Overview

Dataset statistics

Number of variables15
Number of observations64
Missing cells57
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory131.1 B

Variable types

Numeric7
Text4
Categorical3
DateTime1

Dataset

Description충청북도 단양군 소재 공동주택 현황으로 공동주택명, 도로명주소, 지번주소, 세대수, 층수, 동수, 면적별 세대수, 준공연도, 관리사무실 전화번호 및 데이터기준일자 등의 항목으로 구성됨
Author충청북도 단양군
URLhttps://www.data.go.kr/data/15111716/fileData.do

Alerts

135제곱미터 초과 has constant value ""Constant
데이터기준일자 has constant value ""Constant
기타 유의사항 is highly overall correlated with 순번 and 7 other fieldsHigh correlation
85제곱미터 초과~135제곱미터 이하 is highly overall correlated with 층수 and 2 other fieldsHigh correlation
순번 is highly overall correlated with 준공연도 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
층수 is highly overall correlated with 85제곱미터 초과~135제곱미터 이하 and 1 other fieldsHigh correlation
동수 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
60제곱미터 이하 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
60제곱미터 초과~85제곱미터 이하 is highly overall correlated with 85제곱미터 초과~135제곱미터 이하 and 1 other fieldsHigh correlation
준공연도 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
85제곱미터 초과~135제곱미터 이하 is highly imbalanced (85.4%)Imbalance
기타 유의사항 is highly imbalanced (50.2%)Imbalance
관리실 전화번호 has 57 (89.1%) missing valuesMissing
순번 has unique valuesUnique
공공주택명 has unique valuesUnique
지번 has unique valuesUnique
60제곱미터 이하 has 20 (31.2%) zerosZeros
60제곱미터 초과~85제곱미터 이하 has 26 (40.6%) zerosZeros

Reproduction

Analysis started2023-12-12 18:21:28.860379
Analysis finished2023-12-12 18:21:34.620877
Duration5.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.5
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T03:21:34.683130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.15
Q116.75
median32.5
Q348.25
95-th percentile60.85
Maximum64
Range63
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.618987
Coefficient of variation (CV)0.5728919
Kurtosis-1.2
Mean32.5
Median Absolute Deviation (MAD)16
Skewness0
Sum2080
Variance346.66667
MonotonicityStrictly increasing
2023-12-13T03:21:34.803436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
34 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
43 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
64 1
1.6%
63 1
1.6%
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%

공공주택명
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-13T03:21:35.036137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length6.15625
Min length4

Characters and Unicode

Total characters394
Distinct characters105
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)100.0%

Sample

1st row평동연립1차
2nd row매화연립주택1차
3rd row평동연립2차
4th row매화연립주택2차
5th row주공아파트
ValueCountFrequency (%)
평동리 3
 
3.9%
에이스타운 3
 
3.9%
삼성스위트 1
 
1.3%
단아루아파트 1
 
1.3%
천동리연립주택1 1
 
1.3%
단양태양드림빌아파트 1
 
1.3%
영춘빌라 1
 
1.3%
한강빌라 1
 
1.3%
일승빌라 1
 
1.3%
빌리지 1
 
1.3%
Other values (63) 63
81.8%
2023-12-13T03:21:35.371309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빌 24
 
6.1%
라 22
 
5.6%
트 15
 
3.8%
14
 
3.6%
연 14
 
3.6%
립 14
 
3.6%
아 13
 
3.3%
리 12
 
3.0%
파 12
 
3.0%
주 11
 
2.8%
Other values (95) 243
61.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 355
90.1%
Decimal Number 21
 
5.3%
Space Separator 14
 
3.6%
Close Punctuation 2
 
0.5%
Open Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 24
 
6.8%
라 22
 
6.2%
트 15
 
4.2%
연 14
 
3.9%
립 14
 
3.9%
아 13
 
3.7%
리 12
 
3.4%
파 12
 
3.4%
주 11
 
3.1%
동 8
 
2.3%
Other values (88) 210
59.2%
Decimal Number
ValueCountFrequency (%)
1 10
47.6%
2 6
28.6%
0 3
 
14.3%
3 2
 
9.5%
Space Separator
ValueCountFrequency (%)
14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 355
90.1%
Common 39
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 24
 
6.8%
라 22
 
6.2%
트 15
 
4.2%
연 14
 
3.9%
립 14
 
3.9%
아 13
 
3.7%
리 12
 
3.4%
파 12
 
3.4%
주 11
 
3.1%
동 8
 
2.3%
Other values (88) 210
59.2%
Common
ValueCountFrequency (%)
14
35.9%
1 10
25.6%
2 6
15.4%
0 3
 
