Overview

Dataset statistics

Number of variables12
Number of observations128
Missing cells217
Missing cells (%)14.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 KiB
Average record size in memory100.0 B

Variable types

Categorical3
Text6
Numeric3

Alerts

소재지우편번호 is highly overall correlated with WGS84위도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
시설유형 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
관리기관명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
시설유형 is highly imbalanced (67.8%)Imbalance
소재지우편번호 has 11 (8.6%) missing valuesMissing
소재지도로명주소 has 16 (12.5%) missing valuesMissing
소재지지번주소 has 10 (7.8%) missing valuesMissing
WGS84위도 has 10 (7.8%) missing valuesMissing
WGS84경도 has 10 (7.8%) missing valuesMissing
사업자등록번호 has 49 (38.3%) missing valuesMissing
특이사항 has 111 (86.7%) missing valuesMissing
시설명 has unique valuesUnique

Reproduction

Analysis started2024-03-23 02:13:51.013682
Analysis finished2024-03-23 02:14:00.740092
Duration9.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
화성시
12 
안산시
12 
파주시
10 
용인시
9 
고양시
9 
Other values (19)
76 

Length

Max length4
Median length3
Mean length3.078125
Min length3

Unique

Unique3 ?
Unique (%)2.3%

Sample

1st row가평군
2nd row가평군
3rd row고양시
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
화성시 12
 
9.4%
안산시 12
 
9.4%
파주시 10
 
7.8%
용인시 9
 
7.0%
고양시 9
 
7.0%
양평군 8
 
6.2%
시흥시 7
 
5.5%
안성시 7
 
5.5%
남양주시 6
 
4.7%
김포시 5
 
3.9%
Other values (14) 43
33.6%

Length

2024-03-23T02:14:01.043979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화성시 12
 
9.4%
안산시 12
 
9.4%
파주시 10
 
7.8%
용인시 9
 
7.0%
고양시 9
 
7.0%
양평군 8
 
6.2%
시흥시 7
 
5.5%
안성시 7
 
5.5%
남양주시 6
 
4.7%
여주시 5
 
3.9%
Other values (14) 43
33.6%

시설명
Text

UNIQUE 

Distinct128
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-23T02:14:01.828862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length9.109375
Min length3

Characters and Unicode

Total characters1166
Distinct characters162
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)100.0%

Sample

1st row가평군농협효문화센터
2nd row연새장례식장
3rd row㈜헤븐앤어스 명지병원장례식장
4th row국민건강보험공단일산병원장례식장
5th row동국대학교일산병원장례식장
ValueCountFrequency (%)
장례식장 22
 
13.3%
장례문화원 2
 
1.2%
장례문화센터 2
 
1.2%
쉴낙원 2
 
1.2%
경기도의료원 2
 
1.2%
효사랑병원장례식장 1
 
0.6%
용인서울병원 1
 
0.6%
여주시민장례문화원 1
 
0.6%
여주장례식장 1
 
0.6%
한국장례문화원 1
 
0.6%
Other values (131) 131
78.9%
2024-03-23T02:14:03.340368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 232
19.9%
례 124
 
10.6%
식 106
 
9.1%
원 93
 
8.0%
병 53
 
4.5%
38
 
3.3%
화 26
 
2.2%
문 21
 
1.8%
산 17
 
1.5%
양 13
 
1.1%
Other values (152) 443
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1114
95.5%
Space Separator 38
 
3.3%
Close Punctuation 5
 
0.4%
Open Punctuation 5
 
0.4%
Other Symbol 3
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 232
20.8%
례 124
 
11.1%
식 106
 
9.5%
원 93
 
8.3%
병 53
 
4.8%
화 26
 
2.3%
문 21
 
1.9%
산 17
 
1.5%
양 13
 
1.2%
주 11
 
1.0%
Other values (147) 418
37.5%
Space Separator
ValueCountFrequency (%)
38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1117
95.8%
Common 49
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 232
20.8%
례 124
 
