Overview

Dataset statistics

Number of variables6
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory55.3 B

Variable types

Categorical1
Numeric5

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR012

Alerts

WV_NM has constant value ""Constant
WV_MTR_SN is highly overall correlated with LAHigh correlation
LA is highly overall correlated with WV_MTR_SN and 1 other fieldsHigh correlation
LO is highly overall correlated with LA and 1 other fieldsHigh correlation
WVDRCT is highly overall correlated with LOHigh correlation
WV_MTR_SN has unique valuesUnique
LA has unique valuesUnique
LO has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:43:43.270888
Analysis finished2024-03-13 12:43:47.587256
Duration4.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WV_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1979
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1979
2nd row1979
3rd row1979
4th row1979
5th row1979

Common Values

ValueCountFrequency (%)
1979 100
100.0%

Length

2024-03-13T21:43:47.685315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:43:47.823246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1979 100
100.0%

WV_MTR_SN
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:43:48.018909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2024-03-13T21:43:48.239276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

LA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.69447
Minimum34.663896
Maximum34.752531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:43:48.442873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.663896
5-th percentile34.667331
Q134.672232
median34.682505
Q334.712309
95-th percentile34.750426
Maximum34.752531
Range0.08863504
Interquartile range (IQR)0.040076818

Descriptive statistics

Standard deviation0.02739577
Coefficient of variation (CV)0.00078962928
Kurtosis-0.51478948
Mean34.69447
Median Absolute Deviation (MAD)0.01350906
Skewness0.89422445
Sum3469.447
Variance0.0007505282
MonotonicityNot monotonic
2024-03-13T21:43:48.636338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.66389636 1
 
1.0%
34.70307584 1
 
1.0%
34.71937991 1
 
1.0%
34.67819693 1
 
1.0%
34.681855 1
 
1.0%
34.67756959 1
 
1.0%
34.67748421 1
 
1.0%
34.67664291 1
 
1.0%
34.68625653 1
 
1.0%
34.68800945 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
34.66389636 1
1.0%
34.66573414 1
1.0%
34.66595759 1
1.0%
34.66625775 1
1.0%
34.66729378 1
1.0%
34.66733286 1
1.0%
34.66747484 1
1.0%
34.66770699 1
1.0%
34.66788336 1
1.0%
34.66816941 1
1.0%
ValueCountFrequency (%)
34.7525314 1
1.0%
34.75240413 1
1.0%
34.75206961 1
1.0%
34.75202382 1
1.0%
34.75083032 1
1.0%
34.75040439 1
1.0%
34.7492674 1
1.0%
34.74773674 1
1.0%
34.74644161 1
1.0%
34.74483675 1
1.0%

LO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.31244
Minimum127.27876
Maximum127.3437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:43:48.845994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.27876
5-th percentile127.28362
Q1127.2989
median127.31526
Q3127.32581
95-th percentile127.33475
Maximum127.3437
Range0.0649377
Interquartile range (IQR)0.02690945

Descriptive statistics

Standard deviation0.0165326
Coefficient of variation (CV)0.00012985848
Kurtosis-0.85543751
Mean127.31244
Median Absolute Deviation (MAD)0.0121786
Skewness-0.3876117
Sum12731.244
Variance0.00027332686
MonotonicityNot monotonic
2024-03-13T21:43:49.071667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.2852199 1
 
1.0%
127.329397 1
 
1.0%
127.3297968 1
 
1.0%
127.2812546 1
 
1.0%
127.3113797 1
 
1.0%
127.3026961 1
 
1.0%
127.3112068 1
 
1.0%
127.28781 1
 
1.0%
127.3150392 1
 
1.0%
127.318874 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
127.2787632 1
1.0%
127.280934 1
1.0%
127.2812546 1
1.0%
127.2819733 1
1.0%
127.2819879 1
1.0%
127.2837039 1
1.0%
127.2842391 1
1.0%
127.2851331 1
1.0%
127.2852199 1
1.0%
127.2868747 1
1.0%
ValueCountFrequency (%)
127.3437009 1
1.0%
127.3411288 1
1.0%
127.3392302 1
1.0%
127.3369121 1
1.0%
127.3353884 1
1.0%
127.3347191 1
1.0%
127.3330033 1
1.0%
127.3326474 1
1.0%
127.3318687 1
1.0%
127.331211 1
1.0%

SGNFCT_WVHGH
Real number (ℝ)

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78795
Minimum0.576
Maximum0.904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:43:49.307648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.576
5-th percentile0.68885
Q10.7365
median0.788
Q30.84075
95-th percentile0.89025
Maximum0.904
Range0.328
Interquartile range (IQR)0.10425

Descriptive statistics

Standard deviation0.066892553
Coefficient of variation (CV)0.084894413
Kurtosis-0.37713911
Mean0.78795
Median Absolute Deviation (MAD)0.0535
Skewness-0.19194473
Sum78.795
Variance0.0044746136
MonotonicityNot monotonic
2024-03-13T21:43:49.502475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.752 3
 
