Overview

Dataset statistics

Number of variables9
Number of observations885
Missing cells21
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.9 KiB
Average record size in memory75.1 B

Variable types

Categorical3
Text3
Numeric3

Dataset

Description병원평가정보(질병-당뇨병) 현황
Author건강보험심사평가원
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=V928TXT246ZC7IUAVNFW21396593&infSeq=1

Alerts

평가내역 has constant value ""Constant
평가등급 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
소재지도로명주소 has 13 (1.5%) missing valuesMissing
소재지지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:25:35.621759
Analysis finished2023-12-10 21:25:37.513504
Duration1.89 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
수원시
96 
부천시
88 
고양시
67 
성남시
64 
남양주시
 
46
Other values (26)
524 

Length

Max length4
Median length3
Mean length3.0971751
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남양주시
2nd row광명시
3rd row남양주시
4th row안양시
5th row평택시

Common Values

ValueCountFrequency (%)
수원시 96
 
10.8%
부천시 88
 
9.9%
고양시 67
 
7.6%
성남시 64
 
7.2%
남양주시 46
 
5.2%
용인시 46
 
5.2%
안양시 45
 
5.1%
안산시 41
 
4.6%
평택시 38
 
4.3%
화성시 37
 
4.2%
Other values (21) 317
35.8%

Length

2023-12-11T06:25:37.575616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 96
 
10.8%
부천시 88
 
9.9%
고양시 67
 
7.6%
성남시 64
 
7.2%
남양주시 46
 
5.2%
용인시 46
 
5.2%
안양시 45
 
5.1%
안산시 41
 
4.6%
평택시 38
 
4.3%
화성시 37
 
4.2%
Other values (21) 317
35.8%

기관명
Text

Distinct748
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2023-12-11T06:25:37.759387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length6.9050847
Min length3

Characters and Unicode

Total characters6111
Distinct characters319
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

Unique670 ?
Unique (%)75.7%

Sample

1st row365서울내과의원
2nd row365편안한내과의원
3rd rowThe서울아산내과의원
4th row가톨릭성모내과의원
5th row강남뷰베스트의원
ValueCountFrequency (%)
서울내과의원 8
 
0.9%
속편한내과의원 7
 
0.8%
연세내과의원 6
 
0.7%
강내과의원 6
 
0.7%
삼성내과의원 5
 
0.6%
서울삼성내과의원 5
 
0.6%
김내과의원 5
 
0.6%
굿모닝내과의원 5
 
0.6%
정내과의원 5
 
0.6%
현내과의원 5
 
0.6%
Other values (746) 840
93.6%
2023-12-11T06:25:38.135517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 959
 
15.7%
원 897
 
14.7%
과 627
 
10.3%
내 534
 
8.7%
정 117
 
1.9%
성 110
 
1.8%
연 107
 
1.8%
세 104
 
1.7%
한 97
 
1.6%
가 89
 
1.5%
Other values (309) 2470
40.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6061
99.2%
Decimal Number 26
 
0.4%
Space Separator 12
 
0.2%
Uppercase Letter 5
 
0.1%
Lowercase Letter 3
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 959
 
15.8%
원 897
 
14.8%
과 627
 
10.3%
내 534
 
8.8%
정 117
 
1.9%
성 110
 
1.8%
연 107
 
1.8%
세 104
 
1.7%
한 97
 
1.6%
가 89
 
1.5%
Other values (292) 2420
39.9%
Decimal Number
ValueCountFrequency (%)
5 6
23.1%
3 6
23.1%
1 5
19.2%
6 5
19.2%
2 4
15.4%
Uppercase Letter
ValueCountFrequency (%)
D 1
20.0%
T 1
20.0%
K 1
20.0%
O 1
20.0%
S 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
h 1
33.3%
r 1
33.3%
Space Separator
ValueCountFrequency (%)
12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6061
99.2%
Common 42
 