7.7%
3 2
 
5.1%
) 2
 
5.1%
( 2
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 355
90.1%
ASCII 39
 
9.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빌 24
 
6.8%
라 22
 
6.2%
트 15
 
4.2%
연 14
 
3.9%
립 14
 
3.9%
아 13
 
3.7%
리 12
 
3.4%
파 12
 
3.4%
주 11
 
3.1%
동 8
 
2.3%
Other values (88) 210
59.2%
ASCII
ValueCountFrequency (%)
14
35.9%
1 10
25.6%
2 6
15.4%
0 3
 
7.7%
3 2
 
5.1%
) 2
 
5.1%
( 2
 
5.1%

도로명 주소
Text

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-13T03:21:35.597047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length15.6875
Min length13

Characters and Unicode

Total characters1004
Distinct characters62
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

Unique62 ?
Unique (%)96.9%

Sample

1st row단양군 매포읍 평동11길 13-5
2nd row단양군 매포읍 평동15길 10
3rd row단양군 매포읍 평동12길 4
4th row단양군 매포읍 평동2길 31
5th row단양군 단양읍 상진1로 13
ValueCountFrequency (%)
단양군 64
25.0%
단양읍 29
 
11.3%
매포읍 18
 
7.0%
영춘면 7
 
2.7%
단성면 5
 
2.0%
12 4
 
1.6%
강변로 4
 
1.6%
대강면 4
 
1.6%
상진1로 4
 
1.6%
상진2로 3
 
1.2%
Other values (86) 114
44.5%
2023-12-13T03:21:35.975221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192
19.1%
단 98
 
9.8%
양 94
 
9.4%
군 64
 
6.4%
1 63
 
6.3%
읍 47
 
4.7%
길 41
 
4.1%
3 26
 
2.6%
5 23
 
2.3%
2 23
 
2.3%
Other values (52) 333
33.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 587
58.5%
Decimal Number 209
 
20.8%
Space Separator 192
 
19.1%
Dash Punctuation 16
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
단 98
16.7%
양 94
16.0%
군 64
10.9%
읍 47
 
8.0%
길 41
 
7.0%
로 23
 
3.9%
상 19
 
3.2%
매 18
 
3.1%
포 18
 
3.1%
면 17
 
2.9%
Other values (40) 148
25.2%
Decimal Number
ValueCountFrequency (%)
1 63
30.1%
3 26
12.4%
5 23
 
11.0%
2 23
 
11.0%
9 16
 
7.7%
4 13
 
6.2%
6 12
 
5.7%
8 11
 
5.3%
7 11
 
5.3%
0 11
 
5.3%
Space Separator
ValueCountFrequency (%)
192
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 587
58.5%
Common 417
41.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
단 98
16.7%
양 94
16.0%
군 64
10.9%
읍 47
 
8.0%
길 41
 
7.0%
로 23
 
3.9%
상 19
 
3.2%
매 18
 
3.1%
포 18
 
3.1%
면 17
 
2.9%
Other values (40) 148
25.2%
Common
ValueCountFrequency (%)
192
46.0%
1 63
 
15.1%
3 26
 
6.2%
5 23
 
5.5%
2 23
 
5.5%
9 16
 
3.8%
- 16
 
3.8%
4 13
 
3.1%
6 12
 
2.9%
8 11
 
2.6%
Other values (2) 22
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 587
58.5%
ASCII 417
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192
46.0%
1 63
 
15.1%
3 26
 
6.2%
5 23
 
5.5%
2 23
 
5.5%
9 16
 
3.8%
- 16
 
3.8%
4 13
 
3.1%
6 12
 
2.9%
8 11
 
2.6%
Other values (2) 22
 
5.3%
Hangul
ValueCountFrequency (%)
단 98
16.7%
양 94
16.0%
군 64
10.9%
읍 47
 
8.0%
길 41
 
7.0%
로 23
 
3.9%
상 19
 
3.2%
매 18
 
3.1%
포 18
 
3.1%
면 17
 
2.9%
Other values (40) 148
25.2%

지번
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-13T03:21:36.244199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length12.015625
Min length10

Characters and Unicode

Total characters769
Distinct characters46
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