11.1%
식 106
 
9.5%
원 93
 
8.3%
병 53
 
4.7%
화 26
 
2.3%
문 21
 
1.9%
산 17
 
1.5%
양 13
 
1.2%
주 11
 
1.0%
Other values (148) 421
37.7%
Common
ValueCountFrequency (%)
38
77.6%
) 5
 
10.2%
( 5
 
10.2%
- 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1114
95.5%
ASCII 49
 
4.2%
None 3
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 232
20.8%
례 124
 
11.1%
식 106
 
9.5%
원 93
 
8.3%
병 53
 
4.8%
화 26
 
2.3%
문 21
 
1.9%
산 17
 
1.5%
양 13
 
1.2%
주 11
 
1.0%
Other values (147) 418
37.5%
ASCII
ValueCountFrequency (%)
38
77.6%
) 5
 
10.2%
( 5
 
10.2%
- 1
 
2.0%
None
ValueCountFrequency (%)
㈜ 3
100.0%

시설유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
장례식장
111 
사설
 
6
병원
 
5
전문
 
4
공설
 
1

Length

Max length6
Median length4
Mean length3.765625
Min length2

Unique

Unique2 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
장례식장 111
86.7%
사설 6
 
4.7%
병원 5
 
3.9%
전문 4
 
3.1%
공설 1
 
0.8%
병원부대시설 1
 
0.8%

Length

2024-03-23T02:14:03.822459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T02:14:04.303527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장례식장 111
86.7%
사설 6
 
4.7%
병원 5
 
3.9%
전문 4
 
3.1%
공설 1
 
0.8%
병원부대시설 1
 
0.8%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct111
Distinct (%)94.9%
Missing11
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean14404.154
Minimum10017
Maximum18592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-03-23T02:14:04.714290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10017
5-th percentile10831.4
Q112155
median14353
Q317139
95-th percentile18467.4
Maximum18592
Range8575
Interquartile range (IQR)4984

Descriptive statistics

Standard deviation2698.8927
Coefficient of variation (CV)0.18736905
Kurtosis-1.4076972
Mean14404.154
Median Absolute Deviation (MAD)2642
Skewness0.092985853
Sum1685286
Variance7284021.6
MonotonicityNot monotonic
2024-03-23T02:14:05.183944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11435 2
 
1.6%
10867 2
 
1.6%
11027 2
 
1.6%
12550 2
 
1.6%
12585 2
 
1.6%
11416 2
 
1.6%
12805 1
 
0.8%
12764 1
 
0.8%
17366 1
 
0.8%
16060 1
 
0.8%
Other values (101) 101
78.9%
(Missing) 11
 
8.6%
ValueCountFrequency (%)
10017 1
0.8%
10086 1
0.8%
10099 1
0.8%
10117 1
0.8%
10811 1
0.8%
10813 1
0.8%
10836 1
0.8%
10860 1
0.8%
10867 2
1.6%
10913 1
0.8%
ValueCountFrequency (%)
18592 1
0.8%
18589 1
0.8%
18584 1
0.8%
18573 1
0.8%
18552 1
0.8%
18537 1
0.8%
18450 1
0.8%
18390 1
0.8%
18356 1
0.8%
18335 1
0.8%

소재지도로명주소
Text

MISSING 

Distinct112
Distinct (%)100.0%
Missing16
Missing (%)12.5%
Memory size1.1 KiB
2024-03-23T02:14:05.735992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length24.5
Mean length19.383929
Min length14

Characters and Unicode

Total characters2171
Distinct characters154
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

Unique112 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 가평읍 경춘로 1775
2nd row경기도 가평군 청평면 경춘로 1219
3rd row경기도 광명시 디지털로 36
4th row경기도 광명시 광명로 844
5th row경기도 광명시 덕안로 110
ValueCountFrequency (%)
경기도 112
 
21.6%
화성시 12
 
2.3%
안산시 12
 
2.3%
파주시 10
 
1.9%
용인시 9
 
1.7%
양평군 8
 
1.5%
안성시 7
 
1.3%
상록구 7
 
1.3%
시흥시 7
 
1.3%
남양주시 6
 
1.2%
Other values (259) 329
63.4%
2024-03-23T02:14:06.815348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
407
18.7%
경 124
 
