Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 KiB
Average record size in memory140.3 B

Variable types

Numeric3
Categorical11
Text2
Boolean1

Alerts

trget_age_etc_nmpr_cn has constant value ""Constant
instt_nm is highly overall correlated with seq_no and 10 other fieldsHigh correlation
addr is highly overall correlated with seq_no and 10 other fieldsHigh correlation
seq_no is highly overall correlated with valid_pd_begin_de and 5 other fieldsHigh correlation
valid_pd_begin_de is highly overall correlated with seq_no and 6 other fieldsHigh correlation
valid_pd_end_de is highly overall correlated with seq_no and 5 other fieldsHigh correlation
flag_nm is highly overall correlated with area_nm and 3 other fieldsHigh correlation
area_nm is highly overall correlated with flag_nm and 4 other fieldsHigh correlation
safe_cnsdr_act_at is highly overall correlated with area_nm and 4 other fieldsHigh correlation
full_age9_12_trget_nmpr_cn is highly overall correlated with tot_trget_nmpr_cnHigh correlation
full_age13_15_trget_nmpr_cn is highly overall correlated with seq_no and 8 other fieldsHigh correlation
full_age19_24_trget_nmpr_cn is highly overall correlated with valid_pd_begin_de and 3 other fieldsHigh correlation
tot_trget_nmpr_cn is highly overall correlated with flag_nm and 6 other fieldsHigh correlation
oper_state_nm is highly overall correlated with seq_no and 5 other fieldsHigh correlation
safe_cnsdr_act_at is highly imbalanced (59.8%)Imbalance
full_age16_18_trget_nmpr_cn is highly imbalanced (51.1%)Imbalance
full_age19_24_trget_nmpr_cn is highly imbalanced (91.9%)Imbalance
oper_state_nm is highly imbalanced (80.6%)Imbalance
seq_no has unique valuesUnique
crtfc_no_cn has unique valuesUnique
progrm_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:05:48.902707
Analysis finished2023-12-10 10:05:53.593230
Duration4.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean378.01
Minimum1
Maximum10932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:53.731172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum10932
Range10931
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation1865.4444
Coefficient of variation (CV)4.9349075
Kurtosis29.882749
Mean378.01
Median Absolute Deviation (MAD)25.5
Skewness5.5926101
Sum37801
Variance3479882.7
MonotonicityNot monotonic
2023-12-10T19:05:53.999396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
10932 1
1.0%
10931 1
1.0%
10930 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

flag_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
활동
61 
개별
39 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row활동
2nd row활동
3rd row활동
4th row활동
5th row개별

Common Values

ValueCountFrequency (%)
활동 61
61.0%
개별 39
39.0%

Length

2023-12-10T19:05:54.274289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:54.492830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
활동 61
61.0%
개별 39
39.0%

crtfc_no_cn
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:05:54.824828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length14.39
Min length14

Characters and Unicode

Total characters1439
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row0249B06B-09547
2nd row1195A04B-00050
3rd row5070F07A-09514
4th row5070D08B-09448
5th row5070G01A-MD0583
ValueCountFrequency (%)
0249b06b-09547 1
 
1.0%
0018g05a-md0439 1
 
1.0%
5070f07b-08919 1
 
1.0%
4498b06b-08888 1
 
1.0%
5413a01f-08930 1
 
1.0%
0928g07c-md0409 1
 
1.0%
4456a01f-08915 1
 
1.0%
4081f07b-09818 1
 
1.0%
4081f07a-09817 1
 
1.0%
4081f07b-09833 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:05:55.399174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 338
23.5%
9 108
 
7.5%
- 100
 
6.9%
8 96
 
6.7%
1 89
 
6.2%
6 77
 
5.4%
5 76
 
5.3%
4 76
 
5.3%
7 73
 
5.1%
2 70
 
4.9%
Other values (8) 336
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1061
73.7%
Uppercase Letter 278
 
19.3%
Dash Punctuation 100
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 338
31.9%
9 108
 
10.2%
8 96
 
9.0%
1 89
 
8.4%
6 77
 
7.3%
5 76
 
7.2%
4 76
 
7.2%
7 73
 
6.9%
2 70
 
6.6%
3 58
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
A 60
21.6%
D 44
15.8%
B 40
14.4%
G 39
14.0%
M 39
14.0%
F 32
11.5%
C 24
 
8.6%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1161
80.7%
Latin 278
 
19.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 338
29.1%
9 108
 
9.3%
- 100
 
8.6%
8 96
 
8.3%
1 89
 
7.7%
6 77
 
6.6%
5 76
 
6.5%
4 76
 
6.5%
7 73
 
6.3%
2 70
 
6.0%
Latin
ValueCountFrequency (%)
A 60
21.6%
D 44
15.8%
B 40
14.4%
G 39
14.0%
M 39
14.0%
F 32
11.5%
C 24
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 338
23.5%
9 108
 