Unique64 ?
Unique (%)100.0%

Sample

1st row매포읍 평동리 165
2nd row매포읍 평동리 339-1
3rd row매포읍 평동리 263
4th row매포읍 평동리 343-1
5th row단양읍 상진리 978
ValueCountFrequency (%)
단양읍 29
 
15.1%
매포읍 18
 
9.4%
상진리 18
 
9.4%
평동리 17
 
8.9%
영춘면 7
 
3.6%
별곡리 6
 
3.1%
단성면 5
 
2.6%
장림리 4
 
2.1%
대강면 4
 
2.1%
상방리 3
 
1.6%
Other values (74) 81
42.2%
2023-12-13T03:21:36.643435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
128
16.6%
리 64
 
8.3%
읍 47
 
6.1%
1 42
 
5.5%
단 34
 
4.4%
3 33
 
4.3%
- 32
 
4.2%
양 29
 
3.8%
5 26
 
3.4%
2 24
 
3.1%
Other values (36) 310
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 384
49.9%
Decimal Number 225
29.3%
Space Separator 128
 
16.6%
Dash Punctuation 32
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 64
16.7%
읍 47
12.2%
단 34
 
8.9%
양 29
 
7.6%
상 24
 
6.2%
동 19
 
4.9%
평 19
 
4.9%
포 18
 
4.7%
진 18
 
4.7%
매 18
 
4.7%
Other values (24) 94
24.5%
Decimal Number
ValueCountFrequency (%)
1 42
18.7%
3 33
14.7%
5 26
11.6%
2 24
10.7%
9 23
10.2%
6 21
9.3%
0 21
9.3%
7 12
 
5.3%
4 12
 
5.3%
8 11
 
4.9%
Space Separator
ValueCountFrequency (%)
128
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 385
50.1%
Hangul 384
49.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 64
16.7%
읍 47
12.2%
단 34
 
8.9%
양 29
 
7.6%
상 24
 
6.2%
동 19
 
4.9%
평 19
 
4.9%
포 18
 
4.7%
진 18
 
4.7%
매 18
 
4.7%
Other values (24) 94
24.5%
Common
ValueCountFrequency (%)
128
33.2%
1 42
 
10.9%
3 33
 
8.6%
- 32
 
8.3%
5 26
 
6.8%
2 24
 
6.2%
9 23
 
6.0%
6 21
 
5.5%
0 21
 
5.5%
7 12
 
3.1%
Other values (2) 23
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385
50.1%
Hangul 384
49.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
128
33.2%
1 42
 
10.9%
3 33
 
8.6%
- 32
 
8.3%
5 26
 
6.8%
2 24
 
6.2%
9 23
 
6.0%
6 21
 
5.5%
0 21
 
5.5%
7 12
 
3.1%
Other values (2) 23
 
6.0%
Hangul
ValueCountFrequency (%)
리 64
16.7%
읍 47
12.2%
단 34
 
8.9%
양 29
 
7.6%
상 24
 
6.2%
동 19
 
4.9%
평 19
 
4.9%
포 18
 
4.7%
진 18
 
4.7%
매 18
 
4.7%
Other values (24) 94
24.5%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.03125
Minimum5
Maximum422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T03:21:36.791373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19.75
median16
Q355.75
95-th percentile299.7
Maximum422
Range417
Interquartile range (IQR)46

Descriptive statistics

Standard deviation97.658941
Coefficient of variation (CV)1.5493734
Kurtosis3.6300656
Mean63.03125
Median Absolute Deviation (MAD)8
Skewness2.0981376
Sum4034
Variance9537.2688
MonotonicityNot monotonic
2023-12-13T03:21:36.938481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
8 7
 
10.9%
6 6
 
9.4%
16 6
 
9.4%
12 6
 
9.4%
19 2
 
3.1%
15 2
 
3.1%
28 2
 
3.1%
9 2
 
3.1%
18 2
 
3.1%
146 2
 
3.1%
Other values (27) 27
42.2%
ValueCountFrequency (%)
5 1
 
1.6%
6 6
9.4%
8 7
10.9%
9 2
 
3.1%
10 1
 
1.6%
11 1
 
1.6%
12 6
9.4%
13 1
 
1.6%
14 1
 
1.6%
15 2
 
3.1%
ValueCountFrequency (%)
422 1
1.6%
326 1
1.6%
311 1
1.6%
300 1
1.6%
298 1
1.6%
297 1
1.6%
188 1
1.6%
180 1
1.6%
152 1
1.6%
150 1
1.6%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.53125
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T03:21:37.040220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q13
median4
Q35
95-th percentile18
Maximum20
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.7441484
Coefficient of variation (CV)0.85769915
Kurtosis3.996902
Mean5.53125
Median Absolute Deviation (MAD)1
Skewness2.2975722
Sum354
Variance22.506944
MonotonicityNot monotonic
2023-12-13T03:21:37.146987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 23
35.9%
4 18
28.1%
5 11
17.2%
20 3
 
