Overview

Dataset statistics

Number of variables6
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)5.4%
Total size in memory2.9 KiB
Average record size in memory52.4 B

Variable types

Categorical3
Text2
Numeric1

Dataset

Description인천소방학교에서 교육중인 교육훈련 입니다. 응급구조사 2급양성 ,인명구조사 2급심화반 전문구급대원 양성반, 화재진압전술반, 항공기사고대응반 등을 교육 운영 현황 입니다.
Author인천광역시
URLhttps://www.data.go.kr/data/15105869/fileData.do

Alerts

Dataset has 3 (5.4%) duplicate rowsDuplicates
총인원 is highly overall correlated with 횟수High correlation
구분 is highly overall correlated with 기간High correlation
기간 is highly overall correlated with 구분High correlation
횟수 is highly overall correlated with 총인원High correlation
횟수 is highly imbalanced (77.9%)Imbalance

Reproduction

Analysis started2024-04-29 23:01:39.221264
Analysis finished2024-04-29 23:01:41.694314
Duration2.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size580.0 B
집합
35 
특별
6 
원격
5 
시민안전
5 
보수
 
3

Length

Max length4
Median length2
Mean length2.1785714
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신임
2nd row신임
3rd row집합
4th row집합
5th row집합

Common Values

ValueCountFrequency (%)
집합 35
62.5%
특별 6
 
10.7%
원격 5
 
8.9%
시민안전 5
 
8.9%
보수 3
 
5.4%
신임 2
 
3.6%

Length

2024-04-30T08:01:41.776431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T08:01:41.889251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
집합 35
62.5%
특별 6
 
10.7%
원격 5
 
8.9%
시민안전 5
 
8.9%
보수 3
 
5.4%
신임 2
 
3.6%

과정명
Text

Distinct46
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-04-30T08:01:42.079597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length10.392857
Min length5

Characters and Unicode

Total characters582
Distinct characters127
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)67.9%

Sample

1st row제25기 신임소방공무원반
2nd row제26기 신임소방공무원반
3rd row소방위 기본반
4th row소방시설(기계전기)반
5th row교수설계 및 강의능력향상반
ValueCountFrequency (%)
예방행정실무반 3
 
4.1%
보수교육 3
 
4.1%
펌뷸런스반 3
 
4.1%
소방시설(기계전기)반 2
 
2.7%
교수설계 2
 
2.7%
및 2
 
2.7%
강의능력향상반 2
 
2.7%
직장훈련교관양성반 2
 
2.7%
긴급구조통제단운영실무반 2
 
2.7%
특수소방차량조작운영반 2
 
2.7%
Other values (49) 51
68.9%
2024-04-30T08:01:42.413139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
반 50
 
8.6%
18
 
3.1%
방 17
 
2.9%
급 16
 
2.7%
교 14
 
2.4%
무 14
 
2.4%
소 14
 
2.4%
조 13
 
2.2%
1 12
 
2.1%
구 12
 
2.1%
Other values (117) 402
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 514
88.3%
Decimal Number 26
 
4.5%
Space Separator 18
 
3.1%
Close Punctuation 10
 
1.7%
Open Punctuation 10
 
1.7%
Uppercase Letter 3
 
0.5%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
반 50
 
9.7%
방 17
 
3.3%
급 16
 
3.1%
교 14
 
2.7%
무 14
 
2.7%
소 14
 
2.7%
조 13
 
2.5%
구 12
 
2.3%
수 11
 
2.1%
관 11
 
2.1%
Other values (105) 342
66.5%
Decimal Number
ValueCountFrequency (%)
1 12
46.2%
2 8
30.8%
9 4
 
15.4%
5 1
 
3.8%
6 1
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
R 1
33.3%
T 1
33.3%
Space Separator
ValueCountFrequency (%)
18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 514
88.3%
Common 65
 
11.2%
Latin 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
반 50
 
9.7%
방 17
 
3.3%
급 16
 
3.1%
교 14
 
2.7%
무 14
 
2.7%
소 14
 
2.7%
조 13
 
2.5%
구 12
 
2.3%
수 11
 
2.1%
관 11
 
2.1%
Other values (105) 342
66.5%
Common
ValueCountFrequency (%)
18
27.7%
1 12
18.5%
) 10
15.4%
( 10
15.4%
2 8
12.3%
9 4
 
6.2%
· 1
 
1.5%
5 1
 
1.5%
6 1
 
1.5%
Latin
ValueCountFrequency (%)
I 1
33.3%
R 1
33.3%
T 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 514
88.3%
ASCII 67
 
