Overview

Dataset statistics

Number of variables4
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory38.7 B

Variable types

Numeric2
Text1
Categorical1

Dataset

Description광주광역시 북구 동별 전동보장구 소지자 현황(전동보장구 구입에 따른 의료급여 지원 대상자 현황 기반)에 대한 데이터로 소지자 행정동 소지인원 데이터 기준을등을 나타냅니다.
URLhttps://www.data.go.kr/data/15103240/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 has unique valuesUnique
행정동 has unique valuesUnique
소지인원 has 1 (3.6%) zerosZeros

Reproduction

Analysis started2023-12-12 05:35:58.917255
Analysis finished2023-12-12 05:35:59.412121
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T14:35:59.467833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2023-12-12T14:35:59.576496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

행정동
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T14:35:59.740844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3.5
Mean length3.4285714
Min length2

Characters and Unicode

Total characters96
Distinct characters33
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

Unique28 ?
Unique (%)100.0%

Sample

1st row중흥1동
2nd row중흥2동
3rd row중흥3동
4th row중앙동
5th row임동
ValueCountFrequency (%)
중흥1동 1
 
3.6%
중흥2동 1
 
3.6%
양산동 1
 
3.6%
건국동 1
 
3.6%
석곡동 1
 
3.6%
오치2동 1
 
3.6%
오치1동 1
 
3.6%
매곡동 1
 
3.6%
일곡동 1
 
3.6%
삼각동 1
 
3.6%
Other values (18) 18
64.3%
2023-12-12T14:36:00.023347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 29
30.2%
암 6
 
6.2%
1 5
 
5.2%
2 5
 
5.2%
흥 5
 
5.2%
중 4
 
4.2%
3 3
 
3.1%
곡 3
 
3.1%
두 3
 
3.1%
문 3
 
3.1%
Other values (23) 30
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
86.5%
Decimal Number 13
 
13.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 29
34.9%
암 6
 
7.2%
흥 5
 
6.0%
중 4
 
4.8%
곡 3
 
3.6%
두 3
 
3.6%
문 3
 
3.6%
운 3
 
3.6%
치 2
 
2.4%
오 2
 
2.4%
Other values (20) 23
27.7%
Decimal Number
ValueCountFrequency (%)
1 5
38.5%
2 5
38.5%
3 3
23.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
86.5%
Common 13
 
13.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 29
34.9%
암 6
 
7.2%
흥 5
 
6.0%
중 4
 
4.8%
곡 3
 
3.6%
두 3
 
3.6%
문 3
 
3.6%
운 3
 
3.6%
치 2
 
2.4%
오 2
 
2.4%
Other values (20) 23
27.7%
Common
ValueCountFrequency (%)
1 5
38.5%
2 5
38.5%
3 3
23.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
86.5%
ASCII 13
 
13.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 29
34.9%
암 6
 
7.2%
흥 5
 
6.0%
중 4
 
4.8%
곡 3
 
3.6%
두 3
 
3.6%
문 3
 
3.6%
운 3
 
3.6%
치 2
 
2.4%
오 2
 
2.4%
Other values (20) 23
27.7%
ASCII
ValueCountFrequency (%)
1 5
38.5%
2 5
38.5%
3 3
23.1%

소지인원
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.571429
Minimum0
Maximum112
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T14:36:00.351187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q312
95-th percentile66.4
Maximum112
Range112
Interquartile range (IQR)9

Descriptive statistics

Standard deviation25.456468
Coefficient of variation (CV)1.7470125
Kurtosis8.1541438
Mean14.571429
Median Absolute Deviation (MAD)2
Skewness2.8302976
Sum408
Variance648.03175
MonotonicityNot monotonic
2023-12-12T14:36:00.433279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 7
25.0%
4 4
14.3%
6 4
14.3%
2 3
10.7%
12 2
 
7.1%
15 1
 
3.6%
0 1
 
3.6%
38 1
 
3.6%
56 1
 
3.6%
112 1
 
3.6%
Other values (3) 3
10.7%
ValueCountFrequency (%)
0 1
 
3.6%
2 3
10.7%
3 7
25.0%
4 4
14.3%
6 4
14.3%
7 1
 
3.6%
12 2
 
7.1%
15 1
 
3.6%
17 1
 
3.6%
38 1
 
3.6%
ValueCountFrequency (%)
112 1
 
3.6%
72 1
 
3.6%
56 1
 
3.6%
38 1
 
3.6%
17 1
 
3.6%
15 1
 
3.6%
12 2
7.1%
7 1
 
3.6%
6 4
14.3%
4 4
14.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-07-31
28 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-31
2nd row2023-07-31
3rd row2023-07-31
4th row2023-07-31
5th row2023-07-31

Common Values

ValueCountFrequency (%)
2023-07-31 28
100.0%

Length

2023-12-12T14:36:00.528190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:36:00.599117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-31 28
100.0%

Interactions

2023-12-12T14:35:59.149410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:35:59.007680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:35:59.223539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:35:59.086168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:36:00.643365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동소지인원
연번1.0001.0000.657
행정동1.0001.0001.000
소지인원0.6571.0001.000
2023-12-12T14:36:00.730759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소지인원
연번1.0000.247
소지인원0.2471.000

Missing values

2023-12-12T14:35:59.310404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:35:59.379199image/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

연번행정동소지인원데이터기준일자
01중흥1동42023-07-31
12중흥2동32023-07-31
23중흥3동62023-07-31
34중앙동42023-07-31
45임동32023-07-31
56신안동62023-07-31
67용봉동152023-07-31
78운암1동02023-07-31
89운암2동42023-07-31
910운암3동22023-07-31
연번행정동소지인원데이터기준일자
1819두암3동1122023-07-31
1920삼각동32023-07-31
2021일곡동22023-07-31
2122매곡동22023-07-31
2223오치1동72023-07-31
2324오치2동722023-07-31
2425석곡동42023-07-31
2526건국동172023-07-31
2627양산동122023-07-31
2728신용동122023-07-31