Overview

Dataset statistics

Number of variables22
Number of observations317
Missing cells269
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.5 KiB
Average record size in memory182.4 B

Variable types

Numeric6
Text2
DateTime2
Categorical11
Boolean1

Alerts

apr_at has constant value ""Constant
last_load_dttm has constant value ""Constant
edu_sdate has 59 (18.6%) missing valuesMissing
edu_fdate has 59 (18.6%) missing valuesMissing
people has 4 (1.3%) missing valuesMissing
edu_exp has 48 (15.1%) missing valuesMissing
days has 87 (27.4%) missing valuesMissing
lat has 6 (1.9%) missing valuesMissing
lng has 6 (1.9%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-21 12:51:30.111252
Analysis finished2024-04-21 12:51:30.814874
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct317
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5853.6025
Minimum5682
Maximum6026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T21:51:30.943497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5682
5-th percentile5697.8
Q15761
median5840
Q35947
95-th percentile6010.2
Maximum6026
Range344
Interquartile range (IQR)186

Descriptive statistics

Standard deviation104.02495
Coefficient of variation (CV)0.017771099
Kurtosis-1.3459336
Mean5853.6025
Median Absolute Deviation (MAD)93
Skewness0.011458754
Sum1855592
Variance10821.19
MonotonicityNot monotonic
2024-04-21T21:51:31.198726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5999 1
 
0.3%
5707 1
 
0.3%
5805 1
 
0.3%
5713 1
 
0.3%
5712 1
 
0.3%
5711 1
 
0.3%
5710 1
 
0.3%
5709 1
 
0.3%
5708 1
 
0.3%
5706 1
 
0.3%
Other values (307) 307
96.8%
ValueCountFrequency (%)
5682 1
0.3%
5683 1
0.3%
5684 1
0.3%
5685 1
0.3%
5686 1
0.3%
5687 1
0.3%
5688 1
0.3%
5689 1
0.3%
5690 1
0.3%
5691 1
0.3%
ValueCountFrequency (%)
6026 1
0.3%
6025 1
0.3%
6024 1
0.3%
6023 1
0.3%
6022 1
0.3%
6021 1
0.3%
6020 1
0.3%
6019 1
0.3%
6018 1
0.3%
6017 1
0.3%

instt_code
Real number (ℝ)

Distinct13
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3587981.1
Minimum3250000
Maximum6260000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T21:51:31.413456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3250000
Q13300000
median3340000
Q33380000
95-th percentile6260000
Maximum6260000
Range3010000
Interquartile range (IQR)80000

Descriptive statistics

Standard deviation834110.05
Coefficient of variation (CV)0.23247337
Kurtosis6.4915903
Mean3587981.1
Median Absolute Deviation (MAD)40000
Skewness2.9013492
Sum1.13739 × 109
Variance6.9573958 × 1011
MonotonicityNot monotonic
2024-04-21T21:51:31.601509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3330000 52
16.4%
3340000 48
15.1%
3390000 42
13.2%
3380000 28
8.8%
3280000 28
8.8%
6260000 28
8.8%
3260000 27
8.5%
3360000 19
 
6.0%
3250000 18
 
5.7%
3310000 13
 
4.1%
Other values (3) 14
 
4.4%
ValueCountFrequency (%)
3250000 18
 
5.7%
3260000 27
8.5%
3280000 28
8.8%
3300000 8
 
2.5%
3310000 13
 
4.1%
3320000 4
 
1.3%
3330000 52
16.4%
3340000 48
15.1%
3350000 2
 
0.6%
3360000 19
 
6.0%
ValueCountFrequency (%)
6260000 28
8.8%
3390000 42
13.2%
3380000 28
8.8%
3360000 19
 
6.0%
3350000 2
 
0.6%
3340000 48
15.1%
3330000 52
16.4%
3320000 4
 
1.3%
3310000 13
 
4.1%
3300000 8
 
2.5%

edu_nm
Text

Distinct169
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-21T21:51:32.342451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length20
Mean length10.832808
Min length2

Characters and Unicode

Total characters3434
Distinct characters237
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)34.4%

Sample

1st row스마트폰을 활용한 1인 크리에이터1.
2nd row슬기로운 화상솔루션
3rd row안전한 내손 안의 은행(금융)
4th row앱 활용을 통한 삶의 업그레이드
5th row앱으로 소통하기(SNS)
ValueCountFrequency (%)
스마트폰 44
 