11.5%
None 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
반 50
 
9.7%
방 17
 
3.3%
급 16
 
3.1%
교 14
 
2.7%
무 14
 
2.7%
소 14
 
2.7%
조 13
 
2.5%
구 12
 
2.3%
수 11
 
2.1%
관 11
 
2.1%
Other values (105) 342
66.5%
ASCII
ValueCountFrequency (%)
18
26.9%
1 12
17.9%
) 10
14.9%
( 10
14.9%
2 8
11.9%
9 4
 
6.0%
I 1
 
1.5%
5 1
 
1.5%
R 1
 
1.5%
T 1
 
1.5%
None
ValueCountFrequency (%)
· 1
100.0%

교육대상
Text

Distinct36
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-04-30T08:01:42.631149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length10.035714
Min length3

Characters and Unicode

Total characters562
Distinct characters102
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)42.9%

Sample

1st row임용예정자
2nd row임용예정자
3rd row소방위
4th row소방경이하
5th row소방경이하 및 소방안전강사 등
ValueCountFrequency (%)
이하 10
 
8.1%
소방경이하 8
 
6.5%
소방위 8
 
6.5%
및 8
 
6.5%
소방경 4
 
3.3%
등 3
 
2.4%
자격소지자 3
 
2.4%
자격자 3
 
2.4%
無 3
 
2.4%
중 3
 
2.4%
Other values (53) 70
56.9%
2024-04-30T08:01:43.003081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
 
11.9%
소 33
 
5.9%
자 32
 
5.7%
방 29
 
5.2%
이 22
 
3.9%
하 22
 
3.9%
경 16
 
2.8%
급 16
 
2.8%
구 14
 
2.5%
, 13
 
2.3%
Other values (92) 298
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 460
81.9%
Space Separator 67
 
11.9%
Other Punctuation 17
 
3.0%
Decimal Number 10
 
1.8%
Close Punctuation 4
 
0.7%
Open Punctuation 4
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 33
 
7.2%
자 32
 
7.0%
방 29
 
6.3%
이 22
 
4.8%
하 22
 
4.8%
경 16
 
3.5%
급 16
 
3.5%
구 14
 
3.0%
사 13
 
2.8%
조 13
 
2.8%
Other values (84) 250
54.3%
Decimal Number
ValueCountFrequency (%)
1 5
50.0%
2 4
40.0%
9 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 13
76.5%
· 4
 
23.5%
Space Separator
ValueCountFrequency (%)
67
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 457
81.3%
Common 102
 
18.1%
Han 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 33
 
7.2%
자 32
 
7.0%
방 29
 
6.3%
이 22
 
4.8%
하 22
 
4.8%
경 16
 
3.5%
급 16
 
3.5%
구 14
 
3.1%
사 13
 
2.8%
조 13
 
2.8%
Other values (83) 247
54.0%
Common
ValueCountFrequency (%)
67
65.7%
, 13
 
12.7%
1 5
 
4.9%
2 4
 
3.9%
) 4
 
3.9%
· 4
 
3.9%
( 4
 
3.9%
9 1
 
1.0%
Han
ValueCountFrequency (%)
無 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 457
81.3%
ASCII 98
 
17.4%
None 4
 
0.7%
CJK 3
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
68.4%
, 13
 
13.3%
1 5
 
5.1%
2 4
 
4.1%
) 4
 
4.1%
( 4
 
4.1%
9 1
 
1.0%
Hangul
ValueCountFrequency (%)
소 33
 
7.2%
자 32
 
7.0%
방 29
 
6.3%
이 22
 
4.8%
하 22
 
4.8%
경 16
 
3.5%
급 16
 
3.5%
구 14
 
3.1%
사 13
 
2.8%
조 13
 
2.8%
Other values (83) 247
54.0%
None
ValueCountFrequency (%)
· 4
100.0%
CJK
ValueCountFrequency (%)
無 3
100.0%

기간
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size580.0 B
3일
20 
5일
15 
1일
9 
2일
4 
24주
 
2
Other values (6)
6 

Length

Max length4
Median length2
Mean length2.0892857
Min length2

Unique

Unique6 ?
Unique (%)10.7%

Sample

1st row24주
2nd row24주
3rd row3일
4th row5일
5th row3일

Common Values

ValueCountFrequency (%)
3일 20
35.7%
5일 15
26.8%
1일 9
16.1%
2일 4
 
7.1%
24주 2
 
3.6%
3주 1
 
1.8%
10주 1
 
1.8%
2주 1
 
1.8%
4주 1
 
1.8%
4일 1
 
1.8%

Length

2024-04-30T08:01:43.167604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3일 20
35.7%
5일 15
26.8%
1일 9
16.1%
2일 4
 
