Overview

Dataset statistics

Number of variables16
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory147.5 B

Variable types

Text2
Numeric12
Categorical2

Dataset

Description경상북도 소방관련 정보(도내 시군별 의용소방대 인원, 남대, 여대 구분) 의용소방대는 비상근으로서 소방활동상 필요에 의하여 소집된 때에는 출동하여 소방본부장 또는 소방서장의 소방업무를 보조하는 자발적인 민간봉사단체입니다. 의용소방대는 의용소방대 설치 및 운영에 관한 법률(소방본부장 또는 소방서장은 소방업무를 보조하게 하기 위하여 특별시, 광역시와 시 읍 면에 의용소방대를 두며 그 지역의 주민중 희망하는 사람으로서 구성)에 의거 만들어진 협조 단체입니다.
Author경상북도
URLhttps://www.data.go.kr/data/15044821/fileData.do

Alerts

정원계 is highly overall correlated with 현원계 and 4 other fieldsHigh correlation
현원계 is highly overall correlated with 정원계 and 4 other fieldsHigh correlation
남성대정원 is highly overall correlated with 정원계 and 4 other fieldsHigh correlation
남성대현원 is highly overall correlated with 정원계 and 3 other fieldsHigh correlation
여성대정원 is highly overall correlated with 정원계 and 4 other fieldsHigh correlation
여성대현원 is highly overall correlated with 정원계 and 3 other fieldsHigh correlation
혼성대현원 is highly overall correlated with 혼성대정원High correlation
지역대현원 is highly overall correlated with 지역대정원High correlation
전담대정원 is highly overall correlated with 전담대현원High correlation
전담대현원 is highly overall correlated with 전담대정원High correlation
전문대정원 is highly overall correlated with 전문대현원High correlation
전문대현원 is highly overall correlated with 전문대정원High correlation
혼성대정원 is highly overall correlated with 혼성대현원High correlation
지역대정원 is highly overall correlated with 남성대정원 and 2 other fieldsHigh correlation
시군구별 has unique valuesUnique
현원계 has unique valuesUnique
남성대현원 has unique valuesUnique
여성대현원 has unique valuesUnique
혼성대현원 has 15 (62.5%) zerosZeros
지역대현원 has 15 (62.5%) zerosZeros
전담대정원 has 5 (20.8%) zerosZeros
전담대현원 has 5 (20.8%) zerosZeros
전문대정원 has 2 (8.3%) zerosZeros
전문대현원 has 2 (8.3%) zerosZeros

Reproduction

Analysis started2023-12-12 07:52:21.168355
Analysis finished2023-12-12 07:52:36.922812
Duration15.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구별
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T16:52:37.039472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0833333
Min length3

Characters and Unicode

Total characters74
Distinct characters36
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

Unique24 ?
Unique (%)100.0%

Sample

1st row포항북구
2nd row포항남구
3rd row경주시
4th row김천시
5th row안동시
ValueCountFrequency (%)
포항북구 1
 
4.2%
포항남구 1
 
4.2%
울진군 1
 
4.2%
봉화군 1
 
4.2%
예천군 1
 
4.2%
칠곡군 1
 
4.2%
성주군 1
 
4.2%
고령군 1
 
4.2%
청도군 1
 
4.2%
영덕군 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T16:52:37.384730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14
18.9%
9
 
12.2%
4
 
5.4%
4
 
5.4%
3
 
4.1%
3
 
4.1%
3
 
4.1%
2
 
2.7%
2
 
2.7%
2
 
2.7%
Other values (26) 28
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14
18.9%
9
 
12.2%
4
 
5.4%
4
 
5.4%
3
 
4.1%
3
 
4.1%
3
 
4.1%
2
 
2.7%
2
 
2.7%
2
 
2.7%
Other values (26) 28
37.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14
18.9%
9
 
12.2%
4
 
5.4%
4
 
5.4%
3
 
4.1%
3
 
4.1%
3
 
4.1%
2
 
2.7%
2
 
2.7%
2
 
2.7%
Other values (26) 28
37.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
14
18.9%
9
 