7.5%
- 100
 
6.9%
8 96
 
6.7%
1 89
 
6.2%
6 77
 
5.4%
5 76
 
5.3%
4 76
 
5.3%
7 73
 
5.1%
2 70
 
4.9%
Other values (8) 336
23.3%

area_nm
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전라북도
23 
경상북도
21 
경상남도
12 
충청남도
9 
전라남도
8 
Other values (7)
27 

Length

Max length5
Median length4
Mean length4.07
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row부산광역시
2nd row광주광역시
3rd row경상북도
4th row경상북도
5th row경상북도

Common Values

ValueCountFrequency (%)
전라북도 23
23.0%
경상북도 21
21.0%
경상남도 12
12.0%
충청남도 9
 
9.0%
전라남도 8
 
8.0%
대구광역시 6
 
6.0%
강원도 6
 
6.0%
서울특별시 5
 
5.0%
광주광역시 4
 
4.0%
경기도 4
 
4.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T19:05:55.677788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라북도 23
23.0%
경상북도 21
21.0%
경상남도 12
12.0%
충청남도 9
 
9.0%
전라남도 8
 
8.0%
대구광역시 6
 
6.0%
강원도 6
 
6.0%
서울특별시 5
 
5.0%
광주광역시 4
 
4.0%
경기도 4
 
4.0%
Other values (2) 2
 
2.0%

safe_cnsdr_act_at
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
92 
True
 
8
ValueCountFrequency (%)
False 92
92.0%
True 8
 
8.0%
2023-12-10T19:05:55.876975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

full_age9_12_trget_nmpr_cn
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0명
63 
150명
 
5
20명
 
4
100명
 
4
10명
 
3
Other values (16)
21 

Length

Max length4
Median length2
Mean length2.57
Min length2

Unique

Unique13 ?
Unique (%)13.0%

Sample

1st row0명
2nd row0명
3rd row300명
4th row0명
5th row300명

Common Values

ValueCountFrequency (%)
0명 63
63.0%
150명 5
 
5.0%
20명 4
 
4.0%
100명 4
 
4.0%
10명 3
 
3.0%
300명 3
 
3.0%
120명 3
 
3.0%
80명 2
 
2.0%
70명 1
 
1.0%
30명 1
 
1.0%
Other values (11) 11
 
11.0%

Length

2023-12-10T19:05:56.080501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0명 63
63.0%
150명 5
 
5.0%
20명 4
 
4.0%
100명 4
 
4.0%
10명 3
 
3.0%
300명 3
 
3.0%
120명 3
 
3.0%
80명 2
 
2.0%
360명 1
 
1.0%
200명 1
 
1.0%
Other values (11) 11
 
11.0%

full_age13_15_trget_nmpr_cn
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0명
57 
10명
 
4
120명
 
3
100명
 
3
96명
 
3
Other values (20)
30 

Length

Max length4
Median length2
Mean length2.61
Min length2

Unique

Unique10 ?
Unique (%)10.0%

Sample

1st row10명
2nd row40명
3rd row0명
4th row150명
5th row0명

Common Values

ValueCountFrequency (%)
0명 57
57.0%
10명 4
 
4.0%
120명 3
 
3.0%
100명 3
 
3.0%
96명 3
 
3.0%
300명 2
 
2.0%
70명 2
 
2.0%
150명 2
 
2.0%
30명 2
 
2.0%
20명 2
 
2.0%
Other values (15) 20
 
20.0%

Length

2023-12-10T19:05:56.387131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0명 57
57.0%
10명 4
 
4.0%
120명 3
 
3.0%
100명 3
 
3.0%
96명 3
 
3.0%
270명 2
 
2.0%
200명 2
 
2.0%
280명 2
 
2.0%
36명 2
 
2.0%
400명 2
 
2.0%
Other values (15) 20
 
20.0%

full_age16_18_trget_nmpr_cn
Categorical

IMBALANCE 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0명
70 
100명
 
4
120명
 
4
96명
 
3
360명
 
3
Other values (13)
16 

Length

Max length4
Median length2
Mean length2.47
Min length2

Unique

Unique10 ?
Unique (%)10.0%

Sample

1st row0명
2nd row0명
3rd row0명
4th row0명
5th row0명

Common Values

ValueCountFrequency (%)
0명 70
70.0%
100명 4
 
4.0%
120명 4
 
4.0%
96명 3
 
3.0%
360명 3
 
3.0%
20명 2
 
2.0%
10명 2
 
2.0%
200명 2
 
2.0%
400명 1
 
1.0%
5명 1
 
1.0%
Other values (8) 8
 
8.0%

Length

2023-12-10T19:05:56.786588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0명 70
70.0%
120명 4
 
