Overview

Dataset statistics

Number of variables17
Number of observations1761
Missing cells1423
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory252.9 KiB
Average record size in memory147.1 B

Variable types

Numeric8
Categorical6
Text3

Dataset

Description충청남도 재난안전포털에서 제공하는 이재민 임시주거시설 정보입니다.(시설명, 상세주소, 시설면적, 주거능력 등 포함)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=31&beforeMenuCd=DOM_000000201001001000&publicdatapk=15118633

Alerts

시도명 has constant value ""Constant
시군명 is highly overall correlated with 일련번호 and 6 other fieldsHigh correlation
도로명주소코드 is highly overall correlated with 일련번호 and 6 other fieldsHigh correlation
일련번호 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
지역코드 is highly overall correlated with 법정동코드 and 4 other fieldsHigh correlation
시설일련번호 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
시설구분코드 is highly overall correlated with 시설면적 and 2 other fieldsHigh correlation
법정동코드 is highly overall correlated with 지역코드 and 4 other fieldsHigh correlation
행정동코드 is highly overall correlated with 지역코드 and 4 other fieldsHigh correlation
시설면적 is highly overall correlated with 시설구분코드 and 1 other fieldsHigh correlation
주거능력 is highly overall correlated with 시설구분코드 and 1 other fieldsHigh correlation
시설구분 is highly overall correlated with 시설구분코드High correlation
경도 is highly overall correlated with 일련번호 and 6 other fieldsHigh correlation
위도 is highly imbalanced (99.1%)Imbalance
지자체담당자 연락처 has 1420 (80.6%) missing valuesMissing
일련번호 has unique valuesUnique

Reproduction

Analysis started2024-01-09 23:11:37.226466
Analysis finished2024-01-09 23:11:45.694601
Duration8.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean881
Minimum1
Maximum1761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-10T08:11:45.757967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile89
Q1441
median881
Q31321
95-th percentile1673
Maximum1761
Range1760
Interquartile range (IQR)880

Descriptive statistics

Standard deviation508.50123
Coefficient of variation (CV)0.57718641
Kurtosis-1.2
Mean881
Median Absolute Deviation (MAD)440
Skewness0
Sum1551441
Variance258573.5
MonotonicityStrictly increasing
2024-01-10T08:11:45.889336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1171 1
 
0.1%
1182 1
 
0.1%
1181 1
 
0.1%
1180 1
 
0.1%
1179 1
 
0.1%
1178 1
 
0.1%
1177 1
 
0.1%
1176 1
 
0.1%
1175 1
 
0.1%
Other values (1751) 1751
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1761 1
0.1%
1760 1
0.1%
1759 1
0.1%
1758 1
0.1%
1757 1
0.1%
1756 1
0.1%
1755 1
0.1%
1754 1
0.1%
1753 1
0.1%
1752 1
0.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4379155 × 109
Minimum4.413 × 109
Maximum4.4825 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-10T08:11:46.038438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.413 × 109
5-th percentile4.413 × 109
Q14.413 × 109
median4.42 × 109
Q34.471 × 109
95-th percentile4.48 × 109
Maximum4.4825 × 109
Range69500000
Interquartile range (IQR)58000000

Descriptive statistics

Standard deviation27913787
Coefficient of variation (CV)0.006289842
Kurtosis-1.5809973
Mean4.4379155 × 109
Median Absolute Deviation (MAD)7000000
Skewness0.5733678
Sum7.8151692 × 1012
Variance7.7917952 × 1014
MonotonicityNot monotonic
2024-01-10T08:11:46.159552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4413000000 465
26.4%
4420000000 375
21.3%
4471000000 353
20.0%
4479000000 117
 
6.6%
4421000000 112
 
6.4%
4418000000 70
 
4.0%
4481000000 42
 
2.4%
4482500000 42
 
2.4%
4423000000 36
 
2.0%
4415000000 34
 
1.9%
Other values (6) 115
 
6.5%
ValueCountFrequency (%)
4413000000 465
26.4%
4413100000 2
 
0.1%
4415000000 34
 
1.9%
4418000000 70
 
4.0%
4420000000 375
21.3%
4421000000 112
 
6.4%
4423000000 36
 
2.0%
4425000000 9
 
0.5%
4427000000 24
 
1.4%
4471000000 353
20.0%
ValueCountFrequency (%)
4482500000 42
 
2.4%
4481000000 42
 
2.4%
4480000000 24
 
1.4%
4479000000 117
 
6.6%
4477000000 26
 
1.5%
4476000000 30
 
1.7%
4471000000 353
20.0%
4427000000 24
 
1.4%
4425000000 9
 
0.5%
4423000000 36
 
2.0%

시설일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1395
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean702.92675
Minimum1
Maximum1596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-10T08:11:46.280036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q1266
median652
Q31102
95-th percentile1506
Maximum1596
Range1595
Interquartile range (IQR)836