0.7%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 959
 
15.8%
원 897
 
14.8%
과 627
 
10.3%
내 534
 
8.8%
정 117
 
1.9%
성 110
 
1.8%
연 107
 
1.8%
세 104
 
1.7%
한 97
 
1.6%
가 89
 
1.5%
Other values (292) 2420
39.9%
Common
ValueCountFrequency (%)
12
28.6%
5 6
14.3%
3 6
14.3%
1 5
11.9%
6 5
11.9%
2 4
 
9.5%
) 2
 
4.8%
( 1
 
2.4%
. 1
 
2.4%
Latin
ValueCountFrequency (%)
e 1
12.5%
h 1
12.5%
D 1
12.5%
r 1
12.5%
T 1
12.5%
K 1
12.5%
O 1
12.5%
S 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6061
99.2%
ASCII 50
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 959
 
15.8%
원 897
 
14.8%
과 627
 
10.3%
내 534
 
8.8%
정 117
 
1.9%
성 110
 
1.8%
연 107
 
1.8%
세 104
 
1.7%
한 97
 
1.6%
가 89
 
1.5%
Other values (292) 2420
39.9%
ASCII
ValueCountFrequency (%)
12
24.0%
5 6
12.0%
3 6
12.0%
1 5
10.0%
6 5
10.0%
2 4
 
8.0%
) 2
 
4.0%
( 1
 
2.0%
e 1
 
2.0%
h 1
 
2.0%
Other values (7) 7
14.0%

평가내역
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
당뇨병
885 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
당뇨병 885
100.0%

Length

2023-12-11T06:25:38.250741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:25:38.330633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당뇨병 885
100.0%

평가등급
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
양호
885 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양호 885
100.0%

Length

2023-12-11T06:25:38.422919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:25:38.510396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양호 885
100.0%

소재지도로명주소
Text

MISSING 

Distinct862
Distinct (%)98.9%
Missing13
Missing (%)1.5%
Memory size7.0 KiB
2023-12-11T06:25:38.829016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length18.216743
Min length13

Characters and Unicode

Total characters15885
Distinct characters260
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

Unique852 ?
Unique (%)97.7%

Sample

1st row경기도 남양주시 늘을3로 65-12
2nd row경기도 광명시 오리로 976
3rd row경기도 남양주시 진접읍 장현천로 28
4th row경기도 안양시 만안구 안양로 461-1
5th row경기도 평택시 중앙2로 25
ValueCountFrequency (%)
경기도 872
 
22.0%
수원시 95
 
2.4%
부천시 85
 
2.1%
고양시 66
 
1.7%
성남시 64
 
1.6%
남양주시 46
 
1.2%
용인시 44
 
1.1%
안양시 43
 
1.1%
안산시 41
 
1.0%
평택시 38
 
1.0%
Other values (1074) 2578
64.9%
2023-12-11T06:25:39.247639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3100
19.5%
경 910
 
5.7%
시 895
 
5.6%
기 894
 
5.6%
도 893
 
5.6%
로 836
 
5.3%
1 555
 
3.5%
구 380
 
2.4%
2 372
 
2.3%
3 320
 
2.0%
Other values (250) 6730
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10074
63.4%
Space Separator 3100
 
19.5%
Decimal Number 2629
 
16.6%
Dash Punctuation 82
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 910
 
9.0%
시 895
 
8.9%
기 894
 
8.9%
도 893
 
8.9%
로 836
 
8.3%
구 380
 
3.8%
양 246
 
2.4%
안 217
 
2.2%
길 180
 
1.8%
원 173
 
1.7%
Other values (238) 4450
44.2%
Decimal Number
ValueCountFrequency (%)
1 555
21.1%
2 372
14.1%
3 320
12.2%
6 217
 
8.3%
5 214
 
8.1%
4 211
 
8.0%
0 198
 
7.5%
7 187
 
7.1%
8 185
 
7.0%
9 170
 
6.5%
Space Separator
ValueCountFrequency (%)
3100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10074
63.4%
Common 5811
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 910
 