5.8%
기초/활용 28
 
3.7%
활용 27
 
3.5%
엑셀 24
 
3.1%
기초 22
 
2.9%
만들기 22
 
2.9%
파워포인트 21
 
2.8%
itq자격증 18
 
2.4%
활용한 15
 
2.0%
한글 14
 
1.8%
Other values (191) 527
69.2%
2024-04-21T21:51:33.327895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
445
 
13.0%
기 126
 
3.7%
트 100
 
2.9%
용 91
 
2.6%
초 89
 
2.6%
활 88
 
2.6%
스 79
 
2.3%
마 69
 
2.0%
폰 67
 
2.0%
( 63
 
1.8%
Other values (227) 2217
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2559
74.5%
Space Separator 445
 
13.0%
Uppercase Letter 139
 
4.0%
Other Punctuation 89
 
2.6%
Decimal Number 64
 
1.9%
Open Punctuation 63
 
1.8%
Close Punctuation 63
 
1.8%
Letter Number 8
 
0.2%
Dash Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 126
 
4.9%
트 100
 
3.9%
용 91
 
3.6%
초 89
 
3.5%
활 88
 
3.4%
스 79
 
3.1%
마 69
 
2.7%
폰 67
 
2.6%
한 60
 
2.3%
인 60
 
2.3%
Other values (196) 1730
67.6%
Uppercase Letter
ValueCountFrequency (%)
I 33
23.7%
T 31
22.3%
Q 31
22.3%
S 16
11.5%
N 7
 
5.0%
O 4
 
2.9%
M 4
 
2.9%
A 3
 
2.2%
R 2
 
1.4%
Z 2
 
1.4%
Other values (4) 6
 
4.3%
Other Punctuation
ValueCountFrequency (%)
/ 34
38.2%
. 30
33.7%
! 11
 
12.4%
& 11
 
12.4%
' 2
 
2.2%
, 1
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 36
56.2%
2 14
 
21.9%
0 8
 
12.5%
4 4
 
6.2%
3 2
 
3.1%
Letter Number
ValueCountFrequency (%)
Ⅱ 4
50.0%
Ⅰ 4
50.0%
Space Separator
ValueCountFrequency (%)
445
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2559
74.5%
Common 728
 
21.2%
Latin 147
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 126
 
4.9%
트 100
 
3.9%
용 91
 
3.6%
초 89
 
3.5%
활 88
 
3.4%
스 79
 
3.1%
마 69
 
2.7%
폰 67
 
2.6%
한 60
 
2.3%
인 60
 
2.3%
Other values (196) 1730
67.6%
Latin
ValueCountFrequency (%)
I 33
22.4%
T 31
21.1%
Q 31
21.1%
S 16
10.9%
N 7
 
4.8%
O 4
 
2.7%
M 4
 
2.7%
Ⅱ 4
 
2.7%
Ⅰ 4
 
2.7%
A 3
 
2.0%
Other values (6) 10
 
6.8%
Common
ValueCountFrequency (%)
445
61.1%
( 63
 
8.7%
) 63
 
8.7%
1 36
 
4.9%
/ 34
 
4.7%
. 30
 
4.1%
2 14
 
1.9%
! 11
 
1.5%
& 11
 
1.5%
0 8
 
1.1%
Other values (5) 13
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2559
74.5%
ASCII 867
 
25.2%
Number Forms 8
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
445
51.3%
( 63
 
7.3%
) 63
 
7.3%
1 36
 
4.2%
/ 34
 
3.9%
I 33
 
3.8%
T 31
 
3.6%
Q 31
 
3.6%
. 30
 
3.5%
S 16
 
1.8%
Other values (19) 85
 
9.8%
Hangul
ValueCountFrequency (%)
기 126
 
4.9%
트 100
 
3.9%
용 91
 
3.6%
초 89
 
3.5%
활 88
 
3.4%
스 79
 
3.1%
마 69
 
2.7%
폰 67
 
2.6%
한 60
 
2.3%
인 60
 
2.3%
Other values (196) 1730
67.6%
Number Forms
ValueCountFrequency (%)
Ⅱ 4
50.0%
Ⅰ 4
50.0%

edu_sdate
Date

MISSING 

Distinct53
Distinct (%)20.5%
Missing59
Missing (%)18.6%
Memory size2.6 KiB
Minimum2020-01-06 00:00:00
Maximum2021-11-29 00:00:00
2024-04-21T21:51:33.558138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:51:33.802713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

edu_fdate
Date

MISSING 

Distinct58
Distinct (%)22.5%
Missing59
Missing (%)18.6%
Memory size2.6 KiB
Minimum2020-01-17 00:00:00
Maximum2021-12-10 00:00:00
2024-04-21T21:51:34.045697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:51:34.291791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

edu_time
Categorical

Distinct23
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
13:30~15:30
60 
수시
55 
10:00~12:00
49 
09:30~13:00
34 
7
17 
Other values (18)
102 

