Overview

Dataset statistics

Number of variables9
Number of observations1415
Missing cells27
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory103.8 KiB
Average record size in memory75.1 B

Variable types

Categorical3
Text3
Numeric3

Dataset

Description병원평가정보(질병-고혈압) 현황
Author건강보험심사평가원
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=9NVBI7Y204P9ZV6TEPHF21368490&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 21 (1.5%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:55:21.988925
Analysis finished2023-12-10 22:55:24.093381
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
수원시
129 
성남시
114 
부천시
111 
고양시
100 
용인시
90 
Other values (26)
871 

Length

Max length4
Median length3
Mean length3.0897527
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포천시
2nd row양평군
3rd row남양주시
4th row안양시
5th row부천시

Common Values

ValueCountFrequency (%)
수원시 129
 
9.1%
성남시 114
 
8.1%
부천시 111
 
7.8%
고양시 100
 
7.1%
용인시 90
 
6.4%
안양시 78
 
5.5%
안산시 73
 
5.2%
남양주시 62
 
4.4%
의정부시 58
 
4.1%
화성시 57
 
4.0%
Other values (21) 543
38.4%

Length

2023-12-11T07:55:24.174772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 129
 
9.1%
성남시 114
 
8.1%
부천시 111
 
7.8%
고양시 100
 
7.1%
용인시 90
 
6.4%
안양시 78
 
5.5%
안산시 73
 
5.2%
남양주시 62
 
4.4%
의정부시 58
 
4.1%
화성시 57
 
4.0%
Other values (21) 543
38.4%

기관명
Text

Distinct1158
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2023-12-11T07:55:24.394819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length7.0508834
Min length3

Characters and Unicode

Total characters9977
Distinct characters375
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1037 ?
Unique (%)73.3%

Sample

1st row김내과의원
2nd row김동우내과의원
3rd row김마취통증의학과의원
4th row김병수내과의원
5th row김서영의원
ValueCountFrequency (%)
서울내과의원 12
 
0.8%
연세내과의원 12
 
0.8%
속편한내과의원 10
 
0.7%
정내과의원 9
 
0.6%
연세가정의학과의원 9
 
0.6%
김내과의원 7
 
0.5%
한사랑의원 7
 
0.5%
우리내과의원 7
 
0.5%
서울가정의학과의원 6
 
0.4%
서울의원 6
 
0.4%
Other values (1158) 1346
94.1%
2023-12-11T07:55:24.800074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 1566
 
15.7%
원 1436
 
14.4%
과 945
 
9.5%
내 653
 
6.5%
정 249
 
2.5%
성 175
 
1.8%
가 173
 
1.7%
연 156
 
1.6%
한 150
 
1.5%
세 142
 
1.4%
Other values (365) 4332
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9864
98.9%
Decimal Number 56
 
0.6%
Uppercase Letter 19
 
0.2%
Space Separator 16
 
0.2%
Close Punctuation 7
 
0.1%
Open Punctuation 6
 
0.1%
Lowercase Letter 5
 
0.1%
Other Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 1566
 
15.9%
원 1436
 
14.6%
과 945
 
9.6%
내 653
 
6.6%
정 249
 
2.5%
성 175
 
1.8%
가 173
 
1.8%
연 156
 
1.6%
한 150
 
1.5%
세 142
 
1.4%
Other values (343) 4219
42.8%
Uppercase Letter
ValueCountFrequency (%)
S 6
31.6%
D 3
15.8%
O 3
15.8%
K 3
15.8%
E 1
 
5.3%
I 1
 
5.3%
W 1
 
5.3%
T 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
3 14
25.0%
5 14
25.0%
6 13
23.2%
1 8
14.3%
2 7
12.5%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
r 2
40.0%
h 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
· 1
25.0%
& 1
25.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9864
98.9%
Common 89
 
