Overview

Dataset statistics

Number of variables6
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory55.3 B

Variable types

Categorical3
Numeric3

Dataset

DescriptionSample
Author경북대학교 산학협력단
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT02KNU004

Alerts

WTCH_LO is highly overall correlated with REVISN_SLNTY and 3 other fieldsHigh correlation
WTCH_YMD is highly overall correlated with REVISN_SLNTY and 3 other fieldsHigh correlation
WTCH_LA is highly overall correlated with REVISN_SLNTY and 3 other fieldsHigh correlation
PRSR is highly overall correlated with REVISN_SLNTY and 1 other fieldsHigh correlation
REVISN_SLNTY is highly overall correlated with PRSR and 4 other fieldsHigh correlation
REVISN_WTEM is highly overall correlated with PRSR and 4 other fieldsHigh correlation

Reproduction

Analysis started2024-01-14 06:57:23.405688
Analysis finished2024-01-14 06:57:24.626369
Duration1.22 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WTCH_LA
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
38.832
75 
38.728
24 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row38.832
2nd row38.832
3rd row38.832
4th row38.832
5th row38.832

Common Values

ValueCountFrequency (%)
38.832 75
75.8%
38.728 24
 
24.2%

Length

2024-01-14T15:57:24.688222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:24.770596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38.832 75
75.8%
38.728 24
 
24.2%

WTCH_LO
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
137.165
75 
137.031
24 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row137.165
2nd row137.165
3rd row137.165
4th row137.165
5th row137.165

Common Values

ValueCountFrequency (%)
137.165 75
75.8%
137.031 24
 
24.2%

Length

2024-01-14T15:57:24.855875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:24.988558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
137.165 75
75.8%
137.031 24
 
24.2%

WTCH_YMD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
20210121
75 
20210128
24 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210121
2nd row20210121
3rd row20210121
4th row20210121
5th row20210121

Common Values

ValueCountFrequency (%)
20210121 75
75.8%
20210128 24
 
24.2%

Length

2024-01-14T15:57:25.114230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:25.199865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210121 75
75.8%
20210128 24
 
24.2%

PRSR
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean318.18182
Minimum10
Maximum750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-01-14T15:57:25.304449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q1130
median260
Q3505
95-th percentile701
Maximum750
Range740
Interquartile range (IQR)375

Descriptive statistics

Standard deviation221.59608
Coefficient of variation (CV)0.69644483
Kurtosis-1.1221037
Mean318.18182
Median Absolute Deviation (MAD)170
Skewness0.41146806
Sum31500
Variance49104.824
MonotonicityNot monotonic
2024-01-14T15:57:25.452137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2
 
2.0%
140 2
 
2.0%
20 2
 
2.0%
240 2
 
2.0%
230 2
 
2.0%
220 2
 
2.0%
210 2
 
2.0%
200 2
 
2.0%
190 2
 
2.0%
170 2
 
2.0%
Other values (65) 79
79.8%
ValueCountFrequency (%)
10 2
2.0%
20 2
2.0%
30 2
2.0%
40 2
2.0%
50 2
2.0%
60 2
2.0%
70 2
2.0%
80 2
2.0%
90 2
2.0%
100 2
2.0%
ValueCountFrequency (%)
750 1
1.0%
740 1
1.0%
730 1
1.0%
720 1
1.0%
710 1
1.0%
700 1
1.0%
690 1
1.0%
680 1
1.0%
670 1
1.0%
660 1
1.0%

REVISN_SLNTY
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.014602
Minimum33.8956
Maximum34.1255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-01-14T15:57:25.587614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.8956
5-th percentile33.89855
Q133.94755
median34.0506
Q334.0632
95-th percentile34.08576
Maximum34.1255
Range0.2299
Interquartile range (IQR)0.11565

Descriptive statistics

Standard deviation0.067928492
Coefficient of variation (CV)0.0019970391
Kurtosis-1.1092418
Mean34.014602
Median Absolute Deviation (MAD)0.0155
Skewness-0.65708901
Sum3367.4456
Variance0.00461428
MonotonicityNot monotonic
2024-01-14T15:57:25.728476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.915 5
 
5.1%
33.916 4
 
4.0%
34.0641 3
 
3.0%
34.0651 2
 
2.0%
34.0661 2
 
2.0%
34.0494 2
 
2.0%
34.0648 1
 
1.0%
34.0644 1
 
1.0%
34.0638 1
 
1.0%
34.0634 1
 
1.0%
Other values (77) 77
77.8%
ValueCountFrequency (%)
33.8956 1
1.0%
33.8961 1
1.0%
33.8966 1
1.0%
33.8971 1
1.0%
33.8981 1
1.0%
33.8986 1
1.0%
33.9005 1
1.0%
33.9033 1
1.0%
33.9055 1
1.0%
33.9065 1
1.0%
ValueCountFrequency (%)
34.1255 1
1.0%
34.1216 1
1.0%
34.1065 1
1.0%
34.1024 1
1.0%
34.0926 1
1.0%
34.085 1
1.0%
34.0848 1
1.0%
34.0746 1
1.0%
34.0726 1
1.0%
34.072 1
1.0%

