Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells33695
Missing cells (%)17.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory169.0 B

Variable types

Text7
Categorical3
Numeric9

Dataset

Description사이트 아이디,ASP 아이디,강의 아이디,강의 구분,강의 명,수강신청 시작 일자,수강신청 종료 일자,강의 시작 일자,강의 종료 일자,강의대상,정원,수강신청 URL,등록 일자,수정 일자,교육기관 고유아이디,교육기관명,교육기관 자치구,교육기관 위도 좌표,교육기관 경도 좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2561/S/1/datasetView.do

Alerts

ASP 아이디 has constant value ""Constant
강의 구분 has constant value ""Constant
수강신청 시작 일자 is highly overall correlated with 수강신청 종료 일자 and 5 other fieldsHigh correlation
수강신청 종료 일자 is highly overall correlated with 수강신청 시작 일자 and 5 other fieldsHigh correlation
강의 시작 일자 is highly overall correlated with 수강신청 시작 일자 and 4 other fieldsHigh correlation
강의 종료 일자 is highly overall correlated with 수강신청 시작 일자 and 4 other fieldsHigh correlation
등록 일자 is highly overall correlated with 수강신청 시작 일자 and 5 other fieldsHigh correlation
수정 일자 is highly overall correlated with 수강신청 시작 일자 and 5 other fieldsHigh correlation
교육기관 위도 좌표 is highly overall correlated with 교육기관 자치구High correlation
교육기관 경도 좌표 is highly overall correlated with 교육기관 자치구High correlation
교육기관 자치구 is highly overall correlated with 수강신청 시작 일자 and 5 other fieldsHigh correlation
강의 명 has 297 (3.0%) missing valuesMissing
강의대상 has 9189 (91.9%) missing valuesMissing
수강신청 URL has 1156 (11.6%) missing valuesMissing
등록 일자 has 1603 (16.0%) missing valuesMissing
수정 일자 has 6829 (68.3%) missing valuesMissing
교육기관 고유아이디 has 3233 (32.3%) missing valuesMissing
교육기관명 has 3233 (32.3%) missing valuesMissing
교육기관 위도 좌표 has 3994 (39.9%) missing valuesMissing
교육기관 경도 좌표 has 3994 (39.9%) missing valuesMissing
강의 시작 일자 is highly skewed (γ1 = 42.31656278)Skewed
정원 is highly skewed (γ1 = 72.73757586)Skewed
교육기관 위도 좌표 is highly skewed (γ1 = 23.30358418)Skewed
교육기관 경도 좌표 is highly skewed (γ1 = -23.29679259)Skewed
정원 has 2170 (21.7%) zerosZeros

Reproduction

Analysis started2024-04-21 00:39:06.115365
Analysis finished2024-04-21 00:39:20.959156
Duration14.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct171
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T09:39:21.165340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.963
Min length1

Characters and Unicode

Total characters29630
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st row1243
2nd row92
3rd row110
4th rowlc1
5th rowlc1
ValueCountFrequency (%)
lc1 3311
33.1%
279 519
 
5.2%
1243 351
 
3.5%
254 220
 
2.2%
59 199
 
2.0%
186 198
 
2.0%
285 181
 
1.8%
84 179
 
1.8%
310 172
 
1.7%
79 161
 
1.6%
Other values (161) 4509
45.1%
2024-04-21T09:39:21.576562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6700
22.6%
2 3651
12.3%
l 3311
11.2%
c 3311
11.2%
4 2239
 
7.6%
5 1848
 
6.2%
8 1691
 
5.7%
7 1515
 
5.1%
3 1503
 
5.1%
9 1441
 
4.9%
Other values (3) 2420
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22649
76.4%
Lowercase Letter 6981
 
23.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6700
29.6%
2 3651
16.1%
4 2239
 
9.9%
5 1848
 
8.2%
8 1691
 
7.5%
7 1515
 
6.7%
3 1503
 
6.6%
9 1441
 
6.4%
0 1080
 
4.8%
6 981
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
l 3311
47.4%
c 3311
47.4%
n 359
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22649
76.4%
Latin 6981
 
23.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6700
29.6%
2 3651
16.1%
4 2239
 
9.9%
5 1848
 
8.2%
8 1691
 
7.5%
7 1515
 
6.7%
3 1503
 
6.6%
9 1441
 
6.4%
0 1080
 
4.8%
6 981
 
4.3%
Latin
ValueCountFrequency (%)
l 3311
47.4%
c 3311
47.4%
n 359
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6700
22.6%
2 3651
12.3%
l 3311
11.2%
c 3311
11.2%
4 2239
 
7.6%
5 1848
 
6.2%
8 1691
 
5.7%
7 1515
 
5.1%
3 1503
 
5.1%
9 1441
 
4.9%
Other values (3) 2420
 
8.2%

ASP 아이디
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
ASP00001
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASP00001
2nd rowASP00001
3rd rowASP00001
4th rowASP00001
5th rowASP00001

Common Values

ValueCountFrequency (%)
ASP00001 10000
100.0%

Length

2024-04-21T09:39:21.719972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T09:39:21.827399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
asp00001 10000
100.0%
Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T09:39:22.083050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length25.0567
Min length19

Characters and Unicode

Total characters250567
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9994 ?
Unique (%)99.9%

Sample

1st rowASP0000120190000156
2nd rowASP00001E1029001420200000049
3rd rowASP00001E1023000120190000029
4th rowASP00001J406120230108130
5th rowASP00001J404920230107394
ValueCountFrequency (%)
asp00001e0927000920190000001 2
 
< 0.1%
asp00001e1327001220190000002 2
 
< 0.1%
asp00001e1927001120190000011 2
 
< 0.1%
asp00001e1027000120190000120 1
 
< 0.1%
asp00001e1327000520190000029 1
 
< 0.1%
asp00001i119320160000374 1
 
< 0.1%
asp00001l1918000420190000112 1
 
< 0.1%
asp00001m9934201989451 1
 
< 0.1%
asp0000120190000156 1
 
< 0.1%
asp00001e1927000520190000020 1
 
< 0.1%
Other values (9987) 9987
99.9%
2024-04-21T09:39:22.452811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 109909
43.9%
1 34001
 
13.6%
2 25088
 
10.0%
S 11119
 
4.4%
A 10097
 
4.0%
P 10011
 
4.0%
9 8173
 
3.3%
3 7639
 
3.0%
4 7411
 
3.0%
6 5701
 
2.3%
Other values (17) 21418
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 211763
84.5%
Uppercase Letter 38804
 
15.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 11119
28.7%
A 10097
26.0%
P 10011
25.8%
E 3885
 
10.0%
J 2245
 
5.8%
I 533
 
1.4%
C 368
 
0.9%
L 234
 
0.6%
K 102
 
0.3%
H 75
 
0.2%
Other values (7) 135
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 109909
51.9%
1 34001
 
16.1%
2 25088
 
11.8%
9 8173
 
3.9%
3 7639
 
3.6%
4 7411
 
3.5%
6 5701
 
2.7%
7 5000
 
2.4%
8 4431
 
2.1%
5 4410
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 211763
84.5%
Latin 38804
 
15.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 11119
28.7%
A 10097
26.0%
P 10011
25.8%
E 3885
 
10.0%
J 2245
 
5.8%
I 533
 
1.4%
C 368
 
0.9%
L 234
 
0.6%
K 102
 
0.3%
H 75
 
0.2%
Other values (7) 135
 
0.3%
Common
ValueCountFrequency (%)
0 109909
51.9%
1 34001
 
16.1%
2 25088
 
11.8%
9 8173
 
3.9%
3 7639
 
3.6%
4 7411
 
3.5%
6 5701
 
2.7%
7 5000
 
2.4%
8 4431
 
2.1%
5 4410
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 109909
43.9%
1 34001
 
13.6%
2 25088
 
10.0%
S 11119
 
4.4%
A 10097
 
4.0%
P 10011
 
4.0%
9 8173
 
3.3%
3 7639
 
3.0%
4 7411
 
3.0%
6 5701
 
2.3%
Other values (17) 21418
 
8.5%

강의 구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
오프라인
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
오프라인 10000
100.0%

Length

2024-04-21T09:39:22.588190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T09:39:22.687960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오프라인 10000
100.0%