Descriptive statistics

Standard deviation475.69221
Coefficient of variation (CV)0.67673085
Kurtosis-1.1851381
Mean702.92675
Median Absolute Deviation (MAD)412
Skewness0.26295029
Sum1237854
Variance226283.08
MonotonicityNot monotonic
2024-01-10T08:11:46.415736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136 5
 
0.3%
141 5
 
0.3%
140 5
 
0.3%
51 4
 
0.2%
148 4
 
0.2%
152 4
 
0.2%
2 4
 
0.2%
156 4
 
0.2%
35 4
 
0.2%
27 4
 
0.2%
Other values (1385) 1718
97.6%
ValueCountFrequency (%)
1 4
0.2%
2 4
0.2%
3 2
0.1%
5 4
0.2%
6 3
0.2%
7 2
0.1%
8 1
 
0.1%
9 1
 
0.1%
10 4
0.2%
11 3
0.2%
ValueCountFrequency (%)
1596 1
0.1%
1595 1
0.1%
1594 1
0.1%
1593 1
0.1%
1592 1
0.1%
1591 1
0.1%
1590 1
0.1%
1589 1
0.1%
1588 1
0.1%
1587 1
0.1%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
충청남도
1761 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 1761
100.0%

Length

2024-01-10T08:11:46.536601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:11:46.641895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 1761
100.0%

시군명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
천안시
465 
아산시
375 
금산군
353 
청양군
117 
서산시
112 
Other values (11)
339 

Length

Max length7
Median length3
Mean length3.0045429
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
천안시 465
26.4%
아산시 375
21.3%
금산군 353
20.0%
청양군 117
 
6.6%
서산시 112
 
6.4%
보령시 70
 
4.0%
예산군 42
 
2.4%
태안군 42
 
2.4%
논산시 36
 
2.0%
공주시 34
 
1.9%
Other values (6) 115
 
6.5%

Length

2024-01-10T08:11:46.751701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
천안시 467
26.5%
아산시 375
21.3%
금산군 353
20.0%
청양군 117
 
6.6%
서산시 112
 
6.4%
보령시 70
 
4.0%
예산군 42
 
2.4%
태안군 42
 
2.4%
논산시 36
 
2.0%
공주시 34
 
1.9%
Other values (6) 115
 
6.5%

시설구분코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4304373
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-10T08:11:46.866045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile3
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4161665
Coefficient of variation (CV)0.58267973
Kurtosis28.818546
Mean2.4304373
Median Absolute Deviation (MAD)0
Skewness3.8288717
Sum4280
Variance2.0055276
MonotonicityNot monotonic
2024-01-10T08:11:46.989988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 923
52.4%
1 512
29.1%
2 245
 
13.9%
4 41
 
2.3%
6 19
 
1.1%
14 12
 
0.7%
7 9
 
0.5%
ValueCountFrequency (%)
1 512
29.1%
2 245
 
13.9%
3 923
52.4%
4 41
 
2.3%
6 19
 
1.1%
7 9
 
0.5%
14 12
 
0.7%
ValueCountFrequency (%)
14 12
 
0.7%
7 9
 
0.5%
6 19
 
1.1%
4 41
 
2.3%
3 923
52.4%
2 245
 
13.9%
1 512
29.1%

시설구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
경로당
923 
학교
512 
마을회관
245 
관공서
 
41
교회
 
19
Other values (2)
 
21

Length

Max length56
Median length3
Mean length3.2038614
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관공서
2nd row학교
3rd row학교
4th row관공서
5th row학교

Common Values

ValueCountFrequency (%)
경로당 923
52.4%
학교 512
29.1%
마을회관 245
 
13.9%
관공서 41
 
2.3%
교회 19
 
1.1%
공공시설(국·공립도서관, 공립병원, 시·도민회관, 구민회관 주민체육시설, 노인병원, 어린이도서관 등) 12
 
0.7%
기타시설 9
 
0.5%

Length

2024-01-10T08:11:47.101366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:11:47.215190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경로당 923
50.0%
학교 512
27.8%
마을회관 245
 
