Overview

Dataset statistics

Number of variables13
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory113.1 B

Variable types

Text4
Numeric6
Categorical3

Alerts

영업상태명 has constant value ""Constant
의료기관종별명 has constant value ""Constant
진료과목내용 is highly imbalanced (50.2%)Imbalance
사업장명 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
소재지지번주소 has unique valuesUnique
소재지우편번호 has unique valuesUnique
WGS84위도 has unique valuesUnique
WGS84경도 has unique valuesUnique
연면적(㎡) has 7 (16.3%) zerosZeros

Reproduction

Analysis started2024-05-10 21:07:54.547897
Analysis finished2024-05-10 21:08:05.628009
Duration11.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct30
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-05-10T21:08:05.848592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0697674
Min length3

Characters and Unicode

Total characters132
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)51.2%

Sample

1st row가평군
2nd row고양시
3rd row고양시
4th row고양시
5th row과천시
ValueCountFrequency (%)
수원시 4
 
9.3%
고양시 3
 
7.0%
용인시 3
 
7.0%
부천시 3
 
7.0%
안양시 2
 
4.7%
평택시 2
 
4.7%
안산시 2
 
4.7%
성남시 2
 
4.7%
의정부시 1
 
2.3%
하남시 1
 
2.3%
Other values (20) 20
46.5%
2024-05-10T21:08:06.612335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 42
31.8%
양 8
 
6.1%
천 7
 
5.3%
안 5
 
3.8%
주 5
 
3.8%
수 4
 
3.0%
원 4
 
3.0%
남 4
 
3.0%
평 4
 
3.0%
성 4
 
3.0%
Other values (27) 45
34.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 42
31.8%
양 8
 
6.1%
천 7
 
5.3%
안 5
 
3.8%
주 5
 
3.8%
수 4
 
3.0%
원 4
 
3.0%
남 4
 
3.0%
평 4
 
3.0%
성 4
 
3.0%
Other values (27) 45
34.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 42
31.8%
양 8
 
6.1%
천 7
 
5.3%
안 5
 
3.8%
주 5
 
3.8%
수 4
 
3.0%
원 4
 
3.0%
남 4
 
3.0%
평 4
 
3.0%
성 4
 
3.0%
Other values (27) 45
34.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 42
31.8%
양 8
 
6.1%
천 7
 
5.3%
안 5
 
3.8%
주 5
 
3.8%
수 4
 
3.0%
원 4
 
3.0%
남 4
 
3.0%
평 4
 
3.0%
성 4
 
3.0%
Other values (27) 45
34.1%

사업장명
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-05-10T21:08:07.142871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length6
Mean length6.7674419
Min length5

Characters and Unicode

Total characters291
Distinct characters63
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

Unique43 ?
Unique (%)100.0%

Sample

1st row가평군보건소
2nd row일산서구보건소
3rd row고양시덕양구보건소
4th row고양시일산동구보건소
5th row과천시보건소
ValueCountFrequency (%)
가평군보건소 1
 
2.2%
상록수보건소 1
 
2.2%
안양시보건소 1
 
2.2%
만안보건과 1
 
2.2%
안양시 1
 
2.2%
동안구보건소 1
 
2.2%
양주시보건소 1
 
2.2%
양평군보건소 1
 
2.2%
여주시보건소 1
 
2.2%
오산시보건소 1
 
2.2%
Other values (36) 36
78.3%
2024-05-10T21:08:07.997833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
보 44
15.1%
건 44
15.1%
소 44
15.1%
시 30
 
10.3%
구 14
 
4.8%
양 8
 
2.7%
안 7
 
2.4%
수 5
 
1.7%
주 5
 
1.7%
평 5
 
1.7%
Other values (53) 85
29.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 288
99.0%
Space Separator 3
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 44
15.3%
건 44
15.3%
소 44
15.3%
시 30
 
10.4%
구 14
 
4.9%
양 8
 
2.8%
안 7
 
2.4%
수 5
 
1.7%
주 5
 
1.7%
평 5
 
1.7%
Other values (52) 82
28.5%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 288
99.0%
Common 3
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 44
15.3%
건 44
15.3%
소 44
15.3%
시 30
 