강의 명
Text

MISSING 

Distinct8021
Distinct (%)82.7%
Missing297
Missing (%)3.0%
Memory size156.2 KiB
2024-04-21T09:39:22.940409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length101
Median length65
Mean length23.457178
Min length2

Characters and Unicode

Total characters227605
Distinct characters1096
Distinct categories18 ?
Distinct scripts4 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7048 ?
Unique (%)72.6%

Sample

1st row[사무관리,IT관련] &lt;직종설명회&gt;코딩기반 IT융합강사 양성과정(드론&VR) - 영등포여성인력개발센터
2nd row[초등] 자연관찰그리기 &lt;곤충의 다리는 몇 개일까?&gt;
3rd row중식조리기능사 자격증취득 실기집중반
4th row빅데이터 분석 및 시각화
5th row[토]무역실무(무역영어1급)
ValueCountFrequency (%)
2735
 
7.4%
영등포여성인력개발센터 524
 
1.4%
교육서비스 425
 
1.2%
동대문여성인력개발센터 257
 
0.7%
노원여성인력개발센터 237
 
0.6%
강좌명 210
 
0.6%
자격증 190
 
0.5%
구로여성인력개발센터 172
 
0.5%
사무관리,it관련 160
 
0.4%
2019년 146
 
0.4%
Other values (13074) 31734
86.3%
2024-04-21T09:39:23.412550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29001
 
12.7%
) 4626
 
2.0%
( 4618
 
2.0%
[ 4409
 
1.9%
] 4405
 
1.9%
3291
 
1.4%
3107
 
1.4%
2562
 
1.1%
1 2535
 
1.1%
- 2516
 
1.1%
Other values (1086) 166535
73.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151507
66.6%
Space Separator 29001
 
12.7%
Decimal Number 10302
 
4.5%
Close Punctuation 9105
 
4.0%
Open Punctuation 9100
 
4.0%
Other Punctuation 6958
 
3.1%
Uppercase Letter 3583
 
1.6%
Lowercase Letter 3271
 
1.4%
Dash Punctuation 2516
 
1.1%
Other Symbol 900
 
0.4%
Other values (8) 1362
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3291
 
2.2%
3107
 
2.1%
2562
 
1.7%
2513
 
1.7%
2505
 
1.7%
2431
 
1.6%
2365
 
1.6%
2279
 
1.5%
2201
 
1.5%
2033
 
1.3%
Other values (964) 126220
83.3%
Lowercase Letter
ValueCountFrequency (%)
t 929
28.4%
g 450
13.8%
l 449
13.7%
a 240
 
7.3%
e 176
 
5.4%
p 153
 
4.7%
m 136
 
4.2%
o 118
 
3.6%
n 92
 
2.8%
r 84
 
2.6%
Other values (16) 444
13.6%
Uppercase Letter
ValueCountFrequency (%)
T 520
14.5%
A 456
12.7%
I 446
12.4%
S 304
8.5%
N 242
 
6.8%
B 241
 
6.7%
C 218
 
6.1%
Q 166
 
4.6%
P 133
 
3.7%
E 121
 
3.4%
Other values (16) 736
20.5%
Other Punctuation
ValueCountFrequency (%)
, 1636
23.5%
& 1098
15.8%
; 941
13.5%
: 859
12.3%
' 748
10.8%
/ 581
 
8.4%
. 571
 
8.2%
! 228
 
3.3%
? 137
 
2.0%
* 84
 
1.2%
Other values (6) 75
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 2535
24.6%
2 2347
22.8%
0 1693
16.4%
3 814
 
7.9%
6 623
 
6.0%
5 588
 
5.7%
9 559
 
5.4%
4 529
 
5.1%
7 320
 
3.1%
8 294
 
2.9%
Other Symbol
ValueCountFrequency (%)
855
95.0%
23
 
2.6%
6
 
0.7%
5
 
0.6%
4
 
0.4%
3
 
0.3%
2
 
0.2%
1
 
0.1%
1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 4626
50.8%
] 4405
48.4%
57
 
0.6%
12
 
0.1%
3
 
< 0.1%
} 2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 4618
50.7%
[ 4409
48.5%
57
 
0.6%
12
 
0.1%
3
 
< 0.1%
{ 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 420
56.3%
+ 293
39.3%
18
 
2.4%
< 7
 
0.9%
> 7
 
0.9%
= 1
 
0.1%
Letter Number
ValueCountFrequency (%)
27
48.2%
25
44.6%
3
 
5.4%
1
 
1.8%
Other Number
ValueCountFrequency (%)
6
66.7%
1
 
11.1%
1
 
11.1%
1
 
11.1%
Final Punctuation
ValueCountFrequency (%)
26
65.0%
14
35.0%
Initial Punctuation
ValueCountFrequency (%)
21
50.0%
21
50.0%
Space Separator
ValueCountFrequency (%)
29001
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2516
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 456
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 7
100.0%
Control
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151445
66.5%
Common 69188
30.4%
Latin 6910
 
3.0%
Han 62
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3291
 
2.2%
3107
 
2.1%
2562
 
1.7%
2513
 
1.7%
2505
 
1.7%
2431
 
1.6%
2365
 
1.6%
2279
 
1.5%
2201
 
1.5%
2033
 
1.3%
Other values (918) 126158
83.3%
Common
ValueCountFrequency (%)
29001
41.9%
) 4626
 
6.7%
( 4618
 
6.7%
[ 4409
 
6.4%
] 4405
 
6.4%
1 2535
 
3.7%
- 2516
 
3.6%
2 2347
 
3.4%
0 1693
 
2.4%
, 1636
 
2.4%
Other values (56) 11402
 
16.5%
Latin
ValueCountFrequency (%)
t 929
 
13.4%
T 520
 
7.5%
A 456
 
6.6%
g 450
 
6.5%
l 449
 
6.5%
I 446
 
6.5%
S 304
 
4.4%
N 242
 
3.5%
B 241
 
3.5%
a 240
 
3.5%
Other values (46) 2633
38.1%
Han
ValueCountFrequency (%)
3
 
4.8%
3
 
4.8%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
Other values (36) 40
64.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151442
66.5%
ASCII 74867
32.9%
Misc Symbols 868
 
0.4%
None 149
 
0.1%
Punctuation 99
 
< 0.1%
CJK 57
 
< 0.1%
Number Forms 56
 
< 0.1%
Geometric Shapes 32
 
< 0.1%
Arrows 18
 
< 0.1%
Enclosed Alphanum 9
 
< 0.1%
Other values (2) 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29001
38.7%
) 4626
 
6.2%
( 4618
 
6.2%
[ 4409
 
5.9%
] 4405
 
5.9%
1 2535
 
3.4%
- 2516
 
3.4%
2 2347
 
3.1%
0 1693
 
2.3%
, 1636
 
2.2%
Other values (80) 17081
22.8%
Hangul
ValueCountFrequency (%)
3291
 
2.2%
3107
 
2.1%
2562
 
1.7%
2513
 
1.7%
2505
 
1.7%
2431
 
1.6%
2365
 
1.6%
2279
 
1.5%
2201
 
1.5%
2033
 
1.3%
Other values (917) 126155
83.3%
Misc Symbols
ValueCountFrequency (%)
855
98.5%
6
 
0.7%
5
 
0.6%
2
 
0.2%
None
ValueCountFrequency (%)
57
38.3%
57
38.3%
12
 
8.1%
12
 
8.1%
4
 
2.7%
3
 
2.0%
3
 
2.0%
1
 
0.7%
Number Forms
ValueCountFrequency (%)
27
48.2%
25
44.6%
3
 
5.4%
1
 
1.8%
Punctuation
ValueCountFrequency (%)
26
26.3%
21
21.2%
21
21.2%
16
16.2%
14
14.1%
1
 
1.0%
Geometric Shapes
ValueCountFrequency (%)
23
71.9%
4
 
12.5%
3
 
9.4%
1
 
3.1%
1
 
3.1%
Arrows
ValueCountFrequency (%)
18
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
6
66.7%
1
 
11.1%
1
 
11.1%
1
 
11.1%
CJK
ValueCountFrequency (%)
3
 
5.3%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
Other values (33) 36
63.2%
Compat Jamo
ValueCountFrequency (%)
3
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
3
60.0%
1
 