9.0%
시 895
 
8.9%
기 894
 
8.9%
도 893
 
8.9%
로 836
 
8.3%
구 380
 
3.8%
양 246
 
2.4%
안 217
 
2.2%
길 180
 
1.8%
원 173
 
1.7%
Other values (238) 4450
44.2%
Common
ValueCountFrequency (%)
3100
53.3%
1 555
 
9.6%
2 372
 
6.4%
3 320
 
5.5%
6 217
 
3.7%
5 214
 
3.7%
4 211
 
3.6%
0 198
 
3.4%
7 187
 
3.2%
8 185
 
3.2%
Other values (2) 252
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10074
63.4%
ASCII 5811
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3100
53.3%
1 555
 
9.6%
2 372
 
6.4%
3 320
 
5.5%
6 217
 
3.7%
5 214
 
3.7%
4 211
 
3.6%
0 198
 
3.4%
7 187
 
3.2%
8 185
 
3.2%
Other values (2) 252
 
4.3%
Hangul
ValueCountFrequency (%)
경 910
 
9.0%
시 895
 
8.9%
기 894
 
8.9%
도 893
 
8.9%
로 836
 
8.3%
구 380
 
3.8%
양 246
 
2.4%
안 217
 
2.2%
길 180
 
1.8%
원 173
 
1.7%
Other values (238) 4450
44.2%

소재지지번주소
Text

UNIQUE 

Distinct885
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2023-12-11T06:25:39.499168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length43
Mean length28.919774
Min length14

Characters and Unicode

Total characters25594
Distinct characters390
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique885 ?
Unique (%)100.0%

Sample

1st row경기도 남양주시 호평동 617-1번지 금강프라자 403호
2nd row경기도 광명시 광명동 158-85번지
3rd row경기도 남양주시 진접읍 장현리 350-1번지 우경메디프라자 8층 802호
4th row경기도 안양시 만안구 석수동 317-5번지 1층
5th row경기도 평택시 평택동 292-6번지 3층, 5층
ValueCountFrequency (%)
경기도 885
 
16.7%
2층 146
 
2.8%
수원시 96
 
1.8%
부천시 88
 
1.7%
3층 78
 
1.5%
고양시 67
 
1.3%
성남시 64
 
1.2%
용인시 46
 
0.9%
남양주시 46
 
0.9%
안양시 46
 
0.9%
Other values (1906) 3729
70.5%
2023-12-11T06:25:39.859048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4406
 
17.2%
지 952
 
3.7%
1 914
 
3.6%
기 914
 
3.6%
도 909
 
3.6%
시 904
 
3.5%
경 891
 
3.5%
번 883
 
3.5%
동 867
 
3.4%
2 855
 
3.3%
Other values (380) 13099
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14729
57.5%
Decimal Number 5501
 
21.5%
Space Separator 4406
 
17.2%
Dash Punctuation 693
 
2.7%
Other Punctuation 160
 
0.6%
Uppercase Letter 44
 
0.2%
Math Symbol 41
 
0.2%
Open Punctuation 9
 
< 0.1%
Close Punctuation 9
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 952
 
6.5%
기 914
 
6.2%
도 909
 
6.2%
시 904
 
6.1%
경 891
 
6.0%
번 883
 
6.0%
동 867
 
5.9%
호 412
 
2.8%
구 386
 
2.6%
층 363
 
2.5%
Other values (343) 7248
49.2%
Uppercase Letter
ValueCountFrequency (%)
B 6
13.6%
M 5
11.4%
H 4
9.1%
A 4
9.1%
S 4
9.1%
D 3
 
6.8%
C 3
 
6.8%
R 2
 
4.5%
N 2
 
4.5%
L 2
 
4.5%
Other values (7) 9
20.5%
Decimal Number
ValueCountFrequency (%)
1 914
16.6%
2 855
15.5%
3 754
13.7%
0 706
12.8%
4 548
10.0%
5 476
8.7%
6 360
 