Length

Max length11
Median length11
Mean length8.1451104
Min length1

Unique

Unique4 ?
Unique (%)1.3%

Sample

1st row10.5
2nd row4
3rd row10.5
4th row7
5th row3.5

Common Values

ValueCountFrequency (%)
13:30~15:30 60
18.9%
수시 55
17.4%
10:00~12:00 49
15.5%
09:30~13:00 34
10.7%
7 17
 
5.4%
10.5 16
 
5.0%
13:00~15:00 15
 
4.7%
3.5 11
 
3.5%
19:00~22:00 8
 
2.5%
09:00~13:00 8
 
2.5%
Other values (13) 44
13.9%

Length

2024-04-21T21:51:34.520319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13:30~15:30 60
18.5%
수시 55
16.9%
10:00~12:00 49
15.1%
09:30~13:00 34
10.5%
7 17
 
5.2%
10.5 16
 
4.9%
13:00~15:00 15
 
4.6%
3.5 11
 
3.4%
19:00~22:00 8
 
2.5%
09:00~13:00 8
 
2.5%
Other values (15) 52
16.0%

edu_loc
Categorical

Distinct15
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
자택
56 
구청 제2별관 2층 정보화교육장
48 
사상구청 전산교육장
40 
온라인 교육
29 
영도구청 4층 정보화교육장
28 
Other values (10)
116 

Length

Max length18
Median length16
Mean length11.160883
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row구청 제2별관 2층 정보화교육장
2nd row구청 제2별관 2층 정보화교육장
3rd row구청 제2별관 2층 정보화교육장
4th row구청 제2별관 2층 정보화교육장
5th row구청 제2별관 2층 정보화교육장

Common Values

ValueCountFrequency (%)
자택 56
17.7%
구청 제2별관 2층 정보화교육장 48
15.1%
사상구청 전산교육장 40
12.6%
온라인 교육 29
9.1%
영도구청 4층 정보화교육장 28
8.8%
문화복합센터 3층 정보화교육장 25
7.9%
좌1동주민센터 3층 정보화교육장 25
7.9%
강서구청 여성센터 정보화교육장 19
 
6.0%
중구청 전산교육장 18
 
5.7%
남구청 5층 정보화교육장 13
 
4.1%
Other values (5) 16
 
5.0%

Length

2024-04-21T21:51:34.750406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정보화교육장 165
20.8%
전산교육장 60
 
7.6%
3층 57
 
7.2%
자택 56
 
7.1%
제2별관 48
 
6.1%
2층 48
 
6.1%
구청 48
 
6.1%
사상구청 40
 
5.0%
온라인 33
 
4.2%
4층 30
 
3.8%
Other values (17) 208
26.2%

people
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)3.2%
Missing4
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean13.428115
Minimum5
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T21:51:34.962971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q110
median15
Q315
95-th percentile20
Maximum40
Range35
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.9790048
Coefficient of variation (CV)0.37078956
Kurtosis7.0684304
Mean13.428115
Median Absolute Deviation (MAD)3
Skewness1.5676461
Sum4203
Variance24.790489
MonotonicityNot monotonic
2024-04-21T21:51:35.163399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
15 97
30.6%
10 76
24.0%
12 53
16.7%
20 33
 
10.4%
5 27
 
8.5%
16 19
 
6.0%
40 3
 
0.9%
30 2
 
0.6%
24 2
 
0.6%
25 1
 
0.3%
(Missing) 4
 
1.3%
ValueCountFrequency (%)
5 27
 
8.5%
10 76
24.0%
12 53
16.7%
15 97
30.6%
16 19
 
6.0%
20 33
 
10.4%
24 2
 
0.6%
25 1
 
0.3%
30 2
 
0.6%
40 3
 
0.9%
ValueCountFrequency (%)
40 3
 
0.9%
30 2
 
0.6%
25 1
 
0.3%
24 2
 
0.6%
20 33
 
10.4%
16 19
 
6.0%
15 97
30.6%
12 53
16.7%
10 76
24.0%
5 27
 
8.5%

edu_exp
Text

MISSING 

Distinct51
Distinct (%)19.0%
Missing48
Missing (%)15.1%
Memory size2.6 KiB
2024-04-21T21:51:36.409016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length97
Median length91
Mean length12.6171
Min length5