10.4%
구 14
 
4.9%
양 8
 
2.8%
안 7
 
2.4%
수 5
 
1.7%
주 5
 
1.7%
평 5
 
1.7%
Other values (52) 82
28.5%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 288
99.0%
ASCII 3
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
보 44
15.3%
건 44
15.3%
소 44
15.3%
시 30
 
10.4%
구 14
 
4.9%
양 8
 
2.8%
안 7
 
2.4%
수 5
 
1.7%
주 5
 
1.7%
평 5
 
1.7%
Other values (52) 82
28.5%
ASCII
ValueCountFrequency (%)
3
100.0%

인허가일자
Real number (ℝ)

Distinct38
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19869538
Minimum19620101
Maximum20110208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-05-10T21:08:08.352113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19620101
5-th percentile19620201
Q119810701
median19900317
Q319950204
95-th percentile20061162
Maximum20110208
Range490107
Interquartile range (IQR)139502.5

Descriptive statistics

Standard deviation145910.6
Coefficient of variation (CV)0.0073434322
Kurtosis-0.62913866
Mean19869538
Median Absolute Deviation (MAD)70110
Skewness-0.48641252
Sum8.5439012 × 108
Variance2.1289904 × 1010
MonotonicityNot monotonic
2024-05-10T21:08:08.773122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
19620101 3
 
7.0%
19621231 2
 
4.7%
20051025 2
 
4.7%
19810701 2
 
4.7%
19850125 1
 
2.3%
19881121 1
 
2.3%
19900317 1
 
2.3%
19630101 1
 
2.3%
19950314 1
 
2.3%
19900330 1
 
2.3%
Other values (28) 28
65.1%
ValueCountFrequency (%)
19620101 3
7.0%
19621101 1
 
2.3%
19621231 2
4.7%
19630101 1
 
2.3%
19650101 1
 
2.3%
19670101 1
 
2.3%
19800101 1
 
2.3%
19810701 2
4.7%
19830207 1
 
2.3%
19830707 1
 
2.3%
ValueCountFrequency (%)
20110208 1
2.3%
20070912 1
2.3%
20061221 1
2.3%
20060628 1
2.3%
20060101 1
2.3%
20051031 1
2.3%
20051025 2
4.7%
19980101 1
2.3%
19950314 1
2.3%
19950301 1
2.3%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
영업중
43 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 43
100.0%

Length

2024-05-10T21:08:09.263385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T21:08:09.640967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 43
100.0%

의료기관종별명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
보건소
43 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
보건소 43
100.0%

Length

2024-05-10T21:08:09.982514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T21:08:10.359079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보건소 43
100.0%

의료인수(명)
Real number (ℝ)

Distinct17
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1395349
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-05-10T21:08:10.809862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median5
Q312.5
95-th percentile23.7
Maximum45
Range44
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.8306323
Coefficient of variation (CV)1.0849063
Kurtosis6.2017441
Mean8.1395349
Median Absolute Deviation (MAD)4
Skewness2.1177726
Sum350
Variance77.980066
MonotonicityNot monotonic
2024-05-10T21:08:11.169586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 11
25.6%
5 5
11.6%
2 4
 
9.3%
4 3
 
7.0%
6 3
 
7.0%
12 3
 
7.0%
14 2
 
4.7%
15 2
 
4.7%
13 2
 
4.7%
10 1
 
2.3%
Other values (7) 7
16.3%
ValueCountFrequency (%)
1 11
25.6%
2 4
 
9.3%
3 1
 
2.3%
4 3
 
7.0%
5 5
11.6%
6 3
 
7.0%
10 1
 
2.3%
11 1
 
2.3%
12 3
 
7.0%
13 2
 
4.7%
ValueCountFrequency (%)
45 1
 
2.3%
26 1
 
2.3%
24 1
 
2.3%
21 1
 
2.3%
16 1
 
2.3%
15 2
4.7%
14 2
4.7%
13 2
4.7%
12 3
7.0%
11 1
 
2.3%

진료과목내용
Categorical

IMBALANCE 

Distinct7
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
보건소
31 
내과
7 
보건소, 한방내과, 소아치과, 내과
 