13.3%
관공서 41
 
2.2%
교회 19
 
1.0%
공공시설(국·공립도서관 12
 
0.7%
공립병원 12
 
0.7%
시·도민회관 12
 
0.7%
구민회관 12
 
0.7%
주민체육시설 12
 
0.7%
Other values (4) 45
 
2.4%

시설명
Text

Distinct1750
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-01-10T08:11:47.453016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length7.6501988
Min length2

Characters and Unicode

Total characters13472
Distinct characters349
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1741 ?
Unique (%)98.9%

Sample

1st row삽교읍복지회관
2nd row보성초등학교
3rd row용동초등학교
4th row삽교국민체육관
5th row대술초등학교
ValueCountFrequency (%)
경로당 490
 
19.0%
마을회관 121
 
4.7%
강당 111
 
4.3%
체육관 39
 
1.5%
주민자치센터 8
 
0.3%
상곡초등학교 4
 
0.2%
다목적실 4
 
0.2%
금성초등학교 3
 
0.1%
금산군 3
 
0.1%
제원초등학교 3
 
0.1%
Other values (1774) 1794
69.5%
2024-01-10T08:11:47.824974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
당 1051
 
7.8%
리 929
 
6.9%
경 907
 
6.7%
로 900
 
6.7%
819
 
6.1%
교 555
 
4.1%
학 521
 
3.9%
등 406
 
3.0%
1 390
 
2.9%
초 359
 
2.7%
Other values (339) 6635
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11531
85.6%
Decimal Number 974
 
7.2%
Space Separator 819
 
6.1%
Open Punctuation 55
 
0.4%
Close Punctuation 55
 
0.4%
Other Punctuation 23
 
0.2%
Uppercase Letter 15
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
당 1051
 
9.1%
리 929
 
8.1%
경 907
 
7.9%
로 900
 
7.8%
교 555
 
4.8%
학 521
 
4.5%
등 406
 
3.5%
초 359
 
3.1%
관 315
 
2.7%
회 279
 
2.4%
Other values (322) 5309
46.0%
Decimal Number
ValueCountFrequency (%)
1 390
40.0%
2 327
33.6%
3 138
 
14.2%
4 37
 
3.8%
5 23
 
2.4%
6 18
 
1.8%
7 15
 
1.5%
8 11
 
1.1%
9 9
 
0.9%
0 6
 
0.6%
Other Punctuation
ValueCountFrequency (%)
@ 12
52.2%
, 9
39.1%
. 2
 
8.7%
Space Separator
ValueCountFrequency (%)
819
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11531
85.6%
Common 1926
 
14.3%
Latin 15
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
당 1051
 
9.1%
리 929
 
8.1%
경 907
 
7.9%
로 900
 
7.8%
교 555
 
4.8%
학 521
 
4.5%
등 406
 
3.5%
초 359
 
3.1%
관 315
 
2.7%
회 279
 
2.4%
Other values (322) 5309
46.0%
Common
ValueCountFrequency (%)
819
42.5%
1 390
20.2%
2 327
 
17.0%
3 138
 
7.2%
( 55
 
2.9%
) 55
 
2.9%
4 37
 
1.9%
5 23
 
1.2%
6 18
 
0.9%
7 15
 
0.8%
Other values (6) 49
 
2.5%
Latin
ValueCountFrequency (%)
A 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11531
85.6%
ASCII 1941
 
14.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
당 1051
 
9.1%
리 929
 
8.1%
경 907
 
7.9%
로 900
 
7.8%
교 555
 
4.8%
학 521
 
4.5%
등 406
 
3.5%
초 359
 
3.1%
관 315
 
2.7%
회 279
 
2.4%
Other values (322) 5309
46.0%
ASCII
ValueCountFrequency (%)
819
42.2%
1 390
20.1%
2 327
 
16.8%
3 138
 
7.1%
( 55
 
2.8%
) 55
 
2.8%
4 37
 
1.9%
5 23
 
1.2%
6 18
 
0.9%
A 15
 
0.8%
Other values (7) 64
 
3.3%

도로명주소코드
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
4.42e+24
626 
4.41e+24
466 
4.47e+24
353 
4.48e+24
297 
4.43e+24
 
10

Length

Max length8
Median length8
Mean length7.9795571
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.48e+24
2nd row<NA>
3rd row4.48e+24
4th row4.48e+24
5th row<NA>

Common Values

ValueCountFrequency (%)
4.42e+24 626
35.5%
4.41e+24 466
26.5%
4.47e+24 353
20.0%
4.48e+24 297
16.9%
4.43e+24 10
 