REVISN_WTEM
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7535606
Minimum0.4808
Maximum12.258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-01-14T15:57:25.849968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4808
5-th percentile0.52152
Q10.7405
median3.6969
Q311.47
95-th percentile12.24035
Maximum12.258
Range11.7772
Interquartile range (IQR)10.7295

Descriptive statistics

Standard deviation5.0187303
Coefficient of variation (CV)0.87228252
Kurtosis-1.8400493
Mean5.7535606
Median Absolute Deviation (MAD)3.1745
Skewness0.17628134
Sum569.6025
Variance25.187654
MonotonicityNot monotonic
2024-01-14T15:57:25.976295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.47 2
 
2.0%
0.4808 1
 
1.0%
0.4954 1
 
1.0%
0.5046 1
 
1.0%
0.5136 1
 
1.0%
0.5224 1
 
1.0%
0.5312 1
 
1.0%
0.5388 1
 
1.0%
0.546 1
 
1.0%
0.5539 1
 
1.0%
Other values (88) 88
88.9%
ValueCountFrequency (%)
0.4808 1
1.0%
0.4868 1
1.0%
0.4954 1
1.0%
0.5046 1
1.0%
0.5136 1
1.0%
0.5224 1
1.0%
0.5312 1
1.0%
0.5388 1
1.0%
0.546 1
1.0%
0.5539 1
1.0%
ValueCountFrequency (%)
12.258 1
1.0%
12.2575 1
1.0%
12.2545 1
1.0%
12.25 1
1.0%
12.248 1
1.0%
12.2395 1
1.0%
12.2315 1
1.0%
12.2198 1
1.0%
12.214 1
1.0%
12.2128 1
1.0%

Interactions

2024-01-14T15:57:24.160232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:23.615671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:23.927781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:24.249753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:23.706128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:24.009343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:24.352652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:23.804992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:24.082333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:57:26.063083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LAWTCH_LOWTCH_YMDPRSRREVISN_SLNTYREVISN_WTEM
WTCH_LA1.0000.9990.9990.6460.6980.735
WTCH_LO0.9991.0000.9990.6460.6980.735
WTCH_YMD0.9990.9991.0000.6460.6980.735
PRSR0.6460.6460.6461.0000.8970.882
REVISN_SLNTY0.6980.6980.6980.8971.0000.912
REVISN_WTEM0.7350.7350.7350.8820.9121.000
2024-01-14T15:57:26.163813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LOWTCH_YMDWTCH_LA
WTCH_LO1.0000.9720.972
WTCH_YMD0.9721.0000.972
WTCH_LA0.9720.9721.000
2024-01-14T15:57:26.294718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRSRREVISN_SLNTYREVISN_WTEMWTCH_LAWTCH_LOWTCH_YMD
PRSR1.0000.782-0.9790.4790.4790.479
REVISN_SLNTY0.7821.000-0.7810.5210.5210.521
REVISN_WTEM-0.979-0.7811.0000.5520.5520.552
WTCH_LA0.4790.5210.5521.0000.9720.972
WTCH_LO0.4790.5210.5520.9721.0000.972
WTCH_YMD0.4790.5210.5520.9720.9721.000

Missing values

2024-01-14T15:57:24.482310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:57:24.584570image/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

WTCH_LAWTCH_LOWTCH_YMDPRSRREVISN_SLNTYREVISN_WTEM
038.832137.165202101211033.898112.25
138.832137.165202101212033.895612.258
238.832137.165202101213033.896112.2575
338.832137.165202101214033.896612.2545
438.832137.165202101215033.897112.248
538.832137.165202101216033.898612.2395
638.832137.165202101217033.900512.2315
738.832137.165202101218033.903312.2198
838.832137.165202101219033.905512.2128
938.832137.1652021012110033.906512.214
WTCH_LAWTCH_LOWTCH_YMDPRSRREVISN_SLNTYREVISN_WTEM
8938.728137.0312021012815033.975310.7681
9038.728137.0312021012816033.98410.6894
9138.728137.0312021012817033.99310.4992
9238.728137.0312021012818033.994310.1699
9338.728137.0312021012819033.99019.6751
9438.728137.0312021012820034.00189.0948
9538.728137.0312021012821034.04898.5619
9638.728137.0312021012822034.0727.7023
9738.728137.0312021012823034.08486.7027
9838.728137.0312021012824034.09265.758