5.7%
기 118
 
5.4%
도 115
 
5.3%
시 107
 
4.9%
로 97
 
4.5%
1 84
 
3.9%
2 55
 
2.5%
3 41
 
1.9%
5 40
 
1.8%
Other values (144) 983
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1346
62.0%
Space Separator 407
 
18.7%
Decimal Number 397
 
18.3%
Dash Punctuation 21
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 124
 
9.2%
기 118
 
8.8%
도 115
 
8.5%
시 107
 
7.9%
로 97
 
7.2%
길 36
 
2.7%
양 35
 
2.6%
안 32
 
2.4%
주 31
 
2.3%
구 28
 
2.1%
Other values (132) 623
46.3%
Decimal Number
ValueCountFrequency (%)
1 84
21.2%
2 55
13.9%
3 41
10.3%
5 40
10.1%
7 39
9.8%
4 33
 
8.3%
8 31
 
7.8%
9 26
 
6.5%
0 25
 
6.3%
6 23
 
5.8%
Space Separator
ValueCountFrequency (%)
407
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1346
62.0%
Common 825
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 124
 
9.2%
기 118
 
8.8%
도 115
 
8.5%
시 107
 
7.9%
로 97
 
7.2%
길 36
 
2.7%
양 35
 
2.6%
안 32
 
2.4%
주 31
 
2.3%
구 28
 
2.1%
Other values (132) 623
46.3%
Common
ValueCountFrequency (%)
407
49.3%
1 84
 
10.2%
2 55
 
6.7%
3 41
 
5.0%
5 40
 
4.8%
7 39
 
4.7%
4 33
 
4.0%
8 31
 
3.8%
9 26
 
3.2%
0 25
 
3.0%
Other values (2) 44
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1346
62.0%
ASCII 825
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
407
49.3%
1 84
 
10.2%
2 55
 
6.7%
3 41
 
5.0%
5 40
 
4.8%
7 39
 
4.7%
4 33
 
4.0%
8 31
 
3.8%
9 26
 
3.2%
0 25
 
3.0%
Other values (2) 44
 
5.3%
Hangul
ValueCountFrequency (%)
경 124
 
9.2%
기 118
 
8.8%
도 115
 
8.5%
시 107
 
7.9%
로 97
 
7.2%
길 36
 
2.7%
양 35
 
2.6%
안 32
 
2.4%
주 31
 
2.3%
구 28
 
2.1%
Other values (132) 623
46.3%

소재지지번주소
Text

MISSING 

Distinct118
Distinct (%)100.0%
Missing10
Missing (%)7.8%
Memory size1.1 KiB
2024-03-23T02:14:07.504329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length20.262712
Min length15

Characters and Unicode

Total characters2391
Distinct characters148
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

Unique118 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 가평읍 상색리 269-1
2nd row경기도 가평군 청평면 상천리 1148-3
3rd row경기도 광명시 철산동 389번지
4th row경기도 광명시 광명동 290-23번지 지하1층
5th row경기도 광명시 일직동 501번지
ValueCountFrequency (%)
경기도 118
 
21.6%
안산시 12
 
2.2%
화성시 12
 
2.2%
파주시 10
 
1.8%
용인시 9
 
1.6%
양평군 8
 
1.5%
상록구 7
 
1.3%
안성시 7
 
1.3%
시흥시 7
 
1.3%
남양주시 6
 
1.1%
Other values (281) 351
64.2%
2024-03-23T02:14:08.629946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
429
 
17.9%
도 122
 
5.1%
기 122
 
5.1%
경 118
 
4.9%
시 113
 
4.7%
1 98
 
4.1%
- 81
 
3.4%
동 76
 
3.2%
지 75
 
3.1%
번 65
 
2.7%
Other values (138) 1092
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1425
59.6%
Decimal Number 456
 
19.1%
Space Separator 429
 
17.9%
Dash Punctuation 81
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 122
 
8.6%
기 122
 
8.6%
경 118
 
8.3%
시 113
 
7.9%
동 76
 
5.3%
지 75
 
5.3%
번 65
 
4.6%
리 51
 
3.6%
양 35
 
2.5%
주 32
 
2.2%
Other values (126) 616
43.2%
Decimal Number
ValueCountFrequency (%)
1 98
21.5%
2 52
11.4%
4 46
10.1%
5 45
9.9%
7 43
9.4%
3 40
8.8%
8 37
 