20.0%
1
 
20.0%

수강신청 시작 일자
Real number (ℝ)

HIGH CORRELATION 

Distinct1576
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6096904 × 1010
Minimum20061001
Maximum2.024061 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T09:39:23.568324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20061001
5-th percentile20160217
Q120190110
median20190919
Q32.0220328 × 1011
95-th percentile2.0240104 × 1011
Maximum2.024061 × 1011
Range2.0238604 × 1011
Interquartile range (IQR)2.0218309 × 1011

Descriptive statistics

Standard deviation9.4851426 × 1010
Coefficient of variation (CV)1.4350358
Kurtosis-1.4539953
Mean6.6096904 × 1010
Median Absolute Deviation (MAD)29400.5
Skewness0.73908168
Sum6.6096904 × 1014
Variance8.9967929 × 1021
MonotonicityNot monotonic
2024-04-21T09:39:23.723413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202301010000 530
 
5.3%
202401010000 216
 
2.2%
20160302 203
 
2.0%
20190101 138
 
1.4%
20190617 128
 
1.3%
20160301 114
 
1.1%
20160321 105
 
1.1%
20190916 100
 
1.0%
20181217 97
 
1.0%
20160101 91
 
0.9%
Other values (1566) 8278
82.8%
ValueCountFrequency (%)
20061001 2
 
< 0.1%
20091001 3
 
< 0.1%
20100101 3
 
< 0.1%
20100105 1
 
< 0.1%
20101001 4
 
< 0.1%
20101011 1
 
< 0.1%
20101201 6
0.1%
20101228 2
 
< 0.1%
20101229 1
 
< 0.1%
20111201 10
0.1%
ValueCountFrequency (%)
202406100000 1
< 0.1%
202405230000 1
< 0.1%
202405140000 1
< 0.1%
202405130000 1
< 0.1%
202404300000 1
< 0.1%
202404290000 1
< 0.1%
202404260000 2
< 0.1%
202404180000 1
< 0.1%
202404150900 1
< 0.1%
202404150000 1
< 0.1%

수강신청 종료 일자
Real number (ℝ)

HIGH CORRELATION 

Distinct2604
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6407802 × 1010
Minimum0
Maximum2.0240031 × 1012
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T09:39:23.909685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20160321
Q120190329
median20191231
Q32.0220401 × 1011
95-th percentile2.0240306 × 1011
Maximum2.0240031 × 1012
Range2.0240031 × 1012
Interquartile range (IQR)2.0218382 × 1011

Descriptive statistics

Standard deviation9.8784999 × 1010
Coefficient of variation (CV)1.4875511
Kurtosis29.169988
Mean6.6407802 × 1010
Median Absolute Deviation (MAD)29287
Skewness2.2016641
Sum6.6407802 × 1014
Variance9.758476 × 1021
MonotonicityNot monotonic
2024-04-21T09:39:24.153536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202306302359 482
 
4.8%
20160331 266
 
2.7%
20190930 200
 
2.0%
20160315 149
 
1.5%
20191231 144
 
1.4%
20160630 102
 
1.0%
20160610 80
 
0.8%
20190628 79
 
0.8%
20161231 72
 
0.7%
20190329 68
 
0.7%
Other values (2594) 8358
83.6%
ValueCountFrequency (%)
0 3
< 0.1%
20101231 1
 
< 0.1%
20110108 1
 
< 0.1%
20110121 1
 
< 0.1%
20110127 1
 
< 0.1%
20110128 1
 
< 0.1%
20110131 1
 
< 0.1%
20110228 4
< 0.1%
20111011 1
 
< 0.1%
20111230 7
0.1%
ValueCountFrequency (%)
2024003060000 1
 
< 0.1%
2024002270000 1
 
< 0.1%
230506201910 1
 
< 0.1%
230506101800 2
 
< 0.1%
202420200000 1
 
< 0.1%
202412310000 29
0.3%
202412090900 1
 
< 0.1%
202408070000 1
 
< 0.1%
202408010000 1
 
< 0.1%
202407051500 1
 
< 0.1%

강의 시작 일자
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1518
Distinct (%)15.2%
Missing39
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean20199636
Minimum20061001
Maximum23030203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T09:39:24.307522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20061001
5-th percentile20160302
Q120190309
median20191001
Q320220720
95-th percentile20240306
Maximum23030203
Range2969202
Interquartile range (IQR)30411

Descriptive statistics

Standard deviation37741.724
Coefficient of variation (CV)0.0018684359
Kurtosis3175.9372
Mean20199636
Median Absolute Deviation (MAD)19104
Skewness42.316563
Sum2.0120857 × 1011
Variance1.4244377 × 109
MonotonicityNot monotonic
2024-04-21T09:39:24.741972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20230101 482
 
4.8%
20190701 361
 
3.6%
20160401 360
 
3.6%
20191001 329
 
3.3%
20190401 280
 
2.8%
20190101 244
 
2.4%
20160301 191
 
1.9%
20160101 179
 
1.8%
20200401 126
 
1.3%
20190102 101
 
1.0%
Other values (1508) 7308
73.1%
ValueCountFrequency (%)
20061001 2
< 0.1%
20091001 3
< 0.1%
20100101 3
< 0.1%
20100105 1
 
< 0.1%
20101001 4
< 0.1%
20101011 1
 
< 0.1%
20101201 4
< 0.1%
20101228 2
< 0.1%
20101229 1
 
< 0.1%
20110101 2
< 0.1%
ValueCountFrequency (%)
23030203 1
< 0.1%
20241209 1
< 0.1%
20240706 1
< 0.1%
20240618 1
< 0.1%
20240617 1
< 0.1%
20240616 1
< 0.1%
20240613 2
< 0.1%
20240612 1
< 0.1%
20240611 1
< 0.1%
20240610 1
< 0.1%

강의 종료 일자
Real number (ℝ)

HIGH CORRELATION 

Distinct1642
Distinct (%)16.5%
Missing51
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean20199874
Minimum20110108
Maximum20250628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T09:39:24.893126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110108
5-th percentile20160408
Q120190506
median20191231
Q320220920
95-th percentile20240411
Maximum20250628
Range140520
Interquartile range (IQR)30414

Descriptive statistics

Standard deviation24666.197
Coefficient of variation (CV)0.0012211065
Kurtosis-0.57295981
Mean20199874
Median Absolute Deviation (MAD)18988
Skewness-0.036834629
Sum2.0096855 × 1011
Variance6.0842128 × 108
MonotonicityNot monotonic
2024-04-21T09:39:25.036366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20231231 483
 
4.8%
20191231 410
 
4.1%
20160630 366
 
3.7%
20190930 343
 
3.4%
20160331 269
 
2.7%
20190331 213
 
2.1%
20190630 212
 
2.1%
20200630 160
 
1.6%
20161231 72
 
0.7%
20160430 72
 
0.7%
Other values (1632) 7349
73.5%
ValueCountFrequency (%)
20110108 1
 
< 0.1%
20110121 1
 
< 0.1%
20110127 1
 
< 0.1%
20110128 1
 
< 0.1%
20110228 4
< 0.1%
20111011 1
 
< 0.1%
20111230 7
0.1%
20111231 5
0.1%
20160309 2
 
< 0.1%
20160310 5
0.1%
ValueCountFrequency (%)
20250628 1
 
< 0.1%
20241220 1
 
< 0.1%
20241205 1
 
< 0.1%
20241026 1
 
< 0.1%
20241018 1
 
< 0.1%
20240926 1
 
< 0.1%
20240910 1
 
< 0.1%
20240904 1
 
< 0.1%
20240822 6
0.1%
20240821 1
 
< 0.1%

강의대상
Text

MISSING 

Distinct271
Distinct (%)33.4%
Missing9189
Missing (%)91.9%
Memory size156.2 KiB
2024-04-21T09:39:25.373392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length203
Median length184
Mean length14.257707
Min length1

Characters and Unicode

Total characters11563
Distinct characters415
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique220 ?
Unique (%)27.1%

Sample

1st row비문해 성인
2nd row6,7세 및 초등 저학년 어린이를 동반한 가족 15팀 (2인 1팀)
3rd row관내 임산부
4th row배움의 기회를 놓친 성인 누구나
5th row지역주민
ValueCountFrequency (%)
성인 203
 
