Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 KiB
Average record size in memory106.3 B

Variable types

Categorical6
Numeric6

Dataset

DescriptionSample
Author해봄데이터㈜
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT02HBM017

Alerts

WTCH_YR has constant value ""Constant
WETHR_INFO has constant value ""Constant
WTCH_LA is highly overall correlated with WTCH_DD and 2 other fieldsHigh correlation
WTCH_LO is highly overall correlated with FSGR_NM and 1 other fieldsHigh correlation
WTCH_DD is highly overall correlated with WTCH_LA and 3 other fieldsHigh correlation
WTCH_TM is highly overall correlated with FSGR_NM and 2 other fieldsHigh correlation
WTCH_MIN is highly overall correlated with FSGR_NM and 2 other fieldsHigh correlation
WTCH_VAL is highly overall correlated with IEM_NMHigh correlation
FSGR_NM is highly overall correlated with WTCH_LA and 5 other fieldsHigh correlation
SNO is highly overall correlated with WTCH_LA and 5 other fieldsHigh correlation
WTCH_MNTH is highly overall correlated with WTCH_DD and 2 other fieldsHigh correlation
IEM_NM is highly overall correlated with WTCH_VALHigh correlation
WTCH_MIN has 9 (9.0%) zerosZeros

Reproduction

Analysis started2024-01-14 07:01:07.959250
Analysis finished2024-01-14 07:01:12.172080
Duration4.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

FSGR_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
군산
54 
금강하구
18 
태안
18 
아산
10 

Length

Max length4
Median length2
Mean length2.36
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군산 54
54.0%
금강하구 18
 
18.0%
태안 18
 
18.0%
아산 10
 
10.0%

Length

2024-01-14T16:01:12.259631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:01:12.383918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군산 54
54.0%
금강하구 18
 
18.0%
태안 18
 
18.0%
아산 10
 
10.0%

SNO
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4
36 
8
18 
2
18 
1
18 
5
10 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
4 36
36.0%
8 18
18.0%
2 18
18.0%
1 18
18.0%
5 10
 
10.0%

Length

2024-01-14T16:01:12.496357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:01:12.619042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 36
36.0%
8 18
18.0%
2 18
18.0%
1 18
18.0%
5 10
 
10.0%

WTCH_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.225633
Minimum35.76667
Maximum37.20333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:01:12.711045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.76667
5-th percentile35.76667
Q136
median36.08833
Q336.63
95-th percentile37.20333
Maximum37.20333
Range1.43666
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation0.42442614
Coefficient of variation (CV)0.011716183
Kurtosis0.37127278
Mean36.225633
Median Absolute Deviation (MAD)0.08833
Skewness1.1468003
Sum3622.5633
Variance0.18013755
MonotonicityNot monotonic
2024-01-14T16:01:12.903307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
35.76667 18
18.0%
36.08833 18
18.0%
36.0 18
18.0%
36.1 18
18.0%
36.63 18
18.0%
37.20333 10
10.0%
ValueCountFrequency (%)
35.76667 18
18.0%
36.0 18
18.0%
36.08833 18
18.0%
36.1 18
18.0%
36.63 18
18.0%
37.20333 10
10.0%
ValueCountFrequency (%)
37.20333 10
10.0%
36.63 18
18.0%
36.1 18
18.0%
36.08833 18
18.0%
36.0 18
18.0%
35.76667 18
18.0%

WTCH_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.43087
Minimum126.23
Maximum126.52167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:01:13.025542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.23
5-th percentile126.23
Q1126.41833
median126.45333
Q3126.5
95-th percentile126.52167
Maximum126.52167
Range0.29167
Interquartile range (IQR)0.08167