4.0%
100명 4
 
4.0%
96명 3
 
3.0%
360명 3
 
3.0%
20명 2
 
2.0%
10명 2
 
2.0%
200명 2
 
2.0%
390명 1
 
1.0%
280명 1
 
1.0%
Other values (8) 8
 
8.0%

full_age19_24_trget_nmpr_cn
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0명
99 
60명
 
1

Length

Max length3
Median length2
Mean length2.01
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0명
2nd row0명
3rd row0명
4th row0명
5th row0명

Common Values

ValueCountFrequency (%)
0명 99
99.0%
60명 1
 
1.0%

Length

2023-12-10T19:05:57.112860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:57.387032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0명 99
99.0%
60명 1
 
1.0%

trget_age_etc_nmpr_cn
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0명
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0명
2nd row0명
3rd row0명
4th row0명
5th row0명

Common Values

ValueCountFrequency (%)
0명 100
100.0%

Length

2023-12-10T19:05:57.676772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:57.867982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0명 100
100.0%

tot_trget_nmpr_cn
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
100명
12 
120명
10 
150명
8 
20명
8 
300명
7 
Other values (24)
55 

Length

Max length4
Median length4
Mean length3.61
Min length2

Unique

Unique10 ?
Unique (%)10.0%

Sample

1st row10명
2nd row40명
3rd row300명
4th row150명
5th row300명

Common Values

ValueCountFrequency (%)
100명 12
 
12.0%
120명 10
 
10.0%
150명 8
 
8.0%
20명 8
 
8.0%
300명 7
 
7.0%
96명 6
 
6.0%
360명 5
 
5.0%
200명 5
 
5.0%
30명 4
 
4.0%
70명 4
 
4.0%
Other values (19) 31
31.0%

Length

2023-12-10T19:05:58.105720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100명 12
 
12.0%
120명 10
 
10.0%
150명 8
 
8.0%
20명 8
 
8.0%
300명 7
 
7.0%
96명 6
 
6.0%
360명 5
 
5.0%
200명 5
 
5.0%
30명 4
 
4.0%
70명 4
 
4.0%
Other values (19) 31
31.0%

progrm_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:05:58.541875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length24
Mean length16.38
Min length6

Characters and Unicode

Total characters1638
Distinct characters280
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row나는야 모듬북 봉사단
2nd row-주말형 숙박프로그램- “바다야~ 놀자!”
3rd row화랑도 통일 체험활동(초등학교 2박3일)
4th row잡(JOB)학다식 양성소
5th row화랑 운동회(초등학교)
ValueCountFrequency (%)
1박2일 10
 
3.5%
2박3일 10
 
3.5%
미션 6
 
2.1%
청소년 4
 
1.4%
만해마을 4
 
1.4%
함께 4
 
1.4%
3일 3
 
1.1%
반딧불이천문대 3
 
1.1%
up 3
 
1.1%
중학교 3
 
1.1%
Other values (196) 234
82.4%
2023-12-10T19:05:59.342308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184
 
11.2%
( 77
 
4.7%
) 77
 
4.7%
교 68
 
4.2%
학 68
 
4.2%
등 41
 
2.5%
일 32
 
2.0%
리 31
 
1.9%
박 30
 
1.8%
! 28
 
1.7%
Other values (270) 1002
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1104
67.4%
Space Separator 184
 
11.2%
Open Punctuation 78
 
4.8%
Close Punctuation 78
 
4.8%
Decimal Number 65
 
4.0%
Lowercase Letter 47
 
2.9%
Other Punctuation 45
 
2.7%
Uppercase Letter 27
 
1.6%
Dash Punctuation 5
 
0.3%
Math Symbol 3
 
0.2%
Other values (2) 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 68
 
6.2%
학 68
 
6.2%
등 41
 
3.7%
일 32
 
2.9%
리 31
 
2.8%
박 30
 
2.7%
고 27
 
2.4%
중 26
 
2.4%
초 20
 
1.8%
동 19
 
1.7%
Other values (215) 742
67.2%
Lowercase Letter
ValueCountFrequency (%)
e 6
12.8%
p 5
10.6%
h 4
 