6.5%
7 323
 
5.9%
8 311
 
5.7%
9 254
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 141
88.1%
. 17
 
10.6%
/ 2
 
1.2%
Space Separator
ValueCountFrequency (%)
4406
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 693
100.0%
Math Symbol
ValueCountFrequency (%)
~ 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14729
57.5%
Common 10819
42.3%
Latin 46
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 952
 
6.5%
기 914
 
6.2%
도 909
 
6.2%
시 904
 
6.1%
경 891
 
6.0%
번 883
 
6.0%
동 867
 
5.9%
호 412
 
2.8%
구 386
 
2.6%
층 363
 
2.5%
Other values (343) 7248
49.2%
Latin
ValueCountFrequency (%)
B 6
13.0%
M 5
10.9%
H 4
 
8.7%
A 4
 
8.7%
S 4
 
8.7%
D 3
 
6.5%
C 3
 
6.5%
R 2
 
4.3%
N 2
 
4.3%
L 2
 
4.3%
Other values (9) 11
23.9%
Common
ValueCountFrequency (%)
4406
40.7%
1 914
 
8.4%
2 855
 
7.9%
3 754
 
7.0%
0 706
 
6.5%
- 693
 
6.4%
4 548
 
5.1%
5 476
 
4.4%
6 360
 
3.3%
7 323
 
3.0%
Other values (8) 784
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14729
57.5%
ASCII 10864
42.4%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4406
40.6%
1 914
 
8.4%
2 855
 
7.9%
3 754
 
6.9%
0 706
 
6.5%
- 693
 
6.4%
4 548
 
5.0%
5 476
 
4.4%
6 360
 
3.3%
7 323
 
3.0%
Other values (26) 829
 
7.6%
Hangul
ValueCountFrequency (%)
지 952
 
6.5%
기 914
 
6.2%
도 909
 
6.2%
시 904
 
6.1%
경 891
 
6.0%
번 883
 
6.0%
동 867
 
5.9%
호 412
 
2.8%
구 386
 
2.6%
층 363
 
2.5%
Other values (343) 7248
49.2%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct683
Distinct (%)77.3%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean14227.689
Minimum10011
Maximum18616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-11T06:25:39.998914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10011
5-th percentile10374
Q112099.5
median14316
Q316396.5
95-th percentile18135.9
Maximum18616
Range8605
Interquartile range (IQR)4297

Descriptive statistics

Standard deviation2460.7306
Coefficient of variation (CV)0.17295365
Kurtosis-1.1441282
Mean14227.689
Median Absolute Deviation (MAD)2140
Skewness-0.010929433
Sum12563049
Variance6055195.1
MonotonicityNot monotonic
2023-12-11T06:25:40.136433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15865 5
 
0.6%
10905 5
 
0.6%
15360 4
 
0.5%
13618 4
 
0.5%
14072 4
 
0.5%
10500 4
 
0.5%
10386 4
 
0.5%
17051 4
 
0.5%
17909 4
 
0.5%
11695 3
 
0.3%
Other values (673) 842
95.1%
ValueCountFrequency (%)
10011 1
 
0.1%
10067 1
 
0.1%
10071 1
 
0.1%
10073 1
 
0.1%
10080 1
 
0.1%
10082 1
 
0.1%
10083 3
0.3%
10088 1
 
0.1%
10097 2
0.2%
10101 2
0.2%
ValueCountFrequency (%)
18616 1
0.1%
18611 1
0.1%
18594 1
0.1%
18593 1
0.1%
18568 1
0.1%
18567 2
0.2%
18550 1
0.1%
18505 1
0.1%
18476 1
0.1%
18469 1
0.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct872
Distinct (%)98.9%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean37.443821
Minimum36.960846
Maximum38.091074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-11T06:25:40.277474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.960846
5-th percentile37.063352
Q137.293061
median37.430226
Q337.622936
95-th percentile37.78716
Maximum38.091074
Range1.1302284
Interquartile range (IQR)0.3298745