1
보건소, 내과
 
1
한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과, 한방응급과
 
1
Other values (2)
 
2

Length

Max length69
Median length3
Mean length5.0697674
Min length2

Unique

Unique5 ?
Unique (%)11.6%

Sample

1st row보건소
2nd row내과
3rd row내과
4th row보건소
5th row보건소

Common Values

ValueCountFrequency (%)
보건소 31
72.1%
내과 7
 
16.3%
보건소, 한방내과, 소아치과, 내과 1
 
2.3%
보건소, 내과 1
 
2.3%
한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과, 한방응급과 1
 
2.3%
내과, 한방내과 1
 
2.3%
한방내과, 내과 1
 
2.3%

Length

2024-05-10T21:08:11.651715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T21:08:12.047548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보건소 33
57.9%
내과 11
 
19.3%
한방내과 4
 
7.0%
소아치과 1
 
1.8%
한방부인과 1
 
1.8%
한방소아과 1
 
1.8%
한방안·이비인후·피부과 1
 
1.8%
한방신경정신과 1
 
1.8%
한방재활의학과 1
 
1.8%
사상체질과 1
 
1.8%
Other values (2) 2
 
3.5%

연면적(㎡)
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4752.7919
Minimum0
Maximum66011
Zeros7
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-05-10T21:08:12.535088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11252
median2420
Q34589.5
95-th percentile15682.4
Maximum66011
Range66011
Interquartile range (IQR)3337.5

Descriptive statistics

Standard deviation10281.925
Coefficient of variation (CV)2.1633443
Kurtosis31.536438
Mean4752.7919
Median Absolute Deviation (MAD)1463.75
Skewness5.3503577
Sum204370.05
Variance1.0571799 × 108
MonotonicityNot monotonic
2024-05-10T21:08:12.961977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 7
 
16.3%
1.0 2
 
4.7%
7289.0 1
 
2.3%
4837.0 1
 
2.3%
3017.71 1
 
2.3%
1756.2 1
 
2.3%
2420.0 1
 
2.3%
4342.0 1
 
2.3%
2315.58 1
 
2.3%
6553.35 1
 
2.3%
Other values (26) 26
60.5%
ValueCountFrequency (%)
0.0 7
16.3%
1.0 2
 
4.7%
956.25 1
 
2.3%
1089.0 1
 
2.3%
1415.0 1
 
2.3%
1658.0 1
 
2.3%
1755.75 1
 
2.3%
1756.2 1
 
2.3%
2002.85 1
 
2.3%
2054.91 1
 
2.3%
ValueCountFrequency (%)
66011.0 1
2.3%
18644.0 1
2.3%
16615.0 1
2.3%
7289.0 1
2.3%
6553.35 1
2.3%
5915.59 1
2.3%
5675.2 1
2.3%
5573.2 1
2.3%
5537.55 1
2.3%
5328.0 1
2.3%

소재지도로명주소
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-05-10T21:08:13.541501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length32
Mean length25.255814
Min length19

Characters and Unicode

Total characters1086
Distinct characters139
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

Unique43 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 가평읍 가화로 155-18
2nd row경기도 고양시 일산서구 일중로 54 (일산동)
3rd row경기도 고양시 덕양구 원당로33번길 28 (주교동)
4th row경기도 고양시 일산동구 중앙로 1228 (마두동,KT고양지사 일산동구보건소)
5th row경기도 과천시 관문로 69 (중앙동)
ValueCountFrequency (%)
경기도 43
 
17.9%
수원시 4
 
1.7%
고양시 3
 
1.2%
부천시 3
 
1.2%
용인시 3
 
1.2%
중앙로 2
 
0.8%
안산시 2
 
0.8%
기흥구 2
 
0.8%
13 2
 
0.8%
성남시 2
 
0.8%
Other values (172) 174
72.5%
2024-05-10T21:08:14.395777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
197
 
18.1%
기 48
 
4.4%
경 47
 
4.3%
시 45
 
4.1%
동 45
 
4.1%
도 44
 
4.1%
로 43
 
4.0%
) 40
 
3.7%
( 40
 
3.7%
1 35
 
3.2%
Other values (129) 502
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 657
60.5%
Space Separator 197
 
18.1%
Decimal Number 138
 
12.7%
Close Punctuation 40
 
3.7%
Open Punctuation 40
 
3.7%
Other Punctuation 10
 
0.9%
Dash Punctuation 2
 
0.2%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 48
 
7.3%
경 47
 
7.2%
시 45
 
6.8%
동 45
 
6.8%
도 44
 
6.7%
로 43
 
6.5%
구 24
 
3.7%
양 14
 
2.1%
산 13
 
2.0%
안 12
 
1.8%
Other values (111) 322
49.0%
Decimal Number
ValueCountFrequency (%)
1 35
25.4%
5 20
14.5%
3 17
12.3%
2 16
11.6%
4 10
 