0.6%
<NA> 9
 
0.5%

Length

2024-01-10T08:11:47.967925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:11:48.088799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4.42e+24 626
35.5%
4.41e+24 466
26.5%
4.47e+24 353
20.0%
4.48e+24 297
16.9%
4.43e+24 10
 
0.6%
na 9
 
0.5%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct789
Distinct (%)44.9%
Missing3
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.437998 × 109
Minimum4.4131102 × 109
Maximum4.482536 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-10T08:11:48.213694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.4131102 × 109
5-th percentile4.4131118 × 109
Q14.4133256 × 109
median4.420039 × 109
Q34.471034 × 109
95-th percentile4.4800372 × 109
Maximum4.482536 × 109
Range69425824
Interquartile range (IQR)57708403

Descriptive statistics

Standard deviation27877338
Coefficient of variation (CV)0.006281512
Kurtosis-1.5813489
Mean4.437998 × 109
Median Absolute Deviation (MAD)6904004.5
Skewness0.57462448
Sum7.8020005 × 1012
Variance7.7714598 × 1014
MonotonicityNot monotonic
2024-01-10T08:11:48.346141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4413111800 24
 
1.4%
4413310400 21
 
1.2%
4413110700 21
 
1.2%
4413111600 19
 
1.1%
4413111300 17
 
1.0%
4471025022 16
 
0.9%
4413310700 16
 
0.9%
4413325021 13
 
0.7%
4471025025 13
 
0.7%
4471025024 13
 
0.7%
Other values (779) 1585
90.0%
ValueCountFrequency (%)
4413110200 3
 
0.2%
4413110600 1
 
0.1%
4413110700 21
1.2%
4413110800 1
 
0.1%
4413110900 2
 
0.1%
4413111000 3
 
0.2%
4413111100 3
 
0.2%
4413111200 2
 
0.1%
4413111300 17
1.0%
4413111400 3
 
0.2%
ValueCountFrequency (%)
4482536024 2
0.1%
4482536021 1
0.1%
4482535031 1
0.1%
4482535027 1
0.1%
4482535021 2
0.1%
4482534029 1
0.1%
4482534027 1
0.1%
4482534023 2
0.1%
4482534021 1
0.1%
4482533029 1
0.1%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct211
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4379931 × 109
Minimum4.413125 × 109
Maximum4.482536 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-10T08:11:48.505814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.413125 × 109
5-th percentile4.413135 × 109
Q14.413358 × 109
median4.420057 × 109
Q34.471 × 109
95-th percentile4.480032 × 109
Maximum4.482536 × 109
Range69411000
Interquartile range (IQR)57642000

Descriptive statistics

Standard deviation27861727
Coefficient of variation (CV)0.0062780015
Kurtosis-1.5806689
Mean4.4379931 × 109
Median Absolute Deviation (MAD)6900000
Skewness0.57509052
Sum7.8153058 × 1012
Variance7.7627584 × 1014
MonotonicityNot monotonic
2024-01-10T08:11:48.920465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4471000000 293
 
16.6%
4413325000 67
 
3.8%
4413125000 46
 
2.6%
4413325600 42
 
2.4%
4413136000 34
 
1.9%
4413159000 33
 
1.9%
4420035000 33
 
1.9%
4413134000 32
 
1.8%
4420040000 32
 
1.8%
4420036000 29
 
1.6%
Other values (201) 1120
63.6%
ValueCountFrequency (%)
4413125000 46
2.6%
4413131000 4
 
0.2%
4413132000 3
 
0.2%
4413133000 2
 
0.1%
4413134000 32
1.8%
4413135000 23
1.3%
4413136000 34
1.9%
4413137000 24
1.4%
4413151000 1
 
0.1%
4413152000 3
 
0.2%
ValueCountFrequency (%)
4482536000 3
 
0.2%
4482535021 1
 
0.1%
4482535000 3
 
0.2%
4482534000 4
0.2%
4482533027 1
 
0.1%
4482533000 6
0.3%
4482532000 4
0.2%
4482531000 2
 
0.1%
4482525300 8
0.5%
4482525000 9
0.5%

상세주소
Text

Distinct1723
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-01-10T08:11:49.269577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length42
Mean length23.074957
Min length8

Characters and Unicode

Total characters40635
Distinct characters394
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1689 ?
Unique (%)95.9%