Descriptive statistics

Standard deviation0.10069202
Coefficient of variation (CV)0.00079641961
Kurtosis0.14745083
Mean126.43087
Median Absolute Deviation (MAD)0.04667
Skewness-1.2574589
Sum12643.087
Variance0.010138883
MonotonicityNot monotonic
2024-01-14T16:01:13.148821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
126.41833 18
18.0%
126.45333 18
18.0%
126.5 18
18.0%
126.52167 18
18.0%
126.23 18
18.0%
126.48667 10
10.0%
ValueCountFrequency (%)
126.23 18
18.0%
126.41833 18
18.0%
126.45333 18
18.0%
126.48667 10
10.0%
126.5 18
18.0%
126.52167 18
18.0%
ValueCountFrequency (%)
126.52167 18
18.0%
126.5 18
18.0%
126.48667 10
10.0%
126.45333 18
18.0%
126.41833 18
18.0%
126.23 18
18.0%

WTCH_YR
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2022
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 100
100.0%

Length

2024-01-14T16:01:13.291131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:01:13.396467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 100
100.0%

WTCH_MNTH
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
12
54 
10
46 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
12 54
54.0%
10 46
46.0%

Length

2024-01-14T16:01:13.510236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:01:13.619287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12 54
54.0%
10 46
46.0%

WTCH_DD
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.05
Minimum6
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:01:13.717920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q19
median9
Q312
95-th percentile13
Maximum13
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7773832
Coefficient of variation (CV)0.17685405
Kurtosis-1.2620478
Mean10.05
Median Absolute Deviation (MAD)1
Skewness0.3085309
Sum1005
Variance3.1590909
MonotonicityNot monotonic
2024-01-14T16:01:13.849082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9 36
36.0%
12 27
27.0%
8 18
18.0%
10 9
 
9.0%
13 9
 
9.0%
6 1
 
1.0%
ValueCountFrequency (%)
6 1
 
1.0%
8 18
18.0%
9 36
36.0%
10 9
 
9.0%
12 27
27.0%
13 9
 
9.0%
ValueCountFrequency (%)
13 9
 
9.0%
12 27
27.0%
10 9
 
9.0%
9 36
36.0%
8 18
18.0%
6 1
 
1.0%

WTCH_TM
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.59
Minimum9
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:01:13.975001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9
Q110
median11
Q313
95-th percentile15
Maximum16
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9284945
Coefficient of variation (CV)0.16639297
Kurtosis-1.0255668
Mean11.59
Median Absolute Deviation (MAD)2
Skewness0.22241216
Sum1159
Variance3.7190909
MonotonicityNot monotonic
2024-01-14T16:01:14.115797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
13 36
36.0%
10 18
18.0%
9 18
18.0%
11 18
18.0%
15 9
 
9.0%
16 1
 
1.0%
ValueCountFrequency (%)
9 18
18.0%
10 18
18.0%
11 18
18.0%
13 36
36.0%
15 9
 
9.0%
16 1
 
1.0%
ValueCountFrequency (%)
16 1
 
1.0%
15 9
 
9.0%
13 36
36.0%
11 18
18.0%
10 18
18.0%
9 18
18.0%

WTCH_MIN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.7
Minimum0
Maximum55
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:01:14.244106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median40
Q350
95-th percentile55
Maximum55
Range55
Interquartile range (IQR)35

Descriptive statistics

Standard deviation18.037839
Coefficient of variation (CV)0.569017
Kurtosis-1.1314004
Mean31.7
Median Absolute Deviation (MAD)10
Skewness-0.4847543
Sum3170
Variance325.36364
MonotonicityNot monotonic
2024-01-14T16:01:14.366560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
40 27
27.0%
50 18
18.0%
20 10
 
10.0%
5 9
 
9.0%
55 9
 
9.0%
35 9
 
9.0%
15 9
 
9.0%
0 9
 
9.0%
ValueCountFrequency (%)
0 9
 
9.0%
5 9
 
9.0%
15 9
 
9.0%
20 10
 
10.0%
35 9
 
9.0%
40 27
27.0%
50 18
18.0%
55 9
 
9.0%
ValueCountFrequency (%)
55 9
 
9.0%
50 18
18.0%
40 27
27.0%
35 9
 
9.0%
20 10
 
10.0%
15 9
 
9.0%
5 9
 
9.0%
0 9
 
9.0%

WETHR_INFO
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
맑음 100
100.0%

Length

2024-01-14T16:01:14.559420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:01:14.664529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
맑음 100
100.0%

