Overview

Dataset statistics

Number of variables11
Number of observations5829
Missing cells404
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory540.9 KiB
Average record size in memory95.0 B

Variable types

Numeric6
Boolean1
Categorical2
DateTime2

Dataset

Description순번,년도,차수,자치구,개인정보동의여부,1일차분반,2일차분반,기타,등록일,수정일,삭제여부
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15706/S/1/datasetView.do

Alerts

개인정보동의여부 has constant value ""Constant
삭제여부 has constant value ""Constant
순번 is highly overall correlated with 년도 and 1 other fieldsHigh correlation
년도 is highly overall correlated with 순번High correlation
1일차분반 is highly overall correlated with 2일차분반High correlation
2일차분반 is highly overall correlated with 1일차분반High correlation
기타 is highly overall correlated with 순번High correlation
기타 is highly imbalanced (76.5%)Imbalance
2일차분반 has 404 (6.9%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2024-05-11 06:11:56.418564
Analysis finished2024-05-11 06:12:02.154232
Duration5.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5829
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3249.6456
Minimum1
Maximum6410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2024-05-11T15:12:02.264520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile323.4
Q11671
median3258
Q34850
95-th percentile6114.6
Maximum6410
Range6409
Interquartile range (IQR)3179

Descriptive statistics

Standard deviation1849.8252
Coefficient of variation (CV)0.56923906
Kurtosis-1.191582
Mean3249.6456
Median Absolute Deviation (MAD)1590
Skewness-0.019521463
Sum18942184
Variance3421853.2
MonotonicityNot monotonic
2024-05-11T15:12:02.445014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6410 1
 
< 0.1%
2187 1
 
< 0.1%
2189 1
 
< 0.1%
2190 1
 
< 0.1%
2191 1
 
< 0.1%
2192 1
 
< 0.1%
2193 1
 
< 0.1%
2194 1
 
< 0.1%
2195 1
 
< 0.1%
2196 1
 
< 0.1%
Other values (5819) 5819
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
6410 1
< 0.1%
6409 1
< 0.1%
6408 1
< 0.1%
6407 1
< 0.1%
6406 1
< 0.1%
6405 1
< 0.1%
6404 1
< 0.1%
6403 1
< 0.1%
6402 1
< 0.1%
6401 1
< 0.1%

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.1402
Minimum2017
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2024-05-11T15:12:02.580296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32020
95-th percentile2024
Maximum2024
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0656059
Coefficient of variation (CV)0.0010230126
Kurtosis0.22756545
Mean2019.1402
Median Absolute Deviation (MAD)1
Skewness1.0622874
Sum11769568
Variance4.2667276
MonotonicityDecreasing
2024-05-11T15:12:02.703494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2017 1371
23.5%
2018 1368
23.5%
2019 1296
22.2%
2020 550
9.4%
2021 511
 
8.8%
2024 423
 
7.3%
2023 310
 
5.3%
ValueCountFrequency (%)
2017 1371
23.5%
2018 1368
23.5%
2019 1296
22.2%
2020 550
9.4%
2021 511
 
8.8%
2023 310
 
5.3%
2024 423
 
7.3%
ValueCountFrequency (%)
2024 423
 
7.3%
2023 310
 
5.3%
2021 511
 
8.8%
2020 550
9.4%
2019 1296
22.2%
2018 1368
23.5%
2017 1371
23.5%

차수
Real number (ℝ)

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4052153
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2024-05-11T15:12:02.835267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile12
Maximum13
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7519357
Coefficient of variation (CV)0.85170314
Kurtosis-0.29522107
Mean4.4052153
Median Absolute Deviation (MAD)2
Skewness1.0565961
Sum25678
Variance14.077022
MonotonicityNot monotonic
2024-05-11T15:12:02.979807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1306
22.4%
2 1283
22.0%
3 1008
17.3%
11 489
 
8.4%
4 454
 
7.8%
13 289
 
5.0%
10 283
 
4.9%
7 281
 
4.8%
6 244
 
4.2%
8 75
 
1.3%
Other values (3) 117
 
2.0%
ValueCountFrequency (%)
1 1306
22.4%
2 1283
22.0%
3 1008
17.3%
4 454
 
7.8%
5 36
 
0.6%
6 244
 
4.2%
7 281
 
4.8%
8 75
 
1.3%
9 61
 
1.0%
10 283
 
4.9%
ValueCountFrequency (%)
13 289
5.0%
12 20
 
0.3%
11 489
8.4%
10 283
4.9%
9 61
 
1.0%
8 75
 
1.3%
7 281
4.8%
6 244
4.2%
5 36
 
0.6%
4 454
7.8%

자치구
Real number (ℝ)