7.2%
6 10
 
7.2%
0 9
 
6.5%
8 9
 
6.5%
9 7
 
5.1%
7 5
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 9
90.0%
. 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%
Space Separator
ValueCountFrequency (%)
197
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 657
60.5%
Common 427
39.3%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 48
 
7.3%
경 47
 
7.2%
시 45
 
6.8%
동 45
 
6.8%
도 44
 
6.7%
로 43
 
6.5%
구 24
 
3.7%
양 14
 
2.1%
산 13
 
2.0%
안 12
 
1.8%
Other values (111) 322
49.0%
Common
ValueCountFrequency (%)
197
46.1%
) 40
 
9.4%
( 40
 
9.4%
1 35
 
8.2%
5 20
 
4.7%
3 17
 
4.0%
2 16
 
3.7%
4 10
 
2.3%
6 10
 
2.3%
, 9
 
2.1%
Other values (6) 33
 
7.7%
Latin
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 657
60.5%
ASCII 429
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
197
45.9%
) 40
 
9.3%
( 40
 
9.3%
1 35
 
8.2%
5 20
 
4.7%
3 17
 
4.0%
2 16
 
3.7%
4 10
 
2.3%
6 10
 
2.3%
, 9
 
2.1%
Other values (8) 35
 
8.2%
Hangul
ValueCountFrequency (%)
기 48
 
7.3%
경 47
 
7.2%
시 45
 
6.8%
동 45
 
6.8%
도 44
 
6.7%
로 43
 
6.5%
구 24
 
3.7%
양 14
 
2.1%
산 13
 
2.0%
안 12
 
1.8%
Other values (111) 322
49.0%

소재지지번주소
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-05-10T21:08:15.119548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length28
Mean length22.651163
Min length11

Characters and Unicode

Total characters974
Distinct characters125
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

Unique43 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 가평읍 읍내리 624번지 1호
2nd row경기도 고양시일산서구 일산동 1680번지 6호
3rd row경기도 고양시덕양구 주교동 603번지
4th row경기도 고양시 일산동구 마두1동 1010번지 KT고양지사 일산동구보건소
5th row경기도 과천시 중앙동 1번지 3호
ValueCountFrequency (%)
경기도 43
 
19.3%
1호 6
 
2.7%
3호 5
 
2.2%
수원시 4
 
1.8%
부천시 3
 
1.3%
송탄 2
 
0.9%
안양시 2
 
0.9%
성남시 2
 
0.9%
안산시 2
 
0.9%
평택시 2
 
0.9%
Other values (150) 152
68.2%
2024-05-10T21:08:16.144192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
181
18.6%
기 45
 
4.6%
시 45
 
4.6%
동 44
 
4.5%
경 44
 
4.5%
도 44
 
4.5%
지 42
 
4.3%
번 39
 
4.0%
1 38
 
3.9%
3 22
 
2.3%
Other values (115) 430
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 617
63.3%
Space Separator 181
 
18.6%
Decimal Number 167
 
17.1%
Dash Punctuation 4
 
0.4%
Uppercase Letter 2
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 45
 
7.3%
시 45
 
7.3%
동 44
 
7.1%
경 44
 
7.1%
도 44
 
7.1%
지 42
 
6.8%
번 39
 
6.3%
구 21
 
3.4%
호 18
 
2.9%
안 16
 
2.6%
Other values (98) 259
42.0%
Decimal Number
ValueCountFrequency (%)
1 38
22.8%
3 22
13.2%
5 20
12.0%
6 17
10.2%
2 15
 
9.0%
4 13
 
7.8%
8 13
 
7.8%
0 12
 
7.2%
9 10
 
6.0%
7 7
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%
Space Separator
ValueCountFrequency (%)
181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 617
63.3%
Common 355
36.4%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 45
 
