Overview

Dataset statistics

Number of variables4
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory880.0 B
Average record size in memory40.0 B

Variable types

Categorical1
DateTime1
Text1
Numeric1

Dataset

Description의료급여 수급권자의 출산비 조견표로, 출산 구분 별 적용시작일자와 종료일자에 따른 지급액을 나타낸 내역. 컬럼명은 출산구분, 적용시작일자, 적용종료일자, 지급액
URLhttps://www.data.go.kr/data/15121293/fileData.do

Reproduction

Analysis started2023-12-12 22:15:56.336540
Analysis finished2023-12-12 22:15:56.647070
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

출산구분
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
1
11 
2
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 11
50.0%
2 11
50.0%

Length

2023-12-13T07:15:56.699838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:15:56.784293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 11
50.0%
2 11
50.0%

적용시작일자
Date

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum1988-02-15 00:00:00
Maximum1997-09-01 00:00:00
2023-12-13T07:15:56.856366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:56.936241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

적용종료일자
Text

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T07:15:57.108479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters220
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

Unique0 ?
Unique (%)0.0%

Sample

1st row1989-06-30
2nd row1990-01-31
3rd row1991-06-30
4th row1992-04-30
5th row1993-02-28
ValueCountFrequency (%)
1989-06-30 2
9.1%
1990-01-31 2
9.1%
1991-06-30 2
9.1%
1992-04-30 2
9.1%
1993-02-28 2
9.1%
1994-07-31 2
9.1%
1995-03-31 2
9.1%
1995-12-09 2
9.1%
1996-07-31 2
9.1%
1997-08-31 2
9.1%
2023-12-13T07:15:57.424002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 50
22.7%
- 44
20.0%
1 40
18.2%
0 28
12.7%
3 22
10.0%
2 10
 
4.5%
8 6
 
2.7%
6 6
 
2.7%
7 6
 
2.7%
4 4
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
80.0%
Dash Punctuation 44
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 50
28.4%
1 40
22.7%
0 28
15.9%
3 22
12.5%
2 10
 
5.7%
8 6
 
3.4%
6 6
 
3.4%
7 6
 
3.4%
4 4
 
2.3%
5 4
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 50
22.7%
- 44
20.0%
1 40
18.2%
0 28
12.7%
3 22
10.0%
2 10
 
4.5%
8 6
 
2.7%
6 6
 
2.7%
7 6
 
2.7%
4 4
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 50
22.7%
- 44
20.0%
1 40
18.2%
0 28
12.7%
3 22
10.0%
2 10
 
4.5%
8 6
 
2.7%
6 6
 
2.7%
7 6
 
2.7%
4 4
 
1.8%

지급액
Real number (ℝ)

Distinct16
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58372.727
Minimum46400
Maximum76400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T07:15:57.554004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46400
5-th percentile46400
Q150400
median57500
Q365000
95-th percentile70950
Maximum76400
Range30000
Interquartile range (IQR)14600

Descriptive statistics

Standard deviation8921.5193
Coefficient of variation (CV)0.15283712
Kurtosis-0.92926607
Mean58372.727
Median Absolute Deviation (MAD)7300
Skewness0.33871606
Sum1284200
Variance79593506
MonotonicityNot monotonic
2023-12-13T07:15:57.700613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
50400 3
13.6%
46400 3
13.6%
70000 2
 
9.1%
65000 2
 
9.1%
57000 1
 
4.5%
59800 1
 
4.5%
62000 1
 
4.5%
66000 1
 
4.5%
76400 1
 
4.5%
54000 1
 
4.5%
Other values (6) 6
27.3%
ValueCountFrequency (%)
46400 3
13.6%
50000 1
 
4.5%
50400 3
13.6%
53000 1
 
4.5%
54000 1
 
4.5%
55600 1
 
4.5%
57000 1
 
4.5%
58000 1
 
4.5%
59800 1
 
4.5%
61000 1
 
4.5%
ValueCountFrequency (%)
76400 1
4.5%
71000 1
4.5%
70000 2
9.1%
66000 1
4.5%
65000 2
9.1%
62000 1
4.5%
61000 1
4.5%
59800 1
4.5%
58000 1
4.5%
57000 1
4.5%

Interactions

2023-12-13T07:15:56.426134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:15:57.810132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출산구분적용시작일자적용종료일자지급액
출산구분1.0000.0000.0000.000
적용시작일자0.0001.0001.0000.000
적용종료일자0.0001.0001.0000.000
지급액0.0000.0000.0001.000
2023-12-13T07:15:57.935385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지급액출산구분
지급액1.0000.107
출산구분0.1071.000

Missing values

2023-12-13T07:15:56.542832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:15:56.619688image/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

출산구분적용시작일자적용종료일자지급액
011988-02-151989-06-3050400
111989-07-011990-01-3150400
211990-02-011991-06-3050400
311991-07-011992-04-3054000
411992-05-011993-02-2857000
511993-03-011994-07-3159800
611994-08-011995-03-3162000
711995-04-011995-12-0966000
811995-12-101996-07-3170000
911996-08-011997-08-3170000
출산구분적용시작일자적용종료일자지급액
1221989-07-011990-01-3146400
1321990-02-011991-06-3046400
1421991-07-011992-04-3050000
1521992-05-011993-02-2853000
1621993-03-011994-07-3155600
1721994-08-011995-03-3158000
1821995-04-011995-12-0961000
1921995-12-101996-07-3165000
2021996-08-011997-08-3165000
2121997-09-019999-12-3171000