Overview

Dataset statistics

Number of variables11
Number of observations25
Missing cells28
Missing cells (%)10.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory99.1 B

Variable types

Numeric5
Categorical2
Text4

Dataset

Description전북특별자치도 지구별 전원마을 조성사업 추진 현황(지구명, 위치, 사업기간, 계획세대, 입주, 건축 중, 미건축, 입주율 등)
Author전북특별자치도
URLhttps://www.data.go.kr/data/3080902/fileData.do

Alerts

순번 is highly overall correlated with 미건축 and 2 other fieldsHigh correlation
계획세대 is highly overall correlated with 미건축 and 1 other fieldsHigh correlation
입주 is highly overall correlated with 입주율(퍼센트) and 1 other fieldsHigh correlation
미건축 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
입주율(퍼센트) is highly overall correlated with 순번 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 순번 and 4 other fieldsHigh correlation
건축 중 is highly overall correlated with 계획세대 and 2 other fieldsHigh correlation
구분 is highly imbalanced (59.8%)Imbalance
입주 has 6 (24.0%) missing valuesMissing
미건축 has 4 (16.0%) missing valuesMissing
입주율(퍼센트) has 3 (12.0%) missing valuesMissing
비고 has 15 (60.0%) missing valuesMissing
순번 has unique valuesUnique
미건축 has 1 (4.0%) zerosZeros
입주율(퍼센트) has 2 (8.0%) zerosZeros

Reproduction

Analysis started2024-03-14 17:49:20.185490
Analysis finished2024-03-14 17:49:28.325449
Duration8.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T02:49:28.437416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q17
median13
Q319
95-th percentile23.8
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.56613852
Kurtosis-1.2
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum325
Variance54.166667
MonotonicityStrictly increasing
2024-03-15T02:49:28.810163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1
 
4.0%
2 1
 
4.0%
25 1
 
4.0%
24 1
 
4.0%
23 1
 
4.0%
22 1
 
4.0%
21 1
 
4.0%
20 1
 
4.0%
19 1
 
4.0%
18 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
5 1
4.0%
6 1
4.0%
7 1
4.0%
8 1
4.0%
9 1
4.0%
10 1
4.0%
ValueCountFrequency (%)
25 1
4.0%
24 1
4.0%
23 1
4.0%
22 1
4.0%
21 1
4.0%
20 1
4.0%
19 1
4.0%
18 1
4.0%
17 1
4.0%
16 1
4.0%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
준공
23 
추진 중
 
2

Length

Max length4
Median length2
Mean length2.16
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
준공 23
92.0%
추진 중 2
 
8.0%

Length

2024-03-15T02:49:29.338418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:49:29.659783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
준공 23
85.2%
추진 2
 
7.4%
중 2
 
7.4%

지구명
Text

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T02:49:30.434886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.16
Min length2

Characters and Unicode

Total characters54
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)76.0%

Sample

1st row옥산
2nd row뜰아름
3rd row신대
4th row하동
5th row백일
ValueCountFrequency (%)
용정 2
 
8.0%
장금 2
 
8.0%
덕천 2
 
8.0%
금과 1
 
4.0%
옥산 1
 
4.0%
강천산 1
 
4.0%
남계 1
 
4.0%
우동 1
 
4.0%
운산 1
 
4.0%
해당화 1
 
4.0%
Other values (12) 12
48.0%
2024-03-15T02:49:31.514595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 5
 
9.3%
천 4
 
7.4%
용 3
 
5.6%
금 3
 
5.6%
정 3
 
5.6%
덕 2
 
3.7%
장 2
 
3.7%
신 2
 
3.7%
동 2
 
3.7%
운 1
 
1.9%
Other values (27) 27
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 5
 
9.3%
천 4
 
7.4%
용 3
 
5.6%
금 3
 
5.6%
정 3
 
5.6%
덕 2
 
3.7%
장 2
 
3.7%
신 2
 
3.7%
동 2
 
3.7%
운 1
 
1.9%
Other values (27) 27
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 5
 
9.3%
천 4
 
7.4%
용 3
 
5.6%
금 3
 
5.6%
정 3
 
5.6%
덕 2
 
3.7%
장 2
 
3.7%
신 2
 
3.7%
동 2
 
3.7%
운 1
 
1.9%
Other values (27) 27
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 5
 
