Overview

Dataset statistics

Number of variables10
Number of observations57
Missing cells4
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory85.3 B

Variable types

Categorical3
Text4
Numeric3

Dataset

Description경기도 국민안심병원 현황
Author보건복지부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=XMA3NZC2TFEAZBJKYZ9K31799000&infSeq=1

Alerts

데이터기준일자 has constant value ""Constant
정제우편번호 is highly overall correlated with 정제WGS84위도 and 1 other fieldsHigh correlation
정제WGS84위도 is highly overall correlated with 정제우편번호 and 1 other fieldsHigh correlation
정제WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 정제우편번호 and 2 other fieldsHigh correlation
정제도로명주소 has 1 (1.8%) missing valuesMissing
정제우편번호 has 1 (1.8%) missing valuesMissing
정제WGS84위도 has 1 (1.8%) missing valuesMissing
정제WGS84경도 has 1 (1.8%) missing valuesMissing
기관명 has unique valuesUnique
전화번호 has unique valuesUnique
정제지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:29:49.226861
Analysis finished2023-12-10 21:29:50.764603
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Memory size588.0 B
성남시
5 
부천시
5 
화성시
5 
평택시
4 
고양시
4 
Other values (15)
34 

Length

Max length4
Median length3
Mean length3.122807
Min length3

Unique

Unique3 ?
Unique (%)5.3%

Sample

1st row평택시
2nd row평택시
3rd row평택시
4th row화성시
5th row화성시

Common Values

ValueCountFrequency (%)
성남시 5
 
8.8%
부천시 5
 
8.8%
화성시 5
 
8.8%
평택시 4
 
7.0%
고양시 4
 
7.0%
의정부시 4
 
7.0%
시흥시 3
 
5.3%
남양주시 3
 
5.3%
김포시 3
 
5.3%
안산시 3
 
5.3%
Other values (10) 18
31.6%

Length

2023-12-11T06:29:50.821962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 5
 
8.8%
화성시 5
 
8.8%
부천시 5
 
8.8%
평택시 4
 
7.0%
고양시 4
 
7.0%
의정부시 4
 
7.0%
김포시 3
 
5.3%
안산시 3
 
5.3%
용인시 3
 
5.3%
남양주시 3
 
5.3%
Other values (10) 18
31.6%

기관명
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-11T06:29:51.028624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length7.3859649
Min length3

Characters and Unicode

Total characters421
Distinct characters119
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

Unique57 ?
Unique (%)100.0%

Sample

1st row평택굿모닝병원
2nd row평택성모병원
3rd row박병원
4th row화성중앙종합병원
5th row한림대학교 동탄성심병원
ValueCountFrequency (%)
가톨릭대학교 2
 
3.1%
의정부을지대학교병원 1
 
1.5%
메디인병원 1
 
1.5%
자인메디병원 1
 
1.5%
국민건강보험공단 1
 
1.5%
일산병원 1
 
1.5%
동국대학교 1
 
1.5%
일산불교병원 1
 
1.5%
광명성애병원 1
 
1.5%
아이원병원 1
 
1.5%
Other values (54) 54
83.1%
2023-12-11T06:29:51.378169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 63
 
15.0%
병 57
 
13.5%
대 17
 
4.0%
교 16
 
3.8%
학 16
 
3.8%
성 11
 
2.6%
8
 
1.9%
의 8
 
1.9%
부 7
 
1.7%
산 6
 
1.4%
Other values (109) 212
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 413
98.1%
Space Separator 8
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 63
 
15.3%
병 57
 
13.8%
대 17
 
4.1%
교 16
 
3.9%
학 16
 
3.9%
성 11
 
2.7%
의 8
 
1.9%
부 7
 
1.7%
산 6
 
1.5%
천 5
 
1.2%
Other values (108) 207
50.1%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 413
98.1%
Common 8
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 63
 
15.3%
병 57
 
13.8%
대 17
 
4.1%
교 16
 
3.9%
학 16
 
3.9%
성 11
 
2.7%
의 8
 
1.9%
부 7
 
1.7%
산 6
 
1.5%
천 5
 
1.2%
Other values (108) 207
50.1%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 413
98.1%
ASCII 8
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 63
 