3.0%
0.82 3
 
3.0%
0.768 2
 
2.0%
0.701 2
 
2.0%
0.889 2
 
2.0%
0.75 2
 
2.0%
0.731 2
 
2.0%
0.717 2
 
2.0%
0.727 2
 
2.0%
0.829 2
 
2.0%
Other values (74) 78
78.0%
ValueCountFrequency (%)
0.576 1
1.0%
0.665 1
1.0%
0.669 1
1.0%
0.684 1
1.0%
0.686 1
1.0%
0.689 1
1.0%
0.701 2
2.0%
0.704 1
1.0%
0.709 2
2.0%
0.711 1
1.0%
ValueCountFrequency (%)
0.904 1
1.0%
0.903 1
1.0%
0.9 1
1.0%
0.896 1
1.0%
0.895 1
1.0%
0.89 1
1.0%
0.889 2
2.0%
0.888 1
1.0%
0.88 1
1.0%
0.879 1
1.0%

WVDRCT
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.7604
Minimum0.25
Maximum359.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:43:49.680209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile37.261
Q1241.7525
median288.45
Q3317.49
95-th percentile356.34
Maximum359.38
Range359.13
Interquartile range (IQR)75.7375

Descriptive statistics

Standard deviation99.795146
Coefficient of variation (CV)0.39172158
Kurtosis0.63852531
Mean254.7604
Median Absolute Deviation (MAD)39.45
Skewness-1.3466733
Sum25476.04
Variance9959.0712
MonotonicityNot monotonic
2024-03-13T21:43:49.865276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298.4 2
 
2.0%
250.17 1
 
1.0%
329.5 1
 
1.0%
356.91 1
 
1.0%
227.24 1
 
1.0%
299.71 1
 
1.0%
287.82 1
 
1.0%
296.2 1
 
1.0%
239.44 1
 
1.0%
306.92 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.25 1
1.0%
31.92 1
1.0%
32.3 1
1.0%
35.32 1
1.0%
35.57 1
1.0%
37.35 1
1.0%
43.21 1
1.0%
43.96 1
1.0%
44.45 1
1.0%
44.57 1
1.0%
ValueCountFrequency (%)
359.38 1
1.0%
358.57 1
1.0%
358.06 1
1.0%
357.78 1
1.0%
356.91 1
1.0%
356.31 1
1.0%
355.15 1
1.0%
354.77 1
1.0%
354.27 1
1.0%
353.94 1
1.0%

Interactions

2024-03-13T21:43:46.574463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:43.477334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:44.120520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:45.232711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:45.929678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:46.742099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:43.593389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:44.715936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:45.357578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:46.050310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:46.905198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:43.733317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:44.827928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:45.483480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:46.166568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:47.091548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:43.868842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:44.966160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:45.635287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:46.305284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:47.219271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:43.998807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:45.093014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:45.767744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:43:46.431128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:43:50.010165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WV_MTR_SNLALOSGNFCT_WVHGHWVDRCT
WV_MTR_SN1.0000.5320.4280.3910.400
LA0.5321.0000.7040.8880.848
LO0.4280.7041.0000.4870.787
SGNFCT_WVHGH0.3910.8880.4871.0000.654
WVDRCT0.4000.8480.7870.6541.000
2024-03-13T21:43:50.163428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WV_MTR_SNLALOSGNFCT_WVHGHWVDRCT
WV_MTR_SN1.0000.7010.2530.159-0.120
LA0.7011.0000.7120.0060.060
LO0.2530.7121.0000.1670.581
SGNFCT_WVHGH0.1590.0060.1671.0000.419
WVDRCT-0.1200.0600.5810.4191.000

Missing values

2024-03-13T21:43:47.386972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:43:47.530227image/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

WV_NMWV_MTR_SNLALOSGNFCT_WVHGHWVDRCT
01979134.663896127.285220.576250.17
11979234.665958127.2876340.686256.04
21979334.667294127.3166470.799296.14
31979434.665734127.3108340.763289.08
41979534.666258127.3065160.732285.94
51979634.711382127.3437010.738357.78
61979734.667475127.2979290.684259.38
71979834.667333127.293570.711256.23
81979934.667883127.2875380.761251.15
919791034.668169127.3146270.798294.34
WV_NMWV_MTR_SNLALOSGNFCT_WVHGHWVDRCT
9019799134.679593127.3019240.75254.91
9119799234.683573127.3094320.82299.18
9219799334.680058127.2819880.868227.8
9319799434.718819127.3279150.731358.57
9419799534.712689127.3291050.829350.24
9519799634.752024127.3116380.70970.21
9619799734.749267127.3150120.70955.41
9719799834.744837127.3208620.71743.21
9819799934.74041127.3231140.75235.57
99197910034.736814127.3233420.74331.92