Sample

1st row충청남도 예산군 삽교읍 두리3길 35
2nd row충청남도 예산군 삽교읍 목리 산17-1
3rd row충청남도 예산군 삽교읍 삽교평야로 193
4th row충청남도 예산군 삽교읍 신가꽃산서길 115
5th row충청남도 예산군 대술면 화천리 365-0
ValueCountFrequency (%)
충청남도 1761
 
18.8%
천안시 467
 
5.0%
아산시 375
 
4.0%
금산군 353
 
3.8%
동남구 283
 
3.0%
서북구 184
 
2.0%
청양군 117
 
1.3%
서산시 112
 
1.2%
금산읍 72
 
0.8%
보령시 70
 
0.7%
Other values (2524) 5564
59.5%
2024-01-10T08:11:49.770938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7597
 
18.7%
남 2230
 
5.5%
청 1959
 
4.8%
도 1854
 
4.6%
충 1802
 
4.4%
산 1333
 
3.3%
1 1289
 
3.2%
길 1238
 
3.0%
시 1152
 
2.8%
면 1069
 
2.6%
Other values (384) 19112
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26112
64.3%
Space Separator 7597
 
18.7%
Decimal Number 5676
 
14.0%
Dash Punctuation 474
 
1.2%
Open Punctuation 363
 
0.9%
Close Punctuation 363
 
0.9%
Other Punctuation 49
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 2230
 
8.5%
청 1959
 
7.5%
도 1854
 
7.1%
충 1802
 
6.9%
산 1333
 
5.1%
길 1238
 
4.7%
시 1152
 
4.4%
면 1069
 
4.1%
동 748
 
2.9%
로 744
 
2.8%
Other values (367) 11983
45.9%
Decimal Number
ValueCountFrequency (%)
1 1289
22.7%
2 818
14.4%
3 669
11.8%
4 550
9.7%
5 497
 
8.8%
6 402
 
7.1%
7 393
 
6.9%
8 353
 
6.2%
0 353
 
6.2%
9 352
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 48
98.0%
. 1
 
2.0%
Space Separator
ValueCountFrequency (%)
7597
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 474
100.0%
Open Punctuation
ValueCountFrequency (%)
( 363
100.0%
Close Punctuation
ValueCountFrequency (%)
) 363
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26112
64.3%
Common 14522
35.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 2230
 
8.5%
청 1959
 
7.5%
도 1854
 
7.1%
충 1802
 
6.9%
산 1333
 
5.1%
길 1238
 
4.7%
시 1152
 
4.4%
면 1069
 
4.1%
동 748
 
2.9%
로 744
 
2.8%
Other values (367) 11983
45.9%
Common
ValueCountFrequency (%)
7597
52.3%
1 1289
 
8.9%
2 818
 
5.6%
3 669
 
4.6%
4 550
 
3.8%
5 497
 
3.4%
- 474
 
3.3%
6 402
 
2.8%
7 393
 
2.7%
( 363
 
2.5%
Other values (6) 1470
 
10.1%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26112
64.3%
ASCII 14523
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7597
52.3%
1 1289
 
8.9%
2 818
 
5.6%
3 669
 
4.6%
4 550
 
3.8%
5 497
 
3.4%
- 474
 
3.3%
6 402
 
2.8%
7 393
 
2.7%
( 363
 
2.5%
Other values (7) 1471
 
10.1%
Hangul
ValueCountFrequency (%)
남 2230
 
8.5%
청 1959
 
7.5%
도 1854
 
7.1%
충 1802
 
6.9%
산 1333
 
5.1%
길 1238
 
4.7%
시 1152
 
4.4%
면 1069
 
4.1%
동 748
 
2.9%
로 744
 
2.8%
Other values (367) 11983
45.9%

시설면적
Real number (ℝ)

HIGH CORRELATION 

Distinct726
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1046.4708
Minimum15
Maximum15957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-10T08:11:49.905266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile64
Q190
median134
Q3602
95-th percentile7004
Maximum15957
Range15942
Interquartile range (IQR)512

Descriptive statistics

Standard deviation2438.162
Coefficient of variation (CV)2.3298901
Kurtosis11.493444
Mean1046.4708
Median Absolute Deviation (MAD)62
Skewness3.4051162
Sum1842835.1
Variance5944633.7
MonotonicityNot monotonic
2024-01-10T08:11:50.043078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.0 118
 