Characters and Unicode

Total characters3394
Distinct characters229
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)11.5%

Sample

1st row초급, 2주과정
2nd row중급, 2주과정
3rd row초급, 2주과정
4th row중급, 2주과정
5th row초급, 2주과정
ValueCountFrequency (%)
2주과정 82
 
8.9%
중급 62
 
6.8%
과정 58
 
6.3%
인터넷 57
 
6.2%
디지털 57
 
6.2%
코로나로 56
 
6.1%
실시간 56
 
6.1%
강의 56
 
6.1%
인해 56
 
6.1%
기초 41
 
4.5%
Other values (160) 336
36.6%
2024-04-21T21:51:37.891610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
648
 
19.1%
정 178
 
5.2%
과 174
 
5.1%
, 142
 
4.2%
인 119
 
3.5%
로 117
 
3.4%
주 114
 
3.4%
급 110
 
3.2%
초 86
 
2.5%
2 84
 
2.5%
Other values (219) 1622
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2460
72.5%
Space Separator 648
 
19.1%
Other Punctuation 145
 
4.3%
Decimal Number 117
 
3.4%
Uppercase Letter 12
 
0.4%
Open Punctuation 6
 
0.2%
Close Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 178
 
7.2%
과 174
 
7.1%
인 119
 
4.8%
로 117
 
4.8%
주 114
 
4.6%
급 110
 
4.5%
초 86
 
3.5%
지 63
 
2.6%
중 62
 
2.5%
디 62
 
2.5%
Other values (202) 1375
55.9%
Uppercase Letter
ValueCountFrequency (%)
I 3
25.0%
A 3
25.0%
S 2
16.7%
C 1
 
8.3%
P 1
 
8.3%
N 1
 
8.3%
D 1
 
8.3%
Decimal Number
ValueCountFrequency (%)
2 84
71.8%
1 19
 
16.2%
4 9
 
7.7%
3 5
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 142
97.9%
· 2
 
1.4%
& 1
 
0.7%
Space Separator
ValueCountFrequency (%)
648
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2460
72.5%
Common 922
 
27.2%
Latin 12
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 178
 
7.2%
과 174
 
7.1%
인 119
 
4.8%
로 117
 
4.8%
주 114
 
4.6%
급 110
 
4.5%
초 86
 
3.5%
지 63
 
2.6%
중 62
 
2.5%
디 62
 
2.5%
Other values (202) 1375
55.9%
Common
ValueCountFrequency (%)
648
70.3%
, 142
 
15.4%
2 84
 
9.1%
1 19
 
2.1%
4 9
 
1.0%
( 6
 
0.7%
) 6
 
0.7%
3 5
 
0.5%
· 2
 
0.2%
& 1
 
0.1%
Latin
ValueCountFrequency (%)
I 3
25.0%
A 3
25.0%
S 2
16.7%
C 1
 
8.3%
P 1
 
8.3%
N 1
 
8.3%
D 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2458
72.4%
ASCII 932
 
27.5%
Compat Jamo 2
 
0.1%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
648
69.5%
, 142
 
15.2%
2 84
 
9.0%
1 19
 
2.0%
4 9
 
1.0%
( 6
 
0.6%
) 6
 
0.6%
3 5
 
0.5%
I 3
 
0.3%
A 3
 
0.3%
Other values (6) 7
 
0.8%
Hangul
ValueCountFrequency (%)
정 178
 
7.2%
과 174
 
7.1%
인 119
 
4.8%
로 117
 
4.8%
주 114
 
4.6%
급 110
 
4.5%
초 86
 
3.5%
지 63
 
2.6%
중 62
 
2.5%
디 62
 
2.5%
Other values (201) 1373
55.9%
Compat Jamo
ValueCountFrequency (%)
ㆍ 2
100.0%
None
ValueCountFrequency (%)
· 2
100.0%

road_addr
Categorical

Distinct15
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
부산광역시 수영구 연수로 275번길 30
56 
부산광역시 사하구 낙동대로398번길 22, 2층
48 
부산광역시 사상구 학감대로 242
42 
부산광역시 영도구 태종로 423
28 
부산광역시 서구 구덕로 120
27 
Other values (10)
116 