8.1%
6 36
 
7.9%
0 30
 
6.6%
9 29
 
6.4%
Space Separator
ValueCountFrequency (%)
429
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1425
59.6%
Common 966
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 122
 
8.6%
기 122
 
8.6%
경 118
 
8.3%
시 113
 
7.9%
동 76
 
5.3%
지 75
 
5.3%
번 65
 
4.6%
리 51
 
3.6%
양 35
 
2.5%
주 32
 
2.2%
Other values (126) 616
43.2%
Common
ValueCountFrequency (%)
429
44.4%
1 98
 
10.1%
- 81
 
8.4%
2 52
 
5.4%
4 46
 
4.8%
5 45
 
4.7%
7 43
 
4.5%
3 40
 
4.1%
8 37
 
3.8%
6 36
 
3.7%
Other values (2) 59
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1425
59.6%
ASCII 966
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
429
44.4%
1 98
 
10.1%
- 81
 
8.4%
2 52
 
5.4%
4 46
 
4.8%
5 45
 
4.7%
7 43
 
4.5%
3 40
 
4.1%
8 37
 
3.8%
6 36
 
3.7%
Other values (2) 59
 
6.1%
Hangul
ValueCountFrequency (%)
도 122
 
8.6%
기 122
 
8.6%
경 118
 
8.3%
시 113
 
7.9%
동 76
 
5.3%
지 75
 
5.3%
번 65
 
4.6%
리 51
 
3.6%
양 35
 
2.5%
주 32
 
2.2%
Other values (126) 616
43.2%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct118
Distinct (%)100.0%
Missing10
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean37.433723
Minimum36.996747
Maximum38.023551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-03-23T02:14:09.087224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.996747
5-th percentile37.061314
Q137.257284
median37.357777
Q337.647821
95-th percentile37.881142
Maximum38.023551
Range1.0268046
Interquartile range (IQR)0.39053721

Descriptive statistics

Standard deviation0.2585065
Coefficient of variation (CV)0.0069057116
Kurtosis-0.72955758
Mean37.433723
Median Absolute Deviation (MAD)0.15418489
Skewness0.45872831
Sum4417.1794
Variance0.06682561
MonotonicityNot monotonic
2024-03-23T02:14:09.568401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.0235512237 1
 
0.8%
37.2749252448 1
 
0.8%
37.2242557225 1
 
0.8%
37.2403149583 1
 
0.8%
37.2707754145 1
 
0.8%
37.3278973359 1
 
0.8%
37.319308465 1
 
0.8%
37.2315458776 1
 
0.8%
37.2528382671 1
 
0.8%
37.1412846971 1
 
0.8%
Other values (108) 108
84.4%
(Missing) 10
 
7.8%
ValueCountFrequency (%)
36.9967466606 1
0.8%
37.0049595787 1
0.8%
37.0161337727 1
0.8%
37.0165478864 1
0.8%
37.0173057984 1
0.8%
37.0423612446 1
0.8%
37.0646586839 1
0.8%
37.0806285422 1
0.8%
37.1016147537 1
0.8%
37.1140311132 1
0.8%
ValueCountFrequency (%)
38.0235512237 1
0.8%
38.0174251575 1
0.8%
37.9517617585 1
0.8%
37.9088541736 1
0.8%
37.9081637185 1
0.8%
37.9068599484 1
0.8%
37.8766038982 1
0.8%
37.8584559756 1
0.8%
37.8551054144 1
0.8%
37.8479328534 1
0.8%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct118
Distinct (%)100.0%
Missing10
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean127.07599
Minimum126.60805
Maximum127.78824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-03-23T02:14:10.182446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.60805
5-th percentile126.72406
Q1126.82691
median127.0535
Q3127.26139
95-th percentile127.6114
Maximum127.78824
Range1.1801888
Interquartile range (IQR)0.43447517