12.2%
4
 
5.4%
4
 
5.4%
3
 
4.1%
3
 
4.1%
3
 
4.1%
2
 
2.7%
2
 
2.7%
2
 
2.7%
Other values (26) 28
37.8%
Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T16:52:37.565814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.25
Min length2

Characters and Unicode

Total characters54
Distinct characters28
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

Unique15 ?
Unique (%)62.5%

Sample

1st row포항북구
2nd row포항남구
3rd row경주
4th row김천
5th row안동
ValueCountFrequency (%)
안동 3
 
12.5%
포항남구 2
 
8.3%
의성 2
 
8.3%
영주 2
 
8.3%
청도 1
 
4.2%
포항북구 1
 
4.2%
예천 1
 
4.2%
칠곡 1
 
4.2%
성주 1
 
4.2%
고령 1
 
4.2%
Other values (9) 9
37.5%
2023-12-12T16:52:37.908427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
9.3%
4
 
7.4%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
Other values (18) 20
37.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
 
9.3%
4
 
7.4%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
Other values (18) 20
37.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5
 
9.3%
4
 
7.4%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
Other values (18) 20
37.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
5
 
9.3%
4
 
7.4%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
Other values (18) 20
37.0%

정원계
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean556.66667
Minimum240
Maximum1050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:38.068295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum240
5-th percentile260.5
Q1417.5
median520
Q3687.5
95-th percentile951.5
Maximum1050
Range810
Interquartile range (IQR)270

Descriptive statistics

Standard deviation215.64195
Coefficient of variation (CV)0.38738074
Kurtosis-0.059584912
Mean556.66667
Median Absolute Deviation (MAD)150
Skewness0.58619921
Sum13360
Variance46501.449
MonotonicityNot monotonic
2023-12-12T16:52:38.209563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
320 3
 
12.5%
650 1
 
4.2%
240 1
 
4.2%
540 1
 
4.2%
490 1
 
4.2%
500 1
 
4.2%
450 1
 
4.2%
380 1
 
4.2%
470 1
 
4.2%
460 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
240 1
 
4.2%
250 1
 
4.2%
320 3
12.5%
380 1
 
4.2%
430 1
 
4.2%
450 1
 
4.2%
460 1
 
4.2%
470 1
 
4.2%
490 1
 
4.2%
500 1
 
4.2%
ValueCountFrequency (%)
1050 1
4.2%
980 1
4.2%
790 1
4.2%
780 1
4.2%
760 1
4.2%
710 1
4.2%
680 1
4.2%
650 1
4.2%
640 1
4.2%
590 1
4.2%

현원계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.125
Minimum198
Maximum827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:38.336469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile218.35
Q1351.25
median453
Q3584
95-th percentile657.75
Maximum827
Range629
Interquartile range (IQR)232.75

Descriptive statistics

Standard deviation159.31301
Coefficient of variation (CV)0.34851083
Kurtosis-0.26785348
Mean457.125
Median Absolute Deviation (MAD)133
Skewness0.23959457
Sum10971
Variance25380.636
MonotonicityNot monotonic
2023-12-12T16:52:38.464704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
663 1
 
4.2%
262 1
 
4.2%
198 1
 
4.2%
462 1
 
4.2%
444 1
 
4.2%
434 1
 
4.2%
411 1
 
4.2%
313 1
 
4.2%
278 1
 
4.2%
404 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
198 1
4.2%
211 1
4.2%
260 1
4.2%
262 1
4.2%
278 1
4.2%
313 1
4.2%
364 1
4.2%
380 1
4.2%
404 1
4.2%
411 1
4.2%
ValueCountFrequency (%)
827 1
4.2%
663 1
4.2%
628 1
4.2%
624 1
4.2%
614 1
4.2%
599 1
4.2%
579 1
4.2%
530 1
4.2%
510 1
4.2%
495 1
4.2%

남성대정원
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.83333
Minimum100
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:38.601466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile104.5
Q1167.5
median240
Q3362.5
95-th percentile504
Maximum510
Range410
Interquartile range (IQR)195