9.3%
천 4
 
7.4%
용 3
 
5.6%
금 3
 
5.6%
정 3
 
5.6%
덕 2
 
3.7%
장 2
 
3.7%
신 2
 
3.7%
동 2
 
3.7%
운 1
 
1.9%
Other values (27) 27
50.0%

위치
Text

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T02:49:32.289272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters125
Distinct characters46
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)76.0%

Sample

1st row군산 옥산
2nd row군산 나포
3rd row익산 함라
4th row김제 요촌
5th row남원 산내
ValueCountFrequency (%)
진안 5
 
10.0%
완주 4
 
8.0%
고창 4
 
8.0%
순창 4
 
8.0%
산내 3
 
6.0%
부안 3
 
6.0%
군산 2
 
4.0%
남원 2
 
4.0%
정읍 2
 
4.0%
구이 2
 
4.0%
Other values (18) 19
38.0%
2024-03-15T02:49:33.458881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25
20.0%
안 10
 
8.0%
창 8
 
6.4%
산 8
 
6.4%
주 6
 
4.8%
진 5
 
4.0%
완 4
 
3.2%
부 4
 
3.2%
고 4
 
3.2%
순 4
 
3.2%
Other values (36) 47
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100
80.0%
Space Separator 25
 
20.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 10
 
10.0%
창 8
 
8.0%
산 8
 
8.0%
주 6
 
6.0%
진 5
 
5.0%
완 4
 
4.0%
부 4
 
4.0%
고 4
 
4.0%
순 4
 
4.0%
내 3
 
3.0%
Other values (35) 44
44.0%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100
80.0%
Common 25
 
20.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 10
 
10.0%
창 8
 
8.0%
산 8
 
8.0%
주 6
 
6.0%
진 5
 
5.0%
완 4
 
4.0%
부 4
 
4.0%
고 4
 
4.0%
순 4
 
4.0%
내 3
 
3.0%
Other values (35) 44
44.0%
Common
ValueCountFrequency (%)
25
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100
80.0%
ASCII 25
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25
100.0%
Hangul
ValueCountFrequency (%)
안 10
 
10.0%
창 8
 
8.0%
산 8
 
8.0%
주 6
 
6.0%
진 5
 
5.0%
완 4
 
4.0%
부 4
 
4.0%
고 4
 
4.0%
순 4
 
4.0%
내 3
 
3.0%
Other values (35) 44
44.0%

사업기간
Text

Distinct21
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T02:49:34.206986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters225
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

Unique17 ?
Unique (%)68.0%

Sample

1st row2005~2007
2nd row2007~2010
3rd row2008~2014
4th row2008~2010
5th row2007~2010
ValueCountFrequency (%)
2016~2019 2
 
8.0%
2007~2010 2
 
8.0%
2015~2019 2
 
8.0%
2010~2013 2
 
8.0%
2010~2014 1
 
4.0%
2005~2007 1
 
4.0%
2010~2012 1
 
4.0%
2015~2020 1
 
4.0%
2009~2011 1
 
4.0%
2005~2008 1
 
4.0%
Other values (11) 11
44.0%
2024-03-15T02:49:35.090831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 75
33.3%
2 54
24.0%
1 34
15.1%
~ 25
 
11.1%
9 7
 
3.1%
5 7
 
3.1%
6 6
 
2.7%
4 6
 
2.7%
8 5
 
2.2%
7 4
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 200
88.9%
Math Symbol 25
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 75
37.5%
2 54
27.0%
1 34
17.0%
9 7
 
3.5%
5 7
 
3.5%
6 6
 
3.0%
4 6
 
3.0%
8 5
 
2.5%
7 4
 
2.0%
3 2
 
1.0%
Math Symbol
ValueCountFrequency (%)
~ 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 225
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 75
33.3%
2 54
24.0%
1 34
15.1%
~ 25
 
11.1%
9 7
 
3.1%
5 7
 
3.1%
6 6
 
2.7%
4 6
 
2.7%
8 5
 
2.2%
7 4
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 225
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 75
33.3%
2 54
24.0%
1 34
15.1%
~ 25
 
