Overview

Dataset statistics

Number of variables12
Number of observations225
Missing cells3
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.2 KiB
Average record size in memory105.6 B

Variable types

Numeric9
Text1
Categorical2

Dataset

Description광주광역시(소방안전본부)가 보유한 구조대 인명구조장비에 대한 데이터로 장비명, 내용연수 , 인명구조장비의 위치 등에 대한 정보를 제공합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/3076307/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
특수구조대 is highly overall correlated with 동부 and 4 other fieldsHigh correlation
산악구조대 is highly overall correlated with 항공대 and 2 other fieldsHigh correlation
항공대 is highly overall correlated with 산악구조대 and 5 other fieldsHigh correlation
동부 is highly overall correlated with 특수구조대 and 6 other fieldsHigh correlation
서부 is highly overall correlated with 특수구조대 and 5 other fieldsHigh correlation
남부 is highly overall correlated with 특수구조대 and 6 other fieldsHigh correlation
북부 is highly overall correlated with 특수구조대 and 5 other fieldsHigh correlation
광산 is highly overall correlated with 특수구조대 and 5 other fieldsHigh correlation
장비명 has 3 (1.3%) missing valuesMissing
연번 has unique valuesUnique
특수구조대 has 27 (12.0%) zerosZeros
산악구조대 has 141 (62.7%) zerosZeros
항공대 has 140 (62.2%) zerosZeros
동부 has 60 (26.7%) zerosZeros
서부 has 59 (26.2%) zerosZeros
남부 has 55 (24.4%) zerosZeros
북부 has 52 (23.1%) zerosZeros
광산 has 50 (22.2%) zerosZeros

Reproduction

Analysis started2024-03-15 02:20:37.741061
Analysis finished2024-03-15 02:21:00.273402
Duration22.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct225
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113
Minimum1
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T11:21:00.479854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.2
Q157
median113
Q3169
95-th percentile213.8
Maximum225
Range224
Interquartile range (IQR)112

Descriptive statistics

Standard deviation65.096083
Coefficient of variation (CV)0.57607153
Kurtosis-1.2
Mean113
Median Absolute Deviation (MAD)56
Skewness0
Sum25425
Variance4237.5
MonotonicityStrictly increasing
2024-03-15T11:21:00.877753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
170 1
 
0.4%
144 1
 
0.4%
145 1
 
0.4%
146 1
 
0.4%
147 1
 
0.4%
148 1
 
0.4%
149 1
 
0.4%
150 1
 
0.4%
151 1
 
0.4%
Other values (215) 215
95.6%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
225 1
0.4%
224 1
0.4%
223 1
0.4%
222 1
0.4%
221 1
0.4%
220 1
0.4%
219 1
0.4%
218 1
0.4%
217 1
0.4%
216 1
0.4%

장비명
Text

MISSING 

Distinct220
Distinct (%)99.1%
Missing3
Missing (%)1.3%
Memory size1.9 KiB
2024-03-15T11:21:01.947304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length10.166667
Min length4

Characters and Unicode

Total characters2257
Distinct characters279
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique218 ?
Unique (%)98.2%

Sample

1st row화생방분석차(01)
2nd row화생방제독차(02)
3rd row구조공착차(01)
4th row구조버스(02)
5th row산악구조차(03)
ValueCountFrequency (%)
공기호흡기 4
 
1.3%
급류구조용 4
 
1.3%
구조대상자 4
 
1.3%
매몰자 3
 
1.0%
다목적 3
 
1.0%
생물학작용제 2
 
0.7%
단식사다리 2
 
0.7%
수중 2
 
0.7%
공기주입형 2
 
0.7%
누출제어 2
 
0.7%
Other values (270) 275
90.8%
2024-03-15T11:21:03.221777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 219
 
9.7%
( 219
 
9.7%
0 199
 
8.8%
1 89
 
3.9%
83
 
3.7%
62
 
2.7%
2 44
 
1.9%
41
 
1.8%
38
 
1.7%
38
 
1.7%
Other values (269) 1225
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1298
57.5%
Decimal Number 430
 
19.1%
Close Punctuation 219
 
9.7%
Open Punctuation 219
 
9.7%
Space Separator 83
 
3.7%
Uppercase Letter 4
 
0.2%
Other Punctuation 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
62
 
4.8%
41
 
3.2%
38
 
2.9%
38
 
2.9%
33
 
2.5%
31
 
2.4%
27
 
2.1%
26
 
2.0%
23
 
1.8%
21
 
1.6%
Other values (249) 958
73.8%
Decimal Number
ValueCountFrequency (%)
0 199
46.3%
1 89
20.7%
2 44
 