7.6%
109
 
4.1%
있는 73
 
2.7%
마포구에 54
 
2.0%
등록되어 54
 
2.0%
주민등록증(신분증)이 54
 
2.0%
구민 54
 
2.0%
52
 
2.0%
누구나 36
 
1.4%
지역주민 29
 
1.1%
Other values (911) 1942
73.0%
2024-04-21T09:39:25.878989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2488
 
21.5%
280
 
2.4%
259
 
2.2%
252
 
2.2%
252
 
2.2%
, 242
 
2.1%
216
 
1.9%
172
 
1.5%
151
 
1.3%
151
 
1.3%
Other values (405) 7100
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7894
68.3%
Space Separator 2488
 
21.5%
Other Punctuation 390
 
3.4%
Decimal Number 345
 
3.0%
Close Punctuation 132
 
1.1%
Open Punctuation 132
 
1.1%
Dash Punctuation 73
 
0.6%
Math Symbol 61
 
0.5%
Uppercase Letter 27
 
0.2%
Lowercase Letter 17
 
0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
280
 
3.5%
259
 
3.3%
252
 
3.2%
252
 
3.2%
216
 
2.7%
172
 
2.2%
151
 
1.9%
151
 
1.9%
141
 
1.8%
119
 
1.5%
Other values (355) 5901
74.8%
Other Punctuation
ValueCountFrequency (%)
, 242
62.1%
. 52
 
13.3%
? 46
 
11.8%
: 12
 
3.1%
11
 
2.8%
' 8
 
2.1%
* 7
 
1.8%
! 4
 
1.0%
% 2
 
0.5%
& 2
 
0.5%
Other values (3) 4
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 93
27.0%
2 45
13.0%
0 41
11.9%
3 40
11.6%
6 33
 
9.6%
5 29
 
8.4%
4 28
 
8.1%
8 13
 
3.8%
7 13
 
3.8%
9 10
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
N 10
37.0%
O 8
29.6%
S 4
 
14.8%
V 1
 
3.7%
R 1
 
3.7%
T 1
 
3.7%
I 1
 
3.7%
B 1
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
n 3
17.6%
l 3
17.6%
g 3
17.6%
e 3
17.6%
i 2
11.8%
t 2
11.8%
v 1
 
5.9%
Close Punctuation
ValueCountFrequency (%)
) 124
93.9%
] 8
 
6.1%
Open Punctuation
ValueCountFrequency (%)
( 124
93.9%
[ 8
 
6.1%
Math Symbol
ValueCountFrequency (%)
~ 59
96.7%
+ 2
 
3.3%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
2488
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7894
68.3%
Common 3625
31.3%
Latin 44
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
280
 
3.5%
259
 
3.3%
252
 
3.2%
252
 
3.2%
216
 
2.7%
172
 
2.2%
151
 
1.9%
151
 
1.9%
141
 
1.8%
119
 
1.5%
Other values (355) 5901
74.8%
Common
ValueCountFrequency (%)
2488
68.6%
, 242
 
6.7%
) 124
 
3.4%
( 124
 
3.4%
1 93
 
2.6%
- 73
 
2.0%
~ 59
 
1.6%
. 52
 
1.4%
? 46
 
1.3%
2 45
 
1.2%
Other values (25) 279
 
7.7%
Latin
ValueCountFrequency (%)
N 10
22.7%
O 8
18.2%
S 4
 
9.1%
n 3
 
6.8%
l 3
 
6.8%
g 3
 
6.8%
e 3
 
6.8%
i 2
 
4.5%
t 2
 
4.5%
V 1
 
2.3%
Other values (5) 5
11.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7894
68.3%
ASCII 3653
31.6%
Punctuation 11
 
0.1%
Enclosed Alphanum 2
 
< 0.1%
Geometric Shapes 1
 
< 0.1%
Misc Symbols 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2488
68.1%
, 242
 
6.6%
) 124
 
3.4%
( 124
 
3.4%
1 93
 
2.5%
- 73
 
2.0%
~ 59
 
1.6%
. 52
 
1.4%
? 46
 
1.3%
2 45
 
1.2%
Other values (34) 307
 
8.4%
Hangul
ValueCountFrequency (%)
280
 
3.5%
259
 
3.3%
252
 
3.2%
252
 
3.2%
216
 
2.7%
172
 
2.2%
151
 
1.9%
151
 
1.9%
141
 
1.8%
119
 
1.5%
Other values (355) 5901
74.8%
Punctuation
ValueCountFrequency (%)
11
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
1
50.0%
1
50.0%
Misc Symbols
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%

정원
Real number (ℝ)

SKEWED  ZEROS 

Distinct91
Distinct (%)0.9%
Missing77
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean23.30525
Minimum0
Maximum20000
Zeros2170
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T09:39:26.036412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median16
Q320
95-th percentile40
Maximum20000
Range20000
Interquartile range (IQR)14

Descriptive statistics

Standard deviation231.87359
Coefficient of variation (CV)9.9494142
Kurtosis5899.5242
Mean23.30525
Median Absolute Deviation (MAD)6
Skewness72.737576
Sum231258
Variance53765.362
MonotonicityNot monotonic
2024-04-21T09:39:26.197318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2170
21.7%
20 2031
20.3%
15 1067
10.7%
30 594
 
5.9%
10 571
 
5.7%
12 508
 
5.1%
25 500
 
5.0%
16 433
 
4.3%
18 248
 
2.5%
40 224
 
2.2%
Other values (81) 1577
15.8%
ValueCountFrequency (%)
0 2170
21.7%
1 57
 
0.6%
2 52
 
0.5%
3 52
 
0.5%
4 25
 
0.2%
5 98
 
1.0%
6 56
 
0.6%
7 20
 
0.2%
8 64
 
0.6%
9 10
 
0.1%
ValueCountFrequency (%)
20000 1
 
< 0.1%
10000 1
 
< 0.1%
999 32
0.3%
500 4
 
< 0.1%
400 1
 
< 0.1%
350 1
 
< 0.1%
300 2
 
< 0.1%
250 1
 
< 0.1%
200 10
 
0.1%
174 1
 
< 0.1%

수강신청 URL
Text

MISSING 

Distinct7287
Distinct (%)82.4%
Missing1156
Missing (%)11.6%
Memory size156.2 KiB
2024-04-21T09:39:26.405608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length517
Median length396
Mean length129.09566
Min length1

Characters and Unicode

Total characters1141722
Distinct characters184
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6773 ?
Unique (%)76.6%

Sample

1st rowhttp://www.vocation.or.kr/sub2/step2.aspx?listUrl=step1.aspx&cop=CO20190050&center=WMC-01-05&p=1&type=&name=
2nd rowhttp://www.ecoclass.or.kr/?menu=nlist2&dbname=onbbs05&page=1&sel1=0&text1=&sel2=0
3rd rowhttp://www.dobongwoman.or.kr/lecture/lecture.php?lecture_code=5632&category=
4th rowwww.nbedu.or.kr
5th rowhttps://dongjak.seoulwomanup.or.kr/dongjak/edu/selectComorrow.do?sch_class_code=C100129650&returnUrl=%2Fdongjak%2Fedu%2FselectPageListComorrow.do&edust=&sch_course_code=39&sch_domain_code=&sch_group_code=&sch_edust=&sch_edu_fee=&sch_dayofweek=&sch_time_from=&sch_time_to=&sch_lecturer=&sch_class_name=&currentPage=4
ValueCountFrequency (%)
https://www.seoulwomanup.or.kr/womanup/edu/selectprogrampagelistall.do 354
 
4.0%
http://www.jungnanglib.seoul.kr/jnlib/index.php?g_page=event&m_page=event02_02 48
 
0.5%
http://www.ecoclass.or.kr/?menu=nlist2&dbname=onbbs05&page=1&sel1=0&text1=&sel2=0 41
 
0.5%
http://www.jungnanglib.seoul.kr/suplib/index.php?g_page=event&m_page=event02_02 40
 
0.4%
http://www.jungnanglib.seoul.kr/mmlib/index.php?g_page=event&m_page=event02_02 31
 