Descriptive statistics

Standard deviation130.48127
Coefficient of variation (CV)0.481777
Kurtosis-0.877618
Mean270.83333
Median Absolute Deviation (MAD)110
Skewness0.52563839
Sum6500
Variance17025.362
MonotonicityNot monotonic
2023-12-12T16:52:38.706809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
130 3
12.5%
220 3
12.5%
100 2
 
8.3%
360 2
 
8.3%
250 2
 
8.3%
510 2
 
8.3%
400 1
 
4.2%
470 1
 
4.2%
290 1
 
4.2%
460 1
 
4.2%
Other values (6) 6
25.0%
ValueCountFrequency (%)
100 2
8.3%
130 3
12.5%
160 1
 
4.2%
170 1
 
4.2%
180 1
 
4.2%
220 3
12.5%
230 1
 
4.2%
250 2
8.3%
280 1
 
4.2%
290 1
 
4.2%
ValueCountFrequency (%)
510 2
8.3%
470 1
4.2%
460 1
4.2%
400 1
4.2%
370 1
4.2%
360 2
8.3%
290 1
4.2%
280 1
4.2%
250 2
8.3%
230 1
4.2%

남성대현원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.58333
Minimum80
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:38.837294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile87.6
Q1133.25
median211
Q3314.75
95-th percentile377.05
Maximum391
Range311
Interquartile range (IQR)181.5

Descriptive statistics

Standard deviation99.90296
Coefficient of variation (CV)0.44682651
Kurtosis-1.2907528
Mean223.58333
Median Absolute Deviation (MAD)93.5
Skewness0.14629073
Sum5366
Variance9980.6014
MonotonicityNot monotonic
2023-12-12T16:52:38.955068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
225 1
 
4.2%
119 1
 
4.2%
138 1
 
4.2%
116 1
 
4.2%
205 1
 
4.2%
248 1
 
4.2%
174 1
 
4.2%
177 1
 
4.2%
107 1
 
4.2%
189 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
80 1
4.2%
87 1
4.2%
91 1
4.2%
107 1
4.2%
116 1
4.2%
119 1
4.2%
138 1
4.2%
174 1
4.2%
177 1
4.2%
185 1
4.2%
ValueCountFrequency (%)
391 1
4.2%
382 1
4.2%
349 1
4.2%
340 1
4.2%
333 1
4.2%
323 1
4.2%
312 1
4.2%
302 1
4.2%
276 1
4.2%
248 1
4.2%

여성대정원
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.79167
Minimum40
Maximum430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:39.067645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile44.5
Q177.5
median145
Q3205
95-th percentile332.75
Maximum430
Range390
Interquartile range (IQR)127.5

Descriptive statistics

Standard deviation105.30989
Coefficient of variation (CV)0.63904861
Kurtosis0.23098938
Mean164.79167
Median Absolute Deviation (MAD)70
Skewness0.96461571
Sum3955
Variance11090.172
MonotonicityNot monotonic
2023-12-12T16:52:39.167724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
70 4
16.7%
100 3
 
12.5%
40 2
 
8.3%
335 1
 
4.2%
290 1
 
4.2%
200 1
 
4.2%
180 1
 
4.2%
140 1
 
4.2%
160 1
 
4.2%
190 1
 
4.2%
Other values (8) 8
33.3%
ValueCountFrequency (%)
40 2
8.3%
70 4
16.7%
80 1
 
4.2%
100 3
12.5%
120 1
 
4.2%
140 1
 
4.2%
150 1
 
4.2%
160 1
 
4.2%
170 1
 
4.2%
180 1
 
4.2%
ValueCountFrequency (%)
430 1
4.2%
335 1
4.2%
320 1
4.2%
310 1
4.2%
290 1
4.2%
220 1
4.2%
200 1
4.2%
190 1
4.2%
180 1
4.2%
170 1
4.2%

여성대현원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.91667
Minimum34
Maximum279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:39.271453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile41.65
Q167.75
median121
Q3160.5
95-th percentile275.05
Maximum279
Range245
Interquartile range (IQR)92.75

Descriptive statistics

Standard deviation78.001626
Coefficient of variation (CV)0.58684609
Kurtosis-0.60840167
Mean132.91667
Median Absolute Deviation (MAD)47
Skewness0.74269079
Sum3190
Variance6084.2536
MonotonicityNot monotonic
2023-12-12T16:52:39.375940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
279 1
 