10.2%
3 30
 
7.0%
4 20
 
4.7%
5 15
 
3.5%
7 10
 
2.3%
6 10
 
2.3%
8 7
 
1.6%
9 6
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
H 1
25.0%
V 1
25.0%
O 1
25.0%
R 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
/ 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 219
100.0%
Open Punctuation
ValueCountFrequency (%)
( 219
100.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Lowercase Letter
ValueCountFrequency (%)
p 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1298
57.5%
Common 954
42.3%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
62
 
4.8%
41
 
3.2%
38
 
2.9%
38
 
2.9%
33
 
2.5%
31
 
2.4%
27
 
2.1%
26
 
2.0%
23
 
1.8%
21
 
1.6%
Other values (249) 958
73.8%
Common
ValueCountFrequency (%)
) 219
23.0%
( 219
23.0%
0 199
20.9%
1 89
9.3%
83
 
8.7%
2 44
 
4.6%
3 30
 
3.1%
4 20
 
2.1%
5 15
 
1.6%
7 10
 
1.0%
Other values (5) 26
 
2.7%
Latin
ValueCountFrequency (%)
H 1
20.0%
p 1
20.0%
V 1
20.0%
O 1
20.0%
R 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1298
57.5%
ASCII 959
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 219
22.8%
( 219
22.8%
0 199
20.8%
1 89
9.3%
83
 
8.7%
2 44
 
4.6%
3 30
 
3.1%
4 20
 
2.1%
5 15
 
1.6%
7 10
 
1.0%
Other values (10) 31
 
3.2%
Hangul
ValueCountFrequency (%)
62
 
4.8%
41
 
3.2%
38
 
2.9%
38
 
2.9%
33
 
2.5%
31
 
2.4%
27
 
2.1%
26
 
2.0%
23
 
1.8%
21
 
1.6%
Other values (249) 958
73.8%

내용연수
Categorical

Distinct12
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
소모품
58 
10
53 
5
50 
7
22 
3
17 
Other values (7)
25 

Length

Max length3
Median length2
Mean length1.7777778
Min length1

Unique

Unique4 ?
Unique (%)1.8%

Sample

1st row10
2nd row10
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
소모품 58
25.8%
10 53
23.6%
5 50
22.2%
7 22
 
9.8%
3 17
 
7.6%
8 10
 
4.4%
9 8
 
3.6%
12 3
 
1.3%
11 1
 
0.4%
2 1
 
0.4%
Other values (2) 2
 
0.9%

Length

2024-03-15T11:21:03.476238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소모품 58
25.8%
10 53
23.6%
5 50
22.2%
7 22
 
9.8%
3 17
 
7.6%
8 10
 
4.4%
9 8
 
3.6%
12 3
 
1.3%
11 1
 
0.4%
2 1
 
0.4%
Other values (2) 2
 
0.9%

특수구조대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5644444
Minimum0
Maximum198
Zeros27
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T11:21:03.712662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q39
95-th percentile35.8
Maximum198
Range198
Interquartile range (IQR)8

Descriptive statistics

Standard deviation18.219558
Coefficient of variation (CV)2.1273485
Kurtosis54.037032
Mean8.5644444
Median Absolute Deviation (MAD)1
Skewness6.0729804
Sum1927
Variance331.9523
MonotonicityNot monotonic
2024-03-15T11:21:04.126543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 71
31.6%
2 33
14.7%
0 27
 
12.0%
3 11
 
4.9%
5 10
 
4.4%
10 8
 
3.6%
6 6
 
2.7%
4 5
 
2.2%
20 5
 
2.2%
21 4
 
1.8%
Other values (27) 45
20.0%
ValueCountFrequency (%)
0 27
 
12.0%
1 71
31.6%
2 33
14.7%
3 11
 
4.9%
4 5
 
2.2%
5 10
 
4.4%
6 6
 
2.7%
7 3
 
1.3%
8 2
 
0.9%
9 2
 
0.9%
ValueCountFrequency (%)
198 1
 
0.4%
91 1
 
0.4%
73 1
 
0.4%
51 1
 
0.4%
50 1
 
0.4%
49 1
 
0.4%
48 1
 
0.4%
44 3
1.3%
37 1
 
0.4%
36 1
 
0.4%

산악구조대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7333333
Minimum0
Maximum96
Zeros141
Zeros (%)62.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T11:21:04.599630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile16.8
Maximum96
Range96
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.467637
Coefficient of variation (CV)2.8038314
Kurtosis47.243872
Mean3.7333333
Median Absolute Deviation (MAD)0
Skewness6.1298633
Sum840
Variance109.57143
MonotonicityNot monotonic
2024-03-15T11:21:05.031884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 141
62.7%
1 19
 