11.1%
9 7
 
3.1%
5 7
 
3.1%
6 6
 
2.7%
4 6
 
2.7%
8 5
 
2.2%
7 4
 
1.8%

계획세대
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33
Minimum20
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T02:49:35.287225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q123
median31
Q333
95-th percentile53.2
Maximum75
Range55
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.025616
Coefficient of variation (CV)0.39471563
Kurtosis3.3930461
Mean33
Median Absolute Deviation (MAD)8
Skewness1.6328845
Sum825
Variance169.66667
MonotonicityNot monotonic
2024-03-15T02:49:35.486356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20 6
24.0%
30 5
20.0%
31 4
16.0%
33 2
 
8.0%
50 2
 
8.0%
40 1
 
4.0%
41 1
 
4.0%
54 1
 
4.0%
75 1
 
4.0%
32 1
 
4.0%
ValueCountFrequency (%)
20 6
24.0%
23 1
 
4.0%
30 5
20.0%
31 4
16.0%
32 1
 
4.0%
33 2
 
8.0%
40 1
 
4.0%
41 1
 
4.0%
50 2
 
8.0%
54 1
 
4.0%
ValueCountFrequency (%)
75 1
 
4.0%
54 1
 
4.0%
50 2
 
8.0%
41 1
 
4.0%
40 1
 
4.0%
33 2
 
8.0%
32 1
 
4.0%
31 4
16.0%
30 5
20.0%
23 1
 
4.0%

입주
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)68.4%
Missing6
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean20.526316
Minimum7
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T02:49:35.675659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.9
Q113.5
median20
Q323
95-th percentile38
Maximum47
Range40
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation9.9742358
Coefficient of variation (CV)0.48592431
Kurtosis1.5995115
Mean20.526316
Median Absolute Deviation (MAD)6
Skewness1.1244375
Sum390
Variance99.48538
MonotonicityNot monotonic
2024-03-15T02:49:35.983886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20 6
24.0%
13 2
 
8.0%
19 1
 
4.0%
26 1
 
4.0%
28 1
 
4.0%
37 1
 
4.0%
17 1
 
4.0%
10 1
 
4.0%
31 1
 
4.0%
14 1
 
4.0%
Other values (3) 3
12.0%
(Missing) 6
24.0%
ValueCountFrequency (%)
7 1
 
4.0%
8 1
 
4.0%
10 1
 
4.0%
13 2
 
8.0%
14 1
 
4.0%
17 1
 
4.0%
19 1
 
4.0%
20 6
24.0%
26 1
 
4.0%
28 1
 
4.0%
ValueCountFrequency (%)
47 1
 
4.0%
37 1
 
4.0%
31 1
 
4.0%
28 1
 
4.0%
26 1
 
4.0%
20 6
24.0%
19 1
 
4.0%
17 1
 
4.0%
14 1
 
4.0%
13 2
 
8.0%

건축 중
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
<NA>
17 
1
4 
3
2 
2
 
1
8
 
1

Length

Max length4
Median length4
Mean length3.04
Min length1

Unique

Unique2 ?
Unique (%)8.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
68.0%
1 4
 
16.0%
3 2
 
8.0%
2 1
 
4.0%
8 1
 
4.0%

Length

2024-03-15T02:49:36.240158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:49:36.454854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
68.0%
1 4
 
16.0%
3 2
 
8.0%
2 1
 
4.0%
8 1
 
4.0%

미건축
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)71.4%
Missing4
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean18.571429
Minimum0
Maximum47
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T02:49:36.629677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median18
Q324
95-th percentile47
Maximum47
Range47
Interquartile range (IQR)14

Descriptive statistics

Standard deviation12.890196
Coefficient of variation (CV)0.69408746
Kurtosis0.53793111
Mean18.571429
Median Absolute Deviation (MAD)8
Skewness0.76708122
Sum390
Variance166.15714
MonotonicityNot monotonic
2024-03-15T02:49:37.092934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
10 2
 
8.0%
14 2
 
8.0%
23 2
 
8.0%
28 2
 
8.0%
18 2
 
8.0%
47 2
 
8.0%
11 1
 
4.0%
2 1
 
4.0%
3 1
 
4.0%
4 1
 
4.0%
Other values (5) 5
20.0%
(Missing) 4
16.0%
ValueCountFrequency (%)
0 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
10 2
8.0%
11 1
4.0%
14 2
8.0%
16 1
4.0%
18 2
8.0%
20 1
4.0%
ValueCountFrequency (%)
47 2
8.0%
30 1
4.0%
28 2
8.0%
24 1
4.0%
23 2
8.0%
20 1
4.0%
18 2
8.0%
16 1
4.0%
14 2
8.0%
11 1
4.0%

