Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory61.3 B

Variable types

Categorical4
Numeric3

Alerts

year has constant value ""Constant
act_ctprvn_nm is highly overall correlated with ctprvn_code and 1 other fieldsHigh correlation
ctprvn_code_nm is highly overall correlated with ctprvn_code and 1 other fieldsHigh correlation
ctprvn_code is highly overall correlated with ctprvn_code_nm and 1 other fieldsHigh correlation
progrm_co is highly overall correlated with partcptn_nmpr_coHigh correlation
partcptn_nmpr_co is highly overall correlated with progrm_coHigh correlation

Reproduction

Analysis started2023-12-10 09:59:31.159214
Analysis finished2023-12-10 09:59:34.101008
Duration2.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 100
100.0%

Length

2023-12-10T18:59:34.286198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:34.602583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 100
100.0%

ctprvn_code
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20008.77
Minimum20001
Maximum20016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:35.009623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile20001
Q120004
median20008
Q320014
95-th percentile20016
Maximum20016
Range15
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.2220492
Coefficient of variation (CV)0.00026098802
Kurtosis-1.4139129
Mean20008.77
Median Absolute Deviation (MAD)4.5
Skewness-0.028299201
Sum2000877
Variance27.269798
MonotonicityNot monotonic
2023-12-10T18:59:35.320992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20001 12
12.0%
20011 12
12.0%
20008 12
12.0%
20015 11
11.0%
20016 11
11.0%
20004 11
11.0%
20014 10
10.0%
20005 8
8.0%
20007 7
7.0%
20002 5
5.0%
ValueCountFrequency (%)
20001 12
12.0%
20002 5
5.0%
20004 11
11.0%
20005 8
8.0%
20007 7
7.0%
20008 12
12.0%
20011 12
12.0%
20013 1
 
1.0%
20014 10
10.0%
20015 11
11.0%
ValueCountFrequency (%)
20016 11
11.0%
20015 11
11.0%
20014 10
10.0%
20013 1
 
1.0%
20011 12
12.0%
20008 12
12.0%
20007 7
7.0%
20005 8
8.0%
20004 11
11.0%
20002 5
5.0%

ctprvn_code_nm
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
12 
충청남도
12 
경기도
12 
경상남도
11 
제주특별자치도
11 
Other values (6)
42 

Length

Max length7
Median length5
Mean length4.64
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row울산광역시
2nd row경상남도
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 12
12.0%
충청남도 12
12.0%
경기도 12
12.0%
경상남도 11
11.0%
제주특별자치도 11
11.0%
인천광역시 11
11.0%
경상북도 10
10.0%
광주광역시 8
8.0%
울산광역시 7
7.0%
부산광역시 5
5.0%

Length

2023-12-10T18:59:35.648682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 12
12.0%
충청남도 12
12.0%
경기도 12
12.0%
경상남도 11
11.0%
제주특별자치도 11
11.0%
인천광역시 11
11.0%
경상북도 10
10.0%
광주광역시 8
8.0%
울산광역시 7
7.0%
부산광역시 5
5.0%

progrm_relm_nm
Categorical

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
과학정보
10 
기타
10 
진로탐구
9 
교류
9 
문화예술
9 
Other values (7)
53 

Length

Max length6
Median length4
Mean length3.78
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row진로탐구
2nd row환경보존
3rd row건강/스포츠
4th row과학정보
5th row교류

Common Values

ValueCountFrequency (%)
과학정보 10
10.0%
기타 10
10.0%
진로탐구 9
9.0%
교류 9
9.0%
문화예술 9
9.0%
건강/스포츠 8
8.0%
자기개발 8
8.0%
역사탐방 8
8.0%
봉사협력 8
8.0%
환경보존 7
7.0%
Other values (2) 14
14.0%

Length

2023-12-10T18:59:35.923928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
과학정보 10
10.0%
기타 10
10.0%
진로탐구 9
9.0%
교류 9
9.0%
문화예술 9
9.0%
건강/스포츠 8
8.0%
자기개발 8
8.0%
역사탐방 8
8.0%
봉사협력 8
8.0%
환경보존 7
7.0%
Other values (2) 14
14.0%

progrm_co
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean812.8
Minimum1
Maximum11438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:36.334944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median13
Q335.25
95-th percentile5834.95
Maximum11438
Range11437
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation2470.1783
Coefficient of variation (CV)3.0390973
Kurtosis10.048464
Mean812.8
Median Absolute Deviation (MAD)11
Skewness3.2740152
Sum81280
Variance6101780.7
MonotonicityNot monotonic
2023-12-10T18:59:36.671832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
9.0%
2 7
 
7.0%
3 6
 
6.0%
5 5
 
5.0%
8 4
 
4.0%
7 4
 
4.0%
13 4
 
4.0%
4 4
 
4.0%
12 3
 
3.0%
30 3
 
3.0%
Other values (44) 51
51.0%
ValueCountFrequency (%)
1 9
9.0%
2 7
7.0%
3 6
6.0%
4 4
4.0%
5 5
5.0%
6 1
 
1.0%
7 4
4.0%
8 4
4.0%
9 3
 
3.0%
10 1
 
1.0%
ValueCountFrequency (%)
11438 1
1.0%
11336 1
1.0%
11214 1
1.0%
8443 1
1.0%
8361 1
1.0%
5702 1
1.0%
5652 1
1.0%
5639 1
1.0%
5564 1
1.0%
2868 1
1.0%

partcptn_nmpr_co
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20148.57
Minimum2
Maximum228824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:37.171175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile35.45
Q1147
median615.5
Q32180.5
95-th percentile118361
Maximum228824
Range228822
Interquartile range (IQR)2033.5