Descriptive statistics

Standard deviation0.28271104
Coefficient of variation (CV)0.00222474
Kurtosis-0.67999263
Mean127.07599
Median Absolute Deviation (MAD)0.21252992
Skewness0.54650823
Sum14994.967
Variance0.079925534
MonotonicityNot monotonic
2024-03-23T02:14:10.658149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0609862377 1
 
0.8%
127.2283170105 1
 
0.8%
127.2654827562 1
 
0.8%
127.2144834225 1
 
0.8%
127.1482844761 1
 
0.8%
127.1395188474 1
 
0.8%
127.1060170414 1
 
0.8%
127.2114163575 1
 
0.8%
127.1028903173 1
 
0.8%
127.0755950189 1
 
0.8%
Other values (108) 108
84.4%
(Missing) 10
 
7.8%
ValueCountFrequency (%)
126.6080462754 1
0.8%
126.660255538 1
0.8%
126.7063041396 1
0.8%
126.7105517913 1
0.8%
126.7154020313 1
0.8%
126.7211030095 1
0.8%
126.7245821812 1
0.8%
126.7254687115 1
0.8%
126.7279758655 1
0.8%
126.7387979777 1
0.8%
ValueCountFrequency (%)
127.7882350603 1
0.8%
127.6472587207 1
0.8%
127.6424350729 1
0.8%
127.6289443305 1
0.8%
127.6253165893 1
0.8%
127.6253039785 1
0.8%
127.6089479092 1
0.8%
127.5888915032 1
0.8%
127.5887070444 1
0.8%
127.5611511576 1
0.8%

관리기관명
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
화성시청 위생정책과
12 
안산시청 노인복지과
12 
파주시
10 
<NA>
9 
시흥시청
 
7
Other values (42)
78 

Length

Max length19
Median length15
Mean length7.640625
Min length3

Unique

Unique29 ?
Unique (%)22.7%

Sample

1st row가평군청
2nd row가평군청
3rd row덕양구 가정복지과
4th row일산동구 가정복지과
5th row일산동구 가정복지과

Common Values

ValueCountFrequency (%)
화성시청 위생정책과 12
 
9.4%
안산시청 노인복지과 12
 
9.4%
파주시 10
 
7.8%
<NA> 9
 
7.0%
시흥시청 7
 
5.5%
남양주시청 6
 
4.7%
이천시청 5
 
3.9%
여주시청 5
 
3.9%
양주시 사회복지과 5
 
3.9%
김포시 노인장애인과 5
 
3.9%
Other values (37) 52
40.6%

Length

2024-03-23T02:14:11.115629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화성시청 12
 
6.3%
노인복지과 12
 
6.3%
위생정책과 12
 
6.3%
안산시청 12
 
6.3%
파주시 10
 
5.2%
가정복지과 9
 
4.7%
노인장애인과 9
 
4.7%
사회복지과 9
 
4.7%
na 9
 
4.7%
시흥시청 7
 
3.7%
Other values (46) 90
47.1%

연락처
Text

Distinct99
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-23T02:14:11.739689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.132812
Min length12

Characters and Unicode

Total characters1553
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

Unique93 ?
Unique (%)72.7%

Sample

1st row031-581-4442
2nd row031-585-6700
3rd row031-810-5444
4th row031-900-0444
5th row031-961-9400
ValueCountFrequency (%)
031-5189-3157 12
 
9.4%
031-590-2211 6
 
4.7%
031-980-2216 5
 
3.9%
031-887-2264 5
 
3.9%
031-860-2149 4
 
3.1%
031-8036-7445 3
 
2.3%
031-774-1411 1
 
0.8%
031-323-4444 1
 
0.8%
031-275-4444 1
 
0.8%
031-830-8200 1
 
0.8%
Other values (89) 89
69.5%
2024-03-23T02:14:12.846431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 256
16.5%
4 253
16.3%
0 231
14.9%
1 206
13.3%
3 180
11.6%
9 77
 
5.0%
7 76
 
4.9%
5 72
 
4.6%
2 72
 
4.6%
6 70
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1297
83.5%
Dash Punctuation 256
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 253
19.5%
0 231
17.8%
1 206
15.9%
3 180
13.9%
9 77
 