15.3%
병 57
 
13.8%
대 17
 
4.1%
교 16
 
3.9%
학 16
 
3.9%
성 11
 
2.7%
의 8
 
1.9%
부 7
 
1.7%
산 6
 
1.5%
천 5
 
1.2%
Other values (108) 207
50.1%
ASCII
ValueCountFrequency (%)
8
100.0%

신청유형
Categorical

Distinct2
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size588.0 B
A
31 
B
26 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A 31
54.4%
B 26
45.6%

Length

2023-12-11T06:29:51.512551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:29:51.593212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 31
54.4%
b 26
45.6%

전화번호
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-11T06:29:51.807676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.684211
Min length9

Characters and Unicode

Total characters666
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

Unique57 ?
Unique (%)100.0%

Sample

1st row031-659-7700
2nd row031-1800-8800
3rd row031-666-2600
4th row031-352-8114
5th row031-8086-3000
ValueCountFrequency (%)
031-659-7700 1
 
1.8%
031-951-1000 1
 
1.8%
1688-5533 1
 
1.8%
031-900-0114 1
 
1.8%
031-961-7000 1
 
1.8%
02-2680-7114 1
 
1.8%
1833-7588 1
 
1.8%
031-550-1111 1
 
1.8%
031-389-3000 1
 
1.8%
031-980-9114 1
 
1.8%
Other values (47) 47
82.5%
2023-12-11T06:29:52.198930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 140
21.0%
1 117
17.6%
- 104
15.6%
3 71
10.7%
8 40
 
6.0%
6 39
 
5.9%
5 39
 
5.9%
7 37
 
5.6%
9 29
 
4.4%
4 26
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 562
84.4%
Dash Punctuation 104
 
15.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 140
24.9%
1 117
20.8%
3 71
12.6%
8 40
 
7.1%
6 39
 
6.9%
5 39
 
6.9%
7 37
 
6.6%
9 29
 
5.2%
4 26
 
4.6%
2 24
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 666
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 140
21.0%
1 117
17.6%
- 104
15.6%
3 71
10.7%
8 40
 
6.0%
6 39
 
5.9%
5 39
 
5.9%
7 37
 
5.6%
9 29
 
4.4%
4 26
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 140
21.0%
1 117
17.6%
- 104
15.6%
3 71
10.7%
8 40
 
6.0%
6 39
 
5.9%
5 39
 
5.9%
7 37
 
5.6%
9 29
 
4.4%
4 26
 
3.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
2022-02-28
57 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-02-28
2nd row2022-02-28
3rd row2022-02-28
4th row2022-02-28
5th row2022-02-28

Common Values

ValueCountFrequency (%)
2022-02-28 57
100.0%

Length

2023-12-11T06:29:52.328567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:29:52.416151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-02-28 57
100.0%

정제도로명주소
Text

MISSING 

Distinct56
Distinct (%)100.0%
Missing1
Missing (%)1.8%
Memory size588.0 B
2023-12-11T06:29:52.700845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length17.660714
Min length13

Characters and Unicode

Total characters989
Distinct characters123
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

Unique56 ?
Unique (%)100.0%

Sample

1st row경기도 평택시 중앙로 338
2nd row경기도 평택시 평택로 284
3rd row경기도 평택시 송탄로 33
4th row경기도 화성시 향남읍 발안로 5
5th row경기도 화성시 큰재봉길 7
ValueCountFrequency (%)
경기도 56
 
22.8%
부천시 5
 
2.0%
화성시 5
 
2.0%
고양시 4
 
1.6%
의정부시 4
 
1.6%
평택시 4
 
1.6%
성남시 4
 
1.6%
시흥시 3
 
1.2%
김포시 3
 
1.2%
남양주시 3
 
1.2%
Other values (135) 155
63.0%
2023-12-11T06:29:53.187068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
190
19.2%
시 62
 
