Overview

Dataset statistics

Number of variables5
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory46.8 B

Variable types

Text2
Numeric3

Dataset

Description소방관서 1인당 담당 인구 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=TWU4TKHKMP68I4R4VEED30945666&infSeq=1

Alerts

정원(명) is highly overall correlated with 관할인구(명) and 1 other fieldsHigh correlation
관할인구(명) is highly overall correlated with 정원(명) and 1 other fieldsHigh correlation
1인당담당인구(명) is highly overall correlated with 정원(명) and 1 other fieldsHigh correlation
관서명 has unique valuesUnique
관할인구(명) has unique valuesUnique
1인당담당인구(명) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:28:10.375259
Analysis finished2023-12-10 22:28:11.700253
Duration1.32 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct31
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-11T07:28:11.865413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0857143
Min length3

Characters and Unicode

Total characters108
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

Unique27 ?
Unique (%)77.1%

Sample

1st row수원시
2nd row수원시
3rd row성남시
4th row성남시
5th row부천시
ValueCountFrequency (%)
수원시 2
 
5.7%
성남시 2
 
5.7%
평택시 2
 
5.7%
고양시 2
 
5.7%
파주시 1
 
2.9%
양평군 1
 
2.9%
과천시 1
 
2.9%
의정부시 1
 
2.9%
남양주시 1
 
2.9%
포천시 1
 
2.9%
Other values (21) 21
60.0%
2023-12-11T07:28:12.200147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 33
30.6%
천 6
 
5.6%
양 6
 
5.6%
주 5
 
4.6%
성 4
 
3.7%
남 4
 
3.7%
평 4
 
3.7%
군 4
 
3.7%
포 3
 
2.8%
안 3
 
2.8%
Other values (28) 36
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 33
30.6%
천 6
 
5.6%
양 6
 
5.6%
주 5
 
4.6%
성 4
 
3.7%
남 4
 
3.7%
평 4
 
3.7%
군 4
 
3.7%
포 3
 
2.8%
안 3
 
2.8%
Other values (28) 36
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 33
30.6%
천 6
 
5.6%
양 6
 
5.6%
주 5
 
4.6%
성 4
 
3.7%
남 4
 
3.7%
평 4
 
3.7%
군 4
 
3.7%
포 3
 
2.8%
안 3
 
2.8%
Other values (28) 36
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 33
30.6%
천 6
 
5.6%
양 6
 
5.6%
주 5
 
4.6%
성 4
 
3.7%
남 4
 
3.7%
평 4
 
3.7%
군 4
 
3.7%
포 3
 
2.8%
안 3
 
2.8%
Other values (28) 36
33.3%

관서명
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-11T07:28:12.404569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1428571
Min length5

Characters and Unicode

Total characters180
Distinct characters46
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

Unique35 ?
Unique (%)100.0%

Sample

1st row수원소방서
2nd row수원남부소방서
3rd row성남소방서
4th row분당소방서
5th row부천소방서
ValueCountFrequency (%)
수원소방서 1
 
2.9%
하남소방서 1
 
2.9%
오산소방서 1
 
2.9%
여주소방서 1
 
2.9%
양평소방서 1
 
2.9%
과천소방서 1
 
2.9%
고양소방서 1
 
2.9%
일산소방서 1
 
2.9%
의왕소방서 1
 
2.9%
의정부소방서 1
 
2.9%
Other values (25) 25
71.4%
2023-12-11T07:28:12.762136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 35
19.4%
방 35
19.4%
서 35
19.4%
천 6
 
3.3%
주 5
 
2.8%
양 5
 
2.8%
남 4
 
2.2%
산 3
 
1.7%
평 3
 
1.7%
포 3
 
1.7%
Other values (36) 46
25.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 180
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 35
19.4%
방 35
19.4%
서 35
19.4%
천 6
 
3.3%
주 5
 
2.8%
양 5
 
2.8%
남 4
 
2.2%
산 3
 
1.7%
평 3
 
1.7%
포 3
 
1.7%
Other values (36) 46
25.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 180
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 35
19.4%
방 35
19.4%
서 35
19.4%
천 6
 
3.3%
주 5
 
2.8%
양 5
 
2.8%
남 4
 
2.2%
산 3
 
1.7%
평 3
 
1.7%
포 3
 
1.7%
Other values (36) 46
25.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 180
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 35
19.4%
방 35
19.4%
서 35
19.4%
천 6
 
3.3%
주 5
 
2.8%
양 5
 
2.8%
남 4
 
2.2%
산 3
 
1.7%
평 3
 
1.7%
포 3
 
1.7%
Other values (36) 46
25.6%

정원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.25714
Minimum150
Maximum590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:28:12.889767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile183.1
Q1227
median294
Q3344
95-th percentile471.1
Maximum590
Range440
Interquartile range (IQR)117

Descriptive statistics

Standard deviation98.671273
Coefficient of variation (CV)0.32323985
Kurtosis1.1510307
Mean305.25714
Median Absolute Deviation (MAD)63
Skewness0.97242981
Sum10684
Variance9736.0202
MonotonicityNot monotonic
2023-12-11T07:28:13.201885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
294 2
 