4.7%
2 2
 
3.1%
10 2
 
3.1%
18 2
 
3.1%
15 1
 
1.6%
6 1
 
1.6%
17 1
 
1.6%
ValueCountFrequency (%)
2 2
 
3.1%
3 23
35.9%
4 18
28.1%
5 11
17.2%
6 1
 
1.6%
10 2
 
3.1%
15 1
 
1.6%
17 1
 
1.6%
18 2
 
3.1%
20 3
 
4.7%
ValueCountFrequency (%)
20 3
 
4.7%
18 2
 
3.1%
17 1
 
1.6%
15 1
 
1.6%
10 2
 
3.1%
6 1
 
1.6%
5 11
17.2%
4 18
28.1%
3 23
35.9%
2 2
 
3.1%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.953125
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T03:21:37.247168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7405499
Coefficient of variation (CV)0.89116154
Kurtosis4.7719214
Mean1.953125
Median Absolute Deviation (MAD)0
Skewness2.2368045
Sum125
Variance3.0295139
MonotonicityNot monotonic
2023-12-13T03:21:37.343603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 40
62.5%
2 12
 
18.8%
6 3
 
4.7%
3 3
 
4.7%
4 2
 
3.1%
5 2
 
3.1%
7 1
 
1.6%
9 1
 
1.6%
ValueCountFrequency (%)
1 40
62.5%
2 12
 
18.8%
3 3
 
4.7%
4 2
 
3.1%
5 2
 
3.1%
6 3
 
4.7%
7 1
 
1.6%
9 1
 
1.6%
ValueCountFrequency (%)
9 1
 
1.6%
7 1
 
1.6%
6 3
 
4.7%
5 2
 
3.1%
4 2
 
3.1%
3 3
 
4.7%
2 12
 
18.8%
1 40
62.5%

60제곱미터 이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.90625
Minimum0
Maximum422
Zeros20
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T03:21:37.463141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.5
Q331.25
95-th percentile237.8
Maximum422
Range422
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation85.023194
Coefficient of variation (CV)1.8933488
Kurtosis7.2924079
Mean44.90625
Median Absolute Deviation (MAD)9.5
Skewness2.6558603
Sum2874
Variance7228.9435
MonotonicityNot monotonic
2023-12-13T03:21:37.596808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 20
31.2%
8 4
 
6.2%
12 3
 
4.7%
6 3
 
4.7%
16 2
 
3.1%
20 2
 
3.1%
10 2
 
3.1%
54 1
 
1.6%
11 1
 
1.6%
297 1
 
1.6%
Other values (25) 25
39.1%
ValueCountFrequency (%)
0 20
31.2%
2 1
 
1.6%
3 1
 
1.6%
4 1
 
1.6%
5 1
 
1.6%
6 3
 
4.7%
8 4
 
6.2%
9 1
 
1.6%
10 2
 
3.1%
11 1
 
1.6%
ValueCountFrequency (%)
422 1
1.6%
300 1
1.6%
297 1
1.6%
248 1
1.6%
180 1
1.6%
179 1
1.6%
150 1
1.6%
140 1
1.6%
128 1
1.6%
106 1
1.6%

60제곱미터 초과~85제곱미터 이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.53125
Minimum0
Maximum277
Zeros26
Zeros (%)40.6%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T03:21:37.701724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q312.75
95-th percentile77.1
Maximum277
Range277
Interquartile range (IQR)12.75

Descriptive statistics

Standard deviation41.023404
Coefficient of variation (CV)2.3400159
Kurtosis26.038448
Mean17.53125
Median Absolute Deviation (MAD)6
Skewness4.6468179
Sum1122
Variance1682.9196
MonotonicityNot monotonic
2023-12-13T03:21:37.803169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 26
40.6%
8 5
 