8.4%
9 14
 
6.2%
2 10
 
4.4%
5 6
 
2.7%
3 4
 
1.8%
10 4
 
1.8%
11 4
 
1.8%
18 2
 
0.9%
13 2
 
0.9%
Other values (16) 19
 
8.4%
ValueCountFrequency (%)
0 141
62.7%
1 19
 
8.4%
2 10
 
4.4%
3 4
 
1.8%
4 2
 
0.9%
5 6
 
2.7%
6 2
 
0.9%
8 1
 
0.4%
9 14
 
6.2%
10 4
 
1.8%
ValueCountFrequency (%)
96 1
0.4%
90 1
0.4%
41 1
0.4%
30 1
0.4%
28 1
0.4%
27 1
0.4%
26 1
0.4%
25 1
0.4%
19 1
0.4%
18 2
0.9%

항공대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5777778
Minimum0
Maximum100
Zeros140
Zeros (%)62.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T11:21:05.446177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile16.6
Maximum100
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.8521811
Coefficient of variation (CV)2.4742121
Kurtosis63.188027
Mean3.5777778
Median Absolute Deviation (MAD)0
Skewness6.4976259
Sum805
Variance78.361111
MonotonicityNot monotonic
2024-03-15T11:21:05.878343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 140
62.2%
2 14
 
6.2%
1 13
 
5.8%
15 7
 
3.1%
12 6
 
2.7%
10 6
 
2.7%
6 6
 
2.7%
5 5
 
2.2%
7 4
 
1.8%
3 4
 
1.8%
Other values (14) 20
 
8.9%
ValueCountFrequency (%)
0 140
62.2%
1 13
 
5.8%
2 14
 
6.2%
3 4
 
1.8%
4 1
 
0.4%
5 5
 
2.2%
6 6
 
2.7%
7 4
 
1.8%
8 3
 
1.3%
9 1
 
0.4%
ValueCountFrequency (%)
100 1
 
0.4%
30 2
 
0.9%
26 1
 
0.4%
24 1
 
0.4%
23 2
 
0.9%
22 2
 
0.9%
19 1
 
0.4%
18 1
 
0.4%
17 1
 
0.4%
15 7
3.1%

동부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1733333
Minimum0
Maximum133
Zeros60
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T11:21:06.340127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile32.8
Maximum133
Range133
Interquartile range (IQR)8

Descriptive statistics

Standard deviation16.276111
Coefficient of variation (CV)1.9913676
Kurtosis23.325402
Mean8.1733333
Median Absolute Deviation (MAD)2
Skewness4.1974944
Sum1839
Variance264.91179
MonotonicityNot monotonic
2024-03-15T11:21:06.821635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 60
26.7%
1 42
18.7%
2 27
12.0%
5 12
 
5.3%
3 9
 
4.0%
6 6
 
2.7%
23 5
 
2.2%
7 5
 
2.2%
12 5
 
2.2%
10 5
 
2.2%
Other values (28) 49
21.8%
ValueCountFrequency (%)
0 60
26.7%
1 42
18.7%
2 27
12.0%
3 9
 
4.0%
4 4
 
1.8%
5 12
 
5.3%
6 6
 
2.7%
7 5
 
2.2%
8 4
 
1.8%
9 3
 
1.3%
ValueCountFrequency (%)
133 1
0.4%
109 1
0.4%
80 1
0.4%
63 1
0.4%
56 1
0.4%
55 1
0.4%
54 1
0.4%
46 1
0.4%
38 2
0.9%
35 1
0.4%

서부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2711111
Minimum0
Maximum170
Zeros59
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T11:21:07.264518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile32.4
Maximum170
Range170
Interquartile range (IQR)8

Descriptive statistics

Standard deviation15.501219
Coefficient of variation (CV)2.1318913
Kurtosis55.425892
Mean7.2711111
Median Absolute Deviation (MAD)2
Skewness6.1238404
Sum1636
Variance240.28778
MonotonicityNot monotonic
2024-03-15T11:21:07.734244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 59
26.2%
1 41
18.2%
2 33
14.7%
3 12
 
5.3%
5 9
 
4.0%
10 7
 
3.1%
8 7
 
3.1%
6 7
 
3.1%
4 4
 
1.8%
20 3
 
1.3%
Other values (27) 43
19.1%
ValueCountFrequency (%)
0 59
26.2%
1 41
18.2%
2 33
14.7%
3 12
 