6.3%
기 59
 
6.0%
경 58
 
5.9%
도 56
 
5.7%
로 54
 
5.5%
1 32
 
3.2%
구 24
 
2.4%
3 23
 
2.3%
2 23
 
2.3%
Other values (113) 408
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 627
63.4%
Space Separator 190
 
19.2%
Decimal Number 169
 
17.1%
Dash Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 62
 
9.9%
기 59
 
9.4%
경 58
 
9.3%
도 56
 
8.9%
로 54
 
8.6%
구 24
 
3.8%
길 13
 
2.1%
양 13
 
2.1%
성 11
 
1.8%
번 11
 
1.8%
Other values (101) 266
42.4%
Decimal Number
ValueCountFrequency (%)
1 32
18.9%
3 23
13.6%
2 23
13.6%
5 18
10.7%
7 17
10.1%
8 14
8.3%
0 14
8.3%
6 13
7.7%
4 8
 
4.7%
9 7
 
4.1%
Space Separator
ValueCountFrequency (%)
190
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 627
63.4%
Common 362
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 62
 
9.9%
기 59
 
9.4%
경 58
 
9.3%
도 56
 
8.9%
로 54
 
8.6%
구 24
 
3.8%
길 13
 
2.1%
양 13
 
2.1%
성 11
 
1.8%
번 11
 
1.8%
Other values (101) 266
42.4%
Common
ValueCountFrequency (%)
190
52.5%
1 32
 
8.8%
3 23
 
6.4%
2 23
 
6.4%
5 18
 
5.0%
7 17
 
4.7%
8 14
 
3.9%
0 14
 
3.9%
6 13
 
3.6%
4 8
 
2.2%
Other values (2) 10
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 627
63.4%
ASCII 362
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
190
52.5%
1 32
 
8.8%
3 23
 
6.4%
2 23
 
6.4%
5 18
 
5.0%
7 17
 
4.7%
8 14
 
3.9%
0 14
 
3.9%
6 13
 
3.6%
4 8
 
2.2%
Other values (2) 10
 
2.8%
Hangul
ValueCountFrequency (%)
시 62
 
9.9%
기 59
 
9.4%
경 58
 
9.3%
도 56
 
8.9%
로 54
 
8.6%
구 24
 
3.8%
길 13
 
2.1%
양 13
 
2.1%
성 11
 
1.8%
번 11
 
1.8%
Other values (101) 266
42.4%

정제지번주소
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-11T06:29:53.443042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length25
Mean length20.842105
Min length15

Characters and Unicode

Total characters1188
Distinct characters125
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

Unique57 ?
Unique (%)100.0%

Sample

1st row경기도 평택시 합정동 883번지
2nd row경기도 평택시 세교동 439-3번지
3rd row경기도 평택시 장당동 470-4번지
4th row경기도 화성시 향남읍 평리 74-1번지
5th row경기도 화성시 석우동 40번지
ValueCountFrequency (%)
경기도 57
 
21.8%
화성시 5
 
1.9%
부천시 5
 
1.9%
성남시 5
 
1.9%
평택시 4
 
1.5%
고양시 4
 
1.5%
의정부시 4
 
1.5%
안산시 3
 
1.1%
김포시 3
 
1.1%
남양주시 3
 
1.1%
Other values (148) 168
64.4%
2023-12-11T06:29:54.043300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
204
17.2%
기 60
 
5.1%
시 60
 
5.1%
지 58
 
4.9%
경 57
 
4.8%
도 57
 
4.8%
번 57
 
4.8%
동 56
 
4.7%
1 44
 
3.7%
- 33
 
2.8%
Other values (115) 502
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 734
61.8%
Decimal Number 217
 
18.3%
Space Separator 204
 
17.2%
Dash Punctuation 33
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 60
 
8.2%
시 60
 
8.2%
지 58
 
7.9%
경 57
 
7.8%
도 57
 
7.8%
번 57
 
7.8%
동 56
 
7.6%
구 22
 
3.0%
산 14
 
1.9%
양 14
 
1.9%
Other values (103) 279
38.0%
Decimal Number
ValueCountFrequency (%)
1 44
20.3%
3 27
12.4%
4 25
11.5%
6 23
10.6%
2 19
8.8%
9 18
8.3%
5 18
8.3%
8 15
 