Length

Max length27
Median length24
Mean length19.858044
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row부산광역시 사하구 낙동대로398번길 22, 2층
2nd row부산광역시 사하구 낙동대로398번길 22, 2층
3rd row부산광역시 사하구 낙동대로398번길 22, 2층
4th row부산광역시 사하구 낙동대로398번길 22, 2층
5th row부산광역시 사하구 낙동대로398번길 22, 2층

Common Values

ValueCountFrequency (%)
부산광역시 수영구 연수로 275번길 30 56
17.7%
부산광역시 사하구 낙동대로398번길 22, 2층 48
15.1%
부산광역시 사상구 학감대로 242 42
13.2%
부산광역시 영도구 태종로 423 28
8.8%
부산광역시 서구 구덕로 120 27
8.5%
부산 해운대구 재반로211번길 9 25
7.9%
부산 해운대구 양운로 91 25
7.9%
부산광역시 강서구 낙동북로 477(대저1동) 19
 
6.0%
부산광역시 중구로120(지하1층 전산교육장) 18
 
5.7%
부산광역시 남구 못골로 19 13
 
4.1%
Other values (5) 16
 
5.0%

Length

2024-04-21T21:51:38.126637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 259
19.4%
275번길 56
 
4.2%
30 56
 
4.2%
수영구 56
 
4.2%
연수로 56
 
4.2%
부산 52
 
3.9%
해운대구 50
 
3.7%
사하구 48
 
3.6%
낙동대로398번길 48
 
3.6%
22 48
 
3.6%
Other values (32) 607
45.4%

addr
Categorical

Distinct11
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
84 
부산광역시 수영구 망미동 826-54, 2층
56 
부산광역시 사하구 당리동 317-61, 2층
48 
부산광역시 사상구 감전동 138-8
42 
부산광역시 서구 토성동4가 2-3
27 
Other values (6)
60 

Length

Max length25
Median length24
Mean length16.637224
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row부산광역시 사하구 당리동 317-61, 2층
2nd row부산광역시 사하구 당리동 317-61, 2층
3rd row부산광역시 사하구 당리동 317-61, 2층
4th row부산광역시 사하구 당리동 317-61, 2층
5th row부산광역시 사하구 당리동 317-61, 2층

Common Values

ValueCountFrequency (%)
<NA> 84
26.5%
부산광역시 수영구 망미동 826-54, 2층 56
17.7%
부산광역시 사하구 당리동 317-61, 2층 48
15.1%
부산광역시 사상구 감전동 138-8 42
13.2%
부산광역시 서구 토성동4가 2-3 27
 
8.5%
부산광역시 강서구 대저1동 2300 강서구청 19
 
6.0%
부산광역시 중구 120호 18
 
5.7%
부산광역시 남구 대연동 1268-1 13
 
4.1%
부산광역시 동래구 낙민동 150-8 동래구청 7
 
2.2%
부산 금정구 부곡3동 78 2
 
0.6%

Length

2024-04-21T21:51:38.341072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 231
20.5%
2층 104
 
9.2%
na 84
 
7.4%
수영구 56
 
5.0%
망미동 56
 
5.0%
826-54 56
 
5.0%
사하구 48
 
4.3%
당리동 48
 
4.3%
317-61 48
 
4.3%
사상구 42
 
3.7%
Other values (25) 356
31.5%

months
Categorical

Distinct20
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
83 
항시
55 
상시
48 
12
20 
3
12 
Other values (15)
99 

Length

Max length4
Median length3
Mean length2.318612
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 83
26.2%
항시 55
17.4%
상시 48
15.1%
12 20
 
6.3%
3 12
 
3.8%
11 10
 
3.2%
2 10
 
3.2%
9 9
 
2.8%
7 9
 
2.8%
6 9
 
2.8%
Other values (10) 52
16.4%

Length

2024-04-21T21:51:38.560591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 83
26.2%
항시 55
17.4%
상시 48
15.1%
12 20
 
6.3%
3 12
 
3.8%
11 10
 
3.2%
2 10
 
3.2%
9 9
 
2.8%
7 9
 
2.8%
6 9
 
2.8%
Other values (10) 52
16.4%

target
Categorical

Distinct17
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
구민
56 
해운대구민
50 
사하구민
48 
영도구 주민
28 
디지털 취약계층
27 
Other values (12)
108 