4.2%
63 1
 
4.2%
34 1
 
4.2%
162 1
 
4.2%
92 1
 
4.2%
81 1
 
4.2%
160 1
 
4.2%
58 1
 
4.2%
67 1
 
4.2%
129 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
34 1
4.2%
40 1
4.2%
51 1
4.2%
58 1
4.2%
63 1
4.2%
67 1
4.2%
68 1
4.2%
81 1
4.2%
90 1
4.2%
92 1
4.2%
ValueCountFrequency (%)
279 1
4.2%
277 1
4.2%
264 1
4.2%
256 1
4.2%
237 1
4.2%
162 1
4.2%
160 1
4.2%
157 1
4.2%
152 1
4.2%
136 1
4.2%

혼성대정원
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
15 
30
60
90
 
1

Length

Max length2
Median length1
Mean length1.375
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15
62.5%
30 4
 
16.7%
60 4
 
16.7%
90 1
 
4.2%

Length

2023-12-12T16:52:39.499032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:52:39.601110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15
62.5%
30 4
 
16.7%
60 4
 
16.7%
90 1
 
4.2%

혼성대현원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum0
Maximum82
Zeros15
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:39.717362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324.75
95-th percentile58.35
Maximum82
Range82
Interquartile range (IQR)24.75

Descriptive statistics

Standard deviation23.994565
Coefficient of variation (CV)1.5480364
Kurtosis1.2147455
Mean15.5
Median Absolute Deviation (MAD)0
Skewness1.4491893
Sum372
Variance575.73913
MonotonicityNot monotonic
2023-12-12T16:52:39.847075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 15
62.5%
22 1
 
4.2%
15 1
 
4.2%
60 1
 
4.2%
82 1
 
4.2%
44 1
 
4.2%
23 1
 
4.2%
49 1
 
4.2%
47 1
 
4.2%
30 1
 
4.2%
ValueCountFrequency (%)
0 15
62.5%
15 1
 
4.2%
22 1
 
4.2%
23 1
 
4.2%
30 1
 
4.2%
44 1
 
4.2%
47 1
 
4.2%
49 1
 
4.2%
60 1
 
4.2%
82 1
 
4.2%
ValueCountFrequency (%)
82 1
 
4.2%
60 1
 
4.2%
49 1
 
4.2%
47 1
 
4.2%
44 1
 
4.2%
30 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
15 1
 
4.2%
0 15
62.5%

지역대정원
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
15 
20
40
140
 
1
80
 
1

Length

Max length3
Median length1
Mean length1.4166667
Min length1

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0
2nd row0
3rd row140
4th row0
5th row40

Common Values

ValueCountFrequency (%)
0 15
62.5%
20 5
 
20.8%
40 2
 
8.3%
140 1
 
4.2%
80 1
 
4.2%

Length

2023-12-12T16:52:40.063146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:52:40.247041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15
62.5%
20 5
 
20.8%
40 2
 
8.3%
140 1
 
4.2%
80 1
 
4.2%

지역대현원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum0
Maximum96
Zeros15
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:40.413443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q318.5
95-th percentile68.7
Maximum96
Range96
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation24.754446
Coefficient of variation (CV)1.8336627
Kurtosis5.7905207
Mean13.5
Median Absolute Deviation (MAD)0
Skewness2.4070536
Sum324
Variance612.78261
MonotonicityNot monotonic
2023-12-12T16:52:40.603155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 15
62.5%
20 2
 
8.3%
96 1
 
4.2%
30 1
 
4.2%
15 1
 
4.2%
18 1
 
4.2%
33 1
 
4.2%
75 1
 
4.2%
17 1
 
4.2%
ValueCountFrequency (%)
0 15
62.5%
15 1
 
4.2%
17 1
 
4.2%
18 1
 
4.2%
20 2
 
8.3%
30 1
 
4.2%
33 1
 
4.2%
75 1
 
4.2%
96 1
 
4.2%
ValueCountFrequency (%)
96 1
 
4.2%
75 1
 
4.2%
33 1
 
4.2%
30 1
 
4.2%
20 2
 
8.3%
18 1
 
4.2%
17 1
 
4.2%
15 1
 
4.2%
0 15
62.5%

전담대정원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.958333
Minimum0
Maximum120
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:40.810974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median60
Q360
95-th percentile112.75
Maximum120
Range120
Interquartile range (IQR)30