7.8%
6 4
 
6.2%
12 4
 
6.2%
4 3
 
4.7%
9 2
 
3.1%
16 2
 
3.1%
30 1
 
1.6%
10 1
 
1.6%
60 1
 
1.6%
Other values (15) 15
23.4%
ValueCountFrequency (%)
0 26
40.6%
2 1
 
1.6%
3 1
 
1.6%
4 3
 
4.7%
6 4
 
6.2%
8 5
 
7.8%
9 2
 
3.1%
10 1
 
1.6%
11 1
 
1.6%
12 4
 
6.2%
ValueCountFrequency (%)
277 1
1.6%
119 1
1.6%
108 1
1.6%
78 1
1.6%
72 1
1.6%
60 1
1.6%
45 1
1.6%
40 1
1.6%
30 1
1.6%
21 1
1.6%

85제곱미터 초과~135제곱미터 이하
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size644.0 B
0
62 
4
 
1
34
 
1

Length

Max length2
Median length1
Mean length1.015625
Min length1

Unique

Unique2 ?
Unique (%)3.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 62
96.9%
4 1
 
1.6%
34 1
 
1.6%

Length

2023-12-13T03:21:37.922889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:21:38.014811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 62
96.9%
4 1
 
1.6%
34 1
 
1.6%

135제곱미터 초과
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
0
64 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64
100.0%

Length

2023-12-13T03:21:38.117625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:21:38.210851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 64
100.0%

준공연도
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998.0469
Minimum1983
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T03:21:38.297341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1984
Q11990.75
median1994
Q32007
95-th percentile2016.85
Maximum2019
Range36
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation10.907155
Coefficient of variation (CV)0.0054589083
Kurtosis-0.93015505
Mean1998.0469
Median Absolute Deviation (MAD)6.5
Skewness0.57344767
Sum127875
Variance118.96602
MonotonicityNot monotonic
2023-12-13T03:21:38.417382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1993 5
 
7.8%
1992 5
 
7.8%
1994 5
 
7.8%
2014 4
 
6.2%
1984 4
 
6.2%
1999 4
 
6.2%
1985 4
 
6.2%
1989 3
 
4.7%
2016 3
 
4.7%
1991 2
 
3.1%
Other values (19) 25
39.1%
ValueCountFrequency (%)
1983 1
 
1.6%
1984 4
6.2%
1985 4
6.2%
1986 1
 
1.6%
1987 1
 
1.6%
1988 1
 
1.6%
1989 3
4.7%
1990 1
 
1.6%
1991 2
 
3.1%
1992 5
7.8%
ValueCountFrequency (%)
2019 1
 
1.6%
2018 2
3.1%
2017 1
 
1.6%
2016 3
4.7%
2015 1
 
1.6%
2014 4
6.2%
2012 1
 
1.6%
2011 1
 
1.6%
2009 1
 
1.6%
2007 2
3.1%

관리실 전화번호
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing57
Missing (%)89.1%
Memory size644.0 B
2023-12-13T03:21:38.560499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters84
Distinct characters10
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

Unique5 ?
Unique (%)71.4%

Sample

1st row043-423-3086
2nd row043-422-7554
3rd row043-423-5174
4th row043-422-7554
5th row043-421-1760
ValueCountFrequency (%)
043-422-7554 2
28.6%
043-423-3086 1
14.3%
043-423-5174 1
14.3%
043-421-1760 1
14.3%
043-421-1344 1
14.3%
043-422-3324 1
14.3%
2023-12-13T03:21:38.828087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 20
23.8%
- 14
16.7%
3 13
15.5%
2 11
13.1%
0 9
10.7%
5 5
 
6.0%
1 5
 
6.0%
7 4
 
4.8%
6 2
 
2.4%
8 1
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
83.3%
Dash Punctuation 14
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 20
28.6%
3 13
18.6%
2 11
15.7%
0 9
12.9%
5 5
 
7.1%
1 5
 
7.1%
7 4
 
5.7%
6 2
 
2.9%
8 1
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 20
23.8%
- 14
16.7%
3 13
15.5%
2 11
13.1%
0 9
10.7%
5 5
 
6.0%
1 5
 
6.0%
7 4
 
4.8%
6 2
 
2.4%
8 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 20
23.8%
- 14
16.7%
3 13
15.5%
2 11
13.1%
0 9
10.7%
5 5
 
6.0%
1 5
 
6.0%
7 4
 
4.8%
6 2
 
2.4%
8 1
 
1.2%

기타 유의사항
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size644.0 B
관리실 전화번호 없음
57 
<NA>
7 

