Overview

Dataset statistics

Number of variables5
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory47.3 B

Variable types

Text2
Numeric3

Dataset

Description주택용 소방시설 보급 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=W9YMSWTLS7G1ZR7EPWCA20818424&infSeq=1

Alerts

누적 보급 수(가구수) is highly overall correlated with 주택용 소방시설 분말소화기(개) and 1 other fieldsHigh correlation
주택용 소방시설 분말소화기(개) is highly overall correlated with 누적 보급 수(가구수) and 1 other fieldsHigh correlation
주택용 소방시설 주택용 화재경보기(개) is highly overall correlated with 누적 보급 수(가구수) and 1 other fieldsHigh correlation
시군명 has unique valuesUnique
관서명 has unique valuesUnique
누적 보급 수(가구수) has unique valuesUnique
주택용 소방시설 분말소화기(개) has unique valuesUnique
주택용 소방시설 주택용 화재경보기(개) has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:45:28.108215
Analysis finished2023-12-10 21:45:29.462989
Duration1.35 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T06:45:29.591020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Characters and Unicode

Total characters96
Distinct characters38
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

Unique31 ?
Unique (%)100.0%

Sample

1st row수원시
2nd row성남시
3rd row부천시
4th row안양시
5th row안산시
ValueCountFrequency (%)
수원시 1
 
3.2%
의왕시 1
 
3.2%
가평군 1
 
3.2%
동두천시 1
 
3.2%
양주시 1
 
3.2%
포천시 1
 
3.2%
구리시 1
 
3.2%
파주시 1
 
3.2%
남양주시 1
 
3.2%
의정부시 1
 
3.2%
Other values (21) 21
67.7%
2023-12-11T06:45:29.881917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
포 3
 
3.1%
평 3
 
3.1%
성 3
 
3.1%
남 3
 
3.1%
안 3
 
3.1%
Other values (28) 32
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
포 3
 
3.1%
평 3
 
3.1%
성 3
 
3.1%
남 3
 
3.1%
안 3
 
3.1%
Other values (28) 32
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
포 3
 
3.1%
평 3
 
3.1%
성 3
 
3.1%
남 3
 
3.1%
안 3
 
3.1%
Other values (28) 32
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
포 3
 
3.1%
평 3
 
3.1%
성 3
 
3.1%
남 3
 
3.1%
안 3
 
3.1%
Other values (28) 32
33.3%

관서명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T06:45:30.082266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0967742
Min length2

Characters and Unicode

Total characters65
Distinct characters38
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

Unique31 ?
Unique (%)100.0%

Sample

1st row수원
2nd row성남
3rd row부천
4th row안양
5th row안산
ValueCountFrequency (%)
수원 1
 
3.2%
의왕 1
 
3.2%
가평 1
 
3.2%
동두천 1
 
3.2%
양주 1
 
3.2%
포천 1
 
3.2%
구리 1
 
3.2%
파주 1
 
3.2%
남양주 1
 
3.2%
의정부 1
 
3.2%
Other values (21) 21
67.7%
2023-12-11T06:45:30.368322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 6
 
9.2%
주 5
 
7.7%
양 5
 
7.7%
포 3
 
4.6%
성 3
 
4.6%
남 3
 
4.6%
안 3
 
4.6%
평 3
 
4.6%
광 2
 
3.1%
의 2
 
3.1%
Other values (28) 30
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 6
 
9.2%
주 5
 
7.7%
양 5
 
7.7%
포 3
 
4.6%
성 3
 
4.6%
남 3
 
4.6%
안 3
 
4.6%
평 3
 
4.6%
광 2
 
3.1%
의 2
 
3.1%
Other values (28) 30
46.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 6
 
9.2%
주 5
 
7.7%
양 5
 
7.7%
포 3
 
4.6%
성 3
 
4.6%
남 3
 
4.6%
안 3
 
4.6%
평 3
 
4.6%
광 2
 
3.1%
의 2
 
3.1%
Other values (28) 30
46.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 6
 
9.2%
주 5
 
7.7%
양 5
 
7.7%
포 3
 
4.6%
성 3
 
4.6%
남 3
 
4.6%
안 3
 
4.6%
평 3
 
4.6%
광 2
 
3.1%
의 2
 
3.1%
Other values (28) 30
46.2%

누적 보급 수(가구수)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8901.2581
Minimum2344
Maximum21561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T06:45:30.479550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2344
5-th percentile3435
Q15938.5
median7711
Q39923
95-th percentile18705.5
Maximum21561
Range19217
Interquartile range (IQR)3984.5

Descriptive statistics

Standard deviation4732.3932
Coefficient of variation (CV)0.53165442
Kurtosis1.1197684
Mean8901.2581
Median Absolute Deviation (MAD)2022
Skewness1.1782259
Sum275939
Variance22395545
MonotonicityNot monotonic
2023-12-11T06:45:30.600487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
21561 1
 
