Overview

Dataset statistics

Number of variables16
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory138.7 B

Variable types

Numeric6
Categorical10

Dataset

DescriptionSample
Author에이치더블유
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000068

Alerts

CTGRY_MLSFC_NM has constant value ""Constant
SOF has constant value ""Constant
PRCSS_FOM has constant value ""Constant
EXAM_NO has constant value ""Constant
ORPLC_NTN_NM has constant value ""Constant
WRKNG_AREA has constant value ""Constant
ENTRY_YR has constant value ""Constant
ENTRY_MNTH has constant value ""Constant
PHOTO_INFO_ESSN_ID has constant value ""Constant
SEQ_NO is highly overall correlated with TOT_WT and 5 other fieldsHigh correlation
TOT_WT is highly overall correlated with SEQ_NO and 5 other fieldsHigh correlation
VSCR_WT is highly overall correlated with SEQ_NO and 5 other fieldsHigh correlation
NET_WT is highly overall correlated with SEQ_NO and 5 other fieldsHigh correlation
HR is highly overall correlated with SEQ_NO and 5 other fieldsHigh correlation
RN is highly overall correlated with SEQ_NO and 5 other fieldsHigh correlation
SOF_SCTN_UNIT is highly overall correlated with SEQ_NO and 5 other fieldsHigh correlation
SEQ_NO has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:20:57.921388
Analysis finished2023-12-10 14:21:02.202782
Duration4.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SEQ_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean620
Minimum596
Maximum644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:02.295068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum596
5-th percentile598.4
Q1608
median620
Q3632
95-th percentile641.6
Maximum644
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.023046274
Kurtosis-1.2
Mean620
Median Absolute Deviation (MAD)12
Skewness0
Sum30380
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:21:02.484699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
596 1
 
2.0%
633 1
 
2.0%
623 1
 
2.0%
624 1
 
2.0%
625 1
 
2.0%
626 1
 
2.0%
627 1
 
2.0%
628 1
 
2.0%
629 1
 
2.0%
630 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
596 1
2.0%
597 1
2.0%
598 1
2.0%
599 1
2.0%
600 1
2.0%
601 1
2.0%
602 1
2.0%
603 1
2.0%
604 1
2.0%
605 1
2.0%
ValueCountFrequency (%)
644 1
2.0%
643 1
2.0%
642 1
2.0%
641 1
2.0%
640 1
2.0%
639 1
2.0%
638 1
2.0%
637 1
2.0%
636 1
2.0%
635 1
2.0%

CTGRY_MLSFC_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
연체류 해물모둠
49 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연체류 해물모둠
2nd row연체류 해물모둠
3rd row연체류 해물모둠
4th row연체류 해물모둠
5th row연체류 해물모둠

Common Values

ValueCountFrequency (%)
연체류 해물모둠 49
100.0%

Length

2023-12-10T23:21:02.651086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:02.760493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연체류 49
50.0%
해물모둠 49
50.0%

SOF
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
오징어
49 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
오징어 49
100.0%

Length

2023-12-10T23:21:02.854895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:02.946270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오징어 49
100.0%

PRCSS_FOM
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
할복
49 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
할복 49
100.0%

Length

2023-12-10T23:21:03.041103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:03.179701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
할복 49
100.0%

SOF_SCTN_UNIT
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
S
30 
2M
19 

Length

Max length2
Median length1
Mean length1.3877551
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
S 30
61.2%
2M 19
38.8%

Length

2023-12-10T23:21:03.313506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:03.402958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 30
61.2%
2m 19
38.8%

EXAM_NO
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
SQ 1
49 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
SQ 1 49
100.0%

Length

2023-12-10T23:21:03.499404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:03.587710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sq 49
50.0%
1 49
50.0%

ORPLC_NTN_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
한국(원양산)
49 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row한국(원양산)
2nd row한국(원양산)
3rd row한국(원양산)
4th row한국(원양산)
5th row한국(원양산)

Common Values

ValueCountFrequency (%)
한국(원양산) 49
100.0%

Length

2023-12-10T23:21:03.680101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:03.775061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한국(원양산 49
100.0%

WRKNG_AREA
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
대서양
49 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대서양 49
100.0%

Length

2023-12-10T23:21:03.866682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:03.973980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대서양 49
100.0%

ENTRY_YR
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2007
49 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2007
2nd row2007
3rd row2007
4th row2007
5th row2007

Common Values

ValueCountFrequency (%)
2007 49
100.0%

Length

2023-12-10T23:21:04.087897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:04.179088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2007 49
100.0%

ENTRY_MNTH
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
3
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 49
100.0%

Length

2023-12-10T23:21:04.266777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:04.358095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 49
100.0%

TOT_WT
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.61224
Minimum160
Maximum214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:04.492674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile161
Q1167
median177
Q3208
95-th percentile213
Maximum214
Range54
Interquartile range (IQR)41

