Overview

Dataset statistics

Number of variables10
Number of observations61
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory90.2 B

Variable types

Categorical3
Text1
Numeric6

Dataset

Description울산광역시 소방서·안전센터의 소방청 조직계급별 상위부서명 정보, 부서명 정보, 해당직급별 인원수 정보를 데이터로 제공
URLhttps://www.data.go.kr/data/15109128/fileData.do

Alerts

소방령 is highly overall correlated with 소방경 and 2 other fieldsHigh correlation
소방경 is highly overall correlated with 소방령High correlation
소방위 is highly overall correlated with 소방장 and 1 other fieldsHigh correlation
소방장 is highly overall correlated with 소방위 and 2 other fieldsHigh correlation
소방교 is highly overall correlated with 소방위 and 2 other fieldsHigh correlation
소방사 is highly overall correlated with 소방령 and 2 other fieldsHigh correlation
소방정 is highly overall correlated with 소방령High correlation
소방준감 is highly imbalanced (87.9%)Imbalance
소방정 is highly imbalanced (54.5%)Imbalance
소방령 has 39 (63.9%) zerosZeros
소방경 has 7 (11.5%) zerosZeros
소방위 has 4 (6.6%) zerosZeros
소방장 has 6 (9.8%) zerosZeros
소방교 has 7 (11.5%) zerosZeros
소방사 has 9 (14.8%) zerosZeros

Reproduction

Analysis started2023-12-12 10:20:10.825907
Analysis finished2023-12-12 10:20:15.946691
Duration5.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상위부서명
Categorical

Distinct7
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size620.0 B
남부소방서
12 
북부소방서
10 
중부소방서
9 
온산소방서
9 
동부소방서
8 
Other values (2)
13 

Length

Max length5
Median length5
Mean length4.9180328
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남부소방서 12
19.7%
북부소방서 10
16.4%
중부소방서 9
14.8%
온산소방서 9
14.8%
동부소방서 8
13.1%
울주소방서 8
13.1%
소방본부 5
8.2%

Length

2023-12-12T19:20:16.036472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:20:16.197609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남부소방서 12
19.7%
북부소방서 10
16.4%
중부소방서 9
14.8%
온산소방서 9
14.8%
동부소방서 8
13.1%
울주소방서 8
13.1%
소방본부 5
8.2%

부서명
Text

Distinct38
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
2023-12-12T19:20:16.485742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.9016393
Min length3

Characters and Unicode

Total characters421
Distinct characters66
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

Unique34 ?
Unique (%)55.7%

Sample

1st row소방본부
2nd row소방행정과
3rd row119종합상황실
4th row예방안전과
5th row119재난대응과
ValueCountFrequency (%)
소방행정과 7
 
11.5%
예방안전과 7
 
11.5%
119재난대응과 7
 
11.5%
구조대 6
 
9.8%
화산119안전센터 1
 
1.6%
북부소방서 1
 
1.6%
송정119안전센터 1
 
1.6%
온산소방서 1
 
1.6%
온산119안전센터 1
 
1.6%
온양119안전센터 1
 
1.6%
Other values (28) 28
45.9%
2023-12-12T19:20:16.980060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 68
16.2%
전 34
 
8.1%
9 34
 
8.1%
안 33
 
7.8%
터 26
 
6.2%
센 26
 
6.2%
방 21
 
5.0%
과 21
 
5.0%
소 15
 
3.6%
대 13
 
3.1%
Other values (56) 130
30.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 319
75.8%
Decimal Number 102
 
24.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 34
 
10.7%
안 33
 
10.3%
터 26
 
8.2%
센 26
 
8.2%
방 21
 
6.6%
과 21
 
6.6%
소 15
 
4.7%
대 13
 
4.1%
정 10
 
3.1%
서 8
 
2.5%
Other values (54) 112
35.1%
Decimal Number
ValueCountFrequency (%)
1 68
66.7%
9 34
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 319
75.8%
Common 102
 