3.2%
13408 1
 
3.2%
7711 1
 
3.2%
5808 1
 
3.2%
4530 1
 
3.2%
10113 1
 
3.2%
9732 1
 
3.2%
7002 1
 
3.2%
9483 1
 
3.2%
8278 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
2344 1
3.2%
2580 1
3.2%
4290 1
3.2%
4461 1
3.2%
4530 1
3.2%
4669 1
3.2%
5648 1
3.2%
5808 1
3.2%
6069 1
3.2%
6302 1
3.2%
ValueCountFrequency (%)
21561 1
3.2%
19871 1
3.2%
17540 1
3.2%
16345 1
3.2%
13408 1
3.2%
11823 1
3.2%
11460 1
3.2%
10113 1
3.2%
9733 1
3.2%
9732 1
3.2%

주택용 소방시설 분말소화기(개)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7939.2258
Minimum1771
Maximum21508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T06:45:30.745178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1771
5-th percentile2465
Q15166
median6692
Q39675
95-th percentile15947
Maximum21508
Range19737
Interquartile range (IQR)4509

Descriptive statistics

Standard deviation4504.9693
Coefficient of variation (CV)0.56743181
Kurtosis1.5432014
Mean7939.2258
Median Absolute Deviation (MAD)2066
Skewness1.2091578
Sum246116
Variance20294748
MonotonicityNot monotonic
2023-12-11T06:45:30.854562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
21508 1
 
3.2%
13081 1
 
3.2%
6452 1
 
3.2%
5326 1
 
3.2%
2355 1
 
3.2%
9580 1
 
3.2%
7110 1
 
3.2%
5682 1
 
3.2%
9770 1
 
3.2%
7145 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1771 1
3.2%
2355 1
3.2%
2575 1
3.2%
3492 1
3.2%
3918 1
3.2%
4280 1
3.2%
4669 1
3.2%
5006 1
3.2%
5326 1
3.2%
5472 1
3.2%
ValueCountFrequency (%)
21508 1
3.2%
16446 1
3.2%
15448 1
3.2%
14195 1
3.2%
13081 1
3.2%
11447 1
3.2%
11018 1
3.2%
9770 1
3.2%
9580 1
3.2%
8758 1
3.2%

주택용 소방시설 주택용 화재경보기(개)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14242.774
Minimum3098
Maximum41630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T06:45:30.966151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3098
5-th percentile4457.5
Q18688.5
median11871
Q318648
95-th percentile30926
Maximum41630
Range38532
Interquartile range (IQR)9959.5

Descriptive statistics

Standard deviation8805.0606
Coefficient of variation (CV)0.61821247
Kurtosis2.0535449
Mean14242.774
Median Absolute Deviation (MAD)4178
Skewness1.391522
Sum441526
Variance77529091
MonotonicityNot monotonic
2023-12-11T06:45:31.073679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
41630 1
 
3.2%
25953 1
 
3.2%
7693 1
 
3.2%
8896 1
 
3.2%
5797 1
 
3.2%
19919 1
 
3.2%
13662 1
 
3.2%
9841 1
 
3.2%
17829 1
 
3.2%
13036 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
3098 1
3.2%
3118 1
3.2%
5797 1
3.2%
6409 1
3.2%
6713 1
3.2%
7424 1
3.2%
7693 1
3.2%
8481 1
3.2%
8896 1
3.2%
8903 1
3.2%
ValueCountFrequency (%)
41630 1
3.2%
30949 1
3.2%
30903 1
3.2%
25953 1
3.2%
21475 1
3.2%
20501 1
3.2%
19919 1
3.2%
19467 1
3.2%
17829 1
3.2%
15969 1
3.2%

Interactions

2023-12-11T06:45:29.030082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:28.278565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:28.523477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:29.136778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:28.359676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:28.628782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:29.224666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:28.439541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:28.730166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:45:31.153217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명관서명누적 보급 수(가구수)주택용 소방시설 분말소화기(개)주택용 소방시설 주택용 화재경보기(개)
시군명1.0001.0001.0001.0001.000
관서명1.0001.0001.0001.0001.000
누적 보급 수(가구수)1.0001.0001.0000.9020.863
주택용 소방시설 분말소화기(개)1.0001.0000.9021.0000.881
주택용 소방시설 주택용 화재경보기(개)1.0001.0000.8630.8811.000
2023-12-11T06:45:31.237717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
누적 보급 수(가구수)주택용 소방시설 분말소화기(개)주택용 소방시설 주택용 화재경보기(개)
누적 보급 수(가구수)1.0000.9750.944
주택용 소방시설 분말소화기(개)0.9751.0000.956
주택용 소방시설 주택용 화재경보기(개)0.9440.9561.000

Missing values

2023-12-11T06:45:29.346500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:45:29.429754image/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

시군명관서명누적 보급 수(가구수)주택용 소방시설 분말소화기(개)주택용 소방시설 주택용 화재경보기(개)
0수원시수원215612150841630
1성남시성남134081308125953
2부천시부천175401419530903
3안양시안양9733842615969
4안산시안산118231101819467
5용인시용인6692669213384
6평택시평택163451544820501
7광명시광명9351868710253
8시흥시시흥9542770314168
9군포시군포7012609710220
시군명관서명누적 보급 수(가구수)주택용 소방시설 분말소화기(개)주택용 소방시설 주택용 화재경보기(개)
21고양시고양198711644630949
22의정부시의정부9622875814033
23남양주시남양주8278714513036
24파주시파주9483977017829
25구리시구리700256829841
26포천시포천9732711013662
27양주시양주10113958019919
28동두천시동두천453023555797
29가평군가평580853268896
30연천군연천771164527693