Overview

Dataset statistics

Number of variables4
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory38.4 B

Variable types

Categorical2
Numeric2

Dataset

DescriptionSample
Author㈜유에스티21
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT01UST011

Alerts

WTCH_YMDHM has constant value ""Constant
WTCH_LA is highly overall correlated with SSD_GBG_OCCR_AT_CDHigh correlation
SSD_GBG_OCCR_AT_CD is highly overall correlated with WTCH_LAHigh correlation
SSD_GBG_OCCR_AT_CD is highly imbalanced (56.6%)Imbalance

Reproduction

Analysis started2024-03-13 12:43:31.156824
Analysis finished2024-03-13 12:43:32.079849
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WTCH_YMDHM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
202203030000
56 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202203030000
2nd row202203030000
3rd row202203030000
4th row202203030000
5th row202203030000

Common Values

ValueCountFrequency (%)
202203030000 56
100.0%

Length

2024-03-13T21:43:32.173060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:43:32.295271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202203030000 56
100.0%

WTCH_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.13
Minimum29.1
Maximum29.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2024-03-13T21:43:32.410902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.1
5-th percentile29.1
Q129.11
median29.13
Q329.15
95-th percentile29.16
Maximum29.16
Range0.06
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.020180999
Coefficient of variation (CV)0.00069279091
Kurtosis-1.2539308
Mean29.13
Median Absolute Deviation (MAD)0.02
Skewness0
Sum1631.28
Variance0.00040727273
MonotonicityNot monotonic
2024-03-13T21:43:32.571852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
29.1 8
14.3%
29.11 8
14.3%
29.12 8
14.3%
29.13 8
14.3%
29.14 8
14.3%
29.15 8
14.3%
29.16 8
14.3%
ValueCountFrequency (%)
29.1 8
14.3%
29.11 8
14.3%
29.12 8
14.3%
29.13 8
14.3%
29.14 8
14.3%
29.15 8
14.3%
29.16 8
14.3%
ValueCountFrequency (%)
29.16 8
14.3%
29.15 8
14.3%
29.14 8
14.3%
29.13 8
14.3%
29.12 8
14.3%
29.11 8
14.3%
29.1 8
14.3%

WTCH_LO
Real number (ℝ)

Distinct8
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.015
Minimum131.98
Maximum132.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2024-03-13T21:43:32.767238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum131.98
5-th percentile131.98
Q1131.9975
median132.015
Q3132.0325
95-th percentile132.05
Maximum132.05
Range0.07
Interquartile range (IQR)0.035

Descriptive statistics

Standard deviation0.023120239
Coefficient of variation (CV)0.00017513342
Kurtosis-1.2408905
Mean132.015
Median Absolute Deviation (MAD)0.02
Skewness0
Sum7392.84
Variance0.00053454545
MonotonicityIncreasing
2024-03-13T21:43:32.972650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
131.98 7
12.5%
131.99 7
12.5%
132.0 7
12.5%
132.01 7
12.5%
132.02 7
12.5%
132.03 7
12.5%
132.04 7
12.5%
132.05 7
12.5%
ValueCountFrequency (%)
131.98 7
12.5%
131.99 7
12.5%
132.0 7
12.5%
132.01 7
12.5%
132.02 7
12.5%
132.03 7
12.5%
132.04 7
12.5%
132.05 7
12.5%
ValueCountFrequency (%)
132.05 7
12.5%
132.04 7
12.5%
132.03 7
12.5%
132.02 7
12.5%
132.01 7
12.5%
132.0 7
12.5%
131.99 7
12.5%
131.98 7
12.5%

SSD_GBG_OCCR_AT_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size580.0 B
0
51 
1
 
5

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 51
91.1%
1 5
 
8.9%

Length

2024-03-13T21:43:33.192658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:43:33.328681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51
91.1%
1 5
 
8.9%

Interactions

2024-03-13T21:43:31.573002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:31.290126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:31.709030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:31.429093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:43:33.429930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LAWTCH_LOSSD_GBG_OCCR_AT_CD
WTCH_LA1.0000.0000.504
WTCH_LO0.0001.0000.000
SSD_GBG_OCCR_AT_CD0.5040.0001.000
2024-03-13T21:43:33.586057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LAWTCH_LOSSD_GBG_OCCR_AT_CD
WTCH_LA1.0000.0000.514
WTCH_LO0.0001.0000.000
SSD_GBG_OCCR_AT_CD0.5140.0001.000

Missing values

2024-03-13T21:43:31.911064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:43:32.020026image/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_YMDHMWTCH_LAWTCH_LOSSD_GBG_OCCR_AT_CD
020220303000029.1131.980
120220303000029.11131.980
220220303000029.12131.980
320220303000029.13131.980
420220303000029.14131.980
520220303000029.15131.980
620220303000029.16131.980
720220303000029.1131.990
820220303000029.11131.990
920220303000029.12131.990
WTCH_YMDHMWTCH_LAWTCH_LOSSD_GBG_OCCR_AT_CD
4620220303000029.14132.040
4720220303000029.15132.040
4820220303000029.16132.040
4920220303000029.1132.050
5020220303000029.11132.050
5120220303000029.12132.050
5220220303000029.13132.051
5320220303000029.14132.050
5420220303000029.15132.050
5520220303000029.16132.050