6.9%
7 14
 
6.5%
0 14
 
6.5%
Space Separator
ValueCountFrequency (%)
204
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 734
61.8%
Common 454
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 60
 
8.2%
시 60
 
8.2%
지 58
 
7.9%
경 57
 
7.8%
도 57
 
7.8%
번 57
 
7.8%
동 56
 
7.6%
구 22
 
3.0%
산 14
 
1.9%
양 14
 
1.9%
Other values (103) 279
38.0%
Common
ValueCountFrequency (%)
204
44.9%
1 44
 
9.7%
- 33
 
7.3%
3 27
 
5.9%
4 25
 
5.5%
6 23
 
5.1%
2 19
 
4.2%
9 18
 
4.0%
5 18
 
4.0%
8 15
 
3.3%
Other values (2) 28
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 734
61.8%
ASCII 454
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
204
44.9%
1 44
 
9.7%
- 33
 
7.3%
3 27
 
5.9%
4 25
 
5.5%
6 23
 
5.1%
2 19
 
4.2%
9 18
 
4.0%
5 18
 
4.0%
8 15
 
3.3%
Other values (2) 28
 
6.2%
Hangul
ValueCountFrequency (%)
기 60
 
8.2%
시 60
 
8.2%
지 58
 
7.9%
경 57
 
7.8%
도 57
 
7.8%
번 57
 
7.8%
동 56
 
7.6%
구 22
 
3.0%
산 14
 
1.9%
양 14
 
1.9%
Other values (103) 279
38.0%

정제우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)100.0%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean14479.679
Minimum10086
Maximum18592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T06:29:54.177888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10086
5-th percentile10272.75
Q111990.5
median14548.5
Q317012
95-th percentile18379.5
Maximum18592
Range8506
Interquartile range (IQR)5021.5

Descriptive statistics

Standard deviation2691.1474
Coefficient of variation (CV)0.18585685
Kurtosis-1.1954767
Mean14479.679
Median Absolute Deviation (MAD)2515
Skewness-0.060651858
Sum810862
Variance7242274.5
MonotonicityNot monotonic
2023-12-11T06:29:54.318264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17874 1
 
1.8%
11759 1
 
1.8%
10518 1
 
1.8%
10444 1
 
1.8%
10326 1
 
1.8%
14241 1
 
1.8%
14316 1
 
1.8%
11919 1
 
1.8%
15839 1
 
1.8%
10086 1
 
1.8%
Other values (46) 46
80.7%
ValueCountFrequency (%)
10086 1
1.8%
10099 1
1.8%
10113 1
1.8%
10326 1
1.8%
10444 1
1.8%
10475 1
1.8%
10518 1
1.8%
10924 1
1.8%
11686 1
1.8%
11759 1
1.8%
ValueCountFrequency (%)
18592 1
1.8%
18454 1
1.8%
18450 1
1.8%
18356 1
1.8%
18270 1
1.8%
18144 1
1.8%
18114 1
1.8%
17909 1
1.8%
17874 1
1.8%
17825 1
1.8%

정제WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)100.0%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean37.410462
Minimum36.990565
Maximum37.758523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T06:29:54.463914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.990565
5-th percentile37.007732
Q137.268115
median37.392274
Q337.614094
95-th percentile37.75083
Maximum37.758523
Range0.76795781
Interquartile range (IQR)0.3459795

Descriptive statistics

Standard deviation0.21909722
Coefficient of variation (CV)0.0058565762
Kurtosis-0.84424274
Mean37.410462
Median Absolute Deviation (MAD)0.1789948
Skewness-0.13045987
Sum2094.9858
Variance0.048003591
MonotonicityNot monotonic
2023-12-11T06:29:54.593232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.9905649024 1
 