Descriptive statistics

Standard deviation0.21566678
Coefficient of variation (CV)0.0057597429
Kurtosis-0.3633546
Mean37.443821
Median Absolute Deviation (MAD)0.15283358
Skewness0.11095123
Sum33025.45
Variance0.046512161
MonotonicityNot monotonic
2023-12-11T06:25:40.431582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.7400599131 2
 
0.2%
37.3508865176 2
 
0.2%
37.3373682747 2
 
0.2%
37.630306106 2
 
0.2%
37.4080810704 2
 
0.2%
37.3576105787 2
 
0.2%
37.4572994371 2
 
0.2%
37.5037931018 2
 
0.2%
37.3021627847 2
 
0.2%
37.3915329856 2
 
0.2%
Other values (862) 862
97.4%
(Missing) 3
 
0.3%
ValueCountFrequency (%)
36.9608455198 1
0.1%
36.9787555958 1
0.1%
36.9806819927 1
0.1%
36.9847844212 1
0.1%
36.9876822644 1
0.1%
36.9891225123 1
0.1%
36.9912479331 1
0.1%
36.9912796065 1
0.1%
36.9917612086 1
0.1%
36.9918350692 1
0.1%
ValueCountFrequency (%)
38.0910739209 1
0.1%
38.0270087162 1
0.1%
38.0243987043 1
0.1%
37.9998349286 1
0.1%
37.9592303481 1
0.1%
37.9583733138 1
0.1%
37.9576250306 1
0.1%
37.9530792501 1
0.1%
37.9096325441 1
0.1%
37.9054371701 1
0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct872
Distinct (%)98.9%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean127.00242
Minimum126.58437
Maximum127.63728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-11T06:25:40.545799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.58437
5-th percentile126.74595
Q1126.8312
median127.01006
Q3127.12829
95-th percentile127.31368
Maximum127.63728
Range1.0529116
Interquartile range (IQR)0.29708665

Descriptive statistics

Standard deviation0.1921765
Coefficient of variation (CV)0.001513172
Kurtosis0.30082915
Mean127.00242
Median Absolute Deviation (MAD)0.14442774
Skewness0.55926959
Sum112016.14
Variance0.036931809
MonotonicityNot monotonic
2023-12-11T06:25:40.660623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0477635405 2
 
0.2%
127.109487783 2
 
0.2%
127.1157667203 2
 
0.2%
126.833248425 2
 
0.2%
127.2586735757 2
 
0.2%
126.9334110705 2
 
0.2%
127.1640680238 2
 
0.2%
126.7764369853 2
 
0.2%
127.0092247016 2
 
0.2%
126.9813602889 2
 
0.2%
Other values (862) 862
97.4%
(Missing) 3
 
0.3%
ValueCountFrequency (%)
126.5843682181 1
0.1%
126.5856269206 1
0.1%
126.6270138759 1
0.1%
126.6329200073 1
0.1%
126.635154405 1
0.1%
126.6612957364 1
0.1%
126.6629337542 1
0.1%
126.666513227 1
0.1%
126.6666969603 1
0.1%
126.6683606272 1
0.1%
ValueCountFrequency (%)
127.6372797797 1
0.1%
127.6363312817 1
0.1%
127.6334667866 1
0.1%
127.6319470185 1
0.1%
127.6310802657 1
0.1%
127.6297270919 1
0.1%
127.6279989608 1
0.1%
127.5952411602 1
0.1%
127.5934976499 1
0.1%
127.5901207834 1
0.1%

Interactions

2023-12-11T06:25:36.932413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:36.415471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:36.688057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:37.011502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:36.505824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:36.772602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:37.092752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:36.606979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:36.853996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:25:40.738298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명소재지우편번호WGS84위도WGS84경도
시군명1.0000.9920.9640.946
소재지우편번호0.9921.0000.9150.854
WGS84위도0.9640.9151.0000.690
WGS84경도0.9460.8540.6901.000
2023-12-11T06:25:40.811272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명
소재지우편번호1.000-0.9100.1310.921
WGS84위도-0.9101.000-0.1840.777
WGS84경도0.131-0.1841.0000.711
시군명0.9210.7770.7111.000