5.9%
7 76
 
5.9%
5 72
 
5.6%
2 72
 
5.6%
6 70
 
5.4%
8 60
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1553
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 256
16.5%
4 253
16.3%
0 231
14.9%
1 206
13.3%
3 180
11.6%
9 77
 
5.0%
7 76
 
4.9%
5 72
 
4.6%
2 72
 
4.6%
6 70
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 256
16.5%
4 253
16.3%
0 231
14.9%
1 206
13.3%
3 180
11.6%
9 77
 
5.0%
7 76
 
4.9%
5 72
 
4.6%
2 72
 
4.6%
6 70
 
4.5%

사업자등록번호
Text

MISSING 

Distinct68
Distinct (%)86.1%
Missing49
Missing (%)38.3%
Memory size1.1 KiB
2024-03-23T02:14:13.362162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.1139241
Min length1

Characters and Unicode

Total characters720
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)84.8%

Sample

1st row1328210548
2nd row1219958031
3rd row1409121903
4th row7819100261
5th row1538200538
ValueCountFrequency (%)
n 12
 
15.2%
1418200520 1
 
1.3%
279-94-01434 1
 
1.3%
575-85-01645 1
 
1.3%
391-34-00634 1
 
1.3%
5679401568 1
 
1.3%
1278210250 1
 
1.3%
1389131261 1
 
1.3%
1418202135 1
 
1.3%
715-95-00146 1
 
1.3%
Other values (58) 58
73.4%
2024-03-23T02:14:14.318408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 114
15.8%
0 95
13.2%
2 76
10.6%
8 69
9.6%
5 66
9.2%
3 63
8.8%
4 58
8.1%
9 51
7.1%
7 43
 
6.0%
- 38
 
5.3%
Other values (2) 47
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 670
93.1%
Dash Punctuation 38
 
5.3%
Uppercase Letter 12
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 114
17.0%
0 95
14.2%
2 76
11.3%
8 69
10.3%
5 66
9.9%
3 63
9.4%
4 58
8.7%
9 51
7.6%
7 43
 
6.4%
6 35
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 708
98.3%
Latin 12
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 114
16.1%
0 95
13.4%
2 76
10.7%
8 69
9.7%
5 66
9.3%
3 63
8.9%
4 58
8.2%
9 51
7.2%
7 43
 
6.1%
- 38
 
5.4%
Latin
ValueCountFrequency (%)
N 12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 114
15.8%
0 95
13.2%
2 76
10.6%
8 69
9.6%
5 66
9.2%
3 63
8.8%
4 58
8.1%
9 51
7.1%
7 43
 
6.0%
- 38
 
5.3%
Other values (2) 47
6.5%

특이사항
Text

MISSING 

Distinct13
Distinct (%)76.5%
Missing111
Missing (%)86.7%
Memory size1.1 KiB
2024-03-23T02:14:14.678105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length16
Mean length12
Min length2

Characters and Unicode

Total characters204
Distinct characters51
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)70.6%

Sample

1st row잠정휴업
2nd row원광대학교 산본병원 장례식장
3rd row사설
4th row사설
5th row사설
ValueCountFrequency (%)
사설 5
20.8%
잠정휴업 1
 
4.2%
편의점 1
 
4.2%
부대시설(식당 1
 
4.2%
안치실(16기 1
 
4.2%
염습실 1
 
4.2%
빈소(6실 1
 
4.2%
빈소수(12)+안치능력(14 1
 
4.2%
빈소수(6)+안치능력(10 1
 
4.2%
빈소수(7)+안치능력(8 1
 
4.2%
Other values (10) 10
41.7%
2024-03-23T02:14:15.540845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 21
 
10.3%
( 21
 
10.3%
안 10
 
4.9%
빈 10
 
4.9%
치 10
 
4.9%
소 10
 
4.9%
1 9
 
4.4%
력 9
 
4.4%
능 9
 
4.4%
+ 9
 
4.4%
Other values (41) 86
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
54.9%
Decimal Number 29
 
14.2%
Close Punctuation 21
 
10.3%
Open Punctuation 21
 
10.3%
Math Symbol 9
 
4.4%
Space Separator 7
 
3.4%
Other Punctuation 5
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 10
 