6.7%
66.0 32
 
1.8%
100.0 28
 
1.6%
83.0 25
 
1.4%
84.0 19
 
1.1%
85.0 18
 
1.0%
68.0 17
 
1.0%
75.0 16
 
0.9%
72.0 16
 
0.9%
69.0 15
 
0.9%
Other values (716) 1457
82.7%
ValueCountFrequency (%)
15.0 1
 
0.1%
17.0 2
0.1%
23.0 1
 
0.1%
30.0 3
0.2%
31.0 2
0.1%
32.0 2
0.1%
33.0 2
0.1%
35.0 1
 
0.1%
36.0 1
 
0.1%
38.0 1
 
0.1%
ValueCountFrequency (%)
15957.0 1
0.1%
15617.0 1
0.1%
14951.0 1
0.1%
13698.0 1
0.1%
13361.0 1
0.1%
13135.0 1
0.1%
13115.0 1
0.1%
12947.0 1
0.1%
12728.0 1
0.1%
12712.0 1
0.1%

주거능력
Real number (ℝ)

HIGH CORRELATION 

Distinct416
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.71664
Minimum5
Maximum4835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-01-10T08:11:50.181739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile15
Q125
median38
Q3150
95-th percentile1274
Maximum4835
Range4830
Interquartile range (IQR)125

Descriptive statistics

Standard deviation529.23231
Coefficient of variation (CV)2.3446757
Kurtosis17.607594
Mean225.71664
Median Absolute Deviation (MAD)18
Skewness3.9787678
Sum397487
Variance280086.84
MonotonicityNot monotonic
2024-01-10T08:11:50.312642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 173
 
9.8%
20 169
 
9.6%
25 54
 
3.1%
15 52
 
3.0%
40 42
 
2.4%
29 32
 
1.8%
50 31
 
1.8%
27 30
 
1.7%
21 28
 
1.6%
28 26
 
1.5%
Other values (406) 1124
63.8%
ValueCountFrequency (%)
5 2
 
0.1%
7 1
 
0.1%
8 2
 
0.1%
9 1
 
0.1%
10 24
1.4%
11 1
 
0.1%
12 7
 
0.4%
13 3
 
0.2%
14 4
 
0.2%
15 52
3.0%
ValueCountFrequency (%)
4835 1
0.1%
3856 1
0.1%
3844 1
0.1%
3461 1
0.1%
3427 1
0.1%
3395 1
0.1%
3383 1
0.1%
3364 1
0.1%
3356 1
0.1%
3254 1
0.1%

경도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
127
1066 
126
695 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row126
2nd row126
3rd row126
4th row126
5th row126

Common Values

ValueCountFrequency (%)
127 1066
60.5%
126 695
39.5%

Length

2024-01-10T08:11:50.431715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:11:50.529409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
127 1066
60.5%
126 695
39.5%

위도
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
36
1759 
35
 
1
37
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row36
2nd row36
3rd row36
4th row36
5th row36

Common Values

ValueCountFrequency (%)
36 1759
99.9%
35 1
 
0.1%
37 1
 
0.1%

Length

2024-01-10T08:11:50.630548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:11:50.728301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36 1759
99.9%
35 1
 
0.1%
37 1
 
0.1%

지자체담당자 연락처
Text

MISSING 

Distinct180
Distinct (%)52.8%
Missing1420
Missing (%)80.6%
Memory size13.9 KiB
2024-01-10T08:11:51.019578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.97654
Min length8

Characters and Unicode

Total characters4084
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

Unique169 ?
Unique (%)49.6%

Sample

1st row041-339-8480
2nd row041-337-2532
3rd row041-338-4115
4th row041-339-8480
5th row041-335-5009
ValueCountFrequency (%)
041-940-2433 108
31.7%
041-350-3642 23
 
6.7%
041-830-2062 18
 
5.3%
041-940-2122 9
 
2.6%
041-544-8161 2
 
0.6%
041-950-6859 2
 
0.6%
041-542-3171 2
 
0.6%
041-339-8480 2
 
0.6%
041-840-2852 2
 
0.6%
041-531-1590 2
 
0.6%
Other values (170) 171
50.1%
2024-01-10T08:11:51.460284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 742
18.2%
- 680
16.7%
0 651
15.9%
1 461
11.3%
3 459
11.2%
2 316
7.7%
5 208
 
5.1%
9 187
 
4.6%
6 137
 
3.4%
8 136
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3404
83.3%
Dash Punctuation 680
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 742
21.8%
0 651
19.1%
1 461
13.5%
3 459
13.5%
2 316
9.3%
5 208
 
6.1%
9 187
 
5.5%
6 137
 
4.0%
8 136
 
4.0%
7 107
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 680
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4084
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 742
18.2%
- 680
16.7%
0 651
15.9%
1 461
11.3%
3 459
11.2%
2 316
7.7%
5 208
 