0.9%
Latin 24
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 1566
 
15.9%
원 1436
 
14.6%
과 945
 
9.6%
내 653
 
6.6%
정 249
 
2.5%
성 175
 
1.8%
가 173
 
1.8%
연 156
 
1.6%
한 150
 
1.5%
세 142
 
1.4%
Other values (343) 4219
42.8%
Common
ValueCountFrequency (%)
16
18.0%
3 14
15.7%
5 14
15.7%
6 13
14.6%
1 8
9.0%
) 7
7.9%
2 7
7.9%
( 6
 
6.7%
. 2
 
2.2%
· 1
 
1.1%
Latin
ValueCountFrequency (%)
S 6
25.0%
D 3
12.5%
O 3
12.5%
K 3
12.5%
e 2
 
8.3%
r 2
 
8.3%
E 1
 
4.2%
I 1
 
4.2%
W 1
 
4.2%
h 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9864
98.9%
ASCII 112
 
1.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 1566
 
15.9%
원 1436
 
14.6%
과 945
 
9.6%
내 653
 
6.6%
정 249
 
2.5%
성 175
 
1.8%
가 173
 
1.8%
연 156
 
1.6%
한 150
 
1.5%
세 142
 
1.4%
Other values (343) 4219
42.8%
ASCII
ValueCountFrequency (%)
16
14.3%
3 14
12.5%
5 14
12.5%
6 13
11.6%
1 8
7.1%
) 7
 
6.2%
2 7
 
6.2%
S 6
 
5.4%
( 6
 
5.4%
D 3
 
2.7%
Other values (11) 18
16.1%
None
ValueCountFrequency (%)
· 1
100.0%

평가내역
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
고혈압
1415 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고혈압 1415
100.0%

Length

2023-12-11T07:55:24.946136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:55:25.035408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고혈압 1415
100.0%

평가등급
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
양호
1415 

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 1415
100.0%

Length

2023-12-11T07:55:25.150466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:55:25.279577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양호 1415
100.0%

소재지도로명주소
Text

MISSING 

Distinct1371
Distinct (%)98.4%
Missing21
Missing (%)1.5%
Memory size11.2 KiB
2023-12-11T07:55:25.578584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length18.319225
Min length13

Characters and Unicode

Total characters25537
Distinct characters286
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

Unique1348 ?
Unique (%)96.7%

Sample

1st row경기도 포천시 소흘읍 솔모루로 72
2nd row경기도 양평군 양평읍 시민로 37
3rd row경기도 남양주시 평내로29번길 49
4th row경기도 안양시 만안구 석천로 181
5th row경기도 부천시 원미로 143
ValueCountFrequency (%)
경기도 1394
 
21.9%
수원시 127
 
2.0%
성남시 113
 
1.8%
부천시 109
 
1.7%
고양시 97
 
1.5%
용인시 88
 
1.4%
안양시 77
 
1.2%
안산시 71
 
1.1%
남양주시 60
 
0.9%
의정부시 58
 
0.9%
Other values (1463) 4184
65.6%
2023-12-11T07:55:26.059895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4984
19.5%
경 1458
 
5.7%
기 1450
 
5.7%
시 1435
 
5.6%
도 1431
 
5.6%
로 1337
 
5.2%
1 862
 
3.4%
2 612
 
2.4%
구 611
 
2.4%
3 503
 
2.0%
Other values (276) 10854
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16196
63.4%
Space Separator 4984
 
19.5%
Decimal Number 4227
 
16.6%
Dash Punctuation 130
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 1458
 
9.0%
기 1450
 
9.0%
시 1435
 
8.9%
도 1431
 
8.8%
로 1337
 
8.3%
구 611
 
3.8%
양 394
 
2.4%
안 343
 
2.1%
길 294
 
1.8%
남 270
 
1.7%
Other values (264) 7173
44.3%
Decimal Number
ValueCountFrequency (%)
1 862
20.4%
2 612
14.5%
3 503
11.9%
5 359
8.5%
4 345
8.2%
0 337
 
8.0%
7 331
 
7.8%
6 317
 
7.5%
8 294
 
7.0%
9 267
 
6.3%
Space Separator
ValueCountFrequency (%)
4984
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16196
63.4%
Common 9341
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 1458
 
