2.8%
Other values (2) 21
 
5.4%
Hangul
ValueCountFrequency (%)
원 64
 
8.6%
강 61
 
8.2%
자 51
 
6.9%
특 50
 
6.7%
별 50
 
6.7%
치 50
 
6.7%
도 50
 
6.7%
동 38
 
5.1%
시 36
 
4.9%
지 27
 
3.6%
Other values (74) 265
35.7%

모집공고일
Date

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
Minimum2020-02-06 00:00:00
Maximum2022-12-16 00:00:00
2023-12-13T07:22:48.506791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:48.643283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

공급세대
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean377.6
Minimum44
Maximum1206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-13T07:22:48.764386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile50.8
Q1172.25
median263
Q3475.25
95-th percentile964.65
Maximum1206
Range1162
Interquartile range (IQR)303

Descriptive statistics

Standard deviation301.3335
Coefficient of variation (CV)0.79802304
Kurtosis0.25167674
Mean377.6
Median Absolute Deviation (MAD)125
Skewness1.1114596
Sum18880
Variance90801.878
MonotonicityNot monotonic
2023-12-13T07:22:48.903635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
183 2
 
4.0%
89 1
 
2.0%
164 1
 
2.0%
360 1
 
2.0%
549 1
 
2.0%
355 1
 
2.0%
48 1
 
2.0%
440 1
 
2.0%
49 1
 
2.0%
228 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
44 1
2.0%
48 1
2.0%
49 1
2.0%
53 1
2.0%
66 1
2.0%
71 1
2.0%
89 1
2.0%
98 1
2.0%
150 1
2.0%
154 1
2.0%
ValueCountFrequency (%)
1206 1
2.0%
1001 1
2.0%
975 1
2.0%
952 1
2.0%
922 1
2.0%
901 1
2.0%
823 1
2.0%
811 1
2.0%
760 1
2.0%
707 1
2.0%

접수건수
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3697.6
Minimum3
Maximum35625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-13T07:22:49.066377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.45
Q1137
median539
Q32998.25
95-th percentile23119.6
Maximum35625
Range35622
Interquartile range (IQR)2861.25

Descriptive statistics

Standard deviation7590.9586
Coefficient of variation (CV)2.0529421
Kurtosis8.8719298
Mean3697.6
Median Absolute Deviation (MAD)534.5
Skewness3.0168817
Sum184880
Variance57622653
MonotonicityNot monotonic
2023-12-13T07:22:49.205729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3 2
 
4.0%
87 2
 
4.0%
2829 1
 
2.0%
1833 1
 
2.0%
3019 1
 
2.0%
28 1
 
2.0%
6127 1
 
2.0%
5 1
 
2.0%
5452 1
 
2.0%
518 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
3 2
4.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
13 1
2.0%
16 1
2.0%
28 1
2.0%
38 1
2.0%
71 1
2.0%
87 2
4.0%
ValueCountFrequency (%)
35625 1
2.0%
28873 1
2.0%
24925 1
2.0%
20913 1
2.0%
7260 1
2.0%
7160 1
2.0%
7077 1
2.0%
6527 1
2.0%
6127 1
2.0%
5452 1
2.0%

경쟁률
Text

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-13T07:22:49.453988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.08
Min length5

Characters and Unicode

Total characters304
Distinct characters12
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

Unique44 ?
Unique (%)88.0%

Sample

1st row31.7:1
2nd row16.7:1
3rd row1.81:1
4th row45.59:1
5th row0.02:1
ValueCountFrequency (%)
0.02:1 2
 
4.0%
0.08:1 2
 
4.0%
1.12:1 2
 
4.0%
0.016:1 1
 
2.0%
4.08:1 1
 
2.0%
1.02:1 1
 
2.0%
31.7:1 1
 
2.0%
3.87:1 1
 
2.0%
0.05:1 1
 
2.0%
17.5:1 1
 
2.0%
Other values (37) 37
74.0%
2023-12-13T07:22:49.791597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 74
24.3%
. 50
16.4%
: 50
16.4%
0 28
 
9.2%
2 16
 
5.3%
8 16
 
5.3%
4 15
 
4.9%
6 13
 
4.3%
3 12
 
3.9%
9 11
 
3.6%
Other values (2) 19
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 204
67.1%
Other Punctuation 100
32.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 74
36.3%
0 28
 