1.8%
37.7539942635 1
 
1.8%
37.6229829943 1
 
1.8%
37.6454752678 1
 
1.8%
37.6764385211 1
 
1.8%
37.4732140184 1
 
1.8%
37.4479290831 1
 
1.8%
37.6059246921 1
 
1.8%
37.3586407829 1
 
1.8%
37.640963919 1
 
1.8%
Other values (46) 46
80.7%
ValueCountFrequency (%)
36.9905649024 1
1.8%
36.9930565731 1
1.8%
37.0058059754 1
1.8%
37.0083742374 1
1.8%
37.0482621367 1
1.8%
37.1313495282 1
1.8%
37.1412846971 1
1.8%
37.1706289306 1
1.8%
37.1992313944 1
1.8%
37.2046139805 1
1.8%
ValueCountFrequency (%)
37.7585227082 1
1.8%
37.7580480392 1
1.8%
37.7539942635 1
1.8%
37.7497753415 1
1.8%
37.7453057969 1
1.8%
37.7154360459 1
1.8%
37.6827278206 1
1.8%
37.6764385211 1
1.8%
37.6454752678 1
1.8%
37.6424745722 1
1.8%

정제WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)100.0%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean126.97573
Minimum126.66026
Maximum127.62532
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T06:29:54.727038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.66026
5-th percentile126.72748
Q1126.81134
median126.98598
Q3127.1137
95-th percentile127.20628
Maximum127.62532
Range0.96506105
Interquartile range (IQR)0.30235337

Descriptive statistics

Standard deviation0.18216493
Coefficient of variation (CV)0.0014346437
Kurtosis1.2597318
Mean126.97573
Median Absolute Deviation (MAD)0.14478692
Skewness0.63490679
Sum7110.6406
Variance0.033184062
MonotonicityNot monotonic
2023-12-11T06:29:54.871075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1204512355 1
 
1.8%
127.0500967064 1
 
1.8%
126.8356822065 1
 
1.8%
126.7929631952 1
 
1.8%
126.805563051 1
 
1.8%
126.8716164265 1
 
1.8%
126.884976242 1
 
1.8%
127.1338875276 1
 
1.8%
126.9473038902 1
 
1.8%
126.660255538 1
 
1.8%
Other values (46) 46
80.7%
ValueCountFrequency (%)
126.660255538 1
1.8%
126.7105517913 1
1.8%
126.7245821812 1
1.8%
126.7284520798 1
1.8%
126.7347224643 1
1.8%
126.7621105079 1
1.8%
126.7694862939 1
1.8%
126.774853086 1
1.8%
126.789375692 1
1.8%
126.7911902926 1
1.8%
ValueCountFrequency (%)
127.6253165893 1
1.8%
127.2707076272 1
1.8%
127.2114163575 1
1.8%
127.20457392 1
1.8%
127.1799591601 1
1.8%
127.1529109733 1
1.8%
127.1482844761 1
1.8%
127.1338875276 1
1.8%
127.1325173047 1
1.8%
127.1290120515 1
1.8%

Interactions

2023-12-11T06:29:50.112820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:29:49.660935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:29:49.871702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:29:50.203774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:29:49.730588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:29:49.950790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:29:50.323415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:29:49.806472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:29:50.030587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:29:54.967062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명기관명신청유형전화번호정제도로명주소정제지번주소정제우편번호정제WGS84위도정제WGS84경도
시군명1.0001.0000.0001.0001.0001.0000.9980.9860.975
기관명1.0001.0001.0001.0001.0001.0001.0001.0001.000
신청유형0.0001.0001.0001.0001.0001.0000.0000.0000.457
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
정제도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
정제지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
정제우편번호0.9981.0000.0001.0001.0001.0001.0000.9460.731
정제WGS84위도0.9861.0000.0001.0001.0001.0000.9461.0000.475
정제WGS84경도0.9751.0000.4571.0001.0001.0000.7310.4751.000
2023-12-11T06:29:55.075574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명신청유형
시군명1.0000.000
신청유형0.0001.000
2023-12-11T06:29:55.149616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정제우편번호정제WGS84위도정제WGS84경도시군명신청유형
정제우편번호1.000-0.9070.1990.8180.000
정제WGS84위도-0.9071.000-0.2680.7050.000
정제WGS84경도0.199-0.2681.0000.7380.321
시군명0.8180.7050.7381.0000.000
신청유형0.0000.0000.3210.0001.000