8.5%
o 4
 
8.5%
a 4
 
8.5%
s 3
 
6.4%
u 3
 
6.4%
t 3
 
6.4%
r 3
 
6.4%
y 2
 
4.3%
Other values (8) 10
21.3%
Uppercase Letter
ValueCountFrequency (%)
U 4
14.8%
P 3
11.1%
L 3
11.1%
O 3
11.1%
G 3
11.1%
C 2
7.4%
E 2
7.4%
D 2
7.4%
Y 1
 
3.7%
V 1
 
3.7%
Other values (3) 3
11.1%
Decimal Number
ValueCountFrequency (%)
2 26
40.0%
1 16
24.6%
3 15
23.1%
9 2
 
3.1%
5 2
 
3.1%
6 1
 
1.5%
8 1
 
1.5%
4 1
 
1.5%
7 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
! 28
62.2%
' 8
 
17.8%
" 4
 
8.9%
? 2
 
4.4%
. 2
 
4.4%
, 1
 
2.2%
Open Punctuation
ValueCountFrequency (%)
( 77
98.7%
[ 1
 
1.3%
Close Punctuation
ValueCountFrequency (%)
) 77
98.7%
] 1
 
1.3%
Space Separator
ValueCountFrequency (%)
184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
” 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1102
67.3%
Common 460
28.1%
Latin 74
 
4.5%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 68
 
6.2%
학 68
 
6.2%
등 41
 
3.7%
일 32
 
2.9%
리 31
 
2.8%
박 30
 
2.7%
고 27
 
2.5%
중 26
 
2.4%
초 20
 
1.8%
동 19
 
1.7%
Other values (213) 740
67.2%
Latin
ValueCountFrequency (%)
e 6
 
8.1%
p 5
 
6.8%
h 4
 
5.4%
o 4
 
5.4%
a 4
 
5.4%
U 4
 
5.4%
P 3
 
4.1%
s 3
 
4.1%
u 3
 
4.1%
L 3
 
4.1%
Other values (21) 35
47.3%
Common
ValueCountFrequency (%)
184
40.0%
( 77
16.7%
) 77
16.7%
! 28
 
6.1%
2 26
 
5.7%
1 16
 
3.5%
3 15
 
3.3%
' 8
 
1.7%
- 5
 
1.1%
" 4
 
0.9%
Other values (14) 20
 
4.3%
Han
ValueCountFrequency (%)
寧 1
50.0%
安 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1102
67.3%
ASCII 532
32.5%
Punctuation 2
 
0.1%
CJK 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
184
34.6%
( 77
14.5%
) 77
14.5%
! 28
 
5.3%
2 26
 
4.9%
1 16
 
3.0%
3 15
 
2.8%
' 8
 
1.5%
e 6
 
1.1%
p 5
 
0.9%
Other values (43) 90
16.9%
Hangul
ValueCountFrequency (%)
교 68
 
6.2%
학 68
 
6.2%
등 41
 
3.7%
일 32
 
2.9%
리 31
 
2.8%
박 30
 
2.7%
고 27
 
2.5%
중 26
 
2.4%
초 20
 
1.8%
동 19
 
1.7%
Other values (213) 740
67.2%
Punctuation
ValueCountFrequency (%)
“ 1
50.0%
” 1
50.0%
CJK
ValueCountFrequency (%)
寧 1
50.0%
安 1
50.0%

instt_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
영양군청소년수련원
14 
청정청소년수련원(청정테마힐링센터)
12 
임실군청소년수련원
10 
경주시 화랑마을
6 
군포시청소년수련원
6 
Other values (25)
52 

Length

Max length21
Median length18
Mean length11.54
Min length6

Unique

Unique15 ?
Unique (%)15.0%

Sample

1st row부산중구청소년문화의집
2nd row광주동구청소년수련관
3rd row경주시 화랑마을
4th row경주시 화랑마을
5th row경주시 화랑마을

Common Values

ValueCountFrequency (%)
영양군청소년수련원 14
14.0%
청정청소년수련원(청정테마힐링센터) 12
12.0%
임실군청소년수련원 10
 
10.0%
경주시 화랑마을 6
 
6.0%
군포시청소년수련원 6
 
6.0%
창원시 진해청소년야영장 6
 
6.0%
(재)김해시복지재단 김해시청소년수련관 5
 
5.0%
전라남도청소년수련원 5
 
5.0%
만해마을 청소년수련원 5
 
5.0%
대구광역시달서구청소년수련관 4
 
4.0%
Other values (20) 27
27.0%

Length

2023-12-10T19:05:59.659381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영양군청소년수련원 14
 