Descriptive statistics

Standard deviation52412.097
Coefficient of variation (CV)2.6012812
Kurtosis7.7541045
Mean20148.57
Median Absolute Deviation (MAD)549.5
Skewness2.9015139
Sum2014857
Variance2.7470279 × 109
MonotonicityNot monotonic
2023-12-10T18:59:37.527980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 4
 
4.0%
130 3
 
3.0%
115 2
 
2.0%
147 2
 
2.0%
40 2
 
2.0%
1940 1
 
1.0%
3532 1
 
1.0%
7493 1
 
1.0%
413 1
 
1.0%
2072 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
2 1
 
1.0%
10 1
 
1.0%
15 1
 
1.0%
20 1
 
1.0%
25 1
 
1.0%
36 1
 
1.0%
40 2
2.0%
57 1
 
1.0%
60 4
4.0%
72 1
 
1.0%
ValueCountFrequency (%)
228824 1
1.0%
228094 1
1.0%
225823 1
1.0%
203958 1
1.0%
167951 1
1.0%
115751 1
1.0%
115677 1
1.0%
113959 1
1.0%
112768 1
1.0%
111743 1
1.0%

act_ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
12 
충청남도
12 
경기도
12 
경상남도
11 
제주특별자치도
11 
Other values (6)
42 

Length

Max length7
Median length5
Mean length4.64
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row울산광역시
2nd row경상남도
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 12
12.0%
충청남도 12
12.0%
경기도 12
12.0%
경상남도 11
11.0%
제주특별자치도 11
11.0%
인천광역시 11
11.0%
경상북도 10
10.0%
광주광역시 8
8.0%
울산광역시 7
7.0%
부산광역시 5
5.0%

Length

2023-12-10T18:59:37.876609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 12
12.0%
충청남도 12
12.0%
경기도 12
12.0%
경상남도 11
11.0%
제주특별자치도 11
11.0%
인천광역시 11
11.0%
경상북도 10
10.0%
광주광역시 8
8.0%
울산광역시 7
7.0%
부산광역시 5
5.0%

Interactions

2023-12-10T18:59:32.986979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:31.644084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:32.407961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:33.176790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:31.878537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:32.645113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:33.409299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:32.064435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:32.805677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:59:38.053240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_codectprvn_code_nmprogrm_relm_nmprogrm_copartcptn_nmpr_coact_ctprvn_nm
ctprvn_code1.0001.0000.000NaN0.0001.000
ctprvn_code_nm1.0001.0000.0000.5900.2771.000
progrm_relm_nm0.0000.0001.0000.0000.0000.000
progrm_coNaN0.5900.0001.0000.9730.590
partcptn_nmpr_co0.0000.2770.0000.9731.0000.277
act_ctprvn_nm1.0001.0000.0000.5900.2771.000
2023-12-10T18:59:38.334381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
act_ctprvn_nmctprvn_code_nmprogrm_relm_nm
act_ctprvn_nm1.0001.0000.000
ctprvn_code_nm1.0001.0000.000
progrm_relm_nm0.0000.0001.000
2023-12-10T18:59:38.494924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_codeprogrm_copartcptn_nmpr_coctprvn_code_nmprogrm_relm_nmact_ctprvn_nm
ctprvn_code1.000-0.117-0.2150.9890.0000.989
progrm_co-0.1171.0000.8610.3620.0000.362
partcptn_nmpr_co-0.2150.8611.0000.1230.0000.123
ctprvn_code_nm0.9890.3620.1231.0000.0001.000
progrm_relm_nm0.0000.0000.0000.0001.0000.000
act_ctprvn_nm0.9890.3620.1231.0000.0001.000

Missing values

2023-12-10T18:59:33.682400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:59:33.914784image/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

yearctprvn_codectprvn_code_nmprogrm_relm_nmprogrm_copartcptn_nmpr_coact_ctprvn_nm
0201920007울산광역시진로탐구81940울산광역시
1201920015경상남도환경보존7130경상남도
2201920001서울특별시건강/스포츠11438228824서울특별시
3201920001서울특별시과학정보5564111743서울특별시
4201920001서울특별시교류18505서울특별시
5201920016제주특별자치도환경보존581제주특별자치도
6201920016제주특별자치도환경보건457제주특별자치도
7201920016제주특별자치도진로탐구121340제주특별자치도
8201920016제주특별자치도자기개발13147제주특별자치도
9201920016제주특별자치도역사탐방340제주특별자치도
yearctprvn_codectprvn_code_nmprogrm_relm_nmprogrm_copartcptn_nmpr_coact_ctprvn_nm
90201920015경상남도기타255115677경상남도
91201920015경상남도교류571891경상남도
92201920015경상남도과학정보5330경상남도
93201920015경상남도건강/스포츠13534경상남도
94201920016제주특별자치도모험개척5165제주특별자치도
95201920016제주특별자치도기타13327제주특별자치도
96201920016제주특별자치도교류2200제주특별자치도
97201920016제주특별자치도과학정보15202제주특별자치도
98201920002부산광역시기타461122부산광역시
99201920002부산광역시모험개척3115부산광역시