0.3%
http://www.lifelongstudy.ac.kr/traincenter/?listtitle=전체과정목록 29
 
0.3%
26
 
0.3%
https://www.50plus.or.kr/sbc/education.do 24
 
0.3%
https://dongbu.seoulwomanup.or.kr 22
 
0.2%
http://www.bukedu.or.kr/home/homeindex.do 22
 
0.2%
Other values (7287) 8290
92.9%
2024-04-21T09:39:26.770868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 89942
 
7.9%
t 59713
 
5.2%
o 54630
 
4.8%
s 54301
 
4.8%
r 53541
 
4.7%
= 48368
 
4.2%
c 46546
 
4.1%
a 41399
 
3.6%
/ 40278
 
3.5%
& 40235
 
3.5%
Other values (174) 612769
53.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 744084
65.2%
Other Punctuation 142256
 
12.5%
Decimal Number 102942
 
9.0%
Uppercase Letter 72934
 
6.4%
Math Symbol 48426
 
4.2%
Connector Punctuation 26547
 
2.3%
Dash Punctuation 3892
 
0.3%
Other Letter 519
 
< 0.1%
Space Separator 84
 
< 0.1%
Open Punctuation 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
31
 
6.0%
30
 
5.8%
29
 
5.6%
29
 
5.6%
29
 
5.6%
29
 
5.6%
18
 
3.5%
16
 
3.1%
14
 
2.7%
14
 
2.7%
Other values (96) 280
53.9%
Lowercase Letter
ValueCountFrequency (%)
e 89942
 
12.1%
t 59713
 
8.0%
o 54630
 
7.3%
s 54301
 
7.3%
r 53541
 
7.2%
c 46546
 
6.3%
a 41399
 
5.6%
p 38500
 
5.2%
n 34838
 
4.7%
d 33524
 
4.5%
Other values (16) 237150
31.9%
Uppercase Letter
ValueCountFrequency (%)
C 9666
 
13.3%
P 5848
 
8.0%
I 4857
 
6.7%
S 4277
 
5.9%
L 4076
 
5.6%
U 3980
 
5.5%
E 3861
 
5.3%
N 3717
 
5.1%
M 3705
 
5.1%
D 3354
 
4.6%
Other values (16) 25593
35.1%
Decimal Number
ValueCountFrequency (%)
0 26835
26.1%
1 21327
20.7%
2 16153
15.7%
3 7492
 
7.3%
4 6659
 
6.5%
8 5461
 
5.3%
9 5436
 
5.3%
5 5403
 
5.2%
6 4294
 
4.2%
7 3882
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 40278
28.3%
& 40235
28.3%
. 35496
25.0%
: 8801
 
6.2%
% 8633
 
6.1%
? 7980
 
5.6%
; 799
 
0.6%
# 34
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
= 48368
99.9%
+ 44
 
0.1%
| 14
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 26547
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3892
100.0%
Space Separator
ValueCountFrequency (%)
84
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 817018
71.6%
Common 324185
 
28.4%
Hangul 519
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
31
 
6.0%
30
 
5.8%
29
 
5.6%
29
 
5.6%
29
 
5.6%
29
 
5.6%
18
 
3.5%
16
 
3.1%
14
 
2.7%
14
 
2.7%
Other values (96) 280
53.9%
Latin
ValueCountFrequency (%)
e 89942
 
11.0%
t 59713
 
7.3%
o 54630
 
6.7%
s 54301
 
6.6%
r 53541
 
6.6%
c 46546
 
5.7%
a 41399
 
5.1%
p 38500
 
4.7%
n 34838
 
4.3%
d 33524
 
4.1%
Other values (42) 310084
38.0%
Common
ValueCountFrequency (%)
= 48368
14.9%
/ 40278
12.4%
& 40235
12.4%
. 35496
10.9%
0 26835
8.3%
_ 26547
8.2%
1 21327
6.6%
2 16153
 
5.0%
: 8801
 
2.7%
% 8633
 
2.7%
Other values (16) 51512
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1141203
> 99.9%
Hangul 519
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 89942
 
7.9%
t 59713
 
5.2%
o 54630
 
4.8%
s 54301
 
4.8%
r 53541
 
4.7%
= 48368
 
4.2%
c 46546
 
4.1%
a 41399
 
3.6%
/ 40278
 
3.5%
& 40235
 
3.5%
Other values (68) 612250
53.6%
Hangul
ValueCountFrequency (%)
31
 
6.0%
30
 
5.8%
29
 
5.6%
29
 
5.6%
29
 
5.6%
29
 
5.6%
18
 
3.5%
16
 
3.1%
14
 
2.7%
14
 
2.7%
Other values (96) 280
53.9%

등록 일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1727
Distinct (%)20.6%
Missing1603
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean7.3139905 × 1012
Minimum20181113
Maximum2.0240417 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T09:39:26.912962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20181113
5-th percentile20181216
Q120190608
median20200623
Q32.0230103 × 1013
95-th percentile2.0240307 × 1013
Maximum2.0240417 × 1013
Range2.0240397 × 1013
Interquartile range (IQR)2.0230083 × 1013

Descriptive statistics

Standard deviation9.7119536 × 1012
Coefficient of variation (CV)1.3278597
Kurtosis-1.6669007
Mean7.3139905 × 1012
Median Absolute Deviation (MAD)10505
Skewness0.57744181
Sum6.1415579 × 1016
Variance9.4322042 × 1025
MonotonicityNot monotonic
2024-04-21T09:39:27.060727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20230220100130 482
 
4.8%
20240307185000 237
 
2.4%
20230623100000 221
 
2.2%
20240108155100 203
 
2.0%
202304181520 174
 
1.7%
20230926100000 166
 
1.7%
20240206112500 101
 
1.0%
20190529 91
 
0.9%
20220703 82
 
0.8%
20210326 74
 
0.7%
Other values (1717) 6566
65.7%
(Missing) 1603
 
16.0%
ValueCountFrequency (%)
20181113 8
 
0.1%
20181114 11
0.1%
20181115 13
0.1%
20181116 6
 
0.1%
20181117 23
0.2%
20181118 10
 
0.1%
20181120 10
 
0.1%
20181121 22
0.2%
20181122 13
0.1%
20181123 27
0.3%
ValueCountFrequency (%)
20240417204735 2
 
< 0.1%
20240417204734 1
 
< 0.1%
20240417203808 1
 
< 0.1%
20240417203807 5
0.1%
20240417203447 2
 
< 0.1%
20240417203446 2
 
< 0.1%
20240417203337 5
0.1%
20240417203336 1
 
< 0.1%
20240415160406 1
 
< 0.1%
20240412140454 1
 
< 0.1%

수정 일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1195
Distinct (%)37.7%
Missing6829
Missing (%)68.3%
Infinite0
Infinite (%)0.0%
Mean1.8609978 × 1013
Minimum1.7082308 × 1011
Maximum2.0240417 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T09:39:27.216206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7082308 × 1011
5-th percentile2.0230418 × 1011
Q12.0210917 × 1013
median2.0230303 × 1013
Q32.0240108 × 1013
95-th percentile2.0240403 × 1013
Maximum2.0240417 × 1013
Range2.0069594 × 1013
Interquartile range (IQR)2.9191049 × 1010

Descriptive statistics

Standard deviation5.4559756 × 1012
Coefficient of variation (CV)0.29317475
Kurtosis7.4881015
Mean1.8609978 × 1013
Median Absolute Deviation (MAD)9.8050248 × 109
Skewness-3.0794953
Sum5.9012239 × 1016
Variance2.9767669 × 1025
MonotonicityNot monotonic
2024-04-21T09:39:27.382188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20230220100130 482
 