13.7%
2 16
 
7.8%
8 16
 
7.8%
4 15
 
7.4%
6 13
 
6.4%
3 12
 
5.9%
9 11
 
5.4%
5 10
 
4.9%
7 9
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 50
50.0%
: 50
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 74
24.3%
. 50
16.4%
: 50
16.4%
0 28
 
9.2%
2 16
 
5.3%
8 16
 
5.3%
4 15
 
4.9%
6 13
 
4.3%
3 12
 
3.9%
9 11
 
3.6%
Other values (2) 19
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 74
24.3%
. 50
16.4%
: 50
16.4%
0 28
 
9.2%
2 16
 
5.3%
8 16
 
5.3%
4 15
 
4.9%
6 13
 
4.3%
3 12
 
3.9%
9 11
 
3.6%
Other values (2) 19
 
6.2%

평균 분양가(천원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345527.4
Minimum72384
Maximum699900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-13T07:22:49.920084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72384
5-th percentile180698.1
Q1254199.75
median322046
Q3446250
95-th percentile576463.4
Maximum699900
Range627516
Interquartile range (IQR)192050.25

Descriptive statistics

Standard deviation129652.65
Coefficient of variation (CV)0.37523116
Kurtosis0.21490611
Mean345527.4
Median Absolute Deviation (MAD)75057.5
Skewness0.60007174
Sum17276370
Variance1.6809809 × 1010
MonotonicityNot monotonic
2023-12-13T07:22:50.058172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
393421 1
 
2.0%
456300 1
 
2.0%
263506 1
 
2.0%
360225 1
 
2.0%
248246 1
 
2.0%
500000 1
 
2.0%
261399 1
 
2.0%
251800 1
 
2.0%
480700 1
 
2.0%
238249 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
72384 1
2.0%
175083 1
2.0%
175857 1
2.0%
186615 1
2.0%
191197 1
2.0%
200232 1
2.0%
218956 1
2.0%
222674 1
2.0%
222899 1
2.0%
238249 1
2.0%
ValueCountFrequency (%)
699900 1
2.0%
628400 1
2.0%
604358 1
2.0%
542370 1
2.0%
525060 1
2.0%
500000 1
2.0%
483460 1
2.0%
480700 1
2.0%
476400 1
2.0%
466300 1
2.0%

평당 분양가(천원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10211.74
Minimum2895
Maximum17775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-13T07:22:50.231926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2895
5-th percentile6492.2
Q17766
median9588
Q312716.5
95-th percentile15542.65
Maximum17775
Range14880
Interquartile range (IQR)4950.5

Descriptive statistics

Standard deviation3172.2148
Coefficient of variation (CV)0.31064391
Kurtosis-0.2760261
Mean10211.74
Median Absolute Deviation (MAD)1961.5
Skewness0.36088745
Sum510587
Variance10062947
MonotonicityNot monotonic
2023-12-13T07:22:50.381830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11571 1
 
2.0%
13420 1
 
2.0%
8235 1
 
2.0%
11257 1
 
2.0%
7758 1
 
2.0%
15625 1
 
2.0%
7688 1
 
2.0%
10072 1
 
2.0%
14138 1
 
2.0%
7007 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
2895 1
2.0%
5489 1
2.0%
6440 1
2.0%
6556 1
2.0%
6959 1
2.0%
7003 1
2.0%
7007 1
2.0%
7034 1
2.0%
7227 1
2.0%
7416 1
2.0%
ValueCountFrequency (%)
17775 1
2.0%
16664 1
2.0%
15625 1
2.0%
15442 1
2.0%
14961 1
2.0%
14219 1
2.0%
14138 1
2.0%
13714 1
2.0%
13529 1
2.0%
13453 1
2.0%