8.9%
빈 10
 
8.9%
치 10
 
8.9%
소 10
 
8.9%
력 9
 
8.0%
능 9
 
8.0%
수 9
 
8.0%
설 6
 
5.4%
사 5
 
4.5%
장 3
 
2.7%
Other values (26) 31
27.7%
Decimal Number
ValueCountFrequency (%)
1 9
31.0%
6 5
17.2%
0 4
13.8%
2 2
 
6.9%
8 2
 
6.9%
9 2
 
6.9%
4 2
 
6.9%
7 1
 
3.4%
3 1
 
3.4%
5 1
 
3.4%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Math Symbol
ValueCountFrequency (%)
+ 9
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 112
54.9%
Common 92
45.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 10
 
8.9%
빈 10
 
8.9%
치 10
 
8.9%
소 10
 
8.9%
력 9
 
8.0%
능 9
 
8.0%
수 9
 
8.0%
설 6
 
5.4%
사 5
 
4.5%
장 3
 
2.7%
Other values (26) 31
27.7%
Common
ValueCountFrequency (%)
) 21
22.8%
( 21
22.8%
1 9
9.8%
+ 9
9.8%
7
 
7.6%
, 5
 
5.4%
6 5
 
5.4%
0 4
 
4.3%
2 2
 
2.2%
8 2
 
2.2%
Other values (5) 7
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 112
54.9%
ASCII 92
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 21
22.8%
( 21
22.8%
1 9
9.8%
+ 9
9.8%
7
 
7.6%
, 5
 
5.4%
6 5
 
5.4%
0 4
 
4.3%
2 2
 
2.2%
8 2
 
2.2%
Other values (5) 7
 
7.6%
Hangul
ValueCountFrequency (%)
안 10
 
8.9%
빈 10
 
8.9%
치 10
 
8.9%
소 10
 
8.9%
력 9
 
8.0%
능 9
 
8.0%
수 9
 
8.0%
설 6
 
5.4%
사 5
 
4.5%
장 3
 
2.7%
Other values (26) 31
27.7%

Interactions

2024-03-23T02:13:57.949736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:13:56.413382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:13:57.179653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:13:58.203238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:13:56.664137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:13:57.418510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:13:58.470038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:13:56.916355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:13:57.662650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T02:14:15.823577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명시설유형소재지우편번호WGS84위도WGS84경도관리기관명연락처사업자등록번호특이사항
시군명1.0000.9500.9920.9370.9031.0001.0000.0001.000
시설유형0.9501.0000.7320.6140.4100.9660.7210.0000.000
소재지우편번호0.9920.7321.0000.8620.8650.9941.0000.9401.000
WGS84위도0.9370.6140.8621.0000.6990.9460.9390.0000.870
WGS84경도0.9030.4100.8650.6991.0000.9760.9850.8111.000
관리기관명1.0000.9660.9940.9460.9761.0001.0000.9411.000
연락처1.0000.7211.0000.9390.9851.0001.0000.9921.000
사업자등록번호0.0000.0000.9400.0000.8110.9410.9921.0001.000
특이사항1.0000.0001.0000.8701.0001.0001.0001.0001.000
2024-03-23T02:14:16.158976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설유형시군명관리기관명
시설유형1.0000.6620.663
시군명0.6621.0000.872
관리기관명0.6630.8721.000
2024-03-23T02:14:16.542779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명시설유형관리기관명
소재지우편번호1.000-0.9150.1210.8970.4970.774
WGS84위도-0.9151.000-0.2120.6730.3740.583
WGS84경도0.121-0.2121.0000.5870.2240.683
시군명0.8970.6730.5871.0000.6620.872
시설유형0.4970.3740.2240.6621.0000.663
관리기관명0.7740.5830.6830.8720.6631.000

Missing values

2024-03-23T02:13:58.865844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T02:13:59.670310image/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.
2024-03-23T02:14:00.275438image/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