Distinct26
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3291930
Minimum3000000
Maximum9999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2024-05-11T15:12:03.193057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3010000
Q13070000
median3130000
Q33190000
95-th percentile3240000
Maximum9999999
Range6999999
Interquartile range (IQR)120000

Descriptive statistics

Standard deviation1058471.5
Coefficient of variation (CV)0.32153524
Kurtosis36.067625
Mean3291930
Median Absolute Deviation (MAD)60000
Skewness6.1545343
Sum1.918866 × 1010
Variance1.1203619 × 1012
MonotonicityNot monotonic
2024-05-11T15:12:03.467340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3110000 412
 
7.1%
3180000 326
 
5.6%
3190000 315
 
5.4%
3230000 297
 
5.1%
3050000 284
 
4.9%
3100000 279
 
4.8%
3070000 273
 
4.7%
3130000 271
 
4.6%
3150000 267
 
4.6%
3220000 245
 
4.2%
Other values (16) 2860
49.1%
ValueCountFrequency (%)
3000000 194
3.3%
3010000 165
2.8%
3020000 167
2.9%
3030000 176
3.0%
3040000 158
2.7%
3050000 284
4.9%
3060000 224
3.8%
3070000 273
4.7%
3080000 120
2.1%
3090000 164
2.8%
ValueCountFrequency (%)
9999999 141
2.4%
3240000 179
3.1%
3230000 297
5.1%
3220000 245
4.2%
3210000 197
3.4%
3200000 231
4.0%
3190000 315
5.4%
3180000 326
5.6%
3170000 95
 
1.6%
3160000 219
3.8%

개인정보동의여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
True
5829 
ValueCountFrequency (%)
True 5829
100.0%
2024-05-11T15:12:03.599926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

1일차분반
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2842683
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2024-05-11T15:12:03.710470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile13
Maximum19
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0544079
Coefficient of variation (CV)0.76726004
Kurtosis0.93091898
Mean5.2842683
Median Absolute Deviation (MAD)2
Skewness1.1493579
Sum30802
Variance16.438224
MonotonicityNot monotonic
2024-05-11T15:12:03.869414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1313
22.5%
3 1281
22.0%
5 1136
19.5%
7 781
13.4%
9 368
 
6.3%
11 356
 
6.1%
13 154
 
2.6%
15 134
 
2.3%
17 124
 
2.1%
2 73
 
1.3%
Other values (3) 109
 
1.9%
ValueCountFrequency (%)
1 1313
22.5%
2 73
 
1.3%
3 1281
22.0%
4 39
 
0.7%
5 1136
19.5%
6 37
 
0.6%
7 781
13.4%
9 368
 
6.3%
11 356
 
6.1%
13 154
 
2.6%
ValueCountFrequency (%)
19 33
 
0.6%
17 124
 
2.1%
15 134
 
2.3%
13 154
 
2.6%
11 356
 
6.1%
9 368
 
6.3%
7 781
13.4%
6 37
 
0.6%
5 1136
19.5%
4 39
 
0.7%

2일차분반
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)0.2%
Missing404
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean6.4534562
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2024-05-11T15:12:04.008243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median6
Q38
95-th percentile16
Maximum20
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.163653
Coefficient of variation (CV)0.64518188
Kurtosis0.63405545
Mean6.4534562
Median Absolute Deviation (MAD)2
Skewness1.0638635
Sum35010
Variance17.336006
MonotonicityNot monotonic
2024-05-11T15:12:04.150409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1226
21.0%
4 1220
20.9%
6 991
17.0%
8 780
13.4%
10 380
 
6.5%
12 358
 
6.1%
14 162
 
2.8%
16 136
 
2.3%
18 125
 
2.1%
20 33
 
0.6%
(Missing) 404
 
6.9%
ValueCountFrequency (%)
2 1226
21.0%
3 14
 
0.2%
4 1220
20.9%
6 991
17.0%
8 780
13.4%
10 380
 
6.5%
12 358
 
6.1%
14 162
 
2.8%
16 136
 
2.3%
18 125
 
2.1%
ValueCountFrequency (%)
20 33
 
0.6%
18 125
 
2.1%
16 136
 
2.3%
14 162
 
2.8%
12 358
 
6.1%
10 380
 
6.5%
8 780
13.4%
6 991
17.0%
4 1220
20.9%
3 14
 
0.2%

기타
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
<NA>
5173 
A
 
474
온라인수료예정
 
76
온라인기수료
 
62
오프라인
 
29
Other values (2)
 
15

Length

Max length7
Median length4
Mean length3.8100875
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5173
88.7%
A 474
 
8.1%
온라인수료예정 76
 
1.3%
온라인기수료 62
 
1.1%
오프라인 29
 
0.5%
E 13
 
0.2%
관리자입력 2
 
< 0.1%

Length

2024-05-11T15:12:04.648011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:12:04.827829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5173
88.7%
a 474
 