Interactions

2023-12-13T07:22:45.822050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:43.533569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:44.031765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:44.712001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:45.141859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:45.926522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:43.613951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:44.144035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:44.810478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:45.502752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:46.023079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:43.716838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:44.278146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:44.887182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:45.578197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:46.139790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:43.821443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:44.419760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:44.966587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:45.662899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:46.225510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:43.918481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:44.566466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:45.057146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:22:45.740444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:22:50.538273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군단지명주소모집공고일공급세대접수건수경쟁률평균 분양가(천원)평당 분양가(천원)
연번1.0000.9261.0001.0000.7880.5400.0000.7880.4710.673
시군0.9261.0001.0001.0000.8890.5530.0000.9330.0000.541
단지명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
모집공고일0.7880.8891.0001.0001.0000.3970.9370.9690.9650.833
공급세대0.5400.5531.0001.0000.3971.0000.5820.9010.0000.000
접수건수0.0000.0001.0001.0000.9370.5821.0001.0000.0000.236
경쟁률0.7880.9331.0001.0000.9690.9011.0001.0000.9310.000
평균 분양가(천원)0.4710.0001.0001.0000.9650.0000.0000.9311.0000.933
평당 분양가(천원)0.6730.5411.0001.0000.8330.0000.2360.0000.9331.000
2023-12-13T07:22:50.686841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번공급세대접수건수평균 분양가(천원)평당 분양가(천원)시군
연번1.000-0.263-0.444-0.216-0.2050.708
공급세대-0.2631.0000.5100.0930.0100.245
접수건수-0.4440.5101.0000.4750.4480.000
평균 분양가(천원)-0.2160.0930.4751.0000.9720.000
평당 분양가(천원)-0.2050.0100.4480.9721.0000.178
시군0.7080.2450.0000.0000.1781.000

Missing values

2023-12-13T07:22:46.346970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:22:46.469996image/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

연번시군단지명주소모집공고일공급세대접수건수경쟁률평균 분양가(천원)평당 분양가(천원)
01춘천파밀리에 리버파크강원특별자치도 춘천시 근화동 752-112021-06-1189282931.7:139342111571
12춘천모아엘가 그랑데강원특별자치도 춘천시 학곡리 8602021-10-08390652716.7:136480510730
23춘천하우스디 시그니처 98강원특별자치도 춘천시 효자동 302-14번지2022-06-29981771.81:169990016664
34춘천삼부르네상스 더테라스강원특별자치도 춘천시 온의동 산5-1번지2022-06-3066307545.59:160435817775
45원주세경3차아파트강원특별자치도 원주시 단계동 807-1번지2020-03-1334960.02:1723842895
56원주제일풍경채 센텀포레강원특별자치도 원주시 반곡동 1822-112020-07-30120625152.08:12189566440
67원주태장2지구 B-2 태원칸타빌강원특별자치도 원주시 태장동 27372020-11-12901710.08:12226746959
78원주이지더원 3차강원특별자치도 원주시 가곡리 14732021-09-15100171604.39:13060779002
89원주남원주 역세권 A-1BL, 호반써밋강원특별자치도 원주시 무실동 1066-12021-11-052352091367.84:135750010515
910원주초혁신도시 반도유보라 마크브릿지강원특별자치도 원주시 관설동 1426번지2022-04-29476707714.86:137730011097
연번시군단지명주소모집공고일공급세대접수건수경쟁률평균 분양가(천원)평당 분양가(천원)
4041평창엘리엇아파트강원특별자치도 평창군 봉평면 무이리 1181번지2020-02-0615030.02:11758577034
4142평창현대힐스 700강원특별자치도 평창군 대관령면 횡계리 335-46번지2020-06-084430.07:12002327416
4243평창평창진부 웰라움 더퍼스트강원특별자치도 평창군 하진부리 172021-06-082621550.59:12658107818
4344평창평창스위트엠 엘크루강원특별자치도 평창군 평창읍 하리 192-12021-10-22190160.08:12673157862
4445평창더 리치먼드 평창강원특별자치도 평창군 대관령면 횡계리 2592021-11-26267380.14:12787058197
4546정선정선 벨라시티 아파트강원특별자치도 정선군 정선읍 애산리 5112021-04-23154870.56:13218009465
4647철원리치먼드 힐 철원강원특별자치도 철원군 지포리 2272021-03-2518340.016:12457317227
4748철원아데나 퍼스티어강원특별자치도 철원군 동송읍 이평리 6962021-11-252082121.02:12228996556
4849고성아야진 라메르 데시앙강원특별자치도 고성군 토성면 아야진리 산21번지2022-10-2881129363.62:138770011402
4950양양양양 스위트엠 디오션강원특별자치도 양양군 청곡리 87-7번지2022-09-022093051.45:137850011052