Length

Max length15
Median length9
Mean length4.829653
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
구민 56
17.7%
해운대구민 50
15.8%
사하구민 48
15.1%
영도구 주민 28
8.8%
디지털 취약계층 27
8.5%
55세 미만 20
 
6.3%
55세 이상 20
 
6.3%
강서구 주민 19
 
6.0%
지역 주민 18
 
5.7%
전체 11
 
3.5%
Other values (7) 20
 
6.3%

Length

2024-04-21T21:51:38.782920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주민 65
14.3%
구민 56
12.3%
해운대구민 50
11.0%
사하구민 48
10.5%
55세 40
8.8%
영도구 28
 
6.2%
디지털 27
 
5.9%
취약계층 27
 
5.9%
미만 20
 
4.4%
이상 20
 
4.4%
Other values (12) 74
16.3%

period
Categorical

Distinct39
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
상시
104 
수시
32 
항시
27 
2020-11-02
18 
2021-02-22 ~ 마감시까지
 
8
Other values (34)
128 

Length

Max length21
Median length2
Mean length8.1577287
Min length2

Unique

Unique4 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
상시 104
32.8%
수시 32
 
10.1%
항시 27
 
8.5%
2020-11-02 18
 
5.7%
2021-02-22 ~ 마감시까지 8
 
2.5%
2021-01-21 ~ 마감시까지 8
 
2.5%
2020-01-21~2020-01-22 8
 
2.5%
2021-08-23 ~ 마감시 7
 
2.2%
12/1(화)~마감시까지 7
 
2.2%
2021-10-21 ~ 마감시 6
 
1.9%
Other values (29) 92
29.0%

Length

2024-04-21T21:51:39.012955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상시 104
24.7%
52
12.4%
수시 32
 
7.6%
마감시까지 31
 
7.4%
항시 27
 
6.4%
마감시 21
 
5.0%
2020-11-02 18
 
4.3%
2021-01-21 8
 
1.9%
2020-01-21~2020-01-22 8
 
1.9%
2021-02-22 8
 
1.9%
Other values (32) 112
26.6%

tel
Categorical

Distinct11
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
051-610-4301
56 
051-749-4306
52 
051-220-4301
48 
051-310-4301
42 
051-419-4301
28 
Other values (6)
91 

Length

Max length12
Median length12
Mean length11.744479
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row051-220-4301
2nd row051-220-4301
3rd row051-220-4301
4th row051-220-4301
5th row051-220-4301

Common Values

ValueCountFrequency (%)
051-610-4301 56
17.7%
051-749-4306 52
16.4%
051-220-4301 48
15.1%
051-310-4301 42
13.2%
051-419-4301 28
8.8%
1800-0096 27
8.5%
051-461-0120 22
 
6.9%
051-970-4301 19
 
6.0%
051-607-4301 13
 
4.1%
051-550-4309 8
 
2.5%

Length

2024-04-21T21:51:39.242646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
051-610-4301 56
17.7%
051-749-4306 52
16.4%
051-220-4301 48
15.1%
051-310-4301 42
13.2%
051-419-4301 28
8.8%
1800-0096 27
8.5%
051-461-0120 22
 
6.9%
051-970-4301 19
 
6.0%
051-607-4301 13
 
4.1%
051-550-4309 8
 
2.5%

days
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)5.2%
Missing87
Missing (%)27.4%
Infinite0
Infinite (%)0.0%
Mean6.3130435
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T21:51:39.427888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q310
95-th percentile11
Maximum20
Range19
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.4640538
Coefficient of variation (CV)0.70711597
Kurtosis0.31056925
Mean6.3130435
Median Absolute Deviation (MAD)4
Skewness0.71590468
Sum1452
Variance19.927777
MonotonicityNot monotonic
2024-04-21T21:51:39.619114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 73
23.0%
2 35
11.0%
5 35
11.0%
1 32
 
10.1%
3 25
 
7.9%
9 10
 
3.2%
11 6
 
1.9%
20 5
 
1.6%
8 4
 
1.3%
18 3
 
0.9%
Other values (2) 2
 
0.6%
(Missing) 87
27.4%
ValueCountFrequency (%)
1 32
10.1%
2 35
11.0%
3 25
 
7.9%
5 35
11.0%
8 4
 
1.3%
9 10
 
3.2%
10 73
23.0%
11 6
 
1.9%
13 1
 
0.3%
15 1
 
0.3%
ValueCountFrequency (%)
20 5
 
1.6%
18 3
 
0.9%
15 1
 
0.3%
13 1
 
0.3%
11 6
 
1.9%
10 73
23.0%
9 10
 
3.2%
8 4
 
1.3%
5 35
11.0%
3 25
 
7.9%

gugun
Categorical

Distinct12
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
부산광역시 수영구
56 
부산광역시 해운대구
52 
사하구
48 
부산광역시 사상구
42 
부산광역시 영도구
28 
Other values (7)
91 