Missing values

2023-12-11T06:29:50.421120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:29:50.582268image/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.
2023-12-11T06:29:50.708008image/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

시군명기관명신청유형전화번호데이터기준일자정제도로명주소정제지번주소정제우편번호정제WGS84위도정제WGS84경도
0평택시평택굿모닝병원B031-659-77002022-02-28경기도 평택시 중앙로 338경기도 평택시 합정동 883번지1787436.990565127.120451
1평택시평택성모병원B031-1800-88002022-02-28경기도 평택시 평택로 284경기도 평택시 세교동 439-3번지1782537.008374127.074368
2평택시박병원B031-666-26002022-02-28경기도 평택시 송탄로 33경기도 평택시 장당동 470-4번지1778437.048262127.057007
3화성시화성중앙종합병원A031-352-81142022-02-28경기도 화성시 향남읍 발안로 5경기도 화성시 향남읍 평리 74-1번지1859237.13135126.910773
4화성시한림대학교 동탄성심병원B031-8086-30002022-02-28경기도 화성시 큰재봉길 7경기도 화성시 석우동 40번지1845037.216496127.079942
5화성시원광종합병원A031-8077-72002022-02-28경기도 화성시 화산북로 21경기도 화성시 송산동 200-4번지1835637.210062127.00803
6화성시화성디에스병원B1600-59822022-02-28경기도 화성시 남양읍 시청로160번길 46-13경기도 화성시 남양읍 남양리 2319-2번지1827037.199231126.825712
7화성시센트럴아동병원A031-8060-50002022-02-28경기도 화성시 동탄지성로 17경기도 화성시 반송동 93-1번지 동탄위버폴리스 지하1-2층 일부1845437.204614127.07204
8고양시명지병원B031-810-51142022-02-28경기도 고양시 덕양구 화수로14번길 55경기도 고양시 덕양구 화정동 697-1번지1047537.642475126.831745
9구리시한양대학교구리병원B031-560-21142022-02-28경기도 구리시 경춘로 153경기도 구리시 교문동 249-1번지1192337.601188127.132517
시군명기관명신청유형전화번호데이터기준일자정제도로명주소정제지번주소정제우편번호정제WGS84위도정제WGS84경도
47시흥시센트럴병원B031-8041-35312022-02-28경기도 시흥시 공단1대로 237경기도 시흥시 정왕동 1366-11번지1507937.336666126.728452
48안산시고려대학교안산병원B031-412-51142022-02-28경기도 안산시 단원구 적금로 123경기도 안산시 단원구 고잔동 516번지1535537.318859126.824994
49안성시안성성모병원A031-675-60072022-02-28경기도 안성시 시장길 58경기도 안성시 서인동 14번지1759237.005806127.270708
50안양시안양샘병원A031-467-91142022-02-28경기도 안양시 만안구 삼덕로 9경기도 안양시 만안구 안양동 613-6번지1403037.392894126.92482
51여주시세종여주병원B031-880-77002022-02-28경기도 여주시 청심로 39경기도 여주시 하동 435번지1261837.301482127.625317
52용인시연세대학교 용인세브란스병원B1899-10042022-02-28경기도 용인시 기흥구 동백죽전대로 363경기도 용인시 기흥구 중동 1151번지1699537.270775127.148284
53용인시다보스병원A031-8021-21142022-02-28경기도 용인시 처인구 백옥대로1082번길 18경기도 용인시 처인구 김량장동 18-4번지1706337.231546127.211416
54의정부시의정부백병원A031-856-81112022-02-28경기도 의정부시 금신로 322경기도 의정부시 신곡동 519-11번지1177837.745306127.062136
55의정부시추병원A031-845-77772022-02-28경기도 의정부시 평화로 650경기도 의정부시 의정부동 234-2번지1168637.749775127.045207
56평택시박애병원A031-652-21212022-02-28경기도 평택시 평택2로20번길 3경기도 평택시 평택동 41-2번지1790936.993057127.089074