Overview

Dataset statistics

Number of variables4
Number of observations27
Missing cells19
Missing cells (%)17.6%
Duplicate rows1
Duplicate rows (%)3.7%
Total size in memory1.0 KiB
Average record size in memory38.7 B

Variable types

Numeric2
Text2

Dataset

Description전라북도 임실군의 원룸 및 오피스텔 현황 데이터 입니다. 데이터 세부내역에는 주소, 세대수(객실수), 건축연도를 포함하여 제공하고 있습니다.
Author전북특별자치도 임실군
URLhttps://www.data.go.kr/data/15077160/fileData.do

Alerts

Dataset has 1 (3.7%) duplicate rowsDuplicates
순번 has 5 (18.5%) missing valuesMissing
주소 has 5 (18.5%) missing valuesMissing
세대수(객실수) has 5 (18.5%) missing valuesMissing
건축연도 has 4 (14.8%) missing valuesMissing
세대수(객실수) has 1 (3.7%) zerosZeros

Reproduction

Analysis started2024-03-14 19:54:28.345991
Analysis finished2024-03-14 19:54:30.674941
Duration2.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)100.0%
Missing5
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-15T04:54:30.884689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2024-03-15T04:54:31.278730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
13 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
19 1
 
3.7%
18 1
 
3.7%
17 1
 
3.7%
16 1
 
3.7%
15 1
 
3.7%
14 1
 
3.7%
Other values (12) 12
44.4%
(Missing) 5
18.5%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%
17 1
3.7%
16 1
3.7%
15 1
3.7%
14 1
3.7%
13 1
3.7%

주소
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing5
Missing (%)18.5%
Memory size344.0 B
2024-03-15T04:54:32.456586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length25.227273
Min length23

Characters and Unicode

Total characters555
Distinct characters46
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

Unique22 ?
Unique (%)100.0%

Sample

1st row전북특별자치도 임실군 강진면 갈담리 275-1
2nd row전북특별자치도 임실군 덕치면 회문리 452-6
3rd row전북특별자치도 임실군 오수면 금암리 790-3
4th row전북특별자치도 임실군 오수면 오수리 285
5th row전북특별자치도 임실군 오수면 오수리 401-21
ValueCountFrequency (%)
전북특별자치도 22
20.0%
임실군 22
20.0%
임실읍 14
12.7%
이도리 13
11.8%
오수면 3
 
2.7%
오수리 2
 
1.8%
운암면 2
 
1.8%
구고리 1
 
0.9%
533 1
 
0.9%
청웅면 1
 
0.9%
Other values (29) 29
26.4%
2024-03-15T04:54:33.803696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
88
 
15.9%
실 36
 
6.5%
임 36
 
6.5%
도 35
 
6.3%
2 23
 
4.1%
치 23
 
4.1%
북 22
 
4.0%
리 22
 
4.0%
군 22
 
4.0%
전 22
 
4.0%
Other values (36) 226
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 352
63.4%
Decimal Number 94
 
16.9%
Space Separator 88
 
15.9%
Dash Punctuation 20
 
3.6%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 36
 
10.2%
임 36
 
10.2%
도 35
 
9.9%
치 23
 
6.5%
북 22
 
6.2%
리 22
 
6.2%
군 22
 
6.2%
전 22
 
6.2%
자 22
 
6.2%
별 22
 
6.2%
Other values (23) 90
25.6%
Decimal Number
ValueCountFrequency (%)
2 23
24.5%
3 16
17.0%
4 12
12.8%
1 9
 
9.6%
9 8
 
8.5%
7 7
 
7.4%
8 5
 
5.3%
6 5
 
5.3%
5 5
 
5.3%
0 4
 
4.3%
Space Separator
ValueCountFrequency (%)
88
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 352
63.4%
Common 203
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 36
 
10.2%
임 36
 
10.2%
도 35
 
9.9%
치 23
 
6.5%
북 22
 
6.2%
리 22
 
6.2%
군 22
 
6.2%
전 22
 
6.2%
자 22
 
6.2%
별 22
 
6.2%
Other values (23) 90
25.6%
Common
ValueCountFrequency (%)
88
43.3%
2 23
 
11.3%
- 20
 
9.9%
3 16
 
7.9%
4 12
 
5.9%
1 9
 
4.4%
9 8
 
3.9%
7 7
 
3.4%
8 5
 
2.5%
6 5
 
2.5%
Other values (3) 10
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 352
63.4%
ASCII 203
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
88
43.3%
2 23
 
11.3%
- 20
 
9.9%
3 16
 
7.9%
4 12
 
5.9%
1 9
 
4.4%
9 8
 
3.9%
7 7
 
3.4%
8 5
 
2.5%
6 5
 
2.5%
Other values (3) 10
 
4.9%
Hangul
ValueCountFrequency (%)
실 36
 
10.2%
임 36
 
10.2%
도 35
 
9.9%
치 23
 
6.5%
북 22
 
6.2%
리 22
 
6.2%
군 22
 
6.2%
전 22
 
6.2%
자 22
 
6.2%
별 22
 
6.2%
Other values (23) 90
25.6%

세대수(객실수)
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)54.5%
Missing5
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean7.1818182
Minimum0
Maximum16
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-15T04:54:34.037316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median8.5
Q311
95-th percentile12.95
Maximum16
Range16
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.0012985
Coefficient of variation (CV)0.69638334
Kurtosis-1.5534841
Mean7.1818182
Median Absolute Deviation (MAD)4
Skewness-0.042979492
Sum158
Variance25.012987
MonotonicityNot monotonic
2024-03-15T04:54:34.299860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 5
18.5%
11 4
14.8%
12 3
11.1%
1 2
 