11.1%
청정청소년수련원(청정테마힐링센터 12
 
9.5%
임실군청소년수련원 10
 
7.9%
경주시 6
 
4.8%
화랑마을 6
 
4.8%
군포시청소년수련원 6
 
4.8%
창원시 6
 
4.8%
진해청소년야영장 6
 
4.8%
재)김해시복지재단 5
 
4.0%
김해시청소년수련관 5
 
4.0%
Other values (28) 50
39.7%

valid_pd_begin_de
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20195226
Minimum20060627
Maximum20210526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:59.940503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060627
5-th percentile20191113
Q120197956
median20200304
Q320200527
95-th percentile20210331
Maximum20210526
Range149899
Interquartile range (IQR)2570.5

Descriptive statistics

Standard deviation24325.483
Coefficient of variation (CV)0.0012045165
Kurtosis26.86714
Mean20195226
Median Absolute Deviation (MAD)223
Skewness-5.1769282
Sum2.0195226 × 109
Variance5.9172913 × 108
MonotonicityNot monotonic
2023-12-10T19:06:00.171320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20200304 21
21.0%
20191113 12
12.0%
20200401 12
12.0%
20191211 10
10.0%
20201209 9
9.0%
20200429 7
 
7.0%
20200206 6
 
6.0%
20200527 3
 
3.0%
20200205 3
 
3.0%
20200722 2
 
2.0%
Other values (10) 15
15.0%
ValueCountFrequency (%)
20060627 1
 
1.0%
20060928 1
 
1.0%
20061128 1
 
1.0%
20191113 12
12.0%
20191211 10
10.0%
20200205 3
 
3.0%
20200206 6
 
6.0%
20200304 21
21.0%
20200401 12
12.0%
20200429 7
 
7.0%
ValueCountFrequency (%)
20210526 2
 
2.0%
20210428 2
 
2.0%
20210331 2
 
2.0%
20210303 1
 
1.0%
20210203 1
 
1.0%
20210106 2
 
2.0%
20201209 9
9.0%
20200722 2
 
2.0%
20200624 2
 
2.0%
20200527 3
 
3.0%

valid_pd_end_de
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20251713
Minimum20061027
Maximum20260721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:00.525534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20061027
5-th percentile20251112
Q120251210
median20260303
Q320260331
95-th percentile20260526
Maximum20260721
Range199694
Interquartile range (IQR)9121

Descriptive statistics

Standard deviation32857.931
Coefficient of variation (CV)0.0016224766
Kurtosis28.892543
Mean20251713
Median Absolute Deviation (MAD)125
Skewness-5.4538379
Sum2.0251713 × 109
Variance1.0796436 × 109
MonotonicityNot monotonic
2023-12-10T19:06:00.844864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20260303 21
21.0%
20251112 12
12.0%
20260331 12
12.0%
20251210 10
10.0%
20251208 9
9.0%
20260428 7
 
7.0%
20260205 6
 
6.0%
20260526 3
 
3.0%
20260204 3
 
3.0%
20260721 2
 
2.0%
Other values (10) 15
15.0%
ValueCountFrequency (%)
20061027 1
 
1.0%
20070209 1
 
1.0%
20071012 1
 
1.0%
20251112 12
12.0%
20251208 9
9.0%
20251210 10
10.0%
20260105 2
 
2.0%
20260202 1
 
1.0%
20260204 3
 
3.0%
20260205 6
6.0%
ValueCountFrequency (%)
20260721 2
 
2.0%
20260623 2
 
2.0%
20260526 3
 
3.0%
20260525 2
 
2.0%
20260428 7
 
7.0%
20260427 2
 
2.0%
20260331 12
12.0%
20260330 2
 
2.0%
20260303 21
21.0%
20260302 1
 
1.0%

oper_state_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
인증
97 
취소
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인증
2nd row취소
3rd row인증
4th row인증
5th row인증

Common Values

ValueCountFrequency (%)
인증 97
97.0%
취소 3
 
3.0%

Length

2023-12-10T19:06:01.198135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:01.383316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인증 97
97.0%
취소 3
 
3.0%

addr
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경상북도 영양군 수비면 반딧불이로 227 영양군청소년수련원
14 
전북 완주군 구이면 장미로 31
12 
전북 임실군 관촌면 덕천리 481-10번지
10 
경상북도 경주시 석현로 123 (석장동) 화백관 2층 수련활동팀
6 
충청남도 청양군 화성면 정자골길 100-43 군포시청소년수련원
6 
Other values (25)
52 

Length

Max length52
Median length35
Mean length29.46
Min length16

Unique

Unique15 ?
Unique (%)15.0%

Sample

1st row부산 중구 보수동2가 64-1번지
2nd row광주 동구 학동 652 ~730 724-9번지
3rd row경상북도 경주시 석현로 123 (석장동) 화백관 2층 수련활동팀
4th row경상북도 경주시 석현로 123 (석장동) 화백관 2층 수련활동팀
5th row경상북도 경주시 석현로 123 (석장동) 화백관 2층 수련활동팀

