Overview

Dataset statistics

Number of variables4
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory38.8 B

Variable types

Categorical2
Numeric2

Dataset

DescriptionSample
Author㈜유에스티21
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT01UST006

Alerts

SHP_MVMN_YMDHM has constant value ""Constant
SHP_DN_VAL is highly imbalanced (53.5%)Imbalance

Reproduction

Analysis started2024-03-13 12:27:25.239400
Analysis finished2024-03-13 12:27:27.530678
Duration2.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHP_MVMN_YMDHM
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
202204180000
48 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202204180000
2nd row202204180000
3rd row202204180000
4th row202204180000
5th row202204180000

Common Values

ValueCountFrequency (%)
202204180000 48
100.0%

Length

2024-03-13T21:27:27.605467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:27:27.978649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202204180000 48
100.0%

SHP_LA
Real number (ℝ)

Distinct6
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.775
Minimum31.75
Maximum31.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-03-13T21:27:28.111116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.75
5-th percentile31.75
Q131.76
median31.775
Q331.79
95-th percentile31.8
Maximum31.8
Range0.05
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.017258979
Coefficient of variation (CV)0.00054316219
Kurtosis-1.2751304
Mean31.775
Median Absolute Deviation (MAD)0.015
Skewness0
Sum1525.2
Variance0.00029787234
MonotonicityNot monotonic
2024-03-13T21:27:28.346639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
31.75 8
16.7%
31.76 8
16.7%
31.77 8
16.7%
31.78 8
16.7%
31.79 8
16.7%
31.8 8
16.7%
ValueCountFrequency (%)
31.75 8
16.7%
31.76 8
16.7%
31.77 8
16.7%
31.78 8
16.7%
31.79 8
16.7%
31.8 8
16.7%
ValueCountFrequency (%)
31.8 8
16.7%
31.79 8
16.7%
31.78 8
16.7%
31.77 8
16.7%
31.76 8
16.7%
31.75 8
16.7%

SHP_LO
Real number (ℝ)

Distinct8
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.055
Minimum132.02
Maximum132.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-03-13T21:27:28.525486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum132.02
5-th percentile132.02
Q1132.0375
median132.055
Q3132.0725
95-th percentile132.09
Maximum132.09
Range0.07
Interquartile range (IQR)0.035

Descriptive statistics

Standard deviation0.02315535
Coefficient of variation (CV)0.00017534625
Kurtosis-1.2412238
Mean132.055
Median Absolute Deviation (MAD)0.02
Skewness0
Sum6338.64
Variance0.00053617021
MonotonicityIncreasing
2024-03-13T21:27:28.692701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
132.02 6
12.5%
132.03 6
12.5%
132.04 6
12.5%
132.05 6
12.5%
132.06 6
12.5%
132.07 6
12.5%
132.08 6
12.5%
132.09 6
12.5%
ValueCountFrequency (%)
132.02 6
12.5%
132.03 6
12.5%
132.04 6
12.5%
132.05 6
12.5%
132.06 6
12.5%
132.07 6
12.5%
132.08 6
12.5%
132.09 6
12.5%
ValueCountFrequency (%)
132.09 6
12.5%
132.08 6
12.5%
132.07 6
12.5%
132.06 6
12.5%
132.05 6
12.5%
132.04 6
12.5%
132.03 6
12.5%
132.02 6
12.5%

SHP_DN_VAL
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size516.0 B
0
38 
1
8 
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)4.2%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38
79.2%
1 8
 
16.7%
2 1
 
2.1%
3 1
 
2.1%

Length

2024-03-13T21:27:28.883859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:27:29.047960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38
79.2%
1 8
 
16.7%
2 1
 
2.1%
3 1
 
2.1%

Interactions

2024-03-13T21:27:26.913858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:27:26.599977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:27:27.105875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:27:26.739527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:27:29.229304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHP_LASHP_LOSHP_DN_VAL
SHP_LA1.0000.0000.157
SHP_LO0.0001.0000.339
SHP_DN_VAL0.1570.3391.000
2024-03-13T21:27:29.426400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHP_LASHP_LOSHP_DN_VAL
SHP_LA1.0000.0000.087
SHP_LO0.0001.0000.184
SHP_DN_VAL0.0870.1841.000

Missing values

2024-03-13T21:27:27.327383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:27:27.461442image/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

SHP_MVMN_YMDHMSHP_LASHP_LOSHP_DN_VAL
020220418000031.75132.021
120220418000031.76132.020
220220418000031.77132.020
320220418000031.78132.020
420220418000031.79132.020
520220418000031.8132.021
620220418000031.75132.030
720220418000031.76132.030
820220418000031.77132.030
920220418000031.78132.030
SHP_MVMN_YMDHMSHP_LASHP_LOSHP_DN_VAL
3820220418000031.77132.080
3920220418000031.78132.080
4020220418000031.79132.082
4120220418000031.8132.080
4220220418000031.75132.090
4320220418000031.76132.090
4420220418000031.77132.093
4520220418000031.78132.091
4620220418000031.79132.090
4720220418000031.8132.090