입주율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)77.3%
Missing3
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean54.286364
Minimum0
Maximum100
Zeros2
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T02:49:37.452354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q130.325
median58.7
Q384.15
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)53.825

Descriptive statistics

Standard deviation33.401373
Coefficient of variation (CV)0.61528109
Kurtosis-1.1060207
Mean54.286364
Median Absolute Deviation (MAD)29.85
Skewness-0.0418498
Sum1194.3
Variance1115.6517
MonotonicityNot monotonic
2024-03-15T02:49:37.834389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100.0 4
16.0%
0.0 2
 
8.0%
63.3 2
 
8.0%
90.0 1
 
4.0%
54.8 1
 
4.0%
30.3 1
 
4.0%
25.0 1
 
4.0%
37.0 1
 
4.0%
65.0 1
 
4.0%
66.6 1
 
4.0%
Other values (7) 7
28.0%
(Missing) 3
12.0%
ValueCountFrequency (%)
0.0 2
8.0%
6.0 1
4.0%
24.2 1
4.0%
25.0 1
4.0%
30.3 1
4.0%
30.4 1
4.0%
37.0 1
4.0%
40.6 1
4.0%
41.9 1
4.0%
54.8 1
4.0%
ValueCountFrequency (%)
100.0 4
16.0%
93.3 1
 
4.0%
90.0 1
 
4.0%
66.6 1
 
4.0%
65.0 1
 
4.0%
63.3 2
8.0%
62.6 1
 
4.0%
54.8 1
 
4.0%
41.9 1
 
4.0%
40.6 1
 
4.0%

비고
Text

MISSING 

Distinct5
Distinct (%)50.0%
Missing15
Missing (%)60.0%
Memory size328.0 B
2024-03-15T02:49:38.357446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length5.7
Min length4

Characters and Unicode

Total characters57
Distinct characters25
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

Unique4 ?
Unique (%)40.0%

Sample

1st row입주완료
2nd row입주완료
3rd row입주완료
4th row입주완료
5th row입주완료
ValueCountFrequency (%)
입주완료 6
42.9%
분양진행 1
 
7.1%
4 1
 
7.1%
미분양 1
 
7.1%
20 1
 
7.1%
기반조성 1
 
7.1%
완료 1
 
7.1%
11월 1
 
7.1%
분양공고예정 1
 
7.1%
2024-03-15T02:49:39.033337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
완 7
12.3%
료 7
12.3%
입 6
 
10.5%
주 6
 
10.5%
4
 
7.0%
분 3
 
5.3%
양 3
 
5.3%
( 2
 
3.5%
) 2
 
3.5%
1 2
 
3.5%
Other values (15) 15
26.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44
77.2%
Decimal Number 5
 
8.8%
Space Separator 4
 
7.0%
Open Punctuation 2
 
3.5%
Close Punctuation 2
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
완 7
15.9%
료 7
15.9%
입 6
13.6%
주 6
13.6%
분 3
 
6.8%
양 3
 
6.8%
조 1
 
2.3%
예 1
 
2.3%
고 1
 
2.3%
공 1
 
2.3%
Other values (8) 8
18.2%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
0 1
20.0%
2 1
20.0%
4 1
20.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44
77.2%
Common 13
 
22.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
완 7
15.9%
료 7
15.9%
입 6
13.6%
주 6
13.6%
분 3
 
6.8%
양 3
 
6.8%
조 1
 
2.3%
예 1
 
2.3%
고 1
 
2.3%
공 1
 
2.3%
Other values (8) 8
18.2%
Common
ValueCountFrequency (%)
4
30.8%
( 2
15.4%
) 2
15.4%
1 2
15.4%
0 1
 
7.7%
2 1
 
7.7%
4 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44
77.2%
ASCII 13
 
22.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
완 7
15.9%
료 7
15.9%
입 6
13.6%
주 6
13.6%
분 3
 
6.8%
양 3
 
6.8%
조 1
 
2.3%
예 1
 
2.3%
고 1
 
2.3%
공 1
 
2.3%
Other values (8) 8
18.2%
ASCII
ValueCountFrequency (%)
4
30.8%
( 2
15.4%
) 2
15.4%
1 2
15.4%
0 1
 