Descriptive statistics

Standard deviation20.785428
Coefficient of variation (CV)0.11258965
Kurtosis-1.7258348
Mean184.61224
Median Absolute Deviation (MAD)15
Skewness0.30373712
Sum9046
Variance432.03401
MonotonicityIncreasing
2023-12-10T23:21:04.683233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
162 3
 
6.1%
213 3
 
6.1%
161 3
 
6.1%
168 3
 
6.1%
160 2
 
4.1%
210 2
 
4.1%
180 2
 
4.1%
206 2
 
4.1%
207 2
 
4.1%
209 2
 
4.1%
Other values (20) 25
51.0%
ValueCountFrequency (%)
160 2
4.1%
161 3
6.1%
162 3
6.1%
163 1
 
2.0%
164 1
 
2.0%
165 1
 
2.0%
166 1
 
2.0%
167 2
4.1%
168 3
6.1%
169 2
4.1%
ValueCountFrequency (%)
214 1
 
2.0%
213 3
6.1%
212 2
4.1%
211 2
4.1%
210 2
4.1%
209 2
4.1%
208 1
 
2.0%
207 2
4.1%
206 2
4.1%
205 1
 
2.0%

VSCR_WT
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.877551
Minimum34
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:04.837303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile35
Q138
median39
Q356
95-th percentile58
Maximum59
Range25
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.6277218
Coefficient of variation (CV)0.21453314
Kurtosis-1.782083
Mean44.877551
Median Absolute Deviation (MAD)3
Skewness0.44442592
Sum2199
Variance92.693027
MonotonicityNot monotonic
2023-12-10T23:21:05.036063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
38 12
24.5%
56 9
18.4%
37 6
12.2%
39 6
12.2%
57 5
10.2%
35 3
 
6.1%
34 2
 
4.1%
58 2
 
4.1%
59 2
 
4.1%
36 1
 
2.0%
ValueCountFrequency (%)
34 2
 
4.1%
35 3
 
6.1%
36 1
 
2.0%
37 6
12.2%
38 12
24.5%
39 6
12.2%
55 1
 
2.0%
56 9
18.4%
57 5
10.2%
58 2
 
4.1%
ValueCountFrequency (%)
59 2
 
4.1%
58 2
 
4.1%
57 5
10.2%
56 9
18.4%
55 1
 
2.0%
39 6
12.2%
38 12
24.5%
37 6
12.2%
36 1
 
2.0%
35 3
 
6.1%

NET_WT
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.73469
Minimum122
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:05.191552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile123.4
Q1129
median139
Q3152
95-th percentile155.8
Maximum158
Range36
Interquartile range (IQR)23

Descriptive statistics

Standard deviation11.796496
Coefficient of variation (CV)0.084420665
Kurtosis-1.543582
Mean139.73469
Median Absolute Deviation (MAD)12
Skewness0.040253058
Sum6847
Variance139.15731
MonotonicityNot monotonic
2023-12-10T23:21:05.348090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
154 6
 
12.2%
148 3
 
6.1%
142 3
 
6.1%
153 3
 
6.1%
131 2
 
4.1%
151 2
 
4.1%
139 2
 
4.1%
136 2
 
4.1%
123 2
 
4.1%
157 2
 
4.1%
Other values (16) 22
44.9%
ValueCountFrequency (%)
122 1
2.0%
123 2
4.1%
124 2
4.1%
125 1
2.0%
126 2
4.1%
127 2
4.1%
128 2
4.1%
129 2
4.1%
130 2
4.1%
131 2
4.1%
ValueCountFrequency (%)
158 1
 
2.0%
157 2
 
4.1%
154 6
12.2%
153 3
6.1%
152 1
 
2.0%
151 2
 
4.1%
149 1
 
2.0%
148 3
6.1%
143 1
 
2.0%
142 3
6.1%

HR
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.930612
Minimum71.8
Maximum79.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:05.479245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71.8
5-th percentile72.24
Q173.1
median76.6
Q378.5
95-th percentile79
Maximum79.2
Range7.4
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation2.5975312
Coefficient of variation (CV)0.034209275
Kurtosis-1.6191101
Mean75.930612
Median Absolute Deviation (MAD)2.3
Skewness-0.24566918
Sum3720.6
Variance6.7471684
MonotonicityNot monotonic
2023-12-10T23:21:05.646240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
78.9 3
 