5.1%
9 187
 
4.6%
6 137
 
3.4%
8 136
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 742
18.2%
- 680
16.7%
0 651
15.9%
1 461
11.3%
3 459
11.2%
2 316
7.7%
5 208
 
5.1%
9 187
 
4.6%
6 137
 
3.4%
8 136
 
3.3%

Interactions

2024-01-10T08:11:44.403147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:38.405651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:39.207249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.147824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.926966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.686084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.474613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:43.489034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:44.517726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:38.490241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:39.326095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.239237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.014292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.778079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.582979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:43.579247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:44.639536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:38.585934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:39.442103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.338837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.110131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.876408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.677654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:43.674473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:44.745361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:38.680836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:39.564356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.438383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.207964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.974431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.771866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:43.790871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:44.832364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:38.767110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:39.677843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.528481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.301330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.070976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.862942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:43.914437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:44.927217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:38.863033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:39.797496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.631284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.407849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.173523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.958180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:44.039773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:45.027828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:38.977946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:39.922484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.735773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.507246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.274403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:43.056752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:44.165862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:45.118984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:39.100709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.047324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:40.835980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:41.600961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:42.375213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:43.403285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:44.292820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:11:51.563023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호지역코드시설일련번호시군명시설구분코드시설구분도로명주소코드법정동코드행정동코드시설면적주거능력경도위도
일련번호1.0000.7940.9100.8940.4550.4780.9520.9490.9430.2430.2110.7400.000
지역코드0.7941.0000.9151.0000.4810.5130.9580.9930.9820.1730.1530.5960.179
시설일련번호0.9100.9151.0000.8680.5600.5730.9350.9000.8960.4860.3830.8190.000
시군명0.8941.0000.8681.0000.6340.6820.9941.0000.9790.3940.3510.9730.209
시설구분코드0.4550.4810.5600.6341.0001.0000.3900.4680.4770.3730.3270.5300.000
시설구분0.4780.5130.5730.6821.0001.0000.4220.5110.5290.4580.3780.3940.000
도로명주소코드0.9520.9580.9350.9940.3900.4221.0000.9650.9620.3370.3270.6650.021
법정동코드0.9490.9930.9001.0000.4680.5110.9651.0000.9980.2180.1720.5470.183
행정동코드0.9430.9820.8960.9790.4770.5290.9620.9981.0000.2290.1700.5460.248
시설면적0.2430.1730.4860.3940.3730.4580.3370.2180.2291.0000.8530.1140.000
주거능력0.2110.1530.3830.3510.3270.3780.3270.1720.1700.8531.0000.0990.000
경도0.7400.5960.8190.9730.5300.3940.6650.5470.5460.1140.0991.0000.006
위도0.0000.1790.0000.2090.0000.0000.0210.1830.2480.0000.0000.0061.000
2024-01-10T08:11:51.703503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분경도시군명도로명주소코드위도
시설구분1.0000.4210.3960.2870.000
경도0.4211.0000.8630.7950.010
시군명0.3960.8631.0000.9840.115
도로명주소코드0.2870.7950.9841.0000.016
위도0.0000.0100.1150.0161.000
2024-01-10T08:11:51.806130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호지역코드시설일련번호시설구분코드법정동코드행정동코드시설면적주거능력시군명시설구분도로명주소코드경도위도
일련번호1.000-0.2180.0230.032-0.248-0.211-0.040-0.0890.6310.2660.6990.5790.000
지역코드-0.2181.0000.053-0.0950.9810.983-0.009-0.0130.9970.2980.8190.7260.139
시설일련번호0.0230.0531.0000.4390.0660.060-0.305-0.3020.5790.3350.6560.6510.000
시설구분코드0.032-0.0950.4391.000-0.078-0.107-0.604-0.6030.3671.0000.2770.3830.000
법정동코드-0.2480.9810.066-0.0781.0000.977-0.029-0.0210.9970.3590.7330.6620.139
행정동코드-0.2110.9830.060-0.1070.9771.0000.0240.0220.9330.3530.7250.6600.193
시설면적-0.040-0.009-0.305-0.604-0.0290.0241.0000.9280.1650.2520.1460.0870.000
주거능력-0.089-0.013-0.302-0.603-0.0210.0220.9281.0000.1520.2100.1950.0980.000
시군명0.6310.9970.5790.3670.9970.9330.1650.1521.0000.3960.9840.8630.115
시설구분0.2660.2980.3351.0000.3590.3530.2520.2100.3961.0000.2870.4210.000
도로명주소코드0.6990.8190.6560.2770.7330.7250.1460.1950.9840.2871.0000.7950.016
경도0.5790.7260.6510.3830.6620.6600.0870.0980.8630.4210.7951.0000.010
위도0.0000.1390.0000.0000.1390.1930.0000.0000.1150.0000.0160.0101.000