9.0%
기 1450
 
9.0%
시 1435
 
8.9%
도 1431
 
8.8%
로 1337
 
8.3%
구 611
 
3.8%
양 394
 
2.4%
안 343
 
2.1%
길 294
 
1.8%
남 270
 
1.7%
Other values (264) 7173
44.3%
Common
ValueCountFrequency (%)
4984
53.4%
1 862
 
9.2%
2 612
 
6.6%
3 503
 
5.4%
5 359
 
3.8%
4 345
 
3.7%
0 337
 
3.6%
7 331
 
3.5%
6 317
 
3.4%
8 294
 
3.1%
Other values (2) 397
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16196
63.4%
ASCII 9341
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4984
53.4%
1 862
 
9.2%
2 612
 
6.6%
3 503
 
5.4%
5 359
 
3.8%
4 345
 
3.7%
0 337
 
3.6%
7 331
 
3.5%
6 317
 
3.4%
8 294
 
3.1%
Other values (2) 397
 
4.3%
Hangul
ValueCountFrequency (%)
경 1458
 
9.0%
기 1450
 
9.0%
시 1435
 
8.9%
도 1431
 
8.8%
로 1337
 
8.3%
구 611
 
3.8%
양 394
 
2.4%
안 343
 
2.1%
길 294
 
1.8%
남 270
 
1.7%
Other values (264) 7173
44.3%

소재지지번주소
Text

Distinct1412
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2023-12-11T07:55:26.413776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length48
Mean length29.223322
Min length14

Characters and Unicode

Total characters41351
Distinct characters432
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

Unique1409 ?
Unique (%)99.6%

Sample

1st row경기도 포천시 소흘읍 송우리 172-2번지
2nd row경기도 양평군 양평읍 양근리 178-4번지 2층
3rd row경기도 남양주시 평내동 579-3번지 엠투프라자 301호
4th row경기도 안양시 만안구 석수동 289-8번지
5th row경기도 부천시 원미동 54-25번지 2층
ValueCountFrequency (%)
경기도 1415
 
16.6%
2층 270
 
3.2%
수원시 129
 
1.5%
3층 120
 
1.4%
성남시 114
 
1.3%
부천시 111
 
1.3%
고양시 100
 
1.2%
용인시 90
 
1.1%
안양시 78
 
0.9%
안산시 73
 
0.9%
Other values (2843) 6038
70.7%
2023-12-11T07:55:26.936862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7123
 
17.2%
지 1526
 
3.7%
기 1477
 
3.6%
1 1476
 
3.6%
2 1457
 
3.5%
시 1447
 
3.5%
도 1445
 
3.5%
경 1433
 
3.5%
번 1416
 
3.4%
동 1367
 
3.3%
Other values (422) 21184
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23669
57.2%
Decimal Number 8958
 
21.7%
Space Separator 7123
 
17.2%
Dash Punctuation 1097
 
2.7%
Other Punctuation 281
 
0.7%
Uppercase Letter 103
 
0.2%
Math Symbol 80
 
0.2%
Open Punctuation 18
 
< 0.1%
Close Punctuation 18
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 1526
 
6.4%
기 1477
 
6.2%
시 1447
 
6.1%
도 1445
 
6.1%
경 1433
 
6.1%
번 1416
 
6.0%
동 1367
 
5.8%
호 641
 
2.7%
구 631
 
2.7%
층 616
 
2.6%
Other values (378) 11670
49.3%
Uppercase Letter
ValueCountFrequency (%)
A 15
14.6%
B 14
13.6%
C 10
 
9.7%
S 6
 
5.8%
E 6
 
5.8%
R 5
 
4.9%
L 5
 
4.9%
K 5
 
4.9%
T 5
 
4.9%
D 4
 
3.9%
Other values (12) 28
27.2%
Decimal Number
ValueCountFrequency (%)
1 1476
16.5%
2 1457
16.3%
3 1227
13.7%
0 1156
12.9%
4 885
9.9%
5 736
8.2%
6 576
 