IEM_NM
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
DEPTH
12 
TEMP_S
11 
TEMP_B
11 
SAL_S
11 
SAL_B
11 
Other values (4)
44 

Length

Max length6
Median length5
Mean length4.78
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEPTH
2nd rowTEMP_S
3rd rowTEMP_B
4th rowSAL_S
5th rowSAL_B

Common Values

ValueCountFrequency (%)
DEPTH 12
12.0%
TEMP_S 11
11.0%
TEMP_B 11
11.0%
SAL_S 11
11.0%
SAL_B 11
11.0%
PH_S 11
11.0%
PH_B 11
11.0%
DO_S 11
11.0%
DO_B 11
11.0%

Length

2024-01-14T16:01:14.785342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:01:14.938163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
depth 12
12.0%
temp_s 11
11.0%
temp_b 11
11.0%
sal_s 11
11.0%
sal_b 11
11.0%
ph_s 11
11.0%
ph_b 11
11.0%
do_s 11
11.0%
do_b 11
11.0%

WTCH_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.8339
Minimum6
Maximum31.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:01:15.118633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.0855
Q18.11
median9.865
Q318.5625
95-th percentile31.8205
Maximum31.96
Range25.96
Interquartile range (IQR)10.4525

Descriptive statistics

Standard deviation9.2875521
Coefficient of variation (CV)0.62610319
Kurtosis-0.63082088
Mean14.8339
Median Absolute Deviation (MAD)1.99
Skewness1.0437537
Sum1483.39
Variance86.258624
MonotonicityNot monotonic
2024-01-14T16:01:15.300688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.11 5
 
5.0%
31.96 4
 
4.0%
8.12 4
 
4.0%
10.0 3
 
3.0%
8.1 3
 
3.0%
9.86 3
 
3.0%
8.13 3
 
3.0%
15.0 2
 
2.0%
7.61 2
 
2.0%
31.18 2
 
2.0%
Other values (60) 69
69.0%
ValueCountFrequency (%)
6.0 2
2.0%
6.9 1
1.0%
6.98 1
1.0%
7.0 1
1.0%
7.09 1
1.0%
7.31 1
1.0%
7.59 1
1.0%
7.61 2
2.0%
7.82 1
1.0%
7.83 2
2.0%
ValueCountFrequency (%)
31.96 4
4.0%
31.83 1
 
