Overview

Dataset statistics

Number of variables7
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory63.1 B

Variable types

Categorical2
Text1
Numeric4

Dataset

Description대전광역시 동부여성가족원 기별 교육 수료 통계에 대한 데이터로 강좌별, 등록인원, 수료인원, 수료율 등의 항목을 제공합니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15077739/fileData.do

Alerts

등록인원 is highly overall correlated with 수료인원 and 1 other fieldsHigh correlation
수료인원 is highly overall correlated with 등록인원High correlation
미수료인원 is highly overall correlated with 수료율(퍼센트)High correlation
수료율(퍼센트) is highly overall correlated with 미수료인원High correlation
정원 is highly overall correlated with 등록인원High correlation
강좌명 has unique valuesUnique
미수료인원 has 20 (31.2%) zerosZeros

Reproduction

Analysis started2023-12-12 04:24:38.382864
Analysis finished2023-12-12 04:24:40.133396
Duration1.75 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size644.0 B
직업훈련교육과정
31 
문화아카데미과정
15 
가정친화및역량강화
14 
전문지도사양성과정
4 

Length

Max length9
Median length8
Mean length8.28125
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전문지도사양성과정
2nd row전문지도사양성과정
3rd row전문지도사양성과정
4th row전문지도사양성과정
5th row직업훈련교육과정

Common Values

ValueCountFrequency (%)
직업훈련교육과정 31
48.4%
문화아카데미과정 15
23.4%
가정친화및역량강화 14
21.9%
전문지도사양성과정 4
 
6.2%

Length

2023-12-12T13:24:40.485606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:24:40.589010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직업훈련교육과정 31
48.4%
문화아카데미과정 15
23.4%
가정친화및역량강화 14
21.9%
전문지도사양성과정 4
 
6.2%

강좌명
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-12T13:24:40.821413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length7.890625
Min length3

Characters and Unicode

Total characters505
Distinct characters175
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)100.0%

Sample

1st row정리수납전문가
2nd row웃음코칭지도사
3rd row실버인지놀이지도사
4th row전래놀이지도사
5th row손으로만드는퀼트소품
ValueCountFrequency (%)
정리수납전문가 1
 
1.6%
웃음코칭지도사 1
 
1.6%
생활일본어 1
 
1.6%
중국어hsk자격증(3급 1
 
1.6%
이탈리아&브런치 1
 
1.6%
영양만점집밥만들기 1
 
1.6%
홈베이킹 1
 
1.6%
천연발효빵만들기 1
 
1.6%
우리가족미술다지기 1
 
1.6%
가족건강경락마사지 1
 
1.6%
Other values (54) 54
84.4%
2023-12-12T13:24:41.202000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 21
 
4.2%
) 20
 
4.0%
자 14
 
2.8%
기 14
 
2.8%
스 11
 
2.2%
리 11
 
2.2%
사 10
 
2.0%
오 10
 
2.0%
어 10
 
2.0%
증 7
 
1.4%
Other values (165) 377
74.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 449
88.9%
Open Punctuation 21
 
4.2%
Close Punctuation 20
 
4.0%
Other Punctuation 8
 
1.6%
Uppercase Letter 6
 
1.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 14
 
3.1%
기 14
 
3.1%
스 11
 
2.4%
리 11
 
2.4%
사 10
 
2.2%
오 10
 
2.2%
어 10
 
2.2%
증 7
 
1.6%
가 7
 
1.6%
격 7
 
1.6%
Other values (154) 348
77.5%
Uppercase Letter
ValueCountFrequency (%)
H 1
16.7%
I 1
16.7%
K 1
16.7%
S 1
16.7%
T 1
16.7%
Q 1
16.7%
Other Punctuation
ValueCountFrequency (%)
& 5
62.5%
· 3
37.5%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 449
88.9%
Common 50
 
9.9%
Latin 6
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 14
 
3.1%
기 14
 
3.1%
스 11
 
2.4%
리 11
 
2.4%
사 10
 
2.2%
오 10
 
2.2%
어 10
 
2.2%
증 7
 
1.6%
가 7
 
1.6%
격 7
 
1.6%
Other values (154) 348
77.5%
Latin
ValueCountFrequency (%)
H 1
16.7%
I 1
16.7%
K 1
16.7%
S 1
16.7%
T 1
16.7%
Q 1
16.7%
Common
ValueCountFrequency (%)
( 21
42.0%
) 20
40.0%
& 5
 