7.4%
4 1
 
3.7%
0 1
 
3.7%
16 1
 
3.7%
8 1
 
3.7%
9 1
 
3.7%
13 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 5
18.5%
ValueCountFrequency (%)
0 1
 
3.7%
1 2
 
7.4%
2 5
18.5%
4 1
 
3.7%
6 1
 
3.7%
8 1
 
3.7%
9 1
 
3.7%
10 1
 
3.7%
11 4
14.8%
12 3
11.1%
ValueCountFrequency (%)
16 1
 
3.7%
13 1
 
3.7%
12 3
11.1%
11 4
14.8%
10 1
 
3.7%
9 1
 
3.7%
8 1
 
3.7%
6 1
 
3.7%
4 1
 
3.7%
2 5
18.5%

건축연도
Text

MISSING 

Distinct20
Distinct (%)87.0%
Missing4
Missing (%)14.8%
Memory size344.0 B
2024-03-15T04:54:35.035147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.6086957
Min length1

Characters and Unicode

Total characters221
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)73.9%

Sample

1st row2017-03-29
2nd row2013-03-11
3rd row2002-03-25
4th row2015-10-29
5th row2018-08-21
ValueCountFrequency (%)
2018-12-13 2
 
9.1%
2017-12-28 2
 
9.1%
2019-08-27 2
 
9.1%
2019-09-03 1
 
4.5%
2022-04-27 1
 
4.5%
2022-08-10 1
 
4.5%
2018-09-18 1
 
4.5%
2021-01-14 1
 
4.5%
2018-12-24 1
 
4.5%
2018-12-06 1
 
4.5%
Other values (9) 9
40.9%
2024-03-15T04:54:36.109893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 52
23.5%
- 44
19.9%
0 41
18.6%
1 38
17.2%
8 17
 
7.7%
3 7
 
3.2%
9 7
 
3.2%
7 7
 
3.2%
4 3
 
1.4%
5 2
 
0.9%
Other values (2) 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
79.6%
Dash Punctuation 44
 
19.9%
Space Separator 1
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 52
29.5%
0 41
23.3%
1 38
21.6%
8 17
 
9.7%
3 7
 
4.0%
9 7
 
4.0%
7 7
 
4.0%
4 3
 
1.7%
5 2
 
1.1%
6 2
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 221
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 52
23.5%
- 44
19.9%
0 41
18.6%
1 38
17.2%
8 17
 
7.7%
3 7
 
3.2%
9 7
 
3.2%
7 7
 
3.2%
4 3
 
1.4%
5 2
 
0.9%
Other values (2) 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 52
23.5%
- 44
19.9%
0 41
18.6%
1 38
17.2%
8 17
 
7.7%
3 7
 
3.2%
9 7
 
3.2%
7 7
 
3.2%
4 3
 
1.4%
5 2
 
0.9%
Other values (2) 3
 
1.4%

Interactions

2024-03-15T04:54:29.113884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:54:28.546250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:54:29.399877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:54:28.776900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:54:36.372672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번주소세대수(객실수)건축연도
순번1.0001.0000.6440.899
주소1.0001.0001.0001.000
세대수(객실수)0.6441.0001.0000.947
건축연도0.8991.0000.9471.000
2024-03-15T04:54:36.620537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번세대수(객실수)
순번1.0000.444
세대수(객실수)0.4441.000

Missing values

2024-03-15T04:54:29.778368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:54:30.148139image/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.
2024-03-15T04:54:30.496833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

순번주소세대수(객실수)건축연도
01전북특별자치도 임실군 강진면 갈담리 275-122017-03-29
12전북특별자치도 임실군 덕치면 회문리 452-642013-03-11
23전북특별자치도 임실군 오수면 금암리 790-312002-03-25
34전북특별자치도 임실군 오수면 오수리 28522015-10-29
45전북특별자치도 임실군 오수면 오수리 401-2102018-08-21
56전북특별자치도 임실군 운암면 쌍암리 833-322018-12-28
67전북특별자치도 임실군 임실읍 이도리 199-112007-12-28
78전북특별자치도 임실군 임실읍 이도리 239-28162012-06-18
89전북특별자치도 임실군 임실읍 이도리 239-29112012-02-21
910전북특별자치도 임실군 임실읍 이도리 240-382018-12-13
순번주소세대수(객실수)건축연도
1718전북특별자치도 임실군 임실읍 이도리 384-422018-12-24
1819전북특별자치도 임실군 임실읍 정월리 991-6122021-01-14
1920전북특별자치도 임실군 청웅면 구고리 53322018-09-18
2021전북특별자치도 임실군 임실읍 이도리 546-8122022-08-10
2122전북특별자치도 임실군 운암면 운암리 761-362022-04-27
22<NA><NA><NA><NA>
23<NA><NA><NA><NA>
24<NA><NA><NA><NA>
25<NA><NA><NA><NA>
26<NA><NA><NA>

Duplicate rows

Most frequently occurring

순번주소세대수(객실수)건축연도# duplicates
0<NA><NA><NA><NA>4