7.3%
시 45
 
7.3%
동 44
 
7.1%
경 44
 
7.1%
도 44
 
7.1%
지 42
 
6.8%
번 39
 
6.3%
구 21
 
3.4%
호 18
 
2.9%
안 16
 
2.6%
Other values (98) 259
42.0%
Common
ValueCountFrequency (%)
181
51.0%
1 38
 
10.7%
3 22
 
6.2%
5 20
 
5.6%
6 17
 
4.8%
2 15
 
4.2%
4 13
 
3.7%
8 13
 
3.7%
0 12
 
3.4%
9 10
 
2.8%
Other values (5) 14
 
3.9%
Latin
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 617
63.3%
ASCII 357
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
181
50.7%
1 38
 
10.6%
3 22
 
6.2%
5 20
 
5.6%
6 17
 
4.8%
2 15
 
4.2%
4 13
 
3.6%
8 13
 
3.6%
0 12
 
3.4%
9 10
 
2.8%
Other values (7) 16
 
4.5%
Hangul
ValueCountFrequency (%)
기 45
 
7.3%
시 45
 
7.3%
동 44
 
7.1%
경 44
 
7.1%
도 44
 
7.1%
지 42
 
6.8%
번 39
 
6.3%
구 21
 
3.4%
호 18
 
2.9%
안 16
 
2.6%
Other values (98) 259
42.0%

소재지우편번호
Real number (ℝ)

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345389.42
Minimum11922
Maximum487804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-05-10T21:08:16.559029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11922
5-th percentile13501.8
Q1411061.5
median430824
Q3459670
95-th percentile481837.2
Maximum487804
Range475882
Interquartile range (IQR)48608.5

Descriptive statistics

Standard deviation185080.66
Coefficient of variation (CV)0.53586082
Kurtosis-0.34148879
Mean345389.42
Median Absolute Deviation (MAD)25446
Skewness-1.2643806
Sum14851745
Variance3.425485 × 1010
MonotonicityNot monotonic
2024-05-10T21:08:16.973829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
477805 1
 
2.3%
411310 1
 
2.3%
456270 1
 
2.3%
430824 1
 
2.3%
431811 1
 
2.3%
482040 1
 
2.3%
476806 1
 
2.3%
469101 1
 
2.3%
447800 1
 
2.3%
448554 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
11922 1
2.3%
12284 1
2.3%
13346 1
2.3%
14904 1
2.3%
15396 1
2.3%
16076 1
2.3%
16295 1
2.3%
16969 1
2.3%
17730 1
2.3%
17901 1
2.3%
ValueCountFrequency (%)
487804 1
2.3%
483130 1
2.3%
482040 1
2.3%
480012 1
2.3%
477805 1
2.3%
476806 1
2.3%
469101 1
2.3%
467802 1
2.3%
465701 1
2.3%
464802 1
2.3%

WGS84위도
Real number (ℝ)

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.437677
Minimum36.990862
Maximum37.900812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-05-10T21:08:17.374359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.990862
5-th percentile37.072135
Q137.29179
median37.416429
Q337.607499
95-th percentile37.828744
Maximum37.900812
Range0.90994967
Interquartile range (IQR)0.31570867

Descriptive statistics

Standard deviation0.22694733
Coefficient of variation (CV)0.0060620034
Kurtosis-0.35610367
Mean37.437677
Median Absolute Deviation (MAD)0.1438649
Skewness0.19991712
Sum1609.8201
Variance0.051505089
MonotonicityNot monotonic
2024-05-10T21:08:17.788092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
37.8334837912 1
 
2.3%
37.6844794692 1
 
2.3%
37.0001973079 1
 
2.3%
37.3858484345 1
 
2.3%
37.3929692686 1
 
2.3%
37.7860842773 1
 
2.3%
37.4970308152 1
 
2.3%
37.2945968982 1
 
2.3%
37.1590686601 1
 
2.3%
37.3219745051 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
36.9908620219 1
2.3%
37.0001973079 1
2.3%
37.0657848904 1
2.3%
37.1292813141 1
2.3%
37.1590686601 1
2.3%
37.2407024165 1
2.3%
37.2568958654 1
2.3%
37.2587409803 1
2.3%
37.27150183 1
2.3%
37.2725644668 1
2.3%
ValueCountFrequency (%)
37.9008116895 1
2.3%
37.8966147652 1
2.3%
37.8334837912 1
2.3%
37.7860842773 1
2.3%
37.7563478092 1
2.3%
37.7361435376 1
2.3%
37.6844794692 1
2.3%
37.6578291434 1
2.3%
37.6559819175 1
2.3%
37.623745773 1
2.3%

