Overview

Dataset statistics

Number of variables11
Number of observations24
Missing cells12
Missing cells (%)4.5%
Duplicate rows1
Duplicate rows (%)4.2%
Total size in memory2.2 KiB
Average record size in memory93.5 B

Variable types

Text1
Unsupported10

Dataset

Description2017~2021년 특정 질병코드분류별 진료 인원 현황 1. 수진기준(한의분류 제외, 약국 제외), 연령(연말기준) 2. 건강보험 급여실적(의료급여 제외)이며, 비급여는 제외 - 2022년 6월 지급분까지 반영 3. 아래 질병통계 자료는 요양기관에서 환자진료중 진단명이 확정되지 않은 상태에서의 호소, 증세 등에 따라 일차진단명을 부여하고 청구한 내역중 주진단명 기준으로 발췌한 것이므로 최종확정된 질병과는 다를수 있음 -주상병코드: M30, M31, M32, M33, M34, M35, M36
Author국민건강보험공단
URLhttps://www.data.go.kr/data/15103854/fileData.do

Alerts

Dataset has 1 (4.2%) duplicate rowsDuplicates
구분 has 2 (8.3%) missing valuesMissing
실수진자수(약국제외) has 1 (4.2%) missing valuesMissing
Unnamed: 2 has 1 (4.2%) missing valuesMissing
Unnamed: 3 has 1 (4.2%) missing valuesMissing
Unnamed: 4 has 1 (4.2%) missing valuesMissing
Unnamed: 5 has 1 (4.2%) missing valuesMissing
Unnamed: 6 has 1 (4.2%) missing valuesMissing
Unnamed: 7 has 1 (4.2%) missing valuesMissing
Unnamed: 8 has 1 (4.2%) missing valuesMissing
Unnamed: 9 has 1 (4.2%) missing valuesMissing
Unnamed: 10 has 1 (4.2%) missing valuesMissing
실수진자수(약국제외) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 14:32:38.837561
Analysis finished2023-12-12 14:32:39.387997
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing2
Missing (%)8.3%
Memory size324.0 B
2023-12-12T23:32:39.538600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.6363636
Min length2

Characters and Unicode

Total characters124
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row0세
2nd row1-4세
3rd row5-9세
4th row10-14세
5th row15-19세
ValueCountFrequency (%)
0세 1
 
4.5%
1-4세 1
 
4.5%
95-99세 1
 
4.5%
90-94세 1
 
4.5%
85-89세 1
 
4.5%
80-84세 1
 
4.5%
75-79세 1
 
4.5%
70-74세 1
 
4.5%
65-69세 1
 
4.5%
60-64세 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T23:32:39.929009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 22
17.7%
- 20
16.1%
4 14
11.3%
5 14
11.3%
9 14
11.3%
0 12
9.7%
1 6
 
4.8%
2 4
 
3.2%
3 4
 
3.2%
6 4
 
3.2%
Other values (4) 10
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
64.5%
Other Letter 24
 
19.4%
Dash Punctuation 20
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 14
17.5%
5 14
17.5%
9 14
17.5%
0 12
15.0%
1 6
7.5%
2 4
 
5.0%
3 4
 
5.0%
6 4
 
5.0%
7 4
 
5.0%
8 4
 
5.0%
Other Letter
ValueCountFrequency (%)
세 22
91.7%
이 1
 
4.2%
상 1
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100
80.6%
Hangul 24
 
19.4%

Most frequent character per script

Common
ValueCountFrequency (%)
- 20
20.0%
4 14
14.0%
5 14
14.0%
9 14
14.0%
0 12
12.0%
1 6
 
6.0%
2 4
 
4.0%
3 4
 
4.0%
6 4
 
4.0%
7 4
 
4.0%
Hangul
ValueCountFrequency (%)
세 22
91.7%
이 1
 
4.2%
상 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100
80.6%
Hangul 24
 
19.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 22
91.7%
이 1
 
4.2%
상 1
 
4.2%
ASCII
ValueCountFrequency (%)
- 20
20.0%
4 14
14.0%
5 14
14.0%
9 14
14.0%
0 12
12.0%
1 6
 
6.0%
2 4
 
4.0%
3 4
 
4.0%
6 4
 
4.0%
7 4
 
4.0%

실수진자수(약국제외)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.2%
Memory size324.0 B

Missing values

2023-12-12T23:32:38.932270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:32:39.096244image/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.
2023-12-12T23:32:39.264458image/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

구분실수진자수(약국제외)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
0<NA>2017년NaN2018년NaN2019년NaN2020년NaN2021년NaN
1<NA>남자여자남자여자남자여자남자여자남자여자
20세1021590971671870545678427615411
31-4세4620340449323612481636593054225023341638
45-9세1909144321131697223316141670120714941009
510-14세391402509487539563445499614648
615-19세4561093446101050610204519295781029
720-24세71421307102071748214268721437492203
825-29세904294387429739263227996331210783541
930-34세103338819763877103338841076386611494175
구분실수진자수(약국제외)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
1455-59세24149728243210095250410800253010614256610775
1560-64세201776002203838025019782263710557287311917
1665-69세1541513516515615180463082042716423438520
1770-74세1275368313753916148345041626488618275468
1875-79세984257011012859121431831277330414533756
1980-84세50811775621363649162068617948892092
2085-89세189439198483236533257631318808
2190-94세40844089571104813660174
2295-99세2971131689823
23100세이상NaN1NaN1NaN2NaN5NaN3

Duplicate rows

Most frequently occurring

구분# duplicates
0<NA>2