7.7%
2 1
 
7.7%
4 1
 
7.7%

Interactions

2024-03-15T02:49:25.766963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:21.090562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:22.424469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:23.378794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:24.533710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:26.032090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:21.430873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:22.571563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:23.638351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:24.768418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:26.285052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:21.671774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:22.725679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:23.836275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:25.002671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:26.597022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:21.989537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:22.879131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:24.018466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:25.257781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:26.856410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:22.195526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:23.098571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:24.269032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:49:25.490626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:49:39.199058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분지구명위치사업기간계획세대입주건축 중미건축입주율(퍼센트)비고
순번1.0000.8260.7190.9120.6050.2940.0000.3460.4700.7190.751
구분0.8261.0001.0001.0001.0000.000NaN1.0000.645NaN1.000
지구명0.7191.0001.0000.9980.9840.9850.8451.0000.9200.7911.000
위치0.9121.0000.9981.0000.9841.0000.8451.0000.9100.8511.000
사업기간0.6051.0000.9840.9841.0000.9981.0001.0000.9480.9811.000
계획세대0.2940.0000.9851.0000.9981.0000.7730.7260.8160.4620.631
입주0.000NaN0.8450.8451.0000.7731.0000.0000.4400.8090.000
건축 중0.3461.0001.0001.0001.0000.7260.0001.0000.8030.3140.000
미건축0.4700.6450.9200.9100.9480.8160.4400.8031.0000.5661.000
입주율(퍼센트)0.719NaN0.7910.8510.9810.4620.8090.3140.5661.0001.000
비고0.7511.0001.0001.0001.0000.6310.0000.0001.0001.0001.000
2024-03-15T02:49:39.690712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분건축 중
구분1.0000.816
건축 중0.8161.000
2024-03-15T02:49:39.958946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번계획세대입주미건축입주율(퍼센트)구분건축 중
순번1.0000.029-0.4690.573-0.6180.5240.000
계획세대0.0291.0000.2110.581-0.1300.0000.775
입주-0.4690.2111.000-0.3330.7121.0000.000
미건축0.5730.581-0.3331.000-0.4190.5910.621
입주율(퍼센트)-0.618-0.1300.712-0.4191.0001.0000.000
구분0.5240.0001.0000.5911.0001.0000.816
건축 중0.0000.7750.0000.6210.0000.8161.000

Missing values

2024-03-15T02:49:27.226473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:49:27.834891image/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.
2024-03-15T02:49:28.199130image/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

순번구분지구명위치사업기간계획세대입주건축 중미건축입주율(퍼센트)비고
01준공옥산군산 옥산2005~20073020<NA>1063.3입주완료
12준공뜰아름군산 나포2007~20103019<NA>1163.3입주완료
23준공신대익산 함라2008~20144026<NA>1465.0<NA>
34준공하동김제 요촌2008~20103028<NA>293.3<NA>
45준공백일남원 산내2007~20102020<NA><NA>100.0입주완료
56준공주천남원 주천2016~201841371390.0<NA>
67준공덕천완주 구이2005~20093117<NA>1454.8<NA>
78준공광곡완주 구이2005~20103310<NA>2330.3<NA>
89준공학선진안 동향2006~20093131<NA><NA>100.0입주완료
910준공거석진안 부귀2010~20132020<NA><NA>100.0입주완료
순번구분지구명위치사업기간계획세대입주건축 중미건축입주율(퍼센트)비고
1516준공용산고창 부안2010~2014321311840.6<NA>
1617준공해당화고창 해리2012~201433812424.2<NA>
1718준공운산부안 변산2005~2008237<NA>1630.4<NA>
1819준공우동부안 보안2009~20113113<NA>1841.9<NA>
1920준공장금정읍 산내2015~201920<NA><NA>00.0(미분양 20)
2021준공용정완주 상관2016~201950<NA>3476.0<NA>
2122준공장금정읍 산내2015~201920<NA><NA>200.0<NA>
2223준공용정완주 상관2016~201950<NA>347<NA><NA>
2324추진 중남계순창 순창2015~202031<NA>823<NA>기반조성 완료
2425추진 중마이산진안 진안2016~202030<NA><NA>30<NA>11월 분양공고예정