7.1%
24주 2
 
3.6%
3주 1
 
1.8%
10주 1
 
1.8%
2주 1
 
1.8%
4주 1
 
1.8%
4일 1
 
1.8%

횟수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size580.0 B
1
53 
4
 
2
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row1
2nd row1
3rd row8
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 53
94.6%
4 2
 
3.6%
8 1
 
1.8%

Length

2024-04-30T08:01:43.293494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T08:01:43.385487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 53
94.6%
4 2
 
3.6%
8 1
 
1.8%

총인원
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.464286
Minimum10
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2024-04-30T08:01:43.472586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q120
median30
Q340
95-th percentile195
Maximum400
Range390
Interquartile range (IQR)20

Descriptive statistics

Standard deviation78.724136
Coefficient of variation (CV)1.559997
Kurtosis13.096275
Mean50.464286
Median Absolute Deviation (MAD)10
Skewness3.6749248
Sum2826
Variance6197.4896
MonotonicityNot monotonic
2024-04-30T08:01:43.590747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
30 20
35.7%
20 13
23.2%
40 9
16.1%
24 2
 
3.6%
12 2
 
3.6%
133 1
 
1.8%
57 1
 
1.8%
400 1
 
1.8%
10 1
 
1.8%
16 1
 
1.8%
Other values (5) 5
 
8.9%
ValueCountFrequency (%)
10 1
 
1.8%
12 2
 
3.6%
16 1
 
1.8%
20 13
23.2%
24 2
 
3.6%
30 20
35.7%
32 1
 
1.8%
40 9
16.1%
46 1
 
1.8%
57 1
 
1.8%
ValueCountFrequency (%)
400 1
 
1.8%
380 1
 
1.8%
300 1
 
1.8%
160 1
 
1.8%
133 1
 
1.8%
57 1
 
1.8%
46 1
 
1.8%
40 9
16.1%
32 1
 
1.8%
30 20
35.7%

Interactions

2024-04-30T08:01:41.373613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T08:01:43.687227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분과정명교육대상기간횟수총인원
구분1.0000.9880.9930.8370.0000.585
과정명0.9881.0000.9950.9981.0001.000
교육대상0.9930.9951.0000.9721.0000.795
기간0.8370.9980.9721.0000.0000.513
횟수0.0001.0001.0000.0001.0000.760
총인원0.5851.0000.7950.5130.7601.000
2024-04-30T08:01:43.810332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분횟수기간
구분1.0000.0000.597
횟수0.0001.0000.000
기간0.5970.0001.000
2024-04-30T08:01:43.910582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총인원구분기간횟수
총인원1.0000.4390.2890.751
구분0.4391.0000.5970.000
기간0.2890.5971.0000.000
횟수0.7510.0000.0001.000

Missing values

2024-04-30T08:01:41.550465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T08:01:41.649527image/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

구분과정명교육대상기간횟수총인원
0신임제25기 신임소방공무원반임용예정자24주1133
1신임제26기 신임소방공무원반임용예정자24주157
2집합소방위 기본반소방위3일8400
3집합소방시설(기계전기)반소방경이하5일130
4집합교수설계 및 강의능력향상반소방경이하 및 소방안전강사 등3일140
5집합동물구조패러다임반소방위 이하3일130
6집합구급교관양성반1급 응급구조사 및 간호사5일120
7집합직장훈련교관양성반경(센터장),위5일140
8집합긴급구조통제단운영실무반긴급구조통제단업무담당자 및 내근근무자5일130
9집합화재대응능력1급양성반화재대응능력1급 자격취득희망자3주124
구분과정명교육대상기간횟수총인원
46특별재난안전분야종사자반(관리자)긴급구조 및 민·관 기관1일120
47특별119안전캠프반(직무연수)초중고 안전 지도교사2일120
48보수인명구조사 1·2급 보수교육자격소지자1일1380
49보수화재조사관 보수교육자격소지자1일132
50보수상황관리관 보수교육자격소지자1일146
51시민안전시민체험교육(기초소방안전실무)관내시민연중수시130
52시민안전119안전캠프(소방고교 등)초·중·고 청소년1일130
53시민안전재난관리부대 안전교육군, 경 재난관리부대1일140
54시민안전특별시설물관계자반초고층, 지하구 관계자, 자체소방대원 등1일120
55시민안전학교교원 안전요원 직무연수과정 교육반인천시내 초중고 교사2일4300

Duplicate rows

Most frequently occurring

구분과정명교육대상기간횟수총인원# duplicates
2집합펌뷸런스반소방위 이하, 펌블란스 대원 중 無 자격자3일1303
0집합긴급구조통제단운영실무반긴급구조통제단업무담당자 및 내근근무자5일1302
1집합직장훈련교관양성반경(센터장),위5일1402