Missing values

2024-01-10T08:11:45.247210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:11:45.479120image/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.
2024-01-10T08:11:45.631770image/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

일련번호지역코드시설일련번호시도명시군명시설구분코드시설구분시설명도로명주소코드법정동코드행정동코드상세주소시설면적주거능력경도위도지자체담당자 연락처
014481000000761충청남도예산군4관공서삽교읍복지회관4480000000000000268435456.044810253214481025300충청남도 예산군 삽교읍 두리3길 351179.030012636041-339-8480
124481000000757충청남도예산군1학교보성초등학교<NA>44810253264481025326충청남도 예산군 삽교읍 목리 산17-11704.040012636041-337-2532
234481000000756충청남도예산군1학교용동초등학교4480000000000000268435456.044810253344481025300충청남도 예산군 삽교읍 삽교평야로 1931585.023012636041-338-4115
344481000000760충청남도예산군4관공서삽교국민체육관4480000000000000268435456.044810253384481025300충청남도 예산군 삽교읍 신가꽃산서길 1152088.055012636041-339-8480
454481000000312충청남도예산군1학교대술초등학교<NA>44810310214481031021충청남도 예산군 대술면 화천리 365-02521.010012636041-335-5009
564481000000311충청남도예산군1학교대술중학교4480000000000000268435456.044810310214481031000충청남도 예산군 대술면 대술로 135-72038.010012636041-333-5008
674481000000315충청남도예산군1학교신양초등학교4480000000000000268435456.044810320214481032000충청남도 예산군 신양면 청신로 3743220.050012636041-333-1943
784481000000764충청남도예산군1학교신양중학교4480000000000000268435456.044810320214481032000충청남도 예산군 신양면 청신로 4023557.050012636041-333-7032
894481000000782충청남도예산군4관공서신양면사무소(회의실)4480000000000000268435456.044810320214481032000충청남도 예산군 신양면 청신로 372671.07012636041-339-8573
9104480000000189충청남도홍성군1학교금마초등학교4480000000000000268435456.044800320254480000000충청남도 홍성군 금마면 광금북로 4842371.071912636<NA>
일련번호지역코드시설일련번호시도명시군명시설구분코드시설구분시설명도로명주소코드법정동코드행정동코드상세주소시설면적주거능력경도위도지자체담당자 연락처
17511752442700000012충청남도당진시1학교순성초등학교 강당4480000000000000268435456.044270360214427036000충청남도 당진시 순성면 순성로 473711.021512636041-350-3642
17521753442700000091충청남도당진시1학교순성중학교 체육관4480000000000000268435456.044270360214427036000충청남도 당진시 순성면 틀모시로 146701.021212636041-350-3642
17531754442700000011충청남도당진시1학교면천초등학교 강당4430000000000000188743680.044270350214427035000충청남도 당진시 면천면 면천서문1길 33521.015712636041-350-3642
175417554427000000145충청남도당진시1학교정미초등학교 교실4480000000000000268435456.044270340254427000000충청남도 당진시 정미면 운정로 6422825.070012636041-350-3642
175517564481000000317충청남도예산군1학교광시중학교4480000000000000268435456.044810330214481033000충청남도 예산군 광시면 광시소길 201876.013412636041-332-0018
175617574481000000779충청남도예산군1학교웅산초등학교<NA>44810330214481033021충청남도 예산군 광시면 광시리 64-03700.034312636041-332-1560
175717584481000000762충청남도예산군1학교대흥초등학교4480000000000000268435456.044810340214481034000충청남도 예산군 대흥면 의좋은형제길 161285.028812636041-332-0100
1758175944710000001316충청남도금산군3경로당주공아파트2단지경로당4470000000000000037748736.0<NA>4471000000충청남도 금산군90.01512736<NA>
175917604413000000621충청남도천안시3경로당도하1리 경로당4410000000000000264241152.0<NA>4413325000충청남도 천안시 서북구 성환읍65.02012736<NA>
176017614418000000370충청남도보령시3경로당동오1리경로당<NA><NA>4418038000충청남도 보령시 주산면 96-1134.04012636<NA>