6.4%
7 527
 
5.9%
8 475
 
5.3%
9 443
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 255
90.7%
. 22
 
7.8%
/ 2
 
0.7%
@ 1
 
0.4%
& 1
 
0.4%
Space Separator
ValueCountFrequency (%)
7123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1097
100.0%
Math Symbol
ValueCountFrequency (%)
~ 80
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Letter Number
ValueCountFrequency (%)
Ⅲ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23669
57.2%
Common 17575
42.5%
Latin 107
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 1526
 
6.4%
기 1477
 
6.2%
시 1447
 
6.1%
도 1445
 
6.1%
경 1433
 
6.1%
번 1416
 
6.0%
동 1367
 
5.8%
호 641
 
2.7%
구 631
 
2.7%
층 616
 
2.6%
Other values (378) 11670
49.3%
Latin
ValueCountFrequency (%)
A 15
14.0%
B 14
13.1%
C 10
 
9.3%
S 6
 
5.6%
E 6
 
5.6%
R 5
 
4.7%
L 5
 
4.7%
K 5
 
4.7%
T 5
 
4.7%
D 4
 
3.7%
Other values (14) 32
29.9%
Common
ValueCountFrequency (%)
7123
40.5%
1 1476
 
8.4%
2 1457
 
8.3%
3 1227
 
7.0%
0 1156
 
6.6%
- 1097
 
6.2%
4 885
 
5.0%
5 736
 
4.2%
6 576
 
3.3%
7 527
 
3.0%
Other values (10) 1315
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23669
57.2%
ASCII 17680
42.8%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7123
40.3%
1 1476
 
8.3%
2 1457
 
8.2%
3 1227
 
6.9%
0 1156
 
6.5%
- 1097
 
6.2%
4 885
 
5.0%
5 736
 
4.2%
6 576
 
3.3%
7 527
 
3.0%
Other values (33) 1420
 
8.0%
Hangul
ValueCountFrequency (%)
지 1526
 
6.4%
기 1477
 
6.2%
시 1447
 
6.1%
도 1445
 
6.1%
경 1433
 
6.1%
번 1416
 
6.0%
동 1367
 
5.8%
호 641
 
2.7%
구 631
 
2.7%
층 616
 
2.6%
Other values (378) 11670
49.3%
Number Forms
ValueCountFrequency (%)
Ⅲ 2
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1015
Distinct (%)71.8%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean14153.984
Minimum10011
Maximum18611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T07:55:27.112173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10011
5-th percentile10331.2
Q112066
median14112
Q316362
95-th percentile18133.2
Maximum18611
Range8600
Interquartile range (IQR)4296

Descriptive statistics

Standard deviation2461.8506
Coefficient of variation (CV)0.1739334
Kurtosis-1.1322412
Mean14153.984
Median Absolute Deviation (MAD)2184
Skewness0.026035838
Sum19999580
Variance6060708.5
MonotonicityNot monotonic
2023-12-11T07:55:27.238489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14001 5
 
0.4%
15865 5
 
0.4%
17006 5
 
0.4%
10874 5
 
0.4%
10059 5
 
0.4%
12557 4
 
0.3%
10414 4
 
0.3%
16930 4
 
0.3%
10486 4
 
0.3%
10275 4
 
0.3%
Other values (1005) 1368
96.7%
ValueCountFrequency (%)
10011 2
 
0.1%
10018 1
 
0.1%
10019 2
 
0.1%
10039 1
 
0.1%
10040 1
 
0.1%
10059 5
0.4%
10060 1
 
0.1%
10067 1
 
0.1%
10071 3
0.2%
10073 2
 
0.1%
ValueCountFrequency (%)
18611 1
 
0.1%
18600 1
 
0.1%
18598 1
 
0.1%
18593 2
0.1%
18568 1
 
0.1%
18567 1
 
0.1%
18555 1
 
0.1%
18550 3
0.2%
18519 1
 
0.1%
18516 1
 
0.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct1390
Distinct (%)98.4%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean37.447456
Minimum36.961496
Maximum38.099192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T07:55:27.447138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.961496
5-th percentile37.081765
Q137.29606
median37.412457
Q337.62821
95-th percentile37.818558
Maximum38.099192
Range1.1376962
Interquartile range (IQR)0.33214916