Common Values

ValueCountFrequency (%)
경상북도 영양군 수비면 반딧불이로 227 영양군청소년수련원 14
14.0%
전북 완주군 구이면 장미로 31 12
12.0%
전북 임실군 관촌면 덕천리 481-10번지 10
 
10.0%
경상북도 경주시 석현로 123 (석장동) 화백관 2층 수련활동팀 6
 
6.0%
충청남도 청양군 화성면 정자골길 100-43 군포시청소년수련원 6
 
6.0%
경상남도 창원시 진해구 천자로 484 (풍호동) 창원시 진해청소년야영장 6
 
6.0%
경상남도 김해시 진영읍 김해대로 257 김해시청소년수련관 5
 
5.0%
전라남도 완도군 군외면 삼두1길 215 전라남도청소년수련원 5
 
5.0%
강원도 인제군 북면 만해로 91 동국대학교 만해마을청소년수련원 5
 
5.0%
대구광역시 달서구 상화로 420 (상인동) 대구광역시달서구청소년수련관 4
 
4.0%
Other values (20) 27
27.0%

Length

2023-12-10T19:06:01.634649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북 22
 
3.7%
경상북도 21
 
3.5%
수비면 14
 
2.3%
반딧불이로 14
 
2.3%
227 14
 
2.3%
영양군청소년수련원 14
 
2.3%
영양군 14
 
2.3%
31 12
 
2.0%
창원시 12
 
2.0%
경상남도 12
 
2.0%
Other values (154) 451
75.2%

Interactions

2023-12-10T19:05:52.251051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:51.231434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:51.727100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:52.532396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:51.379679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:51.895987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:52.703437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:51.550191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:52.073894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:06:02.321487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noflag_nmcrtfc_no_cnarea_nmsafe_cnsdr_act_atfull_age9_12_trget_nmpr_cnfull_age13_15_trget_nmpr_cnfull_age16_18_trget_nmpr_cnfull_age19_24_trget_nmpr_cntot_trget_nmpr_cnprogrm_nminstt_nmvalid_pd_begin_devalid_pd_end_deoper_state_nmaddr
seq_no1.0000.0001.0000.6330.0000.2290.9300.5570.3960.0001.0001.0001.0000.9190.9631.000
flag_nm0.0001.0001.0000.8660.2670.4560.4430.2900.0000.7751.0000.9450.0000.0000.0000.945
crtfc_no_cn1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
area_nm0.6330.8661.0001.0000.7960.5670.8130.6480.3900.9141.0001.0000.6790.4780.6331.000
safe_cnsdr_act_at0.0000.2671.0000.7961.0000.0000.7830.4050.0001.0001.0001.0000.0540.0000.0001.000
full_age9_12_trget_nmpr_cn0.2290.4561.0000.5670.0001.0000.0000.0000.2620.9551.0000.8780.0570.0000.2290.878
full_age13_15_trget_nmpr_cn0.9300.4431.0000.8130.7830.0001.0000.0000.6590.9361.0000.9520.8530.9380.9300.952
full_age16_18_trget_nmpr_cn0.5570.2901.0000.6480.4050.0000.0001.0000.0000.8091.0000.8260.0000.0000.5570.826
full_age19_24_trget_nmpr_cn0.3960.0001.0000.3900.0000.2620.6590.0001.0000.0001.0001.0000.4490.4970.3961.000
tot_trget_nmpr_cn0.0000.7751.0000.9141.0000.9550.9360.8090.0001.0001.0000.9640.6930.0000.0000.964
progrm_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
instt_nm1.0000.9451.0001.0001.0000.8780.9520.8261.0000.9641.0001.0000.9741.0001.0001.000
valid_pd_begin_de1.0000.0001.0000.6790.0540.0570.8530.0000.4490.6931.0000.9741.0001.0001.0000.974
valid_pd_end_de0.9190.0001.0000.4780.0000.0000.9380.0000.4970.0001.0001.0001.0001.0000.9191.000