4.8%
20240307185000 237
 
2.4%
20230623100000 221
 
2.2%
20240108155100 203
 
2.0%
202304181520 174
 
1.7%
20230926100000 166
 
1.7%
20240206112500 101
 
1.0%
202303171300 73
 
0.7%
20240115161800 48
 
0.5%
20230512170000 34
 
0.3%
Other values (1185) 1432
 
14.3%
(Missing) 6829
68.3%
ValueCountFrequency (%)
170823084229 1
 
< 0.1%
202303171300 73
0.7%
202304181520 174
1.7%
202304191020 8
 
0.1%
20181115161145 1
 
< 0.1%
20181115161153 1
 
< 0.1%
20190114140108 1
 
< 0.1%
20190114140121 1
 
< 0.1%
20190114140123 1
 
< 0.1%
20190114140133 1
 
< 0.1%
ValueCountFrequency (%)
20240417204735 2
 
< 0.1%
20240417204734 1
 
< 0.1%
20240417203808 1
 
< 0.1%
20240417203807 5
0.1%
20240417203447 2
 
< 0.1%
20240417203446 2
 
< 0.1%
20240417203337 5
0.1%
20240417203336 1
 
< 0.1%
20240415160406 1
 
< 0.1%
20240412140454 1
 
< 0.1%
Distinct409
Distinct (%)6.0%
Missing3233
Missing (%)32.3%
Memory size156.2 KiB
2024-04-21T09:39:27.636969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length7.3505246
Min length5

Characters and Unicode

Total characters49741
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)0.9%

Sample

1st rowE10290014
2nd rowE10230001
3rd rowC1018
4th rowJ4051
5th rowE16210004
ValueCountFrequency (%)
s1001 842
 
12.4%
e20270015 218
 
3.2%
e06230001 199
 
2.9%
e17230002 172
 
2.5%
e19230001 135
 
2.0%
e18290002 112
 
1.7%
i1193 86
 
1.3%
e12210002 81
 
1.2%
j1013 80
 
1.2%
e12210058 80
 
1.2%
Other values (399) 4762
70.4%
2024-04-21T09:39:28.040375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15287
30.7%
1 9764
19.6%
2 5904
 
11.9%
E 3722
 
7.5%
3 2587
 
5.2%
7 2435
 
4.9%
9 1998
 
4.0%
4 1673
 
3.4%
6 1235
 
2.5%
8 1212
 
2.4%
Other values (15) 3924
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43022
86.5%
Uppercase Letter 6719
 
13.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3722
55.4%
S 853
 
12.7%
J 691
 
10.3%
I 533
 
7.9%
C 352
 
5.2%
L 234
 
3.5%
A 101
 
1.5%
H 75
 
1.1%
K 49
 
0.7%
G 46
 
0.7%
Other values (5) 63
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 15287
35.5%
1 9764
22.7%
2 5904
 
13.7%
3 2587
 
6.0%
7 2435
 
5.7%
9 1998
 
4.6%
4 1673
 
3.9%
6 1235
 
2.9%
8 1212
 
2.8%
5 927
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 43022
86.5%
Latin 6719
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3722
55.4%
S 853
 
12.7%
J 691
 
10.3%
I 533
 
7.9%
C 352
 
5.2%
L 234
 
3.5%
A 101
 
1.5%
H 75
 
1.1%
K 49
 
0.7%
G 46
 
0.7%
Other values (5) 63
 
0.9%
Common
ValueCountFrequency (%)
0 15287
35.5%
1 9764
22.7%
2 5904
 
13.7%
3 2587
 
6.0%
7 2435
 
5.7%
9 1998
 
4.6%
4 1673
 
3.9%
6 1235
 
2.9%
8 1212
 
2.8%
5 927
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15287
30.7%
1 9764
19.6%
2 5904
 
11.9%
E 3722
 
7.5%
3 2587
 
5.2%
7 2435
 
4.9%
9 1998
 
4.0%
4 1673
 
3.4%
6 1235
 
2.5%
8 1212
 
2.4%
Other values (15) 3924
 
7.9%

교육기관명
Text

MISSING 

Distinct376
Distinct (%)5.6%
Missing3233
Missing (%)32.3%
Memory size156.2 KiB
2024-04-21T09:39:28.267088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length9.3379636
Min length3

Characters and Unicode

Total characters63190
Distinct characters308
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)0.9%

Sample

1st row도봉환경교실
2nd row도봉여성센터
3rd row동대문여성인력개발센터
4th row서초여성인력개발센터
5th row강서꿈꾸는어린이도서관
ValueCountFrequency (%)
자치회관 2154
23.3%
서울시(평생교육과 842
 
9.1%
동대문여성인력개발센터 264
 
2.9%
신대방2동 222
 
2.4%
구로여성인력개발센터 186
 
2.0%
영등포여성인력개발센터 185
 
2.0%
금천구무한상상스페이스 112
 
1.2%
은평여성인력개발센터 95
 
1.0%
종로여성인력개발센터 89
 
1.0%
사당5동 86
 
0.9%
Other values (390) 5002
54.2%
2024-04-21T09:39:28.611731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3426
 
5.4%
2840
 
4.5%
2505
 
4.0%
2260
 
3.6%
2251
 
3.6%
2160
 
3.4%
2085
 
3.3%
1854
 
2.9%
1852
 
2.9%
1599
 
2.5%
Other values (298) 40358
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 56714
89.8%
Space Separator 2505
 
4.0%
Decimal Number 2010
 
3.2%
Open Punctuation 910
 
1.4%
Close Punctuation 910
 
1.4%
Other Punctuation 106
 
0.2%
Math Symbol 19
 
< 0.1%
Lowercase Letter 8
 
< 0.1%
Letter Number 4
 
< 0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3426
 
6.0%
2840
 
5.0%
2260
 
4.0%
2251
 
4.0%
2160
 
3.8%
2085
 
3.7%
1854
 
3.3%
1852
 
3.3%
1599
 
2.8%
1484
 
2.6%
Other values (269) 34903
61.5%
Decimal Number
ValueCountFrequency (%)
2 635
31.6%
1 523
26.0%
5 236
 
11.7%
3 233
 
11.6%
0 133
 
6.6%
4 102
 
5.1%
6 42
 
2.1%
8 40
 
2.0%
7 37
 
1.8%
9 29
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
c 2
25.0%
w 1
12.5%
p 1
12.5%
a 1
12.5%
o 1
12.5%
k 1
12.5%
r 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 1
25.0%
A 1
25.0%
S 1
25.0%
V 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 86
81.1%
? 18
 
17.0%
. 2
 
1.9%
Space Separator
ValueCountFrequency (%)
2505
100.0%
Open Punctuation
ValueCountFrequency (%)
( 910
100.0%
Close Punctuation
ValueCountFrequency (%)
) 910
100.0%
Math Symbol
ValueCountFrequency (%)
~ 19
100.0%
Letter Number
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 56714
89.8%
Common 6460
 
10.2%
Latin 16
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3426
 
6.0%
2840
 
5.0%
2260
 
4.0%
2251
 
4.0%
2160
 
3.8%
2085
 
3.7%
1854
 
3.3%
1852
 
3.3%
1599
 
2.8%
1484
 
2.6%
Other values (269) 34903
61.5%
Common
ValueCountFrequency (%)
2505
38.8%
( 910
 
14.1%
) 910
 
14.1%
2 635
 
9.8%
1 523
 
8.1%
5 236
 
3.7%
3 233
 
3.6%
0 133
 
2.1%
4 102
 
1.6%
, 86
 
1.3%
Other values (7) 187
 
2.9%
Latin
ValueCountFrequency (%)
4
25.0%
c 2
12.5%
D 1
 
6.2%
A 1
 
6.2%
S 1
 
6.2%
V 1
 
6.2%
w 1
 
6.2%
p 1
 
6.2%
a 1
 
6.2%
o 1
 
6.2%
Other values (2) 2
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 56714
89.8%
ASCII 6472
 
10.2%
Number Forms 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3426
 
6.0%
2840
 
5.0%
2260
 
4.0%
2251
 
4.0%
2160
 
3.8%
2085
 
3.7%
1854
 
3.3%
1852
 
3.3%
1599
 
2.8%
1484
 
2.6%
Other values (269) 34903
61.5%
ASCII
ValueCountFrequency (%)
2505
38.7%
( 910
 
14.1%
) 910
 
14.1%
2 635
 
9.8%
1 523
 
8.1%
5 236
 
3.6%
3 233
 
3.6%
0 133
 
2.1%
4 102
 
1.6%
, 86
 
1.3%
Other values (18) 199
 
3.1%
Number Forms
ValueCountFrequency (%)
4
100.0%

교육기관 자치구
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3233 
중구
922 
노원구
628 
동작구
496 
영등포구
443 
Other values (42)
4278 

Length

Max length7
Median length6
Mean length3.4061
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row도봉구
3rd row도봉구
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 3233
32.3%
중구 922
 