5.3%
4 4
 
1.8%
5 9
 
4.0%
6 7
 
3.1%
7 1
 
0.4%
8 7
 
3.1%
9 1
 
0.4%
ValueCountFrequency (%)
170 1
 
0.4%
64 2
0.9%
48 1
 
0.4%
47 1
 
0.4%
46 1
 
0.4%
43 1
 
0.4%
38 3
1.3%
36 1
 
0.4%
33 1
 
0.4%
30 1
 
0.4%

남부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1688889
Minimum0
Maximum154
Zeros55
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T11:21:08.169724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q39
95-th percentile37.6
Maximum154
Range154
Interquartile range (IQR)8

Descriptive statistics

Standard deviation16.962351
Coefficient of variation (CV)2.0764575
Kurtosis32.972142
Mean8.1688889
Median Absolute Deviation (MAD)2
Skewness4.924965
Sum1838
Variance287.72135
MonotonicityNot monotonic
2024-03-15T11:21:08.584988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 55
24.4%
1 42
18.7%
2 30
13.3%
3 13
 
5.8%
10 8
 
3.6%
4 8
 
3.6%
5 7
 
3.1%
8 5
 
2.2%
6 4
 
1.8%
16 4
 
1.8%
Other values (28) 49
21.8%
ValueCountFrequency (%)
0 55
24.4%
1 42
18.7%
2 30
13.3%
3 13
 
5.8%
4 8
 
3.6%
5 7
 
3.1%
6 4
 
1.8%
7 4
 
1.8%
8 5
 
2.2%
9 3
 
1.3%
ValueCountFrequency (%)
154 1
 
0.4%
120 1
 
0.4%
74 1
 
0.4%
59 1
 
0.4%
51 1
 
0.4%
50 1
 
0.4%
46 1
 
0.4%
40 2
0.9%
38 3
1.3%
36 1
 
0.4%

북부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8311111
Minimum0
Maximum150
Zeros52
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T11:21:08.831235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q38
95-th percentile27.6
Maximum150
Range150
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.650547
Coefficient of variation (CV)1.9982908
Kurtosis55.129555
Mean6.8311111
Median Absolute Deviation (MAD)2
Skewness6.0643667
Sum1537
Variance186.33742
MonotonicityNot monotonic
2024-03-15T11:21:09.546587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 52
23.1%
1 42
18.7%
2 35
15.6%
3 13
 
5.8%
10 12
 
5.3%
5 8
 
3.6%
6 7
 
3.1%
19 6
 
2.7%
4 5
 
2.2%
11 5
 
2.2%
Other values (23) 40
17.8%
ValueCountFrequency (%)
0 52
23.1%
1 42
18.7%
2 35
15.6%
3 13
 
5.8%
4 5
 
2.2%
5 8
 
3.6%
6 7
 
3.1%
7 5
 
2.2%
8 3
 
1.3%
9 3
 
1.3%
ValueCountFrequency (%)
150 1
0.4%
63 1
0.4%
47 1
0.4%
42 1
0.4%
39 2
0.9%
38 2
0.9%
32 1
0.4%
30 2
0.9%
28 1
0.4%
26 2
0.9%

광산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5422222
Minimum0
Maximum207
Zeros50
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T11:21:09.962546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q310
95-th percentile36.6
Maximum207
Range207
Interquartile range (IQR)9

Descriptive statistics

Standard deviation18.751649
Coefficient of variation (CV)2.1951722
Kurtosis58.916072
Mean8.5422222
Median Absolute Deviation (MAD)2
Skewness6.4763896
Sum1922
Variance351.62433
MonotonicityNot monotonic
2024-03-15T11:21:10.382447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 50
22.2%
1 45
20.0%
2 26
11.6%
4 12
 
5.3%
5 11
 
4.9%
6 9
 
4.0%
10 8
 
3.6%
3 6
 
2.7%
12 5
 
2.2%
19 5
 
2.2%
Other values (28) 48
21.3%
ValueCountFrequency (%)
0 50
22.2%
1 45
20.0%
2 26
11.6%
3 6
 
2.7%
4 12
 
5.3%
5 11
 
4.9%
6 9
 
4.0%
7 3
 
1.3%
8 4
 
1.8%
9 2
 
0.9%
ValueCountFrequency (%)
207 1
 
0.4%
100 1
 
0.4%
75 1
 
0.4%
71 1
 
0.4%
58 1
 
0.4%
42 1
 
0.4%
41 1
 
0.4%
40 1
 
0.4%
38 3
1.3%
37 1
 
0.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-01-01
225 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-01
2nd row2024-01-01
3rd row2024-01-01
4th row2024-01-01
5th row2024-01-01