8.1%
온라인수료예정 76
 
1.3%
온라인기수료 62
 
1.1%
오프라인 29
 
0.5%
e 13
 
0.2%
관리자입력 2
 
< 0.1%

등록일
Date

Distinct5757
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
Minimum2017-02-13 10:21:32
Maximum2024-04-12 20:37:41
2024-05-11T15:12:05.015205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:05.186336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수정일
Date

Distinct5757
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
Minimum2017-02-13 10:21:32
Maximum2024-04-12 20:37:41
2024-05-11T15:12:05.403177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:05.598604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

삭제여부
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.7 KiB
0
5829 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5829
100.0%

Length

2024-05-11T15:12:05.793320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:12:05.904672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5829
100.0%

Interactions

2024-05-11T15:12:01.141443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:57.654656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:58.430563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:59.133875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:59.768895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.476979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:01.257223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:57.803957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:58.570133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:59.238252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:59.880467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.575842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:01.372707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:57.935796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:58.709068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:59.345631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.022426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.668028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:01.491313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:58.072206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:58.823926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:59.453351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.147636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.769582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:01.615177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:58.186506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:58.926419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:59.574533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.252450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.905159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:01.720120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:58.305946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:59.028348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:11:59.676056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.370859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:00.998227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:12:05.993654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번년도차수자치구1일차분반2일차분반기타
순번1.0000.9320.8770.2810.6090.6000.767
년도0.9321.0000.7620.2200.4410.4140.737
차수0.8770.7621.0000.3080.5620.5590.474
자치구0.2810.2200.3081.0000.0620.0640.212
1일차분반0.6090.4410.5620.0621.0000.9980.585
2일차분반0.6000.4140.5590.0640.9981.0000.183
기타0.7670.7370.4740.2120.5850.1831.000
2024-05-11T15:12:06.138409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번년도차수자치구1일차분반2일차분반기타
순번1.0000.9800.4850.026-0.115-0.0950.540
년도0.9801.0000.3870.022-0.134-0.1120.455
차수0.4850.3871.0000.056-0.010-0.0020.287
자치구0.0260.0220.0561.000-0.015-0.0330.155
1일차분반-0.115-0.134-0.010-0.0151.0000.9330.356
2일차분반-0.095-0.112-0.002-0.0330.9331.0000.110
기타0.5400.4550.2870.1550.3560.1101.000

Missing values

2024-05-11T15:12:01.889898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:12:02.081009image/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

순번년도차수자치구개인정보동의여부1일차분반2일차분반기타등록일수정일삭제여부
06410202433200000Y6<NA><NA>2024-04-12 20:37:41.02024-04-12 20:37:41.00
16409202433190000Y6<NA><NA>2024-04-10 02:05:18.02024-04-10 02:05:18.00
26408202433180000Y6<NA><NA>2024-04-03 16:54:48.02024-04-03 16:54:48.00
36407202433180000Y2<NA><NA>2024-04-03 16:16:58.02024-04-03 16:16:58.00
46406202433100000Y3<NA><NA>2024-04-03 13:50:49.02024-04-03 13:50:49.00
56405202433080000Y3<NA><NA>2024-04-03 12:37:25.02024-04-03 12:37:25.00
66404202433140000Y3<NA><NA>2024-04-02 23:20:32.02024-04-02 23:20:32.00
76403202433110000Y6<NA><NA>2024-04-02 20:15:18.02024-04-02 20:15:18.00
86402202433190000Y6<NA><NA>2024-04-02 18:45:57.02024-04-02 18:45:57.00
96401202433090000Y1<NA><NA>2024-04-02 16:05:45.02024-04-02 16:05:45.00
순번년도차수자치구개인정보동의여부1일차분반2일차분반기타등록일수정일삭제여부
581910201713190000Y72<NA>2017-02-13 10:47:28.02017-02-13 10:47:28.00
58209201713190000Y12<NA>2017-02-13 10:46:10.02017-02-13 10:46:10.00
58218201713190000Y14<NA>2017-02-13 10:39:59.02017-02-13 10:39:59.00
58227201713210000Y78<NA>2017-02-13 10:36:45.02017-02-13 10:36:45.00
58236201713180000Y56<NA>2017-02-13 10:34:47.02017-02-13 10:34:47.00
58245201713190000Y14<NA>2017-02-13 10:33:58.02017-02-13 10:33:58.00
58254201713190000Y312<NA>2017-02-13 10:33:46.02017-02-13 10:33:46.00
58263201713190000Y12<NA>2017-02-13 10:28:27.02017-02-13 10:28:27.00
58272201713140000Y12<NA>2017-02-13 10:25:36.02017-02-13 10:25:36.00
58281201713190000Y12<NA>2017-02-13 10:21:32.02017-02-13 10:21:32.00