24.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 34
 
10.7%
안 33
 
10.3%
터 26
 
8.2%
센 26
 
8.2%
방 21
 
6.6%
과 21
 
6.6%
소 15
 
4.7%
대 13
 
4.1%
정 10
 
3.1%
서 8
 
2.5%
Other values (54) 112
35.1%
Common
ValueCountFrequency (%)
1 68
66.7%
9 34
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 319
75.8%
ASCII 102
 
24.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 68
66.7%
9 34
33.3%
Hangul
ValueCountFrequency (%)
전 34
 
10.7%
안 33
 
10.3%
터 26
 
8.2%
센 26
 
8.2%
방 21
 
6.6%
과 21
 
6.6%
소 15
 
4.7%
대 13
 
4.1%
정 10
 
3.1%
서 8
 
2.5%
Other values (54) 112
35.1%

소방준감
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
0
60 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 60
98.4%
1 1
 
1.6%

Length

2023-12-12T19:20:17.185378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:20:17.304760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 60
98.4%
1 1
 
1.6%

소방정
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size620.0 B
0
51 
1
9 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row0
2nd row3
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 51
83.6%
1 9
 
14.8%
3 1
 
1.6%

Length

2023-12-12T19:20:17.438155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:20:17.563172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51
83.6%
1 9
 
14.8%
3 1
 
1.6%

소방령
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62295082
Minimum0
Maximum7
Zeros39
Zeros (%)63.9%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:20:17.668725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3186348
Coefficient of variation (CV)2.1167559
Kurtosis13.037397
Mean0.62295082
Median Absolute Deviation (MAD)0
Skewness3.4441644
Sum38
Variance1.7387978
MonotonicityNot monotonic
2023-12-12T19:20:17.787316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 39
63.9%
1 18
29.5%
6 1
 
1.6%
4 1
 
1.6%
3 1
 
1.6%
7 1
 
1.6%
ValueCountFrequency (%)
0 39
63.9%
1 18
29.5%
3 1
 
1.6%
4 1
 
1.6%
6 1
 
1.6%
7 1
 
1.6%
ValueCountFrequency (%)
7 1
 
1.6%
6 1
 
1.6%
4 1
 
1.6%
3 1
 
1.6%
1 18
29.5%
0 39
63.9%

소방경
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0163934
Minimum0
Maximum12
Zeros7
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:20:17.943187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile8
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2839425
Coefficient of variation (CV)1.1326869
Kurtosis6.509884
Mean2.0163934
Median Absolute Deviation (MAD)0
Skewness2.4177159
Sum123
Variance5.2163934
MonotonicityNot monotonic
2023-12-12T19:20:18.079311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 31
50.8%
2 11
 
18.0%
0 7
 
11.5%
5 4
 
6.6%
8 3
 
4.9%
3 2
 
3.3%
4 2
 
3.3%
12 1
 
1.6%
ValueCountFrequency (%)
0 7
 
11.5%
1 31
50.8%
2 11
 
18.0%
3 2
 
3.3%
4 2
 
3.3%
5 4
 
6.6%
8 3
 
4.9%
12 1
 
1.6%
ValueCountFrequency (%)
12 1
 
1.6%
8 3
 
4.9%
5 4
 
6.6%
4 2
 
3.3%
3 2
 
3.3%
2 11
 
18.0%
1 31
50.8%
0 7
 
11.5%

소방위
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0163934
Minimum0
Maximum24
Zeros4
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:20:18.215123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q36
95-th percentile9
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.8318481
Coefficient of variation (CV)0.76386515
Kurtosis9.6910462
Mean5.0163934
Median Absolute Deviation (MAD)2
Skewness2.4016539
Sum306
Variance14.68306
MonotonicityNot monotonic
2023-12-12T19:20:18.351101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 14
23.0%
6 9
14.8%
4 8
13.1%
9 5
 