Descriptive statistics

Standard deviation0.21556964
Coefficient of variation (CV)0.0057565899
Kurtosis-0.29587708
Mean37.447456
Median Absolute Deviation (MAD)0.13594185
Skewness0.2247858
Sum52913.255
Variance0.046470271
MonotonicityNot monotonic
2023-12-11T07:55:27.583905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.9982828223 2
 
0.1%
37.3639702274 2
 
0.1%
37.3576105787 2
 
0.1%
37.3373682747 2
 
0.1%
37.4745122029 2
 
0.1%
37.7263677442 2
 
0.1%
37.3231017192 2
 
0.1%
37.5411002724 2
 
0.1%
37.7438009676 2
 
0.1%
37.4080810704 2
 
0.1%
Other values (1380) 1393
98.4%
ValueCountFrequency (%)
36.9614956154 1
0.1%
36.9772202616 1
0.1%
36.9787555958 1
0.1%
36.9806819927 1
0.1%
36.9881523917 1
0.1%
36.9882692644 1
0.1%
36.9883012099 1
0.1%
36.9886155188 1
0.1%
36.9891225123 1
0.1%
36.9892771984 1
0.1%
ValueCountFrequency (%)
38.0991918519 1
0.1%
38.0910739209 1
0.1%
38.0905784202 1
0.1%
38.0898836317 1
0.1%
38.027602793 1
0.1%
38.0270087162 1
0.1%
38.0256395132 1
0.1%
38.0253738214 1
0.1%
38.0244204892 1
0.1%
38.0243987043 1
0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct1390
Distinct (%)98.4%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean127.00528
Minimum126.58256
Maximum127.66156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-11T07:55:27.720475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.58256
5-th percentile126.73907
Q1126.83595
median127.01634
Q3127.12773
95-th percentile127.31928
Maximum127.66156
Range1.0790021
Interquartile range (IQR)0.29177244

Descriptive statistics

Standard deviation0.19516355
Coefficient of variation (CV)0.001536657
Kurtosis0.46295203
Mean127.00528
Median Absolute Deviation (MAD)0.13886174
Skewness0.54825914
Sum179458.46
Variance0.038088813
MonotonicityNot monotonic
2023-12-11T07:55:27.929167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1596245736 2
 
0.1%
126.9306868421 2
 
0.1%
126.9334110705 2
 
0.1%
127.1157667203 2
 
0.1%
126.8665954646 2
 
0.1%
127.0545873719 2
 
0.1%
127.0948684138 2
 
0.1%
127.2160118288 2
 
0.1%
126.8082824681 2
 
0.1%
127.2586735757 2
 
0.1%
Other values (1380) 1393
98.4%
ValueCountFrequency (%)
126.5825555862 1
0.1%
126.5833826942 1
0.1%
126.5843682181 1
0.1%
126.5856269206 1
0.1%
126.5976057487 1
0.1%
126.5982817262 1
0.1%
126.5986406181 1
0.1%
126.6228753128 1
0.1%
126.623439457 1
0.1%
126.623719776 1
0.1%
ValueCountFrequency (%)
127.6615576994 1
0.1%
127.6439730063 1
0.1%
127.6425252062 1
0.1%
127.6386455655 1
0.1%
127.6372797797 1
0.1%
127.6367504612 1
0.1%
127.6363312817 1
0.1%
127.6358927578 1
0.1%
127.6356119984 1
0.1%
127.6355890297 1
0.1%

Interactions

2023-12-11T07:55:23.384299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:22.736403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:23.064254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:23.476861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:22.848543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:23.174869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:23.584983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:22.940078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:23.275362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:55:28.021120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명소재지우편번호WGS84위도WGS84경도
시군명1.0000.9920.9670.944
소재지우편번호0.9921.0000.9140.860
WGS84위도0.9670.9141.0000.688
WGS84경도0.9440.8600.6881.000
2023-12-11T07:55:28.108105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명
소재지우편번호1.000-0.9030.1350.929
WGS84위도-0.9031.000-0.1920.793
WGS84경도0.135-0.1921.0000.709
시군명0.9290.7930.7091.000