Length

Max length10
Median length9
Mean length7.7003155
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 수영구 56
17.7%
부산광역시 해운대구 52
16.4%
사하구 48
15.1%
부산광역시 사상구 42
13.2%
부산광역시 영도구 28
8.8%
부산광역시 서구 27
8.5%
강서구 19
 
6.0%
부산광역시 중구 18
 
5.7%
부산광역시 남구 13
 
4.1%
부산광역시 동래구 8
 
2.5%
Other values (2) 6
 
1.9%

Length

2024-04-21T21:51:39.853017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 250
44.1%
수영구 56
 
9.9%
해운대구 52
 
9.2%
사하구 48
 
8.5%
사상구 42
 
7.4%
영도구 28
 
4.9%
서구 27
 
4.8%
강서구 19
 
3.4%
중구 18
 
3.2%
남구 13
 
2.3%
Other values (3) 14
 
2.5%

data_day
Categorical

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2020-12-31
190 
2021-01-13
56 
2021-01-19
52 
2021-01-21
 
19

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 190
59.9%
2021-01-13 56
 
17.7%
2021-01-19 52
 
16.4%
2021-01-21 19
 
6.0%

Length

2024-04-21T21:51:40.072028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:51:40.251622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 190
59.9%
2021-01-13 56
 
17.7%
2021-01-19 52
 
16.4%
2021-01-21 19
 
6.0%

lat
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)4.2%
Missing6
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean35.138881
Minimum35.091214
Maximum35.243073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T21:51:40.631824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.091214
5-th percentile35.091214
Q135.104225
median35.152612
Q335.174512
95-th percentile35.182318
Maximum35.243073
Range0.15185892
Interquartile range (IQR)0.07028739

Descriptive statistics

Standard deviation0.035918186
Coefficient of variation (CV)0.0010221778
Kurtosis-1.2978155
Mean35.138881
Median Absolute Deviation (MAD)0.0297063
Skewness0.10928385
Sum10928.192
Variance0.0012901161
MonotonicityNot monotonic
2024-04-21T21:51:40.826314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
35.174512 56
17.7%
35.10422461 48
15.1%
35.152612 42
13.2%
35.09121408 28
8.8%
35.09753855 27
8.5%
35.1823183 25
7.9%
35.17088596 25
7.9%
35.121299 19
 
6.0%
35.1020144 18
 
5.7%
35.136501 13
 
4.1%
Other values (3) 10
 
3.2%
ValueCountFrequency (%)
35.09121408 28
8.8%
35.09753855 27
8.5%
35.1020144 18
 
5.7%
35.10422461 48
15.1%
35.121299 19
 
6.0%
35.136501 13
 
4.1%
35.152612 42
13.2%
35.17088596 25
7.9%
35.174512 56
17.7%
35.1823183 25
7.9%
ValueCountFrequency (%)
35.243073 2
 
0.6%
35.211951 1
 
0.3%
35.196527 7
 
2.2%
35.1823183 25
7.9%
35.174512 56
17.7%
35.17088596 25
7.9%
35.152612 42
13.2%
35.136501 13
 
4.1%
35.121299 19
 
6.0%
35.10422461 48
15.1%

lng
Real number (ℝ)

MISSING 

Distinct14
Distinct (%)4.5%
Missing6
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean129.05194
Minimum128.97455
Maximum129.17421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T21:51:41.026416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.97455
5-th percentile128.97455
Q1128.99116
median129.06791
Q3129.10107
95-th percentile129.17421
Maximum129.17421
Range0.19965471
Interquartile range (IQR)0.109914

Descriptive statistics

Standard deviation0.062948517
Coefficient of variation (CV)0.0004877766
Kurtosis-1.0856809
Mean129.05194
Median Absolute Deviation (MAD)0.0513583
Skewness0.28412072
Sum40135.154
Variance0.0039625158
MonotonicityNot monotonic
2024-04-21T21:51:41.232169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
129.101071 54
17.0%
128.97455429 48
15.1%
128.991157 42
13.2%
129.0679144 28
8.8%
129.023424 27
8.5%
129.1192727 25
7.9%
129.174209 25
7.9%
128.980589 19
 