Missing values

2023-12-11T06:25:37.221809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:25:37.346379image/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.
2023-12-11T06:25:37.443405image/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

시군명기관명평가내역평가등급소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0남양주시365서울내과의원당뇨병양호경기도 남양주시 늘을3로 65-12경기도 남양주시 호평동 617-1번지 금강프라자 403호1214537.660241127.247882
1광명시365편안한내과의원당뇨병양호경기도 광명시 오리로 976경기도 광명시 광명동 158-85번지1422037.479514126.854881
2남양주시The서울아산내과의원당뇨병양호경기도 남양주시 진접읍 장현천로 28경기도 남양주시 진접읍 장현리 350-1번지 우경메디프라자 8층 802호1201137.719196127.183892
3안양시가톨릭성모내과의원당뇨병양호경기도 안양시 만안구 안양로 461-1경기도 안양시 만안구 석수동 317-5번지 1층1397037.411051126.910816
4평택시강남뷰베스트의원당뇨병양호경기도 평택시 중앙2로 25경기도 평택시 평택동 292-6번지 3층, 5층1790936.991761127.089856
5안산시강남수내과의원당뇨병양호경기도 안산시 상록구 석호로 288경기도 안산시 상록구 본오동 882-7번지1555237.293369126.862544
6안성시강내과의원당뇨병양호경기도 안성시 금산길 8경기도 안성시 연지동 5번지 2층1758737.011305127.270927
7화성시강내과의원당뇨병양호경기도 화성시 영통로61번길 6경기도 화성시 반월동 864-1번지 제일프라자1837737.235483127.061144
8부천시강내과의원당뇨병양호경기도 부천시 장말로 202경기도 부천시 상동 236-52번지 2층1461837.492686126.764327
9수원시강영찬가정의학과의원당뇨병양호경기도 수원시 장안구 창룡대로 185경기도 수원시 장안구 연무동 246-24번지1621937.292532127.030385
시군명기관명평가내역평가등급소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
875양평군김동우내과의원당뇨병양호경기도 양평군 양평읍 시민로 37경기도 양평군 양평읍 양근리 178-4번지 2층1255737.490542127.495724
876용인시김람훈내과의원당뇨병양호경기도 용인시 기흥구 구성로 93경기도 용인시 기흥구 언남동 336-3번지 언남프라자1691837.29453127.12008
877김포시김봉환내과의원당뇨병양호경기도 김포시 김포한강2로 110경기도 김포시 장기동 2055-3번지 센타프라자 303,304호1008837.639467126.671641
878군포시김성근내과의원당뇨병양호경기도 군포시 광정로 80경기도 군포시 산본동 1123-5번지 신원빌딩 404호1586537.360296126.929585
879안산시김성봉내과의원당뇨병양호경기도 안산시 상록구 용신로 393경기도 안산시 상록구 본오동 874-8번지 청암빌딩 210,304호1553237.301887126.865914
880고양시김소은내과의원당뇨병양호경기도 고양시 덕양구 충장로 140경기도 고양시 덕양구 행신동 943번지 3층 302,303호1049537.624738126.835202
881고양시김앤유내과의원당뇨병양호경기도 고양시 덕양구 권율대로 900경기도 고양시 덕양구 신원동 628번지 S프라자 3층1057137.663462126.890787
882성남시김영수내과의원당뇨병양호경기도 성남시 분당구 백현로 206경기도 성남시 분당구 정자동 102번지 한솔근린상가 101~103호1360737.36752127.118549
883부천시김영희의원당뇨병양호경기도 부천시 범안로 6경기도 부천시 괴안동 39-3번지1469637.480802126.805342
884광명시김완중내과의원당뇨병양호경기도 광명시 철산로 27경기도 광명시 철산동 245-4번지 명동프라자 3층1423637.476832126.868985