Length

Max length11
Median length11
Mean length10.234375
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관리실 전화번호 없음
2nd row관리실 전화번호 없음
3rd row관리실 전화번호 없음
4th row관리실 전화번호 없음
5th row<NA>

Common Values

ValueCountFrequency (%)
관리실 전화번호 없음 57
89.1%
<NA> 7
 
10.9%

Length

2023-12-13T03:21:38.974932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:21:39.095609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관리실 57
32.0%
전화번호 57
32.0%
없음 57
32.0%
na 7
 
3.9%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
Minimum2023-01-06 00:00:00
Maximum2023-01-06 00:00:00
2023-12-13T03:21:39.176362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:39.269048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T03:21:33.541937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:29.411639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.135834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.822581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.544871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.242812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.915665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.625793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:29.509877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.226855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.926024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.633211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.361692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.003641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.701099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:29.599451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.306515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.023551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.712382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.448184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.084325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.782841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:29.717232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.411016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.127637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.816566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.553593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.167753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.869663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:29.829351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.519363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.241011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.928195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.640682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.281627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.941778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:29.912936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.604832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.333194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.016364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.723842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.375292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:34.013837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.007404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:30.720530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:31.435984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.119003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:32.822633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:33.456125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:21:39.359277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번공공주택명도로명 주소지번세대수층수동수60제곱미터 이하60제곱미터 초과~85제곱미터 이하85제곱미터 초과~135제곱미터 이하준공연도관리실 전화번호
순번1.0001.0001.0001.0000.0000.5620.0000.0000.3640.2000.9780.719
공공주택명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명 주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.000
지번1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
세대수0.0001.0001.0001.0001.0000.8300.7380.8780.6180.0830.3181.000
층수0.5621.0001.0001.0000.8301.0000.0710.6510.7760.8270.4950.913
동수0.0001.0001.0001.0000.7380.0711.0000.8240.6900.5320.3000.642
60제곱미터 이하0.0001.0001.0001.0000.8780.6510.8241.0000.5780.0000.0000.956
60제곱미터 초과~85제곱미터 이하0.3641.0001.0001.0000.6180.7760.6900.5781.0000.9280.2520.852
85제곱미터 초과~135제곱미터 이하0.2001.0001.0001.0000.0830.8270.5320.0000.9281.0000.4201.000
준공연도0.9781.0000.0001.0000.3180.4950.3000.0000.2520.4201.0000.719
관리실 전화번호0.7191.0001.0001.0001.0000.9130.6420.9560.8521.0000.7191.000
2023-12-13T03:21:39.513560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기타 유의사항85제곱미터 초과~135제곱미터 이하
기타 유의사항1.0001.000
85제곱미터 초과~135제곱미터 이하1.0001.000
2023-12-13T03:21:39.624852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번세대수층수동수60제곱미터 이하60제곱미터 초과~85제곱미터 이하준공연도85제곱미터 초과~135제곱미터 이하기타 유의사항
순번1.000-0.2100.432-0.306-0.1640.0850.9930.1031.000
세대수-0.2101.0000.3670.6670.6800.255-0.1870.0371.000
층수0.4320.3671.000-0.0170.2590.3760.4470.7651.000
동수-0.3060.667-0.0171.0000.4420.041-0.2900.3801.000
60제곱미터 이하-0.1640.6800.2590.4421.000-0.327-0.1450.0001.000
60제곱미터 초과~85제곱미터 이하0.0850.2550.3760.041-0.3271.0000.0880.6601.000
준공연도0.993-0.1870.447-0.290-0.1450.0881.0000.2631.000
85제곱미터 초과~135제곱미터 이하0.1030.0370.7650.3800.0000.6600.2631.0001.000
기타 유의사항1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T03:21:34.393348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:21:34.558256image/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

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45주공아파트단양군 단양읍 상진1로 13단양읍 상진리 978300573000001985043-423-3086<NA>2023-01-06
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순번공공주택명도로명 주소지번세대수층수동수60제곱미터 이하60제곱미터 초과~85제곱미터 이하85제곱미터 초과~135제곱미터 이하135제곱미터 초과준공연도관리실 전화번호기타 유의사항데이터기준일자
5455다채움3단양군 단양읍 상진1길 10-14단양읍 상진리 93-484180002014<NA>관리실 전화번호 없음2023-01-06
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6162단양코아루해피트리단양군 단양읍 상진2로 31단양읍 상진리 1038298205179119002017043-421-1344<NA>2023-01-06
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