Missing values

2023-12-11T07:55:23.734861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:55:23.898061image/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.
2023-12-11T07:55:24.027324image/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
0포천시김내과의원고혈압양호경기도 포천시 소흘읍 솔모루로 72경기도 포천시 소흘읍 송우리 172-2번지1117437.827924127.146384
1양평군김동우내과의원고혈압양호경기도 양평군 양평읍 시민로 37경기도 양평군 양평읍 양근리 178-4번지 2층1255737.490542127.495724
2남양주시김마취통증의학과의원고혈압양호경기도 남양주시 평내로29번길 49경기도 남양주시 평내동 579-3번지 엠투프라자 301호1222337.645874127.235643
3안양시김병수내과의원고혈압양호경기도 안양시 만안구 석천로 181경기도 안양시 만안구 석수동 289-8번지1396837.412735126.908859
4부천시김서영의원고혈압양호경기도 부천시 원미로 143경기도 부천시 원미동 54-25번지 2층1456737.495475126.79145
5군포시김성근내과의원고혈압양호경기도 군포시 광정로 80경기도 군포시 산본동 1123-5번지 신원빌딩 404호1586537.360296126.929585
6안산시김성봉내과의원고혈압양호경기도 안산시 상록구 용신로 393경기도 안산시 상록구 본오동 874-8번지 청암빌딩 210,304호1553237.301887126.865914
7군포시김성진내과의원고혈압양호경기도 군포시 고산로 693경기도 군포시 산본동 1060-1번지 하나로빌딩 301, 302호1580237.371977126.935031
8용인시김앤서내과의원고혈압양호경기도 용인시 기흥구 동백3로11번길 8경기도 용인시 기흥구 중동 853-3번지 V프라자 201, 204호1700637.269628127.153591
9의정부시김연종내과의원고혈압양호경기도 의정부시 용민로7번길 5경기도 의정부시 용현동 407-9번지1178437.734535127.079552
시군명기관명평가내역평가등급소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
1405남양주시블루밍의원고혈압양호경기도 남양주시 진접읍 내각1로73번길 3경기도 남양주시 진접읍 내각리 747번지 백석빌딩 2층1207137.704623127.164736
1406화성시사단법인 공감직업환경의학센터 향남공감의원고혈압양호경기도 화성시 향남읍 행정서로3길 34-6경기도 화성시 향남읍 행정리 434-3번지 (3,4층)1859837.130749126.918726
1407시흥시사랑내과의원고혈압양호경기도 시흥시 배곧3로 80경기도 시흥시 배곧동 194-1번지 센터프라자 4층 402호1501037.370535126.729088
1408성남시사랑의내과소아청소년과의원고혈압양호경기도 성남시 중원구 둔촌대로 381경기도 성남시 중원구 상대원동 5430-6번지 2층1340237.43366127.158997
1409군포시사랑이가득한소아과의원고혈압양호경기도 군포시 금산로 98경기도 군포시 산본동 213-7번지 1층1580637.370725126.936972
1410군포시산본가정의학과의원고혈압양호경기도 군포시 고산로677번길 40경기도 군포시 산본동 1066-2번지 203호1580237.372839126.930631
1411용인시삼성SDI(주) 기흥부속의원고혈압양호경기도 용인시 기흥구 공세로 150-34경기도 용인시 기흥구 공세동 428-4번지 삼성물산(주) 1층1708437.237623127.111133
1412용인시삼성굿모닝의원고혈압양호경기도 용인시 수지구 손곡로 83경기도 용인시 수지구 동천동 873-3번지 동천타워 304호1682837.335968127.09323
1413화성시삼성내과의원고혈압양호경기도 화성시 동탄반석로 204경기도 화성시 반송동 88-1번지 제일프라자 6층 602호1845337.206654127.072829
1414고양시삼성내과의원고혈압양호경기도 고양시 일산서구 강선로 152경기도 고양시 일산서구 일산동 1088번지 두레빌딩 402호1036037.676475126.769853