Overview

Dataset statistics

Number of variables3
Number of observations1828
Missing cells0
Missing cells (%)0.0%
Duplicate rows66
Duplicate rows (%)3.6%
Total size in memory48.3 KiB
Average record size in memory27.1 B

Variable types

Numeric3

Dataset

Description서울특별시 성동구 무지개장난감세상 홈페이지 휴일정보DB 자료입니다. 휴일정보에 대한 연도, 월, 일 정보를 포함하고 있습니다.
Author서울특별시 성동구
URLhttps://www.data.go.kr/data/15060857/fileData.do

Alerts

Dataset has 66 (3.6%) duplicate rowsDuplicates
연도 is highly skewed (γ1 = -29.99697108)Skewed

Reproduction

Analysis started2023-12-12 00:14:52.417525
Analysis finished2023-12-12 00:14:53.925355
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

SKEWED 

Distinct18
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.227
Minimum7
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2023-12-12T09:14:53.984615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile2007
Q12012
median2016
Q32020
95-th percentile2022
Maximum2022
Range2015
Interquartile range (IQR)8

Descriptive statistics

Standard deviation66.569415
Coefficient of variation (CV)0.033066025
Kurtosis903.01776
Mean2013.227
Median Absolute Deviation (MAD)4
Skewness-29.996971
Sum3680179
Variance4431.487
MonotonicityNot monotonic
2023-12-12T09:14:54.135395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2020 261
14.3%
2015 123
 
6.7%
2018 120
 
6.6%
2014 119
 
6.5%
2012 117
 
6.4%
2013 117
 
6.4%
2017 117
 
6.4%
2019 117
 
6.4%
2016 115
 
6.3%
2021 114
 
6.2%
Other values (8) 508
27.8%
ValueCountFrequency (%)
7 2
 
0.1%
2006 53
2.9%
2007 58
3.2%
2008 68
3.7%
2009 65
3.6%
2010 64
3.5%
2011 86
4.7%
2012 117
6.4%
2013 117
6.4%
2014 119
6.5%
ValueCountFrequency (%)
2022 112
6.1%
2021 114
6.2%
2020 261
14.3%
2019 117
6.4%
2018 120
6.6%
2017 117
6.4%
2016 115
6.3%
2015 123
6.7%
2014 119
6.5%
2013 117
6.4%

월
Real number (ℝ)

Distinct12
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5103939
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2023-12-12T09:14:54.300847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4287495
Coefficient of variation (CV)0.52665777
Kurtosis-1.2080166
Mean6.5103939
Median Absolute Deviation (MAD)3
Skewness0.00021755095
Sum11901
Variance11.756323
MonotonicityNot monotonic
2023-12-12T09:14:54.439493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 173
9.5%
10 164
9.0%
9 164
9.0%
3 156
8.5%
12 154
8.4%
8 153
8.4%
6 150
8.2%
2 150
8.2%
4 146
8.0%
1 145
7.9%
Other values (2) 273
14.9%
ValueCountFrequency (%)
1 145
7.9%
2 150
8.2%
3 156
8.5%
4 146
8.0%
5 173
9.5%
6 150
8.2%
7 138
7.5%
8 153
8.4%
9 164
9.0%
10 164
9.0%
ValueCountFrequency (%)
12 154
8.4%
11 135
7.4%
10 164
9.0%
9 164
9.0%
8 153
8.4%
7 138
7.5%
6 150
8.2%
5 173
9.5%
4 146
8.0%
3 156
8.5%

일
Real number (ℝ)

Distinct32
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.275164
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2023-12-12T09:14:54.593379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median15
Q323
95-th percentile29
Maximum78
Range77
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.0257975
Coefficient of variation (CV)0.59088056
Kurtosis-0.025491986
Mean15.275164
Median Absolute Deviation (MAD)8
Skewness0.22339938
Sum27923
Variance81.46502
MonotonicityNot monotonic
2023-12-12T09:14:54.742743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 84
 
4.6%
6 71
 
3.9%
25 70
 
3.8%
9 70
 
3.8%
15 69
 
3.8%
5 69
 
3.8%
3 67
 
3.7%
13 62
 
3.4%
2 60
 
3.3%
19 60
 
3.3%
Other values (22) 1146
62.7%
ValueCountFrequency (%)
1 84
4.6%
2 60
3.3%
3 67
3.7%
4 59
3.2%
5 69
3.8%
6 71
3.9%
7 54
3.0%
8 53
2.9%
9 70
3.8%
10 57
3.1%
ValueCountFrequency (%)
78 1
 
0.1%
31 31
1.7%
30 52
2.8%
29 53
2.9%
28 54
3.0%
27 56
3.1%
26 58
3.2%
25 70
3.8%
24 55
3.0%
23 55
3.0%

Interactions

2023-12-12T09:14:53.377624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:52.610795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:53.002706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:53.553029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:52.771204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:53.132198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:53.670601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:52.893290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:53.248707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:14:54.837282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월일
연도1.0000.0400.000
월0.0401.0000.000
일0.0000.0001.000
2023-12-12T09:14:54.935116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월일
연도1.000-0.0240.014
월-0.0241.0000.042
일0.0140.0421.000

Missing values

2023-12-12T09:14:53.832950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:14:53.899637image/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

연도월일
020061224
120061217
220061210
32006123
420061126
520061119
620061112
72006115
820061029
920061022
연도월일
181820221127
181920221128
18202022124
18212022125
182220221211
182320221212
182420221218
182520221219
182620221225
182720221226

Duplicate rows

Most frequently occurring

연도월일# duplicates
120152233
020081192
220206142
320206152
420206212
520206222
620206282
720206292
82020752
92020762