10.0%
· 3
 
6.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 449
88.9%
ASCII 53
 
10.5%
None 3
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 21
39.6%
) 20
37.7%
& 5
 
9.4%
H 1
 
1.9%
I 1
 
1.9%
3 1
 
1.9%
K 1
 
1.9%
S 1
 
1.9%
T 1
 
1.9%
Q 1
 
1.9%
Hangul
ValueCountFrequency (%)
자 14
 
3.1%
기 14
 
3.1%
스 11
 
2.4%
리 11
 
2.4%
사 10
 
2.2%
오 10
 
2.2%
어 10
 
2.2%
증 7
 
1.6%
가 7
 
1.6%
격 7
 
1.6%
Other values (154) 348
77.5%
None
ValueCountFrequency (%)
· 3
100.0%

정원
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size644.0 B
20
42 
16
10 
25
5 
30
 
4
15
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row15

Common Values

ValueCountFrequency (%)
20 42
65.6%
16 10
 
15.6%
25 5
 
7.8%
30 4
 
6.2%
15 3
 
4.7%

Length

2023-12-12T13:24:41.350289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:24:41.455871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 42
65.6%
16 10
 
15.6%
25 5
 
7.8%
30 4
 
6.2%
15 3
 
4.7%

등록인원
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.859375
Minimum3
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T13:24:41.576355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8.15
Q112.75
median16
Q319
95-th percentile21.85
Maximum29
Range26
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation4.8627747
Coefficient of variation (CV)0.3066183
Kurtosis1.215895
Mean15.859375
Median Absolute Deviation (MAD)3
Skewness0.27040432
Sum1015
Variance23.646577
MonotonicityNot monotonic
2023-12-12T13:24:41.703879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15 8
12.5%
20 7
10.9%
16 7
10.9%
18 5
7.8%
11 5
7.8%
12 5
7.8%
19 5
7.8%
17 4
 
6.2%
14 4
 
6.2%
9 2
 
3.1%
Other values (8) 12
18.8%
ValueCountFrequency (%)
3 1
 
1.6%
6 1
 
1.6%
8 2
 
3.1%
9 2
 
3.1%
11 5
7.8%
12 5
7.8%
13 2
 
3.1%
14 4
6.2%
15 8
12.5%
16 7
10.9%
ValueCountFrequency (%)
29 2
 
3.1%
28 1
 
1.6%
22 1
 
1.6%
21 2
 
3.1%
20 7
10.9%
19 5
7.8%
18 5
7.8%
17 4
6.2%
16 7
10.9%
15 8
12.5%

수료인원
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum3
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T13:24:41.820966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q110
median14
Q317
95-th percentile20
Maximum26
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.5981363
Coefficient of variation (CV)0.34060269
Kurtosis0.015553729
Mean13.5
Median Absolute Deviation (MAD)3
Skewness0.087977724
Sum864
Variance21.142857
MonotonicityNot monotonic
2023-12-12T13:24:41.932389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
14 7
10.9%
15 6
 
9.4%
18 5
 
7.8%
16 5
 
7.8%
17 5
 
7.8%
9 5
 
7.8%
10 4
 
6.2%
11 4
 
6.2%
12 4
 
6.2%
20 3
 
4.7%
Other values (9) 16
25.0%
ValueCountFrequency (%)
3 1
 
1.6%
5 1
 
1.6%
6 3
4.7%
7 1
 
1.6%
8 3
4.7%
9 5
7.8%
10 4
6.2%
11 4
6.2%
12 4
6.2%
13 3
4.7%
ValueCountFrequency (%)
26 1
 
1.6%
24 1
 
1.6%
20 3
4.7%
19 2
 
3.1%
18 5
7.8%
17 5
7.8%
16 5
7.8%
15 6
9.4%
14 7
10.9%
13 3
4.7%

미수료인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.34375
Minimum0
Maximum10
Zeros20
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T13:24:42.104047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6.85
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3517555
Coefficient of variation (CV)1.0034157
Kurtosis0.69208596
Mean2.34375
Median Absolute Deviation (MAD)2
Skewness0.99274646
Sum150
Variance5.530754
MonotonicityNot monotonic
2023-12-12T13:24:42.251977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 20
31.2%
3 11
17.2%
1 9
14.1%
2 7
 