Descriptive statistics

Standard deviation36.414855
Coefficient of variation (CV)0.74379278
Kurtosis-0.65493674
Mean48.958333
Median Absolute Deviation (MAD)30
Skewness0.2956213
Sum1175
Variance1326.0417
MonotonicityNot monotonic
2023-12-12T16:52:41.013584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
60 8
33.3%
30 6
25.0%
0 5
20.8%
90 2
 
8.3%
115 1
 
4.2%
120 1
 
4.2%
100 1
 
4.2%
ValueCountFrequency (%)
0 5
20.8%
30 6
25.0%
60 8
33.3%
90 2
 
8.3%
100 1
 
4.2%
115 1
 
4.2%
120 1
 
4.2%
ValueCountFrequency (%)
120 1
 
4.2%
115 1
 
4.2%
100 1
 
4.2%
90 2
 
8.3%
60 8
33.3%
30 6
25.0%
0 5
20.8%

전담대현원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.583333
Minimum0
Maximum114
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:41.201655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120.75
median45.5
Q357
95-th percentile102.4
Maximum114
Range114
Interquartile range (IQR)36.25

Descriptive statistics

Standard deviation32.274524
Coefficient of variation (CV)0.77614087
Kurtosis-0.075879255
Mean41.583333
Median Absolute Deviation (MAD)20
Skewness0.53021057
Sum998
Variance1041.6449
MonotonicityNot monotonic
2023-12-12T16:52:41.437959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 5
20.8%
106 1
 
4.2%
52 1
 
4.2%
82 1
 
4.2%
47 1
 
4.2%
28 1
 
4.2%
56 1
 
4.2%
51 1
 
4.2%
53 1
 
4.2%
74 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
0 5
20.8%
17 1
 
4.2%
22 1
 
4.2%
25 1
 
4.2%
26 1
 
4.2%
28 1
 
4.2%
30 1
 
4.2%
44 1
 
4.2%
47 1
 
4.2%
48 1
 
4.2%
ValueCountFrequency (%)
114 1
4.2%
106 1
4.2%
82 1
4.2%
74 1
4.2%
63 1
4.2%
60 1
4.2%
56 1
4.2%
53 1
4.2%
52 1
4.2%
51 1
4.2%

전문대정원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.666667
Minimum0
Maximum80
Zeros2
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:41.674643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.5
Q130
median30
Q340
95-th percentile78.5
Maximum80
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation20.144406
Coefficient of variation (CV)0.5493929
Kurtosis0.85952823
Mean36.666667
Median Absolute Deviation (MAD)0
Skewness0.70916532
Sum880
Variance405.7971
MonotonicityNot monotonic
2023-12-12T16:52:41.882619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
30 15
62.5%
60 2
 
8.3%
40 2
 
8.3%
80 2
 
8.3%
0 2
 
8.3%
70 1
 
4.2%
ValueCountFrequency (%)
0 2
 
8.3%
30 15
62.5%
40 2
 
8.3%
60 2
 
8.3%
70 1
 
4.2%
80 2
 
8.3%
ValueCountFrequency (%)
80 2
 
8.3%
70 1
 
4.2%
60 2
 
8.3%
40 2
 
8.3%
30 15
62.5%
0 2
 
8.3%

전문대현원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.041667
Minimum0
Maximum67
Zeros2
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T16:52:42.085035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q122
median27
Q330.25
95-th percentile60.95
Maximum67
Range67
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation16.956413
Coefficient of variation (CV)0.56442982
Kurtosis0.42855008
Mean30.041667
Median Absolute Deviation (MAD)5
Skewness0.64218161
Sum721
Variance287.51993
MonotonicityNot monotonic
2023-12-12T16:52:42.267816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
22 4
16.7%
28 3
12.5%
55 2
 