6.0%
129.0313443 18
 
5.7%
129.08411 13
 
4.1%
Other values (4) 12
 
3.8%
ValueCountFrequency (%)
128.97455429 48
15.1%
128.980589 19
 
6.0%
128.991157 42
13.2%
129.023424 27
8.5%
129.0313443 18
 
5.7%
129.0679144 28
8.8%
129.08411 13
 
4.1%
129.090549 1
 
0.3%
129.092133 2
 
0.6%
129.093097 7
 
2.2%
ValueCountFrequency (%)
129.174209 25
7.9%
129.1192727 25
7.9%
129.101071 54
17.0%
129.10107 2
 
0.6%
129.093097 7
 
2.2%
129.092133 2
 
0.6%
129.090549 1
 
0.3%
129.08411 13
 
4.1%
129.0679144 28
8.8%
129.0313443 18
 
5.7%

apr_at
Boolean

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size445.0 B
False
317 
ValueCountFrequency (%)
False 317
100.0%
2024-04-21T21:51:41.420547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2021-04-01 05:53:03
317 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01 05:53:03
2nd row2021-04-01 05:53:03
3rd row2021-04-01 05:53:03
4th row2021-04-01 05:53:03
5th row2021-04-01 05:53:03

Common Values

ValueCountFrequency (%)
2021-04-01 05:53:03 317
100.0%

Length

2024-04-21T21:51:41.586168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:51:41.750945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 317
50.0%
05:53:03 317
50.0%

Sample

skeyinstt_codeedu_nmedu_sdateedu_fdateedu_timeedu_locpeopleedu_exproad_addraddrmonthstargetperiodteldaysgugundata_daylatlngapr_atlast_load_dttm
059993340000스마트폰을 활용한 1인 크리에이터1.2021-03-292021-11-2610.5구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43013사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
160003340000슬기로운 화상솔루션2021-03-292021-11-264구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43011사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
260013340000안전한 내손 안의 은행(금융)2021-03-292021-11-2610.5구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43013사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
360023340000앱 활용을 통한 삶의 업그레이드2021-03-292021-11-267구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43012사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
460033340000앱으로 소통하기(SNS)2021-03-292021-11-263.5구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43011사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
560043340000앱으로 소통하기(인스타)(1)2021-03-292021-11-263.5구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43011사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
660053340000앱으로 소통하기(카톡)(1)2021-03-292021-11-263.5구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43011사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
760063340000엑셀 중급1.2021-03-292021-11-2610.5구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43011사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
860073340000엑셀 초급1.2021-03-292021-11-2610.5구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43013사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
960083340000엑셀 초급2.2021-03-292021-11-2610.5구청 제2별관 2층 정보화교육장15<NA>부산광역시 사하구 낙동대로398번길 22, 2층부산광역시 사하구 당리동 317-61, 2층상시사하구민상시051-220-43013사하구2020-12-3135.104225128.974554N2021-04-01 05:53:03
skeyinstt_codeedu_nmedu_sdateedu_fdateedu_timeedu_locpeopleedu_exproad_addraddrmonthstargetperiodteldaysgugundata_daylatlngapr_atlast_load_dttm
30757613260000키오스크 활용하기<NA><NA>수시온라인 교육5디지털 기초 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03
30857623260000한글 초급<NA><NA>수시온라인 교육5디지털 기초 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03
30957633260000엑셀 초급<NA><NA>수시온라인 교육5디지털 기초 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03
31057643260000파워포인트 초급<NA><NA>수시온라인 교육5디지털 기초 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03
31157653260000코딩 만들기 초급<NA><NA>수시온라인 교육5디지털 기초 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03
31257663260000미니앨범 만들기<NA><NA>수시온라인 교육5디지털 생활 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03
31357673260000블로그 만들기<NA><NA>수시온라인 교육5디지털 생활 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03
31457683260000안전한 내손 안의 은행(금융)<NA><NA>수시온라인 교육5디지털 생활 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03
31557693260000앱 활용을 통한 삶의 업그레이드<NA><NA>수시온라인 교육5디지털 생활 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03
31657703260000앱으로 소통하기(SNS)<NA><NA>수시온라인 교육5디지털 생활 과정부산광역시 서구 구덕로 120부산광역시 서구 토성동4가 2-3항시디지털 취약계층항시1800-00965부산광역시 서구2020-12-3135.097539129.023424N2021-04-01 05:53:03