WGS84경도
Real number (ℝ)

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.0397
Minimum126.72298
Maximum127.64041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-05-10T21:08:18.172547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72298
5-th percentile126.77582
Q1126.8616
median127.03958
Q3127.14456
95-th percentile127.48327
Maximum127.64041
Range0.91743379
Interquartile range (IQR)0.28295847

Descriptive statistics

Standard deviation0.21656579
Coefficient of variation (CV)0.0017047095
Kurtosis0.56031953
Mean127.0397
Median Absolute Deviation (MAD)0.13501803
Skewness0.81853566
Sum5462.7071
Variance0.04690074
MonotonicityNot monotonic
2024-05-10T21:08:18.555362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
127.5106028489 1
 
2.3%
126.775785416 1
 
2.3%
127.2698253514 1
 
2.3%
126.9329501354 1
 
2.3%
126.9520679152 1
 
2.3%
127.0461998949 1
 
2.3%
127.4867072092 1
 
2.3%
127.6404126303 1
 
2.3%
127.0778607261 1
 
2.3%
127.0973688191 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
126.7229788379 1
2.3%
126.7739160993 1
2.3%
126.775785416 1
2.3%
126.7761530044 1
2.3%
126.7809633628 1
2.3%
126.7852958731 1
2.3%
126.7960281955 1
2.3%
126.7999425034 1
2.3%
126.8152687237 1
2.3%
126.8310296898 1
2.3%
ValueCountFrequency (%)
127.6404126303 1
2.3%
127.5106028489 1
2.3%
127.4867072092 1
2.3%
127.452368203 1
2.3%
127.2698253514 1
2.3%
127.250724877 1
2.3%
127.2145359087 1
2.3%
127.2012929149 1
2.3%
127.1792150942 1
2.3%
127.171958185 1
2.3%

Interactions

2024-05-10T21:08:03.096797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:55.818339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:57.340472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:58.737113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:00.059272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:01.773512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:03.350168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:56.087474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:57.585749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:58.997070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:00.407849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:01.979361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:03.585430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:56.352655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:57.821459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:59.247941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:00.645632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:02.184581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:03.825240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:56.607596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:58.066958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:59.462323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:00.900821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:02.396939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:04.069596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:56.852072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:58.272416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:59.666770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:01.138515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:02.628526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:04.304607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:57.093542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:58.505436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:07:59.858133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:01.585121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:08:02.859943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T21:08:18.816068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자의료인수(명)진료과목내용연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0001.0000.8910.6590.7550.0001.0001.0000.8220.9940.983
사업장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인허가일자0.8911.0001.0000.3270.0000.0001.0001.0000.0000.0000.478
의료인수(명)0.6591.0000.3271.0000.0000.7261.0001.0000.4660.0000.239
진료과목내용0.7551.0000.0000.0001.0000.0001.0001.0000.0000.3940.000
연면적(㎡)0.0001.0000.0000.7260.0001.0001.0001.0000.1640.0000.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지우편번호0.8221.0000.0000.4660.0000.1641.0001.0001.0000.5630.835
WGS84위도0.9941.0000.0000.0000.3940.0001.0001.0000.5631.0000.000
WGS84경도0.9831.0000.4780.2390.0000.0001.0001.0000.8350.0001.000
2024-05-10T21:08:19.368708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자의료인수(명)연면적(㎡)소재지우편번호WGS84위도WGS84경도진료과목내용
인허가일자1.0000.0140.105-0.126-0.172-0.0510.000
의료인수(명)0.0141.0000.1570.2260.093-0.0110.000
연면적(㎡)0.1050.1571.000-0.219-0.217-0.2400.000
소재지우편번호-0.1260.226-0.2191.0000.0950.4750.007
WGS84위도-0.1720.093-0.2170.0951.000-0.1990.189
WGS84경도-0.051-0.011-0.2400.475-0.1991.0000.000
진료과목내용0.0000.0000.0000.0070.1890.0001.000

Missing values

2024-05-10T21:08:04.823471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T21:08:05.444412image/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.