6.1%
79.0 3
 
6.1%
73.7 3
 
6.1%
72.9 3
 
6.1%
78.5 2
 
4.1%
73.0 2
 
4.1%
77.5 2
 
4.1%
77.4 2
 
4.1%
72.2 2
 
4.1%
72.3 2
 
4.1%
Other values (25) 25
51.0%
ValueCountFrequency (%)
71.8 1
 
2.0%
72.2 2
4.1%
72.3 2
4.1%
72.5 1
 
2.0%
72.6 1
 
2.0%
72.9 3
6.1%
73.0 2
4.1%
73.1 1
 
2.0%
73.2 1
 
2.0%
73.3 1
 
2.0%
ValueCountFrequency (%)
79.2 1
 
2.0%
79.1 1
 
2.0%
79.0 3
6.1%
78.9 3
6.1%
78.8 1
 
2.0%
78.7 1
 
2.0%
78.6 1
 
2.0%
78.5 2
4.1%
78.4 1
 
2.0%
78.3 1
 
2.0%

PHOTO_INFO_ESSN_ID
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
data/1607816339_TCzx
49 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdata/1607816339_TCzx
2nd rowdata/1607816339_TCzx
3rd rowdata/1607816339_TCzx
4th rowdata/1607816339_TCzx
5th rowdata/1607816339_TCzx

Common Values

ValueCountFrequency (%)
data/1607816339_TCzx 49
100.0%

Length

2023-12-10T23:21:05.810034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:05.914249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
data/1607816339_tczx 49
100.0%

RN
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:21:06.039946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q114
median26
Q338
95-th percentile47.6
Maximum50
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.54956501
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)12
Skewness0
Sum1274
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:21:06.241379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

Interactions

2023-12-10T23:21:00.960980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:58.288906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:58.797793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.315765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.931910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.420713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:01.038496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:58.369090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:58.884124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.418978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.025192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.500206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:01.122707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:58.452991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:58.976522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.552953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.115490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.590754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:01.209078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:58.553555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.055058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.668413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.190807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.684160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:01.298270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:58.638905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.134844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.763194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.264737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.778035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:01.394172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:58.722042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.234621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:59.854040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.341408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:00.871748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:21:06.365637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOSOF_SCTN_UNITTOT_WTVSCR_WTNET_WTHRRN
SEQ_NO1.0000.9980.9480.9090.9260.8351.000
SOF_SCTN_UNIT0.9981.0001.0001.0001.0001.0000.998
TOT_WT0.9481.0001.0000.6740.9490.8160.967
VSCR_WT0.9091.0000.6741.0000.7860.8450.906
NET_WT0.9261.0000.9490.7861.0000.8650.915
HR0.8351.0000.8160.8450.8651.0000.838
RN1.0000.9980.9670.9060.9150.8381.000
2023-12-10T23:21:06.528128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOTOT_WTVSCR_WTNET_WTHRRNSOF_SCTN_UNIT
SEQ_NO1.0000.9990.7820.986-0.5591.0000.872
TOT_WT0.9991.0000.7810.987-0.5590.9990.957
VSCR_WT0.7820.7811.0000.710-0.8990.7820.978
NET_WT0.9860.9870.7101.000-0.5030.9860.923
HR-0.559-0.559-0.899-0.5031.000-0.5590.934
RN1.0000.9990.7820.986-0.5591.0000.872
SOF_SCTN_UNIT0.8720.9570.9780.9230.9340.8721.000

Missing values

2023-12-10T23:21:01.521680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:21:02.071552image/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

SEQ_NOCTGRY_MLSFC_NMSOFPRCSS_FOMSOF_SCTN_UNITEXAM_NOORPLC_NTN_NMWRKNG_AREAENTRY_YRENTRY_MNTHTOT_WTVSCR_WTNET_WTHRPHOTO_INFO_ESSN_IDRN
0596연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731603712376.9data/1607816339_TCzx2
1597연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731603812276.3data/1607816339_TCzx3
2598연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731613712477.0data/1607816339_TCzx4
3599연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731613512678.3data/1607816339_TCzx5
4600연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731613412778.9data/1607816339_TCzx6
5601연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731623512778.4data/1607816339_TCzx7
6602연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731623412879.0data/1607816339_TCzx8
7603연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731623912375.9data/1607816339_TCzx9
8604연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731633912476.1data/1607816339_TCzx10
9605연체류 해물모둠오징어할복SSQ 1한국(원양산)대서양200731643912576.2data/1607816339_TCzx11
SEQ_NOCTGRY_MLSFC_NMSOFPRCSS_FOMSOF_SCTN_UNITEXAM_NOORPLC_NTN_NMWRKNG_AREAENTRY_YRENTRY_MNTHTOT_WTVSCR_WTNET_WTHRPHOTO_INFO_ESSN_IDRN
39635연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732105615473.3data/1607816339_TCzx41
40636연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732105715372.9data/1607816339_TCzx42
41637연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732115715473.0data/1607816339_TCzx43
42638연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732115715473.0data/1607816339_TCzx44
43639연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732125915372.2data/1607816339_TCzx45
44640연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732125815472.6data/1607816339_TCzx46
45641연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732135915472.3data/1607816339_TCzx47
46642연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732135615773.7data/1607816339_TCzx48
47643연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732135615773.7data/1607816339_TCzx49
48644연체류 해물모둠오징어할복2MSQ 1한국(원양산)대서양200732145615873.8data/1607816339_TCzx50