8.2%
2 5
 
8.2%
5 5
 
8.2%
0 4
 
6.6%
8 4
 
6.6%
7 2
 
3.3%
1 2
 
3.3%
Other values (3) 3
 
4.9%
ValueCountFrequency (%)
0 4
 
6.6%
1 2
 
3.3%
2 5
 
8.2%
3 14
23.0%
4 8
13.1%
5 5
 
8.2%
6 9
14.8%
7 2
 
3.3%
8 4
 
6.6%
9 5
 
8.2%
ValueCountFrequency (%)
24 1
 
1.6%
16 1
 
1.6%
10 1
 
1.6%
9 5
 
8.2%
8 4
 
6.6%
7 2
 
3.3%
6 9
14.8%
5 5
 
8.2%
4 8
13.1%
3 14
23.0%

소방장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5737705
Minimum0
Maximum17
Zeros6
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:20:18.471021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile12
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6989143
Coefficient of variation (CV)0.8087232
Kurtosis1.6123563
Mean4.5737705
Median Absolute Deviation (MAD)2
Skewness1.2143293
Sum279
Variance13.681967
MonotonicityNot monotonic
2023-12-12T19:20:18.603484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 9
14.8%
3 9
14.8%
6 7
11.5%
1 7
11.5%
0 6
9.8%
2 6
9.8%
4 5
8.2%
8 2
 
3.3%
9 2
 
3.3%
11 2
 
3.3%
Other values (5) 6
9.8%
ValueCountFrequency (%)
0 6
9.8%
1 7
11.5%
2 6
9.8%
3 9
14.8%
4 5
8.2%
5 9
14.8%
6 7
11.5%
7 2
 
3.3%
8 2
 
3.3%
9 2
 
3.3%
ValueCountFrequency (%)
17 1
 
1.6%
14 1
 
1.6%
13 1
 
1.6%
12 1
 
1.6%
11 2
 
3.3%
9 2
 
3.3%
8 2
 
3.3%
7 2
 
3.3%
6 7
11.5%
5 9
14.8%

소방교
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0983607
Minimum0
Maximum18
Zeros7
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:20:18.733434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile9
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2337023
Coefficient of variation (CV)0.78902336
Kurtosis4.3025345
Mean4.0983607
Median Absolute Deviation (MAD)2
Skewness1.4711056
Sum250
Variance10.456831
MonotonicityNot monotonic
2023-12-12T19:20:18.864967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 13
21.3%
4 9
14.8%
5 8
13.1%
0 7
11.5%
9 4
 
6.6%
1 4
 
6.6%
3 4
 
6.6%
7 4
 
6.6%
6 4
 
6.6%
8 2
 
3.3%
Other values (2) 2
 
3.3%
ValueCountFrequency (%)
0 7
11.5%
1 4
 
6.6%
2 13
21.3%
3 4
 
6.6%
4 9
14.8%
5 8
13.1%
6 4
 
6.6%
7 4
 
6.6%
8 2
 
3.3%
9 4
 
6.6%
ValueCountFrequency (%)
18 1
 
1.6%
10 1
 
1.6%
9 4
 
6.6%
8 2
 
3.3%
7 4
 
6.6%
6 4
 
6.6%
5 8
13.1%
4 9
14.8%
3 4
 
6.6%
2 13
21.3%

소방사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8032787
Minimum0
Maximum15
Zeros9
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:20:19.009089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q310
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7638208
Coefficient of variation (CV)0.82088438
Kurtosis-1.2043646
Mean5.8032787
Median Absolute Deviation (MAD)4
Skewness0.37202435
Sum354
Variance22.693989
MonotonicityNot monotonic
2023-12-12T19:20:19.154932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 10
16.4%
0 9
14.8%
6 5
8.2%
1 5
8.2%
8 5
8.2%
3 4
 
6.6%
10 3
 
4.9%
9 3
 
4.9%
13 3
 
4.9%
12 3
 
4.9%
Other values (5) 11
18.0%
ValueCountFrequency (%)
0 9
14.8%
1 5
8.2%
2 10
16.4%
3 4
 