9.2%
노원구 628
 
6.3%
동작구 496
 
5.0%
영등포구 443
 
4.4%
동대문구 425
 
4.2%
종로구 424
 
4.2%
서대문구 335
 
3.4%
은평구 311
 
3.1%
중랑구 300
 
3.0%
Other values (37) 2483
24.8%

Length

2024-04-21T09:39:28.747252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3233
32.3%
중구 922
 
9.2%
노원구 696
 
7.0%
동작구 512
 
5.1%
영등포구 455
 
4.5%
동대문구 425
 
4.2%
종로구 424
 
4.2%
서대문구 339
 
3.4%
은평구 331
 
3.3%
마포구 320
 
3.2%
Other values (18) 2343
23.4%

교육기관 위도 좌표
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct346
Distinct (%)5.8%
Missing3994
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean37.713645
Minimum37.36614
Maximum127.05814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T09:39:28.881565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.36614
5-th percentile37.480361
Q137.512357
median37.55654
Q337.575951
95-th percentile37.634616
Maximum127.05814
Range89.691997
Interquartile range (IQR)0.0635941

Descriptive statistics

Standard deviation3.8276691
Coefficient of variation (CV)0.10149295
Kurtosis541.30959
Mean37.713645
Median Absolute Deviation (MAD)0.029979
Skewness23.303584
Sum226508.15
Variance14.651051
MonotonicityNot monotonic
2024-04-21T09:39:29.036588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5645124 842
 
8.4%
37.5123569 218
 
2.2%
37.5962 199
 
2.0%
37.508042 172
 
1.7%
37.48021 135
 
1.4%
37.485615 86
 
0.9%
37.55673 81
 
0.8%
37.559685 80
 
0.8%
37.5662 80
 
0.8%
37.54126 80
 
0.8%
Other values (336) 4033
40.3%
(Missing) 3994
39.9%
ValueCountFrequency (%)
37.3661397 5
 
0.1%
37.450092 13
0.1%
37.452358 4
 
< 0.1%
37.456768 19
0.2%
37.46193 3
 
< 0.1%
37.4631387 24
0.2%
37.463215 31
0.3%
37.46327 3
 
< 0.1%
37.464007 4
 
< 0.1%
37.466076 4
 
< 0.1%
ValueCountFrequency (%)
127.058137 11
 
0.1%
37.682959 1
 
< 0.1%
37.67867 17
0.2%
37.678669 6
 
0.1%
37.668774 4
 
< 0.1%
37.668629 7
 
0.1%
37.664188 4
 
< 0.1%
37.6631556 33
0.3%
37.662108 11
 
0.1%
37.658021 13
 
0.1%

교육기관 경도 좌표
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct345
Distinct (%)5.7%
Missing3994
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean126.80328
Minimum37.642191
Maximum127.1738
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T09:39:29.195465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.642191
5-th percentile126.84766
Q1126.92049
median126.96696
Q3127.01417
95-th percentile127.08406
Maximum127.1738
Range89.53161
Interquartile range (IQR)0.09368

Descriptive statistics

Standard deviation3.8201816
Coefficient of variation (CV)0.030126835
Kurtosis541.09895
Mean126.80328
Median Absolute Deviation (MAD)0.047035
Skewness-23.296793
Sum761580.51
Variance14.593787
MonotonicityNot monotonic
2024-04-21T09:39:29.328912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9757372 842
 
8.4%
126.9395531 218
 
2.2%
127.084062 199
 
2.0%
126.9472 172
 
1.7%
127.01417 135
 
1.4%
126.966957 86
 
0.9%
126.92049 81
 
0.8%
127.159754 80
 
0.8%
126.901642 80
 
0.8%
126.94629 80
 
0.8%
Other values (335) 4033
40.3%
(Missing) 3994
39.9%
ValueCountFrequency (%)
37.6421907 11
 
0.1%
126.810089 1
 
< 0.1%
126.813683 1
 
< 0.1%
126.829604 60
0.6%
126.829956 63
0.6%
126.829963 25
 
0.2%
126.831451 4
 
< 0.1%
126.83152 3
 
< 0.1%
126.83153 12
 
0.1%
126.833168 1
 
< 0.1%
ValueCountFrequency (%)
127.173801 6
 
0.1%
127.159754 80
0.8%
127.145362 7
 
0.1%
127.142891 15
 
0.1%
127.139587 19
 
0.2%
127.128075 1
 
< 0.1%
127.127117 6
 
0.1%
127.126595 1
 
< 0.1%
127.123344 5
 
0.1%
127.122429 8
 
0.1%

Interactions

2024-04-21T09:39:19.321660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:10.672063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:11.809774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:12.863339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.011580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.972711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:15.969518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:16.974422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:18.152948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:19.425581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:10.845412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:11.923075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:13.143949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.129421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:15.079439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:16.085428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:17.133366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:18.274976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:19.524210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:10.969155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:12.044513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:13.254457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.236084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:15.193547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:16.200577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:17.284418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:18.604310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:19.629539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:11.088848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:12.186739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:13.358981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.344501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:15.302772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:16.312927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:17.399791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:18.702051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:19.731433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:11.198887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:12.314803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:13.473296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.446908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:15.419657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:16.433010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:17.519224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:18.808625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:19.840899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:11.336110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:12.437959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:13.577574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.558267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:15.543566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:16.550164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:17.637225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:18.929163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:19.950307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:11.456823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:12.544075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:13.700207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.672353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:15.653800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:16.664639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:17.779162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:19.041960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:20.059921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:11.583609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:12.657995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:13.806008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.780229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:15.763879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:16.776783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:17.923602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:19.145742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:20.146732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:11.703141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:12.760509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:13.905528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:14.873934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:15.861289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:16.878017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:18.048513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:39:19.237853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T09:39:29.424654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수강신청 시작 일자수강신청 종료 일자강의 시작 일자강의 종료 일자정원등록 일자수정 일자교육기관 자치구교육기관 위도 좌표교육기관 경도 좌표
수강신청 시작 일자1.0000.2640.0000.9610.0050.9940.0080.9610.1110.111
수강신청 종료 일자0.2641.0000.0000.7660.0000.2680.100NaNNaNNaN
강의 시작 일자0.0000.0001.0000.0000.0000.0000.0330.0000.0000.000
강의 종료 일자0.9610.7660.0001.0000.0170.9130.2430.7650.1710.171
정원0.0050.0000.0000.0171.0000.0040.0000.0000.0000.000
등록 일자0.9940.2680.0000.9130.0041.0001.0000.9850.1100.110
수정 일자0.0080.1000.0330.2430.0001.0001.0000.9330.0000.000
교육기관 자치구0.961NaN0.0000.7650.0000.9850.9331.0000.8390.839
교육기관 위도 좌표0.111NaN0.0000.1710.0000.1100.0000.8391.0000.997
교육기관 경도 좌표0.111NaN0.0000.1710.0000.1100.0000.8390.9971.000
2024-04-21T09:39:29.577575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수강신청 시작 일자수강신청 종료 일자강의 시작 일자강의 종료 일자정원등록 일자수정 일자교육기관 위도 좌표교육기관 경도 좌표교육기관 자치구
수강신청 시작 일자1.0000.9740.9590.9440.2670.9660.8420.1650.0730.860
수강신청 종료 일자0.9741.0000.9610.9640.2680.9790.8690.1270.0751.000
강의 시작 일자0.9590.9611.0000.9820.2210.9500.8780.1270.0590.000
강의 종료 일자0.9440.9640.9821.0000.2250.9330.8480.1120.0470.414
정원0.2670.2680.2210.2251.0000.208-0.2320.0450.0490.000
등록 일자0.9660.9790.9500.9330.2081.0001.0000.2100.2240.911
수정 일자0.8420.8690.8780.848-0.2321.0001.000-0.1130.0980.843
교육기관 위도 좌표0.1650.1270.1270.1120.0450.210-0.1131.0000.3820.701
교육기관 경도 좌표0.0730.0750.0590.0470.0490.2240.0980.3821.0000.701
교육기관 자치구0.8601.0000.0000.4140.0000.9110.8430.7010.7011.000

Missing values

2024-04-21T09:39:20.328494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T09:39:20.577022image/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.
2024-04-21T09:39:20.808620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