oper_state_nm0.9630.0001.0000.6330.0000.2290.9300.5570.3960.0001.0001.0001.0000.9191.0001.000
addr1.0000.9451.0001.0001.0000.8780.9520.8261.0000.9641.0001.0000.9741.0001.0001.000
2023-12-10T19:06:02.644542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
full_age19_24_trget_nmpr_cnfull_age9_12_trget_nmpr_cnfull_age13_15_trget_nmpr_cninstt_nmflag_nmoper_state_nmarea_nmfull_age16_18_trget_nmpr_cnsafe_cnsdr_act_ataddrtot_trget_nmpr_cn
full_age19_24_trget_nmpr_cn1.0000.2020.5050.8450.0000.2590.2860.0000.0000.8450.000
full_age9_12_trget_nmpr_cn0.2021.0000.0000.4050.3580.1750.2140.0000.0000.4050.602
full_age13_15_trget_nmpr_cn0.5050.0001.0000.5680.3330.7690.3940.0000.6130.5680.526
instt_nm0.8450.4050.5681.0000.7030.8450.8920.3420.8451.0000.618
flag_nm0.0000.3580.3330.7031.0000.0000.6710.2050.1710.7030.582
oper_state_nm0.2590.1750.7690.8450.0001.0000.4700.4030.0000.8450.000
area_nm0.2860.2140.3940.8920.6710.4701.0000.2650.6050.8920.546
full_age16_18_trget_nmpr_cn0.0000.0000.0000.3420.2050.4030.2651.0000.2890.3420.335
safe_cnsdr_act_at0.0000.0000.6130.8450.1710.0000.6050.2891.0000.8450.851
addr0.8450.4050.5681.0000.7030.8450.8920.3420.8451.0000.618
tot_trget_nmpr_cn0.0000.6020.5260.6180.5820.0000.5460.3350.8510.6181.000
2023-12-10T19:06:02.974059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_novalid_pd_begin_devalid_pd_end_deflag_nmarea_nmsafe_cnsdr_act_atfull_age9_12_trget_nmpr_cnfull_age13_15_trget_nmpr_cnfull_age16_18_trget_nmpr_cnfull_age19_24_trget_nmpr_cntot_trget_nmpr_cninstt_nmoper_state_nmaddr
seq_no1.000-0.596-0.9920.0000.4700.0000.1750.7690.4030.2590.0000.8450.8260.845
valid_pd_begin_de-0.5961.0000.6010.0000.4380.0940.2440.5690.3220.5560.3630.7770.9950.777
valid_pd_end_de-0.9920.6011.0000.0000.4700.0000.1750.7690.4030.2590.0000.8450.8260.845
flag_nm0.0000.0000.0001.0000.6710.1710.3580.3330.2050.0000.5820.7030.0000.703
area_nm0.4700.4380.4700.6711.0000.6050.2140.3940.2650.2860.5460.8920.4700.892
safe_cnsdr_act_at0.0000.0940.0000.1710.6051.0000.0000.6130.2890.0000.8510.8450.0000.845
full_age9_12_trget_nmpr_cn0.1750.2440.1750.3580.2140.0001.0000.0000.0000.2020.6020.4050.1750.405
full_age13_15_trget_nmpr_cn0.7690.5690.7690.3330.3940.6130.0001.0000.0000.5050.5260.5680.7690.568
full_age16_18_trget_nmpr_cn0.4030.3220.4030.2050.2650.2890.0000.0001.0000.0000.3350.3420.4030.342
full_age19_24_trget_nmpr_cn0.2590.5560.2590.0000.2860.0000.2020.5050.0001.0000.0000.8450.2590.845
tot_trget_nmpr_cn0.0000.3630.0000.5820.5460.8510.6020.5260.3350.0001.0000.6180.0000.618
instt_nm0.8450.7770.8450.7030.8920.8450.4050.5680.3420.8450.6181.0000.8451.000
oper_state_nm0.8260.9950.8260.0000.4700.0000.1750.7690.4030.2590.0000.8451.0000.845
addr0.8450.7770.8450.7030.8920.8450.4050.5680.3420.8450.6181.0000.8451.000

Missing values

2023-12-10T19:05:52.979923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:05:53.441462image/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