6.6%
5 2
 
3.3%
6 5
8.2%
7 2
 
3.3%
8 5
8.2%
9 3
 
4.9%
10 3
 
4.9%
ValueCountFrequency (%)
15 2
 
3.3%
14 2
 
3.3%
13 3
4.9%
12 3
4.9%
11 3
4.9%
10 3
4.9%
9 3
4.9%
8 5
8.2%
7 2
 
3.3%
6 5
8.2%

Interactions

2023-12-12T19:20:14.868483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:11.285583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.180135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.765195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:13.376747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:14.123159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:14.982684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:11.376640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.273968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.866233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:13.515007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:14.249298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:15.112444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:11.480057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.379533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.964394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:13.642755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:14.383450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:15.227003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:11.572029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.476161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:13.071368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:13.741247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:14.499493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:15.360957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:11.978098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.578566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:13.172045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:13.859663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:14.630916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:15.506051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.078627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:12.670355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:13.267050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:13.992043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:14.747111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:20:19.257121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상위부서명부서명소방준감소방정소방령소방경소방위소방장소방교소방사
상위부서명1.0000.0000.2920.4000.4120.0000.4760.3440.3710.000
부서명0.0001.0001.0000.0000.0000.0000.8740.9000.7470.913
소방준감0.2921.0001.0000.0000.0000.0000.0000.0000.0000.000
소방정0.4000.0000.0001.0000.9730.5280.5230.3570.3710.381
소방령0.4120.0000.0000.9731.0000.8380.6120.7320.6870.426
소방경0.0000.0000.0000.5280.8381.0000.7810.6110.7870.406
소방위0.4760.8740.0000.5230.6120.7811.0000.8300.8700.495
소방장0.3440.9000.0000.3570.7320.6110.8301.0000.7630.752
소방교0.3710.7470.0000.3710.6870.7870.8700.7631.0000.614
소방사0.0000.9130.0000.3810.4260.4060.4950.7520.6141.000
2023-12-12T19:20:19.730370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방준감상위부서명소방정
소방준감1.0000.2970.000
상위부서명0.2971.0000.281
소방정0.0000.2811.000
2023-12-12T19:20:19.849255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방령소방경소방위소방장소방교소방사상위부서명소방준감소방정
소방령1.0000.829-0.006-0.149-0.332-0.5390.2550.0000.778
소방경0.8291.0000.2270.095-0.099-0.1700.0000.0000.399
소방위-0.0060.2271.0000.5270.6160.4380.1780.0000.394
소방장-0.1490.0950.5271.0000.6320.6280.1690.0000.210
소방교-0.332-0.0990.6160.6321.0000.7000.1520.0000.252
소방사-0.539-0.1700.4380.6280.7001.0000.0000.0000.254
상위부서명0.2550.0000.1780.1690.1520.0001.0000.2970.281
소방준감0.0000.0000.0000.0000.0000.0000.2971.0000.000
소방정0.7780.3990.3940.2100.2520.2540.2810.0001.000

Missing values

2023-12-12T19:20:15.690673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:20:15.877444image/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

상위부서명부서명소방준감소방정소방령소방경소방위소방장소방교소방사
0소방본부소방본부10000000
1소방본부소방행정과03689600
2소방본부119종합상황실014181392
3소방본부예방안전과01333220
4소방본부119재난대응과017122417183
5중부소방서중부소방서01002110
6중부소방서성남119안전센터000155410
7중부소방서병영119안전센터0001551014
8중부소방서태화119안전센터00016559
9중부소방서유곡119안전센터000278813
상위부서명부서명소방준감소방정소방령소방경소방위소방장소방교소방사
51북부소방서예방안전과00124222
52북부소방서119재난대응과00156321
53울주소방서울주소방서01000000
54울주소방서구조대00013616
55울주소방서언양119안전센터00011614915
56울주소방서범서119안전센터00019956
57울주소방서두서119안전센터00015643
58울주소방서소방행정과00123122
59울주소방서119재난대응과00156150
60울주소방서예방안전과00124141