8.3%
27 2
 
8.3%
0 2
 
8.3%
30 2
 
8.3%
53 1
 
4.2%
31 1
 
4.2%
67 1
 
4.2%
24 1
 
4.2%
Other values (5) 5
20.8%
ValueCountFrequency (%)
0 2
8.3%
16 1
 
4.2%
21 1
 
4.2%
22 4
16.7%
24 1
 
4.2%
25 1
 
4.2%
26 1
 
4.2%
27 2
8.3%
28 3
12.5%
30 2
8.3%
ValueCountFrequency (%)
67 1
 
4.2%
62 1
 
4.2%
55 2
8.3%
53 1
 
4.2%
31 1
 
4.2%
30 2
8.3%
28 3
12.5%
27 2
8.3%
26 1
 
4.2%
25 1
 
4.2%

Interactions

2023-12-12T16:52:34.231045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:21.763372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.097949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:24.248742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.559061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.608414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.745438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.841192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.769827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.147675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.040299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.895431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:34.337750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:21.883462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.223513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:24.332305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.643951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.710541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.849717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.921845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.859360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.230488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.108052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:33.067093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:34.433926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.022302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.326334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:24.719130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.753571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.803164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.944621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.993495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.944590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.309999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.175584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:33.176098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:34.531615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.143250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.422453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:24.795630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.845868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.884288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.031823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.065909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:30.021075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.380358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.234711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:33.258435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:34.641049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.266751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.541572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:24.889742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.938102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.968767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.132868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.140929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:30.098294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.456504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.312392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:33.396426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:34.757793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.388405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.649966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:24.986429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.025251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.065126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.216504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.221292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:30.495024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.544616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.387871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:33.516704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:34.847484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.490577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.727254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.067208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.113943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.147234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.294794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.292816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:30.575078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.613544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.460341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:33.619140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:34.926757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.589847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.798863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.138143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.190887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.237210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.378527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.356985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:30.663219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.675797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.527472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:33.706407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:35.054138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.681769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.904187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.208150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.281458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.317199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.462535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.431921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:30.735917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.748145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.594079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:33.802863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:35.257438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.779474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:23.988012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.293892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.362509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.400795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.552881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.506092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:30.828578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.820558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.663960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:33.900450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:35.500226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.878196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:24.069578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.384814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.441796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.486622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.630985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.587288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:30.942347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.894661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.726655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:34.005129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:35.826014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:22.982464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:24.156999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:25.480073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:26.526505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:27.643428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:28.739111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:29.687374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.044149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:31.970877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:32.806391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:34.116882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:52:42.433796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구별관할소방서정원계현원계남성대정원남성대현원여성대정원여성대현원혼성대정원혼성대현원지역대정원지역대현원전담대정원전담대현원전문대정원전문대현원
시군구별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관할소방서1.0001.0000.8770.7680.7700.8110.9150.7650.0000.6750.0000.1430.8030.0000.8400.912
정원계1.0000.8771.0000.7800.6750.5920.7790.6930.0000.0000.0000.0000.0000.0000.0000.000
현원계1.0000.7680.7801.0000.6720.5580.6860.7470.0000.0000.4680.3770.0000.7350.6870.557
남성대정원1.0000.7700.6750.6721.0000.9250.6190.5150.0000.0000.7680.7260.0000.0000.3770.319
남성대현원1.0000.8110.5920.5580.9251.0000.6000.2320.0000.0000.0000.0000.0000.0000.1290.407
여성대정원1.0000.9150.7790.6860.6190.6001.0000.9100.0000.0000.5530.5730.5710.0000.6460.399
여성대현원1.0000.7650.6930.7470.5150.2320.9101.0000.0000.2720.7300.7600.6320.1970.5400.582
혼성대정원1.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.5100.0000.000
혼성대현원1.0000.6750.0000.0000.0000.0000.0000.2721.0001.0000.0000.0000.4810.5780.0000.000
지역대정원1.0000.0000.0000.4680.7680.0000.5530.7300.0000.0001.0001.0000.3350.0000.0000.324
지역대현원1.0000.1430.0000.3770.7260.0000.5730.7600.0000.0001.0001.0000.7290.4670.0000.303
전담대정원1.0000.8030.0000.0000.0000.0000.5710.6320.0000.4810.3350.7291.0000.9970.3090.528
전담대현원1.0000.0000.0000.7350.0000.0000.0000.1970.5100.5780.0000.4670.9971.0000.0000.422
전문대정원1.0000.8400.0000.6870.3770.1290.6460.5400.0000.0000.0000.0000.3090.0001.0000.820
전문대현원1.0000.9120.0000.5570.3190.4070.3990.5820.0000.0000.3240.3030.5280.4220.8201.000
2023-12-12T16:52:42.682034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
혼성대정원지역대정원
혼성대정원1.0000.000
지역대정원0.0001.000
2023-12-12T16:52:42.889309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정원계현원계남성대정원남성대현원여성대정원여성대현원혼성대현원지역대현원전담대정원전담대현원전문대정원전문대현원혼성대정원지역대정원
정원계1.0000.9690.8720.8840.8340.757-0.2760.1570.1170.0920.288-0.1000.0000.000
현원계0.9691.0000.8160.8530.8340.811-0.2540.1310.1610.1450.315-0.0470.0000.246
남성대정원0.8720.8161.0000.9830.5780.473-0.3480.010-0.153-0.1530.117-0.2110.0000.505
남성대현원0.8840.8530.9831.0000.5740.492-0.3170.071-0.083-0.0890.144-0.1830.0000.000
여성대정원0.8340.8340.5780.5741.0000.965-0.2200.021-0.033-0.0590.3980.0970.0000.330
여성대현원0.7570.8110.4730.4920.9651.000-0.1650.0430.0310.0150.3360.0740.0000.507
혼성대현원-0.276-0.254-0.348-0.317-0.220-0.1651.000-0.201-0.0810.022-0.0540.1650.9220.000
지역대현원0.1570.1310.0100.0710.0210.043-0.2011.0000.4530.361-0.090-0.2470.0000.973
전담대정원0.1170.161-0.153-0.083-0.0330.031-0.0810.4531.0000.973-0.033-0.1670.0000.204
전담대현원0.0920.145-0.153-0.089-0.0590.0150.0220.3610.9731.000-0.075-0.1980.2800.000
전문대정원0.2880.3150.1170.1440.3980.336-0.054-0.090-0.033-0.0751.0000.8210.0000.000
전문대현원-0.100-0.047-0.211-0.1830.0970.0740.165-0.247-0.167-0.1980.8211.0000.0000.169
혼성대정원0.0000.0000.0000.0000.0000.0000.9220.0000.0000.2800.0000.0001.0000.000
지역대정원0.0000.2460.5050.0000.3300.5070.0000.9730.2040.0000.0000.1690.0001.000