Common Values

ValueCountFrequency (%)
2024-01-01 225
100.0%

Length

2024-03-15T11:21:10.682236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T11:21:10.996277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-01 225
100.0%

Interactions

2024-03-15T11:20:57.162788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:38.464250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:40.398102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:42.846754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:45.587054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:47.731241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:49.749197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:52.401052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:55.039772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:57.607895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:38.663470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:40.643555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:43.086983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:45.843609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:47.968116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:49.992126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:52.693443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:55.286933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:57.809063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:38.938917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:40.974277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:43.363744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:46.124360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:48.235390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:50.312558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:53.000507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:55.542166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:57.974356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:39.086493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:41.211573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:43.640548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:46.294876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:48.483401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:50.602286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:53.288816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:55.765112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:58.214071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:39.225538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:41.464273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:43.909874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:46.454131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:48.633139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:50.837686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:53.564186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:56.031990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:58.468866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:39.462197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:41.725404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:44.178377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:46.702713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:48.874303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:51.208047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:53.830439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:56.353853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:58.681577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:39.717108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:41.989056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:44.468991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:46.957530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:49.036977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:51.455898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:54.162054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:56.631813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:59.076328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:39.906173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:42.262839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:44.771449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:47.222583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:49.285815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:51.759201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:54.458192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:56.808934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:59.317157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:40.166215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:42.585714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:45.071866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:47.480852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:49.504566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:52.132496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:54.753385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:20:56.986219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T11:21:11.206244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번내용연수특수구조대산악구조대항공대동부서부남부북부광산
연번1.0000.5890.2660.1590.5090.2150.3040.3060.2430.215
내용연수0.5891.0000.0000.0000.2290.0000.0000.0000.0000.000
특수구조대0.2660.0001.0000.8650.7680.9490.9140.8940.9940.963
산악구조대0.1590.0000.8651.0000.5970.7580.7090.7480.8750.909
항공대0.5090.2290.7680.5971.0000.7990.9240.7500.7600.705
동부0.2150.0000.9490.7580.7991.0000.9460.9100.9720.899
서부0.3040.0000.9140.7090.9240.9461.0000.9020.9470.892
남부0.3060.0000.8940.7480.7500.9100.9021.0000.9100.941
북부0.2430.0000.9940.8750.7600.9720.9470.9101.0000.969
광산0.2150.0000.9630.9090.7050.8990.8920.9410.9691.000
2024-03-15T11:21:11.740373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번특수구조대산악구조대항공대동부서부남부북부광산내용연수
연번1.0000.135-0.110-0.014-0.004-0.007-0.0010.0020.0560.290
특수구조대0.1351.0000.3870.4480.6130.5950.6270.6620.6750.000
산악구조대-0.1100.3871.0000.5820.5080.4980.5190.4780.4590.000
항공대-0.0140.4480.5821.0000.5740.5700.5790.5660.5480.000
동부-0.0040.6130.5080.5741.0000.9400.9120.9110.8640.000
서부-0.0070.5950.4980.5700.9401.0000.9060.9070.8450.000
남부-0.0010.6270.5190.5790.9120.9061.0000.8870.8890.000
북부0.0020.6620.4780.5660.9110.9070.8871.0000.8230.000
광산0.0560.6750.4590.5480.8640.8450.8890.8231.0000.000
내용연수0.2900.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-15T11:20:59.567306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T11:21:00.053771image/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

연번장비명내용연수특수구조대산악구조대항공대동부서부남부북부광산데이터기준일자
01화생방분석차(01)10100000002024-01-01
12화생방제독차(02)10100000002024-01-01
23구조공착차(01)8100111112024-01-01
34구조버스(02)8100111112024-01-01
45산악구조차(03)8020100002024-01-01
56생활안전차(05)8001010012024-01-01
67견인차(02)8001000002024-01-01
78구조보트운반트레일러(08)10200110112024-01-01
89구조장비운송 트레일러(09)10200001002024-01-01
910유조차(12)10001000002024-01-01
연번장비명내용연수특수구조대산악구조대항공대동부서부남부북부광산데이터기준일자
215216방사능보호복(04)5500500052024-01-01
216217인명구조경보기(01)3259033263517222024-01-01
217218대원위치추적장치(01)50915000002024-01-01
218219대원탈출장비(01)소모품80314211026182024-01-01
219220관절보호대(02)소모품21111517671092024-01-01
220221안전안경(01)소모품1491726202019212024-01-01
221222패드형 인명구조매트(01)7505655552024-01-01
222223공기주입형 인명구조매트(02)7100131112024-01-01
223224수중펌프5210022012024-01-01
224225내화학용 수중펌프(02)5000111112024-01-01