시군명시설명시설유형소재지우편번호소재지도로명주소소재지지번주소WGS84위도WGS84경도관리기관명연락처사업자등록번호특이사항
0가평군가평군농협효문화센터장례식장12426경기도 가평군 가평읍 경춘로 1775경기도 가평군 가평읍 상색리 269-137.798016127.481417가평군청031-581-44421328210548<NA>
1가평군연새장례식장장례식장12449경기도 가평군 청평면 경춘로 1219경기도 가평군 청평면 상천리 1148-337.766152127.445921가평군청031-585-67001219958031<NA>
2고양시㈜헤븐앤어스 명지병원장례식장장례식장<NA><NA><NA><NA><NA>덕양구 가정복지과031-810-5444<NA><NA>
3고양시국민건강보험공단일산병원장례식장장례식장<NA><NA><NA><NA><NA>일산동구 가정복지과031-900-0444<NA><NA>
4고양시동국대학교일산병원장례식장장례식장<NA><NA><NA><NA><NA>일산동구 가정복지과031-961-9400<NA><NA>
5고양시쉴낙원일산장례식장장례식장<NA><NA><NA><NA><NA>일산동구 가정복지과031-923-7000<NA><NA>
6고양시원당연세장례식장장례식장<NA><NA><NA><NA><NA>덕양구 가정복지과031-966-4444<NA>잠정휴업
7고양시원당장례식장 (주)문베스트장례식장<NA><NA><NA><NA><NA>덕양구 가정복지과031-965-4444<NA><NA>
8고양시일산백장례서비스(주)장례식장<NA><NA><NA><NA><NA>일산서구 가정복지과031-910-7444<NA><NA>
9고양시일산복음병원장례식장장례식장<NA><NA><NA><NA><NA>일산동구 가정복지과031-977-6000<NA><NA>
시군명시설명시설유형소재지우편번호소재지도로명주소소재지지번주소WGS84위도WGS84경도관리기관명연락처사업자등록번호특이사항
118화성시송산장례문화원장례식장18552경기도 화성시 송산면 화성로 284경기도 화성시 송산면 육일리 412-2번지37.202648126.725469화성시청 위생정책과031-5189-31575509700107<NA>
119화성시원광종합병원장례식장장례식장18356경기도 화성시 화산북로 21경기도 화성시 송산동 200-86번지37.210062127.00803화성시청 위생정책과031-5189-31576274300345<NA>
120화성시조암장례식장장례식장18584경기도 화성시 장안면 사랑길 111경기도 화성시 장안면 사랑리 52837.080629126.823242화성시청 위생정책과031-5189-31571449301053<NA>
121화성시하늘가장례식장장례식장18589경기도 화성시 향남읍 발안로 322경기도 화성시 향남읍 관리 295-14번지37.132853126.942004화성시청 위생정책과031-5189-31571438200735<NA>
122화성시한림대학교동탄성심병원장례식장장례식장18450경기도 화성시 큰재봉길 7경기도 화성시 석우동 40번지37.216496127.079942화성시청 위생정책과031-5189-31571358551063<NA>
123화성시함백산추모공원(달빛쉼터)장례식장18288경기도 화성시 매송면 서해로 2448-32경기도 화성시 매송면 숙곡리 산12-537.270621126.910073화성시청 위생정책과031-5189-31574458801781<NA>
124화성시현대장례문화원장례식장18573경기도 화성시 우정읍 남양만로 662경기도 화성시 우정읍 이화리 439-1번지37.042361126.798045화성시청 위생정책과031-5189-31571248219795<NA>
125화성시화성유일병원 장례식장장례식장18256경기도 화성시 남양읍 남양로920번길 6경기도 화성시 남양읍 북양리 691-137.217785126.832659화성시청 위생정책과031-5189-31574269001712<NA>
126화성시화성장례문화원장례식장18537경기도 화성시 마도면 쌍송북로 111경기도 화성시 마도면 두곡리 501번지37.201724126.787951화성시청 위생정책과031-5189-31572258513193<NA>
127화성시화성중앙병원장례식장장례식장18592경기도 화성시 향남읍 발안로 5경기도 화성시 향남읍 평리 74-1번지37.131548126.910806화성시청 위생정책과031-5189-31573150905031<NA>