1.0%
31.82 1
 
1.0%
31.77 1
 
1.0%
31.66 1
 
1.0%
31.56 1
 
1.0%
31.36 1
 
1.0%
31.18 2
2.0%
31.12 2
2.0%
31.11 2
2.0%

Interactions

2024-01-14T16:01:11.280645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:08.375243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:08.933241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:09.389813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.269304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.785675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:11.364523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:08.475671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:09.011879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:09.492265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.354248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.878138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:11.450854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:08.566367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:09.079974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:09.824718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.433210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.963867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:11.593981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:08.655549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:09.153596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:09.962199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.515646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:11.053252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:11.726775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:08.742259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:09.229241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.105959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.603223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:11.133437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:11.837414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:08.835007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:09.297927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.191779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:10.694725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:01:11.206738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T16:01:15.426740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
FSGR_NMSNOWTCH_LAWTCH_LOWTCH_MNTHWTCH_DDWTCH_TMWTCH_MINIEM_NMWTCH_VAL
FSGR_NM1.0000.8830.8830.8830.2550.6950.6780.8810.0000.000
SNO0.8831.0000.9910.9950.1120.7350.7030.8610.0000.000
WTCH_LA0.8830.9911.0000.9950.1120.7700.7700.7690.0000.000
WTCH_LO0.8830.9950.9951.0000.1120.7700.7030.7940.0000.000
WTCH_MNTH0.2550.1120.1120.1121.0000.9870.8480.9810.0000.453
WTCH_DD0.6950.7350.7700.7700.9871.0000.9660.9370.0000.515
WTCH_TM0.6780.7030.7700.7030.8480.9661.0000.9170.0000.494
WTCH_MIN0.8810.8610.7690.7940.9810.9370.9171.0000.0000.066
IEM_NM0.0000.0000.0000.0000.0000.0000.0000.0001.0000.759
WTCH_VAL0.0000.0000.0000.0000.4530.5150.4940.0660.7591.000
2024-01-14T16:01:15.560800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SNOFSGR_NMIEM_NMWTCH_MNTH
SNO1.0000.8720.0000.133
FSGR_NM0.8721.0000.0000.167
IEM_NM0.0000.0001.0000.000
WTCH_MNTH0.1330.1670.0001.000
2024-01-14T16:01:15.901997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LAWTCH_LOWTCH_DDWTCH_TMWTCH_MINWTCH_VALFSGR_NMSNOWTCH_MNTHIEM_NM
WTCH_LA1.000-0.016-0.702-0.017-0.0550.0030.8720.8600.1330.000
WTCH_LO-0.0161.0000.1540.480-0.249-0.0090.8720.8970.1330.000
WTCH_DD-0.7020.1541.000-0.302-0.289-0.1070.5200.6020.8810.000
WTCH_TM-0.0170.480-0.3021.0000.3150.1220.5020.5640.6400.000
WTCH_MIN-0.055-0.249-0.2890.3151.0000.0900.7480.7740.8600.000
WTCH_VAL0.003-0.009-0.1070.1220.0901.0000.0000.0000.4730.531
FSGR_NM0.8720.8720.5200.5020.7480.0001.0000.8720.1670.000
SNO0.8600.8970.6020.5640.7740.0000.8721.0000.1330.000
WTCH_MNTH0.1330.1330.8810.6400.8600.4730.1670.1331.0000.000
IEM_NM0.0000.0000.0000.0000.0000.5310.0000.0000.0001.000

Missing values

2024-01-14T16:01:11.954451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T16:01:12.104512image/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

FSGR_NMSNOWTCH_LAWTCH_LOWTCH_YRWTCH_MNTHWTCH_DDWTCH_TMWTCH_MINWETHR_INFOIEM_NMWTCH_VAL
0군산835.76667126.4183320221210105맑음DEPTH10.0
1군산835.76667126.4183320221210105맑음TEMP_S9.87
2군산835.76667126.4183320221210105맑음TEMP_B10.42
3군산835.76667126.4183320221210105맑음SAL_S31.36
4군산835.76667126.4183320221210105맑음SAL_B31.77
5군산835.76667126.4183320221210105맑음PH_S7.88
6군산835.76667126.4183320221210105맑음PH_B7.87
7군산835.76667126.4183320221210105맑음DO_S9.41
8군산835.76667126.4183320221210105맑음DO_B9.52
9군산236.08833126.4533320221291350맑음DEPTH11.0
FSGR_NMSNOWTCH_LAWTCH_LOWTCH_YRWTCH_MNTHWTCH_DDWTCH_TMWTCH_MINWETHR_INFOIEM_NMWTCH_VAL
90태안436.63126.2320221081340맑음DEPTH10.0
91태안436.63126.2320221081340맑음TEMP_S18.79
92태안436.63126.2320221081340맑음TEMP_B18.26
93태안436.63126.2320221081340맑음SAL_S31.56
94태안436.63126.2320221081340맑음SAL_B31.66
95태안436.63126.2320221081340맑음PH_S8.1
96태안436.63126.2320221081340맑음PH_B8.11
97태안436.63126.2320221081340맑음DO_S9.2
98태안436.63126.2320221081340맑음DO_B9.39
99아산537.20333126.4866720221061620맑음DEPTH15.0