Sample

시군명사업장명인허가일자영업상태명의료기관종별명의료인수(명)진료과목내용연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군가평군보건소19621231영업중보건소14보건소1755.75경기도 가평군 가평읍 가화로 155-18경기도 가평군 가평읍 읍내리 624번지 1호47780537.833484127.510603
1고양시일산서구보건소19800101영업중보건소1내과1.0경기도 고양시 일산서구 일중로 54 (일산동)경기도 고양시일산서구 일산동 1680번지 6호41131037.684479126.775785
2고양시고양시덕양구보건소19830207영업중보건소5내과1.0경기도 고양시 덕양구 원당로33번길 28 (주교동)경기도 고양시덕양구 주교동 603번지41281237.657829126.83103
3고양시고양시일산동구보건소20060628영업중보건소5보건소3063.57경기도 고양시 일산동구 중앙로 1228 (마두동,KT고양지사 일산동구보건소)경기도 고양시 일산동구 마두1동 1010번지 KT고양지사 일산동구보건소41081337.655982126.776153
4과천시과천시보건소19980101영업중보건소3보건소0.0경기도 과천시 관문로 69 (중앙동)경기도 과천시 중앙동 1번지 3호42771437.430243126.986779
5광명시광명시보건소19830707영업중보건소15보건소, 한방내과, 소아치과, 내과3138.0경기도 광명시 오리로 613 (하안동, 광명시보건소)경기도 광명시 하안1동 230번지 ,광명시보건소42385137.455411126.878165
6광주시광주시보건소19620101영업중보건소1보건소2122.0경기도 광주시 파발로 194 (경안동)경기도 광주시 경안동 11546480237.416429127.250725
7구리시구리시보건소19880102영업중보건소4보건소, 내과0.0경기도 구리시 건원대로34번길 84 (인창동, 구리보건소)경기도 구리시 인창동 674번지 3호1192237.604751127.145075
8군포시군포시보건소19900309영업중보건소4보건소5573.2경기도 군포시 군포로 221 (부곡동)경기도 군포시 부곡동 770-143502037.333165126.925975
9김포시김포시보건소19620101영업중보건소6내과66011.0경기도 김포시 사우중로 108 (사우동)경기도 김포시 사우동 869번지 김포보건소41573037.623746126.722979
시군명사업장명인허가일자영업상태명의료기관종별명의료인수(명)진료과목내용연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
33용인시기흥구보건소20051025영업중보건소45보건소7289.0경기도 용인시 기흥구 신갈로58번길 11 (신갈동, 기흥구 보건소)경기도 용인시 기흥구 구갈동 352번지 3호1696937.272564127.106678
34의왕시의왕시보건소19900330영업중보건소5보건소5328.0경기도 의왕시 오봉로 34 (고천동, 의왕시보건소)경기도 의왕시 고천동1607637.343791126.972013
35의정부시의정부시보건소19950314영업중보건소4한방내과, 내과0.0경기도 의정부시 범골로 131 (의정부동)경기도 의정부시 의정부동 516번지48001237.736144127.039582
36이천시이천시보건소19620101영업중보건소1보건소2183.4경기도 이천시 증신로153번길 13 (증포동)경기도 이천시 증포동 152번지 2호46780237.288983127.452368
37파주시파주시보건소19630101영업중보건소26보건소2054.91경기도 파주시 후곡로 13 (금촌동)경기도 파주시 금촌동 953번지 1호41382637.756348126.773916
38평택시평택시평택보건소19881121영업중보건소1내과0.0경기도 평택시 평택5로 56 (비전동)경기도 평택시 비전동 8501790136.990862127.111977
39평택시평택시 송탄보건소19810701영업중보건소5내과1415.0경기도 평택시 서정로 295 (이충동)경기도 평택시 이충동 10번지 24호 송탄 보건복지센터 및 송탄 보건소1773037.065785127.0665
40포천시포천시보건소19850125영업중보건소16보건소3294.0경기도 포천시 포천로 1612 (신읍동)경기도 포천시 신읍동 164번지 3호48780437.900812127.201293
41하남시하남시보건소19900317영업중보건소15보건소1658.0경기도 하남시 대청로 10 (신장동)경기도 하남시 신장동 520번지46570137.539276127.214536
42화성시화성시보건소19621101영업중보건소14보건소2002.85경기도 화성시 향남읍 3.1만세로 1055경기도 화성시 향남읍 발안리 309번지44592337.129281126.904564