seq_noflag_nmcrtfc_no_cnarea_nmsafe_cnsdr_act_atfull_age9_12_trget_nmpr_cnfull_age13_15_trget_nmpr_cnfull_age16_18_trget_nmpr_cnfull_age19_24_trget_nmpr_cntrget_age_etc_nmpr_cntot_trget_nmpr_cnprogrm_nminstt_nmvalid_pd_begin_devalid_pd_end_deoper_state_nmaddr
01활동0249B06B-09547부산광역시N0명10명0명0명0명10명나는야 모듬북 봉사단부산중구청소년문화의집2020072220260721인증부산 중구 보수동2가 64-1번지
110930활동1195A04B-00050광주광역시N0명40명0명0명0명40명-주말형 숙박프로그램- “바다야~ 놀자!”광주동구청소년수련관2006112820071012취소광주 동구 학동 652 ~730 724-9번지
23활동5070F07A-09514경상북도N300명0명0명0명0명300명화랑도 통일 체험활동(초등학교 2박3일)경주시 화랑마을2020072220260721인증경상북도 경주시 석현로 123 (석장동) 화백관 2층 수련활동팀
34활동5070D08B-09448경상북도N0명150명0명0명0명150명잡(JOB)학다식 양성소경주시 화랑마을2020062420260623인증경상북도 경주시 석현로 123 (석장동) 화백관 2층 수련활동팀
45개별5070G01A-MD0583경상북도N300명0명0명0명0명300명화랑 운동회(초등학교)경주시 화랑마을2020062420260623인증경상북도 경주시 석현로 123 (석장동) 화백관 2층 수련활동팀
56개별0928G01A-MD0570전라북도N360명0명0명0명0명360명사계절 썰매(초등학교)청정청소년수련원(청정테마힐링센터)2020052720260526인증전북 완주군 구이면 장미로 31
67활동5211A04B-09405경상남도N0명120명0명0명0명120명지금까지 이런 익사이팅은 없었다!(주)지랜드2020052720260526인증경상남도 김해시 가야테마길 161 (어방동) 가야테마파크내
710931활동0973A06F-00018서울특별시N20명20명0명60명0명100명가족과 함께 문화-역사 속으로(사회봉사활동 프로그램)영등포청소년문화의집2006092820070209취소서울특별시 영등포구 영등포로64길 15 (신길동) 영등포청소년문화의집
89개별0928G01C-MD0572전라북도N0명0명360명0명0명360명사계절 썰매(고등학교)청정청소년수련원(청정테마힐링센터)2020052720260526인증전북 완주군 구이면 장미로 31
910활동0018A07B-09980전라북도N0명60명0명0명0명60명하이-파이브, Go! (중학교)임실군청소년수련원2021052620260525인증전북 임실군 관촌면 덕천리 481-10번지
seq_noflag_nmcrtfc_no_cnarea_nmsafe_cnsdr_act_atfull_age9_12_trget_nmpr_cnfull_age13_15_trget_nmpr_cnfull_age16_18_trget_nmpr_cnfull_age19_24_trget_nmpr_cntrget_age_etc_nmpr_cntot_trget_nmpr_cnprogrm_nminstt_nmvalid_pd_begin_devalid_pd_end_deoper_state_nmaddr
9091개별0928G04B-MD0401전라북도N0명120명0명0명0명120명모험체험활동(중학교)청정청소년수련원(청정테마힐링센터)2019111320251112인증전북 완주군 구이면 장미로 31
9192개별0928G04C-MD0402전라북도N0명0명120명0명0명120명모험활동(고등학교)청정청소년수련원(청정테마힐링센터)2019111320251112인증전북 완주군 구이면 장미로 31
9293개별0928G04A-MD0396전라북도N120명0명0명0명0명120명고무보트(초등학교)청정청소년수련원(청정테마힐링센터)2019111320251112인증전북 완주군 구이면 장미로 31
9394활동0928F07C-08845전라북도N0명0명360명0명0명360명완주에서 함께 하는 터울림(고등학교2박3일)청정청소년수련원(청정테마힐링센터)2019111320251112인증전북 완주군 구이면 장미로 31
9495활동0972A01F-08826서울특별시N0명10명10명0명0명20명청소년 숲길 트레킹서울시립광진청소년센터2019111320251112인증서울특별시 광진구 구천면로 2 (광장동313-3번지)
9596활동3260F07B-08855경상북도N0명200명0명0명0명200명'자연에 놀다' (중학교 2박 3일 캠프)영양군청소년수련원2019111320251112인증경상북도 영양군 수비면 반딧불이로 227 영양군청소년수련원
9697활동5294A01B-08785경상북도N0명30명0명0명0명30명안녕(安寧)하십니까?경산시계림청소년수련원2019111320251112인증경상북도 경산시 자인면 일연로 311-6 경산시계림청소년수련원
9798활동0972A01A-08825서울특별시N10명0명0명0명0명10명자전거안전지킴이 "따릉따릉"서울시립광진청소년센터2019111320251112인증서울특별시 광진구 구천면로 2 (광장동313-3번지)
9899개별0928G04C-MD0398전라북도N0명0명120명0명0명120명고무보트(고등학교)청정청소년수련원(청정테마힐링센터)2019111320251112인증전북 완주군 구이면 장미로 31
99100개별0928G04A-MD0400전라북도N120명0명0명0명0명120명모험체험활동(초등학교)청정청소년수련원(청정테마힐링센터)2019111320251112인증전북 완주군 구이면 장미로 31