Overview

Dataset statistics

Number of variables5
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1010.0 B
Average record size in memory48.1 B

Variable types

Numeric2
Categorical2
Text1

Dataset

Description이 데이터는 관악구 수방 장비 현황에 대한 데이터로 연번, 시도명, 시군구명, 행정동, 수중펌프 의 등 내용을 포함하고 있습니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15119534/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
연번 has unique valuesUnique
행정동 has unique valuesUnique

Reproduction

Analysis started2024-04-20 23:26:52.299337
Analysis finished2024-04-20 23:26:53.352324
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T08:26:53.454702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.56407607
Kurtosis-1.2
Mean11
Median Absolute Deviation (MAD)5
Skewness0
Sum231
Variance38.5
MonotonicityStrictly increasing
2024-04-21T08:26:53.668539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
21 1
 
4.8%
20 1
 
4.8%
19 1
 
4.8%
18 1
 
4.8%
17 1
 
4.8%
16 1
 
4.8%
15 1
 
4.8%
14 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
3 1
4.8%
4 1
4.8%
5 1
4.8%
6 1
4.8%
7 1
4.8%
8 1
4.8%
9 1
4.8%
10 1
4.8%
ValueCountFrequency (%)
21 1
4.8%
20 1
4.8%
19 1
4.8%
18 1
4.8%
17 1
4.8%
16 1
4.8%
15 1
4.8%
14 1
4.8%
13 1
4.8%
12 1
4.8%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size296.0 B
서울특별시
21 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 21
100.0%

Length

2024-04-21T08:26:53.892630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T08:26:54.102742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 21
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size296.0 B
관악구
21 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관악구 21
100.0%

Length

2024-04-21T08:26:54.293630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T08:26:54.457043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관악구 21
100.0%

행정동
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size296.0 B
2024-04-21T08:26:55.013530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.1428571
Min length3

Characters and Unicode

Total characters87
Distinct characters32
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

Unique21 ?
Unique (%)100.0%

Sample

1st row보라매동
2nd row은천동
3rd row성현동
4th row중앙동
5th row청림동
ValueCountFrequency (%)
보라매동 1
 
4.8%
신사동 1
 
4.8%
삼성동 1
 
4.8%
서림동 1
 
4.8%
신원동 1
 
4.8%
서원동 1
 
4.8%
난향동 1
 
4.8%
난곡동 1
 
4.8%
미성동 1
 
4.8%
조원동 1
 
4.8%
Other values (11) 11
52.4%
2024-04-21T08:26:55.854528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
25.3%
동 21
24.1%
성 4
 
4.6%
신 3
 
3.4%
원 3
 
3.4%
림 3
 
3.4%
청 2
 
2.3%
대 2
 
2.3%
현 2
 
2.3%
서 2
 
2.3%
Other values (22) 23
26.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
74.7%
Space Separator 22
 
25.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
32.3%
성 4
 
6.2%
신 3
 
4.6%
원 3
 
4.6%
림 3
 
4.6%
청 2
 
3.1%
대 2
 
3.1%
현 2
 
3.1%
서 2
 
3.1%
난 2
 
3.1%
Other values (21) 21
32.3%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
74.7%
Common 22
 
25.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
32.3%
성 4
 
6.2%
신 3
 
4.6%
원 3
 
4.6%
림 3
 
4.6%
청 2
 
3.1%
대 2
 
3.1%
현 2
 
3.1%
서 2
 
3.1%
난 2
 
3.1%
Other values (21) 21
32.3%
Common
ValueCountFrequency (%)
22
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
74.7%
ASCII 22
 
25.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
100.0%
Hangul
ValueCountFrequency (%)
동 21
32.3%
성 4
 
6.2%
신 3
 
4.6%
원 3
 
4.6%
림 3
 
4.6%
청 2
 
3.1%
대 2
 
3.1%
현 2
 
3.1%
서 2
 
3.1%
난 2
 
3.1%
Other values (21) 21
32.3%

수중펌프
Real number (ℝ)

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.142857
Minimum23
Maximum263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T08:26:56.055136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile25
Q131
median37
Q361
95-th percentile233
Maximum263
Range240
Interquartile range (IQR)30

Descriptive statistics

Standard deviation65.245142
Coefficient of variation (CV)1.0171848
Kurtosis5.4286396
Mean64.142857
Median Absolute Deviation (MAD)10
Skewness2.4484448
Sum1347
Variance4256.9286
MonotonicityNot monotonic
2024-04-21T08:26:56.246944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
33 2
 
9.5%
25 2
 
9.5%
47 2
 
9.5%
54 1
 
4.8%
233 1
 
4.8%
31 1
 
4.8%
37 1
 
4.8%
35 1
 
4.8%
23 1
 
4.8%
61 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
23 1
4.8%
25 2
9.5%
27 1
4.8%
28 1
4.8%
31 1
4.8%
32 1
4.8%
33 2
9.5%
35 1
4.8%
37 1
4.8%
44 1
4.8%
ValueCountFrequency (%)
263 1
4.8%
233 1
4.8%
104 1
4.8%
100 1
4.8%
65 1
4.8%
61 1
4.8%
54 1
4.8%
47 2
9.5%
44 1
4.8%
37 1
4.8%

Interactions

2024-04-21T08:26:52.793219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T08:26:52.493464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T08:26:52.944268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T08:26:52.655088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T08:26:56.386081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동수중펌프
연번1.0001.0000.739
행정동1.0001.0001.000
수중펌프0.7391.0001.000
2024-04-21T08:26:56.530894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수중펌프
연번1.0000.002
수중펌프0.0021.000

Missing values

2024-04-21T08:26:53.128762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T08:26:53.289904image/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

연번시도명시군구명행정동수중펌프
01서울특별시관악구보라매동54
12서울특별시관악구은천동65
23서울특별시관악구성현동33
34서울특별시관악구중앙동44
45서울특별시관악구청림동27
56서울특별시관악구행운동25
67서울특별시관악구청룡동28
78서울특별시관악구낙성대동47
89서울특별시관악구인헌동25
910서울특별시관악구남현동32
연번시도명시군구명행정동수중펌프
1112서울특별시관악구신사동263
1213서울특별시관악구조원동233
1314서울특별시관악구미성동100
1415서울특별시관악구난곡동61
1516서울특별시관악구난향동23
1617서울특별시관악구서원동35
1718서울특별시관악구신원동47
1819서울특별시관악구서림동33
1920서울특별시관악구삼성동37
2021서울특별시관악구대학동31