사이트 아이디ASP 아이디강의 아이디강의 구분강의 명수강신청 시작 일자수강신청 종료 일자강의 시작 일자강의 종료 일자강의대상정원수강신청 URL등록 일자수정 일자교육기관 고유아이디교육기관명교육기관 자치구교육기관 위도 좌표교육기관 경도 좌표
269831243ASP00001ASP0000120190000156오프라인[사무관리,IT관련] &lt;직종설명회&gt;코딩기반 IT융합강사 양성과정(드론&VR) - 영등포여성인력개발센터20190213201903052019030620190306<NA>40http://www.vocation.or.kr/sub2/step2.aspx?listUrl=step1.aspx&cop=CO20190050&center=WMC-01-05&p=1&type=&name=20190214<NA><NA><NA><NA><NA><NA>
1657292ASP00001ASP00001E1029001420200000049오프라인[초등] 자연관찰그리기 &lt;곤충의 다리는 몇 개일까?&gt;20200601202006302020060120200630<NA>0http://www.ecoclass.or.kr/?menu=nlist2&dbname=onbbs05&page=1&sel1=0&text1=&sel2=020200520<NA>E10290014도봉환경교실도봉구37.572529126.998063
23389110ASP00001ASP00001E1023000120190000029오프라인중식조리기능사 자격증취득 실기집중반20190712201909272019071220190927<NA>16http://www.dobongwoman.or.kr/lecture/lecture.php?lecture_code=5632&category=20190531<NA>E10230001도봉여성센터도봉구37.663156127.030142
7314lc1ASP00001ASP00001J406120230108130오프라인빅데이터 분석 및 시각화2023120112002024021624002024030220240614<NA>20www.nbedu.or.kr2023122211070020231222110700<NA><NA><NA><NA><NA>
8425lc1ASP00001ASP00001J404920230107394오프라인[토]무역실무(무역영어1급)2023061901002023072209002023072220231111<NA>15https://dongjak.seoulwomanup.or.kr/dongjak/edu/selectComorrow.do?sch_class_code=C100129650&returnUrl=%2Fdongjak%2Fedu%2FselectPageListComorrow.do&edust=&sch_course_code=39&sch_domain_code=&sch_group_code=&sch_edust=&sch_edu_fee=&sch_dayofweek=&sch_time_from=&sch_time_to=&sch_lecturer=&sch_class_name=&currentPage=42023062310000020230623100000<NA><NA><NA><NA><NA>
12575lc1ASP00001ASP00001C101820220101008오프라인[무료교육]영상콘텐츠 편집전문가2022051700002022070300002022070720220901<NA>20www.job2060.or.kr2022051716054220220517160542C1018동대문여성인력개발센터동대문구37.577251127.030136
10883lc1ASP00001ASP00001J405120230106249오프라인[유료]꽃차 마이스터과정(저녁)2023010100002023063023592023010120231231<NA>12https://www.seoulwomanup.or.kr/womanup/edu/selectProgramPageListAll.do2023022010013020230220100130J4051서초여성인력개발센터서초구37.483499127.035562
14756173ASP00001ASP00001E1621000420210000002오프라인[꿈꾸는 어린이도서관] [꿈꾸는온라인독서회] 창의쏙쏙 독서 플러스 초등1-220210311202103052021031120210527<NA>0https://lib.gangseo.seoul.kr/education/detail/1?lecture_id=296420210305<NA>E16210004강서꿈꾸는어린이도서관강서구37.49416126.90077
27786lc1ASP00001ASP00001S1001201989186오프라인사주?명리로 어떻게 운명 추측이 가능한가?2019011510002019022523002019022620190226<NA>70<NA>2019011414013320190114140133S1001서울시(평생교육과)중구37.564512126.975737
15330lc1ASP00001ASP00001AA018420200101422오프라인성인문해교육지원사업 초등1단계2023030600002023121500002023030620231215비문해 성인<NA>http://gaw.or.kr2020100817102620201008171026AA0184강감찬관악종합사회복지관(구 선의관악종합사회복지관)관악구37.488381126.946659
사이트 아이디ASP 아이디강의 아이디강의 구분강의 명수강신청 시작 일자수강신청 종료 일자강의 시작 일자강의 종료 일자강의대상정원수강신청 URL등록 일자수정 일자교육기관 고유아이디교육기관명교육기관 자치구교육기관 위도 좌표교육기관 경도 좌표
17956279ASP00001ASP00001E1127001420200000012오프라인줌바댄스(B반)20200316202003182020040120200630<NA>50http://lll.nowon.kr/lms/menu.jsp?org_seq=157&at_cd=l_lecture_viw&mno=8_4_1&term_seq=1&edu_insti_seq=0&open_course_seq=11037&num=&org_type=2&page=null20200214<NA>E11270014상계3,4동 자치회관노원구37.55688126.91909
239441255ASP00001ASP00001E0927000920190000004오프라인한글 2010 기초20190524201906032019060420190627누구나0http://www.gangbuk.go.kr/edu/selectEdcLctreView.do?edcLctreNo=24472&key=31&searchInsttNo=3&searchCtgryNo=&searchProgrsSttus=20190525<NA>E09270009번1동 자치회관강북구37.60273126.91549
6136lc1ASP00001ASP00001J401420240109183오프라인쉽게 배워 활용하는 AI데이터라벨러2024030409002024033100002024040420240423<NA>20https://50plus.or.kr/scc/education-detail.do?id=382866602024022611250020240226112500<NA><NA><NA><NA><NA>
17480279ASP00001ASP00001E1127000320200000021오프라인하모니카 교실 [방문접수]20200311202003132020040120200630<NA>20http://lll.nowon.kr/lms/menu.jsp?org_seq=144&at_cd=l_lecture_viw&mno=8_4_1&term_seq=1&edu_insti_seq=0&open_course_seq=12327&num=&org_type=2&page=null20200304<NA>E11270003월계3동 자치회관노원구37.5556126.9342
5307lc1ASP00001ASP00001J400420240109239오프라인온라인 쇼핑몰 시작하기 입문2024022709002024030517002024031920240418<NA>16https://www.50plus.or.kr/gdc/education-detail.do?id=382555452024030718500020240307185000<NA><NA><NA><NA><NA>
19421116ASP00001ASP00001E1027000120190000120오프라인한글서예20190916201910312019100120191231<NA>0http://jachi.dobong.go.kr/JUMIN/sub03/sub03_01_view.aspx?LectureCode=360620190907<NA>E10270001쌍문1동 자치회관도봉구37.5559126.93815
6709lc1ASP00001ASP00001J403320240108821오프라인[취업맞춤형]청소연구소 매니저 양성 과정2024010300002024022300002024022320240223<NA>30https://dongbu.seoulwomanup.or.kr/dongbu/edu/selectRealEduProgram.do?sch_class_code=C100138085&returnUrl=%2Fdongbu%2Fedu%2FselectPageListRealEduProgram.do&edust=&sch_course_code=&sch_domain_code=&sch_group_code=&sch_edust=&sch_edu_fee=&sch_dayofweek=&sch_time_from=&sch_time_to=&sch_lecturer=&sch_class_name=%EC%B2%AD%EC%86%8C%EC%97%B0%EA%B5%AC%EC%86%8C&currentPage=12024011516180020240115161800<NA><NA><NA><NA><NA>
16179lc1ASP00001ASP00001S100120200101158오프라인[테스트 강좌] 디지털 시민교육 연습용 강좌2020062410002020071618002020071320200716<NA>50<NA>2020062408064120200624080641S1001서울시(평생교육과)중구37.564512126.975737
4632186ASP00001ASP00001I138120160000025오프라인손뜨개교실20160220201603202016030120160331<NA>20http://jachi.jongno.go.kr/JUMIN/sub03/sub03_01_view.aspx?LectureCode=12219<NA><NA>I1381교남동 자치회관종로구37.571869126.961967
19948310ASP00001ASP00001E1723000220190000276오프라인[목요일야간] 캘리그라피(고급) - 구로여성인력개발센터20190103201909052019090520191107<NA>20http://www.vocation.or.kr/sub2/step2.aspx?listUrl=step1.aspx&cop=CO20190082&center=WMC-01-04&p=&type=&name=20190829<NA>E17230002구로여성인력개발센터구로구37.508042126.9472