10.9%
5 6
 
9.4%
4 5
 
7.8%
7 2
 
3.1%
6 2
 
3.1%
8 1
 
1.6%
10 1
 
1.6%
ValueCountFrequency (%)
0 20
31.2%
1 9
14.1%
2 7
 
10.9%
3 11
17.2%
4 5
 
7.8%
5 6
 
9.4%
6 2
 
3.1%
7 2
 
3.1%
8 1
 
1.6%
10 1
 
1.6%
ValueCountFrequency (%)
10 1
 
1.6%
8 1
 
1.6%
7 2
 
3.1%
6 2
 
3.1%
5 6
 
9.4%
4 5
 
7.8%
3 11
17.2%
2 7
 
10.9%
1 9
14.1%
0 20
31.2%

수료율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.34375
Minimum46
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T13:24:42.428982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile58.75
Q175
median88.5
Q3100
95-th percentile100
Maximum100
Range54
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.182729
Coefficient of variation (CV)0.16618357
Kurtosis0.11836131
Mean85.34375
Median Absolute Deviation (MAD)11.5
Skewness-0.8717378
Sum5462
Variance201.1498
MonotonicityNot monotonic
2023-12-12T13:24:42.624071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
100 19
29.7%
75 5
 
7.8%
80 3
 
4.7%
93 3
 
4.7%
94 3
 
4.7%
91 2
 
3.1%
88 2
 
3.1%
90 2
 
3.1%
83 2
 
3.1%
92 2
 
3.1%
Other values (17) 21
32.8%
ValueCountFrequency (%)
46 1
1.6%
50 1
1.6%
53 1
1.6%
58 1
1.6%
63 1
1.6%
64 2
3.1%
67 1
1.6%
68 1
1.6%
69 1
1.6%
73 2
3.1%
ValueCountFrequency (%)
100 19
29.7%
94 3
 
4.7%
93 3
 
4.7%
92 2
 
3.1%
91 2
 
3.1%
90 2
 
3.1%
89 1
 
1.6%
88 2
 
3.1%
87 1
 
1.6%
86 1
 
1.6%

Interactions

2023-12-12T13:24:39.649708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:38.702386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.006230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.329800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.723518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:38.784198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.086149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.408074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.795550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:38.856522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.155537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.487655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.878538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:38.930546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.250976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:24:39.565305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:24:42.751375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분강좌명정원등록인원수료인원미수료인원수료율(퍼센트)
구분1.0001.0000.3580.2670.2900.0000.000
강좌명1.0001.0001.0001.0001.0001.0001.000
정원0.3581.0001.0000.7510.5280.6530.000
등록인원0.2671.0000.7511.0000.9020.4330.000
수료인원0.2901.0000.5280.9021.0000.0000.220
미수료인원0.0001.0000.6530.4330.0001.0000.950
수료율(퍼센트)0.0001.0000.0000.0000.2200.9501.000
2023-12-12T13:24:42.890346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분정원
구분1.0000.295
정원0.2951.000
2023-12-12T13:24:42.985262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록인원수료인원미수료인원수료율(퍼센트)구분정원
등록인원1.0000.8760.253-0.0430.0000.521
수료인원0.8761.000-0.1990.3880.1750.325
미수료인원0.253-0.1991.000-0.9540.0000.331
수료율(퍼센트)-0.0430.388-0.9541.0000.0000.000
구분0.0000.1750.0000.0001.0000.295
정원0.5210.3250.3310.0000.2951.000

Missing values

2023-12-12T13:24:39.986135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:24:40.092510image/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

구분강좌명정원등록인원수료인원미수료인원수료율(퍼센트)
0전문지도사양성과정정리수납전문가2020200100
1전문지도사양성과정웃음코칭지도사2015150100
2전문지도사양성과정실버인지놀이지도사201816289
3전문지도사양성과정전래놀이지도사201110191
4직업훈련교육과정손으로만드는퀼트소품151211192
5직업훈련교육과정라탄공예15118373
6직업훈련교육과정한식조리기능사(오전20990100
7직업훈련교육과정한식조리기능사(오후)20330100
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9직업훈련교육과정제과·제빵기능사(오후)2016160100
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