Missing values

2023-12-12T16:52:36.242442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:52:36.849373image/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

시군구별관할소방서정원계현원계남성대정원남성대현원여성대정원여성대현원혼성대정원혼성대현원지역대정원지역대현원전담대정원전담대현원전문대정원전문대현원
0포항북구포항북구76066325022533527900001151066053
1포항남구포항남구640530250217320256000030264031
2경주시경주1050827460391310237001409660488055
3김천시김천790510510349220122000030173022
4안동시안동710579400323120900040301201143022
5구미시구미9806245103334302640000004027
6영주시영주430364220185806830220030227067
7영천시영천59048136030215012030152020003024
8상주시상주78062847038217015200201590633016
9문경시문경5604952902761901570000008062
시군구별관할소방서정원계현원계남성대정원남성대현원여성대정원여성대현원혼성대정원혼성대현원지역대정원지역대현원전담대정원전담대현원전문대정원전문대현원
14영양군안동25021110080404000202060443027
15영덕군영덕460380100871601366044201890743021
16청도군청도470404230189140129004033605300
17고령군고령320278130107706730230060513030
18성주군성주3803132201777058000060563022
19칠곡군칠곡450411180174180160604900003028
20예천군예천5004342802481008160470030283030
21봉화군영주4904442202051009200807560473025
22울진군울진54046213011620016230302017100826055
23울릉군포항남구24019817013840340000003026