5.7%
289 2
 
5.7%
240 1
 
2.9%
284 1
 
2.9%
150 1
 
2.9%
350 1
 
2.9%
275 1
 
2.9%
442 1
 
2.9%
394 1
 
2.9%
190 1
 
2.9%
Other values (23) 23
65.7%
ValueCountFrequency (%)
150 1
2.9%
167 1
2.9%
190 1
2.9%
194 1
2.9%
203 1
2.9%
214 1
2.9%
217 1
2.9%
222 1
2.9%
223 1
2.9%
231 1
2.9%
ValueCountFrequency (%)
590 1
2.9%
532 1
2.9%
445 1
2.9%
442 1
2.9%
430 1
2.9%
394 1
2.9%
381 1
2.9%
379 1
2.9%
350 1
2.9%
338 1
2.9%

관할인구(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean388269.49
Minimum42062
Maximum1074971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:28:13.311013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42062
5-th percentile73340.9
Q1188771.5
median355413
Q3530570
95-th percentile826333.8
Maximum1074971
Range1032909
Interquartile range (IQR)341798.5

Descriptive statistics

Standard deviation253601.18
Coefficient of variation (CV)0.65315763
Kurtosis0.23513358
Mean388269.49
Median Absolute Deviation (MAD)166712
Skewness0.75578484
Sum13589432
Variance6.4313557 × 1010
MonotonicityNot monotonic
2023-12-11T07:28:13.416651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
630908 1
 
2.9%
560056 1
 
2.9%
229849 1
 
2.9%
113150 1
 
2.9%
122323 1
 
2.9%
78137 1
 
2.9%
490088 1
 
2.9%
586447 1
 
2.9%
463724 1
 
2.9%
737353 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
42062 1
2.9%
62150 1
2.9%
78137 1
2.9%
91546 1
2.9%
113150 1
2.9%
122323 1
2.9%
146701 1
2.9%
160221 1
2.9%
188701 1
2.9%
188842 1
2.9%
ValueCountFrequency (%)
1074971 1
2.9%
910814 1
2.9%
790128 1
2.9%
737353 1
2.9%
641660 1
2.9%
630908 1
2.9%
586447 1
2.9%
560056 1
2.9%
548228 1
2.9%
512912 1
2.9%

1인당담당인구(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1189.0857
Minimum182
Maximum2146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:28:13.515957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182
5-th percentile353.5
Q1771.5
median1278
Q31671.5
95-th percentile1823.5
Maximum2146
Range1964
Interquartile range (IQR)900

Descriptive statistics

Standard deviation534.10582
Coefficient of variation (CV)0.44917352
Kurtosis-1.0927761
Mean1189.0857
Median Absolute Deviation (MAD)434
Skewness-0.28527147
Sum41618
Variance285269.02
MonotonicityNot monotonic
2023-12-11T07:28:13.625167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2146 1
 
2.9%
1767 1
 
2.9%
1074 1
 
2.9%
471 1
 
2.9%
431 1
 
2.9%
521 1
 
2.9%
1667 1
 
2.9%
1675 1
 
2.9%
1686 1
 
2.9%
1668 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
182 1
2.9%
280 1
2.9%
385 1
2.9%
431 1
2.9%
471 1
2.9%
521 1
2.9%
548 1
2.9%
674 1
2.9%
771 1
2.9%
772 1
2.9%
ValueCountFrequency (%)
2146 1
2.9%
1827 1
2.9%
1822 1
2.9%
1776 1
2.9%
1767 1
2.9%
1712 1
2.9%
1704 1
2.9%
1686 1
2.9%
1675 1
2.9%
1668 1
2.9%

Interactions

2023-12-11T07:28:11.183051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:10.565512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:10.860015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:11.277903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:10.654051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:10.954232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:11.371747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:10.739278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:11.061508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:28:13.704689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명관서명정원(명)관할인구(명)1인당담당인구(명)
시군명1.0001.0000.9640.9620.860
관서명1.0001.0001.0001.0001.000
정원(명)0.9641.0001.0000.8270.000
관할인구(명)0.9621.0000.8271.0000.760
1인당담당인구(명)0.8601.0000.0000.7601.000
2023-12-11T07:28:13.783101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정원(명)관할인구(명)1인당담당인구(명)
정원(명)1.0000.7670.519
관할인구(명)0.7671.0000.926
1인당담당인구(명)0.5190.9261.000

Missing values

2023-12-11T07:28:11.535228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:28:11.655169image/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

시군명관서명정원(명)관할인구(명)1인당담당인구(명)
0수원시수원소방서2946309082146
1수원시수원남부소방서3175600561767
2성남시성남소방서3294409651340
3성남시분당소방서2954815531632
4부천시부천소방서4457901281776
5안양시안양소방서3005482281827
6안산시안산소방서4306416601492
7용인시용인소방서59010749711822
8평택시평택소방서2983554131193
9평택시송탄소방서289223116772
시군명관서명정원(명)관할인구(명)1인당담당인구(명)
25고양시일산소방서3505864471675
26의정부시의정부소방서2754637241686
27남양주시남양주소방서4427373531668
28파주시파주소방서3944953151257
29구리시구리소방서194188701973
30포천시포천소방서381146701385
31양주시양주소방서307243432793
32동두천시동두천소방서16791546548
33가평군가평소방서22262150280
34연천군연천소방서23142062182